diff --git a/simulations/README.md b/simulations/README.md index df75e53..88ceabd 100644 --- a/simulations/README.md +++ b/simulations/README.md @@ -1,8 +1,8 @@ # Hyperpolarized Simulation Tools -## Creating Simulated Data +## Pharmacokinetic (PK) Models -This directory includes tools for simulating metabolic imaging data based on kinetic models. These include +The `pk_models` directory includes tools for simulating metabolic imaging data based on kinetic models. These include `realistic_input_function` - create a gamma-variate function for simulating the bolus input @@ -10,22 +10,28 @@ This directory includes tools for simulating metabolic imaging data based on kin See [Kinetic Modeling Notebook](../demo_notebooks/Test_Kinetic_Modeling.ipynb) for examples -## Metabolic Phantom +## Anatomical Phantoms -The file metabolic_phantom.m provides a matlab function for automatically generating standardized 3-dimensional perfusion and metabolism maps for simulated experiments. The script DEMO.m demonstrates the usage of this function. Examples of the resulting parameter maps are shown below. +This framework generates anatomically realistic hyperpolarized 13C MRI data by combining anatomical and PK models. This was started with brain and heart phantoms, and built modularly to accomodate multiple anatomies and kinetic models. It also includes experimental factors that incorporate the spatial resolution, SNR, and coil sensitivity profiles. + +Brain phantoms Heart phantoms + +## Metabolic Shapes Phantom + +In `metabolic_shapes_phantom`, the file metabolic_phantom.m provides a matlab function for automatically generating standardized a geometric shape-based 3-dimensional perfusion and metabolism maps for simulated experiments. The script DEMO.m demonstrates the usage of this function. Examples of the resulting parameter maps are shown below. kTRANS (perfusion) map: -Example kTRANS map 16 voxels Example kTRANS map 256 voxels +Example kTRANS map 16 voxels Example kTRANS map 256 voxels kPL (metabolism) map: -Example kPL map 16 voxels Example kPL map 256 voxels +Example kPL map 16 voxels Example kPL map 256 voxels ## Monte Carlo Simulations of Analysis Metrics Given the experimental challenges of time, cost, and lack of ground truth for HP studies, simulations are an invaluable tool for evaluating data acquisition and analysis strategies. -This toolbox includes a Monte Carlo based simulation tool `HP_montecarlo_evaluation()` which uses a 2-site kinetic model (`simulate_Nsite_model()`) with added noise and your choice of experiment parameters (e.g. flip angles, timings) and analysis metric(s) (e.g. kPL, AUCratios). +This toolbox includes a Monte Carlo based simulation tool `HP_montecarlo_evaluation()` in `montecarlo_simulations` which uses a 2-site kinetic model (`simulate_Nsite_model()`) with added noise and your choice of experiment parameters (e.g. flip angles, timings) and analysis metric(s) (e.g. kPL, AUCratios). It evalutes the sensitivity of this combination of experiment parameters and analysis metric to changes in kinetic rate, noise level, bolus characteristics, relaxation rates, and B1+ (e.g. flip angle). Several examples including different acquisition schemes, different kinetic model approaches, and comparison to an AUCratio are in the `test_HP_montecarlo_evaluation.m` script, which has the expected output: @@ -34,13 +40,15 @@ Several examples including different acquisition schemes, different kinetic mode ### Examples ``` -Larson PEZ, Chen HY, Gordon JW, Korn N, Maidens J, Arcak M, Tang S, Criekinge M, Carvajal L, Mammoli D, Bok R, Aggarwal R, Ferrone M, Slater JB, Nelson SJ, Kurhanewicz J, Vigneron DB. Investigation of analysis methods for hyperpolarized 13C-pyruvate metabolic MRI in prostate cancer patients. NMR Biomed. 2018 Nov;31(11):e3997. +Larson PEZ, Chen HY, Gordon JW, Korn N, Maidens J, Arcak M, Tang S, Criekinge M, Carvajal L, Mammoli D, Bok R, Aggarwal R, Ferrone M, Slater JB, Nelson SJ, Kurhanewicz J, Vigneron DB. +Investigation of analysis methods for hyperpolarized 13C-pyruvate metabolic MRI in prostate cancer patients. NMR Biomed. 2018 Nov;31(11):e3997. https://doi.org/10.1002/nbm.3997. Epub 2018 Sep 19. PMID: 30230646; PMCID: PMC6392436. ``` * See Figures 4-6 ``` -James Bankson, Peder E.Z. Larson, Analysis and visualization of hyperpolarized 13C MR data, Editor(s): Peder E.Z. Larson, Advances in Magnetic Resonance Technology and Applications, Academic Press, +James Bankson, Peder E.Z. Larson, Analysis and visualization of hyperpolarized 13C MR data, +Editor(s): Peder E.Z. Larson, Advances in Magnetic Resonance Technology and Applications, Academic Press, Volume 3, 2021, Pages 129-155, ISSN 2666-9099, ISBN 9780128222690, https://doi.org/10.1016/B978-0-12-822269-0.00004-X ``` * Figures 6.8 and 6.9 @@ -50,7 +58,9 @@ Volume 3, 2021, Pages 129-155, ISSN 2666-9099, ISBN 9780128222690, https://doi.o Simulates the effect of pulse sequence parameters (flip angles, phase encoding, etc) on pH measurements for HP bicarbonate studies. Based on: ``` -Korenchan, DE, Gordon, JW, Subramaniam, S, et al. Using bidirectional chemical exchange for improved hyperpolarized [13C]bicarbonate pH imaging. Magn Reson Med. 2019; 82: 959–972. doi: 10.1002/mrm.27780 +Korenchan, DE, Gordon, JW, Subramaniam, S, et al. +Using bidirectional chemical exchange for improved hyperpolarized [13C]bicarbonate pH imaging. +Magn Reson Med. 2019; 82: 959–972. doi: 10.1002/mrm.27780 ``` https://doi.org/10.1002/mrm.27780 ## diff --git a/simulations/anatomical_phantoms/brainweb_metabolic_phantom.m b/simulations/anatomical_phantoms/brainweb_metabolic_phantom.m deleted file mode 100644 index cb1d2b7..0000000 --- a/simulations/anatomical_phantoms/brainweb_metabolic_phantom.m +++ /dev/null @@ -1,297 +0,0 @@ -function [kTRANS, kMaps_out, Mz0Maps_out, metImages, w] = brainweb_metabolic_phantom(kineticRates, ktransScales, Mz0, sampSize, outputSize, simParams, inputFunction, isFuzzy, linear_kTRANS_grad, augmentParams, brain_idx, augmentSeed) -% BRAINWEB_METABOLIC_PHANTOM generates standardized 3-dimensional perfusion -% and metabolism maps for simulated experiments. Supports 3 chemical pool -% kinetic rate mapping. -% -% Toolboxes required: Image Processing -% -% Parameters: -% kineticRates = the kinetic rates to simulate, [(# of chemical -% pools) - 1, 3 (tissue types: vasc, gm, wm)] -% ktransScales = volume transfer constants for different tissue -% compartments [vasculature, gray matter, white matter] -% if linear_kTRANS_grad=true, should include low -% and high kTRANS [[vasc_low, GM_low, WM_low]; [vasc_hi, GM_hi, WM_hi]] -% Mz0 = Initial magnetization per metabolite per compartment, -% size [Nmets 3] in order vessels, gm, wm -% sampSize = 3x3 or 1x3 vector for desired sampling ("acquisition") matrix size of each -% dimension for all mets -% [met dim(nx,ny,nz)], default = [16 16 8] -% outputSize = 1x3 vector for desored output matrix size, -% default = [64 64 8] -% simParams = parameters used for the kinetic simulations: -% Tarrival, Tbolus, TR, Nt, R1, flips; if empty won't -% generate metImages, default = empty struct -% inputFunction = [1 Nt] vector as input into each voxel -% -% Optional Parameters: -% isFuzzy = logical flag to indicate if fuzzy tissue boundaries -% will be used, default = true -% linear_kTRANS_grad = boolean flag for whether kTRANS should be set to -% a gradient -% augmentParams = random augmentation parameters XTranslation, -% YTranslation etc, if not defined a single -% unaugmented phantom will be generated, -% default = empty struct -% brain_idx = specify if want to use a specific brain (1-20), -% default = randomly choose a brain -% augmentSeed = random seed for augmentations, default no seed -% -% Outputs: -% kTRANS = generated perfusion map -% kMaps = generated rate maps for 1->2 and 1->3 -% Mz0Maps = generated Mz0 maps -% metImages = simulated metabolite dynamic images -% w = simulated coil sensitivity maps -% -% Author: -% Jasmine Hu -% Anna Bennett -% Sule Sahin -% -% Copyright, 2024 - - % parse input arguments - arguments - kineticRates (:,3) double {mustBeNumeric} = [0.1, 0.2, 0.3; 0, 0, 0] - ktransScales (:,3) double {mustBeNumeric} = [1, 0.3, 0.3] - Mz0 (:,3) double {mustBeNumeric} = [0, 0, 0] - sampSize (:,3) double {mustBeInteger} = [16, 16, 8] - outputSize (1,3) double {mustBeInteger} = [64, 64, 8] - simParams struct = struct([]) - inputFunction double = [] - isFuzzy double {mustBeNumericOrLogical} = true - linear_kTRANS_grad double {mustBeNumericOrLogical} = false - augmentParams struct = struct() - brain_idx {mustBeInteger} = [] %TODO: should be int between 1-19 - augmentSeed double {mustBeInteger, mustBePositive, mustBeNonzero} = [] - end - - if augmentSeed - rng(augmentSeed) - else - rng("shuffle") - end - - if and(linear_kTRANS_grad, size(ktransScales,1)==1) - ktransScales = repmat(ktransScales, [2 1]); - warning("kTRANS linear gradient is 'true' but low/high kTRANS values not defined. Using user-defined kTRANS values as both low and high.") - elseif and(~linear_kTRANS_grad, size(ktransScales,1)>1) - ktransScales = squeeze(ktransScales(1,:)); - warning("kTRANS linear gradient is 'false' but low/high kTRANS values defined. Using first row of kTRANS values.") - end - - nMets = size(kineticRates,1) + 1; - - if size(sampSize) == [1 3] - maxSampSize = sampSize; - sampSize = repmat(sampSize, [nMets 1]); - else - [~,i] = max(sampSize(:,1)); - maxSampSize = sampSize(i,:); - end - - % add resources dir to path - fileDir = split(mfilename('fullpath'),'/'); - utilDir = fullfile(string(join(fileDir(1:end-1),'/')),'/util'); - addpath(utilDir) - - % define default augmentation parameters and set augmentParams to them - % if not defined - defaultAugParams.XReflection = false; defaultAugParams.YReflection = false; - defaultAugParams.Rotation = [0 0]; defaultAugParams.Scale = [1 1]; - defaultAugParams.XShear = [0 0]; defaultAugParams.YShear = [0 0]; - defaultAugParams.XTranslation = [0 0]; defaultAugParams.YTranslation = [0 0]; - augs = fieldnames(defaultAugParams); - for f=1:size(augs,1) - if ~isfield(augmentParams,augs{f}) - augmentParams(1).(augs{f}) = defaultAugParams.(augs{f}); - end - end - - % load base anatomical information - if isempty(brain_idx) - brain_idx = randi(19); - end - if isFuzzy - brainwebFile = 'brainweb_fuzzy.mat'; - else - brainwebFile = 'brainweb.mat'; - end - baseMaskFile = dir(fullfile(utilDir,num2str(brain_idx),brainwebFile)); - if isempty(baseMaskFile) - error('Error. \nBrainWeb mask file, %s, not found in resources directory.',brainwebFile) - end - - load(fullfile(baseMaskFile.folder,brainwebFile),'im_mask'); - vasc_mask = squeeze(im_mask(:,:,:,1)); - gm_mask = squeeze(im_mask(:,:,:,2)); - wm_mask = squeeze(im_mask(:,:,:,3)); - brain_mask = vasc_mask + gm_mask + wm_mask; - maskSize = size(im_mask,1:3); - - weights = coil_dist_map(brain_mask, simParams.coil_lim); - - nTissues = 3; - if size(im_mask,4) ~= nTissues - error('Unexpected number of tissues present in the imported masks, ask for help, idk.'); - end - - % parameters for output map generation - permuted_mask = double(permute(im_mask,[4 1 2 3])); - sumWeights = sum(im_mask,4); - - if linear_kTRANS_grad - % generate kTRANS gradients per compartment - grad_vasc = generate_linear_gradient(maskSize, ktransScales(1,1), ktransScales(2,1)); - grad_gm = generate_linear_gradient(maskSize, ktransScales(1,2), ktransScales(2,2)); - grad_wm = generate_linear_gradient(maskSize, ktransScales(1,3), ktransScales(2,3)); - - % generate the kTRANS masked volume - kTRANS_vasc = squeeze(permuted_mask(1,:,:,:)) .* grad_vasc; - kTRANS_gm = squeeze(permuted_mask(2,:,:,:)) .* grad_gm; - kTRANS_wm = squeeze(permuted_mask(3,:,:,:)) .* grad_wm; - kTRANS_wSum = kTRANS_vasc + kTRANS_gm + kTRANS_wm; - kTRANS = squeeze(kTRANS_wSum)./sumWeights; - kTRANS(isnan(kTRANS)) = 0; - else - kTRANS = create_map(permuted_mask, ktransScales, sumWeights); - end - - % generate the kinetic rate maps - k_1_2_MAP = create_map(permuted_mask, kineticRates(1,:), sumWeights); - k_1_3_MAP = create_map(permuted_mask, kineticRates(2,:), sumWeights); - - % generate Mz0 maps - Mz0P_MAP = create_map(permuted_mask, Mz0(1,:), sumWeights); - Mz0L_MAP = create_map(permuted_mask, Mz0(2,:), sumWeights); - Mz0B_MAP = create_map(permuted_mask, Mz0(3,:), sumWeights); - - %adjust FOV in z - cropidx1 = randi([40 60]); % TODO: make this optional? - cropidx2 = randi([300 320]); - kTRANS = kTRANS(:,:,cropidx1:cropidx2); - k_1_2_MAP = k_1_2_MAP(:,:,cropidx1:cropidx2); - k_1_3_MAP = k_1_3_MAP(:,:,cropidx1:cropidx2); - Mz0P_MAP = Mz0P_MAP(:,:,cropidx1:cropidx2); - Mz0L_MAP = Mz0L_MAP(:,:,cropidx1:cropidx2); - Mz0B_MAP = Mz0B_MAP(:,:,cropidx1:cropidx2); - w = weights(:,:,cropidx1:cropidx2); - - % resample/downsample maps to desired SAMPLE size - kTRANS = imresize3(kTRANS, maxSampSize); - k_1_2_MAP = imresize3(k_1_2_MAP, maxSampSize); - k_1_3_MAP = imresize3(k_1_3_MAP, maxSampSize); - Mz0P_MAP = imresize3(Mz0P_MAP, maxSampSize); - Mz0L_MAP = imresize3(Mz0L_MAP, maxSampSize); - Mz0B_MAP = imresize3(Mz0B_MAP, maxSampSize); - w = imresize3(w, maxSampSize); - - % Random Augmentations (requires image processing toolbox) - tform = randomAffine2d('Rotation',augmentParams.Rotation, 'Scale', ... - augmentParams.Scale, 'XReflection', augmentParams.XReflection, ... - 'YReflection', augmentParams.YReflection, 'XTranslation', ... - augmentParams.XTranslation, 'YTranslation', augmentParams.YTranslation, ... - 'XShear', augmentParams.XShear, 'YShear', augmentParams.YShear); - outputView = affineOutputView(size(kTRANS),tform); - kTRANS = imwarp(kTRANS,tform,OutputView=outputView); - k_1_2_MAP = imwarp(k_1_2_MAP,tform,OutputView=outputView); - k_1_3_MAP = imwarp(k_1_3_MAP,tform,OutputView=outputView); - Mz0P_MAP = imwarp(Mz0P_MAP,tform,OutputView=outputView); - Mz0L_MAP = imwarp(Mz0L_MAP,tform,OutputView=outputView); - Mz0B_MAP = imwarp(Mz0B_MAP,tform,OutputView=outputView); - w = imwarp(w,tform,OutputView=outputView); - - %flip 3rd dimension to match in vivo convention - kTRANS = flip(kTRANS,3); - kMaps = flip(cat(4,k_1_2_MAP,k_1_3_MAP),3); - Mz0Maps = flip(cat(4,Mz0P_MAP,Mz0L_MAP, Mz0B_MAP),3); - w = flip(w,3); - w = w ./ max(w(:)); % normalize coil sens weights to max of 1 after imresize - - if ~isempty(simParams) % output simulated metabolite dynamic images - % simulate signals - % store simulation parameters a in a struct, - % eventually this should be a custom class - - if isempty(inputFunction) - inputFunction = zeros([1 simParams.Nt]); - end - - % first generate voxelwise dynamics - metImages_sp = zeros(cat(2,maxSampSize,[nMets, simParams.Nt])); - metImages = zeros(cat(2,outputSize,[nMets, simParams.Nt])); - for Ix = 1:maxSampSize(1) - for Iy = 1:maxSampSize(2) - for Iz = 1:maxSampSize(3) - Mz0_vx = [Mz0Maps(Ix, Iy, Iz, 1) Mz0Maps(Ix, Iy, Iz, 2) Mz0Maps(Ix, Iy, Iz, 3)]; - [metImages_sp(Ix,Iy,Iz,:,:), ~] = simulate_Nsite_model(Mz0_vx, simParams.R1, [kMaps(Ix,Iy,Iz,1) 0; kMaps(Ix,Iy,Iz,2) 0], simParams.flips, simParams.TR, inputFunction*kTRANS(Ix,Iy,Iz) ); - end - end - end - - % multiply by coil sens weights - metImages_sp = metImages_sp .* repmat(w, [1 1 1 size(metImages_sp,4) size(metImages_sp,5)]); - - % multi res capability - metImages_mres = cell(nMets,1); % cast metImages into cell to allow different matrix sizes - for Imet=1:nMets - - % add rician noise - std_noise = max(sum(squeeze(metImages_sp(:,:,:,Imet,:)),4),[],'all') ./ (simParams.SNR(Imet) * sqrt(simParams.Nt)); - noise_R = randn(cat(2,sampSize(Imet,:),[simParams.Nt]))* std_noise; - noise_I = randn(cat(2,sampSize(Imet,:),[simParams.Nt]))* std_noise; - - if maxSampSize == sampSize(Imet,:) - temp = squeeze(metImages_sp(:,:,:,Imet,:)); - metImages_mres{Imet} = sqrt((temp + noise_R).^2 + noise_I.^2); - else - temp=zeros(cat(2,sampSize(Imet,:),simParams.Nt)); - for t=1:simParams.Nt - temp(:,:,:,t) = imresize3(metImages_sp(:,:,:,Imet,t), sampSize(Imet,:), 'box'); %box downsampling will effectively average across voxels - end - metImages_mres{Imet} = sqrt((temp + noise_R).^2 + noise_I.