On the R 2 ⁎ relaxometry in complex multi-peak multi-Echo chemical shift-based water-fat quantification: Applications to the neuromuscular diseases.

PURPOSE: Investigation of the feasibility of the R2 ⁎ mapping techniques by using latest theoretical models corrected for confounding factors and optimized for signal to noise ratio.

THEORY AND METHODS: The improvement of the performance of state of the art magnetic resonance imaging (MRI) relaxometry algorithms is challenging because of a non-negligible bias and still unresolved numerical instabilities. Here, R2 ⁎ mapping reconstructions, including complex fitting with multi-spectral fat-correction by using single-decay and double-decay formulation, are deeply studied in order to investigate and identify optimal configuration parameters and minimize the occurrence of numerical artifacts. The effects of echo number, echo spacing, and fat/water relaxation model type are evaluated through both simulated and in-vivo data. We also explore the stability and feasibility of the fat/water relaxation model by analyzing the impact of high percentage of fat infiltrations and local transverse relaxation differences among biological species.

RESULTS: The main limits of the MRI relaxometry are the presence of bias and the occurrence of artifacts, which significantly affect its accuracy. Chemical-shift complex R2 ⁎ -correct single-decay reconstructions exhibit a large bias in presence of a significant difference in the relaxation rates of fat and water and with fat concentration larger than 30%. We find that for fat-dominated tissues or in patients affected by extensive iron deposition, MRI reconstructions accounting for multi-exponential relaxation time provide accurate R2 ⁎ measurements and are less prone to numerical artifacts.

CONCLUSIONS: Complex fitting and fat-correction with multi-exponential decay formulation outperforms the conventional single-decay approximation in various diagnostic scenarios. Although it still lacks of numerical stability, which requires model enhancement and support from spectroscopy, it offers promising perspectives for the development of relaxometry as a reliable tool to improve tissue characterization and monitoring of neuromuscular disorders.