Quantification of Hepatic Steatosis With a Multistep Adaptive Fitting MRI Approach: Prospective Validation Against MR Spectroscopy

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1 Gastrointestinal Imaging Original Research MRI Versus MR Spectroscopy in Quantifying Hepatic Steatosis Gastrointestinal Imaging Original Research Mustafa R. Bashir 1 Xiaodong Zhong 2 Marcel D. Nickel 3 Ghaneh Fananapazir 1 Stephan A. R. Kannengiesser 3 Berthold Kiefer 3 Brian M. Dale 4 Bashir MR, Zhong X, Nickel MD, et al. Keywords: fat fraction, hepatic steatosis, proton density fat fraction DOI:1.2214/AJR Received January 6, 214; accepted after revision June 27, 214. M. R. Bashir is a consultant to Siemens Healthcare and receives research support from Siemens Healthcare. X. Zhong, M. D. Nickel, S. A. R. Kannengiesser, B. Kiefer, and B. M. Dale are employees of Siemens Healthcare. 1 Department of Radiology, Duke University Medical Center, DUMC Box 388, Durham, NC Address correspondence to M. R. Bashir (mustafa.bashir@duke.edu). 2 MR R&D Collaborations, Siemens Healthcare, Atlanta, GA. 3 Siemens AG Healthcare Sector, Erlangen, Germany. 4 MR R&D Collaborations, Siemens Healthcare, Morrisville, NC. AJ15; 24: X/15/ American Roentgen Ray Society Quantification of Hepatic Steatosis With a Multistep Adaptive Fitting MRI Approach: Prospective Validation Against MR Spectroscopy OBJECTIVE. The purpose of this study is to prospectively compare hybrid and complex chemical shift based MRI fat quantification methods against MR spectroscopy (MRS) for the measurement of hepatic steatosis. SUBJECTS AND METHODS. Forty-two subjects (18 men and 24 women; mean ± SD age, 52.8 ± 14 years) were prospectively enrolled and imaged at 3 T with a chemical shift based MRI sequence and a single-voxel MRS sequence, each in one breath-hold. Proton density fat fraction and rate constant ( ) using both single- and dual- hybrid fitting methods, as well as proton density fat fraction and maps using a complex fitting method, were generated. A single radiologist colocalized volumes of interest on the proton density fat fraction and maps according to the spectroscopy measurement voxel. Agreement among the three MRI methods and the MRS proton density fat fraction values was assessed using linear regression, intraclass correlation coefficient (ICC), and Bland-Altman analysis. RESULTS. Correlation between the MRI and MRS measures of proton density fat fraction was excellent. Linear regression coefficients ranged from.98 to 1.1, and intercepts ranged from 1.12% to.49%. Agreement measured by ICC was also excellent (.99 for all three methods). Bland-Altman analysis showed excellent agreement, with mean differences of 1.% to.6% (SD, %). CONCLUSION. The described MRI-based liver proton density fat fraction measures are clinically feasible and accurate. The validation of proton density fat fraction quantification methods is an important step toward wide availability and acceptance of the MRI-based measurement of proton density fat fraction as an accurate and generalizable biomarker. M RI measures of proton density fat fraction are becoming important and generally accepted tools in the evaluation of hepatic steatosis [1 3]. Nonalcoholic fatty liver disease is associated with diabetes, obesity, and hypertension and is estimated to affect up to 8 million Americans [4]. Nonalcoholic fatty liver disease has been shown to be a risk factor for malignancy, cardiovascular disease, and sudden death [5 8]. Proton density fat fraction, the ratio of fat protons to the sum of fat and water protons in a given volume, is an MRIbased measure of liver fat content that has been strongly correlated with histopathologic measures of steatosis [9, 1]. This important measure is gaining traction in the research community as a biomarker in chronic liver disease. Liver fat quantification may also have a role in oncology, where hepatic steatosis has been shown as a marker of liver injury from certain chemotherapeutic agents [11, 12]. Over the last few decades, substantial advances have been made by the scientific community in developing accurate robust measures of the degree of hepatic steatosis [1 3, 13 3]. It has been well shown that it is possible to measure the severity of liver fatty metamorphosis; however, many of these techniques are technical, require offline processing, and are not widely available in clinical practice [13 21]. In fact, in the United States, only one commercial vendor has received approval from the Food and Drug Administration for an MRI-based fat quantification method [2]. For proton density fat fraction to be considered a generalizable biomarker, it must be broadly available from multiple vendors, on multiple hardware platforms. Quantitative methods face a particular challenge as commercial platforms move toward widebore (7 cm) MRI systems, which may have difficulties with field inhomogeneity and AJR:24, February

