Proton Density Fat Fraction Estimation Research Articles

Correcting gradient chain induced fat quantification errors in radial multi-echo Dixon imaging using a gradient modulation transfer function

Purpose: Multi-echo Stack-of-stars (SoS) radial k-space trajectories with golden angle ordering are becoming popular for free-breathing abdominal Dixon imaging and proton density fat fraction (PDFF) mapping. Gradient chain imperfections including eddy currents and system delays are known to affect the image quality of radial imaging and to confound the estimation of PDFF mapping. This work proposes a retrospective trajectory correction method based on a simple gradient modulation transfer function (GMTF) measurement to predict and correct gradient chain induced k-space trajectory errors.Methods: The GMTF was measured using the standard hardware of a 3 Tesla scanner on a phantom using the thin slice method and was applied to a 3D radial SoS Dixon imaging sequence. The impact of the GMTF-based correction on image reconstruction and PDFF quantification was investigated using numerical simulations and validated on experimental phantom data as well as on in vivo leg and liver data of healthy volunteers.Results: Correcting the k-space trajectories with the measured GMTF during image reconstruction reduced PDFF quantification errors for phantom and in vivo acquisitions. A Bland-Altman comparison of the measured PDFF phantom and reference data confirmed that the GMTF correction narrowed down the limits of agreement (LoA) from 1.3% ± 8.1% (uncorrected) to 1.9% ± 5.4% (GMTF-corrected) over the full PDFF range (0%–100%) and from −0.26% ± 2.8% (uncorrected) to 0.12% ± 1.5% (GMTF-corrected) within the 0%–50% PDFF range. Liver PDFF estimation was improved by reducing the standard deviation of the mean liver PDFF and the bias of the mean liver PDFF for all subjects.Conclusion: The proposed GMTF-based k-space trajectory correction is a fast alternative method for avoiding PDFF quantitation errors caused by gradient-system induced k-space trajectory errors in 3D radial multi-echo gradient-echo acquisitions.

Evaluation of proton density fat fraction (PDFF) obtained from a vendor-neutral MRI sequence and MRQuantif software.

To validate the proton density fat fraction (PDFF) obtained by the MRQuantif software from 2D chemical shift encoded MR (CSE-MR) data in comparison with the histological steatosis data. This study, pooling data from 3 prospective studies spread over time between January 2007 and July 2020, analyzed 445 patients who underwent 2D CSE-MR and liver biopsy. MR derived liver iron concentration (MR-LIC) and PDFF was calculated using the MRQuantif software. The histological standard steatosis score (SS) served as reference. In order to get a value more comparable to PDFF, histomorphometry fat fraction (HFF) were centrally determined for 281 patients. Spearman correlation and the Bland and Altman method were used for comparison. Strong correlations were found between PDFF and SS (rs = 0.84, p < 0.001) or HFF (rs = 0.87, p < 0.001). Spearman's coefficients increased to 0.88 (n = 324) and 0.94 (n = 202) when selecting only the patients without liver iron overload. The Bland and Altman analysis between PDFF and HFF found a mean bias of 5.4% ± 5.7 [95% CI 4.7, 6.1]. The mean bias was 4.7% ± 3.7 [95% CI 4.2, 5.3] and 7.1% ± 8.8 [95% CI 5.2, 9.0] for the patients without and with liver iron overload, respectively. The PDFF obtained by MRQuantif from a 2D CSE-MR sequence is highly correlated with the steatosis score and very close to the fat fraction estimated by histomorphometry. Liver iron overload reduced the performance of steatosis quantification and joint quantification is recommended. This device-independent method can be particularly useful for multicenter studies. The quantification of liver steatosis using a vendor-neutral 2D chemical-shift MR sequence, processed by MRQuantif, is well correlated to steatosis score and histomorphometric fat fraction obtained from biopsy, whatever the magnetic field and the MR device used. • The PDFF measured by MRQuantif from 2D CSE-MR sequence data is highly correlated to hepatic steatosis. • Steatosis quantification performance is reduced in case of significant hepatic iron overload. • This vendor-neutral method may allow consistent estimation of PDFF in multicenter studies.

Intra-examination agreement between multi-echo gradient echo and confounder-corrected chemical shift-encoded MR sequences for R2* estimation as a biomarker of liver iron content in patients with a wide range of T2*/R2* and proton density fat fraction values.