^2); - clear temp; - end - end - - % resize to desired OUTPUT size - for Imet = 1:size(metImages_sp,4) - temp = metImages_mres{Imet}; - for It = 1:size(metImages_sp,5) - metImages(:,:,:,Imet,It) = imresize3(squeeze(temp(:,:,:,It)), outputSize, 'lanczos3'); - %metImages(:,:,:,Imet,It) = zeropad(squeeze(temp(:,:,:,It)), outputSize(1:2)); - end - end - - else - metImages = 0; - end - - % resample maps to desired OUTPUT size - kTRANS = imresize3(kTRANS, outputSize, 'lanczos3'); - kMaps_out = zeros(cat(2,outputSize,nMets-1)); - Mz0Maps_out = zeros(cat(2,outputSize,nMets)); - for n=1:nMets-1 - kMaps_out(:,:,:,n) = imresize3(kMaps(:,:,:,n), outputSize, 'lanczos3'); - end - for n=1:nMets - Mz0Maps_out(:,:,:,n) = imresize3(Mz0Maps(:,:,:,n), outputSize, 'lanczos3'); - end - -end - -function [grad] = generate_linear_gradient(maskSize, kTRANS_low, kTRANS_high) - - x = linspace(-1, 1, maskSize(1)); - y = linspace(-1, 1, maskSize(2)); - z = linspace(-1, 1, maskSize(3)); - [X, Y, Z] = meshgrid(x, y, z); - grad = 0.5*(kTRANS_high - kTRANS_low)*Y + 0.5*(kTRANS_low + kTRANS_high); -end - -function [map] = create_map(mask, rates, sumWeights) - map_wSum = pagemtimes(rates,mask); - map = squeeze(map_wSum)./sumWeights; - map(isnan(map)) = 0; -end \ No newline at end of file diff --git a/simulations/anatomical_phantoms/brainweb_metabolic_phantom_test.m b/simulations/anatomical_phantoms/brainweb_metabolic_phantom_test.m deleted file mode 100644 index d211138..0000000 --- a/simulations/anatomical_phantoms/brainweb_metabolic_phantom_test.m +++ /dev/null @@ -1,93 +0,0 @@ -% Quick testing script for the brain web basic metabolic phantom -clear; close all; - -kineticRates = [0, 0.05, 0.03; - 0, 0.02, 0.01]; % in order [vasc, GM, WM] -ktransScales = [1, 0.2, 0.2; - 3, 0.4, 0.4]; -isFuzzy = true; -sampSize = [16 16 8]; -outputSize = [32 32 8]; -linear_kTRANS_grad = true; - -% define simulation parameters: Tarrival, Tbolus, TR, Nt, R1, flips, -% std_noise -simParams.Tarrival = 0; -simParams.Tbolus = 8; -simParams.TR = 4; -simParams.Nt = 30; -simParams.R1 = [1/30 1/25 1/25]; -simParams.flips = repmat([20; 30; 30],[1 simParams.Nt])*pi/180; -simParams.SNR = [150 40 20]; -simParams.coil_lim = [0.4 1.2]; - -% define augmentation parameters -augmentParams.XTranslation = [-1 1]; -augmentParams.YTranslation = [-1 1]; -augmentParams.Scale = [0.95 1.1]; -augmentParams.XReflection = true; -augmentParams.Rotation = [-5 5]; - -% input funciton and Mz0 -input_function = realistic_input_function(simParams.Nt, simParams.TR, simParams.Tarrival, simParams.Tbolus); -Mz0 = [input_function(1), input_function(1)*.5, input_function(1)*.5; - 0, input_function(1)*.01, input_function(1)*.01; - 0, input_function(1)*.005, input_function(1)*.005]; - -[k_trans, k_maps, Mz0_maps, metImages, w] = brainweb_metabolic_phantom(kineticRates, ktransScales, Mz0, sampSize, outputSize, simParams, input_function, isFuzzy, linear_kTRANS_grad, augmentParams); - -%% visualize kTRANS and k maps - -slices = 1:size(k_trans,3); -tpts = 1:3:simParams.Nt; - -% visualize kTRANS -figure, -imagescn(k_trans(:,:,slices),[0 max(k_trans(:,:,slices),[],'all')], [1 numel(slices)]); colormap fire; - -% visualize kinetic rates -figure, -imagescn(k_maps(:,:,slices,1),[0 max(k_maps(:,:,slices,1),[],'all')], [1 numel(slices)]); colormap fire; - -figure, -imagescn(k_maps(:,:,slices,2),[0 max(k_maps(:,:,slices,2),[],'all')], [1 numel(slices)]); colormap fire; - -% visualize Mz0 maps -figure, -imagescn(Mz0_maps(:,:,slices,1),[0 max(Mz0_maps(:,:,slices,1),[],'all')], [1 numel(slices)]); colormap fire; - -figure, -imagescn(Mz0_maps(:,:,slices,2),[0 max(Mz0_maps(:,:,slices,2),[],'all')], [1 numel(slices)]); colormap fire; - -figure, -imagescn(Mz0_maps(:,:,slices,3),[0 max(Mz0_maps(:,:,slices,3),[],'all')], [1 numel(slices)]); colormap fire; - -%% visualize metImages - -%pyruvate -figure, -imagescn(squeeze(metImages(:,:,slices,1,tpts)),[0 max(squeeze(metImages(:,:,slices,1,tpts)),[],'all')], [numel(slices) numel(tpts)]); colormap fire; - -%lactate -figure, -imagescn(squeeze(metImages(:,:,slices,2,tpts)),[0 max(squeeze(metImages(:,:,slices,2,tpts)),[],'all')], [numel(slices) numel(tpts)]); colormap fire; - -%bicarb -figure, -imagescn(squeeze(metImages(:,:,slices,3,tpts)),[0 max(squeeze(metImages(:,:,slices,3,tpts)),[],'all')], [numel(slices) numel(tpts)]); colormap fire; - -%% visualize AUCs -%pyruvate -pyrAUC = sum(squeeze(metImages(:,:,:,1,:)),length(size(squeeze(metImages(:,:,:,1,:))))); -figure, -imagescn(pyrAUC,[0 max(pyrAUC,[],'all')], [1 numel(slices)]); colormap fire; - -%lactate -lacAUC = sum(squeeze(metImages(:,:,:,2,:)),length(size(squeeze(metImages(:,:,:,2,:))))); -figure, -imagescn(lacAUC,[0 max(lacAUC,[],'all')], [1 numel(slices)]); colormap fire; - -%bicarb -bicAUC = sum(squeeze(metImages(:,:,:,3,:)),length(size(squeeze(metImages(:,:,:,3,:))))); -figure, -imagescn(bicAUC,[0 max(bicAUC,[],'all')], [1 numel(slices)]); colormap fire; diff --git a/simulations/anatomical_phantoms/cardiac_metabolic_phantom.m b/simulations/anatomical_phantoms/cardiac_metabolic_phantom.m deleted file mode 100644 index 0f66f99..0000000 --- a/simulations/anatomical_phantoms/cardiac_metabolic_phantom.m +++ /dev/null @@ -1,161 +0,0 @@ -function [kTRANS, kMaps, Mz0Maps, metImages] = cardiac_metabolic_phantom(kineticRates, ktransScales, Mz0_constants, matSize, simParams, heart_idx) -% CARDIAC_METABOLIC_PHANTOM generates standardized 3-dimensional perfusion -% and metabolism maps for simulated experiments. Supports 3 chemical pool -% kinetic rate mapping. -% -% Toolboxes required: Image Processing -% -% Parameters: -% kineticRates = the kinetic rates to simulate, [(# of chemical -% pools) - 1, 4 (tissue types: LV, RV, LV Myocardium, RV Myocardium)] -% ktransScales = volume transfer constants for different tissue -% compartments [LV, RV, LV Myocardium, Rv Myocardium] -% Mz0_constants = Initial magnetization constants per metabolite per compartment, -% size [Nmets 3] in order LV, RV, LV Myocardium, RV Myocardium -% matSize = 1x3 vector for desired matrix size of each -% dimension -% [met dim(nx,ny,nz)], default = [32 32 5] -% simParams = parameters used for the kinetic simulations: -% Tarrival, Tbolus, TR, Nt, R1, flips; if empty won't -% generate metImages, default = empty struct -% inputFunction = [1 Nt] vector as input into each voxel -% -% Optional Parameters: -% heart_idx = heart mask used for (1-20). default = random mask -% -% Outputs: -% kTRANS = generated perfusion map -% kMaps = generated rate maps for 1->2 and 1->3 -% Mz0Maps = generated Mz0 maps -% metImages = simulated metabolite dynamic images -% -% Copyright, 2024 - - % parse input arguments - arguments % !! some may not be robust enough after changes !! - kineticRates (:,4) double {mustBeNumeric} = [0.1, 0.2, 0.3; 0, 0, 0] - ktransScales (:,4) double {mustBeNumeric} = [1, 0.3, 0.3] - Mz0_constants (:,4) double {mustBeNumeric} = [0, 0, 0] - matSize (:,3) double {mustBeInteger} = [32, 32, 5] - simParams struct = struct([]) - heart_idx {mustBeInteger, mustBePositive, mustBeNonzero} = [] - end - - nMets = size(kineticRates,1) + 1; - - % load masks - if isempty(heart_idx) - heart_idx = randi(20); - end - current_path = pwd; - mask_path = fullfile(current_path,'util/_src_cardiac',num2str(heart_idx), 'cardiac_masks.mat'); - load(mask_path,'im_mask'); - - nTissues = 4; - if size(im_mask,4) ~= nTissues - error('Unexpected number of tissues present in the imported masks, ask for help, idk.'); - end - - % parameters for output map generation - permuted_mask = double(permute(im_mask,[4 1 2 3])); - sumWeights = sum(im_mask,4); - - kTRANS = create_map(permuted_mask, ktransScales, sumWeights); - - % generate Tarrival maps - Tarrival_MAP = create_map(permuted_mask, simParams.Tarrival, sumWeights); - Tarrival_MAP(sumWeights == 0) = (simParams.Tarrival(3)+simParams.Tarrival(4)) / 2; - Tarrival_MAP = squeeze(Tarrival_MAP); - - % generate Mz0 - % this is kinda awkward because you need to regenerate the input - % functions later - Mz0 = zeros(nMets, nTissues); - for tissue = 1:nTissues - input_function = realistic_input_function(simParams.Nt, simParams.TR, simParams.Tarrival(tissue), simParams.Tbolus); - Mz0(:,tissue) = input_function(1) .* Mz0_constants(:,tissue); - end - - % generate the kinetic rate maps - k_1_2_MAP = create_map(permuted_mask, kineticRates(1,:), sumWeights); - k_1_3_MAP = create_map(permuted_mask, kineticRates(2,:), sumWeights); - - % generate Mz0 maps - Mz0P_MAP = create_map(permuted_mask, Mz0(1,:), sumWeights); - Mz0L_MAP = create_map(permuted_mask, Mz0(2,:), sumWeights); - Mz0B_MAP = create_map(permuted_mask, Mz0(3,:), sumWeights); - - small_matSize = [matSize(1)/2, matSize(2)/2, matSize(3)]; - - % resample/downsample maps to desired matrix size - kTRANS = imresize3(kTRANS, small_matSize); - k_1_2_MAP = imresize3(k_1_2_MAP, small_matSize); - k_1_3_MAP = imresize3(k_1_3_MAP, small_matSize); - Mz0P_MAP = imresize3(Mz0P_MAP, small_matSize); - Mz0L_MAP = imresize3(Mz0L_MAP, small_matSize); - Mz0B_MAP = imresize3(Mz0B_MAP, small_matSize); - Tarrival_MAP = imresize3(Tarrival_MAP, small_matSize, "linear"); - - % upsample maps for blur - kTRANS = imresize3(kTRANS, matSize); - k_1_2_MAP = imresize3(k_1_2_MAP, matSize); - k_1_3_MAP = imresize3(k_1_3_MAP, matSize); - Mz0P_MAP = imresize3(Mz0P_MAP, matSize); - Mz0L_MAP = imresize3(Mz0L_MAP, matSize); - Mz0B_MAP = imresize3(Mz0B_MAP, matSize); - Tarrival_MAP = imresize3(Tarrival_MAP, matSize, "linear"); - - % account for downsampling changing kinetic rates - % TODO: not a perfect solution, areas that should have lower rates go higher - k_1_2_resize_error = max(kineticRates(1,:),[],'all') / max(k_1_2_MAP,[],'all'); - k_1_3_resize_error = max(kineticRates(2,:),[],'all') / max(k_1_3_MAP,[],'all'); - kTRANS_resize_error = max(ktransScales,[],'all') / max(kTRANS,[],'all'); - k_1_2_MAP = k_1_2_MAP .* k_1_2_resize_error; - k_1_3_MAP = k_1_3_MAP .* k_1_3_resize_error; - kTRANS = kTRANS .* kTRANS_resize_error; - - - kMaps = cat(4,k_1_2_MAP,k_1_3_MAP); - Mz0Maps = cat(4,Mz0P_MAP,Mz0L_MAP, Mz0B_MAP); - - if ~isempty(simParams) % output simulated metabolite dynamic images - % simulate signals - % store simulation parameters a in a struct, - - % generate voxelwise dynamics - metImages = zeros(cat(2,matSize,[nMets, simParams.Nt])); - for Ix = 1:matSize(1) - for Iy = 1:matSize(2) - for Iz = 1:matSize(3) - % generate input_function - Tarrival_vx = Tarrival_MAP(Ix, Iy, Iz); - input_function_vx = realistic_input_function(simParams.Nt, simParams.TR, Tarrival_vx, simParams.Tbolus); - Mz0_vx = [Mz0Maps(Ix, Iy, Iz, 1) Mz0Maps(Ix, Iy, Iz, 2) Mz0Maps(Ix, Iy, Iz, 3)]; - [metImages(Ix,Iy,Iz,:,:), ~] = simulate_Nsite_model(Mz0_vx, simParams.R1, [kMaps(Ix,Iy,Iz,1) 0; kMaps(Ix,Iy,Iz,2) 0], simParams.flips, simParams.TR, input_function_vx*kTRANS(Ix,Iy,Iz) ); - end - end - end - end - - % add rician noise - metImages_w_noise = zeros(size(metImages)); - for Imet=1:nMets - ImetImage = squeeze(metImages(:,:,:,Imet,:)); - std_noise = max(sum(ImetImage,4),[],'all') ./ (simParams.SNR(Imet) * sqrt(simParams.Nt)); - noise_R = randn(cat(2,matSize,[simParams.Nt]))* std_noise; - noise_I = randn(cat(2,matSize,[simParams.Nt]))* std_noise; - metImages_w_noise(:,:,:,Imet,:) = sqrt((ImetImage+ noise_R).^2 + noise_I.^2); - end - - metImages = metImages_w_noise; - -end - - -function [map] = create_map(mask, rates, sumWeights) - % apply rates over masks - map_wSum = pagemtimes(rates,mask); - map = squeeze(map_wSum)./sumWeights; - map(isnan(map)) = 0; -end - diff --git a/simulations/anatomical_phantoms/cardiac_metabolic_phantom_test.m b/simulations/anatomical_phantoms/cardiac_metabolic_phantom_test.m deleted file mode 100644 index 19ef2bb..0000000 --- a/simulations/anatomical_phantoms/cardiac_metabolic_phantom_test.m +++ /dev/null @@ -1,109 +0,0 @@ -% Quick testing script for the cardiac metabolic phantom -clear; close all; -% -% kineticRates = [0.0075, 0.0045, 0.0264, 0.0179; -% 0.0011, 0.0005, 0.0100, 0.0017]; % in order [lv, rv, lv_mc, rv_mc]. - - -kineticRates = [0.0075, 0.0045, 0.06, 0.02; - 0.0011, 0.0005, 0.0400, 0.01]; % in order [lv, rv, lv_mc, rv_mc]. -%ktransScales = [1.2, 1, 0.2, 0.2]; -ktransScales = [1, 1, -0.5, -0.5]; - -matSize = [32,32,5]; - -heart_idx = 1; - -% define simulation parameters: Tarrival, Tbolus, TR, Nt, R1, flips, -% std_noise -simParams.Tarrival = [7,0,10,14]; % in order lv, rv, lvmy, rvmy -simParams.Tbolus = 1; -simParams.TR = 3; % changes over time, but this seems like a pretty good estimate -% TR = 3 * 60/heart_rates(I_subject); -% subject_ids = [6 7 8 9 10 11 13 18]; -% heart_rates = [76 80 69 64 50 77 61 57]; -simParams.Nt = 20; -simParams.R1 = [1/30 1/25 1/25]; -simParams.flips = repmat([20; 30; 30],[1 simParams.Nt])*pi/180; -simParams.SNR = [150 40 20]; -simParams.coil_lim = [0.4 1.2]; % TODO: account for coil sensitivity - -Mz0_constants = [1, 1, 0.5, 0.5; - 0, 0, 0.01, 0.01; - 0, 0, 0.005, 0.005]; - -[k_trans, k_maps, Mz0_maps, metImages] = cardiac_metabolic_phantom(kineticRates, ktransScales, Mz0_constants, matSize, simParams, heart_idx); - -slices = 1:size(k_trans,3); -tpts = 1:simParams.Nt; - -%% visualize kTRANS and k maps - -% visualize kTRANS -figure("Name","kTRANS") -imagescn(k_trans(:,:,slices),[0 max(k_trans(:,:,slices),[],'all')], [1 numel(slices)]); colormap hot; - -% visualize kinetic rates -figure("Name","kPL"); -imagescn(k_maps(:,:,slices,1),[0 max(k_maps(:,:,slices,1),[],'all')], [1 numel(slices)]); colormap hot; - -figure("Name","kPB"); -imagescn(k_maps(:,:,slices,2),[0 max(k_maps(:,:,slices,2),[],'all')], [1 numel(slices)]); colormap hot; - - -%% visualize Mz0 maps -figure("Name","Mz0 maps Pyruvate"); -scale = [0 max(Mz0_maps(:,:,slices,1),[],'all')]; -if scale(2) == 0 - scale(2) = 1; -end -imagescn(Mz0_maps(:,:,slices,1), scale, [1 numel(slices)]); colormap hot; - -figure("Name","Mz0 maps Lactate"); -scale = [0 max(Mz0_maps(:,:,slices,2),[],'all')]; -if scale(2) == 0 - scale(2) = 1; -end -imagescn(Mz0_maps(:,:,slices,3), scale, [1 numel(slices)]); colormap hot; - - -figure("Name","Mz0 maps Bicarb"); -scale = [0 max(Mz0_maps(:,:,slices,3),[],'all')]; -if scale(2) == 0 - scale(2) = 1; -end -imagescn(Mz0_maps(:,:,slices,3), scale, [1 numel(slices)]); colormap hot; - - -%% visualize metImages -%pyruvate -figure("Name","Pyruvate Met Images"); -imagescn(squeeze(metImages(:,:,slices,1,tpts)),[0 max(squeeze(metImages(:,:,slices,1,tpts)),[],'all')], [numel(slices) numel(tpts)]); colormap hot; - -%lactate -figure("Name","Lactate Met Images"); -imagescn(squeeze(metImages(:,:,slices,2,tpts)),[0 max(squeeze(metImages(:,:,slices,2,tpts)),[],'all')], [numel(slices) numel(tpts)]); colormap hot; - -%bicarb -figure("Name","Bicarb Met Images"); -imagescn(squeeze(metImages(:,:,slices,3,tpts)),[0 max(squeeze(metImages(:,:,slices,3,tpts)),[],'all')], [numel(slices) numel(tpts)]); colormap hot; - - -%% visualize AUCs - -%pyruvate -pyrAUC = sum(squeeze(metImages(:,:,:,1,:)),length(size(squeeze(metImages(:,:,:,1,:))))); -figure("Name","Pyruvate AUC"); -imagescn(pyrAUC,[0 max(pyrAUC,[],'all')], [1 numel(slices)]); colormap hot; - - -%lactate -lacAUC = sum(squeeze(metImages(:,:,:,2,:)),length(size(squeeze(metImages(:,:,:,2,:))))); -figure("Name","Lactate AUC"); -imagescn(lacAUC,[0 max(lacAUC,[],'all')], [1 numel(slices)]); colormap hot; - - -%bicarb -bicAUC = sum(squeeze(metImages(:,:,:,3,:)),length(size(squeeze(metImages(:,:,:,3,:))))); -figure("Name","Bicarb AUC"); -imagescn(bicAUC,[0 max(bicAUC,[],'all')], [1 numel(slices)]); colormap hot; diff --git a/simulations/anatomical_phantoms/metabolite.m b/simulations/anatomical_phantoms/metabolite.m new file mode 100644 index 0000000..61bee9f --- /dev/null +++ b/simulations/anatomical_phantoms/metabolite.m @@ -0,0 +1,34 @@ +classdef metabolite + properties + Mz0 (1,1) {mustBeNumeric} + R1 (1,1) {mustBeNumeric} + K (1,2) {mustBeNumeric} + end + + methods + function obj = metabolite(args) % name=value arguments + % Parameters: + % Mz0 = initial magnetization. size = (1,1) + % R1 = relaxation rate. size = (1,1) + % k = kinetic rate(s). size = (1,1) for only forward rate, or (1,2) for forward and reverse rate + arguments + args.Mz0 (1,1) {mustBeNumeric} = 0 + args.R1 (1,1) {mustBeNumeric} = 0 + args.k (1,:) {mustBeNumeric} = 0 + end + + obj.Mz0 = args.Mz0; + obj.R1 = args.R1; + + % parse k + if size(args.k, 2) == 1 + obj.K = [args.k, 0]; + elseif size(args.k, 2) == 2 + obj.K = args.k; + else + error("size of `k` must have size of either (1,1) (forward rate only) or (1,2) (both forward and reverse rates)"); + end + + end + end +end diff --git a/simulations/anatomical_phantoms/mri_system.m b/simulations/anatomical_phantoms/mri_system.m new file mode 100644 index 0000000..3b28d9c --- /dev/null +++ b/simulations/anatomical_phantoms/mri_system.m @@ -0,0 +1,361 @@ +classdef mri_system + methods (Static) + function [met_images_mres, coil_sens_weights] = run_mri_system(met_images, sample_size, snr, coil_lim, tissue_struct, output_size, augment_params, opts) + % Wrapper for easy use of mri system model + % Parameters: + % met_images = single-resolution metabolite images. size = (row, col, slice, met, tpt) + % sample_size = desired size of multires images. size = (met, dim) + % snr = signal-to-noise ratio. size = (1, met) + % coil_lim = coil limits. [min, max] + % tissue_struct = tissue structure. size = (1,1). type = tissue_structure + % output_size = desired output_size. size = (met, dim) + % augment_params = augmentation parameters. struct + % Options: + % include_bg_noise = boolean flag to include background noise + % Outputs: + % met_images_mres = multiresolution metabolite images. + % size = (1, met) cell array. size of each cell = (row, col, slice, time_pt) + % coil_sens_weights = coil sensitivity weights + + + % argument validation + arguments + met_images (:,:,:,:,:) {mustBeNumeric} + sample_size (:,3) {mustBeNumeric} + snr (1,:) {mustBeNumeric} + coil_lim (1,2) {mustBeNumeric} + tissue_struct (1,1) tissue_structure + output_size (:,3) {mustBeNumeric} = NaN + augment_params struct = struct() + opts.include_bg_noise (1,1) logical = true + end + + n_mets = size(met_images, 4); + if size(sample_size, 1) ~= n_mets && size(sample_size, 1) ~= 1 + error('Mismatched array sizes: the 1st dimension of `sample_size` must