Learning a preconditioner to accelerate compressed sensing reconstructions in MRI
Purpose To learn a preconditioner that accelerates parallel imaging (PI) and compressed sensing (CS) reconstructions. Methods A convolutional neural network (CNN) with residual connections was used to train a preconditioning operator. Training and validation data were simulated using 50% brain image...
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| Vydané v: | Magnetic resonance in medicine Ročník 87; číslo 4; s. 2063 - 2073 |
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| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
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United States
Wiley Subscription Services, Inc
01.04.2022
John Wiley and Sons Inc |
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| ISSN: | 0740-3194, 1522-2594, 1522-2594 |
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| Abstract | Purpose
To learn a preconditioner that accelerates parallel imaging (PI) and compressed sensing (CS) reconstructions.
Methods
A convolutional neural network (CNN) with residual connections was used to train a preconditioning operator. Training and validation data were simulated using 50% brain images and 50% white Gaussian noise images. Each multichannel training example contains a simulated sampling mask, complex coil sensitivity maps, and two regularization parameter maps. The trained model was integrated in the preconditioned conjugate gradient (PCG) method as part of the split Bregman CS method. The acceleration performance was compared with that of a circulant PI‐CS preconditioner for varying undersampling factors, number of coil elements and anatomies.
Results
The learned preconditioner reduces the number of PCG iterations by a factor of 4, yielding a similar acceleration as an efficient circulant preconditioner. The method generalizes well to different sampling schemes, coil configurations and anatomies.
Conclusion
It is possible to learn adaptable preconditioners for PI and CS reconstructions that meet the performance of state‐of‐the‐art preconditioners. Further acceleration could be achieved by optimizing the network architecture and the training set. Such a preconditioner could also be integrated in fully learned reconstruction methods to accelerate the training process of unrolled networks. |
|---|---|
| AbstractList | Purpose
To learn a preconditioner that accelerates parallel imaging (PI) and compressed sensing (CS) reconstructions.
Methods
A convolutional neural network (CNN) with residual connections was used to train a preconditioning operator. Training and validation data were simulated using 50% brain images and 50% white Gaussian noise images. Each multichannel training example contains a simulated sampling mask, complex coil sensitivity maps, and two regularization parameter maps. The trained model was integrated in the preconditioned conjugate gradient (PCG) method as part of the split Bregman CS method. The acceleration performance was compared with that of a circulant PI‐CS preconditioner for varying undersampling factors, number of coil elements and anatomies.
Results
The learned preconditioner reduces the number of PCG iterations by a factor of 4, yielding a similar acceleration as an efficient circulant preconditioner. The method generalizes well to different sampling schemes, coil configurations and anatomies.
Conclusion
It is possible to learn adaptable preconditioners for PI and CS reconstructions that meet the performance of state‐of‐the‐art preconditioners. Further acceleration could be achieved by optimizing the network architecture and the training set. Such a preconditioner could also be integrated in fully learned reconstruction methods to accelerate the training process of unrolled networks. To learn a preconditioner that accelerates parallel imaging (PI) and compressed sensing (CS) reconstructions.PURPOSETo learn a preconditioner that accelerates parallel imaging (PI) and compressed sensing (CS) reconstructions.A convolutional neural network (CNN) with residual connections was used to train a preconditioning operator. Training and validation data were simulated using 50% brain images and 50% white Gaussian noise images. Each multichannel training example contains a simulated sampling mask, complex coil sensitivity maps, and two regularization parameter maps. The trained model was integrated in the preconditioned conjugate gradient (PCG) method as part of the split Bregman CS method. The acceleration performance was compared with that of a circulant PI-CS preconditioner for varying undersampling factors, number of coil elements and anatomies.METHODSA convolutional neural network (CNN) with residual connections was used to train a preconditioning