To investigate the intra-examination agreement between multi-echo gradient echo (MEGE) and confounder-corrected chemical shift-encoded (CSE) sequences for liver T2*/R2* estimations in a wide range of T2*/R2* and proton density fat fraction (PDFF) values. Exploratorily, to search for the T2*/R2* value where the agreement line breaks and examine differences between regions of low and high agreement. Consecutive patients at risk for liver iron overload who underwent MEGE and CSE sequences within the same exam at 1.5T were retrospectively selected. Regions of interest were drawn in the right and one in the left liver lobes on post-processed images for R2*(sec-1) and PDFF (%) estimation. Agreement between MEGE-R2* and CSE-R2* was evaluated using intra-class correlation coefficient (ICC) and Bland-Altman analysis. 95% confidence intervals (CI) were computed. Segment-and-regression analysis was performed to find the point where the agreement between sequences is interrupted. Regions of low and high agreement were examined using tree-based partitioning analyses. 49 patients were included. Mean MEGE-R2* was 94.2s-1 (range: 31.0-737.1) and mean CSE-R2* 87.7 (29.7-748.1). Mean CSE-PDFF was 9.12% (0.1-43.3). Agreement was strong for R2* estimations (ICC: 0.992,95%CI 0.987,0.996), but the relation was nonlinear and possibly heteroskedastic. Lower agreement occurred when MEGE-R2* > 235s-1, with MEGE-R2* values consistently lower than CSE-R2*. Higher agreement was observed when PDFF < 14%. MEGE-R2* and CSE-R2* strongly agree, though at higher iron content, MEGE-R2* is consistently lower than CSE-R2*. In this preliminary dataset, a breaking point for agreement was found at R2* > 235. Lower agreement was observed in patients with moderate to severe liver steatosis.

MRI correlates of motoneuron loss in SMA

BackgroundMagnetic resonance imaging (MRI) is currently explored as supplemental tool to monitor disease progression and treatment response in various neuromuscular disorders. We here assessed the utility of a multi-parametric magnetic resonance imaging (MRI) protocol including quantitative water T2 mapping, Dixon-based proton density fat fraction (PDFF) estimation and diffusion tensor imaging (DTI) to detect loss of spinal motor neurons and subsequent muscle damage in adult SMA patients.MethodsSixteen SMA patients and 13 age-matched controls were enrolled in this prospective, longitudinal study. All participants underwent MRI imaging including measurements of Dixon-based PDFF and DTI of the sciatic nerve. SMA patients furthermore underwent measurements of muscle water T2 (T2w) of the biceps femoris muscle (BFM) and quadriceps femoris muscle (QFM). Ten participants returned for a second scan six months later. MRI parameter were correlated with clinical data. All patients were on nusinersen treatment.ResultsThere were significantly higher intramuscular fat fractions in the BFM and QFM of SMA patients compared to healthy controls at baseline and after 6 months. Furthermore, T2 values significantly correlated positively with intramuscular fat fractions. The Hammersmith functional motor scale significantly correlated with the QFM’s intramuscular fat fractions. DTI scans of the sciatic nerve were not significantly different between the two groups.ConclusionThis study demonstrates that, water T2 mapping and Dixon-based PDFF estimation may distinguish between adult SMA patients and controls, due to massive intramuscular fat accumulation in SMA. More extensive long-term studies are warranted to further evaluate these two modalities as surrogate markers in SMA patients during treatment.

Addressing concomitant gradient phase errors in time-interleaved chemical shift-encoded MRI fat fraction and R2 * mapping with a pass-specific phase fitting method.

