Deep Multi-contrast Cardiac MRI Reconstruction via vSHARP with Auxiliary Refinement Network

TL;DR

This paper introduces a deep learning reconstruction method for multi-contrast cardiac MRI that combines vSHARP with an auxiliary refinement network to improve image quality from undersampled data.

Contribution

It presents a novel integration of vSHARP with a variational network as an auxiliary refinement to enhance MCCMRI reconstruction accuracy.

Findings

Outperforms traditional reconstruction methods.

Achieves higher quality images from undersampled data.

Adapts effectively to diverse MCCMRI data.

Abstract

Cardiac MRI (CMRI) is a cornerstone imaging modality that provides in-depth insights into cardiac structure and function. Multi-contrast CMRI (MCCMRI), which acquires sequences with varying contrast weightings, significantly enhances diagnostic capabilities by capturing a wide range of cardiac tissue characteristics. However, MCCMRI is often constrained by lengthy acquisition times and susceptibility to motion artifacts. To mitigate these challenges, accelerated imaging techniques that use k-space undersampling via different sampling schemes at acceleration factors have been developed to shorten scan durations. In this context, we propose a deep learning-based reconstruction method for 2D dynamic multi-contrast, multi-scheme, and multi-acceleration MRI. Our approach integrates the state-of-the-art vSHARP model, which utilizes half-quadratic variable splitting and ADMM optimization, with a Variational Network serving as an Auxiliary Refinement Network (ARN) to better adapt to the diverse nature of MCCMRI data. Specifically, the subsampled k-space data is fed into the ARN, which produces an initial prediction for the denoising step used by vSHARP. This, along with the subsampled k-space, is then used by vSHARP to generate high-quality 2D sequence predictions. Our method outperforms traditional reconstruction techniques and other vSHARP-based models.