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Noise properties of proton density fat fraction estimated using chemical shift-encoded MRI.

ABSTRACT: PURPOSE:The purpose of this work is to characterize the noise distribution of proton density fat fraction (PDFF) measured using chemical shift-encoded MRI, and to provide alternative strategies to reduce bias in PDFF estimation. THEORY:We derived the probability density function for PDFF estimated using chemical shift-encoded MRI, and found it to exhibit an asymmetric noise distribution that contributes to signal-to-noise-ratio dependent bias. METHODS:To study PDFF noise bias, we performed (at 1.5?T) numerical simulations, phantom acquisitions, and a retrospective in vivo experiment. In each experiment, we compared the performance of three statistics (mean, median, and maximum likelihood estimator) in estimating the PDFF in a region of interest. RESULTS:We demonstrated the presence of the asymmetric noise distribution in simulations, phantoms, and in vivo. In each experiment we demonstrated that both the median and proposed maximum likelihood estimator statistics outperformed the mean statistic in mitigating noise-related bias for low signal-to-noise-ratio acquisitions. CONCLUSIONS:Characterization of the noise distribution of PDFF estimated using chemical shift-encoded MRI enabled new strategies based on median and maximum likelihood estimator statistics to mitigate noise-related bias for accurate PDFF measurement from a region of interest. Such strategies are important for quantitative chemical shift-encoded MRI applications that typically operate in low signal-to-noise-ratio regimes. Magn Reson Med 80:685-695, 2018. © 2018 International Society for Magnetic Resonance in Medicine.

SUBMITTER: Roberts NT

PROVIDER: S-EPMC5910302 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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Noise properties of proton density fat fraction estimated using chemical shift-encoded MRI.

Roberts Nathan T NT Hernando Diego D Holmes James H JH Wiens Curtis N CN Reeder Scott B SB

Magnetic resonance in medicine 20180110 2

<h4>Purpose</h4>The purpose of this work is to characterize the noise distribution of proton density fat fraction (PDFF) measured using chemical shift-encoded MRI, and to provide alternative strategies to reduce bias in PDFF estimation.<h4>Theory</h4>We derived the probability density function for PDFF estimated using chemical shift-encoded MRI, and found it to exhibit an asymmetric noise distribution that contributes to signal-to-noise-ratio dependent bias.<h4>Methods</h4>To study PDFF noise bi ...[more]

PMID: 29322549

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Project description:BackgroundWhole-organ, noninvasive techniques for the detection and quantification of nonalcoholic fatty liver disease features have clinical and research applications. However, the effect of time of day, hydration status, and meals are unknown factors with potential to impact bias, precision, reproducibility, and repeatability of chemical shift-encoded MRI (CSE-MRI) to quantify liver proton density fat fraction (PDFF).PurposeTo assess the effect of diurnal variation on PDFF using CSE-MRI, including the effect of time of day, the effect of meals and hydration status, as well as the day to day variability.Study typeProspective.SubjectsEleven healthy subjects and nine patients with observed hepatic steatosis.Field strength/sequencesA commercial quantitative confounder-corrected CSE-MRI sequence (IDEAL IQ) and an MR spectroscopy (MRS) sequence (multiecho STEAM) were acquired at 1.5T.AssessmentMRI-PDFF and MRS-PDFF estimates were compared across six visits (before and after a controlled breakfast, before and after an uncontrolled lunch, at approximately 4 pm, and then before breakfast on the following day) with three repeated measures for a total of 360 MRI-PDFF and MRS-PDFF measurements.Statistical testsLinear regression, Bland-Altman analysis, and mixed effect models were used to determine the bias, precision, and repeatability of PDFF measurements.ResultsNo statistically significant linear trend was observed across visits for either MRI-PDFF or MRS-PDFF (P = 0.31 and 0.37, respectively). The repeatability was measured to be 0.86% for MRI-PDFF and 1.1% for MRS-PDFF over all six visits. For MRI-PDFF, the variability between all six visits (0.94%) was only slightly higher than within each visit (0.66%), with P < 0.001. For MRS-PDFF, the variability between all six visits was 1.29%, compared with 0.87% within each visit (P < 0.001).Data conclusionOur results may indicate that it is not necessary to control for the time of day or the fasting/fed state of the patient when measuring PDFF using CSE-MRI.Level of evidence2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:407-414.