Concomitant gradients induce phase errors that increase quadratically with distance from isocenter. This work proposes a complex-based fitting method that addresses concomitant gradient phase errors in chemical shift encoded (CSE) MRI estimation of proton density fat fraction (PDFF) and R2 * through joint estimation of pass-specific phase terms. This method is applicable to time-interleaved multi-echo gradient-echo acquisitions (i.e., multi-pass acquisitions) and does not require prior knowledge of gradient waveforms typically needed to address concomitant gradient phase errors. A CSE-MRI spoiled gradient echo signal model, with pass-specific phase terms, is introduced for non-linear least squares estimation of PDFF and R2 * in the presence of concomitant gradient phase errors. Cramér-Rao lower bound analysis was used to determine noise performance tradeoffs of the proposed fitting method, which was then validated in both phantom and in vivo experiments. The proposed fitting method removed PDFF and R2 * estimation errors up to 12% and 10 s-1 , respectively, at ±12 cm off isocenter (S/I) in a water phantom. In healthy volunteers, PDFF and R2 * bias was reduced by ~10% (12 cm off-isocenter) and ~30 s-1 (16 cm off-isocenter), respectively. An evaluation in 29 clinical liver datasets demonstrated reduced PDFF bias and variability (8.4% improvement in the coefficient of variation), even with the imaging volume centered at isocenter. Concomitant gradient induced phase errors in multi-pass CSE-MRI acquisitions can result in PDFF and R2 * estimation biases away from isocenter. The proposed fitting method enables accurate PDFF and R2 * quantification in the presence of concomitant gradient phase errors without knowledge of imaging gradient waveforms.

Multisite multivendor validation of a quantitative MRI and CT compatible fat phantom.

Chemical shift-encoded magnetic resonance imaging enables accurate quantification of liver fat content though estimation of proton density fat-fraction (PDFF). Computed tomography (CT) is capable of quantifying fat, based on decreased attenuation with increased fat concentration. Current quantitative fat phantoms do not accurately mimic the CT number of human liver. The purpose of this work was to develop and validate an optimized phantom that simultaneously mimics the MRI and CT signals of fatty liver. An agar-based phantom containing 12 vials doped with iodinated contrast, and with a granular range of fat fractions was designed and constructed within a novel CT and MR compatible spherical housing design. A four-site, three-vendor validation study was performed. MRI (1.5T and 3T) and CT images were obtained using each vendor's PDFF and CT reconstruction, respectively. An ROI centered in each vial was placed to measure MRI-PDFF (%) and CT number (HU). Mixed-effects model, linear regression, and Bland-Altman analysis were used for statistical analysis. MRI-PDFF agreed closely with nominal PDFF values across both field strengths and all MRI vendors. A linear relationship (slope=-0.54±0.01%/HU, intercept= 37.15±0.03%) with an R2 of 0.999 was observed between MRI-PDFF and CT number, replicating established in vivo signal behavior. Excellent test-retest repeatability across vendors (MRI: mean=-0.04%, 95% limits of agreement=[-0.24%, 0.16%]; CT: mean= 0.16 HU, 95% limits of agreement=[-0.15HU, 0.47HU]) and good reproducibility using GE scanners (MRI: mean=-0.21%, 95% limits of agreement=[-1.47%, 1.06%]; CT: mean=-0.18HU, 95% limits of agreement=[-1.96HU, 1.6HU]) were demonstrated. The proposed fat phantom successfully mimicked quantitative liver signal for both MRI and CT. The proposed fat phantom in this study may facilitate broader application and harmonization of liver fat quantification techniques using MRI and CT across institutions, vendors and imaging platforms.

Limits of Fat Quantification in the Presence of Iron Overload.

Chemical shift encoded magnetic resonance imaging (CSE-MRI)-based tissue fat quantification is confounded by increased R2* signal decay rate caused by the presence of excess iron deposition. To determine the upper limit of R2* above which it is no longer feasible to quantify proton density fat fraction (PDFF) reliably, using CSE-MRI. Prospective. Cramér-Rao lower bound (CRLB) calculations, Monte Carlo simulations, phantom experiments, and a prospective study in 26 patients with known or suspected liver iron overload. Multiecho gradient echo at 1.5 T and 3.0 T. CRLB calculations were used to develop an empirical relationship between the maximum R2* value above which PDFF estimation will achieve a desired number of effective signal averages. A single voxel multi-TR, multi-TE stimulated echo acquisition mode magnetic resonance spectroscopy acquisition was used as a reference standard to estimate PDFF. Reconstructed PDFF and R2* maps were analyzed by one analyst using multiple regions of interest drawn in all nine Couinaud segments. None. Simulations, phantom experiments, and in vivo measurements demonstrated unreliable PDFF estimates with increased R2*, with PDFF errors as large as 20% at an R2* of 1000 s-1 . For typical optimized Cartesian acquisitions (TE1=0.75 msec, ΔTE=0.67 msec at 1.5 T, TE1=0.65 msec, ΔTE=0.58 msec at 3.0 T), an empirical relationship between PDFF estimation errors and acquisition parameters was developed that suggests PDFF estimates are unreliable above an R2* of ~538 s-1 and ~779 s-1 at 1.5 T and 3 T, respectively. This empirical relationship was further investigated with phantom experiments and in vivo measurements, with PDFF errors at an R2* of 1000 s-1 at 3.0 T as large as 10% with TE1=1.24 msec, ΔTE=1.01 msec compared to 3% with TE1=0.65 msec, ΔTE=0.58 msec. We successfully developed a theoretically-based empirical formula that may provide an easily calculable guideline to identify R2* values above which PDFF is not reliable in research and clinical applications using CSE-MRI to quantify PDFF in the presence of iron overload. 1 TECHNICAL EFFICACY STAGE: 1.

