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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=yes">
<meta name="description" content="Open NeuroPET project description, BIDS standard">
<meta name="author" content="Dr Cyril Pernet">
<link href="https://fonts.googleapis.com/css?family=Raleway:100,200,300,400,500,600,700,800,900&display=swap"
rel="stylesheet">
<title>OpenNeuro PET - data processing</title>
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Open NeuroPET
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<h3>Welcome to Open NeuroPET</h3>
<h5>Share, Curate and Analyze </br> Brain Positron Emission Tomography Data</h5>
<a href="#about" class="main-stroked-button">Learn More</a>
<a href="https://public-neuro.github.io/" target="_blank" class="main-filled-button">Share
securly EU Data</a>
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class="main-filled-button">Access Data</a>
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<h3>Data Curation made easy</h3>
<h5>Automated Data Curation and Validation</h5>
<a href="https://bids-specification.readthedocs.io/en/stable/modality-specific-files/positron-emission-tomography.html"
target="_blank" class="main-stroked-button">Check the BIDS Specification</a>
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class="main-filled-button">Download PET2BIDS</a>
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<h3>Brain PET analysis pipelines</h3>
<h5>Automate Data Processing</h5>
<a href="data_processing.html#about" class="main-stroked-button">Learn More</a>
<a href="https://github.com/nipreps/petprep" class="main-filled-button">Get PETPrep</a>
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<div class="section-heading">
<h6>Automated data processing</h6>
<h2>PET Preprocessing</h2>
</div>
<p>Once data are curated into BIDS, it becomes easier to preprocess and analyze them using
standardized tools and pipelines. Check <a href="https://github.com/nipreps/petprep"
target="_blank">PETPrep</a> for automated preprocessing. PETPrep is a robust and
open-source preprocessing
pipeline for positron emission tomography (PET) imaging developed within the NiPreps
framework. It provides a robust, automated, and BIDS-compliant solution for transforming raw
PET data into analysis-ready outputs with minimal user input. PETPrep performs essential
preprocessing steps such as motion correction, segmentation, co-registration between PET and
MRI, partial volume correction, normalization, and extraction of regional time–activity
curves. By
integrating best-in-class tools from widely used neuroimaging software packages, PETPrep
ensures high-quality, reproducible preprocessing while offering comprehensive visual reports
and detailed error tracking to support transparency and quality control in PET studies.</p>
</br>
<p> Get in touch with the team if you want to organize demo ore courses</p>
</div>
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<h6>Automated data processing</h6>
<h2>PET Modelling</h2>
</div>
<p> <a href="https://github.com/mathesong/bloodstream" target="_blank">Bloodstream</a> and <a
href="https://github.com/mathesong/petfit" target="_blank">PETFit</a> are two new
open-source BIDS apps that
provide access to the capabilities of the kinfitr R package for PET modelling as automated
pipelines for BIDS data. Both tools are designed for reproducible processing while remaining
highly customisable through browser-based configuration interfaces. Bloodstream processes
raw PET BIDS arterial blood data, producing quantification-ready input functions through
modelling and interpolation of parent fraction, plasma-to-blood ratio, and
metabolite-corrected
arterial input function curves. PETFit ingests derivative PET time activity curve data (e.g.
from
PETPrep) and input function data (e.g. from Bloodstream) to perform pharmacokinetic
modelling,
supporting both invasive models and reference tissue approaches along with detailed analysis
reports </p>
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<h6>Data Curation</h6>
<h2>Helping you curate data</h2>
</div>
<p>The BIDS extension for Positron Emission Tomography (PET-BIDS) (Norgaard et al., 2022)
provides nomenclature for structured data and metadata, including all the necessary
information to share and report on PET blood and metabolite (Knudsen et al., 2020). Here we
present <a href="https://github.com/openneuropet/PET2BIDS " target="_blank"> PET2BIDS</a>,
developed in both Matlab and Python, allowing the conversion of DICOM (DICOM
PS3.3 2020b - Information Object Definitions, 2020) and ECAT (CTI/Siemens proprietary
data format) PET imaging data and metadata (e.g., timing information such as ‘time zero’ or
blood measurements) into files that follow the BIDS specification (nifti, json, tsv).</p>
</div>
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</section>
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