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Maier O, Baete SH, Fyrdahl A, Hammernik K, Harrevelt S, Kasper L, Karakuzu A, Loecher M, Patzig F, Tian Y, Wang K, Gallichan D, Uecker M, Knoll F. CG-SENSE revisited: Results from the first ISMRM reproducibility challenge. Magn Reson Med 2020; 85:1821-1839. [PMID: 33179826 DOI: 10.1002/mrm.28569] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Revised: 10/02/2020] [Accepted: 10/02/2020] [Indexed: 11/06/2022]
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.
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Affiliation(s)
- Oliver Maier
- Institute of Medical Engineering, Graz University of Technology, Graz, Austria
| | - Steven Hubert Baete
- Center for Biomedical Imaging, New York University School of Medicine, New York, NY, USA
| | - Alexander Fyrdahl
- Department of Clinical Physiology, Karolinska University Hospital, and Karolinska Institutet, Stockholm, Sweden
| | - Kerstin Hammernik
- Department of Computing, Imperial College London, London, UK.,Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria
| | - Seb Harrevelt
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Lars Kasper
- Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland.,Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland.,Techna Institute, University Health Network, Toronto, ON, Canada
| | - Agah Karakuzu
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montréal, Montréal, QC, Canada
| | - Michael Loecher
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Franz Patzig
- Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland
| | - Ye Tian
- Utah Center for Advanced Imaging Research (UCAIR), Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT, USA.,Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA
| | - Ke Wang
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA
| | | | - Martin Uecker
- Institute for Diagnostic and Interventional Radiology, University Medical Center Göttingen, Göttingen, Germany.,German Centre for Cardiovascular Research (DZHK), Berlin, Germany.,Cluster of Excellence "Multiscale Bioimaging: from Molecular Machines to Networks of Excitable Cells" (MBExC), University of Göttingen, Göttingen, Germany.,Campus Institute Data Science (CIDAS), University of Göttingen, Göttingen, Germany
| | - Florian Knoll
- Center for Biomedical Imaging, New York University School of Medicine, New York, NY, USA
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Arshad M, Qureshi M, Inam O, Omer H. Transfer learning in deep neural network based under-sampled MR image reconstruction. Magn Reson Imaging 2020; 76:96-107. [PMID: 32980504 DOI: 10.1016/j.mri.2020.09.018] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2020] [Revised: 08/10/2020] [Accepted: 09/08/2020] [Indexed: 01/27/2023]
Abstract
In Magnetic Resonance Imaging (MRI), the success of deep learning-based under-sampled MR image reconstruction depends on: (i) size of the training dataset, (ii) generalization capabilities of the trained neural network. Whenever there is a mismatch between the training and testing data, there is a need to retrain the neural network from scratch with thousands of MR images obtained using the same protocol. This may not be possible in MRI as it is costly and time consuming to acquire data. In this research, a transfer learning approach i.e. end-to-end fine tuning is proposed for U-Net to address the data scarcity and generalization problems of deep learning-based MR image reconstruction. First the generalization capabilities of a pre-trained U-Net (initially trained on the human brain images of 1.5 T scanner) are assessed for: (a) MR images acquired from MRI scanners of different magnetic field strengths, (b) MR images of different anatomies and (c) MR images under-sampled by different acceleration factors. Later, end-to-end fine tuning of the pre-trained U-Net is proposed for the reconstruction of the above-mentioned MR images (i.e. (a), (b) and (c)). The results show successful reconstructions obtained from the proposed method as reflected by the Structural SIMilarity index, Root Mean Square Error, Peak Signal-to-Noise Ratio and central line profile of the reconstructed images.
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Affiliation(s)
- Madiha Arshad
- Medical Image Processing Research Group (MIPRG), Department of Electrical and Computer Engineering, COMSATS University Islamabad, Pakistan
| | - Mahmood Qureshi
- Medical Image Processing Research Group (MIPRG), Department of Electrical and Computer Engineering, COMSATS University Islamabad, Pakistan
| | - Omair Inam
- Medical Image Processing Research Group (MIPRG), Department of Electrical and Computer Engineering, COMSATS University Islamabad, Pakistan
| | - Hammad Omer
- Medical Image Processing Research Group (MIPRG), Department of Electrical and Computer Engineering, COMSATS University Islamabad, Pakistan.
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