Assessment of the generalization of learned image reconstruction and the potential for transfer learning
Florian Knoll,Kerstin Hammernik,Kerstin Hammernik,Erich Kobler,Thomas Pock,Thomas Pock,Michael P. Recht,Daniel K. Sodickson +7 more
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TLDR
In this paper, the authors evaluated the generalization ability of learned image reconstruction with respect to deviations in the acquisition settings between training and testing, and provided an outlook for the potential of transfer learning to fine-tune trainings to a particular target application using only a small number of training cases.Abstract:
Purpose Although deep learning has shown great promise for MR image reconstruction, an open question regarding the success of this approach is the robustness in the case of deviations between training and test data. The goal of this study is to assess the influence of image contrast, SNR, and image content on the generalization of learned image reconstruction, and to demonstrate the potential for transfer learning. Methods Reconstructions were trained from undersampled data using data sets with varying SNR, sampling pattern, image contrast, and synthetic data generated from a public image database. The performance of the trained reconstructions was evaluated on 10 in vivo patient knee MRI acquisitions from 2 different pulse sequences that were not used during training. Transfer learning was evaluated by fine-tuning baseline trainings from synthetic data with a small subset of in vivo MR training data. Results Deviations in SNR between training and testing led to substantial decreases in reconstruction image quality, whereas image contrast was less relevant. Trainings from heterogeneous training data generalized well toward the test data with a range of acquisition parameters. Trainings from synthetic, non-MR image data showed residual aliasing artifacts, which could be removed by transfer learning-inspired fine-tuning. Conclusion This study presents insights into the generalization ability of learned image reconstruction with respect to deviations in the acquisition settings between training and testing. It also provides an outlook for the potential of transfer learning to fine-tune trainings to a particular target application using only a small number of training cases.read more
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An overview of deep learning in medical imaging focusing on MRI
Alexander Lundervold,Alexander Lundervold,Arvid Lundervold,Arvid Lundervold,Arvid Lundervold +4 more
TL;DR: This paper indicates how deep learning has been applied to the entire MRI processing chain, from acquisition to image retrieval, from segmentation to disease prediction, and provides a starting point for people interested in experimenting and contributing to the field of deep learning for medical imaging.
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Deep Magnetic Resonance Image Reconstruction: Inverse Problems Meet Neural Networks
TL;DR: Several signal processing issues for maximizing the potential of deep reconstruction in fast MRI are discussed, which may facilitate further development of the networks and performance analysis from a theoretical point of view.
Journal ArticleDOI
fastMRI: A Publicly Available Raw k-Space and DICOM Dataset of Knee Images for Accelerated MR Image Reconstruction Using Machine Learning.
Florian Knoll,Jure Zbontar,Anuroop Sriram,Matthew J. Muckley,Mary Bruno,Aaron Defazio,Marc Parente,Krzysztof J. Geras,Joe Katsnelson,Hersh Chandarana,Zizhao Zhang,Michal Drozdzalv,Adriana Romero,Michael G. Rabbat,Pascal Vincent,James Pinkerton,Duo Wang,Nafissa Yakubova,Erich James Owens,C. Lawrence Zitnick,Michael P. Recht,Daniel K. Sodickson,Yvonne W. Lui +22 more
TL;DR: A publicly available dataset containing k-space data as well as Digital Imaging and Communications in Medicine image data of knee images for accelerated MR image reconstruction using machine learning is presented.
Journal ArticleDOI
Deep-Learning Methods for Parallel Magnetic Resonance Imaging Reconstruction: A Survey of the Current Approaches, Trends, and Issues
Florian Knoll,Kerstin Hammernik,Chi Zhang,Steen Moeller,Thomas Pock,Daniel K. Sodickson,Mehmet Akcakaya +6 more
TL;DR: An overview of the recent machine-learning approaches that have been proposed specifically for improving parallel imaging is provided and a general background introduction to parallel MRI is given and structured around the classical view of image- and k-space-based methods.
Journal ArticleDOI
Plug-and-Play Methods for Magnetic Resonance Imaging: Using Denoisers for Image Recovery
Rizwan Ahmad,Charles A. Bouman,Gregery T. Buzzard,Stanley H. Chan,Sizhuo Liu,Edward T. Reehorst,Philip Schniter +6 more
TL;DR: This article describes the use of plug-and-play (PnP) algorithms for MRI image recovery and describes how the result of the PnP method can be interpreted as a solution to an equilibrium equation, allowing convergence analysis from this perspective.
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Kerstin Hammernik,Teresa Klatzer,Erich Kobler,Michael P. Recht,Daniel K. Sodickson,Thomas Pock,Thomas Pock,Florian Knoll +7 more
TL;DR: In this paper, a variational network approach is proposed to reconstruct the clinical knee imaging protocol for different acceleration factors and sampling patterns using retrospectively and prospectively undersampled data.