equal n_mets OR 1'); + end + if size(snr, 2) ~= n_mets + error('mismatched array sizes: the 2nd dimension of `snr` must equal n_mets'); + end + if (size(output_size, 1) ~= n_mets) && (size(output_size, 1) ~= 1) && (all(~isnan(output_size), 'all')) + error('mismatched array sizes: the 1st dimension of `output_size` must equal n_mets OR 1'); + end + + % run the system + if ~isempty(fieldnames(augment_params)) + cell_augment_params = namedargs2cell(augment_params); % unpack the augmentation parameters + [met_images, transform] = mri_system.augment(met_images, cell_augment_params{:}); + tissue_struct = tissue_struct.apply_transforms(transform.tform2d, transform.z_translation); + end + [met_images, coil_sens_weights] = mri_system.apply_coil_lim(met_images, coil_lim, tissue_struct.Mask); + met_images_mres = mri_system.make_met_images_multires(met_images, sample_size); + if opts.include_bg_noise + [met_images_mres, ~] = mri_system.add_rician_noise(met_images_mres, snr); + else + [~, met_images_mres] = mri_system.add_rician_noise(met_images_mres, snr); + end + + if all(~isnan(output_size), 'all') + met_images_mres = mri_system.upsample_to_output_size(met_images_mres, output_size); + end + end + + function met_images_multires = make_met_images_multires(met_images, sample_size) + % Converts single-resolution dynamic metabolite images to multiresolution dynamic metabolite images + % Parameters: + % met_images = single-resolution dynamic metabolite images. + % size = (row, col, slice, metabolite, time_pt) + % sample_size = desired matrix sizes for each metabolite. + % size = (metabolite, dim) OR (1, dim) + % + % Outputs: + % met_images_multires = multiresolution dynamic metabolite images. + % size = (1, met) cell array, where size of each cell = (row, col, slice, time_pt) + arguments + met_images (:,:,:,:,:) {mustBeNumeric} + sample_size (:,3) {mustBeInteger, mustBePositive} + end + + n_mets = size(met_images, 4); + + if size(sample_size, 1) == 1 + sample_size = repmat(sample_size, [n_mets, 1]); + elseif size(sample_size, 1) ~= n_mets + error('Mismatched array sizes: the 1st dimension of `sample_size` must equal n_mets OR 1'); + end + + n_mets = size(met_images, 4); + n_time_pts = size(met_images, 5); + met_images_multires = cell(1, n_mets); + + for Imet = 1:n_mets + met_images_multires{Imet} = zeros( ... + cat(2, sample_size(Imet, :), n_time_pts) ... + ); + for time_pt = 1:n_time_pts + met_images_multires{Imet}(:, :, :, time_pt) = ... + imresize3(squeeze(met_images(:, :, :, Imet, time_pt)), sample_size(Imet, :), 'box'); % box averages surrounding voxels + end + end + end + + + function [met_images_w_noise, met_images_w_noise_no_bg] = add_rician_noise(met_images, snr) + % Adds rician noise to dynamic metabolite images + % Parameters: + % met_images = dynamic metabolite images. either: + % - (1, met) cell array, where size of each cell = (row, col, slice, time_pt); OR + % - numerical array. size = (met, row, col, slice, time_pt) + % snr = signal-to-noise ratio per metabolite. (1, metabolite) + % + % Outputs: + % met_images_w_noise = metabolite images with rician noise. + % size = (1, met) cell array, where size of each + % cell = (row, col, slice, time_pt) + % met_images_w_noise_no_bg = metabolite images with rician noise, without background noise + % size = (1, met) cell array, where size of each + % cell = (row, col, slice, time_pt) + + % argument validation/input parsing + arguments + met_images + snr (1,:) {mustBeNumeric} + end + + % convert to cell array if not already a cell array + if isnumeric(met_images) + if numel(size(met_images)) ~= 5 + error("met_images must either be a numeric array with size = [nmets, row, col, slice, time_pt] OR be a [1, nmets] cell array where each cell = [row, col, slice, time_pt],"); + end + + n_mets = size(met_images, 1); + cell_met_images = cell(1, n_mets); + for imet = 1:n_mets + single_met_image = met_images(imet,:,:,:,:); + single_met_image = reshape(single_met_image, size(single_met_image, 2:numel(size(single_met_image)))); % remove first dimension (without squeeze() lest another dim = 1) + + cell_met_images{imet} = single_met_image; + end + + met_images = cell_met_images; + % error if not either numeric or cell + elseif ~iscell(met_images) + error("met_images must either be a numeric array with size = [nmets, row, col, slice, time_pt] OR be a [1, nmets] cell array where each cell = [row, col, slice, time_pt],"); + end + + % error if number of metabolites don't match in met_images and snr + if numel(met_images) ~= size(snr, 2) + error("mismatched array sizes. met_images_multires and SNR must both have length = n_mets"); + end + + met_images_w_noise = met_images; + met_images_w_noise_no_bg = met_images; + n_mets = numel(met_images); + for imet = 1:n_mets + [image_w_bg, image_w_out_bg] = mri_system.add_rician_noise_image(met_images{imet}, snr(imet)); + met_images_w_noise{imet} = image_w_bg; + met_images_w_noise_no_bg{imet} = image_w_out_bg; + end + end + + function output_met_images = upsample_to_output_size(met_images_lowres, output_size) + % Upsamples dynamic metabolite images to output size + % Parameters: + % met_images_lowres = un-resized metabolite images. + % size = (1, met) cell array, where each cell = (row, col, slice, time_pt) + % output_size = desired output matrix size. size = (met, dim) OR (1, dim) + % Outputs: + % output_met_images = upsampled met images + arguments + met_images_lowres + output_size (:,3) {mustBeNumeric} + end + + n_mets = numel(met_images_lowres); + if size(output_size, 1) == 1 + output_size = repmat(output_size, [n_mets, 1]); + elseif size(output_size, 1) ~= n_mets + error("mismatched array sizes. 1st dimension of `output_size` should both equal n_mets OR 1"); + end + + % upsample + output_met_images = cell(1, n_mets); + for i_met = 1:n_mets + n_tpts = size(met_images_lowres{i_met}, 4); + met_output_size = output_size(i_met, :); + output_met_images{i_met} = zeros(cat(2, met_output_size, n_tpts)); + for i_tpt = 1:n_tpts + output_met_images{i_met}(:,:,:,i_tpt) = imresize3(met_images_lowres{i_met}(:,:,:,i_tpt), met_output_size); + end + end + end + + function [augmented_met_images, transform] = augment(met_images, augs) + % Augments image + % Positional Parameters: + % met_images = metabolite images. size = (row, col, slice, met, tpt) + % Name-Value Parameters: see randomAffine2d + % Outputs: + % augmented_met_images = augmented metabolite images. size = (row, col, slice, met, tpt) + % transform = transformation applied to images + % transform.tform2d = transform applied to images. type = affinetform2d + % transform.z_translation = z translation applied to images. size = (1,1) + arguments + met_images (:,:,:,:,:) {mustBeNumeric} + augs.XTranslation (1,2) {mustBeNumeric} = [0,0] + augs.YTranslation (1,2) {mustBeNumeric} = [0,0] + augs.ZTranslation (1,2) {mustBeNumeric} = [0,0] + augs.Rotation (1,2) {mustBeNumeric} = [0,0] + augs.Scale (1,2) {mustBeNumeric} = [1,1] + augs.XReflection (1,1) {mustBeNumericOrLogical} = false + augs.YReflection (1,1) {mustBeNumericOrLogical} = false + augs.XShear (1,2) {mustBeNumeric} = [0,0] + augs.YShear (1,2) {mustBeNumeric} = [0,0] + augs.Seed (1,1) {mustBeNumeric} = NaN + end + + % initialize rng + if ~isnan(augs.Seed) + rng(augs.Seed); + else + rng("shuffle"); + end + + tform = randomAffine2d(... + XTranslation=augs.XTranslation, ... + YTranslation=augs.YTranslation, ... + Rotation=augs.Rotation, ... + Scale=augs.Scale, ... + XReflection=augs.XReflection, ... + YReflection=augs.YReflection, ... + XShear=augs.XShear, ... + YShear=augs.YShear ... + ); + + output_view = affineOutputView(size(met_images, [1,2]), tform, BoundsStyle="CenterOutput"); + augmented_met_images = zeros(size(met_images)); + for i_met = 1:size(met_images, 4) + for i_tpt = 1:size(met_images, 5) + augmented_met_images(:,:,:,i_met,i_tpt) = imwarp(met_images(:,:,:,i_met,i_tpt), tform, OutputView=output_view); + end + end + + % z translation + offset = randi(augs.ZTranslation); + aug_met_img_ztrans = zeros(size(augmented_met_images)); + n_slices = size(met_images,3); + cutoff = n_slices - abs(offset); + if offset > 0 + aug_met_img_ztrans(:,:, 1:cutoff, :,:) = augmented_met_images(:,:, (offset + 1):n_slices ,:,:); + augmented_met_images = aug_met_img_ztrans; + elseif offset < 0 + aug_met_img_ztrans(:,:, (abs(offset) + 1):n_slices, :,:) = augmented_met_images(:,:, 1:cutoff, :,:); + augmented_met_images = aug_met_img_ztrans; + end + + transform.tform2d = tform; + transform.z_translation = offset; + end + + function [met_images_w_coil_lim, coil_sens_weights] = apply_coil_lim(met_images, coil_lim, tissue_mask) + % Parameters: + % met_images = metabolite images. size = (row, col, slice, met, time_pt) + % coil_lim = coil sensitivity limits. [min, max] + % tissue_mask = tissue mask. size = (row, col, slice, tissue) + % Outputs: + % met_images_w_coil_lim = metabolite images with coil sensitivity. size = (row, col, slice, met, time_pt) + % coil_sens_weights = coil sensitivity weights + + % argument validation + arguments + met_images (:,:,:,:,:) {mustBeNumeric} + coil_lim (1,2) {mustBeNumeric} + tissue_mask (:,:,:,:) {mustBeNumeric} + end + + if any(size(met_images, 1:3) ~= size(tissue_mask, 1:3)) + error("mismatched array sizes. `met_images` and `tissue_mask` must have the same number of rows, cols, and slices"); + end + + mask = sum(tissue_mask, 4); + coil_sens_weights = mri_system.coil_dist_map(mask, coil_lim); + + n_mets = size(met_images, 4); + n_tpts = size(met_images, 5); + expanded_coil_sens_weights = repmat(coil_sens_weights, [1, 1, 1, n_mets, n_tpts]); + + met_images_w_coil_lim = met_images .* expanded_coil_sens_weights; + end + end + + methods (Static, Access = private) + function [met_image_w_noise, met_image_w_noise_no_bg] = add_rician_noise_image(met_image, snr) + % Adds rician noise to single dynamic metabolite image + % Parameters: + % met_image = single met image. size = (row, col, slice, time_pt) + % snr = signal-to-noise ratio. size = (1,1) + % Outputs: + % met_image_w_noise = metabolite image with noise. size = (row, col, slice, time_pt) + % met_image_w_noise_no_bg = metabolite images with noise, without background noise. size = (row, col, slice, time_pt) + + arguments + met_image (:,:,:,:) {mustBeNumeric} + snr (1,1) {mustBeNumeric} + end + + nt = size(met_image, 4); + sample_size = size(met_image, 1:3); + + std_noise = max(sum(met_image, 4), [], 'all') ./ (snr * sqrt(nt)); + noise_R = randn(cat(2, sample_size, nt)) * std_noise; + noise_I = randn(cat(2, sample_size, nt)) * std_noise; + + met_image_w_noise = sqrt((met_image + noise_R).^2 + noise_I.^2); + + met_image_w_noise_no_bg = met_image_w_noise; + met_image_w_noise_no_bg(met_image == 0) = 0; + end + + % taken from brainweb + function [weights] = coil_dist_map(mask, lim) + maskSize = size(mask); + weights = zeros(maskSize); + + % create y gradient + x = linspace(-1, 1, maskSize(1)); + y = linspace(-1, 1, maskSize(2)); + z = linspace(-1, 1, maskSize(3)); + [~, Y, Z] = meshgrid(x, y, z); + + % y gradient + %lim = [0.6, 1.2]; + grady = 0.5*(lim(2) - lim(1))*Y + 0.5*(lim(1) + lim(2)); + + % z gradient + gradz = (1 - abs(Z).^2) + 0.6; + gradz = gradz ./ max(gradz, [], 'all'); + + grad = grady .* gradz; + + for z=1:maskSize(3) + mask_sl = squeeze(mask(:,:,z)); + + % get outline/perim of mask + mask_sl = imfill(bwmorph(bwareaopen(mask_sl,300),"fill"),"holes"); + %figure, imagesc(mask); axis off square; + bw2 = bwperim(mask_sl); + %figure, imagesc(bw2) + + %reverse_mask + mask_rev = imcomplement(mask_sl); + + % calculate weights based on distance from mask perim + w = bwdist(bw2) .^0.5; + weights(:,:,z) = ((1 - (w ./max(w(:)))) .* grad(:,:,z) .* mask_sl) + mask_rev; + %figure, imagesc(weights) + end + + weights(isnan(weights)) = 0; + end + end +end diff --git a/simulations/anatomical_phantoms/pk_model.m b/simulations/anatomical_phantoms/pk_model.m new file mode 100644 index 0000000..0fa8cad --- /dev/null +++ b/simulations/anatomical_phantoms/pk_model.m @@ -0,0 +1,263 @@ +classdef pk_model + methods (Static) + function [met_images, dynamics_low_ktrans, dynamics_high_ktrans, images_low_ktrans, images_high_ktrans] = run_pk_model(mz0, r1, k, flips, tr, tissue_struct, opts) + % Wrapper for easy use of pk model. + % Parameters: + % mz0 = initial magnetization of each metabolite in each tissue. size = (met, tissue) + % r1 = relaxation rates. size = (1, met) + % k = forward kinetic rates of product metabolites. size = (met-1, tissue) + % flips = flip angles (rad). size = (met, time_pt) + % tr = temporal resolution. size = (1,1) + % tissue_struct = tissue_structure. size = (1,1), type = tissue_structure + % Additional options: + % input_function = additional input for substrate per tissue per time point. size = (tissue, tpt). may not be provided if `t_arrival` and `t_bolus` are provided. + % t_arrival = arrival time of substrate. size = (1, tissue). must be provided with `t_bolus`. + % t_bolus = time it takes for bolus to enter. size = (1,1). must be provided with `t_arrival` + % Outputs: + % met_images = dynamic metabolite images. size = (row, col, slice, tissue, met, time_pt) + % dynamics_low_ktrans = metabolite dynamices with k_trans = 0. size = (tissue, met, time_pt) + % dynamics_high_ktrans = metabolite dynamices with k_trans = 1. size = (tissue, met, time_pt) + % images_low_ktrans = metabolite images with k_trans = 0. size = (row, col, slice, tissue, met, time_pt) + % images_high_ktrans = metabolite images with k_trans = 1. size = (row, col, slice, tissue, met, time_pt) + arguments + mz0 (:,:) {mustBeNumeric} + r1 (1,:) {mustBeNumeric} + k (:,:) {mustBeNumeric} + flips (:,:) {mustBeNumeric} + tr (1,1) {mustBeNumeric} + tissue_struct (1,1) tissue_structure + opts.input_function (:,:) {mustBeNumeric} = NaN + opts.t_arrival (1,:) {mustBeNumeric} = NaN + opts.t_bolus (1,1) {mustBeNumeric} = NaN + end + n_tissues = size(mz0, 2); + dynamics_low_ktrans = pk_model.generate_all_met_dynamics(mz0, r1, k, zeros(1, n_tissues), flips, tr, ... + input_function=opts.input_function, t_arrival=opts.t_arrival, t_bolus=opts.t_bolus); + dynamics_high_ktrans = pk_model.generate_all_met_dynamics(mz0, r1, k, ones(1, n_tissues), flips, tr, ... + input_function=opts.input_function, t_arrival=opts.t_arrival, t_bolus=opts.t_bolus); + + images_low_ktrans = pk_model.generate_met_images(tissue_struct, dynamics_low_ktrans); + images_high_ktrans = pk_model.generate_met_images(tissue_struct, dynamics_high_ktrans); + + met_images = pk_model.apply_k_trans(images_low_ktrans, images_high_ktrans, tissue_struct.K_trans_map); + end + + + function met_dynamics = generate_all_met_dynamics(mz0, r1, k, k_trans, flips, tr, opts) + % Wrapper for easy use of generate_met_dynamics(). Accepts either input_function OR t_arrival and t_bolus + % Parameters: + % mz0 = initial magnetization of each metabolite in each tissue. size = (met, tissue) + % r1 = relaxation rates. size = (1, met) + % k = forward kinetic rates of product metabolites. size = (met-1, tissue) + % k_trans = volumetric transfer constants. size = (1, tissue) + % flips = flip angles (rad). size = (met, time_pt) + % tr = temporal resolution. size = (1,1) + % Additional options: + % input_function = additional input for substrate per tissue per time point. size = (tissue, tpt). may not be provided if `t_arrival` and `t_bolus` are provided. + % t_arrival = arrival time of substrate. size = (1, tissue). must be provided with `t_bolus`. + % t_bolus = time it takes for bolus to enter. size = (1,1). must be provided with `t_arrival` + % Outputs: + % met_dynamics = metabolite dynamics. size = (tissue, met, time_pt) + + % argument validation + arguments + mz0 (:,:) {mustBeNumeric} + r1 (1,:) {mustBeNumeric} + k (:,:) {mustBeNumeric} + k_trans (1,:) {mustBeNumeric} + flips (:,:) {mustBeNumeric} + tr (1,1) {mustBeNumeric} + opts.input_function (:,:) {mustBeNumeric} = NaN + opts.t_arrival (1,:) {mustBeNumeric} = NaN + opts.t_bolus (1,1) {mustBeNumeric} = NaN + end + + % input function, t_arrival, t_bolus parsing + provided_opts = [~any(isnan(opts.input_function), 'all'), ~any(isnan(opts.t_arrival), 'all'), ~isnan(opts.t_bolus)]; % e.g. if only input function is provided, this is [1,0,0] + + if provided_opts == [1,0,0] % case when only input_function is provided + input_function = opts.input_function; + elseif provided_opts == [0,1,1] % case when t_arrival and t_bolus are provided, but not input_function + % verify t_arrival and t_bolus + n_tissues = size(mz0, 2); + n_tpts = size(flips, 2); + if size(opts.t_arrival, 1) ~= 1 + error("`t_arrival` must have a size = (1, tissue)"); + end + if size(opts.t_arrival, 2) ~= n_tissues + error("mismatched number of tissues in `Mz0` and `t_arrival`"); + end + % create input_function + input_function = zeros(n_tissues, n_tpts); + for i_tissue = 1:n_tissues + input_function(i_tissue, :) = realistic_input_function(n_tpts, tr, opts.t_arrival(i_tissue), opts.t_bolus); + end + elseif provided_opts == [0,0,0] % case when nothing is provided + n_tissues = size(mz0, 2); + n_tpts = size(flips, 2); + input_function = zeros(n_tissues, n_tpts); + else + error("Provide EITHER `input_function` OR both `t_arrival` and `t_bolus`") + end + + % validate the rest of the arguments + [n_mets, n_tissues, n_tpts] = pk_model.validate_pk_args(mz0, r1, k, k_trans, flips, input_function); + + % migrate everything over to compartment-specific pk params + met_dynamics = zeros(n_tissues, n_mets, n_tpts); + for i_tissue = 1:n_tissues + % get substrate + substrate = metabolite( ... + Mz0=mz0(1,i_tissue), ... + R1=r1(1), ... + k=[0,0]); + + % get products + products(1, n_mets-1) = metabolite; + for i_met = 2:n_mets + products(i_met-1) = metabolite( ... + Mz0=mz0(i_met, i_tissue), ... + R1=r1(i_met), ... + k=[k(i_met - 1, i_tissue), 0]); + end + + % put it all into pk_params + tissue_pk_params = pk_params(... + substrate=substrate, ... + products=products, ... + TR=tr, ... + input_function=input_function(i_tissue, :), ... + flips=flips); + + % generate met dynamics + met_dynamics(i_tissue,:,:) = pk_model.generate_met_dynamics(tissue_pk_params, k_trans(i_tissue)); + end + end + + + function met_dynamics = generate_met_dynamics(params, k_trans) + % Generates metabolite dynamics from PK parameters + % Parameters: + % params = pk parameters. size = (1,1), type = pk_params + % k_trans = volumetric transfer constant. size = (1,1) + % Outputs: + % met_dynamics = metabolite dynamics. size = (met, tpt) + arguments + params (1,1) pk_params + k_trans (1,1) {mustBeNumeric} = 1; + end + + % prep everything for simulate_Nsite_model() + Mz0 = params.get_Mz0(); + R1 = params.get_R1(); + k = params.get_kinetic_rates(); + flips = params.get_flips(); + TR = params.TR; + input_function = params.InputFunction; + + [met_dynamics, ~] = simulate_Nsite_model(Mz0, R1, k, flips, TR, input_function .