2020
DOI: 10.21105/joss.02727
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PyQMRI: An accelerated Python based Quantitative MRI toolbox

Abstract: Various medical examinations are seeing a shift to a more patient centric and personalized view, based on quantitative instead of qualitative observations and comparisons. This trend has also affected medical imaging, and particularly quantitative MRI (qMRI) gained importance in recent years. qMRI aims to identify the underlying biophysical and tissue parameters that determine contrast in an MR imaging experiment. In addition to contrast information, qMRI permits insights into diseases by providing biophysical… Show more

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Cited by 10 publications
(15 citation statements)
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“…For example, the computation time for model-based T 1 reconstruction presented here has been reduced from around 4 h in CPU (40-core 2.3 GHz Intel Xeon E5–2650 server with a RAM size of 512 GB) to 6 min using GPUs (Tesla V100 SXM2, NVIDIA, Santa Clara, CA). Other smart computational strategies [ 25 , 46 , 66 ] may also be employed to reduce the memory and computational time. The other limitation might be that model-based reconstructions are sensitive to model mismatch, e.g.…”
Section: Discussionmentioning
confidence: 99%
“…For example, the computation time for model-based T 1 reconstruction presented here has been reduced from around 4 h in CPU (40-core 2.3 GHz Intel Xeon E5–2650 server with a RAM size of 512 GB) to 6 min using GPUs (Tesla V100 SXM2, NVIDIA, Santa Clara, CA). Other smart computational strategies [ 25 , 46 , 66 ] may also be employed to reduce the memory and computational time. The other limitation might be that model-based reconstructions are sensitive to model mismatch, e.g.…”
Section: Discussionmentioning
confidence: 99%
“…42 The proposed reconstruction and fitting approach is integrated into a recently published Python framework for quantitative MRI. 48 This framework allows for an easy adaption to different signal models and, thus, a broad application of the proposed method. Adaptions to the signal model can be made by simply editing text files.…”
Section: Discussionmentioning
confidence: 99%
“…The analyses with the proposed method were done by implementing the FFC signal model in PyQMRI 48 . All fittings were performed on a desktop PC equipped with an Intel(R) Core(TM) i7‐6700K CPU @ 4.00GHz with 64 gigabyte of RAM and a NVIDIA GeForce GTX 1080 Ti GPU with 12 gigabyte of RAM.…”
Section: Methodsmentioning
confidence: 99%
“…The analyses with the proposed method were done by implementing the FFC signal model in PyQMRI [46]. All fittings were performed on a desktop PC equipped with an Intel(R) Core(TM) i7-6700K CPU @ 4.00GHz with 64 gigabyte of RAM and a NVIDIA GeForce GTX 1080 Ti GPU with 12 gigabyte of RAM.…”
Section: Data Processing and Correctionsmentioning
confidence: 99%
“…The Code used for this publication is integrated in PyQMRI [46] and is available at https://github.com/IMTtugraz/ PyQMRI.…”
Section: Code Availability Statementmentioning
confidence: 99%