Learning a preconditioner to accelerate compressed sensing reconstructions in MRI.

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

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SUBMITTER: Koolstra K 

PROVIDER: S-EPMC9299023 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature