MRI Image Recovery using Damped Denoising Vector AMP

TL;DR

This paper introduces DD-VAMP++, a novel MRI image recovery method that enhances convergence and accuracy by adapting vector AMP with damping and initialization strategies, outperforming existing algorithms on real data.

Contribution

The paper develops DD-VAMP++, a new algorithm combining VAMP with damping and initialization techniques tailored for MRI, addressing limitations of standard VAMP in practical scenarios.

Findings

Outperforms existing algorithms in convergence speed

Achieves higher accuracy in MRI image recovery

Effective on real MRI data from fastMRI database

Abstract

Motivated by image recovery in magnetic resonance imaging (MRI), we propose a new approach to solving linear inverse problems based on iteratively calling a deep neural-network, sometimes referred to as plug-and-play recovery. Our approach is based on the vector approximate message passing (VAMP) algorithm, which is known for mean-squared error (MSE)-optimal recovery under certain conditions. The forward operator in MRI, however, does not satisfy these conditions, and thus we design new damping and initialization schemes to help VAMP. The resulting DD-VAMP++ algorithm is shown to outperform existing algorithms in convergence speed and accuracy when recovering images from the fastMRI database for the practical case of Cartesian sampling.