ArticleMagnetic resonance in medicine2023
MAGORINO: Magnitude-only fat fraction and R
Article in Magnetic resonance in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Comparison of Vendor-Independent Software Tools for Liver Proton Density Fat Fraction Estimation at 1.5 T.Diagnostics (Basel, Switzerland) · 2024Article
- MAGORINO: Magnitude-only fat fraction and RMagnetic resonance in medicine · 2023Article
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Authors and funding
5 authors.
Funding
Abstract
purposeMagnitude-based fitting of chemical shift-encoded data enables proton density fat fraction (PDFF) and
methodsSimulations of multi-echo gradient-echo signal intensities are used to investigate the performance and behavior of MAGORINO over the space of clinically plausible PDFF,
resultsSimulations 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.
conclusionThe MAGORINO algorithm reduces Rician noise-related bias in PDFF and
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