A theoretical triglyceride model was developed for in vivo human liver fat 1H MRS characterization, using the number of double bonds (–CH=CH–), number of methylene-interrupted double bonds (–CH=CH–CH2–CH=CH–) and average fatty acid chain length. Five 3 T, single-voxel, stimulated echo acquisition mode spectra (STEAM) were acquired consecutively at progressively longer TEs in a fat–water emulsion phantom and in 121 human subjects with known or suspected nonalcoholic fatty liver disease. T2-corrected peak areas were calculated. Phantom data were used to validate the model. Human data were used in the model to determine the complete liver fat spectrum. In the fat–water emulsion phantom, the spectrum predicted by the model (based on known fatty acid chain distribution) agreed closely with spectroscopic measurement. In human subjects, areas of CH2 peaks at 2.1 and 1.3 ppm were linearly correlated (slope, 0.172; r = 0.991), as were the 0.9 ppm CH3 and 1.3 ppm CH2 peaks (slope, 0.125; r = 0.989). The 2.75 ppm CH2 peak represented 0.6% of the total fat signal in high-liver-fat subjects. These values predict that 8.6% ofm the total fat signal overlies the water peak. The triglyceride model can characterize human liver fat spectra. This allows more accurate determination of liver fat fraction from MRI and MRS.

Quantification of fat has been investigated using images acquired from multiple gradient echos. The evolution of the signal with echo time and flip angle was measured in phantoms of known fat and water composition and in 21 research subjects with fatty liver. Data were compared to different models of the signal equation, in which each model makes different assumptions about the T1 and/ or T2* relaxation effects. A range of T1, T2*, fat fraction and number of echos was investigated to cover situations of relevance to clinical imaging. Results indicate that quantification is most accurate at low flip angles (to minimize T1 effects) with a small number of echos (to minimize spectral broadening effects). At short echo times the spectral broadening effects manifest as a short apparent T2 for the fat component.

Hepatic steatosis is characterized by abnormal and excessive accumulation of lipids within hepatocytes. It is an important feature of diffuse liver disease, and the histological hallmark of nonalcoholic fatty liver disease (NAFLD). Other conditions associated with steatosis include alcoholic liver disease, viral hepatitis, human immunodeficiency virus (HIV) and genetic lipodystrophies, cystic fibrosis liver disease, and hepatotoxicity from various therapeutic agents. Liver biopsy, the current clinical gold standard for assessment of liver fat, is invasive and has sampling errors, and is not optimal for screening, monitoring, clinical decision‐making, or well suited for many types of research studies. Noninvasive methods that accurately and objectively quantify liver fat are needed. Ultrasound (US) and computed tomography (CT) can be used to assess liver fat but have limited accuracy as well as other limitations. Magnetic resonance (MR) techniques can decompose the liver signal into its fat and water signal components and therefore assess liver fat more directly than CT or US. Most MR techniques measure the signal fat‐fraction (the fraction of the liver MR signal attributable to liver fat), which may be confounded by numerous technical and biological factors and may not reliably reflect fat content. By addressing the factors that confound the signal fat‐fraction, advanced MR techniques measure the proton density fat‐fraction (the fraction of the liver proton density attributable to liver fat), which is a fundamental tissue property and a direct measure of liver fat content. These advanced techniques show promise for accurate fat quantification and are likely to be commercially available soon. J. Magn. Reson. Imaging 2011;. © 2011 Wiley‐Liss, Inc.