Project description:Fat fraction quantification and assessment of its distribution in the hepatic tissue become more important with the growing epidemic of obesity, and the increasing prevalence of diabetes mellitus type 2 and non-alcoholic fatty liver disease. At 3Tesla, the multi-echo, chemical-shift-encoded magnetic resonance imaging (CSE-MRI)-based acquisition allows the measurement of proton density fat-fraction (PDFF) even in clinical protocols. Further improvements in SNR can be achieved by the use of phased array coils and increased static magnetic field. The purpose of the study is to evaluate the feasibility of PDFF imaging using a multi-echo CSE-MRI technique at ultra-high magnetic field (7Tesla). Thirteen volunteers (M/F) with a broad range of age, body mass index, and hepatic PDFF were measured at 3 and 7T by multi-gradient-echo MRI and single-voxel spectroscopy MRS. All measurements were performed in breath-hold (exhalation); the MRI protocols were optimized for a short measurement time, thus minimizing motion-related problems. 7T data were processed off-line using Matlab® (MRI:multi-gradient-echo) and jMRUI (MRS), respectively. For quantitative validation of the PDFF results, a similar protocol was performed at 3T, including on-line data processing provided by the system manufacturer, and correlation analyses between 7 and 3T data were performed off-line. The multi-echo CSE-MRI measurements at 7T with a phased-array coil configuration and an optimal post-processing yielded liver volume coverage ranging from 30 to 90% for high- and low-BMI subjects, respectively. PDFFs ranged between 1 and 20%. We found significant correlations between 7T MRI and -MRS measurements (R2 ≅ 0.97; p < 0.005), and between MRI-PDFF at 7T and 3T fields (R2 ≅ 0.94; p < 0.005) in the evaluated volumes. Based on the measurements and analyses performed, the multi-echo CSE-MRI method using a 32-channel coil at 7T showed its aptitude for MRI-based quantitation of PDFF in the investigated volumes. The results are the first step toward qMRI of the whole liver at 7T with further improvements in hardware.

Project description:Texture analysis (TA) has shown promise as a surrogate marker for tissue structure, based on conventional and quantitative MRI sequences. Chemical-shift-encoding-based MRI (CSE-MRI)-derived proton density fat fraction (PDFF) of paraspinal muscles has been associated with various medical conditions including lumbar back pain (LBP) and neuromuscular diseases (NMD). Its application has been shown to improve the prediction of paraspinal muscle strength beyond muscle volume. Since mean PDFF values do not fully reflect muscle tissue structure, the purpose of our study was to investigate PDFF-based TA of paraspinal muscles as a predictor of muscle strength, as compared to mean PDFF. We performed 3T-MRI of the lumbar spine in 26 healthy subjects (age = 30 ± 6 years; 15 females) using a six-echo 3D spoiled gradient echo sequence for chemical-shift-encoding-based water-fat separation. Erector spinae (ES) and psoas (PS) muscles were segmented bilaterally from level L2-L5 to extract mean PDFF and texture features. Muscle flexion and extension strength was measured with an isokinetic dynamometer. Out of the eleven texture features extracted for each muscle, Kurtosis(global) of ES showed the highest significant correlation (r = 0.59, p = 0.001) with extension strength and Variance(global) of PS showed the highest significant correlation (r = 0.63, p = 0.001) with flexion strength. Using multivariate linear regression models, Kurtosis(global) of ES and BMI were identified as significant predictors of extension strength (R2adj = 0.42; p < 0.001), and Variance(global) and Skewness(global) of PS were identified as significant predictors of flexion strength (R2adj = 0.59; p = 0.001), while mean PDFF was not identified as a significant predictor. TA of CSE-MRI-based PDFF maps improves the prediction of paraspinal muscle strength beyond mean PDFF, potentially reflecting the ability to quantify the pattern of muscular fat infiltration. In the future, this may help to improve the pathophysiological understanding, diagnosis, monitoring and treatment evaluation of diseases with paraspinal muscle involvement, e.g., NMD and LBP.