2 eddy currents, two important confounders in proton density fat fraction measurement, particularly for methods based on complexnumber calculations [26]. The purpose of this study was to prospectively compare MRI-based measures of proton density fat fraction against reference standard MR spectroscopy (MRS) for the quantification of hepatic steatosis severity, which are implemented inline on a commercially available wide-bore MRI system. Subjects and Methods Subjects The institutional review board approved this prospective HIPAA-compliant study. Written informed consent was obtained from all subjects. The author who is not an employee of Siemens Healthcare had control of data and information that might have presented a conflict of interest for the duration of the study. Forty-two subjects (18 men and 24 women) were consecutively enrolled who presented for clinical abdominal MRI, predominantly for either chronic liver disease or oncologic indications. Subject age, weight, and body mass index (weight in kilograms divided by the square of height in meters) were recorded, and relevant clinical data were collected from the electronic medical record. It should be noted that data from a subset of these subjects (n = 3) were used in a recent technical description of one of the imaging methods presented here [31]. The current work differs from that manuscript in that it is a clinical rather than technical validation with differences in data analysis, involves a larger cohort of subjects, and evaluates or compares multiple image reconstruction algorithms. A C MRI All imaging was performed on one of two identical 3-T clinical MRI systems (Magnetom Skyra, Siemens Healthcare), using anterior 18-channel flexible array coils in combination with the tablemounted spine coil array. Before IV contrast material administration, a whole-liver volume acquisition was performed using a six-echo 3D spoiled gradient-echo acquisition. Two-dimensional parallel acceleration was used to allow whole liver coverage in a single breath-hold (controlled aliasing in parallel imaging results in higher acceleration) [32]. Imaging parameters included TR/first TE of 8.9/1.23, echo spacing of 1.23 ms, flip angle of 4, receiver bandwidth of 185 Hz/pixel, FOV of cm, acquisition matrix of interpolated to an image matrix of with 6 slices, spatial resolution of mm 3, parallel imaging factor of 2 2, and acqui- Fig. 1 5-year-old man with hepatic steatosis. A G, Representative images from MRI are shown. Proton density fat fraction (A) and effective (D) were reconstructed using hybrid method with single fitting. Proton density fat fraction (B) and water (E) were reconstructed using hybrid method with dual fitting. Proton density fat fraction (C) and effective (F) were reconstructed using complex method. Fat was reconstructed using hybrid method with dual fitting (G); note severe noise throughout image. White squares show ROI locations, colocalized with single-voxel spectroscopy, which yielded reference proton density fat fraction value of 23.9%. (Fig. 1 continues on next page) B 298 AJR:24, February 215

