Reproducibility of MRI‐determined proton density fat fraction across two different MR scanner platforms

Purpose:

To evaluate magnetic resonance imaging (MRI)‐determined proton density fat fraction (PDFF) reproducibility across two MR scanner platforms and, using MR spectroscopy (MRS)‐determined PDFF as reference standard, to confirm MRI‐determined PDFF estimation accuracy.

Materials and Methods:

This prospective, cross‐sectional, crossover, observational pilot study was approved by an Institutional Review Board. Twenty‐one subjects gave written informed consent and underwent liver MRI and MRS at both 1.5T (Siemens Symphony scanner) and 3T (GE Signa Excite HD scanner). MRI‐determined PDFF was estimated using an axial 2D spoiled gradient‐recalled echo sequence with low flip‐angle to minimize T1 bias and six echo‐times to permit correction of T2* and fat‐water signal interference effects. MRS‐determined PDFF was estimated using a stimulated‐echo acquisition mode sequence with long repetition time to minimize T1 bias and five echo times to permit T2 correction. Interscanner reproducibility of MRI determined PDFF was assessed by correlation analysis; accuracy was assessed separately at each field strength by linear regression analysis using MRS‐determined PDFF as reference standard.

Results:

1.5T and 3T MRI‐determined PDFF estimates were highly correlated (r = 0.992). MRI‐determined PDFF estimates were accurate at both 1.5T (regression slope/intercept = 0.958/‐0.48) and 3T (slope/intercept = 1.020/0.925) against the MRS‐determined PDFF reference.

Conclusion:

MRI‐determined PDFF estimation is reproducible and, using MRS‐determined PDFF as reference standard, accurate across two MR scanner platforms at 1.5T and 3T. J. Magn. Reson. Imaging 2011;. © 2011 Wiley‐Liss, Inc.     

Comparison of R2* correction methods for accurate fat quantification in fatty liver

Purpose:

To compare the performance of fat fraction quantification using single‐R₂* and dual‐R₂* correction methods in patients with fatty liver, using MR spectroscopy (MRS) as the reference standard.

Materials and Methods:

From a group of 97 patients, 32 patients with hepatic fat fraction greater than 5%, as measured by MRS, were identified. In these patients, chemical shift encoded fat‐water imaging was performed, covering the entire liver in a single breathhold. Fat fraction was measured from the imaging data by postprocessing using 6 different models: single‐ and dual‐R₂* correction, each performed with complex fitting, magnitude fitting, and mixed magnitude/complex fitting to compare the effects of phase error correction. Fat fraction measurements were compared with co‐registered spectroscopy measurements using linear regression.

Results:

Linear regression demonstrated higher agreement with MRS using single‐R₂* correction compared with dual‐R₂* correction. Among single‐R₂* models, all 3 fittings methods performed similarly well (slope = 1.0 ± 0.06, r² = 0.89–0.91).

Conclusion:

Single‐R₂* modeling is more accurate than dual‐R₂* modeling for hepatic fat quantification in patients, even in those with high hepatic fat concentrations. J. Magn. Reson. Imaging 2013;37:414–422. © 2012 Wiley Periodicals, Inc.     

T(1) independent, T(2) (*) corrected chemical shift based fat-water separation with multi-peak fat spectral modeling is an accurate and precise measure of hepatic steatosis

Purpose:

To determine the precision and accuracy of hepatic fat‐fraction measured with a chemical shift‐based MRI fat‐water separation method, using single‐voxel MR spectroscopy (MRS) as a reference standard.

Materials and Methods:

In 42 patients, two repeated measurements were made using a T₁‐independent, T‐corrected chemical shift‐based fat‐water separation method with multi‐peak spectral modeling of fat, and T₂‐corrected single voxel MR spectroscopy. Precision was assessed through calculation of Bland‐Altman plots and concordance correlation intervals. Accuracy was assessed through linear regression between MRI and MRS. Sensitivity and specificity of MRI fat‐fractions for diagnosis of steatosis using MRS as a reference standard were also calculated.

Results:

Statistical analysis demonstrated excellent precision of MRI and MRS fat‐fractions, indicated by 95% confidence intervals (units of absolute percent) of [?2.66%,2.64%] for single MRI ROI measurements, [?0.81%,0.80%] for averaged MRI ROI, and [?2.70%,2.87%] for single‐voxel MRS. Linear regression between MRI and MRS indicated that the MRI method is highly accurate. Sensitivity and specificity for detection of steatosis using averaged MRI ROI were 100% and 94%, respectively. The relationship between hepatic fat‐fraction and body mass index was examined.

Conclusion:

Fat‐fraction measured with T₁‐independent T‐corrected MRI and multi‐peak spectral modeling of fat is a highly precise and accurate method of quantifying hepatic steatosis. J. Magn. Reson. Imaging 2011;33:873–881. © 2011 Wiley‐Liss, Inc.

     

Quantification of hepatic steatosis with MRI: The effects of accurate fat spectral modeling

Purpose

To develop a chemical‐shift–based imaging method for fat quantification that accounts for the complex spectrum of fat, and to compare this method with MR spectroscopy (MRS). Quantitative noninvasive biomarkers of hepatic steatosis are urgently needed for the diagnosis and management of nonalcoholic fatty liver disease (NAFLD).

