Quantitative proton MR techniques for measuring fat.
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ABSTRACT:
SUBMITTER: Hu HH
PROVIDER: S-EPMC4001818 | biostudies-other | 2013 Dec
REPOSITORIES: biostudies-other
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Project description:PurposeTo investigate the effect of fat saturation (FatSat) on quantitative UTE imaging of variable knee tissues on a 3T scanner.MethodsThree quantitative UTE imaging techniques, including the UTE multi-echo sequence for T2∗ measurement, the adiabatic T1ρ prepared UTE sequence for T1ρ measurement, and the magnetization transfer (MT)-prepared UTE sequence for MT ratio (MTR) and macromolecular proton fraction (MMF) measurements were used in this study. Twelve samples of cartilage and twelve samples of meniscus, as well as six whole knee cadaveric specimens, were imaged with the three above-mentioned UTE sequences with and without FatSat. The difference, correlation, and agreement between the UTE measurements with and without FatSat were calculated to investigate the effects of FatSat on quantification.ResultsFat was well-suppressed using all three UTE sequences when FatSat was deployed. For the small sample study, the quantification difference ratio (QDR) values of all the measured biomarkers ranged from 0.7% to 12.6%, whereas for the whole knee joint specimen study, the QDR values ranged from 0.2% to 12.0%. Except for T1ρ in muscle and MMF in meniscus (p > 0.05), most of the measurements showed statistical differences for T1ρ , MTR, and MMF (p < 0.05) between FatSat and non-FatSat scans. Most of the measurements for T2∗ showed no significant differences (p > 0.05). Strong correlations were found for all the biomarkers between measurements with and without FatSat.ConclusionThe UTE biomarkers showed good correlation and agreement with some slight differences between the scans with and without FatSat.
| S-EPMC9017280 | biostudies-literature
Project description:To compare the accuracy of several magnetic resonance (MR) imaging-based methods for hepatic proton-density fat fraction (FF) estimation at 3.0 T, with spectroscopy as the reference technique.This prospective study was institutional review board approved and HIPAA compliant. Informed consent was obtained. One hundred sixty-three subjects (39 with known hepatic steatosis, 110 with steatosis risk factors, 14 without risk factors) underwent proton MR spectroscopy and non-T1-weighted gradient-echo MR imaging of the liver. At spectroscopy, the reference FF was determined from frequency-selective measurements of fat and water proton densities. At imaging, FF was calculated by using two-, three-, or six-echo methods, with single-frequency and multifrequency fat signal modeling. The three- and six-echo methods corrected for T2*; the two-echo methods did not. For each imaging method, the fat estimation accuracy was assessed by using linear regression between the imaging FF and spectroscopic FF. Binary classification accuracy of imaging was assessed at four reference spectroscopic thresholds (0.04, 0.06, 0.08, and 0.10 FF).Regression intercept of two-, three-, and six-echo methods were -0.0211, 0.0087, and -0.0062 (P <.001 for all three) without multifrequency modeling and -0.0237 (P <.001), 0.0022, and -0.0007 with multifrequency modeling, respectively. Regression slope of two-, three-, and six-echo methods were 0.8522, 0.8528, and 0.7544 (P <.001 for all three) without multifrequency modeling and 0.9994, 0.9775, and 0.9821 with multifrequency modeling, respectively. Significant deviation of intercept and slope from 0 and 1, respectively, indicated systematic error. Classification accuracy was 82.2%-90.1%, 93.9%-96.3%, and 83.4%-89.6% for two-, three-, and six-echo methods without multifrequency modeling and 88.3%-92.0%, 95.1%-96.3%, and 94.5%-96.3% with multifrequency modeling, respectively, depending on the FF threshold. T2*-corrected (three- and six-echo) multifrequency imaging methods had the overall highest FF estimation and classification accuracy. Among methods without multifrequency modeling, the T2-corrected three-echo method had the highest accuracy.Non-T1-weighted MR imaging with T2 correction and multifrequency modeling helps accurately estimate hepatic proton-density FF at 3.0 T.