3 MRI Versus MR Spectroscopy in Quantifying Hepatic Steatosis sition time of 21 seconds. In particular, the work of Levin et al. [33] has shown that the use of inand opposed-phase echo pairs can provide accurate proton density fat fraction measurements, and on the basis of the work of Johnson et al. [34], we think that a flip angle of 4 combined with a TR of 8.9 would minimize T1-related effects while providing adequate signal-to-noise ratio (SNR). Image Reconstruction Inline image reconstruction was performed using a multistep adaptive fitting algorithm that has been previously described [31]. In brief, this hybrid magnitude-complex technique uses the D F G Fig. 1 (continued) 5-year-old man with hepatic steatosis. A G, Representative images from MRI are shown. Proton density fat fraction (A) and effective (D) were reconstructed using hybrid method with single fitting. Proton density fat fraction (B) and water (E) were reconstructed using hybrid method with dual fitting. Proton density fat fraction (C) and effective (F) were reconstructed using complex method. Fat was reconstructed using hybrid method with dual fitting (G); note severe noise throughout image. White squares show ROI locations, colocalized with single-voxel spectroscopy, which yielded reference proton density fat fraction value of 23.9%. Levenberg-Marquardt fitting algorithm to solve for the values of water signal intensity (SI), fat SI, and rate constant ( ), according to a model adapted from that described by Yu et al. [21], and allows the independent estimation of the of fat and of water, first described by Chebrolu et al. [35]. Our model corrects for the of water, the of fat, and the complex multipeak spectrum of fat, as first described by Yu et al. [21] and first shown in the liver by Reeder et al. [36]. A published multipeak spectral model of fat was incorporated as an a priori model, rather than modeling the fat peak spectrum directly, to reduce the number of free parameters in the model; a number of other fat peak models have also been described [21, 31, 37, 38]. Two reconstructions were performed inline: image set A, with water assumed equal to fat (termed the effective ), similar to past descriptions of correction [21, 38 4]; and image set B, with the values of fat and water assumed to be independent of one another, also as previously described [35]. Precise reconstruction times were not recorded; however, whether the combined reconstruction required more than 4 minutes was assessed, beginning from the end of the pulse sequence acquisition to the time of arrival of the final image in the image archive. E AJR:24, February

4 A second image reconstruction was performed offline using the raw datasets from the above acquisitions (image set C) [31]. This complex-based method first determines the field map on a lower resolution based on variable projection, formulated independently of eddy current effects, and then estimates the phase map between images acquired with opposed readout polarity for correction of undesired effects from eddy currents or gradient delays. Next, fitting is performed to the phase corrected complex data to obtain the value, which is assumed to be equal for fat and water in this method, as well as water SI and fat SI. MR Spectroscopy Technique A single-voxel MRS acquisition was performed as the reference standard for proton density fat fraction measured with the MRI techniques. A cm 3 voxel was placed in a homogeneous portion of the mid-to-posterior liver on the basis of scout images, avoiding large vessels, bile ducts, masses and other obvious abnormalities. A stimulated echo acquisition mode acquisition was performed, with the following parameters: TR, 3; TE, 12, 24, 36, 48, and 72; mixing time, 1 ms; receiver bandwidth, 12 Hz; 124 readout points; and breath-hold time, 15 seconds. Reference standard fat fraction values were calculated inline with separate correction of both fat and water peaks, extrapolated to a TE of, using the high-speed multiple-echo acquisition method [41]. Image Analysis Image analysis was performed by a single board-certified abdominal radiologist with 3 years of postfellowship experience in abdominal MRI. Measurements were performed using OsiriX MD (version 1.3, Pixmeo Sarl). Measurements were performed on MR images using cm 3 volumes of interest (VOIs). Colocalization was performed using the reference image generated by the MRI console, showing the location of the MRS voxel overlaid on the fat fraction images according to the location of the voxel in the DICOM metadata. The VOI was then copied to all fat fraction and series, and mean values were calculated for each VOI. A C Statistical Analysis All statistical analyses were performed using SPSS software (version 2., IBM). The proton density fat fraction values measured using each of the MRI techniques were independently compared against the MRS values using a variety