From 7c44049815a9fdcc52c4b573fcc5ed47523f0aba Mon Sep 17 00:00:00 2001 From: Hui Xue Date: Wed, 17 Dec 2025 05:56:03 +0000 Subject: [PATCH 1/2] update and fix --- docs/index.md | 6 +- pyproject.toml | 14 +--- uv.lock | 211 +++++++++++++++++++++++++++++++++++-------------- 3 files changed, 157 insertions(+), 74 deletions(-) diff --git a/docs/index.md b/docs/index.md index 108ec21..d8e45ee 100644 --- a/docs/index.md +++ b/docs/index.md @@ -8,10 +8,10 @@ This repository contains the Pytorch code in our paper [SNRAware: Improved Deep author = {Xue, Hui and Hooper, Sarah M. and Pierce, Iain and Davies, Rhodri H. and Stairs, John and Naegele, Joseph and Campbell-Washburn, Adrienne E. and Manisty, Charlotte and Moon, James C. and Treibel, Thomas A. and Hansen, Michael S. and Kellman, Peter}, title = {SNRAware: Improved Deep Learning MRI Denoising with Signal-to-noise Ratio Unit Training and G-factor Map Augmentation}, journal = {Radiology: Artificial Intelligence}, - volume = {0}, - number = {ja}, + volume = {7}, + number = {6}, pages = {e250227}, - year = {0}, + year = {2025}, doi = {10.1148/ryai.250227}, note ={PMID: 41123451}, URL = {https://doi.org/10.1148/ryai.250227} diff --git a/pyproject.toml b/pyproject.toml index 51dcbd5..5eecb9d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -10,8 +10,9 @@ authors = [ keywords = [] license = "MIT" dependencies = [ - "mkl-fft>=2.0.0", - "mkl-service>=2.4.2", + "mkl", + "mkl-fft", + "mkl-service", "typing_extensions", "click", "h5py", @@ -78,15 +79,6 @@ build-backend = "uv_build" [tool.uv.build-backend] module-name = ["snraware"] -[[tool.uv.index]] -name = "mkl" -url = "https://urob.github.io/numpy-mkl" - -[tool.uv.sources] -numpy = { index = "mkl" } -scipy = { index = "mkl" } -mkl-service = { index = "mkl" } - [project.scripts] [tool.pyright] diff --git a/uv.lock b/uv.lock index 59f20d9..c7dbed0 100644 --- a/uv.lock +++ b/uv.lock @@ -1393,25 +1393,25 @@ wheels = [ [[package]] name = "intel-cmplr-lib-ur" version = "2025.3.0" 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"sha256:695b6be076a7bd2037b0cdbe5ee025f9ae74934cc78bdf25df8b52e8f746e457", size = 249097, upload-time = "2025-10-22T17:57:00.279Z" }, ] [[package]] From f0689acc08d6d3dcea75818df9cc3975b3e0afdd Mon Sep 17 00:00:00 2001 From: Hui Xue Date: Fri, 19 Dec 2025 23:43:36 +0000 Subject: [PATCH 2/2] udpate the model downloading --- docs/index.md | 32 +++++++++++++++++++++++++++----- 1 file changed, 27 insertions(+), 5 deletions(-) diff --git a/docs/index.md b/docs/index.md index ed36bcd..c3df8bb 100644 --- a/docs/index.md +++ b/docs/index.md @@ -1,6 +1,10 @@ # SNRAware -This repository contains the Pytorch code in our paper [SNRAware: Improved Deep Learning MRI Denoising with Signal-to-noise Ratio Unit Training and G-factor Map Augmentation](https://pubs.rsna.org/doi/full/10.1148/ryai.250227) published at the Radiology: Artificial Intelligence: +This repository contains the Pytorch code two of our papers: + +[SNRAware: Improved Deep Learning MRI Denoising with Signal-to-noise Ratio Unit Training and G-factor Map Augmentation](https://pubs.rsna.org/doi/full/10.1148/ryai.250227) published at the Radiology: Artificial Intelligence + +[Imaging Transformer for MRI Denoising: a Scalable Model Architecture that enables Low SNR Imaging across Applications](). ```latex @article{ @@ -18,6 +22,19 @@ This repository contains the Pytorch code in our paper [SNRAware: Improved Deep } ``` +[Imaging Transformer for MRI Denoising: a Scalable Model Architecture that enables Low SNR Imaging across Applications]: +```latex +@misc{xue2024imagingtransformermridenoising, + title={Imaging transformer for MRI denoising with the SNR unit training: enabling generalization across field-strengths, imaging contrasts, and anatomy}, + author={Hui Xue and Sarah Hooper and Azaan Rehman and Iain Pierce and Thomas Treibel and Rhodri Davies and W Patricia Bandettini and Rajiv Ramasawmy and Ahsan Javed and Zheren Zhu and Yang Yang and James Moon and Adrienne Campbell and Peter Kellman}, + year={2024}, + eprint={2404.02382}, + archivePrefix={arXiv}, + primaryClass={eess.IV}, + url={https://arxiv.org/abs/2404.02382}, +} +``` + - Model type: Imaging AI, non-generative - License: MIT @@ -76,8 +93,13 @@ To test the model, ```bash # download the model from the huggingface # small model -wget https://huggingface.co/microsoft/SNRAware/resolve/main/small/snraware_small_model.pts -wget https://huggingface.co/microsoft/SNRAware/resolve/main/small/snraware_small_model.yaml +wget --directory-prefix=./small/ https://huggingface.co/microsoft/SNRAware/resolve/main/small/snraware_small_model.pts +wget --directory-prefix=./small/ https://huggingface.co/microsoft/SNRAware/resolve/main/small/snraware_small_model.yaml + +# or install the huggingface cli +curl -LsSf https://hf.co/cli/install.sh | bash +# download model +hf download Microsoft/SNRAware --local-dir . # a test data is provided at ./test/data/inference # input data are [H, W, Frame] 3D complex tensor, input_real.npy and input_imag.npy store the @@ -85,8 +107,8 @@ wget https://huggingface.co/microsoft/SNRAware/resolve/main/small/snraware_small # gmap.npy is the g-factor map for all frames or for every frame, [H, W, 1 or Frame] # let's use the small model to run a inference -export model_file=snraware_small_model.pts -export config_file=snraware_small_model.yaml +export model_file=./small/snraware_small_model.pts +export config_file=./small/snraware_small_model.yaml # run the inference uv run python3 ./src/snraware/projects/mri/denoising/run_inference.py --input_dir ./test/data/phantom --output_dir /tmp/phantom_res_inference --saved_model_path $model_file --saved_config_path $config_file --batch_size 1 --input_fname input --gmap_fname gmap