| S-EPMC3042639 | biostudies-other
Project description:Purpose To determine the linearity, bias, and precision of hepatic proton density fat fraction (PDFF) measurements by using magnetic resonance (MR) imaging across different field strengths, imager manufacturers, and reconstruction methods. Materials and Methods This meta-analysis was performed in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A systematic literature search identified studies that evaluated the linearity and/or bias of hepatic PDFF measurements by using MR imaging (hereafter, MR imaging-PDFF) against PDFF measurements by using colocalized MR spectroscopy (hereafter, MR spectroscopy-PDFF) or the precision of MR imaging-PDFF. The quality of each study was evaluated by using the Quality Assessment of Studies of Diagnostic Accuracy 2 tool. De-identified original data sets from the selected studies were pooled. Linearity was evaluated by using linear regression between MR imaging-PDFF and MR spectroscopy-PDFF measurements. Bias, defined as the mean difference between MR imaging-PDFF and MR spectroscopy-PDFF measurements, was evaluated by using Bland-Altman analysis. Precision, defined as the agreement between repeated MR imaging-PDFF measurements, was evaluated by using a linear mixed-effects model, with field strength, imager manufacturer, reconstruction method, and region of interest as random effects. Results Twenty-three studies (1679 participants) were selected for linearity and bias analyses and 11 studies (425 participants) were selected for precision analyses. MR imaging-PDFF was linear with MR spectroscopy-PDFF (R2 = 0.96). Regression slope (0.97; P < .001) and mean Bland-Altman bias (-0.13%; 95% limits of agreement: -3.95%, 3.40%) indicated minimal underestimation by using MR imaging-PDFF. MR imaging-PDFF was precise at the region-of-interest level, with repeatability and reproducibility coefficients of 2.99% and 4.12%, respectively. Field strength, imager manufacturer, and reconstruction method each had minimal effects on reproducibility. Conclusion MR imaging-PDFF has excellent linearity, bias, and precision across different field strengths, imager manufacturers, and reconstruction methods. © RSNA, 2017 Online supplemental material is available for this article. An earlier incorrect version of this article appeared online. This article was corrected on October 2, 2017.
| S-EPMC5813433 | biostudies-literature
Project description:Since the brain's gray matter (GM) and white matter (WM) metabolite concentrations differ, their partial volumes can vary the voxel's ¹H MR spectroscopy (¹H-MRS) signal, reducing sensitivity to changes. While single-voxel ¹H-MRS cannot differentiate between WM and GM signals, partial volume correction is feasible by MR spectroscopic imaging (MRSI) using segmentation of the MRI acquired for VOI placement. To determine the magnitude of this effect on metabolic quantification, we segmented a 1-mm³ resolution MRI into GM, WM and CSF masks that were co-registered with the MRSI grid to yield their partial volumes in approximately every 1 cm³ spectroscopic voxel. Each voxel then provided one equation with two unknowns: its i- metabolite's GM and WM concentrations C(i) (GM) , C(i) (WM) . With the voxels' GM and WM volumes as independent coefficients, the over-determined system of equations was solved for the global averaged C(i) (GM) and C(i) (WM) . Trading off local concentration differences offers three advantages: (i) higher sensitivity due to combined data from many voxels; (ii) improved specificity to WM versus GM changes; and (iii) reduced susceptibility to partial volume effects. These improvements made no additional demands on the protocol, measurement time or hardware. Applying this approach to 18 volunteered 3D MRSI sets of 480 voxels each yielded N-acetylaspartate, creatine, choline and myo-inositol C(i) (GM) concentrations of 8.5 ± 0.7, 6.9 ± 0.6, 1.2 ± 0.2, 5.3 ± 0.6 mM, respectively, and C(i) (WM) concentrations of 7.7 ± 0.6, 4.9 ± 0.5, 1.4 ± 0.1 and 4.4 ± 0.6mM, respectively. We showed that unaccounted voxel WM or GM partial volume can vary absolute quantification by 5-10% (more for ratios), which can often double the sample size required to establish statistical significance.