of statistical methods. Intraclass correlation coefficients (ICCs) with 95% CIs were calculated using a twoway mixed model to assess agreement between MRI-based fat fraction values and MRS-based values. Linear regression was performed to determine the correlation ( ), slope, and intercept as measures of correlation between MRI-based and MRS-based measures. Bland-Altman analysis was also performed to assess the degree of systematic and random bias between the MRI measures of proton density fat fraction and reference standard MRS. For comparisons that provided p values, p <.5 was considered statistically significant. Because MRS measures, not, no reference standard was available for validation of the MRI-derived measurements of. To evaluate agreement among the MRI methods, we calculated ICCs using a two-way mixed model for Fig year-old woman without hepatic steatosis. A G, Representative images from MRI are shown. Proton density fat fraction (A) and effective (D) were reconstructed using hybrid method with single fitting. Proton density fat fraction (B) and water (E) were reconstructed using hybrid method with dual fitting. Proton density fat fraction (C) and effective (F) were reconstructed using complex method. Fat was reconstructed using hybrid method with dual fitting (G); note severe noise throughout image. White squares show ROI locations, colocalized with single-voxel spectroscopy, which yielded reference proton density fat fraction value of 2.1%. (Fig. 2 continues on next page) B 3 AJR:24, February 215

5 MRI Versus MR Spectroscopy in Quantifying Hepatic Steatosis F G Fig. 2 (continued) 26-year-old woman without hepatic steatosis. A G, Representative images from MRI are shown. Proton density fat fraction (A) and effective (D) were reconstructed using hybrid method with single fitting. Proton density fat fraction (B) and water (E) were reconstructed using hybrid method with dual fitting. Proton density fat fraction (C) and effective (F) were reconstructed using complex method. Fat was reconstructed using hybrid method with dual fitting (G); note severe noise throughout image. White squares show ROI locations, colocalized with single-voxel spectroscopy, which yielded reference proton density fat fraction value of 2.1%. each pairing, as well as all three of the following values: effective from image set A, water from image set B, and effective from image set C. Finally, we performed a linear regression analysis for all three MRI methods between (effective or water, as appropriate) and proton density fat fraction values to determine whether they were related. D Results Forty-two subjects (18 men and 24 women) were enrolled in this study, with a mean age of 52.8 ± 14 years (range, 2 8 years). The mean subject weight was 85.3 ± 22.4 kg (range, kg), and mean body mass index was 29.3 ± 5.9 (range, ). Twenty-two subjects were being imaged for liver metastasis, 14 for chronic liver disease or hepatocellular carcinoma, three for indeterminate liver lesions, two for pain, and one to evaluate the cause of biliary obstruction. By MRS, proton density fat fraction values ranged from 1.9% to 3.3%; 23 of 42 subjects (55%) had at least mild hepatic steatosis, defined as proton density fat fraction greater than 5.56%. Figures 1 and 2 show representative images from subjects with hepatic steatosis and normal proton density fat fraction, including proton density fat fraction and maps reconstructed using all three methods. There is slightly greater central noise in proton density fat fraction images generated using the dual- fitting method, likely reflecting the model s instability related to the greater number of degrees of freedom. Agreement with the reference standard proton density fat fraction values was excellent, and although no reference standard value was available, the values (effective or water ) calculated with the three methods agreed well with one another. Note that fat values were extremely noisy, likely owing to the combination of model instability and low fat signal; this phenomenon has been described more completely in the work of Horng et al. [42]. Also note that the use of dual- correction introduces spuriously high water values in the subcutaneous and intraabdominal fat, which was also found by Horng et al. Results of the statistical analyses comparing MRI-derived measures of proton density fat fraction and MRS measures are shown in Table 1. Note that ICCs were high for all three comparisons (.99 for all three measures). Linear regression showed excellent correlation between each imaging measure and the reference spectroscopy, with slopes of E AJR:24, February