Project description:PurposeTo compare longitudinal hepatic proton density fat fraction (PDFF) changes estimated by magnitude- vs. complex-based chemical-shift-encoded MRI during a weight loss surgery (WLS) program in severely obese adults with biopsy-proven nonalcoholic fatty liver disease (NAFLD).MethodsThis was a secondary analysis of a prospective dual-center longitudinal study of 54 adults (44 women; mean age 52 years; range 27-70 years) with obesity, biopsy-proven NAFLD, and baseline PDFF ≥ 5%, enrolled in a WLS program. PDFF was estimated by confounder-corrected chemical-shift-encoded MRI using magnitude (MRI-M)- and complex (MRI-C)-based techniques at baseline (visit 1), after a 2- to 4-week very low-calorie diet (visit 2), and at 1, 3, and 6 months (visits 3 to 5) after surgery. At each visit, PDFF values estimated by MRI-M and MRI-C were compared by a paired t test. Rates of PDFF change estimated by MRI-M and MRI-C for visits 1 to 3, and for visits 3 to 5 were assessed by Bland-Altman analysis and intraclass correlation coefficients (ICCs).ResultsMRI-M PDFF estimates were lower by 0.5-0.7% compared with those of MRI-C at all visits (p < 0.001). There was high agreement and no difference between PDFF change rates estimated by MRI-M vs. MRI-C for visits 1 to 3 (ICC 0.983, 95% CI 0.971, 0.99; bias = - 0.13%, p = 0.22), or visits 3 to 5 (ICC 0.956, 95% CI 0.919-0.977%; bias = 0.03%, p = 0.36).ConclusionAlthough MRI-M underestimates PDFF compared with MRI-C cross-sectionally, this bias is consistent and MRI-M and MRI-C agree in estimating the rate of hepatic PDFF change longitudinally.Key points• MRI-M demonstrates a significant but small and consistent bias (0.5-0.7%; p < 0.001) towards underestimation of PDFF compared with MRI-C at 3 T. • Rates of PDFF change estimated by MRI-M and MRI-C agree closely (ICC 0.96-0.98) in adults with severe obesity and biopsy- proven NAFLD enrolled in a weight loss surgery program. • Our findings support the use of either MRI technique (MRI-M or MRI-C) for clinical care or by individual sites or for multi-center trials that include PDFF change as an endpoint. However, since there is a bias in their measurements, the same technique should be used in any given patient for longitudinal follow-up.

Project description:The recently developed magnetic resonance imaging (MRI) proton density fat fraction (PDFF) allows measurement of the fat in all segments of hepatic tissue. However, it is time consuming and inconvenient to measure each segment repeatedly. Moreover, volume of each segment also should be adjusted with arithmetic mean of the selected segments when total amount of liver fat is estimated. Therefore, we try to develop a clinically-relevant and applicable method of estimating hepatic fat in PDFF image.A total of 164 adults were enrolled. We addressed the measurement frequency and segment selection to determine the optimal method of measuring intrahepatic fat. Total hepatic fat was estimated by the weighted mean of each segment reflecting their respective segmental volumes. We designed 2 models. In Model 1, we determined the segment order by which the mean was closest to the whole weighted mean. In Model 2, we determined the segment order by which the arithmetic mean of the selected segments was closest to the whole weighted mean.Fat fraction (FF) was most important risk factor of hepatic heterogeneity in multivariable analysis (β = 0.534, P < .001). In severe fatty liver (FF > 22.1%), intrahepatic fat variability was 2.47% (1.16-6.26%). The arithmetic mean total intrahepatic FF was 12.66%. But the weighted mean that applied to each segmental volume was 12.90%. In Model 1, arithmetic mean of segments 4 and 5 was closest to the total estimated hepatic fat amount. However, when we added segment 8, the mean of segments 4, 5, and 8 was significantly different from the estimated total hepatic fat amount (P = .0021). In Model 2, arithmetic mean of segments 4 and 5 was closest to the total estimated hepatic fat amount. There was a significant reduction in variability between segment 4 and segments 4 and 5 (P < .0001).Averaging the mean hepatic FF of segments 4 and 5 was the most reasonable method for estimating total intrahepatic fat in practice.

Project description:PurposeTo explore, at a high field strength of 7T, the performance of various fat spectral models on the quantification of triglyceride composition and proton density fat fraction (PDFF) using chemical-shift encoded MRI (CSE-MRI).MethodsMR data was acquired from CSE-MRI experiments for various fatty materials, including oil and butter samples and in vivo brown and white adipose mouse tissues. Triglyceride composition and PDFF were estimated using various a priori 6- or 9-peak fat spectral models. To serve as references, NMR spectroscopy experiments were conducted to obtain material specific fat spectral models and triglyceride composition estimates for the same fatty materials. Results obtained using the spectroscopy derived material specific models were compared to results obtained using various published fat spectral models.ResultsUsing a 6-peak fat spectral model to quantify triglyceride composition may lead to large biases at high field strengths. When using a 9-peak model, triglyceride composition estimations vary greatly depending on the relative amplitudes of the chosen a priori spectral model, while PDFF estimations show small variations across spectral models. Material specific spectroscopy derived spectral models produce estimations that better correlate with NMR spectroscopy estimations in comparison to those obtained using non-material specific models.ConclusionAt a high field strength of 7T, a material specific 9-peak fat spectral model, opposed to a widely accepted or generic human liver model, is necessary to accurately quantify triglyceride composition when using CSE-MRI estimation methods that assume the spectral model to be known as a priori information. CSE-MRI allows for the quantification of the spatial distribution of triglyceride composition for certain in vivo applications. Additionally, PDFF quantification is shown to be independent of the chosen a priori spectral model, which agrees with previously reported results obtained at lower field strengths (e.g. 3T).

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Noise properties of proton density fat fraction estimated using chemical shift-encoded MRI.

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Noise properties of proton density fat fraction estimated using chemical shift-encoded MRI.

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