Temperature-corrected proton density fat fraction estimation using chemical shift-encoded MRI in phantoms.

Chemical shift-encoded MRI (CSE-MRI) is well-established to quantify proton density fat fraction (PDFF) as a quantitative biomarker of hepatic steatosis. However, temperature is known to bias PDFF estimation in phantom studies. In this study, strategies were developed and evaluated to correct for the effects of temperature on PDFF estimation through simulations, temperature-controlled experiments, and a multi-center, multi-vendor phantom study. A technical solution that assumes and automatically estimates a uniform, global temperature throughout the phantom is proposed. Computer simulations modeled the effect of temperature on PDFF estimation using magnitude-, complex-, and hybrid-based CSE-MRI methods. Phantom experiments were performed to assess the temperature correction on PDFF estimation at controlled phantom temperatures. To assess the temperature correction method on a larger scale, the proposed method was applied to data acquired as part of a nine-site multi-vendor phantom study and compared to temperature-corrected PDFF estimation using an a priori guess for ambient room temperature. Simulations and temperature-controlled experiments show that as temperature deviates further from the assumed temperature, PDFF bias increases. Using the proposed correction method and a reasonable a priori guess for ambient temperature, PDFF bias and variability were reduced using magnitude-based CSE-MRI, across MRI systems, field strengths, protocols, and varying phantom temperature. Complex and hybrid methods showed little PDFF bias and variability both before and after correction. Correction for temperature reduces temperature-related PDFF bias and variability in phantoms across MRI vendors, sites, field strengths, and protocols for magnitude-based CSE-MRI, even without a priori information about the temperature.

Differences in multi-echo chemical shift encoded MRI proton density fat fraction estimation based on multifrequency fat peaks selection in non-alcoholic fatty liver disease patients

To compare the performance of multi-echo chemical-shift-encoded (MECSE) magnetic resonance imaging (MRI) proton density fat fraction (PDFF) estimation, considering three different fat frequency peak combinations, for the quantification of steatosis in patients with non-alcoholic fatty liver disease (NAFLD). The present study was a prospective cross-sectional research of 121 patients with metabolic syndrome and evidence of hepatic steatosis on ultrasound, who underwent a 3 T MRI examination. All patients were studied with a multifrequency MECSE sequence. The PDFF was calculated using six peaks (MECSEp123456), three peaks (MECSEp456), and a single peak (MECSEp5) model. The two simpler fat peak models were compared to the six peaks model, which was considered the reference standard. Linearity was evaluated using linear regression while agreement was described using Bland-Altman analysis. The mean age was 47 (±9) years and BMI was 29.9 (±2.9) kg/m2. Steatosis distribution was 15%/31%/54% (S1/S2/S3, respectively). Compared to MECSEp123456, both models provided linear PDFF measurements (R2= 0.99 and 0.97, MECSEp456 and MECSEp5 respectively). Regression slope (0.92; p<0.001) and mean Bland-Altman bias (-1.5%; 95% limits of agreement: -3.19%, 0.22%) indicated minimal underestimation by using PDFF-MECSEp456. Nonetheless, mean differences in PDFF estimations varied from -1.5% (MECSEp456,p=0.006) to -2.2% (MECSEp5,p<0.001) when compared to full six fat frequencies model. Although simpler spectral fat MECSE analysis shows a linear relationship with the standard six peaks model, their variation in estimated PDFF values introduces a low but clinically significant bias in fat quantification and steatosis grading in NAFLD patients.

Prospective comparison of longitudinal change in hepatic proton density fat fraction (PDFF) estimated by magnitude-based MRI (MRI-M) and complex-based MRI (MRI-C).