6 TABLE 1: Performance of Three MRI-Based Measures of Proton Density Fat Fraction Compared With Reference Standard MR Spectroscopy Statistical Analysis Hybrid Method (Single ) Hybrid Method (Dual ) Complex Method Intraclass correlation coefficient (95% CI).99 (.99 1.).99 (.98 1.).99 (.99 1.) Linear regression Slope 1.1 ±.3 ( ) 1.1 ±.3 ( ).98 ±.3 ( ) Intercept (%) 1.12 ±.34 ( 1.8 to.43).49 ±.39 (.29 to 1.3).1 ±.32 (.8 to.5) Correlation coefficient ( ) Bland-Altman bias (%) 1. ± 1.4 ( 3.7 to 1.7).6 ± 1.6 ( 2.4 to 3.7).4 ± 1.3 ( 2.9 to 2.2) Note Except where noted otherwise, data are mean ± SD (95% CI). 1.1 ±.3 (image set A), 1.1 ±.3 (image set B), and.98 ±.3 (image set C), with 95% CIs all containing slope = 1. The intercepts were 1.12% ±.34% (image set A),.49% ±.39% (image set B), and.1% ±.32% (image set C), respectively. Notably, the intercept using the hybrid MRI method with single modeling was significantly less than % at the 95% confidence level, showing a small but statistically significant bias in this measure. Figure 3 shows plots of the MRIbased measurements against MRS from all three methods, with lines for the least-squares regression and unity on each plot. MRI Proton Density Fat Fraction (%) MRI Proton Density Fat Fraction (%) MRS Proton Density Fat Fraction (%) Slope = 1.1 ±.3 Intercept = 1.12% ±.34% =.97 Slope =.98 ±.3 Intercept =.1% ±.32% = MRS Proton Density Fat Fraction (%) Bland-Altman analysis also showed excellent agreement between MRI- and MRSbased proton density fat fraction measures (Fig. 4 and Table 1). The largest systematic bias of 1.% was again shown by the hybrid MRI method (image set A) with single modeling. Random error was also small for all three methods, with SDs of 1.4%, 1.6%, and 1.3%, respectively. Bland-Altman plots comparing each of the three MRI-based methods against MRS are shown in Figure 4. By MRS, values of water ranged from 2.8 to 5.2 s 1. Using the three image reconstruction methods, values (either single A C MRI Proton Density Fat Fraction (%) Slope = 1.1 ±.3 Intercept =.49% ±.39% =.96 effective or water ) ranged from 21.8 to 75.9 s 1, a range of values similar to those published by other groups [42, 43]. Although no reference standard for was available, there was strong agreement among the values measured within the VOIs using the three image reconstruction methods. For pairings, ICC values were.997 (image sets A vs B),.994 (image sets B vs C), and.993 (image sets A vs C). The ICC for values from all three methods was.996. Linear regression analysis between and proton density fat fraction values showed a moderate correlation ( =.36.42; p < MRS Proton Density Fat Fraction (%) Fig. 3 Results of linear regression analysis for three methods. A C, Scatterplots show results for hybrid method with single fitting (A), hybrid method with dual fitting (B), and complex method (C). Solid lines delineate linear regression lines, and dashed lines show unity or perfect correlation for reference. MRS = MR spectroscopy. B 32 AJR:24, February 215

7 MRI Versus MR Spectroscopy in Quantifying Hepatic Steatosis MRS MRI Proton Density Fat Fraction (%) MRS MRI Proton Density Fat Fraction (%).1 for all three MRI methods). A scatterplot of the effective versus proton density fat fraction data for image set A is shown in Figure 5. Regression line slopes were and intercepts were % across the three image reconstruction methods. Discussion This study found excellent agreement among three methods of measuring hepatic (s 1 ) Bottom 95% CI: 3.7% (MRS + MRI Proton Density Fat Fraction) / 2 (%) A Top 95% CI: 1.7% Top 95% CI: 2.2% Bottom 95% CI: 2.9% (MRS + MRI Proton Density Fat Fraction) / 2 (%) MRI Proton Density Fat Fraction (%) C MRS MRI Proton Density Fat Fraction (%) (MRS + MRI Proton Density Fat Fraction) / 2 (%) B Fig. 4 Results of Bland-Altman analysis for three methods. A C, Scatterplots show results for hybrid method with single fitting (A), hybrid method with dual fitting (B), and complex method (C). Solid