| S-EPMC3449040 | biostudies-other
Project description:To evaluate the diagnostic performance of magnetic resonance (MR) imaging-estimated proton density fat fraction (PDFF) for assessing hepatic steatosis in nonalcoholic fatty liver disease (NAFLD) by using centrally scored histopathologic validation as the reference standard.This prospectively designed, cross-sectional, internal review board-approved, HIPAA-compliant study was conducted in 77 patients who had NAFLD and liver biopsy. MR imaging-PDFF was estimated from magnitude-based low flip angle multiecho gradient-recalled echo images after T2* correction and multifrequency fat modeling. Histopathologic scoring was obtained by consensus of the Nonalcoholic Steatohepatitis (NASH) Clinical Research Network Pathology Committee. Spearman correlation, additivity and variance stabilization for regression for exploring the effect of a number of potential confounders, and receiver operating characteristic analyses were performed.Liver MR imaging-PDFF was systematically higher, with higher histologic steatosis grade (P < .001), and was significantly correlated with histologic steatosis grade (ρ = 0.69, P < .001). The correlation was not confounded by age, sex, lobular inflammation, hepatocellular ballooning, NASH diagnosis, fibrosis, or magnetic field strength (P = .65). Area under the receiver operating characteristic curves was 0.989 (95% confidence interval: 0.968, 1.000) for distinguishing patients with steatosis grade 0 (n = 5) from those with grade 1 or higher (n = 72), 0.825 (95% confidence interval: 0.734, 0.915) to distinguish those with grade 1 or lower (n = 31) from those with grade 2 or higher (n = 46), and 0.893 (95% confidence interval: 0.809, 0.977) to distinguish those with grade 2 or lower (n = 58) from those with grade 3 (n = 19).MR imaging-PDFF showed promise for assessment of hepatic steatosis grade in patients with NAFLD. For validation, further studies with larger sample sizes are needed.
| S-EPMC3632805 | biostudies-other
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.
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Project description:Background Upper extremity MRI and proton MR spectroscopy are increasingly considered to be outcome measures in Duchenne muscular dystrophy (DMD) clinical trials. Purpose To demonstrate the feasibility of acquiring upper extremity MRI and proton (1H) MR spectroscopy measures of T2 and fat fraction in a large, multicenter cohort (ImagingDMD) of ambulatory and nonambulatory individuals with DMD; compare upper and lower extremity muscles by using MRI and 1H MR spectroscopy; and correlate upper extremity MRI and 1H MR spectroscopy measures to function. Materials and Methods In this prospective cross-sectional study, MRI and 1H MR spectroscopy and functional assessment data were acquired from participants with DMD and unaffected control participants at three centers (from January 28, 2016, to April 24, 2018). T2 maps of the shoulder, upper arm, forearm, thigh, and calf were generated from a spin-echo sequence (repetition time msec/echo time msec, 3000/20-320). Fat fraction maps were generated from chemical shift-encoded imaging (eight echo times). Fat fraction and 1H2O T2 in the deltoid and biceps brachii were measured from single-voxel 1H MR spectroscopy (9000/11-243). Groups were compared by using Mann-Whitney test, and relationships between MRI and 1H MR spectroscopy and arm function were assessed by using Spearman correlation. Results This study evaluated 119 male participants with DMD (mean age, 12 years ± 3 [standard deviation]) and 38 unaffected male control participants (mean age, 12 years ± 3). Deltoid and biceps brachii muscles were different in participants with DMD versus control participants in all age groups by using quantitative T2 MRI (P < .001) and 1H MR spectroscopy fat fraction (P < .05). The deltoid, biceps brachii, and triceps brachii were affected to the same extent (P > .05) as the soleus and medial gastrocnemius. Negative correlations were observed between arm function and MRI (T2: range among muscles, ρ = -0.53 to -0.73 [P < .01]; fat fraction, ρ = -0.49 to -0.70 [P < .01]) and 1H MR spectroscopy fat fraction (ρ = -0.64 to -0.71; P < .01). Conclusion This multicenter study demonstrated early and progressive involvement of upper extremity muscles in Duchenne muscular dystrophy (DMD) and showed the feasibility of MRI and 1H MR spectroscopy to track disease progression over a wide range of ages in participants with DMD. © RSNA, 2020 Online supplemental material is available for this article.
