GrappaNet: Combining Parallel Imaging with Deep Learning for Multi-Coil MRI Reconstruction

TL;DR

GrappaNet is a deep learning framework that combines traditional parallel imaging with neural networks to achieve high-quality MRI reconstructions at high acceleration factors, outperforming existing methods.

Contribution

It introduces a novel end-to-end trainable neural network architecture that integrates parallel imaging techniques for improved MRI reconstruction at high acceleration.

Findings

Outperforms competing methods at 4x and 8x acceleration

Achieves higher quality reconstructions on fastMRI dataset

Enables progressive MRI reconstruction with neural networks

Abstract

Magnetic Resonance Image (MRI) acquisition is an inherently slow process which has spurred the development of two different acceleration methods: acquiring multiple correlated samples simultaneously (parallel imaging) and acquiring fewer samples than necessary for traditional signal processing methods (compressed sensing). Both methods provide complementary approaches to accelerating the speed of MRI acquisition. In this paper, we present a novel method to integrate traditional parallel imaging methods into deep neural networks that is able to generate high quality reconstructions even for high acceleration factors. The proposed method, called GrappaNet, performs progressive reconstruction by first mapping the reconstruction problem to a simpler one that can be solved by a traditional parallel imaging methods using a neural network, followed by an application of a parallel imaging method, and finally fine-tuning the output with another neural network. The entire network can be trained end-to-end. We present experimental results on the recently released fastMRI dataset and show that GrappaNet can generate higher quality reconstructions than competing methods for both $4\times$ and $8\times$ acceleration.