CG‐SENSE revisited: Results from the first ISMRM reproducibility challenge

Purpose The aim of this work is to shed light on the issue of reproducibility in MR image reconstruction in the context of a challenge. Participants had to recreate the results of “Advances in sensitivity encoding with arbitrary k‐space trajectories" by Pruessmann et al. Methods The task of the...

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Published in	Magnetic resonance in medicine Vol. 85; no. 4; pp. 1821 - 1839
Main Authors	Maier, Oliver, Baete, Steven Hubert, Fyrdahl, Alexander, Hammernik, Kerstin, Harrevelt, Seb, Kasper, Lars, Karakuzu, Agah, Loecher, Michael, Patzig, Franz, Tian, Ye, Wang, Ke, Gallichan, Daniel, Uecker, Martin, Knoll, Florian
Format	Journal Article
Language	English
Published	United States Wiley Subscription Services, Inc 01.04.2021
Subjects	Algorithms Brain - diagnostic imaging CG‐SENSE Data acquisition Humans Image processing Image Processing, Computer-Assisted Image reconstruction Magnetic Resonance Imaging MRI nonuniform sampling NUFFT Outliers (statistics) Pixels Programming languages Regularization Reproducibility Reproducibility of Results Similarity CG-SENSE image reconstruction nonuniform sampling MRI reproducibility NUFFT
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ISSN	0740-3194 1522-2594 1522-2594
DOI	10.1002/mrm.28569

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Abstract	Purpose The aim of this work is to shed light on the issue of reproducibility in MR image reconstruction in the context of a challenge. Participants had to recreate the results of “Advances in sensitivity encoding with arbitrary k‐space trajectories" by Pruessmann et al. Methods The task of the challenge was to reconstruct radially acquired multicoil k‐space data (brain/heart) following the method in the original paper, reproducing its key figures. Results were compared to consolidated reference implementations created after the challenge, accounting for the two most common programming languages used in the submissions (Matlab/Python). Results Visually, differences between submissions were small. Pixel‐wise differences originated from image orientation, assumed field‐of‐view, or resolution. The reference implementations were in good agreement, both visually and in terms of image similarity metrics. Discussion and Conclusion While the description level of the published algorithm enabled participants to reproduce CG‐SENSE in general, details of the implementation varied, for example, density compensation or Tikhonov regularization. Implicit assumptions about the data lead to further differences, emphasizing the importance of sufficient metadata accompanying open datasets. Defining reproducibility quantitatively turned out to be nontrivial for this image reconstruction challenge, in the absence of ground‐truth results. Typical similarity measures like NMSE of SSIM were misled by image intensity scaling and outlier pixels. Thus, to facilitate reproducibility, researchers are encouraged to publish code and data alongside the original paper. Future methodological papers on MR image reconstruction might benefit from the consolidated reference implementations of CG‐SENSE presented here, as a benchmark for methods comparison.
AbstractList	Purpose The aim of this work is to shed light on the issue of reproducibility in MR image reconstruction in the context of a challenge. Participants had to recreate the results of “Advances in sensitivity encoding with arbitrary k‐space trajectories" by Pruessmann et al. Methods The task of the challenge was to reconstruct radially acquired multicoil k‐space data (brain/heart) following the method in the original paper, reproducing its key figures. Results were compared to consolidated reference implementations created after the challenge, accounting for the two most common programming languages used in the submissions (Matlab/Python). Results Visually, differences between submissions were small. Pixel‐wise differences originated from image orientation, assumed field‐of‐view, or resolution. The reference implementations were in good agreement, both visually and in terms of image similarity metrics. Discussion and Conclusion While the description level of the published algorithm enabled participants to reproduce CG‐SENSE in general, details of the implementation varied, for example, density compensation or Tikhonov regularization. Implicit assumptions about the data lead to further differences, emphasizing the importance of sufficient metadata accompanying open datasets. Defining reproducibility quantitatively turned out to be nontrivial for this image reconstruction challenge, in the absence of ground‐truth results. Typical similarity measures like NMSE of SSIM were misled by image intensity scaling and outlier pixels. Thus, to facilitate reproducibility, researchers are encouraged to publish code and data alongside the original paper. Future methodological papers on MR image reconstruction might benefit from the consolidated reference implementations of CG‐SENSE presented here, as a benchmark for methods comparison. The aim of this work is to shed light on the issue of reproducibility in MR image reconstruction in the context of a challenge. Participants had to recreate the results of "Advances in sensitivity encoding with arbitrary k-space trajectories" by Pruessmann et al. METHODS: The task of the challenge was to reconstruct radially acquired multicoil k-space data (brain/heart) following the method in the original paper, reproducing its key figures. Results were compared to consolidated reference implementations created after the challenge, accounting for the two most common programming languages used in the submissions (Matlab/Python). Visually, differences between submissions were small. Pixel-wise differences originated from image orientation, assumed field-of-view, or resolution. The reference implementations were in good agreement, both visually and in terms of image similarity metrics. While the description level of the published algorithm enabled participants to reproduce CG-SENSE in general, details of the implementation varied, for example, density compensation