To 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). This was a secondary analysis of a prospective dual-center longitudinal study of 54 adults (44 women; mean age 52years; range 27-70years) 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 6months (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). MRI-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). Although 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. • 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 3T. • 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.

Proton density fat fraction MRI of vertebral bone marrow: Accuracy, repeatability, and reproducibility among readers, field strengths, and imaging platforms.

Chemical shift-encoding based water-fat MRI is an emerging method to noninvasively assess proton density fat fraction (PDFF), a promising quantitative imaging biomarker for estimating tissue fat concentration. However, in vivo validation of PDFF is still lacking for bone marrow applications. To determine the accuracy and precision of MRI-determined vertebral bone marrow PDFF among different readers and across different field strengths and imager manufacturers. Repeatability/reproducibility. Twenty-four adult volunteers underwent lumbar spine MRI with one 1.5T and two different 3.0T MR scanners from two vendors on the same day. 1.5T and 3.0T/3D spoiled-gradient echo multipoint Dixon sequences. Two independent readers measured intravertebral PDFF for the three most central slices of the L1-5 vertebral bodies. Single-voxel MR spectroscopy (MRS)-determined PDFF served as the reference standard for PDFF estimation. Accuracy and bias were assessed by Pearson correlation, linear regression analysis, and Bland-Altman plots. Repeatability and reproducibility were evaluated by Wilcoxon signed rank test, Friedman test, and coefficients of variation. Intraclass correlation coefficients were used to validate intra- and interreader as well as intraimager agreements. MRI-based PDFF estimates of lumbar bone marrow were highly correlated (r2 = 0.899) and accurate (mean bias, -0.6%) against the MRS-determined PDFF reference standard. PDFF showed high linearity (r2 = 0.972-0.978) and small mean bias (0.6-1.5%) with 95% limits of agreement within ±3.4% across field strengths, imaging platforms, and readers. Repeatability and reproducibility of PDFF were high, with the mean overall coefficient of variation being 0.86% and 2.77%, respectively. The overall intraclass correlation coefficient was 0.986 as a measure for an excellent interreader agreement. MRI-based quantification of vertebral bone marrow PDFF is highly accurate, repeatable, and reproducible among readers, field strengths, and MRI platforms, indicating its robustness as a quantitative imaging biomarker for multicentric studies. 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1762-1772.

Magnitude-intrinsic water-fat ambiguity can be resolved with multipeak fat modeling and a multipoint search method.

PurposeTo develop a postprocessing algorithm for multiecho chemical‐shift encoded water–fat separation that estimates proton density fat fraction (PDFF) maps over the full dynamic range (0‐100%) using multipeak fat modeling and multipoint search optimization. To assess its accuracy, reproducibility, and agreement with state‐of‐the‐art complex‐based methods, and to evaluate its robustness to artefacts in abdominal PDFF maps.MethodsWe introduce MAGO (MAGnitude‐Only), a magnitude‐based reconstruction that embodies multipeak liver fat spectral modeling and multipoint optimization, and which is compatible with asymmetric echo acquisitions. MAGO is assessed first for accuracy and reproducibility on publicly available phantom data. Then, MAGO is applied to N = 178 UK Biobank cases, in which its liver PDFF measures are compared using Bland‐Altman analysis with those from a version of the hybrid iterative decomposition of water and fat with echo asymmetry and least squares estimation (IDEAL) algorithm, LiverMultiScan IDEAL (LMS IDEAL, Perspectum Diagnostics Ltd, Oxford, UK). Finally, MAGO is tested on a succession of high field challenging cases for which LMS IDEAL generated artefacts in the PDFF maps.ResultsPhantom data showed accurate, reproducible MAGO PDFF values across manufacturers, field strengths, and acquisition protocols. Moreover, we report excellent agreement between MAGO and LMS IDEAL for 6‐echo, 1.5 tesla human acquisitions (bias = −0.02% PDFF, 95% confidence interval = ±0.13% PDFF). When tested on 12‐echo, 3 tesla cases from different manufacturers, MAGO was shown to be more robust to artefacts compared to LMS IDEAL.ConclusionMAGO resolves the water–fat ambiguity over the entire fat fraction dynamic range without compromising accuracy, therefore enabling robust PDFF estimation where phase data is inaccessible or unreliable and complex‐based and hybrid methods fail.