* k_trans); + end + + function met_images = generate_met_images(tissue_struct, met_dynamics) + % Parameters: + % met_dynamics = metabolite dynamics. size = (tissue, met, time_pt) + % tissue_struct = tissue_structure. size = (1,1), type = tissue_structure + % Outputs: + % met_images = dynamic metabolite images. + % size = (row, col, slice, tissue, met, time_pt) + arguments + tissue_struct (1,1) tissue_structure + met_dynamics {mustBeNumeric} + end + + % validate args + if size(tissue_struct.Mask, 4) ~= size(met_dynamics,1) + error("mismatched number of tissues in tissue structure and met dynamics"); + end + + time_pts = size(met_dynamics, 3); + n_mets = size(met_dynamics, 2); + + % reshape + expand met dynamic + met_dynamics = reshape(met_dynamics, cat(2,[1,1,1],size(met_dynamics))); + met_dynamics = repmat(met_dynamics, cat(2, size(tissue_struct.Mask, 1:3), [1,1,1])); + + % expand mask + mask = tissue_struct.Mask; % = (row, col, slice, tissue) + mask = repmat(mask, cat(2,[1,1,1,1,n_mets,time_pts])); + + % multiply + met_images = met_dynamics .* mask; + + % sum across tissues + met_images = squeeze(sum(met_images, 4)); + end + + function met_images = apply_k_trans(met_images_low_ktrans, met_images_high_ktrans, k_trans_map) + % Parameters: + % met_images_low_ktrans = met_images where k_trans = 0 (no additional input). + % size = (row, col, slice, met, time_pt) + % met_images_high_ktrans = met_images where k_trans = 1 (100% additional input). + % size = (row, col, slice, met, time_pt) + % k_trans_map = map of k_trans values. size = (row, col, slice) + % Outputs: + % met_images = met images with k_trans applied. size = (row, col, slice, met time_pt) + + % argument validation + arguments + met_images_low_ktrans (:,:,:,:,:) {mustBeNumeric} + met_images_high_ktrans (:,:,:,:,:) {mustBeNumeric} + k_trans_map (:,:,:) {mustBeNumeric} + end + + if any(size(met_images_low_ktrans) ~= size(met_images_high_ktrans)) + error("mismatched array sizes. `met_images_low_ktrans` and `met_images_high_ktrans` must have the same size"); + end + + if any(size(k_trans_map) ~= size(met_images_low_ktrans, 1:3)) + error("mismatched array sizes. `k_trans_map` mut have the same number of rows, columns, and slices as `met_images_low_ktrans`"); + end + + % expand k_trans_map + n_mets = size(met_images_low_ktrans, 4); + n_tpts = size(met_images_low_ktrans, 5); + full_ktrans_map = repmat(k_trans_map, 1, 1, 1, n_mets, n_tpts); + + % interpolate + met_images = met_images_low_ktrans + (met_images_high_ktrans - met_images_low_ktrans) .* full_ktrans_map; + end + end + + + methods (Static, Access = private) + function [n_mets, n_tissues, n_tpts] = validate_pk_args(mz0, r1, k, k_trans, flips, input_function, t_arrival) + % validates arguments for `run_pk_model` + n_mets = size(mz0, 1); + if size(r1, 2) ~= n_mets + error("mismatched number of metabolites in `Mz0` and `R1`"); + end + if size(flips, 1) ~= n_mets + error("mismatched number of metabolites in `Mz0` and `flips`"); + end + if size(k, 1) ~= n_mets - 1 + error("mismatched number of metabolits in `Mz0` and `k`. `k` should have n_mets-1 rows"); + end + + n_tissues = size(mz0, 2); + if size(k, 2) ~= n_tissues + error("mismatched number of tissues in `Mz0` and `k`"); + end + if size(input_function, 1) ~= n_tissues + error("mismatched number of tissues in `Mz0` and `input_function`"); + end + if size(k_trans, 2) ~= n_tissues + error("mismatched number of tissues in `Mz0` and `k_trans`"); + end + + n_tpts = size(flips, 2); + if size(input_function, 2) ~= n_tpts; + error("mismatched number of time points in `flips` and `input_function`"); + end + end + end +end diff --git a/simulations/anatomical_phantoms/pk_params.m b/simulations/anatomical_phantoms/pk_params.m new file mode 100644 index 0000000..bcb1ed1 --- /dev/null +++ b/simulations/anatomical_phantoms/pk_params.m @@ -0,0 +1,76 @@ +classdef pk_params + properties + Substrate (1,1) metabolite + Products (1,:) metabolite + TR (1,1) {mustBeNumeric} + InputFunction (1,:) {mustBeNumeric} + Flips (:,:) {mustBeNumeric} + end + methods + function obj = pk_params(args) + % Parameters: + % substrate = substrate metabolite. size = (1,1), type = metabolite + % products = product metabolites. size = (1, n_mets - 1), type = metabolite + % TR = repetition time. size = (1,1) + % input_function = additional input of substrate. size = (1, time_pt) + % flips = flip angles (rad). size = (met, time_pt) + + % argument validation + arguments + args.substrate (1,1) metabolite + args.products (1,:) metabolite + args.TR (1,1) {mustBeNumeric} + args.input_function (1,:) {mustBeNumeric} + args.flips (:,:) {mustBeNumeric} + end + + n_mets = numel(args.substrate) + numel(args.products); + if size(args.flips, 1) ~= n_mets + error("Mismatched number of metabolites (substrate + number of products) and rows in `flips`"); + end + + nt = numel(args.input_function); + if size(args.flips, 2) ~= nt + error("mismatched number of time points in `input_function` and `flips`"); + end + + obj.Substrate = args.substrate; + obj.Products = args.products; + obj.TR = args.TR; + obj.InputFunction = args.input_function; + obj.Flips = args.flips; + end + + % various getters + function Mz0 = get_Mz0(pk_params) + % Outputs: Mz0 = (1, met) + Mz0 = zeros(1, numel(pk_params.Products) + 1); + Mz0(1) = pk_params.Substrate.Mz0; + for met = 1:numel(pk_params.Products) + Mz0(met + 1) = pk_params.Products(met).Mz0; + end + end + + function R1 = get_R1(pk_params) + % Outputs: R1 = (1, met) + R1 = zeros(1, numel(pk_params.Products) + 1); + R1(1) = pk_params.Substrate.R1; + for met = 1:numel(pk_params.Products) + R1(met + 1) = pk_params.Products(met).R1; + end + end + + function flips = get_flips(pk_params) + % Outputs: flips = (met, time_pt) + flips = pk_params.Flips; + end + + function k = get_kinetic_rates(pk_params) + % Outputs: kinetic rates = (met, fw/rv) + k = zeros(numel(pk_params.Products), 2); + for met = 1:numel(pk_params.Products) + k(met, :) = pk_params.Products(met).K; + end + end + end +end diff --git a/simulations/anatomical_phantoms/tissue_structure.m b/simulations/anatomical_phantoms/tissue_structure.m new file mode 100644 index 0000000..3da808e --- /dev/null +++ b/simulations/anatomical_phantoms/tissue_structure.m @@ -0,0 +1,220 @@ +classdef tissue_structure + properties + Name string + Mask (:,:,:,:) {mustBeNumeric} % size = (row, col, slice, tissue) + Tissues (1,:) string % size = (1, tissue) + K_trans_map (:,:,:) {mustBeNumeric} % size = (row, col, slice) + end + methods + function obj = tissue_structure(name, mask, tissues, dims) + % Parameters: + % name = name of tissue structure. size = (1,1), type = string + % mask = tissue masks. size = (row, col, slice, tissue), type = numeric or logical + % tissues = names of tissues. size = (1, tissue), type = string. purely semantic right now + % dims = (optional) desired size of mask. [rows, cols, slices] + arguments + name (1,1) string + mask (:,:,:,:) {mustBeNumericOrLogical} + tissues (1,:) string + dims (1,3) {mustBeInteger} = [0,0,0] + end + + if numel(tissues) ~= size(mask, 4) + error("Mismatched number of tissues in `mask` and `tissues`") + end + + obj.Name = name; + obj.Tissues = tissues; + + if islogical(mask) + % cast to numeric if mask is logical + obj.Mask = double(mask); + else + obj.Mask = mask; + end + + if dims ~= [0,0,0] + obj = obj.downscale_mask(dims); + end + + obj = obj.normalize_mask(); + end + + + function obj = create_k_trans_map(obj, k_trans) + % Parameters: + % k_trans = k_trans values. size = (1, tissue); OR + % (2, tissue) where row 1 = min k_trans, row 2 = max k_trans. if this is the case, k_trans will follow a linear gradient + + % argument validation + arguments + obj + k_trans (:,:) {mustBeNumeric} + end + + if size(k_trans, 1) > 2 + error('length of 1st dimension of k_trans must be either 1 or 2'); + end + + if size(k_trans, 2) ~= size(obj.Mask, 4) + error('mismatched array sizes: 2nd dimension of `k_trans` and 4th dimension of `tissue_structure.Mask` must both equal number of tissues') + end + + % case where k_trans is constant (not gradient) + % mask = (row, col, slice, tissue) + if size(k_trans, 1) == 1 + mask = permute(obj.Mask, [4,1,2,3]); + obj.K_trans_map = squeeze(pagemtimes(k_trans, mask)); + return + end + + % case where k_trans is a gradient + n_tissues = size(k_trans, 2); + mask_size = size(obj.Mask, 1:3); + + k_trans_map = zeros(size(obj.Mask)); + for i_tissue = 1:n_tissues + gradient = generate_linear_gradient(mask_size, k_trans(1, i_tissue), k_trans(2, i_tissue)); + k_trans_map(:,:,:,i_tissue) = squeeze(obj.Mask(:,:,:,i_tissue)) .* gradient; + end + + obj.K_trans_map = sum(k_trans_map, 4); + + % helper function from `brainweb_metabolic_phantom` + function grad = generate_linear_gradient(maskSize, kTRANS_low, kTRANS_high) + x = linspace(-1, 1, maskSize(1)); + y = linspace(-1, 1, maskSize(2)); + z = linspace(-1, 1, maskSize(3)); + [~, Y, ~] = meshgrid(x, y, z); + grad = 0.5*(kTRANS_high - kTRANS_low)*Y + 0.5*(kTRANS_low + kTRANS_high); + end + end + + + function obj = downscale_mask(obj, dims) + % downscales mask (because large masks can cause issues with memory requests) + % Parameters: + % dims = desired dimensions. [rows, cols, slices] + arguments + obj + dims (1,3) {mustBeNumeric} = [100,100,100] + end + + n_tissues = size(obj.Mask, 4); + new_mask = zeros(cat(2, dims, n_tissues)); + + for i_tissue = 1:n_tissues + new_mask(:,:,:,i_tissue) = imresize3(obj.Mask(:,:,:,i_tissue), dims, "cubic"); + end + + obj.Mask = new_mask; + end + + + function obj = normalize_mask(obj) + % normalizes voxels so that the sum of the masks of each tissue in any given voxel is no more than 1 + norm_weights = sum(obj.Mask, 4); + norm_weights(norm_weights < 1) = 1; % only normalize voxels with a sum > 1 + obj.Mask = obj.Mask ./ repmat(norm_weights, [1,1,1, size(obj.Mask,4)]); + end + + + function obj = apply_transforms(obj, tform2d, z_translation) + % applies transformations to obj.Mask + % Parameters: + % tform2d = transform to apply to each slice. type = affinetform2d + % z_translation = z translation + arguments + obj + tform2d (1,1) affinetform2d + z_translation (1,1) {mustBeNumeric} + end + + output_view = affineOutputView(size(obj.Mask, [1,2]), tform2d, BoundsStyle="CenterOutput"); + for i_tissue = 1:size(obj.Mask, 4) + obj.Mask(:,:,:,i_tissue) = imwarp(obj.Mask(:,:,:,i_tissue), tform2d, OutputView=output_view); + end + + % z translation + mask_ztrans = zeros(size(obj.Mask)); + n_slices = size(obj.Mask,3); + cutoff = n_slices - abs(z_translation); + if z_translation > 0 + mask_ztrans(:,:, 1:cutoff, :,:) = obj.Mask(:,:, (z_translation + 1):n_slices ,:,:); + obj.Mask = mask_ztrans; + elseif z_translation < 0 + mask_ztrans(:,:, (abs(z_translation) + 1):n_slices, :,:) = obj.Mask(:,:, 1:cutoff, :,:); + obj.Mask = mask_ztrans; + end + + end + + + function [rgb, colors, fig] = plot_alpha_composite_image(obj, opts) + % plots color visualization of mask + % Optional Parameters: + % slice = slice to plot. size = (1,1) + % order = layer order of each tissue. size = (1, tissue) + % Outputs: + % rgb = rgb alpha-composited volume. size = (row, col, slice, 3) + % colors = rgb triplets of each tissue. size = (tissue, 3) + % fig = the figure that this creates + + % https://en.wikipedia.org/wiki/Alpha_compositing + % performs the 'over' operation (basically, laying transparent layers on top of each other): + % a_0 = a_a + a_b * (1 - a_a) + % C_0 = (C_a * a_a + C_b * a_b * (1 - a_a)) / a_0 + % where a_0, a_a, and a_b are the alpha values of the pixels + % and C_0, C_a, and C_b are the color components of the pixels + + arguments + obj + opts.slice (1,1) {mustBeNumeric} = NaN + opts.order (1,:) {mustBeNumeric} = NaN + end + + n_tissues = size(obj.Mask, 4); + + if any(isnan(opts.order)) + opts.order = 1:n_tissues; + elseif numel(opts.order) ~= n_tissues + error('mismatched number of tissues in `order` and `tissue_structure.Mask`'); + end + + % find rgb + norm_mask = obj.Mask ./ max(sum(obj.Mask, 4), [], 'all'); + + colors = lines(n_tissues); + rgb = zeros(cat(2, size(norm_mask, 1:3), 3)); % rgb version of this + alpha = ones(size(norm_mask, 1:3)); % alpha values (initialized as 1) + + for i_tissue = opts.order + mask_alpha = norm_mask(:,:,:,i_tissue); + alpha_0 = mask_alpha + alpha .* (1 - mask_alpha); + for color_channel = 1:3 + C_a = colors(i_tissue, color_channel); + C_b = rgb(:,:,:,color_channel); + alpha_a = mask_alpha; + alpha_b = alpha; + rgb(:,:,:,color_channel) = (C_a .* alpha_a + C_b .* alpha_b .* (1 - alpha_a)) ./ alpha_0; + end + alpha = alpha_0; + end + + if isnan(opts.slice) + return + end + + % plot + fig = figure; + imshow(squeeze(rgb(:,:,opts.slice,:))); + + % legend for this + fake_legend_lines = zeros(1, n_tissues); + for i_tissue = 1:n_tissues + fake_legend_lines(i_tissue) = line(NaN, NaN, 'color', colors(i_tissue, :)); + end + legend(fake_legend_lines, num2cell(obj.Tissues)); + end + end +end diff --git a/simulations/anatomical_phantoms/usage/brainweb_metabolic_phantom.m b/simulations/anatomical_phantoms/usage/brainweb_metabolic_phantom.m new file mode 100644 index 0000000..85ce3c2 --- /dev/null +++ b/simulations/anatomical_phantoms/usage/brainweb_metabolic_phantom.m @@ -0,0 +1,99 @@ +clear; close all; +addpath('../'); +addpath('../../pk_models/'); +addpath('../../../utilities/') + +%% PARAMETERS +% tissue +mask = load('../util/brainweb/1/brainweb_fuzzy.mat').im_mask; + +k_trans = [1, 0.2, 0.2; + 3, 0.4, 0.4]; + +% pk model params +tr = 3; +n_t = 20; +flips = repmat([20; 30; 30;], 1, n_t) .* (pi/180); +r1 = [1/30, 1/25, 1/20]; % pyr, lac, bic +k = [0, 0.03, 0.025; % lac, in order [vasc, gm, wm] + 0, 0.01, 0.005]; % bic +t_arrival = [0, 0, 0]; % vasc, gm, wm +t_bolus = 8; + +% input function and mz0 +n_tissues = 3; +input_function = zeros(n_tissues, n_t); +for i_tissue = 1:n_tissues + input_function(i_tissue, :) = realistic_input_function(n_t, tr, t_arrival(i_tissue), t_bolus); +end +mz0 = [input_function(1), input_function(1)*.5, input_function(1)*.5; + 0, input_function(1)*.01, input_function(1)*.01; + 0, input_function(1)*.005, input_function(1)*.005]; + +% mri +coil_lim = [0.2, 0.6]; +augment_params = struct(... + "XTranslation", [-1,1], ... + "YTranslation", [-1,1], ... + "Scale", [0.95,1.1], ... + "XReflection", true, ... + "Rotation", [-5,5], ... + "ZTranslation", [-20, 20]); + +sample_size = [32 32 8; 16 16 8; 16 16 8]; +snr = [150 40 20]; +output_size = [64 64 8]; + +%% RUNNING THE MODEL ----------------------------------------------------------- + +% tissue +brain = tissue_structure("brain", mask, ["vasc", "gm", "wm"], [100,100,100]); +brain = brain.create_k_trans_map(k_trans); + +% pk model +images = pk_model.run_pk_model(mz0, r1, k, flips, tr, brain, input_function=input_function); + +% mri +met_images_mres = mri_system.run_mri_system(images, sample_size, snr, coil_lim, brain, output_size, augment_params, include_bg_noise=false); + +%% DISPLAY --------------------------------------------------------------------- +slices = 10:5:40; +figure(Name='kTRANS'); +imagescn(brain.K_trans_map(:,:,slices), [0 max(brain.K_trans_map(:,:,slices), [], 'all')], [1 numel(slices)]); +colormap fire; + +k_trans_dwnszd = imresize3(imresize3(brain.K_trans_map, [16 16 8]), [32 32 8]); +slices = 1:size(k_trans_dwnszd, 3); +figure(Name='kTRANS downsized'); +imagescn(k_trans_dwnszd(:,:,slices), [0 max(k_trans_dwnszd(:,:,slices), [], 'all')], [1 numel(slices)]); +colormap fire; +%% +slices = 1:size(met_images_mres{1}, 3); +time_pts = 1:3:n_t; +figure(Name='Pyruvate (unified)'); +imagescn(met_images_mres{1}(:,:,slices,time_pts), [0, max(met_images_mres{1}(:,:,slices,time_pts), [], 'all')], [numel(slices) numel(time_pts)]); +colormap fire; + +figure(Name='Lactate (unified)'); +imagescn(met_images_mres{2}(:,:,slices,time_pts), [0, max(met_images_mres{2}(:,:,slices,time_pts), [], 'all')], [numel(slices) numel(time_pts)]); +colormap fire; + +figure(Name='Bicarb (unified)'); +imagescn(met_images_mres{3}(:,:,slices,time_pts), [0, max(met_images_mres{3}(:,:,slices,time_pts), [], 'all')], [numel(slices) numel(time_pts)]); +colormap fire; +%% +% AUCs +pyrAUC = sum(met_images_mres{1}, 4); +figure(Name='Pyr AUC (unified)'); +imagescn(pyrAUC, [0 max(pyrAUC, [], 'all')], [1 numel(slices)]); +colormap fire; + +lacAUC = sum(met_images_mres{2}, 4); +figure(Name='Lac AUC (unified)'); +imagescn(lacAUC, [0 max(lacAUC, [], 'all')], [1 numel(slices)]); +colormap fire; + +bicAUC = sum(met_images_mres{3}, 4); +figure(Name='Bic AUC (unified)'); +imagescn(bicAUC, [0 max(bicAUC, [], 'all')], [1 numel(slices)]); +colormap fire; diff --git a/simulations/anatomical_phantoms/usage/cardiac_metabolic_phantom.m b/simulations/anatomical_phantoms/usage/cardiac_metabolic_phantom.m new file mode 100644 index 0000000..a6d2892 --- /dev/null +++ b/simulations/anatomical_phantoms/usage/cardiac_metabolic_phantom.m @@ -0,0 +1,98 @@ +clear; close all; +addpath('../'); % phantom functions +addpath('../../pk_models/'); % realistic_input_function, simulate_Nsite_model +addpath('../../../utilities/'); % fire + +%% PARAMETERS ---------------------------------------------------- +% tissue +mask = load('../util/cardiac/cardiac_mask_10.mat').masks; + +k_trans = [1, 1, 0.2, 0.4]; + +% pk model params +tr = 3.6; +n_t = 30; +flips = repmat([20; 30; 30], 1, n_t) .