or Tikhonov regularization. Implicit assumptions about the data lead to further differences, emphasizing the importance of sufficient metadata accompanying open datasets. Defining reproducibility quantitatively turned out to be nontrivial for this image reconstruction challenge, in the absence of ground-truth results. Typical similarity measures like NMSE of SSIM were misled by image intensity scaling and outlier pixels. Thus, to facilitate reproducibility, researchers are encouraged to publish code and data alongside the original paper. Future methodological papers on MR image reconstruction might benefit from the consolidated reference implementations of CG-SENSE presented here, as a benchmark for methods comparison. PurposeThe aim of this work is to shed light on the issue of reproducibility in MR image reconstruction in the context of a challenge. Participants had to recreate the results of “Advances in sensitivity encoding with arbitrary k‐space trajectories" by Pruessmann et al.MethodsThe task of the challenge was to reconstruct radially acquired multicoil k‐space data (brain/heart) following the method in the original paper, reproducing its key figures. Results were compared to consolidated reference implementations created after the challenge, accounting for the two most common programming languages used in the submissions (Matlab/Python).ResultsVisually, differences between submissions were small. Pixel‐wise differences originated from image orientation, assumed field‐of‐view, or resolution. The reference implementations were in good agreement, both visually and in terms of image similarity metrics.Discussion and ConclusionWhile the description level of the published algorithm enabled participants to reproduce CG‐SENSE in general, details of the implementation varied, for example, density compensation or Tikhonov regularization. Implicit assumptions about the data lead to further differences, emphasizing the importance of sufficient metadata accompanying open datasets. Defining reproducibility quantitatively turned out to be nontrivial for this image reconstruction challenge, in the absence of ground‐truth results. Typical similarity measures like NMSE of SSIM were misled by image intensity scaling and outlier pixels. Thus, to facilitate reproducibility, researchers are encouraged to publish code and data alongside the original paper. Future methodological papers on MR image reconstruction might benefit from the consolidated reference implementations of CG‐SENSE presented here, as a benchmark for methods comparison. The aim of this work is to shed light on the issue of reproducibility in MR image reconstruction in the context of a challenge. Participants had to recreate the results of "Advances in sensitivity encoding with arbitrary k-space trajectories" by Pruessmann et al. METHODS: The task of the challenge was to reconstruct radially acquired multicoil k-space data (brain/heart) following the method in the original paper, reproducing its key figures. Results were compared to consolidated reference implementations created after the challenge, accounting for the two most common programming languages used in the submissions (Matlab/Python).PURPOSEThe aim of this work is to shed light on the issue of reproducibility in MR image reconstruction in the context of a challenge. Participants had to recreate the results of "Advances in sensitivity encoding with arbitrary k-space trajectories" by Pruessmann et al. METHODS: The task of the challenge was to reconstruct radially acquired multicoil k-space data (brain/heart) following the method in the original paper, reproducing its key figures. Results were compared to consolidated reference implementations created after the challenge, accounting for the two most common programming languages used in the submissions (Matlab/Python).Visually, differences between submissions were small. Pixel-wise differences originated from image orientation, assumed field-of-view, or resolution. The reference implementations were in good agreement, both visually and in terms of image similarity metrics.RESULTSVisually, differences between submissions were small. Pixel-wise differences originated from image orientation, assumed field-of-view, or resolution. The reference implementations were in good agreement, both visually and in terms of image similarity metrics.While the description level of the published algorithm enabled participants to reproduce CG-SENSE in general, details of the implementation varied, for example, density compensation or Tikhonov regularization. Implicit assumptions about the data lead to further differences, emphasizing the importance of sufficient metadata accompanying open datasets. Defining reproducibility quantitatively turned out to be nontrivial for this image reconstruction challenge, in the absence of ground-truth results. Typical similarity measures like NMSE of SSIM were misled by image intensity scaling and outlier pixels. Thus, to facilitate reproducibility, researchers are encouraged to publish code and data alongside the original paper. Future methodological papers on MR image reconstruction might benefit from the consolidated reference implementations of CG-SENSE presented here, as a benchmark for methods comparison.DISCUSSION AND CONCLUSIONWhile the description level of the published algorithm enabled participants to reproduce CG-SENSE in general, details of the implementation varied, for example, density compensation or Tikhonov regularization. Implicit assumptions about the data lead to further differences, emphasizing the importance of sufficient metadata accompanying open datasets. Defining reproducibility quantitatively turned out to be nontrivial for this image reconstruction challenge, in the absence of ground-truth results. Typical similarity measures like NMSE of SSIM were misled by image intensity scaling and outlier pixels. Thus, to facilitate reproducibility, researchers are encouraged to publish code and data alongside the original paper. Future methodological papers on MR image reconstruction might benefit from the consolidated reference implementations of CG-SENSE presented here, as a benchmark for methods comparison.