Automated CT and MRI Liver Segmentation and Biometry Using a Generalized Convolutional Neural Network.

To assess feasibility of training a convolutional neural network (CNN) to automate liver segmentation across different imaging modalities and techniques used in clinical practice and apply this to enable automation of liver biometry. We trained a 2D U-Net CNN for liver segmentation in two stages using 330 abdominal MRI and CT exams acquired at our institution. First, we trained the neural network with non-contrast multi-echo spoiled-gradient-echo (SGPR)images with 300 MRI exams to provide multiple signal-weightings. Then, we used transfer learning to generalize the CNN with additional images from 30 contrast-enhanced MRI and CT exams.We assessed the performance of the CNN using a distinct multi-institutional data set curated from multiple sources (n = 498 subjects). Segmentation accuracy was evaluated by computing Dice scores. Utilizing these segmentations, we computed liver volume from CT and T1-weighted (T1w) MRI exams, and estimated hepatic proton- density-fat-fraction (PDFF) from multi-echo T2*w MRI exams. We compared quantitative volumetry and PDFF estimates between automated and manual segmentation using Pearson correlation and Bland-Altman statistics. Dice scores were 0.94 ± 0.06 for CT (n = 230), 0.95 ± 0.03 (n = 100) for T1w MR, and 0.92 ± 0.05 for T2*w MR (n = 169). Liver volume measured by manual and automated segmentation agreed closely for CT (95% limit-of-agreement (LoA) = [-298 mL, 180 mL]) and T1w MR (LoA = [-358 mL, 180 mL]). Hepatic PDFF measured by the two segmentations also agreed closely (LoA = [-0.62%, 0.80%]). Utilizing a transfer-learning strategy, we have demonstrated the feasibility of a CNN to be generalized to perform liver segmentations across different imaging techniques and modalities. With further refinement and validation, CNNs may have broad applicability for multimodal liver volumetry and hepatic tissue characterization.

Validation of a standardized MRI method for liver fat and T2* quantification.

PurposeSeveral studies have demonstrated the accuracy, precision, and reproducibility of proton density fat fraction (PDFF) quantification using vendor-specific image acquisition protocols and PDFF estimation methods. The purpose of this work is to validate a confounder-corrected, cross-vendor, cross field-strength, in-house variant LMS IDEAL of the IDEAL method licensed from the University of Wisconsin, which has been developed for routine clinical use.MethodsLMS IDEAL is implemented using a combination of patented and/or published acquisition and some novel model fitting methods required to correct confounds which result from the imaging and estimation processes, including: water-fat ambiguity; T2* relaxation; multi-peak fat modelling; main field inhomogeneity; T1 and noise bias; bipolar readout gradients; and eddy currents. LMS IDEAL has been designed to use image acquisition protocols that can be installed on most MRI scanners and cloud-based image processing to provide fast, standardized clinical results. Publicly available phantom data were used to validate LMS IDEAL PDFF calculations against results from originally published IDEAL methodology. LMS PDFF and T2* measurements were also compared with an independent technique in human volunteer data (n = 179) acquired as part of the UK Biobank study.ResultsWe demonstrate excellent agreement of LMS IDEAL across vendors, field strengths, and over a wide range of PDFF and T2* values in the phantom study. The performance of LMS IDEAL was then assessed in vivo against widely accepted PDFF and T2* estimation methods (LMS Dixon and LMS T2*, respectively), demonstrating the robustness of LMS IDEAL to potential sources of error.ConclusionThe development and clinical validation of the LMS IDEAL algorithm as a chemical shift-encoded MRI method for PDFF and T2* estimation contributes towards robust, unbiased applications for quantification of hepatic steatosis and iron overload, which are key features of chronic liver disease.

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

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. 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. 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. 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. 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.

MRI proton density fat fraction is robust across the biologically plausible range of triglyceride spectra in adults with nonalcoholic steatohepatitis.

