Model-Based Iterative Reconstruction for Radial Fast Spin-Echo MRI

TL;DR

This paper introduces a novel model-based iterative reconstruction method for radial fast spin-echo MRI that produces accurate spin-density and relaxivity maps, overcoming artifacts and enabling T2 quantification from a single data set.

Contribution

The work presents a new inverse problem formulation and optimization-based reconstruction technique that directly extracts relaxation maps from radial fast spin-echo MRI data, handling multi-coil data and prior knowledge.

Findings

Produces artifact-free spin-density and relaxivity maps

Handles multi-coil data effectively

Demonstrates robustness in simulations and in vivo experiments

Abstract

In radial fast spin-echo MRI, a set of overlapping spokes with an inconsistent T2 weighting is acquired, which results in an averaged image contrast when employing conventional image reconstruction techniques. This work demonstrates that the problem may be overcome with the use of a dedicated reconstruction method that further allows for T2 quantification by extracting the embedded relaxation information. Thus, the proposed reconstruction method directly yields a spin-density and relaxivity map from only a single radial data set. The method is based on an inverse formulation of the problem and involves a modeling of the received MRI signal. Because the solution is found by numerical optimization, the approach exploits all data acquired. Further, it handles multi-coil data and optionally allows for the incorporation of additional prior knowledge. Simulations and experimental results for a phantom and human brain in vivo demonstrate that the method yields spin-density and relaxivity maps that are neither affected by the typical artifacts from TE mixing, nor by streaking artifacts from the incomplete k-space coverage at individual echo times.