Author	Wang, Ke Uecker, Martin Maier, Oliver Hammernik, Kerstin Loecher, Michael Kasper, Lars Karakuzu, Agah Baete, Steven Hubert Patzig, Franz Gallichan, Daniel Knoll, Florian Harrevelt, Seb Tian, Ye Fyrdahl, Alexander
AuthorAffiliation	17 German Centre for Cardiovascular Research (DZHK), Berlin, Germany 2 Center for Biomedical Imaging, New York University School of Medicine, New York, NY, USA 9 Techna Institute, University Health Network, Toronto, ON, Canada 6 Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands 7 Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland 19 Campus Institute Data Science (CIDAS), University of Göttingen, Göttingen, Germany 1 Institute of Medical Engineering, Graz University of Technology, Graz, Austria 13 Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA 10 NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montréal, Montréal, QC, Canada 16 Institute for Diagnostic and Interventional Radiology, University Medical Center Göttingen, Göttingen, Germany 5 Institute of Computer Graphics and Vision, Graz
AuthorAffiliation_xml	– name: 2 Center for Biomedical Imaging, New York University School of Medicine, New York, NY, USA – name: 9 Techna Institute, University Health Network, Toronto, ON, Canada – name: 11 Department of Radiology, Stanford University, Stanford, CA, USA – name: 19 Campus Institute Data Science (CIDAS), University of Göttingen, Göttingen, Germany – name: 3 Department of Clinical Physiology, Karolinska University Hospital, and Karolinska Institutet, Stockholm, Sweden – name: 1 Institute of Medical Engineering, Graz University of Technology, Graz, Austria – name: 13 Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA – name: 14 Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA – name: 16 Institute for Diagnostic and Interventional Radiology, University Medical Center Göttingen, Göttingen, Germany – name: 6 Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands – name: 8 Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland – name: 4 Department of Computing, Imperial College London, London, UK – name: 18 Cluster of Excellence “Multiscale Bioimaging: from Molecular Machines to Networks of Excitable Cells” (MBExC), University of Göttingen, Göttingen, Germany – name: 12 Utah Center for Advanced Imaging Research (UCAIR), Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT, USA – name: 10 NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montréal, Montréal, QC, Canada – name: 17 German Centre for Cardiovascular Research (DZHK), Berlin, Germany – name: 5 Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria – name: 7 Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland – name: 15 Cardiff University Brain Research Imaging Centre, Cardiff, UK
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Snippet	Purpose The aim of this work is to shed light on the issue of reproducibility in MR image reconstruction in the context of a challenge. Participants had to... The aim of this work is to shed light on the issue of reproducibility in MR image reconstruction in the context of a challenge. Participants had to recreate... PurposeThe aim of this work is to shed light on the issue of reproducibility in MR image reconstruction in the context of a challenge. Participants had to...
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SubjectTerms	Algorithms Brain - diagnostic imaging CG‐SENSE Data acquisition Humans Image processing Image Processing, Computer-Assisted Image reconstruction Magnetic Resonance Imaging MRI nonuniform sampling NUFFT Outliers (statistics) Pixels Programming languages Regularization Reproducibility Reproducibility of Results Similarity
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Title	CG‐SENSE revisited: Results from the first ISMRM reproducibility challenge
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