MAGORINO : Magnitude‐only fat fraction and R 2 estimation with Rician noise modeling

Magnitude-based fitting of chemical shift-encoded data enables proton density fat fraction (PDFF) and 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 sou...

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Vydáno v:	Magnetic resonance in medicine Ročník 89; číslo 3; s. 1173 - 1192
Hlavní autoři:	Bray, Timothy J. P., Bainbridge, Alan, Lim, Emma, Hall‐Craggs, Margaret A., Zhang, Hui
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States John Wiley and Sons Inc 01.03.2023
Témata:	Adipose Tissue - diagnostic imaging Algorithms Computer Simulation Likelihood Functions Magnetic Resonance Imaging - methods Normal Distribution Protons Reproducibility of Results s—Computer Processing and Modeling computer-assisted imaging processing magnetic resonance imaging radiology
ISSN:	0740-3194, 1522-2594, 1522-2594
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Abstract	Magnitude-based fitting of chemical shift-encoded data enables proton density fat fraction (PDFF) and 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 estimation with Rician noise modeling (MAGORINO). 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, , 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. 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. The MAGORINO algorithm reduces Rician noise-related bias in PDFF and 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.
AbstractList	Click here for author‐reader discussions Magnitude-based fitting of chemical shift-encoded data enables proton density fat fraction (PDFF) and 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 estimation with Rician noise modeling (MAGORINO). 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, , 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. 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. The MAGORINO algorithm reduces Rician noise-related bias in PDFF and 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. Magnitude-based fitting of chemical shift-encoded data enables proton density fat fraction (PDFF) and R 2 * $$ {R}_2^{\ast } $$ 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 * $$ {R}_2^{\ast } $$ estimation with Rician noise modeling (MAGORINO).PURPOSEMagnitude-based fitting of chemical shift-encoded data enables proton density fat fraction (PDFF) and R 2 * $$ {R}_2^{\ast } $$ 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 * $$ {R}_2^{\ast } $$ estimation with Rician noise modeling (MAGORINO).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 * $$ {R}_2^{\ast } $$ , 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.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, R 2 * $$ {R}_2^{\ast } $$ , 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.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.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.The MAGORINO algorithm reduces Rician noise-related bias in PDFF and R 2 * $$ {R}_2^{\ast } $$ 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.CONCLUSIONThe MAGORINO algorithm reduces Rician noise-related bias in PDFF and R 2 * $$ {R}_2^{\ast } $$ 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.
Author	Zhang, Hui Bray, Timothy J. P. Lim, Emma Bainbridge, Alan Hall‐Craggs, Margaret A.
AuthorAffiliation	4 Department of Imaging Imperial College Healthcare NHS Trust London United Kingdom 2 Department of Imaging University College London Hospital London United Kingdom 5 Department of Computer Science and Centre for Medical Image Computing University College London London United Kingdom 3 Department of Medical Physics University College London Hospitals London United Kingdom 1 Centre for Medical Imaging University College London London United Kingdom
AuthorAffiliation_xml	– name: 1 Centre for Medical Imaging University College London London United Kingdom – name: 2 Department of Imaging University College London Hospital London United Kingdom – name: 4 Department of Imaging Imperial College Healthcare NHS Trust London United Kingdom – name: 5 Department of Computer Science and Centre for Medical Image Computing University College London London United Kingdom – name: 3 Department of Medical Physics University College London Hospitals London United Kingdom
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Snippet	Click here for author‐reader discussions Magnitude-based fitting of chemical shift-encoded data enables proton density fat fraction (PDFF) and estimation where complex-based methods fail or when phase... Magnitude-based fitting of chemical shift-encoded data enables proton density fat fraction (PDFF) and R 2 * $$ {R}_2^{\ast } $$ estimation where complex-based...
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SubjectTerms	Adipose Tissue - diagnostic imaging Algorithms Computer Simulation Likelihood Functions Magnetic Resonance Imaging - methods Normal Distribution Protons Reproducibility of Results s—Computer Processing and Modeling
Title	MAGORINO : Magnitude‐only fat fraction and R 2 estimation with Rician noise modeling
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