4.5 Article

Contrast-agent-based perfusion MRI code repository and testing framework: ISMRM Open Science Initiative for Perfusion Imaging (OSIPI)

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MAGNETIC RESONANCE IN MEDICINE
卷 -, 期 -, 页码 -

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WILEY
DOI: 10.1002/mrm.29826

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dynamic contrast-enhanced MRI; dynamic susceptibility-contrast MRI; open source; OSIPI; perfusion; software

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This article introduces a community-led open-source code repository established by the ISMRM Open Science Initiative for Perfusion Imaging (OSIPI) to promote sharing and testing of perfusion imaging processing software. The repository contains multiple implementations of perfusion processing steps and provides an automated unit-testing framework for evaluation. The results show that differences in output parameters mainly occur in the pharmacokinetic models, while other aspects of the implementations have similar output values.
Purpose: Software has a substantial impact on quantitative perfusion MRI values. The lack of generally accepted implementations, code sharing and transparent testing reduces reproducibility, hindering the use of perfusion MRI in clinical trials. To address these issues, the ISMRM Open Science Initiative for Perfusion Imaging (OSIPI) aimed to establish a community-led, centralized repository for sharing open-source code for processing contrast-based perfusion imaging, incorporating an open-source testing framework. Methods: A repository was established on the OSIPI GitHub website. Python was chosen as the target software language. Calls for code contributions were made to OSIPI members, the ISMRM Perfusion Study Group, and publicly via OSIPI websites. An automated unit-testing framework was implemented to evaluate the output of code contributions, including visual representation of the results. Results: The repository hosts 86 implementations of perfusion processing steps contributed by 12 individuals or teams. These cover all core aspects of DCE and DSC-MRI processing, including multiple implementations of the same functionality. Tests were developed for 52 implementations, covering five analysis steps. For T1 mapping, signal-to-concentration conversion and population AIF functions, different implementations resulted in near-identical output values. For the five pharmacokinetic models tested (Tofts, extended Tofts-Kety, Patlak, two-compartment exchange, and two-compartment uptake), differences in output parameters were observed between contributions. Conclusions: The OSIPI DCE-DSC code repository represents a novel community-led model for code sharing and testing. The repository facilitates the re-use of existing code and the benchmarking of new code, promoting enhanced reproducibility in quantitative perfusion imaging.

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