operator. Training and validation data were simulated using 50% brain images and 50% white Gaussian noise images. Each multichannel training example contains a simulated sampling mask, complex coil sensitivity maps, and two regularization parameter maps. The trained model was integrated in the preconditioned conjugate gradient (PCG) method as part of the split Bregman CS method. The acceleration performance was compared with that of a circulant PI-CS preconditioner for varying undersampling factors, number of coil elements and anatomies.The learned preconditioner reduces the number of PCG iterations by a factor of 4, yielding a similar acceleration as an efficient circulant preconditioner. The method generalizes well to different sampling schemes, coil configurations and anatomies.RESULTSThe learned preconditioner reduces the number of PCG iterations by a factor of 4, yielding a similar acceleration as an efficient circulant preconditioner. The method generalizes well to different sampling schemes, coil configurations and anatomies.It is possible to learn adaptable preconditioners for PI and CS reconstructions that meet the performance of state-of-the-art preconditioners. Further acceleration could be achieved by optimizing the network architecture and the training set. Such a preconditioner could also be integrated in fully learned reconstruction methods to accelerate the training process of unrolled networks.CONCLUSIONIt is possible to learn adaptable preconditioners for PI and CS reconstructions that meet the performance of state-of-the-art preconditioners. Further acceleration could be achieved by optimizing the network architecture and the training set. Such a preconditioner could also be integrated in fully learned reconstruction methods to accelerate the training process of unrolled networks. PurposeTo learn a preconditioner that accelerates parallel imaging (PI) and compressed sensing (CS) reconstructions.MethodsA convolutional neural network (CNN) with residual connections was used to train a preconditioning operator. Training and validation data were simulated using 50% brain images and 50% white Gaussian noise images. Each multichannel training example contains a simulated sampling mask, complex coil sensitivity maps, and two regularization parameter maps. The trained model was integrated in the preconditioned conjugate gradient (PCG) method as part of the split Bregman CS method. The acceleration performance was compared with that of a circulant PI‐CS preconditioner for varying undersampling factors, number of coil elements and anatomies.ResultsThe learned preconditioner reduces the number of PCG iterations by a factor of 4, yielding a similar acceleration as an efficient circulant preconditioner. The method generalizes well to different sampling schemes, coil configurations and anatomies.ConclusionIt is possible to learn adaptable preconditioners for PI and CS reconstructions that meet the performance of state‐of‐the‐art preconditioners. Further acceleration could be achieved by optimizing the network architecture and the training set. Such a preconditioner could also be integrated in fully learned reconstruction methods to accelerate the training process of unrolled networks. To learn a preconditioner that accelerates parallel imaging (PI) and compressed sensing (CS) reconstructions. A convolutional neural network (CNN) with residual connections was used to train a preconditioning operator. Training and validation data were simulated using 50% brain images and 50% white Gaussian noise images. Each multichannel training example contains a simulated sampling mask, complex coil sensitivity maps, and two regularization parameter maps. The trained model was integrated in the preconditioned conjugate gradient (PCG) method as part of the split Bregman CS method. The acceleration performance was compared with that of a circulant PI-CS preconditioner for varying undersampling factors, number of coil elements and anatomies. The learned preconditioner reduces the number of PCG iterations by a factor of 4, yielding a similar acceleration as an efficient circulant preconditioner. The method generalizes well to different sampling schemes, coil configurations and anatomies. It is possible to learn adaptable preconditioners for PI and CS reconstructions that meet the performance of state-of-the-art preconditioners. Further acceleration could be achieved by optimizing the network architecture and the training set. Such a preconditioner could also be integrated in fully learned reconstruction methods to accelerate the training process of unrolled networks. |
| Author | Remis, Rob Koolstra, Kirsten |
