Learning a preconditioner to accelerate compressed sensing reconstructions in MRI

Magnetic Resonance in Medicine, vol. 87, pp. 2063–2073

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.

Authors 2

1. Kirsten Koolstra corresponding Aachen Division of Image Processing Department of Radiology Leiden University Medical Center Leiden the Netherlands Division of Image Processing Department of Radiology, Leiden University Medical Center

   Leiden University · Leiden University Medical Center

   Affiliation as printed

   Division of Image Processing Department of Radiology Leiden University Medical Center Leiden The Netherlands

   Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands

2. Rob Remis

   Delft University of Technology

   Affiliation as printed

   Circuits & Systems Group Electrical Engineering Mathematics and Computer Science Faculty Delft University of Technology Delft The Netherlands

   Circuits & Systems Group, Electrical Engineering, Mathematics and Computer Science Faculty, Delft University of Technology, Delft, The Netherlands