High Speed Compressed Sensing Reconstruction in Dynamic Parallel MRI Using Augmented Lagrangian and Parallel Processing

TL;DR

This paper introduces fast, GPU-accelerated Augmented Lagrangian algorithms for compressed sensing MRI reconstruction, significantly improving speed and performance, especially for dynamic MRI with large, ill-conditioned transfer matrices.

Contribution

It presents novel Augmented Lagrangian based reconstruction algorithms and a sampling pattern-aware computational method for faster MRI image reconstruction.

Findings

Outperforms previous methods in dynamic MRI reconstruction

Achieves significant speedup with GPU parallelization

Handles large, ill-conditioned transfer matrices effectively

Abstract

Magnetic Resonance Imaging (MRI) is one of the fields that the compressed sensing theory is well utilized to reduce the scan time significantly leading to faster imaging or higher resolution images. It has been shown that a small fraction of the overall measurements are sufficient to reconstruct images with the combination of compressed sensing and parallel imaging. Various reconstruction algorithms has been proposed for compressed sensing, among which Augmented Lagrangian based methods have been shown to often perform better than others for many different applications. In this paper, we propose new Augmented Lagrangian based solutions to the compressed sensing reconstruction problem with analysis and synthesis prior formulations. We also propose a computational method which makes use of properties of the sampling pattern to significantly improve the speed of the reconstruction for the proposed algorithms in Cartesian sampled MRI. The proposed algorithms are shown to outperform earlier methods especially for the case of dynamic MRI for which the transfer function tends to be a very large matrix and significantly ill conditioned. It is also demonstrated that the proposed algorithm can be accelerated much further than other methods in case of a parallel implementation with graphics processing units (GPUs).