| AuthorAffiliation | 1 Division of Image Processing Department of Radiology Leiden University Medical Center Leiden The Netherlands 2 Circuits & Systems Group Electrical Engineering Mathematics and Computer Science Faculty Delft University of Technology Delft The Netherlands |
| AuthorAffiliation_xml | – name: 2 Circuits & Systems Group Electrical Engineering Mathematics and Computer Science Faculty Delft University of Technology Delft The Netherlands – name: 1 Division of Image Processing Department of Radiology Leiden University Medical Center Leiden The Netherlands |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34752655$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1109/TBME.2018.2821699 10.1016/j.neuroimage.2013.05.041 10.1002/mrm.27371 10.1002/mrm.27813 10.1109/ISBI.2017.7950457 10.1007/978-3-030-32248-9_3 10.1137/080725891 10.1109/TMI.2018.2833635 10.1109/TMI.2019.2954121 10.1002/mrm.27825 10.1038/nature25988 10.1002/mrm.26977 10.1002/mrm.27355 10.1109/ISBI.2011.5872579 10.1017/CBO9780511804441 10.1002/mrm.25222 10.1002/mrm.24751 10.1073/pnas.1907377117 10.1097/RLI.0b013e318271869c 10.1007/978-3-319-10404-1_18 10.1109/ISBI.2016.7493320 10.1109/EMBC.2019.8857141 10.1002/mrm.21236 10.1109/TMI.2012.2188039 10.1002/mrm.22161 10.1007/s12021-017-9354-9 10.1109/ACCESS.2020.3034287 |
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| Copyright | 2021 The Authors. published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine. 2021 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine. 2021. This article is published under http://creativecommons.org/licenses/by-nc/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| References | 2013; 48 2009; 62 2011 2015; 73 2020; 39 2004 2018; 65 2007; 57 2012; 31 2020; 8 2019; 82 2019; 81 2018; 555 2013; 80 2019 2020; 117 2017 2016 2014; 17 2016; 29 2009; 2 2014; 71 2018; 16 2018; 37 2018; 79 e_1_2_6_10_1 e_1_2_6_19_1 e_1_2_6_13_1 e_1_2_6_14_1 e_1_2_6_11_1 e_1_2_6_12_1 e_1_2_6_17_1 e_1_2_6_18_1 e_1_2_6_15_1 e_1_2_6_16_1 e_1_2_6_21_1 e_1_2_6_20_1 e_1_2_6_9_1 e_1_2_6_8_1 Yang Y (e_1_2_6_29_1) 2016; 29 e_1_2_6_5_1 e_1_2_6_4_1 e_1_2_6_7_1 e_1_2_6_6_1 e_1_2_6_25_1 e_1_2_6_24_1 e_1_2_6_3_1 e_1_2_6_23_1 e_1_2_6_2_1 e_1_2_6_22_1 e_1_2_6_28_1 e_1_2_6_27_1 e_1_2_6_26_1 |
| References_xml | – volume: 2 start-page: 323 year: 2009 end-page: 343 article-title: The split Bregman method for L1‐regularized problems publication-title: SIAM J Imaging Sci – volume: 62 start-page: 1574 year: 2009 end-page: 1584 article-title: Accelerating SENSE using compressed sensing publication-title: Magn Reson Med – volume: 57 start-page: 1086 year: 2007 end-page: 1098 article-title: Undersampled radial MRI with multiple coils. Iterative image reconstruction using a total variation constraint publication-title: Magn Reson Med – volume: 79 start-page: 3055 year: 2018 end-page: 3071 article-title: Learning a variational network for reconstruction of accelerated MRI data publication-title: Magn Reson Med – volume: 31 start-page: 1250 year: 2012 end-page: 1262 article-title: Fast l1‐SPIRiT compressed sensing parallel imaging MRI: scalable parallel implementation and clinically feasible runtime publication-title: IEEE Trans Med Imaging – volume: 73 start-page: 1034 year: 2015 end-page: 1040 article-title: Fast reconstruction for multichannel compressed sensing using a hierarchically semiseparable solver publication-title: Magn Reson Med – volume: 71 start-page: 990 year: 2014 end-page: 1001 article-title: ESPIRiT‐an eigenvalue approach to autocalibrating parallel MRI: where SENSE meets GRAPPA publication-title: Magn Reson Med – start-page: 1039 year: 2011 end-page: 1043 – volume: 8 start-page: 204825 year: 2020 end-page: 204838 article-title: An adaptive intelligence algorithm for undersampled knee MRI reconstruction publication-title: IEEE Access – start-page: 21 year: 2019 end-page: 29 – volume: 48 start-page: 10 year: 2013 end-page: 16 article-title: Free‐breathing contrast‐enhanced multiphase MRI of the liver using a combination of compressed sensing, parallel imaging, and golden‐angle radial sampling publication-title: Invest Radiol – volume: 82 start-page: 1398 year: 2019 end-page: 1411 article-title: Deep residual network for off‐resonance artifact correction with application to pediatric body MRA with 3D cones publication-title: Magn Reson Med – volume: 39 start-page: 1646 year: 2020 end-page: 1654 article-title: Accelerating non‐cartesian MRI reconstruction convergence using k‐space preconditioning publication-title: IEEE Trans Med Imaging – volume: 82 start-page: 1343 year: 2019 end-page: 1358 article-title: Highly accelerated multishot echo planar imaging through synergistic machine learning and joint reconstruction publication-title: Magn Reson Med – volume: 65 start-page: 1985 year: 2018 end-page: 1995 article-title: Deep residual learning for accelerated