Deep Parallel MRI Reconstruction Network Without Coil Sensitivities

TL;DR

This paper introduces a deep neural network for parallel MRI reconstruction that does not require coil sensitivity maps, effectively combining multi-coil data into high-quality images without prior sensitivity estimation.

Contribution

The novel network architecture maps the proximal gradient scheme for pMRI, enabling coil-sensitivity-free reconstruction and adaptive multi-coil image combination from incomplete data.

Findings

Demonstrates promising reconstruction quality on various pMRI datasets.

Outperforms traditional methods that rely on sensitivity maps.

Efficiently extracts sparse features for improved image quality.

Abstract

We propose a novel deep neural network architecture by mapping the robust proximal gradient scheme for fast image reconstruction in parallel MRI (pMRI) with regularization function trained from data. The proposed network learns to adaptively combine the multi-coil images from incomplete pMRI data into a single image with homogeneous contrast, which is then passed to a nonlinear encoder to efficiently extract sparse features of the image. Unlike most of existing deep image reconstruction networks, our network does not require knowledge of sensitivity maps, which can be difficult to estimate accurately, and have been a major bottleneck of image reconstruction in real-world pMRI applications. The experimental results demonstrate the promising performance of our method on a variety of pMRI imaging data sets.