Multicenter, multivendor phantom study to validate proton density fat fraction and T2* values calculated using vendor-provided 6-point DIXON methods

Hayashi, Tatsuya; Fukuzawa, Kei; Yamazaki, Hidekazu; Konno, Takashi; Miyati, Tosiaki; Kotoku, J.; Oba, Hiroshi; Kondo, Hiroshi; Toyoda, Keiko; Saitoh, Satoshi

doi:10.1016/j.clinimag.2018.01.011

Cited by 12 publications

(7 citation statements)

References 25 publications

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“…Indeed, MRI-PDFF results may be influenced by several factors including the MRI device model, the calculation method (Liver MultiScan for study cohort A and clinical procedure for study cohort B), the size of the ROI and the manual selection of the MRI-PDFF ROI by the radiologist (Campo, et al 2017). Some studies assessed the accuracy and reproducibility of MRI-PDFF for multivendor MRI on phantoms , Hayashi, et al 2018. With a standard reconstruction algorithm, the relative error is inversely proportional to the true fat fraction of phantom.…”

Section: Discussionmentioning

confidence: 99%

Improved Ultrasound Attenuation Measurement Method for the Non-invasive Evaluation of Hepatic Steatosis Using FibroScan

Audière¹,

Labourdette²,

Miette³

et al. 2021

Ultrasound in Medicine & Biology

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Section: Discussionmentioning

confidence: 99%

Improved Ultrasound Attenuation Measurement Method for the Non-invasive Evaluation of Hepatic Steatosis Using FibroScan

Audière¹,

Labourdette²,

Miette³

et al. 2021

Ultrasound in Medicine & Biology

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“…훾 is the hydrogen gyromagnetic ratio in MHz/T, 퐵 0 is the main static magnetic field in Tesla, and 훿 푛 indicates the chemical shift of the 푛 molecule in parts per million, i.e., its precession deviation in relation to that of a reference (usually tetramethylsilane, Si(CH 3 ) 4 ). The convenient echo times for in-phase and opposed-phase can then be calculated according to (4), where 푓 푤푎푡푒푟 and 푓 푓푎푡 are water and fat frequencies, respectively, resulting in half the period of the difference in precession between those molecules.…”

Section: Magnitude Gradient Echo Signalmentioning

confidence: 99%

“…Signals with seven echoes were generated based on (2) considering the effects of water molecule and six fat moieties, at echo times chosen for water and methylene to be in-phase and opposed-phase in a 3 Tesla magnetic field, according to (4). It has been suggested that the proportion between fat moieties tends to be similar regardless the fat fraction [15].…”

Section: Obtaining a Complete Gre Signalmentioning

confidence: 99%

“…Among noninvasive quantitative approaches for estimation of hepatic fat content, magnetic resonance imaging (MRI) has shown great reproducibility [3,4] and good agreement to clinical and research gold standards, biopsy, and magnetic resonance spectroscopy, respectively [5,6]. Quantitative assessment of liver steatosis with MRI can be done by estimating the hepatic proton density fat fraction based on chemical shift images (from here on called proton density fat fraction, PDFF), which may carry magnitude or complex information.…”

Section: Introductionmentioning

confidence: 99%

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Full-Range Liver Fat Fraction Estimation in Magnitude MRI Using a Signal Shape Descriptor

Costa

Filho

Flores

et al. 2019

Concepts in Magnetic Resonance Part A

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Current methods for estimation of proton density fat fraction (PDFF) of the liver using magnitude magnetic resonance (MR) imaging face the challenge of correctly estimating it when fat is the dominant molecule; i.e., PDFF is more than 50%. Therefore, the accuracy of the methods is limited to half-range operation. We introduce a method based on neural networks for regression capable of estimating over the full range of fat fractions. We built a neural network based on the angles and distances between the data in the discrete MR signal (ADALIFE), using these as features associated with different PDFFs and as input for the network. Tests were performed using ADALIFE and Multi-interference, a state-of-the-art method to estimate PDFFs, with simulated signals at various signal-to-noise (SNR) values. Results were compared in order to verify repeatability and agreement using Bland-Altman and REC curves. Results for Multi-interference were similar to its in vivo literature, showing the relevance of a simulation. ADALIFE was able to correctly estimate fat fractions up to 100%, breaking the current paradigm for full-range estimation using only offline postprocessing. Within half range, our method outperformed Multi-interference in repeatability and agreement, with narrower limits of agreement and lower expected error at any SNR.

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“…It has been estimated by a meta study that NAFLD has a prevalence of 25% among global population (1) and of 30% in the United States according to another study including 6000 subjects (2). Amongst noninvasive quantitative approaches for estimation of hepatic fat content, magnetic resonance imaging (MRI) has shown great reproducibility (3,4) and good agreement to clinical and research gold standards, biopsy and spectroscopy, respectively (5,6). Quantitative assessment of liver steatosis with MRI can be done by estimating the hepatic proton density fat fraction based on chemical shift images (from here on called fat fraction, PDFF), which may carry magnitude or complex information.…”

Section: Introductionmentioning

confidence: 99%

A proposal for full-range fat fraction estimation using magnitude MR imaging

Costa¹

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Current methods for estimation of proton density fat fraction (PDFF) of the liver using magnitude magnetic resonance (MR) imaging face the challenge of correctly estimating it when fat is the dominant molecule, i.e. PDFF is more than 50%. Therefore, the accuracy of the methods is limited to half-range operation. We introduce a method based on neural networks for regression capable of estimating over the full range of fat fractions. We built a neural network based on the angles and distances between the data in the discrete MR signal (ADALIFE), using these as features associated to different PDFFs and as input for the network. Tests were performed assessing ADALIFE against dual echo, triple echo, and especially Multi-interference, a state-of-the-art method to estimate PDFFs, with simulated signals at various signal-to-noise (SNR) values. Results were compared in order to verify repeatability and agreement using regression analysis, Bland-Altman and REC curves. Results for Multi-interference were similar to its in-vivo literature, showing the relevance of a simulation. ADALIFE was able to correctly estimate fat fractions up to 100%, breaking the current paradigm for full-range estimation using only off-line post processing. Within half-range, our method outperformed Multi-interference in repeatability and agreement, with narrower limits of agreement and lower expected error at any SNR.

show abstract

Multicenter, multivendor phantom study to validate proton density fat fraction and T2* values calculated using vendor-provided 6-point DIXON methods

Cited by 12 publications

References 25 publications

Improved Ultrasound Attenuation Measurement Method for the Non-invasive Evaluation of Hepatic Steatosis Using FibroScan

Improved Ultrasound Attenuation Measurement Method for the Non-invasive Evaluation of Hepatic Steatosis Using FibroScan

Full-Range Liver Fat Fraction Estimation in Magnitude MRI Using a Signal Shape Descriptor

A proposal for full-range fat fraction estimation using magnitude MR imaging

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