Denoising Auto-encoding Priors in Undecimated Wavelet Domain for MR Image Reconstruction

TL;DR

This paper introduces a novel MRI reconstruction method that leverages undecimated wavelet transform and a denoising autoencoder to learn priors, improving image quality from under-sampled data.

Contribution

It proposes a new prior learning approach using undecimated wavelet transform and autoencoders integrated into iterative reconstruction for MRI.

Findings

Effective reconstruction with various sampling trajectories.

Superior performance compared to traditional methods.

Robustness across different undersampling ratios.

Abstract

Compressive sensing is an impressive approach for fast MRI. It aims at reconstructing MR image using only a few under-sampled data in k-space, enhancing the efficiency of the data acquisition. In this study, we propose to learn priors based on undecimated wavelet transform and an iterative image reconstruction algorithm. At the stage of prior learning, transformed feature images obtained by undecimated wavelet transform are stacked as an input of denoising autoencoder network (DAE). The highly redundant and multi-scale input enables the correlation of feature images at different channels, which allows a robust network-driven prior. At the iterative reconstruction, the transformed DAE prior is incorporated into the classical iterative procedure by the means of proximal gradient algorithm. Experimental comparisons on different sampling trajectories and ratios validated the great potential of the presented algorithm.