lines delineate 95% CIs, and dashed lines show central bias. MRS = MR spectroscopy. Fig. 5 Scatterplot showing results of linear regression analysis of effective versus proton density fat fraction for hybrid image reconstruction method with single fitting. and proton density fat fraction values were significantly correlated (r 2 =.42; p <.1). Regression line slope was.95, and intercept was 38.3%. Top 95% CI: 3.7% Bottom 95% CI: 2.4% proton density fat fraction by MRI and reference standard -corrected single-voxel spectroscopy. An extensive body of literature has shown the accuracy of using T1-insensitive chemical shift methods that incorporate correction, correction for the complex multipeak lipid spectrum, and eddy current compensation for measuring hepatic proton density fat fraction [2, 3, 21 27, 29, 39]. T1 weighting is minimized during the acquisition step by using low flip angles relative to the TR. Two of the methods evaluated in this study were hybrid magnitude-complex techniques, which are inherently insensitive to eddy currents, incorporate a published fat spectral model, and model -based signal decay [31, 37]. The third was a complex-based method that incorporated field map correction and eddy current compensation by variable projection at a low-resolution level [31]. Two of these three image reconstruction techniques were implemented to run inline on the MRI systems at the time of our study, with no user interaction beyond prescription of the scan volume; the third (complex method) was implemented shortly after our original data collection concluded, and results were reconstructed using same raw data. All image reconstructions processed in a clinically feasible amount of time. Thus, these techniques are feasible for use in routine clinical imaging. Our results show small errors in agreement between the MRI-based methods and refer- AJR:24, February

8 ence standard MRS, characterized by small deviations of the slopes of the regression lines from 1. and the intercepts from.%. In principle, regression analyses are prone to overstating the strength of correlations in the presence of outlying values, but the data in this study did not contain any such outliers. Also, the ICC and Bland-Altman analyses, which are more robust measures of agreement, show excellent results. In particular, the Bland-Altman analyses show that the absolute amounts of systematic bias ( 1.%) and random error (SD 1.4%) are small and are unlikely to be important in the clinical setting. Notably, we found a significant correlation between in vivo and proton density fat fraction values in our study for all three image reconstruction techniques, with similar correlation coefficients and regression lines among the three techniques. This is in contrast to a recent study by Kühn et al. [44], which found no association between values and grades of hepatic steatosis. During the testing and development of the MRI techniques used in the current work, we found no statistically significant correlation between measured and proton density fat fraction values in phantoms, in which the ground truth and proton density fat fraction values were known to be independent. The fact that water and proton density fat fraction were correlated when water and fat were calculated separately further suggests that this correlation is not caused by weighting of the effective toward fat as proton density fat fraction increases, when a single effective is calculated. Our data therefore suggest that the correlation between and proton density fat fraction may represent a true biologic relationship in vivo. It has been shown that iron can accumulate within the liver with progression of chronic liver disease, though the relationship between progressed liver disease (with greater amounts of inflammation and fibrosis) and liver fat and iron deposition is complex [45, 46]. The difference between our finding and that of Kühn et al. [44] could be explained by differences in patient population. Our study population was largely composed of patients with extrahepatic primary tumors (52%) and chronic liver disease (mainly hepatitis C and nonalcoholic steatohepatitis, 33%), whereas the prevalence and type of chronic liver disease was not reported in the article by Kühn et al. [44]. Nonetheless, additional in vivo