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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Vydáno v:	Magnetic resonance in medicine Ročník 87; číslo 4; s. 2063 - 2073
Hlavní autoři:	Koolstra, Kirsten, Remis, Rob
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Wiley Subscription Services, Inc 01.04.2022 John Wiley and Sons Inc
Témata:	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 split Bregman deep learning parallel imaging compressed sensing MR image reconstruction preconditioning
ISSN:	0740-3194, 1522-2594, 1522-2594
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Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	0740-3194 1522-2594 1522-2594
DOI:	10.1002/mrm.29073