* (pi/180); +r1 = [1/30 1/25 1/20]; +k = [0.013, 0.010, 0.025, 0.020; + 0.001, 0.001, 0.010, 0.001]; % in order [lv, rv, lv_mc, rv_mc]. + +t_arrival = [6,0,8,2]; +t_bolus = 1; + +% input function and mz0 +n_tissues = size(k, 2); +n_mets = size(flips, 1); + +input_function = zeros(n_tissues, n_t); +for i_tissue = 1:n_tissues + input_function(i_tissue, :) = realistic_input_function(n_t, tr, t_arrival(i_tissue), t_bolus); +end + +mz0_constants = [1, 1, 0.5, 0.5; + 0, 0, 0.01, 0.01; + 0, 0, 0.005, 0.005]; + +mz0 = repmat(reshape(input_function(:, 1), 1, n_tissues), 3, 1) .* mz0_constants; + +% mri +coil_lim = [0.4 1.2]; +sample_size = [25,25,5; 13,13,5; 13,13,5]; +snr = [220 70 12]; +output_size = [32,32,5]; + +%% RUNNING THE MODEL ---------------------------------------------- +% tissue +heart = tissue_structure("heart", mask, ["lv" "rv" "lvmy" "rvmy"]); +heart = heart.create_k_trans_map(k_trans); + +% pk model +images = pk_model.run_pk_model(mz0, r1, k, flips, tr, heart, input_function=input_function); + +% mri +met_images_mres = mri_system.run_mri_system(images, sample_size, snr, coil_lim, heart, output_size, include_bg_noise=false); + +%% DISPLAY -------------------------------------------------------- +slices = 1:5:46; +figure(Name='kTRANS'); +imagescn(heart.K_trans_map(:,:,slices), [0 max(heart.K_trans_map(:,:,slices), [], 'all')], [1 numel(slices)]); +colormap hot; + +slices = 1:size(met_images_mres{1}, 3); +time_pts = 1:n_t; +figure(Name='Pyruvate'); +imagescn(met_images_mres{1}(:,:,slices,time_pts), [0, max(met_images_mres{1}(:,:,slices,time_pts), [], 'all')], [numel(slices) numel(time_pts)]); +colormap hot; + +figure(Name='Lactate'); +imagescn(met_images_mres{2}(:,:,slices,time_pts), [0, max(met_images_mres{2}(:,:,slices,time_pts), [], 'all')], [numel(slices) numel(time_pts)]); +colormap hot; + +figure(Name='Bicarb (unified)'); +imagescn(met_images_mres{3}(:,:,slices,time_pts), [0, max(met_images_mres{3}(:,:,slices,time_pts), [], 'all')], [numel(slices) numel(time_pts)]); +colormap hot; + +%% AUCs +pyrAUC = sum(met_images_mres{1}, 4); +lacAUC = sum(met_images_mres{2}, 4); +bicAUC = sum(met_images_mres{3}, 4); + +% AUC ratios +lac_to_pyr = lacAUC ./ pyrAUC; +bic_to_pyr = bicAUC ./ pyrAUC; + +% remove the background noise +parts_to_keep = sum(heart.Mask(), 4); +parts_to_keep = imresize3(parts_to_keep, output_size, "nearest"); +lac_to_pyr(parts_to_keep == 0) = 0; +bic_to_pyr(parts_to_keep == 0) = 0; + +figure(Name='Lac/Pyr AUC (unified)'); +imagescn(lac_to_pyr, [0, 0.5], [1 numel(slices)]); +colormap hot; + +figure(Name='Bic/Pyr AUC (unified)'); +imagescn(bic_to_pyr, [0, 0.15], [1 numel(slices)]); +colormap hot; + + diff --git a/simulations/anatomical_phantoms/usage/concise_unified_phantom_test.m b/simulations/anatomical_phantoms/usage/concise_unified_phantom_test.m new file mode 100644 index 0000000..041d8cd --- /dev/null +++ b/simulations/anatomical_phantoms/usage/concise_unified_phantom_test.m @@ -0,0 +1,56 @@ +clear; close all; +addpath('../') + +% TISSUE STRUCTURE +mask = load('util/mask.mat').masks; +heart = tissue_structure("heart", mask, ["lv" "rv" "lvmy" "rvmy"]); +k_trans = [1, 1, 0.2, 0.4]; +heart = heart.create_k_trans_map(k_trans); + +% PK MODEL PARAMETERS +tr = 3.6; +n_t = 30; +flips = repmat([20; 30; 30;], 1, n_t) .* (pi/180); + +n_compartments = 4; + +% METABOLITE AND/OR TISSUE SPECIFIC PARAMETERS +mz0 = [0,1,0,0; % pyr + 0,0,0,0; % lac + 0,0,0,0]; % bic + +r1 = [1/30, 1/25, 1/25]; % pyr, lac, bic + +k = [0.0075, 0.0045, 0.06, 0.02; % lac + 0.0011, 0.0005, 0.04, 0.01]; % bic + +t_arrival = [7, 0, 10, 14]; % lv, rv, lvmy, rvmy +t_bolus = 10; + + +% RUN PK MODEL +images = pk_model.run_pk_model(mz0, r1, k, flips, tr, heart, t_arrival=t_arrival, t_bolus=t_bolus); + +% RUN MRI SYSTEM +sample_size = [32 32 11; 16 16 11; 24 24 11]; +SNR = [150 40 20]; +coil_lim = [0.4 1.2]; +output_size = [32 32 11; 32 32 11; 32 32 11]; +augmentation_params = struct(... + "XTranslation", [-1,1], ... + "YTranslation", [-1,1], ... + "Scale", [0.95,1.1], ... + "XReflection", true, ... + "Rotation", [-5,5]); + +met_images_mres = mri_system.run_mri_system(images, sample_size, SNR, coil_lim, heart.Mask, output_size, augmentation_params); + +%% DISPLAY +figure; +imagescn(met_images_mres{1}(:,:,5,:), [0, max(met_images_mres{1}(:,:,5,:), [], 'all')]) + +figure; +imagescn(met_images_mres{2}(:,:,5,:), [0, max(met_images_mres{2}(:,:,5,:), [], 'all')]) + +figure; +imagescn(met_images_mres{3}(:,:,5,:), [0, max(met_images_mres{3}(:,:,5,:), [], 'all')]) diff --git a/simulations/anatomical_phantoms/usage/generate_brainweb_figures.m b/simulations/anatomical_phantoms/usage/generate_brainweb_figures.m new file mode 100644 index 0000000..295b210 --- /dev/null +++ b/simulations/anatomical_phantoms/usage/generate_brainweb_figures.m @@ -0,0 +1,347 @@ +% copying over brainweb +clear; close all; +addpath('../'); % phantom functions +addpath('../../pk_models/'); % realistic_input_function, simulate_Nsite_model +addpath('../../../utilities/'); % fire + +%% settings for figure generation +export_path = ''; + +to_export = ~isempty(export_path); % save a couple function calls +if to_export + disp("exporting to `" + string(export_path) + "`"); + mkdir(export_path); +else + disp("not exporting") +end + + +%% PARAMETERS +disp('setting up...'); +t_all = tic; +tic +% tissue +mask = double(load('../util/brainweb/1/brainweb_fuzzy.mat').im_mask); + +k_trans = [1, 0.2, 0.2; + 3, 0.4, 0.4]; + +% pk model params +tr = 3; +n_t = 20; +flips = repmat([20; 30; 30;], 1, n_t) .* (pi/180); +r1 = [1/30, 1/25, 1/20]; % pyr, lac, bic +k = [0, 0.03, 0.025; % lac, in order [vasc, gm, wm] + 0, 0.01, 0.005]; % bic +t_arrival = [0, 0, 0]; % vasc, gm, wm +t_bolus = 8; + +% input function and mz0 +n_tissues = 3; +input_function = zeros(n_tissues, n_t); +for i_tissue = 1:n_tissues + input_function(i_tissue, :) = realistic_input_function(n_t, tr, t_arrival(i_tissue), t_bolus); +end +mz0 = [input_function(1), input_function(1)*.5, input_function(1)*.5; + 0, input_function(1)*.01, input_function(1)*.01; + 0, input_function(1)*.005, input_function(1)*.005]; + +% mri +coil_lim = [0.2, 0.6]; +augment_params = struct(... + "XTranslation", [5,5], ... + "YTranslation", [-7,-7], ... + "ZTranslation", [2, 2], ... + "Scale", [1.1,1.1], ... + "XReflection", true, ... + "Rotation", [5,5]); + +sample_size = [32 32 8; 16 16 8; 16 16 8]; +snr = [150 40 20]; % pyr_range = [70 320]; lac_range = [15 75] +output_size = [64 64 8]; + + +% VARIATIONS +% anatomy +%mask = double(load('../util/brainweb/2/brainweb_fuzzy.mat').im_mask); + +% low SNR +%snr = [70, 15, 10]; + +% high SNR +%snr = [320 75 40]; + +% small sample size +%sample_size = [24 24 8; 12 12 8; 12 12 8]; + +% large sample size +%sample_size = [48 48 8; 24 24 8; 24 24 8]; + +% augmentations +%augment_params = struct(... +% "XTranslation", [5,5], ... +% "YTranslation", [-1,1], ... +% "Scale", [0.95,1.1], ... +% "XReflection", true, ... +% "Rotation", [-5,5], ... +% "ZTranslation", [-5, 5]); + + + +disp(['took ', num2str(toc), 's', newline]); + +%% RUNNING THE MODEL ----------------------------------------------------------- +% tissue +disp('creating tissue structure...'); tic; +brain = tissue_structure("brain", mask, ["vasc", "gm", "wm"], [100,100,100]); +brain = brain.create_k_trans_map(k_trans); +disp(['took ', num2str(toc), 's', newline]); + +% pk model +disp('running pk model...'); tic; +[met_images, ~, met_dynamics, ~, met_images_no_ktrans] = pk_model.run_pk_model(mz0, r1, k, flips, tr, brain, input_function=input_function); +disp(['took ', num2str(toc), 's', newline]); + +% mri +disp('running mri system...'); tic; +cell_augment_params = namedargs2cell(augment_params); % unpack the augmentation parameters +[met_images_aug, transform] = mri_system.augment(met_images, cell_augment_params{:}); +brain = brain.apply_transforms(transform.tform2d, transform.z_translation); +[met_images_coil_lim, coil_sens_weights] = mri_system.apply_coil_lim(met_images_aug, coil_lim, brain.Mask); +met_images_mres = mri_system.make_met_images_multires(met_images_coil_lim, sample_size); +[met_images_mres_noise, met_images_mres_no_bg] = mri_system.add_rician_noise(met_images_mres, snr); + +met_images_upsampled = mri_system.upsample_to_output_size(met_images_mres_noise, output_size); +met_images_upsampled_no_bg = mri_system.upsample_to_output_size(met_images_mres_no_bg, output_size); +disp(['took ', num2str(toc), 's', newline]); + + +%% DISPLAY --------------------------------------------------------------------- +disp('plotting...'); tic; + +%% tissue ---------------------------------- +% mask ------ +% alpha composite +brain.plot_alpha_composite_image(slice=50, order=[2,3,1]); % plot vasculature last +if to_export; saveas(gcf, fullfile(export_path, 'alpha_composite.png')); end + +% multislice tissues +slices = 10:10:100; +permuted_masks = permute(brain.Mask(:,:,slices,:), [1,2,4,3]); +f = display_tiled_images(permuted_masks, false, 'mask', ["Vasc", "GM", "WM"], cmap=@gray); +f.Position = [0, 0, 1000, 500]; +if to_export; saveas(gcf, fullfile(export_path, 'mask_multislice.png')); end + +% middle slice tissues +slice = round(size(brain.Mask, 3) / 2); +figure; imshow(brain.Mask(:,:,slice,1)); if to_export; saveas(gcf, fullfile(export_path, 'mask_vasc_middle.png')); end +figure; imshow(brain.Mask(:,:,slice,2)); if to_export; saveas(gcf, fullfile(export_path, 'mask_gm_middle.png')); end +figure; imshow(brain.Mask(:,:,slice,3)); if to_export; saveas(gcf, fullfile(export_path, 'mask_wm_middle.png')); end + +% kTRANS ------ +% multislice kTRANS +slices = 10:10:100; +f = display_tiled_images(brain.K_trans_map(:,:,slices), true, 'ktrans', [], cmap=@parula); +f.Position = [0, 0, 1440, 185]; +if to_export; saveas(gcf, fullfile(export_path, 'ktrans_multislice.png')); end + +% middle slice kTRANS +slice = round(size(brain.K_trans_map, 3) / 2); +figure; imshow(brain.K_trans_map(:,:,slice), [0, max(brain.K_trans_map, [], 'all')]); +colormap parula; colorbar; +if to_export; saveas(gcf, fullfile(export_path, 'ktrans_middle.png')); end + + +%% pk ----------------------------------------- +% met dynamics ------ +% size(met_dynamics) = [tissue, met, time_pt] +time_pts = 1:size(met_dynamics, 3); + +f = figure(Name='met dynamics'); + +% construction +t = tiledlayout(size(met_dynamics, 1), 1); +tissue_names = ["Vasculature", "Gray Matter", "White Matter"]; +for i_tissue = 1:size(met_dynamics, 1) + nexttile; + hold on; + for i_met = 1:size(met_dynamics, 2) + plot(time_pts, squeeze(met_dynamics(i_tissue, i_met, :))); + end + xlabel('time point'); + ylabel('signal') + leg = legend(["Pyruvate", "Lactate", "Bicarbonate"]); + title(tissue_names(i_tissue)); + hold off; +end +f.Position = [0, 0, 1000, 600]; +if to_export; saveas(f, fullfile(export_path, 'met_dynamics.png')); end + + +% met images (no ktrans) --- +slice = round(size(met_images_no_ktrans, 3) / 2); +time_pts = 1:3:n_t; +f = display_tiled_images(met_images_no_ktrans(:,:,slice,:,time_pts), true, 'met images no ktrans', ["Pyruvate", "Lactate", "Bicarbonate"]); +f.Position = [0, 0, 1440, 475]; +if to_export; saveas(gcf, fullfile(export_path, '1-met_img_no_ktrans.png')); end + +% met images (ktrans) --- +slice = round(size(met_images, 3) / 2); +time_pts = 1:3:n_t; +f = display_tiled_images(met_images(:,:,slice,:,time_pts), true, 'met images', ["Pyruvate", "Lactate", "Bicarbonate"]); +f.Position = [0, 0, 1440, 475]; +if to_export; saveas(gcf, fullfile(export_path, '2-met_img_w_ktrans.png')); end + + +%% met images of various mri steps ----- + +% augmentations --- +slice = round(size(met_images_aug, 3) / 2); +time_pts = 1:3:n_t; +f = display_tiled_images(met_images_aug(:,:,slice,:,time_pts), true, 'met images (augmentations)', ["Pyruvate", "Lactate", "Bicarbonate"]); +f.Position = [0, 0, 1440, 475]; +if to_export; saveas(gcf, fullfile(export_path, '3-met_img_w_augs.png')); end + +% coil limits --- +slice = round(size(met_images_coil_lim, 3) / 2); +time_pts = 1:3:n_t; +f = display_tiled_images(met_images_coil_lim(:,:,slice,:,time_pts), true, 'met images (coil lim)', ["Pyruvate", "Lactate", "Bicarbonate"]); +f.Position = [0, 0, 1440, 475]; +if to_export; saveas(gcf, fullfile(export_path, '4-met_img_w_coil_lims.png')); end + +% coil limit maps --- +% multislice +slices = 10:10:100; +f = display_tiled_images(coil_sens_weights(:,:,slices), true, 'Coil Sensitivity Map', [], cmap=@parula); +f.Position = [0, 0, 1440, 185]; +if to_export; saveas(gcf, fullfile(export_path, 'coil_sensitivity.png')); end + +% single slice +slice = round(size(coil_sens_weights, 3) / 2); +figure; imshow(coil_sens_weights(:,:,slice), [0 max(coil_sens_weights, [], 'all')]); +colormap parula; colorbar; +if to_export; saveas(gcf, fullfile(export_path, 'coil_sensitivity_middle.png')); end + +% multiresolution --- +slice = round(size(met_images_mres{1}, 3) / 2); +time_pts = 1:3:n_t; +f = display_tiled_images(met_images_mres, true, 'met images multires', ["Pyruvate", "Lactate", "Bicarbonate"], slice, time_pts); +f.Position = [0, 0, 1440, 475]; +if to_export; saveas(gcf, fullfile(export_path, '5-met_images_mres.png')); end + +% noise --- +slice = round(size(met_images_mres_no_bg{1}, 3) / 2); +time_pts = 1:3:n_t; +f = display_tiled_images(met_images_mres_no_bg, true, 'met images noisy', ["Pyruvate", "Lactate", "Bicarbonate"], slice, time_pts); +f.Position = [0, 0, 1440, 475]; +if to_export; saveas(gcf, fullfile(export_path, '6-met_images_noise.png')); end + + +%% final met images (after mri) + +slice = round(size(met_images_upsampled_no_bg{1}, 3) / 2); +time_pts = 1:3:n_t; +f = display_tiled_images(met_images_upsampled_no_bg, true, 'final met images!', ["Pyruvate", "Lactate", "Bicarbonate"], slice, time_pts); +f.Position = [0, 0, 1440, 475]; +if to_export; saveas(gcf, fullfile(export_path, '7-met_images_upsampled_no_bg.png')); end + + +%% AUCs +aucs = cell(1,3); + +for i = 1:3 + aucs{i} = sum(met_images_upsampled_no_bg{i}, 4); + % turn it into (row, col, 1, slice) + new_size = cat(2, size(aucs{i}, 1,2), 1, size(aucs{i}, 3)); + aucs{i} = reshape(aucs{i}, new_size); +end + +f = display_tiled_images(aucs, true, 'aucs', ["Pyruvate", "Lactate", "Bicarbonate"]); +f.Position = [0, 0, 1440, 565]; +if to_export; saveas(gcf, fullfile(export_path, 'aucs.png')); end + +disp(['took ', num2str(toc), 's', newline]); +disp('done! (plots might take a while to load)'); +disp(['took a total of ', num2str(toc(t_all)), 's']); + + +function [fig, I] = display_tiled_images(I, has_colorbar, figurename, labels, slice, time_pts, opts) + % I = (row, col, slice). 