investigations are needed to further define the relationship between and proton density fat fraction. A large proportion of the in vivo validations of MRI-based techniques for quantification of hepatic steatosis have been performed at a small number of sites, on a limited number of MRI platforms, and, in some cases, in a research rather than a clinical setting [2, 17, 21, 25, 26]. To be widely usable, these methods are evolving to become robust to MRI vendor, MRI system, field strength, site, and operator [3]. The image acquisition in our study was performed by clinical MRI technologists, with fully automated image postprocessing (for image sets A and B) yielding proton density fat fraction maps without the need for user interaction with the raw or image data. In addition, this study was performed on widely available commercial MRI systems in a busy clinical practice and shows the suitability of these techniques in a clinical setting. Simple workflow and reliable functionality of the technique are important for such methods to gain wide acceptance in fast-paced clinical settings, and such implementations have been shown by other groups [2, 21, 22, 24, 25]. In theory, further automation could be achieved by combining these techniques with liver sampling or segmentation methods, further simplifying workflow and providing user-independent measures of proton density fat fraction [47 51]. For MRI-based measures of proton density fat fraction to become widely accepted biomarkers in clinical medicine and multicenter trials, their accuracy and robustness must be established across a variety of platforms. For this reason, we designed our study and portions of our statistical analysis in a manner similar to previous validations, which were performed using MRI systems from a different manufacturer [2, 26]. Mashhood et al. [3] have also shown the robustness of methods across commercial MRI platforms, albeit with variations on only a single image reconstruction method. Additional trials are needed to establish the reproducibility of proton density fat fraction measures obtained with variations in scanner hardware, pulse sequence parameters, and reconstruction techniques. This study has several limitations. First, we did not directly compare these techniques with methods developed using other vendor platforms. We did not directly evaluate the contribution of individual components of the image reconstruction chain on the accuracy of the results, because this has been performed extensively in previous studies [2, 24 27]. In addition, we did not perform direct parameter optimization on our pulse sequence, but rather used published data to inform our choices of TE and various other parameters [24, 34, 38, 52]. An additional limitation is that we did not explore the effect of low SNR scenarios on the image reconstruction. Our technologists were instructed to place the spectroscopy voxels in the posterior portion of the liver, close to the spine coil array, where SNR is relatively high and respiratory motion less pronounced. Prior work by Liu et al. [17] has shown that low SNR can cause substantial bias in the proton density fat fraction measurement, and this is a potential area for future technical development with our method. However, this work also has several notable strengths. First, the MRI technologists participating in this study received no special training beyond instruction on placement of the spectroscopy voxels. Image reconstructions were accomplished rapidly on commercial MR reconstruction engines with no modification beyond the installation of the pulse sequence and reconstruction program. Data acquisition was performed on wide-bore MRI systems, and the results were accurate despite the technical challenges inherent in such systems, such as phase errors related to eddy currents and field inhomogeneity. The methods thus performed well even in the fast-paced clinical setting, with equipment that accepts certain technical limitations to improve patient comfort. In conclusion, accurate quantification of hepatic steatosis can be performed on widely available commercial wide-bore MRI systems in a busy clinical practice. These techniques can be used without advanced postprocessing or user interaction. The validation of proton density fat fraction quantification