MRI using magnitude and phase networks publication-title: IEEE Trans Biomed Eng – volume: 81 start-page: 116 year: 2019 end-page: 128 article-title: Assessment of the generalization of learned image reconstruction and the potential for transfer learning publication-title: Magn Reson Med – volume: 555 start-page: 487 year: 2018 end-page: 492 article-title: Image reconstruction by domain‐transform manifold learning publication-title: Nature – volume: 117 start-page: 30088 year: 2020 end-page: 300950095 article-title: On instabilities of deep learning in image reconstruction and the potential costs of AI publication-title: Proc Nat Acad Sci – volume: 17 start-page: 138 year: 2014 end-page: 145 – start-page: 6818 year: 2019 end-page: 6821 – year: 2004 – volume: 81 start-page: 670 year: 2019 end-page: 685 article-title: Accelerating compressed sensing in parallel imaging reconstructions using an efficient circulant preconditioner for cartesian trajectories publication-title: Magn Reson Med – volume: 29 start-page: 10 year: 2016 end-page: 18 article-title: Deep ADMM‐Net for compressive sensing MRI publication-title: Proc 30th Int Neural Inf Process Syst – volume: 37 start-page: 1289 year: 2018 end-page: 1296 article-title: Image reconstruction is a new frontier of machine learning publication-title: IEEE Trans Med Imaging – start-page: 514 year: 2016 end-page: 517 – volume: 16 start-page: 425 year: 2018 end-page: 430 article-title: PRIM: an efficient preconditioning iterative reweighted least squares method for parallel brain MRI reconstruction publication-title: Neuroinformatics – volume: 80 start-page: 62 year: 2013 end-page: 79 article-title: The WU‐Minn human connectome project: an overview publication-title: NeuroImage – start-page: 15 year: 2017 end-page: 18 – ident: e_1_2_6_17_1 doi: 10.1109/TBME.2018.2821699 – ident: e_1_2_6_26_1 doi: 10.1016/j.neuroimage.2013.05.041 – ident: e_1_2_6_9_1 doi: 10.1002/mrm.27371 – ident: e_1_2_6_24_1 doi: 10.1002/mrm.27813 – ident: e_1_2_6_14_1 doi: 10.1109/ISBI.2017.7950457 – ident: e_1_2_6_23_1 doi: 10.1007/978-3-030-32248-9_3 – ident: e_1_2_6_25_1 doi: 10.1137/080725891 – volume: 29 start-page: 10 year: 2016 ident: e_1_2_6_29_1 article-title: Deep ADMM‐Net for compressive sensing MRI publication-title: Proc 30th Int Neural Inf Process Syst – ident: e_1_2_6_15_1 doi: 10.1109/TMI.2018.2833635 – ident: e_1_2_6_10_1 doi: 10.1109/TMI.2019.2954121 – ident: e_1_2_6_27_1 doi: 10.1002/mrm.27825 – ident: e_1_2_6_16_1 doi: 10.1038/nature25988 – ident: e_1_2_6_18_1 doi: 10.1002/mrm.26977 – ident: e_1_2_6_20_1 doi: 10.1002/mrm.27355 – ident: e_1_2_6_5_1 doi: 10.1109/ISBI.2011.5872579 – ident: e_1_2_6_11_1 doi: 10.1017/CBO9780511804441 – ident: e_1_2_6_12_1 doi: 10.1002/mrm.25222 – ident: e_1_2_6_28_1 doi: 10.1002/mrm.24751 – ident: e_1_2_6_21_1 doi: 10.1073/pnas.1907377117 – ident: e_1_2_6_3_1 doi: 10.1097/RLI.0b013e318271869c – ident: e_1_2_6_7_1 doi: 10.1007/978-3-319-10404-1_18 – ident: e_1_2_6_13_1 doi: 10.1109/ISBI.2016.7493320 – ident: e_1_2_6_22_1 doi: 10.1109/EMBC.2019.8857141 – ident: e_1_2_6_4_1 doi: 10.1002/mrm.21236 – ident: e_1_2_6_6_1 doi: 10.1109/TMI.2012.2188039 – ident: e_1_2_6_2_1 doi: 10.1002/mrm.22161 – ident: e_1_2_6_8_1 doi: 10.1007/s12021-017-9354-9 – ident: e_1_2_6_19_1 doi: 10.1109/ACCESS.2020.3034287 |
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To learn a preconditioner that accelerates parallel imaging (PI) and compressed sensing (CS) reconstructions.
Methods
A convolutional neural network... To learn a preconditioner that accelerates parallel imaging (PI) and compressed sensing (CS) reconstructions. A convolutional neural network (CNN) with... PurposeTo learn a preconditioner that accelerates parallel imaging (PI) and compressed sensing (CS) reconstructions.MethodsA convolutional neural network (CNN)... To learn a preconditioner that accelerates parallel imaging (PI) and compressed sensing (CS) reconstructions.PURPOSETo learn a preconditioner that accelerates... |
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| SubjectTerms | Acceleration Algorithms Artificial neural networks Brain - diagnostic imaging compressed sensing Computer architecture Conditioned stimulus deep learning Image Processing, Computer-Assisted - methods Image reconstruction Magnetic resonance imaging Magnetic Resonance Imaging - methods Medical imaging MR image reconstruction Neural networks Neural Networks, Computer Neuroimaging parallel imaging Parameter sensitivity Preconditioning Random noise Regularization Sampling split Bregman Technical Note Technical Notes—Computer Processing and Modeling Training |
| Title | Learning a preconditioner to accelerate compressed sensing reconstructions in MRI |
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