1 row, slice columns + % I = (row, col, met, time). met rows, time columns + % I = {met} --> (row, col, slice, time). met rows, time columns. assumes all have the same number of timesteps + + % stored as: + % I = {met} --> (row, col, time/slice). met rows, time/slice columns. assumes all have the same number of timesteps + + % slice and time_pts is only for the 3rd option because there's not a great way to just remove that dimension + + arguments + I + has_colorbar + figurename + labels + slice (1,1) = 1 % these two are optional, won't be used unless I is a cell + time_pts = NaN + opts.cmap = @fire + end + + % keep the dimensions consistent + % in the form {met} --> (row, col, time) + if iscell(I) + if any(isnan(time_pts)) + time_pts = 1:size(I{1}, 4); + end + for row = 1:numel(I) + I{row} = squeeze(I{row}(:, :, slice, time_pts)); + end + else + % convert (x,y,z) --> (x,y,1,z) + I = squeeze(I); + if numel(size(I)) == 3 + new_shape = cat(2, size(I, 1:2), 1, size(I, 3)); + I = reshape(I, new_shape); + end + % turn it into cell + cell_I = cell([1, size(I, 3)]); + for row = 1:size(I, 3) + cell_I{row} = squeeze(I(:,:,row,:)); + end + + I = cell_I; + end + + + % setup + fig = figure(Name=figurename); + + if has_colorbar + n_cols = size(I{1}, 3) + 1; + else + n_cols = size(I{1}, 3); + end + + + % construction + t = tiledlayout(numel(I), n_cols); + t.Padding = 'none'; + t.TileSpacing = 'none'; + for row = 1:numel(I) + scale = [0, max(I{row}(:,:,:), [], 'all')]; + for col = 1:size(I{row}, 3) + nexttile; + imshow(I{row}(:,:,col), scale); + if col == 1 && ~isempty(labels) + ylabel(labels(row)); + end + end + + if has_colorbar + cb_ax = nexttile(); + axis(cb_ax, 'off'); + colorbar('location','west'); + clim(scale); + end + end + + colormap(opts.cmap()); +end diff --git a/simulations/anatomical_phantoms/usage/generate_heart_figures.m b/simulations/anatomical_phantoms/usage/generate_heart_figures.m new file mode 100644 index 0000000..4848b99 --- /dev/null +++ b/simulations/anatomical_phantoms/usage/generate_heart_figures.m @@ -0,0 +1,374 @@ +% copying over cardiac +clear; close all; +addpath('../'); % phantom functions +addpath('../../pk_models/'); % realistic_input_function, simulate_Nsite_model +addpath('../../../utilities/'); % fire + +%% settings for figure generation +export_path = ''; + +to_export = ~isempty(export_path); % save a couple function calls +if to_export + disp("exporting to `" + string(export_path) + "`"); + mkdir(export_path); +else + disp("not exporting") +end + + +%% PARAMETERS +disp('setting up...'); +t_all = tic; +tic + +% tissue +mask = load('../util/cardiac/cardiac_mask_1.mat').masks; + +k_trans = [1, 1, 0.2, 0.4]; + +% pk model params +tr = 3.6; +n_t = 30; +flips = repmat([20; 30; 30], 1, n_t) .* (pi/180); +r1 = [1/30 1/25 1/20]; +k = [0.013, 0.010, 0.025, 0.020; + 0.001, 0.001, 0.010, 0.001]; % in order [lv, rv, lv_mc, rv_mc]. + +t_arrival = [6,0,8,2]; +t_bolus = 1; + +% input function and mz0 +n_tissues = size(k, 2); +n_mets = size(flips, 1); + +input_function = zeros(n_tissues, n_t); +for i_tissue = 1:n_tissues + input_function(i_tissue, :) = realistic_input_function(n_t, tr, t_arrival(i_tissue), t_bolus); +end + +mz0_constants = [1, 1, 0.5, 0.5; + 0, 0, 0.01, 0.01; + 0, 0, 0.005, 0.005]; + +mz0 = repmat(reshape(input_function(:, 1), 1, n_tissues), 3, 1) .* mz0_constants; + +% mri +% augment_params should be deterministic +augment_params = struct(... + "XTranslation", [5,5], ... + "YTranslation", [-7,-7], ... + "ZTranslation", [2,2], ... + "Scale", [1.1, 1.1], ... + "Rotation", [5,5]); + +coil_lim = [0.4 1.2]; +sample_size = [25,25,5; 13,13,5; 13,13,5]; +snr = [220 70 12]; +output_size = [32,32,5]; +disp(['took ', num2str(toc), 's', newline]); + + +% VARIATIONS +% anatomy +%mask = double(load('../util/cardiac/cardiac_mask_2.mat').masks); + +% low SNR +%snr = [70, 15, 10]; + +% high SNR +%snr = [320 75 40]; + +% small sample size +%sample_size = [12 12 8; 8 8 8; 8 8 8]; + +% large sample size +%sample_size = [48 48 8; 24 24 8; 24 24 8]; + + +% augmentations +%augment_params = struct(... +% "XTranslation", [-4,-4], ... +% "YTranslation", [9,9], ... +% "ZTranslation", [-3,-3], ... +% "Scale", [0.8, 0.8], ... +% "Rotation", [12,12]); + + + +%% RUNNING THE MODEL ----------------------------------------------------------- +% tissue +disp('creating tissue structure...'); tic; +heart = tissue_structure("heart", mask, ["lv", "rv", "lvmy", "rvmy"]); +heart = heart.create_k_trans_map(k_trans); +disp(['took ', num2str(toc), 's', newline]); + +% pk model +disp('running pk model...'); tic; +[met_images, ~, met_dynamics, ~, met_images_no_ktrans] = pk_model.run_pk_model(mz0, r1, k, flips, tr, heart, input_function=input_function); +disp(['took ', num2str(toc), 's', newline]); + +% mri +disp('running mri system...'); tic; +cell_augment_params = namedargs2cell(augment_params); % unpack the augmentation parameters +[met_images_aug, transform] = mri_system.augment(met_images, cell_augment_params{:}); +heart = heart.apply_transforms(transform.tform2d, transform.z_translation); +[met_images_coil_lim, coil_sens_weights] = mri_system.apply_coil_lim(met_images_aug, coil_lim, heart.Mask); +met_images_mres = mri_system.make_met_images_multires(met_images_coil_lim, sample_size); +[met_images_mres_noise, met_images_mres_no_bg] = mri_system.add_rician_noise(met_images_mres, snr); + +met_images_upsampled = mri_system.upsample_to_output_size(met_images_mres_noise, output_size); +met_images_upsampled_no_bg = mri_system.upsample_to_output_size(met_images_mres_no_bg, output_size); +disp(['took ', num2str(toc), 's', newline]); + + +%% DISPLAY --------------------------------------------------------------------- +disp('plotting...'); tic; + +%% tissue ---------------------------------- +% mask ------ +% alpha composite +heart.plot_alpha_composite_image(slice=23); % plot vasculature last +if to_export; saveas(gcf, fullfile(export_path, 'mask_alpha_composite.png')); end + +% multislice tissues +slices = 5:5:46; +permuted_masks = permute(heart.Mask(:,:,slices,:), [1,2,4,3]); +f = display_tiled_images(permuted_masks, false, 'mask', ["LV", "RV", "LVMY", "RVMY"], cmap=@gray); +f.Position = [0, 0, 1000, 500]; +if to_export; saveas(gcf, fullfile(export_path, 'mask_multislice.png')); end + +% middle slice tissues +slice = round(size(heart.Mask, 3) / 2); +figure; imshow(heart.Mask(:,:,slice,1)); if to_export; saveas(gcf, fullfile(export_path, 'mask_lv_middle.png')); end +figure; imshow(heart.Mask(:,:,slice,2)); if to_export; saveas(gcf, fullfile(export_path, 'mask_rv_middle.png' )); end +figure; imshow(heart.Mask(:,:,slice,3)); if to_export; saveas(gcf, fullfile(export_path, 'mask_lvmy_middle.png')); end +figure; imshow(heart.Mask(:,:,slice,4)); if to_export; saveas(gcf,fullfile(export_path, 'mask_rvmy_middle.png')); end + +% kTRANS ------ +% multislice kTRANS +slices = 5:5:46; +f = display_tiled_images(heart.K_trans_map(:,:,slices), true, 'ktrans', [], cmap=@parula); +f.Position = [0, 0, 1440, 185]; +if to_export; saveas(gcf, fullfile(export_path, 'ktrans_multislice.png')); end + +% middle slice kTRANS +slice = round(size(heart.K_trans_map, 3) / 2); +figure; imshow(heart.K_trans_map(:,:,slice), [0, max(heart.K_trans_map, [], 'all')]); +colormap parula; colorbar; +if to_export; saveas(gcf, fullfile(export_path, 'ktrans_middle.png')); end + + + +%% pk ----------------------------------------- +% met dynamics ------ +% size(met_dynamics) = [tissue, met, time_pt] +time_pts = 1:size(met_dynamics, 3); + +f = figure(Name='met dynamics'); + +% construction +t = tiledlayout(size(met_dynamics, 1), 1); +tissue_names = ["LV", "RV", "LVMY", "RVMY"]; +for i_tissue = 1:size(met_dynamics, 1) + nexttile; + hold on; + for i_met = 1:size(met_dynamics, 2) + plot(time_pts, squeeze(met_dynamics(i_tissue, i_met, :))); + end + xlabel('time point'); + ylabel('signal') + leg = legend(["Pyruvate", "Lactate", "Bicarbonate"]); + title(tissue_names(i_tissue)); + hold off; +end +f.Position = [0, 0, 1000, 600]; +if to_export; saveas(f, fullfile(export_path, 'met_dynamics.png')); end + + +% met images (no ktrans) --- +slice = round(size(met_images_no_ktrans, 3) / 2); +time_pts = 1:3:n_t; +f = display_tiled_images(met_images_no_ktrans(:,:,slice,:,time_pts), true, 'met images no ktrans', ["Pyruvate", "Lactate", "Bicarbonate"]); +f.Position = [0, 0, 1440, 475]; +if to_export; saveas(gcf, fullfile(export_path, '1-met_img_no_ktrans.png')); end + +% met images (ktrans) --- +slice = round(size(met_images, 3) / 2); +time_pts = 1:3:n_t; +f = display_tiled_images(met_images(:,:,slice,:,time_pts), true, 'met images', ["Pyruvate", "Lactate", "Bicarbonate"]); +f.Position = [0, 0, 1440, 475]; +if to_export; saveas(gcf, fullfile(export_path, '2-met_img_w_ktrans.png')); end + + +%% met images of various mri steps ----- + +% augmentations --- +slice = round(size(met_images_aug, 3) / 2); +time_pts = 1:3:n_t; +f = display_tiled_images(met_images_aug(:,:,slice,:,time_pts), true, 'met images (augmentations)', ["Pyruvate", "Lactate", "Bicarbonate"]); +f.Position = [0, 0, 1440, 475]; +if to_export; saveas(gcf, fullfile(export_path, '3-met_img_w_augs.png')); end + +% coil limits --- +slice = round(size(met_images_coil_lim, 3) / 2); +time_pts = 1:3:n_t; +f = display_tiled_images(met_images_coil_lim(:,:,slice,:,time_pts), true, 'met images (coil lim)', ["Pyruvate", "Lactate", "Bicarbonate"]); +f.Position = [0, 0, 1440, 475]; +if to_export; saveas(gcf, fullfile(export_path, '4-met_img_w_coil_lims.png')); end + +% coil limit maps --- +% multislice +slices = 5:5:46; +f = display_tiled_images(coil_sens_weights(:,:,slices), true, 'Coil Sensitivity Map', [], cmap=@parula); +f.Position = [0, 0, 1440, 185]; +if to_export; saveas(gcf, fullfile(export_path, 'coil_sensitivity_multislice.png')); end + +% single slice +slice = round(size(coil_sens_weights, 3) / 2); +figure; imshow(coil_sens_weights(:,:,slice), [0 max(coil_sens_weights, [], 'all')]); +colormap parula; colorbar; +if to_export; saveas(gcf, fullfile(export_path, 'coil_sensitivity_middle.png')); end + +% multiresolution --- +slice = round(size(met_images_mres{1}, 3) / 2); +time_pts = 1:3:n_t; +f = display_tiled_images(met_images_mres, true, 'met images multires', ["Pyruvate", "Lactate", "Bicarbonate"], slice, time_pts); +f.Position = [0, 0, 1440, 475]; +if to_export; saveas(gcf, fullfile(export_path, '5-met_images_mres.png')); end + +% noise --- +slice = round(size(met_images_mres_no_bg{1}, 3) / 2); +time_pts = 1:3:n_t; +f = display_tiled_images(met_images_mres_no_bg, true, 'met images noisy', ["Pyruvate", "Lactate", "Bicarbonate"], slice, time_pts); +f.Position = [0, 0, 1440, 475]; +if to_export; saveas(gcf, fullfile(export_path, '6-met_images_noise.png')); end + + +%% final met images (after mri) -------- + +slice = round(size(met_images_upsampled_no_bg{1}, 3) / 2); +time_pts = 1:3:n_t; +f = display_tiled_images(met_images_upsampled_no_bg, true, 'final met images!', ["Pyruvate", "Lactate", "Bicarbonate"], slice, time_pts); +f.Position = [0, 0, 1440, 475]; +if to_export; saveas(gcf, fullfile(export_path, '7-met_images_upsampled_no_bg.png')); end + + +%% AUC (ratios) +aucs = cell(1,3); + +for i = 1:3 + aucs{i} = sum(met_images_upsampled_no_bg{i}, 4); +end + +lac_to_pyr_AUC = aucs{2} ./ aucs{1}; +lac_to_pyr_AUC(aucs{1} < max(aucs{1}, [], 'all') * 0.1) = 0; % remove the artifacts caused by different sample sizes +bic_to_pyr_AUC = aucs{3} ./ aucs{1}; +bic_to_pyr_AUC(aucs{1} < max(aucs{1}, [], 'all') * 0.1) = 0; % threshold tuned by hand lol + +slice = round(size(lac_to_pyr_AUC, 3) / 2); +figure(Name="lac-pyr auc"); imshow(lac_to_pyr_AUC(:,:,slice), [0 max(lac_to_pyr_AUC(:,:,slice), [], 'all')]); +colormap hot; colorbar; +if to_export; saveas(gcf, fullfile(export_path, 'auc_ratio_lac_pyr.png')); end + + +figure(Name="bic-pyr auc"); imshow(bic_to_pyr_AUC(:,:,slice), [0 max(bic_to_pyr_AUC(:,:,slice), [], 'all')]); +colormap hot; colorbar; +if to_export; saveas(gcf, fullfile(export_path, 'auc_ratio_bic_pyr.png')); end + + +% make the aucs cell array play nice with the display function +for i = 1:3 + % turn it into (row, col, 1, slice) + new_size = cat(2, size(aucs{i}, 1,2), 1, size(aucs{i}, 3)); + aucs{i} = reshape(aucs{i}, new_size); +end + +f = display_tiled_images(aucs, true, 'aucs', ["Pyruvate", "Lactate", "Bicarbonate"]); +f.Position = [0, 0, 1440, 565]; +if to_export; saveas(gcf, fullfile(export_path, 'aucs.png')); end + +disp(['took ', num2str(toc), 's', newline]); +disp('done! (plots might take a while to load)'); +disp(['took a total of ', num2str(toc(t_all)), 's']); + + +function [fig, I] = display_tiled_images(I, has_colorbar, figurename, labels, slice, time_pts, opts) + % I = (row, col, slice). 1 row, slice columns + % I = (row, col, met, time). met rows, time columns + % I = {met} --> (row, col, slice, time). met rows, time columns. assumes all have the same number of timesteps + + % stored as: + % I = {met} --> (row, col, time/slice). met rows, time/slice columns. assumes all have the same number of timesteps + + % slice and time_pts is only for the 3rd option because there's not a great way to just remove that dimension + + arguments + I + has_colorbar + figurename + labels + slice (1,1) = 1 % these two are optional, won't be used unless I is a cell + time_pts = NaN + opts.cmap = @hot + end + + % keep the dimensions consistent + % in the form {met} --> (row, col, time) + if iscell(I) + if any(isnan(time_pts)) + time_pts = 1:size(I{1}, 4); + end + for row = 1:numel(I) + I{row} = squeeze(I{row}(:, :, slice, time_pts)); + end + else + % convert (x,y,z) --> (x,y,1,z) + I = squeeze(I); + if numel(size(I)) == 3 + new_shape = cat(2, size(I, 1:2), 1, size(I, 3)); + I = reshape(I, new_shape); + end + % turn it into cell + cell_I = cell([1, size(I, 3)]); + for row = 1:size(I, 3) + cell_I{row} = squeeze(I(:,:,row,:)); + end + + I = cell_I; + end + + + % setup + fig = figure(Name=figurename); + + if has_colorbar + n_cols = size(I{1}, 3) + 1; + else + n_cols = size(I{1}, 3); + end + + + % construction + t = tiledlayout(numel(I), n_cols); + t.Padding = 'none'; + t.TileSpacing = 'none'; + for row = 1:numel(I) + scale = [0, max(I{row}(:,:,:), [], 'all')]; + for col = 1:size(I{row}, 3) + nexttile; + imshow(I{row}(:,:,col), scale); + if col == 1 && ~isempty(labels) + ylabel(labels(row)); + end + end + + if has_colorbar + cb_ax = nexttile(); + axis(cb_ax, 'off'); + colorbar('location','west'); + clim(scale); + end + end + + colormap(opts.cmap()); +end diff --git a/simulations/anatomical_phantoms/usage/verbose_unified_phantom_test.m b/simulations/anatomical_phantoms/usage/verbose_unified_phantom_test.m new file mode 100644 index 0000000..74dd9ee --- /dev/null +++ b/simulations/anatomical_phantoms/usage/verbose_unified_phantom_test.m @@ -0,0 +1,239 @@ +clear; close all; +addpath('../'); +mask = load('util/mask.mat').masks; + +create_graphs = true; + +% tissue_structure +heart = tissue_structure("heart", mask, ["lv" "rv" "lvmy" "rvmy"]); + +% GLOBAL PK PARAMS +TR = 3.6; +Nt = 30; +flips = repmat([20; 30; 30;], 1, Nt) .* (pi/180); + +% LV PK PARAMS ------------------ +disp("LV stuff") +% - metabolites +pyr = metabolite( ... + Mz0=0, ... + R1=1/30, ... + k=[0,0]); + +lac = metabolite( ... + Mz0=0, ... + R1=1/25, ... + k=0.0075); + +bic = metabolite( ... + Mz0=0, ... + R1=1/25, ... + k=0.0011); + +% - input function +Tarrival = 7; +Tbolus = 10; +input_function = realistic_input_function(Nt, TR, Tarrival, Tbolus); + +% - put it all into pk_params +lv_pk_params = pk_params(... + substrate = pyr, ... + products=[lac, bic], ... + TR=TR, ... + input_function=input_function, ... + flips=flips); + +% simulate lv metabolite dynamics +lv_met_dynamics = pk_model.generate_met_dynamics(lv_pk_params); + +% plotting +if create_graphs + tpts = 1:30; + figure; plot(tpts, lv_met_dynamics(1,:)) + hold on + plot(tpts, lv_met_dynamics(2,:)) + plot(tpts, lv_met_dynamics(3,:)) + hold off +end + + + + +% RV PK PARAMS ---------------- +disp("RV stuff") +% metabolites +pyr = metabolite( ... + Mz0=1, ... + R1=1/30, ... + k=[0,0]); + +lac = metabolite( ... + Mz0=0, ... + R1=1/25, ... + k=[0.0045,0]); + +bic = metabolite( ... + Mz0=0, ... + R1=1/25, ... + k=[0.0005,0]); + +% - input function +Tarrival = 0; +Tbolus = 10; +input_function = realistic_input_function(Nt, TR, Tarrival, Tbolus); + +% - put it all into pk_params +rv_pk_params = pk_params(... + substrate = pyr, ... + products=[lac, bic], ... + TR=TR, ... + input_function=input_function, ... + flips=flips); + +% simulate lv metabolite dynamics +rv_met_dynamics = pk_model.generate_met_dynamics(rv_pk_params); + +% plotting +if create_graphs + tpts = 1:30; + figure; plot(tpts, rv_met_dynamics(1,:)) + hold on + plot(tpts, rv_met_dynamics(2,:)) + plot(tpts, rv_met_dynamics(3,:)) + hold off +end + + + + +% LVMY PK PARAMS ---------------- +disp("LVMY stuff") +% metabolites +pyr = metabolite( ... + Mz0=0, ... + R1=1/30, ... + k=[0,0]); + +lac = metabolite( ... + Mz0=0, ... + R1=1/25, ... + k=[0.06,0]); + +bic = metabolite( ... + Mz0=0, ... + R1=1/25, ... + k=[0.04,0]); + +% - input function +Tarrival = 10; +Tbolus = 10; +input_function = realistic_input_function(Nt, TR, Tarrival, Tbolus); + + +% - put it all into pk_params +lvmy_pk_params = pk_params(... + substrate = pyr, ... + products=[lac, bic], ... + TR=TR, ... + input_function=input_function, ... + flips=flips); + +% simulate lv metabolite dynamics +lvmy_met_dynamics = pk_model.generate_met_dynamics(lvmy_pk_params); + +% plotting +if create_graphs + tpts = 1:30; + figure; plot(tpts, lvmy_met_dynamics(1,:)) + hold on + plot(tpts, lvmy_met_dynamics(2,:)) + plot(tpts, lvmy_met_dynamics(3,:)) + hold off +end + + + + +% RVMY PK PARAMS ---------------- +disp("LVMY stuff") +% metabolites +pyr = metabolite( ... + Mz0=0, ... + R1=1/30, ... + k=[0,0]); + +lac = metabolite( ... + Mz0=0, ... + R1=1/25, ... + k=[0.02,0]); + +bic = metabolite( ... + Mz0=0, ... + R1=1/25, ... + k=[0.01,0]); + +% - input function +Tarrival = 14; +Tbolus = 10; +input_function = realistic_input_function(Nt, TR, Tarrival, Tbolus); + +% - put it all into pk_params +rvmy_pk_params = pk_params(... + substrate = pyr, ... + products=[lac, bic], ... + TR=TR, ... + input_function=input_function, ... + flips=flips); + +% simulate lv metabolite dynamics +rvmy_met_dynamics = pk_model.generate_met_dynamics(rvmy_pk_params); + +% plotting +if create_graphs + tpts = 1:30; + figure; plot(tpts, rvmy_met_dynamics(1,:)) + hold on + plot(tpts, rvmy_met_dynamics(2,:)) + plot(tpts, rvmy_met_dynamics(3,:)) + hold off +end + + +% PK MODEL ------------------ +disp("pk model time") +met_dynamics = cat(3, lv_met_dynamics, rv_met_dynamics, lvmy_met_dynamics, rvmy_met_dynamics); +met_dynamics = permute(met_dynamics, [3,1,2]); % = (tissue, met, time_pt) + +met_images = pk_model.generate_met_images(heart, met_dynamics); + +% MRI SYSTEM ---------------- +disp("mri time") +sample_size = [32 32 11; 16 16 11; 24 24 11]; + +augmentation_params = struct(... + "XTranslation", [-1,1], ... + "YTranslation", [-1,1], ... + "Scale", [0.95,1.1], ... + "XReflection", true, ... + "Rotation", [-5,5]); +augmentation_params = namedargs2cell(augmentation_params); +coil_lim = [0.4, 1.2]; +SNR = [150 40 20]; +output_size = [32 32 11; 32 32 11; 32 32 11]; + +met_images = mri_system.augment(met_images, augmentation_params{:}); +met_images = mri_system.apply_coil_lim(met_images, coil_lim, heart.Mask); +met_images_mres = mri_system.make_met_images_multires(met_images, sample_size); +met_images_mres = mri_system.add_rician_noise(met_images_mres, SNR); +met_images_mres = mri_system.upsample_to_output_size(met_images_mres, output_size); + + +%% DISPLAY +figure; +imagescn(met_images_mres{1}(:,:,5,:), [0, max(met_images_mres{1}(:,:,5,:), [], 'all')]) + +figure; +imagescn(met_images_mres{2}(:,:,5,:), [0, max(met_images_mres{2}(:,:,5,:), [], 'all')]) + +figure; +imagescn(met_images_mres{3}(:,:,5,:), [0, max(met_images_mres{3}(:,:,5,:), [], 'all')]) diff --git a/simulations/anatomical_phantoms/util/_src_cardiac/1/cardiac_masks.mat b/simulations/anatomical_phantoms/util/_src_cardiac/1/cardiac_masks.mat deleted file mode 100644 index e61b767..0000000 Binary files a/simulations/anatomical_phantoms/util/_src_cardiac/1/cardiac_masks.mat and /dev/null differ diff --git a/simulations/anatomical_phantoms/util/_src_cardiac/10/cardiac_masks.mat 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a/simulations/anatomical_phantoms/util/_src_cardiac/9/cardiac_masks.mat and /dev/null differ diff --git a/simulations/anatomical_phantoms/util/_src_cardiac/create_cardiac_masks/create_cardiac_masks.m b/simulations/anatomical_phantoms/util/_src_cardiac/create_cardiac_masks/create_cardiac_masks.m deleted file mode 100644 index d8d6972..0000000 --- a/simulations/anatomical_phantoms/util/_src_cardiac/create_cardiac_masks/create_cardiac_masks.m +++ /dev/null @@ -1,127 +0,0 @@ -function [cardiac_masks, layered_masks] = create_cardiac_masks(vertices, tolerance, maskSize) -% CREATE_CARDIAC_MASKS creates cardiac masks from point cloud data for -% cardiac_metabolic_phantom -% Parameters: -% vertices = struct with fields mc, lv, rv, each of which are Nx3 -% matrices of xyz coordinates -% tolerance = how "off" a point can be to be included in a -% slice. defaults to 1. -% maskSize = how large the masks should be. [xmin, xmax, xres; -% ymin, ymax, yres; zmin, zmax, zres;]. default = -% size chosen based on point cloud data -% -% Outputs: -% cardiac_masks = 4d array of masks (nx, ny, nz, tissue) -% layered_masks = 3d array of unseparated masks - - - arguments - vertices struct - tolerance (1,1) double = 1 - maskSize (3,3) double = zeros(3) - end - - % default maskSize - if ~any(maskSize,'all') - all_vertices = cat(1, vertices.mc, vertices.lv, vertices.rv); - % get reasonable "window size" - x_range = max(all_vertices(:,1),[],'all') - min(all_vertices(:,1),[],'all'); - y_range = max(all_vertices(:,2),[],'all') - min(all_vertices(:,2),[],'all'); - % get x y bounds - ranges = [x_range, y_range]; - for dim = 1:numel(ranges) - offset = 0.25 * ranges(1); - maskSize(dim,1) = min(all_vertices(:,dim),[],'all') - offset; - maskSize(dim,2) = max(all_vertices(:,dim),[],'all') + offset; - maskSize(dim,3) = round(maskSize(dim,2) - maskSize(dim,1) + 1); - end - % get z bounds - maskSize(3,1) = min(all_vertices(:,3),[],'all'); - maskSize(3,2) = max(all_vertices(:,3),[],'all'); - maskSize(3,3) = round(maskSize(3,2) - maskSize(3,1) + 1); - end - - - % read points into 3d array - point_map = zeros(maskSize(2,3), maskSize(1,3), maskSize(3,3), 3); - tissues = fieldnames(vertices); - - x = linspace(maskSize(1,1),maskSize(1,2),maskSize(1,3)); - y = linspace(maskSize(2,1),maskSize(2,2),maskSize(2,3)); - z = linspace(maskSize(3,1),maskSize(3,2),maskSize(3,3)); - [X,Y,Z] = meshgrid(x,y,z); - - for tissue = 1:numel(tissues) - tissue_name = tissues{tissue}; % TODO: tissue vs tissue name is bad - tissue_vertices = vertices.(tissue_name); - tissue_point_map = zeros(size(X)); - - % plot each point on the map - for point = 1:size(tissue_vertices,1) - % find distance from each point from the map - xDiff = abs(X - tissue_vertices(point,1)); - yDiff = abs(Y - tissue_vertices(point,2)); - zDiff = abs(Z - tissue_vertices(point,3)); - totalDiff = xDiff + yDiff + zDiff; - % add points within tolerance to map - tissue_point_map(totalDiff <= tolerance) = 1; - end - point_map(:,:,:,tissue) = tissue_point_map; - end - - - % create convex hulls of each slice - conv_hulls = zeros(size(point_map)); - slices = size(point_map, 3); - for tissue = 1:numel(tissues) - for slice = 1:slices - conv_hulls(:,:,slice,tissue) = bwconvhull(point_map(:,:,slice,tissue)); - end - end - - % layer hulls - % mc = 1, lv = 2, rv = 3 - layered_masks = int8(conv_hulls(:,:,:,1)); % mc - layered_masks(conv_hulls(:,:,:,2) == 1) = 2; % lv - layered_masks(conv_hulls(:,:,:,3) == 1) = 3; % rv - - % separate rv and lv myocardium - old_layered_masks = zeros(size(layered_masks)); - while ~isequal(old_layered_masks, layered_masks) - old_layered_masks = layered_masks; - for z = 1:size(layered_masks, 3) - % get maps rv lv and mc maps - slice = layered_masks(:,:,z); - mc_map = slice == 1; - lv_map = (slice == 2) | (slice == 4); - rv_map = (slice == 3) | (slice == 5); - kernel = [1,1,1 ; 1,0,1 ; 1,1,1]; - - % get pixels around the lv/rv - lv_conv_result = conv2(lv_map, kernel, "same"); - rv_conv_result = conv2(rv_map,kernel,"same"); - lv_mc = (lv_conv_result > 0) & mc_map; - rv_mc = (rv_conv_result > 0) & mc_map; - - % update layered masks - new_slice = slice; - new_slice(lv_mc == 1) = 4; - new_slice(rv_mc == 1) = 5; - layered_masks(:,:,z) = new_slice; - end - end - - % assume non-labeled myocardium is rv myocardium (TODO: maybe add more convolutions along the z axis) - layered_masks(layered_masks == 1) = 4; - - layered_masks = layered_masks - 1; - layered_masks(layered_masks < 0) = 0; - - % separate masks - cardiac_masks = zeros(cat(2,size(layered_masks),4)); - cardiac_masks(:,:,:,1) = layered_masks == 1; - cardiac_masks(:,:,:,2) = layered_masks == 2; - cardiac_masks(:,:,:,3) = layered_masks == 3; - cardiac_masks(:,:,:,4) = layered_masks == 4; - -end \ No newline at end of file diff --git a/simulations/anatomical_phantoms/util/_src_cardiac/create_cardiac_masks/create_cardiac_masks_test.m b/simulations/anatomical_phantoms/util/_src_cardiac/create_cardiac_masks/create_cardiac_masks_test.m deleted file mode 100644 index 554f217..0000000 --- a/simulations/anatomical_phantoms/util/_src_cardiac/create_cardiac_masks/create_cardiac_masks_test.m +++ /dev/null @@ -1,57 +0,0 @@ -% testing script for create_cardiac_masks -clear; close all; -% PCA modes are from the Cardiac Atlas Project and was derived from the UK -% Biobank Study - -% the HDF5 files are too large for GitHub, but can be found here: -% https://www.cardiacatlas.org/biventricular-modes/ -% after download, place the .h5 file in the same directory as this script - -% read point cloud data -sourceFile = './UKBRVLV_All.h5'; -PCA_mode = 20; -pc = h5read(sourceFile, '/COEFF'); -ev = h5read(sourceFile, '/LATENT'); -mu = h5read(sourceFile, '/MU'); -S = mu + (1.5 .* sqrt(ev(1)) .* pc(:,PCA_mode)'); -N = length(S); -ed = reshape(S(1:N/2), 3, [])'; -es = reshape(S((N/2+1):end), 3, [])'; - -vertices = struct; -% define areas of the heart -% these was separated by hand -lv = ed(1:1428,:); -rv = ed(1429:3056,:); -mc = ed(3057:end,:); - -figure('Color', 'w'); -plot3(lv(:,1), lv(:,2), lv(:,3), 'b.'); -hold on; -plot3(rv(:,1), rv(:,2), rv(:,3), 'r.'); -plot3(mc(:,1), mc(:,2), mc(:,3), 'g.'); -axis vis3d -axis equal - -% put it all in a struct -vertices.mc = mc; -vertices.lv = lv; -vertices.rv = rv; - -maskSize = [-100,100,201; - -100,100,201; - -50,50,11]; - -[im_mask, layered_masks] = create_cardiac_masks(vertices, 2, maskSize); - -slices = 1:round(size(im_mask,3)/10):size(im_mask,3); - -%% visualize im_mask - -figure; -imagescn(im_mask(:,:,slices,:), [0, 1], [numel(slices), size(im_mask,4)]); - -%% visualize layered_masks - -figure; -imagescn(layered_masks(:,:,slices,:),[0 max(layered_masks(:,:,slices,:),[],'all')], [1, numel(slices)]); \ No newline at end of file diff --git a/simulations/anatomical_phantoms/util/1/brainweb.mat b/simulations/anatomical_phantoms/util/brainweb/1/brainweb.mat similarity index 100% rename from simulations/anatomical_phantoms/util/1/brainweb.mat rename to simulations/anatomical_phantoms/util/brainweb/1/brainweb.mat diff --git a/simulations/anatomical_phantoms/util/1/brainweb_fuzzy.mat b/simulations/anatomical_phantoms/util/brainweb/1/brainweb_fuzzy.mat similarity index 100% rename from simulations/anatomical_phantoms/util/1/brainweb_fuzzy.mat rename to simulations/anatomical_phantoms/util/brainweb/1/brainweb_fuzzy.mat diff --git a/simulations/anatomical_phantoms/util/10/brainweb.mat b/simulations/anatomical_phantoms/util/brainweb/10/brainweb.mat similarity index 100% rename from simulations/anatomical_phantoms/util/10/brainweb.mat rename to simulations/anatomical_phantoms/util/brainweb/10/brainweb.mat diff --git a/simulations/anatomical_phantoms/util/10/brainweb_fuzzy.mat b/simulations/anatomical_phantoms/util/brainweb/10/brainweb_fuzzy.mat similarity index 100% rename from simulations/anatomical_phantoms/util/10/brainweb_fuzzy.mat rename to simulations/anatomical_phantoms/util/brainweb/10/brainweb_fuzzy.mat diff --git a/simulations/anatomical_phantoms/util/11/brainweb.mat b/simulations/anatomical_phantoms/util/brainweb/11/brainweb.mat similarity index 100% rename from simulations/anatomical_phantoms/util/11/brainweb.mat rename to simulations/anatomical_phantoms/util/brainweb/11/brainweb.mat diff --git a/simulations/anatomical_phantoms/util/11/brainweb_fuzzy.mat b/simulations/anatomical_phantoms/util/brainweb/11/brainweb_fuzzy.mat similarity index 100% rename from simulations/anatomical_phantoms/util/11/brainweb_fuzzy.mat rename to simulations/anatomical_phantoms/util/brainweb/11/brainweb_fuzzy.mat diff --git a/simulations/anatomical_phantoms/util/12/brainweb.mat b/simulations/anatomical_phantoms/util/brainweb/12/brainweb.mat similarity index 100% rename from simulations/anatomical_phantoms/util/12/brainweb.mat rename to simulations/anatomical_phantoms/util/brainweb/12/brainweb.mat diff --git a/simulations/anatomical_phantoms/util/12/brainweb_fuzzy.mat b/simulations/anatomical_phantoms/util/brainweb/12/brainweb_fuzzy.mat similarity index 100% rename from simulations/anatomical_phantoms/util/12/brainweb_fuzzy.mat rename to simulations/anatomical_phantoms/util/brainweb/12/brainweb_fuzzy.mat diff --git a/simulations/anatomical_phantoms/util/13/brainweb.mat b/simulations/anatomical_phantoms/util/brainweb/13/brainweb.mat similarity index 100% rename from simulations/anatomical_phantoms/util/13/brainweb.mat rename to simulations/anatomical_phantoms/util/brainweb/13/brainweb.mat diff --git a/simulations/anatomical_phantoms/util/13/brainweb_fuzzy.mat b/simulations/anatomical_phantoms/util/brainweb/13/brainweb_fuzzy.mat similarity index 100% rename from simulations/anatomical_phantoms/util/13/brainweb_fuzzy.mat rename to simulations/anatomical_phantoms/util/brainweb/13/brainweb_fuzzy.mat diff --git a/simulations/anatomical_phantoms/util/14/brainweb.mat 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file mode 100644 index 0000000..5cc5019 Binary files /dev/null and b/simulations/anatomical_phantoms/util/cardiac/cardiac_mask_9.mat differ diff --git a/simulations/anatomical_phantoms/util/cardiac/create_cardiac_masks/bresenham.m b/simulations/anatomical_phantoms/util/cardiac/create_cardiac_masks/bresenham.m new file mode 100644 index 0000000..f9e7a58 --- /dev/null +++ b/simulations/anatomical_phantoms/util/cardiac/create_cardiac_masks/bresenham.m @@ -0,0 +1,39 @@ +function [x, y]=bresenham(x1,y1,x2,y2) + +%Matlab optmized version of Bresenham line algorithm. No loops. +%Format: +% [x y]=bham(x1,y1,x2,y2) +% +%Input: +% (x1,y1): Start position +% (x2,y2): End position +% +%Output: +% x y: the line coordinates from (x1,y1) to (x2,y2) +% +%Usage example: +% [x y]=bham(1,1, 10,-5); +% plot(x,y,'or'); +x1=round(x1); x2=round(x2); +y1=round(y1); y2=round(y2); +dx=abs(x2-x1); +dy=abs(y2-y1); +steep=abs(dy)>abs(dx); +if steep t=dx;dx=dy;dy=t; end + +%The main algorithm goes here. +if dy==0 + q=zeros(dx+1,1); +else + q=[0;diff(mod([floor(dx/2):-dy:-dy*dx+floor(dx/2)]',dx))>=0]; +end + +%and ends here. + +if steep + if y1<=y2 y=[y1:y2]'; else y=[y1:-1:y2]'; end + if x1<=x2 x=x1+cumsum(q);else x=x1-cumsum(q); end +else + if x1<=x2 x=[x1:x2]'; else x=[x1:-1:x2]'; end + if y1<=y2 y=y1+cumsum(q);else y=y1-cumsum(q); end +end diff --git a/simulations/anatomical_phantoms/util/cardiac/create_cardiac_masks/check_connection.m b/simulations/anatomical_phantoms/util/cardiac/create_cardiac_masks/check_connection.m new file mode 100644 index 0000000..a1ce4a5 --- /dev/null +++ b/simulations/anatomical_phantoms/util/cardiac/create_cardiac_masks/check_connection.m @@ -0,0 +1,49 @@ +function isconnected = check_connection(BW, pt1, pt2) + % checks if 2 points are connected by 1s on a black and white image. + % basically just a breadth-first search pathfinding algorithm that + % returns true if a path exists and false if path doesn't exist. + % + % ARGS: + % BW = binary image. size [N x M] + % pt1 = coordinates of the first point. size [1 2] + % pt2 = coordinates of the second point. size [1 2] + + % if the 2 points are the same, just say they're connected + if pt1 == pt2 + isconnected = true; + return + end + + isconnected = false; + directions = [0 1; 0 -1; 1 0; -1 0; ... % 4 cardinal directions + 1 1; 1 -1; -1 1; -1 -1]; % 4 corners + + queue = {pt1}; + + visited = zeros(size(BW)); + visited(pt1(1), pt1(2)) = 1; % mark initial point as visited + + while ~isempty(queue) + current_point = queue{1}; + % check each direction + for direction_n = 1:size(directions, 1) + neighbor_idx = current_point + directions(direction_n, :); + % ensure valid idx + if any(neighbor_idx < 1) || any(neighbor_idx > size(BW)) + continue + end + % add to queue if unsearched point + if BW(neighbor_idx(1), neighbor_idx(2)) == 1 & ... + visited(neighbor_idx(1), neighbor_idx(2)) == 0 + queue{end + 1} = neighbor_idx; + visited(neighbor_idx(1), neighbor_idx(2)) = 1; + if neighbor_idx == pt2 + isconnected = true; + return + end + end + end + % pop point of queue off the queue + queue = queue(2:end); + end +end diff --git a/simulations/anatomical_phantoms/util/cardiac/create_cardiac_masks/connectivity.txt b/simulations/anatomical_phantoms/util/cardiac/create_cardiac_masks/connectivity.txt new file mode 100644 index 0000000..6693e7a --- /dev/null +++ b/simulations/anatomical_phantoms/util/cardiac/create_cardiac_masks/connectivity.txt @@ -0,0 +1,11616 @@ +1487 749 1432 +1487 1432 20 +749 1132 332 +749 332 1432 +1133 182 1132 +1133 1132 749 +359 1133 749 +359 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read_atlas(stage, plot) + % ARGS: + % stage = either "ED" for end diastole or "ES" for end systole + % plot = logical flag to plot after reading + arguments + stage string {mustBeMember(stage, ["ED", "ES"])} = "ED" + plot logical = false + end + + pc = h5read('UKBRVLV.h5', '/COEFF'); % read the principal components + ev = h5read('UKBRVLV.h5', '/LATENT'); % read the eigenvalues + mu = h5read('UKBRVLV.h5', '/MU'); % read the mean shape + + deviations = rand([200,1]); % how many standard deviations to vary each pc + scaled_ev = deviations .* sqrt(ev); % scaled eigenvalues + variations = repmat(scaled_ev', [size(pc, 1), 1]) .