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Project description:Preoperative clinical magnetic resonance imaging (MRI) protocols for gliomas, brain tumors with dismal outcomes due to their infiltrative properties, still rely on conventional structural MRI, which does not deliver information on tumor genotype and is limited in the delineation of diffuse gliomas. The GliMR COST action wants to raise awareness about the state of the art of advanced MRI techniques in gliomas and their possible clinical translation or lack thereof. This review describes current methods, limits, and applications of advanced MRI for the preoperative assessment of glioma, summarizing the level of clinical validation of different techniques. In this first part, we discuss dynamic susceptibility contrast and dynamic contrast-enhanced MRI, arterial spin labeling, diffusion-weighted MRI, vessel imaging, and magnetic resonance fingerprinting. The second part of this review addresses magnetic resonance spectroscopy, chemical exchange saturation transfer, susceptibility-weighted imaging, MRI-PET, MR elastography, and MR-based radiomics applications. Evidence Level: 3 Technical Efficacy: Stage 2.
| S-EPMC10946498 | biostudies-literature
Project description:Preoperative clinical MRI protocols for gliomas, brain tumors with dismal outcomes due to their infiltrative properties, still rely on conventional structural MRI, which does not deliver information on tumor genotype and is limited in the delineation of diffuse gliomas. The GliMR COST action wants to raise awareness about the state of the art of advanced MRI techniques in gliomas and their possible clinical translation. This review describes current methods, limits, and applications of advanced MRI for the preoperative assessment of glioma, summarizing the level of clinical validation of different techniques. In this second part, we review magnetic resonance spectroscopy (MRS), chemical exchange saturation transfer (CEST), susceptibility-weighted imaging (SWI), MRI-PET, MR elastography (MRE), and MR-based radiomics applications. The first part of this review addresses dynamic susceptibility contrast (DSC) and dynamic contrast-enhanced (DCE) MRI, arterial spin labeling (ASL), diffusion-weighted MRI, vessel imaging, and magnetic resonance fingerprinting (MRF). EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 2.
| S-EPMC10947037 | biostudies-literature
Project description:To retrospectively assess whether magnetic resonance (MR) imaging with opposed-phase and in-phase gradient-echo (GRE) sequences and MR feature analysis can differentiate angiomyolipomas (AMLs) that contain minimal fat from clear cell renal cell carcinomas (RCCs), with particular emphasis on small (<3-cm) masses.Institutional review board approval and a waiver of informed consent were obtained for this HIPAA-compliant study. MR images from 108 pathologically proved renal masses (88 clear cell RCCs and 20 minimal fat AMLs from 64 men and 44 women) at two academic institutions were evaluated. The signal intensity (SI) of each renal mass and spleen on opposed-phase and in-phase GRE images was used to calculate an SI index and tumor-to-spleen SI ratio. Two radiologists who were blinded to the pathologic results independently assessed the subjective presence of intravoxel fat (ie, decreased SI on opposed-phase images compared with that on in-phase images), SI on T1-weighted and T2-weighted images, cystic degeneration, necrosis, hemorrhage, retroperitoneal collaterals, and renal vein thrombosis. Results were analyzed by using the Wilcoxon rank sum test, two-tailed Fisher exact test, and multivariate logistic regression analysis for all renal masses and for small masses. A P value of less than .05 was considered to indicate a statistically significant difference.There were no differences between minimal fat AMLs and clear cell RCCs for the SI index (8.05%±14.46 vs 14.99%±19.9; P=.146) or tumor-to-spleen ratio (-8.96%±16.6 and -15.8%±22.4; P=.227) when all masses or small masses were analyzed. Diagnostic accuracy (area under receiver operating characteristic curve) for the SI index and tumor-to-spleen ratio was 0.59. Intratumoral necrosis and larger size were predictive of clear cell RCC (P<.001) for all lesions, whereas low SI (relative to renal parenchyma SI) on T2-weighted images, smaller size, and female sex correlated with minimal fat AML (P<.001) for all lesions.The diagnostic accuracy of opposed-phase and in-phase GRE MR imaging for the differentiation of minimal fat AML and clear cell RCC is poor. In this cohort, low SI on T2-weighted images relative to renal parenchyma and small size suggested minimal fat AML, whereas intratumoral necrosis and large size argued against this diagnosis.
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