The magnetic resonance imaging–estimated proton density fat fraction (MRI-PDFF) is a novel imaging-based biomarker that allows fat mapping of the entire liver, whereas the magnetic resonance spectroscopy–measured proton density fat fraction (MRS-PDFF) provides a biochemical measure of liver fat in small regions of interest. Cross-sectional studies have shown that MRI-PDFF correlates with MRS-PDFF. The aim of this study was to show the utility of MRI-PDFF in assessing quantitative changes in liver fat through a three-way comparison of MRI-PDFF and MRS-PDFF with the liver histology–determined steatosis grade at two time points in patients with nonalcoholic fatty liver disease (NAFLD). Fifty patients with biopsy-proven NAFLD who participated in a randomized trial underwent a paired evaluation with liver biopsy, MRI-PDFF, and MRS-PDFF at the baseline and 24 weeks. The mean age and body mass index were 47.8 ± 11.7 years and 30.7 ± 6.5 kg/m2, respectively. MRI-PDFF showed a robust correlation with MRS-PDFF both at week 0 and at week 24 (r = 0.98, P < 0.0001 for both). Cross-sectionally, MRI-PDFF and MRS-PDFF increased with increases in the histology-determined steatosis grade both at week 0 and at week 24 (P < 0.05 for all). Longitudinally, patients who had a decrease (≥1%) or increase (≥1%) in MRI-PDFF (confirmed by MRS-PDFF) showed a parallel decrease or increase in their body weight and serum alanine aminotransferase and aspartate aminotransferase levels at week 24 (P < 0.05). This small increase or decrease in liver fat could not be quantified with histology. Conclusion In this longitudinal study, MRI-PDFF correlated well with MRS-PDFF and was more sensitive than the histology-determined steatosis grade in quantifying increases or decreases in the liver fat content. Therefore, it could be used to quantify changes in liver fat in future clinical trials.

Relaxation- and interference-corrected fat quantification at low-flip-angle multiecho GRE MR imaging provides high diagnostic and fat-grading accuracy in NAFLD.

Purpose:To prospectively compare an investigational version of a complex-based chemical shift-based fat fraction magnetic resonance (MR) imaging method with MR spectroscopy for the quantifi cation of hepatic steatosis. Materials and Methods:This study was approved by the institutional review board and was HIPAA compliant. Written informed consent was obtained before all studies. Fifty-fi ve patients (31 women, 24 men; age range, 24-71 years) were prospectively imaged at 1.5 T with quantitative MR imaging and single-voxel MR spectroscopy, each within a single breath hold. The effects of T2* correction, spectral modeling of fat, and magnitude fi tting for eddy current correction on fat quantifi cation with MR imaging were investigated by reconstructing fat fraction images from the same source data with different combinations of error correction. Single-voxel T2-corrected MR spectroscopy was used to measure fat fraction and served as the reference standard. All MR spectroscopy data were postprocessed at a separate institution by an MR physicist who was blinded to MR imaging results. Fat fractions measured with MR imaging and MR spectroscopy were compared statistically to determine the correlation ( r 2 ), and the slope and intercept as measures of agreement between MR imaging and MR spectroscopy fat fraction measurements, to determine whether MR imaging can help quantify fat, and examine the importance of T2* correction, spectral modeling of fat, and eddy current correction. Two-sided t tests (signifi cance level, P = .05) were used to determine whether estimated slopes and intercepts were signifi cantly different from 1.0 and 0.0 , respectively. Sensitivity and specifi city for the classifi cation of clinically signifi cant steatosis were evaluated. Results:Overall, there was excellent correlation between MR imaging and MR spectroscopy for all reconstruction combinations. However, agreement was only achieved when T2* correction, spectral modeling of fat, and magnitude fi tting for eddy current correction were used ( r 2 = 0.99; slope 6 standard deviation = 1.00 6 0.01, P = .77; intercept 6 standard deviation = 0.2% 6 0.1, P = .19 ). Conclusion:T1-independent chemical shift-based water-fat separation MR imaging methods can accurately quantify fat over the entire liver, by using MR spectroscopy as the reference standard, when T2* correction, spectral modeling of fat, and eddy current correction methods are used.q RSNA, 2011

Purpose: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. Materials and Methods: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. Results:Liver MR imaging-PDFF was systematically higher, with higher histologic steatosis grade (P , .001), and was significantly correlated with histologic steatosis grade (r = 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). Conclusion: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.q RSNA, 2013

Purpose: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. Materials and Methods: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 sixecho 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 classifi cation accuracy of imaging was assessed at four reference spectroscopic thresholds (0.04, 0.06, 0.08, and 0.10 FF). Results:Regression intercept of two-, three-, and six-echo methods were 2 0.0211, 0.0087, and 2 0.0062 ( P , .001 for all three) without multifrequency modeling and 2 0.0237 ( P , .001), 0.0022, and 2 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. Signifi cant 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 classifi cation accuracy. Among methods without multifrequency modeling, the T2 * -corrected threeecho method had the highest accuracy.