Materials and Methods

Hepatic steatosis was measured with “fat‐fraction” images in 31 patients using a multiecho chemical‐shift–based water‐fat separation method at 1.5T. Fat‐fraction images were reconstructed using a conventional signal model that considers fat as a single peak at –210 Hz relative to water (“single peak” reconstruction). Fat‐fraction images were also reconstructed from the same source images using two methods that account for the complex spectrum of fat; precalibrated and self‐calibrated “multipeak” reconstruction. Single‐voxel MRS that was coregistered with imaging was performed for comparison.

Results

Imaging and MRS demonstrated excellent correlation with single peak reconstruction (r² = 0.91), precalibrated multipeak reconstruction (r² = 0.94), and self‐calibrated multipeak reconstruction (r² = 0.91). However, precalibrated multipeak reconstruction demonstrated the best agreement with MRS, with a slope statistically equivalent to 1 (0.96 ± 0.04; P = 0.4), compared to self‐calibrated multipeak reconstruction (0.83 ± 0.05, P = 0.001) and single‐peak reconstruction (0.67 ± 0.04, P < 0.001).

Conclusion

Accurate spectral modeling is necessary for accurate quantification of hepatic steatosis with MRI. J. Magn. Reson. Imaging 2009;29:1332–1339. © 2009 Wiley‐Liss, Inc.     

In vivo 1H‐MRS hepatic lipid profiling in nonalcoholic fatty liver disease: An animal study at 9.4 T

The applicability of the in vivo proton magnetic resonance spectroscopy hepatic lipid profiling (MR‐HLP) technique in nonalcoholic fatty liver disease was investigated. Using magnetic resonance spectroscopy, the relative fractions of diunsaturated (f_di), monounsaturated (f_mono), and saturated (f_sat) fatty acids as well as total hepatic lipid content were estimated in the livers of 8 control and 23 CCl4‐treated rats at 9.4 T. The mean steatosis, necrosis, inflammation, and fibrosis scores of the treated group were all significantly higher than those of the control group (P < 0.01). There was a strong correlation between the histopathologic parameters and the MR‐HLP parameters (r = 0.775, P < 0.01) where both steatosis and fibrosis are positively correlated with f_mono and negatively correlated with f_di. Both necrosis and inflammation, however, were not correlated with any of the MR‐HLP parameters. Hepatic lipid composition appears to be changed in association with the severity of steatosis and fibrosis in nonalcoholic fatty liver disease, and these changes can be depicted in vivo by using the MR‐HLP method at 9.4 T. Thus, while it may not likely be that MR‐HLP helps differentiate between steatohepatitis in its early stages and simple steatosis, these findings altogether are in support of potential applicability of in vivo MR‐HLP at high field in nonalcoholic fatty liver disease. Magn Reson Med 70:620–629, 2013. © 2012 Wiley Periodicals, Inc.     

Hepatic fat quantification using chemical shift MR imaging and MR spectroscopy in the presence of hepatic iron deposition: validation in phantoms and in patients with chronic liver disease

Purpose:

To compare the accuracy of four chemical shift magnetic resonance imaging (MRI) (CS‐MRI) analysis methods and MR spectroscopy (MRS) with and without T2‐correction in fat quantification in the presence of excess iron.

Materials and Methods:

CS‐MRI with six opposed‐ and in‐phase acquisitions and MRS with five‐echo acquisitions (TEs of 20, 30, 40, 50, 60 msec) were performed at 1.5 T on phantoms containing various fat fractions (FFs), on phantoms containing various iron concentrations, and in 18 patients with chronic liver disease. For CS‐MRI, FFs were estimated with the dual‐echo method, with two T2*‐correction methods (triple‐ and multiecho), and with multiinterference methods that corrected for both T2* and spectral interference effects. For MRS, FF was estimated without T2‐correction (single‐echo MRS) and with T2‐correction (multiecho MRS).

Results:

In the phantoms, T2*‐ or T2‐correction methods for CS‐MRI and MRS provided unbiased estimations of FFs (mean bias, ?1.1% to 0.5%) regardless of iron concentration, whereas the dual‐echo method (?5.5% to ?8.4%) and single‐echo MRS (12.1% to 37.3%) resulted in large biases in FFs. In patients, the FFs estimated with triple‐echo (R = 0.98), multiecho (R = 0.99), and multiinterference (R = 0.99) methods had stronger correlations with multiecho MRS FFs than with the dual‐echo method (R = 0.86; P ≤ 0.011). The FFs estimated with multiinterference method showed the closest agreement with multiecho MRS FFs (the 95% limit‐of‐agreement, ?0.2 ± 1.1).

Conclusion:

T2*‐ or T2‐correction methods are effective in correcting the confounding effects of iron, enabling an accurate fat quantification throughout a wide range of iron concentrations. Spectral modeling of fat may further improve the accuracy of CS‐MRI in fat quantification. J. Magn. Reson. Imaging 2011;33:1390–1398. © 2011 Wiley‐Liss, Inc.