Proton density fat fraction (PDFF) estimation requires spectral modeling of the hepatic triglyceride (TG) signal. Deviations in the TG spectrum may occur, leading to bias in PDFF quantification. To investigate the effects of varying six-peak TG spectral models on PDFF estimation bias. Retrospective secondary analysis of prospectively acquired clinical research data. Forty-four adults with biopsy-confirmed nonalcoholic steatohepatitis. Confounder-corrected chemical-shift-encoded 3T MRI (using a 2D multiecho gradient-recalled echo technique with magnitude reconstruction) and MR spectroscopy. In each patient, 61 pairs of colocalized MRI-PDFF and MRS-PDFF values were estimated: one pair used the standard six-peak spectral model, the other 60 were six-peak variants calculated by adjusting spectral model parameters over their biologically plausible ranges. MRI-PDFF values calculated using each variant model and the standard model were compared, and the agreement between MRI-PDFF and MRS-PDFF was assessed. MRS-PDFF and MRI-PDFF were summarized descriptively. Bland-Altman (BA) analyses were performed between PDFF values calculated using each variant model and the standard model. Linear regressions were performed between BA biases and mean PDFF values for each variant model, and between MRI-PDFF and MRS-PDFF. Using the standard model, mean MRS-PDFF of the study population was 17.9 ± 8.0% (range: 4.1-34.3%). The difference between the highest and lowest mean variant MRI-PDFF values was 1.5%. Relative to the standard model, the model with the greatest absolute BA bias overestimated PDFF by 1.2%. Bias increased with increasing PDFF (P < 0.0001 for 59 of the 60 variant models). MRI-PDFF and MRS-PDFF agreed closely for all variant models (R2 = 0.980, P < 0.0001). Over a wide range of hepatic fat content, PDFF estimation is robust across the biologically plausible range of TG spectra. Although absolute estimation bias increased with higher PDFF, its magnitude was small and unlikely to be clinically meaningful. 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:995-1002.

Accuracy of PDFF estimation by magnitude-based and complex-based MRI in children with MR spectroscopy as a reference.

To assess and compare the accuracy of magnitude-based magnetic resonance imaging (MRI-M) and complex-based MRI (MRI-C) for estimating hepatic proton density fat fraction (PDFF) in children, using MR spectroscopy (MRS) as the reference standard. A secondary aim was to assess the agreement between MRI-M and MRI-C. This was a HIPAA-compliant, retrospective analysis of data collected in children enrolled in prospective, Institutional Review Board (IRB)-approved studies between 2012 and 2014. Informed consent was obtained from 200 children (ages 8-19 years) who subsequently underwent 3T MR exams that included MRI-M, MRI-C, and T1 -independent, T2 -corrected, single-voxel stimulated echo acquisition mode (STEAM) MRS. Both MRI methods acquired six echoes at low flip angles. T2*-corrected PDFF parametric maps were generated. PDFF values were recorded from regions of interest (ROIs) drawn on the maps in each of the nine Couinaud segments and three ROIs colocalized to the MRS voxel location. Regression analyses assessing agreement with MRS were performed to evaluate the accuracy of each MRI method, and Bland-Altman and intraclass correlation coefficient (ICC) analyses were performed to assess agreement between the MRI methods. MRI-M and MRI-C PDFF were accurate relative to the colocalized MRS reference standard, with regression intercepts of 0.63% and -0.07%, slopes of 0.998 and 0.975, and proportion-of-explained-variance values (R2 ) of 0.982 and 0.979, respectively. For individual Couinaud segments and for the whole liver averages, Bland-Altman biases between MRI-M and MRI-C were small (ranging from 0.04 to 1.11%) and ICCs were high (≥0.978). Both MRI-M and MRI-C accurately estimated hepatic PDFF in children, and high intermethod agreement was observed. 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2017;46:1641-1647.

Proton Density Fat Fraction Measurements at 1.5- and 3-T Hepatic MR Imaging: Same-Day Agreement among Readers and across Two Imager Manufacturers.

