Reconstructing Multi-echo Magnetic Resonance Images via Structured Deep Dictionary Learning

TL;DR

This paper introduces a structured deep dictionary learning approach for reconstructing multi-echo MRI images, significantly reducing scan time while improving image quality over existing compressed sensing methods.

Contribution

The work presents a novel structured deep dictionary learning method that enhances MRI reconstruction quality and speed compared to traditional compressed sensing techniques.

Findings

Scan time reduced by half with improved image quality.

Structured deep dictionaries outperform traditional CS algorithms.

Experimental validation on real datasets confirms effectiveness.

Abstract

Multi-echo magnetic resonance (MR) images are acquired by changing the echo times (for T2 weighted) or relaxation times (for T1 weighted) of scans. The resulting (multi-echo) images are usually used for quantitative MR imaging. Acquiring MR images is a slow process and acquiring multi scans of the same cross section for multi-echo imaging is even slower. In order to accelerate the scan, compressed sensing (CS) based techniques have been advocating partial K-space (Fourier domain) scans; the resulting images are reconstructed via structured CS algorithms. In recent times, it has been shown that instead of using off-the-shelf CS, better results can be obtained by adaptive reconstruction algorithms based on structured dictionary learning. In this work, we show that the reconstruction results can be further improved by using structured deep dictionaries. Experimental results on real datasets show that by using our proposed technique the scan-time can be cut by half compared to the state-of-the-art.