MAGORINO: Magnitude-only fat fraction and R2* estimation with Rician noise modelling

TL;DR

MAGORINO is a novel algorithm that improves magnitude-only fat fraction and R2* estimation by modeling Rician noise, reducing bias and enhancing accuracy in clinical MRI data analysis.

Contribution

It introduces Rician noise modeling into magnitude-only fitting, outperforming Gaussian noise models and addressing a key bias in fat fraction and R2* estimation.

Findings

Rician noise modeling increases likelihood of global optimum matching ground truth.

MAGORINO shows good agreement with reference values in phantom experiments.

In vivo results confirm simulation-based performance improvements.

Abstract

Purpose: Magnitude-based fitting of chemical shift-encoded data enables proton density fat fraction (PDFF) and R2* estimation where complex-based methods fail or when phase data is inaccessible or unreliable. However, traditional magnitude-based fitting algorithms do not account for Rician noise, creating a source of bias. To address these issues, we propose an algorithm for Magnitude-Only PDFF and R2* estimation with Rician Noise modelling (MAGORINO). Methods: Simulations of multi-echo gradient echo signal intensities are used to investigate the performance and behavior of MAGORINO over the space of clinically-plausible PDFF, R2* and signal-to-noise ratio (SNR) values. Fitting performance is assessed through detailed simulation, including likelihood function visualization, and in a multi-site, multi-vendor and multi-field-strength phantom dataset and in vivo. Results: Simulations show that Rician noise-based magnitude fitting outperforms existing Gaussian noise-based fitting and reveals two key mechanisms underpinning the observed improvement. Firstly, the likelihood functions exhibit two local optima; Rician noise modelling increases the chance the global optimum corresponds to the ground truth. Secondly, when the global optimum corresponds to ground truth for both noise models, the optimum from Rician noise modelling is closer to ground truth. Multisite phantom experiments show good agreement of MAGORINO PDFF with reference values, and in vivo experiments replicate the performance benefits observed in simulation. Conclusion: MAGORINO reduces Rician noise-related bias in PDFF and R2* estimation, thus addressing a key limitation of existing magnitude-only fitting methods. Our results offer an insight into the importance of the noise model for selecting the correct optimum when multiple plausible optima exist.