Purpose To determine the agreement of proton density fat fraction (PDFF) measurements obtained with hepatic magnetic resonance (MR) imaging among readers, imager manufacturers, and field strengths. Materials and Methods This HIPAA-compliant study was approved by the institutional review board. After providing informed consent, 24 adult volunteers underwent imaging with one 1.5-T MR unit (Ingenia; Philips Healthcare, Best, the Netherlands) and two different 3.0-T units (750 W [GE Healthcare, Waukesha, Wis] and Ingenia) on the same day to estimate hepatic PDFF. A single-breath-hold multipoint Dixon-based acquisition was performed with commercially available pulse sequences provided by the MR imager manufacturers (mDIXON Quant [Philips Healthcare], IDEAL IQ [GE Healthcare]). Five readers placed one large region of interest, inclusive of as much liver parenchyma as possible in the right lobe while avoiding large vessels, on imager-generated parametric maps to measure hepatic PDFF. Two-way single-measure intraclass correlation coefficients (ICCs) were used to assess interreader agreement and agreement across the three imaging platforms. Results Excellent interreader agreement for hepatic PDFF measurements was obtained with mDIXON Quant and the Philips 1.5-T unit (ICC, 0.995; 95% confidence interval [CI]: 0.991, 0.998), mDIXON Quant and the Philips 3.0-T unit (ICC, 0.992; 95% CI: 0.986, 0.996), and IDEAL IQ and the GE 3.0-T unit (ICC, 0.966; 95% CI: 0.939, 0.984). Individual reader ICCs for hepatic PDFF measurements across all three imager manufacturer-field strength combinations also showed excellent interimager agreement, ranging from 0.914 to 0.954. Conclusion Estimation of PDFF with hepatic MR imaging by using multipoint Dixon techniques is highly reproducible across readers, field strengths, and imaging platforms. © RSNA, 2017.

Effect of intravenous gadoxetate disodium and flip angle on hepatic proton density fat fraction estimation with six-echo, gradient-recalled-echo, magnitude-based MR imaging at 3T.

The aim of the study was to determine in patients undergoing gadoxetate disodium (Gx)-enhanced MR exams whether proton density fat fraction (PDFF) estimation accuracy of magnitude-based multi-gradient-echo MRI (MRI-M) could be improved by using high flip angle (FA) on post-contrast images. Thirty-one adults with known or suspected hepatic steatosis undergoing 3T clinical Gx-enhanced liver MRI were enrolled prospectively. MR spectroscopy (MRS), the reference standard, was performed before Gx to measure MRS-PDFF. Low (10°)- and high (50°)-flip angle (FA) MRI-M sequences were acquired before and during the hepatobiliary phase after Gx administration; MRI-PDFF was estimated in the MRS-PDFF voxel location. Linear regression parameters (slope, intercept, average bias, R 2) were calculated for MRS-PDFF as a function of MRI-PDFF for each MRI-M sequence (pre-Gx low-FA, pre-Gx high-FA, post-Gx low-FA, post-Gx high-FA) for all patients and for patients with MRS-PDFF<10%. Regression parameters were compared (Bonferroni-adjusted bootstrap-based tests). Three of the four MRI-M sequences (pre-Gx low-FA, post-Gx low-FA, post-Gx high-FA) provided relatively unbiased PDFF estimates overall and in the low-PDFF range, with regression slopes close to 1 and intercepts and biases close to zero. Pre-Gx high-FA MRI overestimated PDFF in proportion to MRS-PDFF, with slopes of 0.72 (overall) and 0.63 (low-PDFF range). Based on regression bias closest to 0, the post-Gx high-FA sequence was the most accurate overall and in the low-PDFF range. This sequence provided statistically significant improvements in at least two regression parameters compared to every other sequence. In patients undergoing Gx-enhanced MR exams, PDFF estimation accuracy of MRI-M can be improved by using high-FA on post-contrast images.

Correction of phase errors in quantitative water-fat imaging using a monopolar time-interleaved multi-echo gradient echo sequence.

To propose a phase error correction scheme for monopolar time-interleaved multi-echo gradient echo water-fat imaging that allows accurate and robust complex-based quantification of the proton density fat fraction (PDFF). A three-step phase correction scheme is proposed to address a) a phase term induced by echo misalignments that can be measured with a reference scan using reversed readout polarity, b) a phase term induced by the concomitant gradient field that can be predicted from the gradient waveforms, and c) a phase offset between time-interleaved echo trains. Simulations were carried out to characterize the concomitant gradient field-induced PDFF bias and the performance estimating the phase offset between time-interleaved echo trains. Phantom experiments and in vivo liver and thigh imaging were performed to study the relevance of each of the three phase correction steps on PDFF accuracy and robustness. The simulation, phantom, and in vivo results showed in agreement with the theory an echo time-dependent PDFF bias introduced by the three phase error sources. The proposed phase correction scheme was found to provide accurate PDFF estimation independent of the employed echo time combination. Complex-based time-interleaved water-fat imaging was found to give accurate and robust PDFF measurements after applying the proposed phase error correction scheme. Magn Reson Med 78:984-996, 2017. © 2016 International Society for Magnetic Resonance in Medicine.