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

Bray, Timothy; Bainbridge, Alan; Lim, Emma; Hall-Craggs, Margaret; Zhang, Hui; (2023) MAGORINO: Magnitude-only fat fraction and R2* estimation with Rician noise modelling. Magnetic Resonance in Medicine , 89 (3) pp. 1173-1192. 10.1002/mrm.29493. Green open access

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Abstract

Purpose: Magnitude-based fitting of chemical shift–encoded data enables proton density fat fraction (PDFF) and R∗ 2 estimation where complex-based methods fail or when phase data are 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 R∗ 2 estimation with Rician noise modeling (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, R∗ 2, and SNR values. Fitting performance is assessed through detailed simulation, including likelihood function visualization, and in a multisite, multivendor, and multi-field-strength phantom data set 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. First, the likelihood functions exhibit two local optima; Rician noise modeling increases the chance that the global optimum corresponds to the ground truth. Second, when the global optimum corresponds to ground truth for both noise models, the optimum from Rician noise modeling 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: The MAGORINO algorithm reduces Rician noise–related bias in PDFF and R∗ 2 estimation, thus addressing a key limitation of existing magnitude-only fitting methods. Our results offer insight into the importance of the noise model for selecting the correct optimum when multiple plausible optima exist.

Type: Article
Title: MAGORINO: Magnitude-only fat fraction and R2* estimation with Rician noise modelling
Open access status: An open access version is available from UCL Discovery
DOI: 10.1002/mrm.29493
Publisher version: https://doi.org/10.1002/mrm.29493
Language: English
Additional information: © 2022 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Keywords: computer-assisted imaging processing, magnetic resonance imaging, radiology
UCL classification: UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL
URI: https://discovery.ucl.ac.uk/id/eprint/10157182
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