* pc; + summed_variations = sum(variations,2)'; + + % generate the first principal mode + % with 1.5 times the standard deviation + S = mu + summed_variations; + + % get ED & ES points, & convert to 3 columns matrix [x, y, z] + N = length(S); + if stage == "ED" + pts = reshape(S(1:N/2), 3, [])'; + else + pts = reshape(S((N/2+1):end), 3, [])'; + end + + % only take some of the points + % these separations were from https://github.com/ComputationalPhysiology/ukb-atlas/blob/main/src/ukb/surface.py + % and i kinda trust them more than my own lol + LV = pts(1:1500, :); + RV = pts(cat(2, 1501:3224, 5730:5808), :); + EPI = pts(3225:5582, :); + everything_else = pts(5583:end, :); + + if plot + % plot ED points in blue + % plot ES points in red + figure('Color', 'w'); + hold on; + plot3(LV(:,1), LV(:,2), LV(:,3), 'r.'); + plot3(RV(:,1), RV(:,2), RV(:,3), 'g.'); + plot3(EPI(:,1), EPI(:,2), EPI(:,3), 'b.'); + plot3(everything_else(:,1), everything_else(:,2), everything_else(:,3), 'y.'); + + axis vis3d + axis equal + end +end + +% ======================= +% == READ CONNECTIVITY == +% ======================= +function connectivity = read_connectivity(path) + arguments + path string = 'connectivity.txt' + end + connectivity = readmatrix(path) + 1; % +1 to account for the fact that this came from python, and python uses 0-indexing + % tbh, that's the main reason why i wanted a wrapper function for this + % lol +end + +% =================== +% == PROJECT EDGES == +% =================== +function slice = trace_surface(zlevel, slice_size, pts, faces, pt_idxs, face_idxs) + % ARGS: + % zlevel = z coordinate of slice + % slice_size = scalar of how big the slice should be + % pts = Nx3 array of points + % faces = Nx3 array of pt idxs that define triangles + % pt_idxs = 1xN array of idxs of all points to look at + % face_idxs = 1xN array of idxs of all faces to look at + % OUT: + % slice = 2d matrix slice with points and (most) lines + + slice = zeros(slice_size); + offset = slice_size / 2; + + for face_idx = face_idxs + face = faces(face_idx, :); + % if any point index in face isn't in pt_idxs, skip this face + % because i guess there's points in connectivity.txt that don't exist/aren't relevant to the parts we're looking at + if any(~ismember(face, pt_idxs)) + continue + end + + verts = pts(face, :); + n_verts = size(verts, 2); % i mean, this is gonna be 3 because everything is a triangle, but... NO MAGIC NUMBERS + + % for each pair of vertices, find intersecting points + intersection_coords = {}; + for vert_idx = 1:n_verts + vert1 = verts(vert_idx, :); + vert2 = verts(mod(vert_idx, n_verts) + 1, :); + % funny thing about this line: it's supposed to be something + % like vert2 = verts(mod(vert_idx + 1, 3), :), but because + % matlab uses 1-indexing, it turns into + % mod((vert_idx + 1) - 1, n_verts) + 1, so that's what that does + if (vert1(3) >= zlevel && vert2(3) <= zlevel) || ... + (vert1(3) <= zlevel && vert2(3) >= zlevel) + xy = find_plane_intersection(vert1, vert2, zlevel); + intersection_coords{numel(intersection_coords) + 1} = [round(xy(1)) + offset, round(xy(2)) + offset]; + slice(round(xy(1)) + offset, round(xy(2)) + offset) = 1; % yes, it's code duplication, but i'm lazy + end + + % if there's 2 intersection coordinates, draw a line between them + if numel(intersection_coords) == 2 + x1 = intersection_coords{1}(1); + y1 = intersection_coords{1}(2); + x2 = intersection_coords{2}(1); + y2 = intersection_coords{2}(2); + slice = draw_line(slice, x1, y1, x2, y2); + end + end + end +end + +% ====================== +% == HELPER FUNCTIONS == +% ====================== +function xy = find_plane_intersection(a, b, z) + % a and b are coordinates [x,y,z] + % z is the plane at z=z + % xy are the xy coordinates where line ab intersects z + xy = [... + a(1) + (((z - a(3)) * (b(1) - a(1))) / (b(3) - a(3))), ... + a(2) + (((z - a(3)) * (b(2) - a(2))) / (b(3) - a(3))) ... + ]; +end + +% quick wrapper function to draw a bresenham line +function I_new = draw_line(I, x1, y1, x2, y2) + [x,y] = bresenham(x1, y1, x2, y2); + line_indices = sub2ind(size(I), x, y); + I_new = I; + I_new(line_indices) = 1; +end + +function [new_endpt_x, new_endpt_y] = remove_erroneous_endpoints(I, endpt_x, endpt_y) + % removes pixels that could be considered offshoots + % this is a very "this just needs to work" kind of function + kernel = ones(5); + n_neighbors = conv2(I, kernel, 'same'); % includes the corner itself + + new_endpt_x = []; + new_endpt_y = []; + for endpt_idx = 1:numel(endpt_x) + if n_neighbors(endpt_x(endpt_idx), endpt_y(endpt_idx)) <= 3 + new_endpt_x(numel(new_endpt_x) + 1, 1) = endpt_x(endpt_idx); + new_endpt_y(numel(new_endpt_y) + 1, 1) = endpt_y(endpt_idx); + else + disp("removed erroneous endpoint") + end + end +end + +function I = remove_blobs(I, blob_size) + % removes small blobs of isolated pixels. intended to work with even + kernel = ones(blob_size + 2) .* 100; + kernel(2:(blob_size + 1), 2:(blob_size + 1)) = 1; + conved = conv2(I, kernel, 'same'); + blob_locations = conved < 100 & conved ~= 0; + if mod(blob_size, 2) == 0 + new_kernel = zeros(blob_size + 1); + new_kernel(2:end, 2:end) = 1; % see, kernels with even dimensions are weird, so this manipulates the anchor of the kernel so it actually catches the right pixels + else + new_kernel = ones(blob_size); + end + to_remove = conv2(blob_locations, new_kernel, 'same'); + I(to_remove ~= 0) = 0; +end + + + +% ===================== +% == CLOSE BIG HOLES == +% ===================== +function bresenhamified = close_big_holes(I) + I = bwskel(logical(I)); + I = remove_blobs(I, 5); % i think 5 is reasonable + bresenhamified = I; + % identify endpoints + kernel = [1 1 1; 1 0 1; 1 1 1]; + surrounding_pixels = conv2(I, kernel, 'same'); + endpoints = (surrounding_pixels == 1 & I == 1); + endpt_indices = find(endpoints); + n_endpts = numel(endpt_indices); + if n_endpts == 0 + return + end + + [endpt_x, endpt_y] = ind2sub(size(endpoints), endpt_indices); + + if mod(numel(endpt_x), 2) == 1 + figure; imshow(I); + error("there's a weird outcrop somewhere idk help") + end + + % multiple gaps case + if numel(endpt_indices) > 2 + distances = zeros(n_endpts); + for endpt_idx1 = 1:numel(endpt_indices) + for endpt_idx2 = 1:numel(endpt_indices) + % check if the points are connected + pt1 = [endpt_x(endpt_idx1), endpt_y(endpt_idx1)]; + pt2 = [endpt_x(endpt_idx2), endpt_y(endpt_idx2)]; + is_connected = check_connection(I, pt1, pt2); + % if not connected, set it to distance between 2 points + if ~is_connected + dx = pt1(1) - pt2(1); + dy = pt1(2) - pt2(2); + distances(endpt_idx1, endpt_idx2) = sqrt(dx.^2 + dy.^2); + end + end + end + + remaining_endpts = 1:n_endpts; + while any(distances > 0, 'all') + % find minimum nonzero distance, fill that one in first + nonzero_min = min(distances(distances > 0)); + [pt1_idx, pt2_idx] = ind2sub(size(distances), find(distances == nonzero_min, 1)); + bresenhamified = draw_line(bresenhamified, endpt_x(pt1_idx), endpt_y(pt1_idx), endpt_x(pt2_idx), endpt_y(pt2_idx)); + + % set these guys to 0 + distances(pt1_idx,:) = 0; + distances(:,pt1_idx) = 0; + distances(pt2_idx,:) = 0; + distances(:,pt2_idx) = 0; + + % recheck connectivity + remaining_endpts(pt1_idx) = 0; + remaining_endpts(pt2_idx) = 0; + for endpt_idx1 = remaining_endpts(remaining_endpts > 0) + for endpt_idx2 = remaining_endpts(remaining_endpts > 0) + pt1 = [endpt_x(endpt_idx1), endpt_y(endpt_idx1)]; + pt2 = [endpt_x(endpt_idx2), endpt_y(endpt_idx2)]; + is_connected = check_connection(bresenhamified, pt1, pt2); % TODO: consider making a wrapper function for checking connectivity of a set of points + if is_connected + distances(endpt_idx1, endpt_idx2) = 0; + end + end + end + end + end + + + % now there should only be two endpoints + % re-skeletonize (in case some corners were introduced and endpoints + % can't be found) + bresenhamified = bwskel(logical(bresenhamified)); + surrounding_pixels = conv2(bresenhamified, kernel, 'same'); + endpoints = (surrounding_pixels == 1 & bresenhamified == 1); + endpt_indices = find(endpoints); + [endpt_x, endpt_y] = ind2sub(size(endpoints), endpt_indices); + if numel(endpt_x) ~= 2 + warning('something looks awefully suspicious. double check this layer') + [endpt_x, endpt_y] = remove_erroneous_endpoints(bresenhamified, endpt_x, endpt_y); + end + bresenhamified = draw_line(bresenhamified, endpt_x(1), endpt_y(1), endpt_x(2), endpt_y(2)); +end + + +% ================ +% == FLOOD-FILL == +% ================ +function filled = fill_outline(outline) + inverse_fill = imfill(outline, [1,1]); + filled = ~inverse_fill + outline; +end + +% ============================ +% == distinguish myocardium == +% ============================ +function new_mask = separate_myocardium(og_layered) + % og_layered = row x col x slice, 1 = lv, 2 = rv, 3 = epi + % outputs a new layered mask where 1 = lv, 2 = rv, 3 = lvmy, 4 = rvmy + new_mask = og_layered; + + % find distances + lv_dist = bwdist(new_mask == 1); + + rv_dist = bwdist(new_mask == 2); + + % for each voxel, see if a) it's epi, b) if it's closer + for row = 1:size(new_mask, 1) + for col = 1:size(new_mask, 2) + for slice = 1:size(new_mask, 3) + if new_mask(row,col,slice) == 3 && rv_dist(row,col,slice) < lv_dist(row,col,slice) + new_mask(row,col,slice) = 4; + end + end + end + end +end + + +function [filled, closed, traced] = create_one_slice(zlevel, slice_size, pts, connections, pt_idxs_to_use, face_idxs_to_use) + traced = trace_surface(zlevel, slice_size, pts, connections, pt_idxs_to_use, face_idxs_to_use); + closed = close_big_holes(traced); + filled = fill_outline(closed); +end + + +%% +% ========== +% == MAIN == +% ========== +clear; close all; + +% read atlas and connectivity, declare ranges +[pts, ~, ~, ~] = read_atlas("ED", false); +connections = read_connectivity(); + +LV_pts = 1:1500; +RV_pts = 1501:3224; +EPI_pts = 3225:5582; +LV_faces = 1:3072; +RV_faces = cat(2, 3073:4480, 4480:6752); +EPI_faces = 6753:11616; + +% parameters for slices +z_values = -40:2:50; +slice_size = 150; + +% creating masks +layered_masks = zeros(slice_size, slice_size, numel(z_values)); +traced_masks = zeros(slice_size, slice_size, numel(z_values)); +closed_masks = zeros(slice_size, slice_size, numel(z_values)); + +for z_idx = 1:numel(z_values) + z = z_values(z_idx); + % generate each slice + [LV_slice, LV_closed, LV_traced] = create_one_slice(z, slice_size, pts, connections, LV_pts, LV_faces); + [RV_slice, RV_closed, RV_traced] = create_one_slice(z, slice_size, pts, connections, RV_pts, RV_faces); + [EPI_slice, EPI_closed, EPI_traced] = create_one_slice(z, slice_size, pts, connections, EPI_pts, EPI_faces); + + % layer everything + layered_slice = EPI_slice .* 3; + layered_slice(LV_slice > 0) = 1; + layered_slice(RV_slice > 0) = 2; + layered_masks(:,:,z_idx) = layered_slice; + + % also do the traced masks (for debugging purposes) + closed_masks(:,:,z_idx) = EPI_closed; + traced_masks(:,:,z_idx) = EPI_traced; + fprintf("z slice at %i (slice #%i) has been made\n", z, z_idx); +end + +layered_masks = separate_myocardium(layered_masks); + +% separate out masks +masks = zeros(slice_size, slice_size, numel(z_values), 4); +masks(:,:,:,1) = layered_masks == 1; +masks(:,:,:,2) = layered_masks == 2; +masks(:,:,:,3) = layered_masks == 3; +masks(:,:,:,4) = layered_masks == 4; + +%% +for slice = 1:46 + figure; imshow(layered_masks(:,:,slice), [0, max(layered_masks, [], 'all')]); +end + +%% +matlab.io.saveVariablesToScript('outputs/cardiac_mask_10.m', {'masks'}) diff --git a/simulations/metabolic_phantom/DEMO.m b/simulations/metabolic_shapes_phantom/DEMO.m similarity index 100% rename from simulations/metabolic_phantom/DEMO.m rename to simulations/metabolic_shapes_phantom/DEMO.m diff --git a/simulations/metabolic_phantom/README.md b/simulations/metabolic_shapes_phantom/README.md similarity index 100% rename from simulations/metabolic_phantom/README.md rename to simulations/metabolic_shapes_phantom/README.md diff --git a/simulations/metabolic_phantom/demo_metabolic_phantom_fit.m b/simulations/metabolic_shapes_phantom/demo_metabolic_phantom_fit.m similarity index 100% rename from simulations/metabolic_phantom/demo_metabolic_phantom_fit.m rename to simulations/metabolic_shapes_phantom/demo_metabolic_phantom_fit.m diff --git a/simulations/metabolic_phantom/kPL_16.png b/simulations/metabolic_shapes_phantom/kPL_16.png similarity index 100% rename from simulations/metabolic_phantom/kPL_16.png rename to simulations/metabolic_shapes_phantom/kPL_16.png diff --git a/simulations/metabolic_phantom/kPL_256.png b/simulations/metabolic_shapes_phantom/kPL_256.png similarity index 100% rename from simulations/metabolic_phantom/kPL_256.png rename to simulations/metabolic_shapes_phantom/kPL_256.png diff --git a/simulations/metabolic_phantom/kTRANS_16.png b/simulations/metabolic_shapes_phantom/kTRANS_16.png similarity index 100% rename from simulations/metabolic_phantom/kTRANS_16.png rename to simulations/metabolic_shapes_phantom/kTRANS_16.png diff --git a/simulations/metabolic_phantom/kTRANS_256.png b/simulations/metabolic_shapes_phantom/kTRANS_256.png similarity index 100% rename from simulations/metabolic_phantom/kTRANS_256.png rename to simulations/metabolic_shapes_phantom/kTRANS_256.png diff --git a/simulations/metabolic_phantom/metabolic_phantom.m b/simulations/metabolic_shapes_phantom/metabolic_phantom.m similarity index 100% rename from simulations/metabolic_phantom/metabolic_phantom.m rename to simulations/metabolic_shapes_phantom/metabolic_phantom.m diff --git a/simulations/metabolic_phantom/rectangle_shape.m b/simulations/metabolic_shapes_phantom/rectangle_shape.m similarity index 100% rename from simulations/metabolic_phantom/rectangle_shape.m rename to simulations/metabolic_shapes_phantom/rectangle_shape.m diff --git a/simulations/metabolic_phantom/sphere_fcn.m b/simulations/metabolic_shapes_phantom/sphere_fcn.m similarity index 100% rename from simulations/metabolic_phantom/sphere_fcn.m rename to simulations/metabolic_shapes_phantom/sphere_fcn.m diff --git a/simulations/HP_montecarlo_evaluation.m b/simulations/montecarlo_simulations/HP_montecarlo_evaluation.m similarity index 100% rename from simulations/HP_montecarlo_evaluation.m rename to simulations/montecarlo_simulations/HP_montecarlo_evaluation.m diff --git a/simulations/test_HP_montecarlo_evaluation.m b/simulations/montecarlo_simulations/test_HP_montecarlo_evaluation.m similarity index 100% rename from simulations/test_HP_montecarlo_evaluation.m rename to simulations/montecarlo_simulations/test_HP_montecarlo_evaluation.m diff --git a/simulations/realistic_input_function.m b/simulations/pk_models/realistic_input_function.m similarity index 100% rename from simulations/realistic_input_function.m rename to simulations/pk_models/realistic_input_function.m diff --git a/simulations/simulate_2site_model.m b/simulations/pk_models/simulate_2site_model.m similarity index 100% rename from simulations/simulate_2site_model.m rename to simulations/pk_models/simulate_2site_model.m diff --git a/simulations/simulate_Nsite_model.m b/simulations/pk_models/simulate_Nsite_model.m similarity index 100% rename from simulations/simulate_Nsite_model.m rename to simulations/pk_models/simulate_Nsite_model.m diff --git a/utilities/fire.m b/utilities/fire.m new file mode 100644 index 0000000..28f409b --- /dev/null +++ b/utilities/fire.m @@ -0,0 +1,300 @@ +function p = fire(m) +%FIRE Blue-Purple Hot colormap +% +% FIRE(M) returns an M-by-3 matrix containing a "fire" colormap. +% FIRE, by itself, is the same length as the current figure's +% colormap. If no figure exists, MATLAB creates one. +% +% To add this colormap as a default map, use 'addpath' with the +% directory containing 'fire.m'. +% +% To reset the colormap of the current figure use 'colormap(fire)'. +% +% see also: HSV, GRAY, HOT, COOL, BONE, COPPER, FLAG, PINK, COLORMAP, +% RGBPLOT. +% +% To create any custom colormap, see the directions on line 23 of this +% m-file. + +if nargin < 1 + m = size(get(gcf,'colormap'),1); +end + +%You can replace this M x 3 matrix with any matrix whose values range +%between 0 and 1 to create a new colormap file. Use copy / paste to create +%a matrix like the one below, you do not have to add these values +%manually. To create a new colormap, change 'cmap_mat' to the desired +%matrix, rename the function *and* the m-file from 'fire' to your desired +%colormap name. + +cmap_mat=[ + 0 0 0 + 0 0 0.0275 + 0 0 0.0588 + 0 0 0.0863 + 0 0 0.1176 + 0 0 0.1490 + 0 0 0.1765 + 0 0 0.2078 + 0 0 0.2392 + 0 0 0.2549 + 0 0 0.2706 + 0 0 0.2902 + 0 0 0.3059 + 0 0 0.3216 + 0 0 0.3412 + 0 0 0.3569 + 0.0039 0 0.3765 + 0.0157 0 0.3922 + 0.0275 0 0.4078 + 0.0392 0 0.4235 + 0.0510 0 0.4431 + 0.0627 0 0.4588 + 0.0745 0 0.4745 + 0.0863 0 0.4902 + 0.0980 0 0.5098 + 0.1098 0 0.5255 + 0.1216 0 0.5412 + 0.1333 0 0.5608 + 0.1451 0 0.5765 + 0.1569 0 0.5922 + 0.1686 0 0.6118 + 0.1804 0 0.6275 + 0.1922 0 0.6471 + 0.2039 0 0.6588 + 0.2157 0 0.6706 + 0.2275 0 0.6863 + 0.2392 0 0.6980 + 0.2510 0 0.7098 + 0.2627 0 0.7255 + 0.2745 0 0.7373 + 0.2863 0 0.7529 + 0.2980 0 0.7647 + 0.3098 0 0.7804 + 0.3216 0 0.7922 + 0.3333 0 0.8078 + 0.3451 0 0.8196 + 0.3569 0 0.8353 + 0.3686 0 0.8471 + 0.3843 0 0.8627 + 0.3961 0 0.8627 + 0.4078 0 0.8667 + 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1.0000 1.0000 + 1.0000 1.0000 1.0000 + 1.0000 1.0000 1.0000 + 1.0000 1.0000 1.0000 + ]; + +%interpolate values +xin=linspace(0,1,m)'; +xorg=linspace(0,1,size(cmap_mat,1)); + +p(:,1)=interp1(xorg,cmap_mat(:,1),xin,'linear'); +p(:,2)=interp1(xorg,cmap_mat(:,2),xin,'linear'); +p(:,3)=interp1(xorg,cmap_mat(:,3),xin,'linear'); + + + + +