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

arXiv: https://arxiv.org/abs/1603.00040 · HTML: https://mlsanity.com/paper/1603.00040/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Feb 2016.

## Built on by (most cited first, 3 of 3)

- [Unsupervised learning of MRI tissue properties using MRI physics models](https://mlsanity.com/paper/2107.02704/) (2021)
- [Accelerating multiparametric quantitative MRI using self-supervised scan-specific implicit neural representation with model reinforcement](https://mlsanity.com/paper/2508.00891/) (2025)
- [Compound Attention and Neighbor Matching Network for Multi-contrast MRI Super-resolution](https://mlsanity.com/paper/2307.02148/) (2023)

## Abstract (arXiv)

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.