methods in this and other works represents an important step toward wide availability and acceptance of these MRIbased measures of hepatic steatosis as an accurate biomarker. Further trials will focus on directly establishing agreement between fat fraction measures obtained using different equipment and reconstruction algorithms. In addition, the reproducibility of these methods should be confirmed through validation of the techniques at other centers. References 1. Noureddin M, Lam J, Peterson MR, et al. Utility of magnetic resonance imaging versus histology for quantifying changes in liver fat in nonalcoholic fatty liver disease trials. Hepatology 213; 58: AJR:24, February 215

9 MRI Versus MR Spectroscopy in Quantifying Hepatic Steatosis 2. Meisamy S, Hines CD, Hamilton G, et al. Quanti- 17. Liu CY, McKenzie CA, Yu H, Brittain JH, Reeder magnetic resonance imaging. J Magn Reson Im- fication of hepatic steatosis with T1-independent, T2-corrected MR imaging with spectral modeling of fat: blinded comparison with MR spectros- SB. Fat quantification with IDEAL gradient echo imaging: correction of bias from T 1 and noise. Magn Reson Med 27; 58: aging 213; 37: Zhong X, Nickel MD, Kannengiesser SA, Dale BM, Kiefer B, Bashir MR. Liver fat quantification copy. Radiology 211; 258: Reeder SB, McKenzie CA, Pineda AR, et al. Wa- using a multi-step adaptive fitting approach with 3. Reeder SB, Cruite I, Hamilton G, Sirlin CB. Quantitative assessment of liver fat with magnetic resonance imaging and spectroscopy. J Magn Reson Imaging 211; 34: Clark JM, Diehl AM. Defining nonalcoholic fatty liver disease: implications for epidemiologic studies. Gastroenterology 23; 124: Adams LA, Lymp JF, St Sauver J, et al. The natural history of nonalcoholic fatty liver disease: a population-based cohort study. Gastroenterology 25; 129: Matteoni CA, Younossi ZM, Gramlich T, Boparai N, Liu YC, McCullough AJ. Nonalcoholic fatty liver disease: a spectrum of clinical and pathological severity. Gastroenterology 1999; 116: Targher G, Bertolini L, Poli F, et al. Nonalcoholic fatty liver disease and risk of future cardiovascular events among type 2 diabetic patients. Diabetes 25; 54: Rubinstein E, Lavine JE, Schwimmer JB. Hepatic, cardiovascular, and endocrine outcomes of the histological subphenotypes of nonalcoholic fatty liver disease. Semin Liver Dis 28; 28: Idilman IS, Aniktar H, Idilman R, et al. Hepatic steatosis: quantification by proton density fat fraction with MR imaging versus liver biopsy. Radiology 213; 267: Tang A, Tan J, Sun M, et al. Nonalcoholic fatty liver disease: MR imaging of liver proton density fat fraction to assess hepatic steatosis. Radiology 213; 267: Stock W, Douer D, DeAngelo DJ, et al. Prevention and management of asparaginase/pegasparaginase-associated toxicities in adults and older adolescents: recommendations of an expert panel. Leuk Lymphoma 211; 52: Sahoo S, Hart J. Histopathological features of L- asparaginase-induced liver disease. Semin Liver Dis 23; 23: Dixon WT. Simple proton spectroscopic imaging. Radiology 1984; 153: Glover GH, Schneider E. Three-point Dixon technique for true water/fat decomposition with B inhomogeneity correction. Magn Reson Med 1991; 18: Ma J. Breath-hold water and fat imaging using a dual-echo two-point Dixon technique with an efficient and robust phase-correction algorithm. Magn Reson Med 24; 52: ter-fat separation with IDEAL gradient-echo imaging. J Magn Reson Imaging 27; 25: Schuchmann S, Weigel C, Albrecht L, et al. Noninvasive quantification of hepatic fat fraction by fast 1., 1.5 and 3. T MR imaging. Eur J Radiol 27; 62: Kim H, Taksali SE, Dufour S, et al. Comparative MR study of hepatic fat quantification using single-voxel proton spectroscopy, two-point dixon and three-point IDEAL. Magn Reson Med 28; 59: Yu H, Shimakawa A, McKenzie CA, Brodsky E, Brittain JH, Reeder SB. Multiecho water-fat separation and simultaneous R2 estimation with multifrequency fat spectrum modeling. Magn Reson Med 28; 6: Yokoo T, Bydder M, Hamilton G, et al. Nonalcoholic fatty liver disease: diagnostic and fat-grading accuracy of low-flip-angle multiecho gradient-recalled-echo MR imaging at 1.5 T. Radiology 29; 251: Börnert P, Koken P, Eggers H. Spiral water-fat imaging with integrated off-resonance correction on a clinical scanner. J Magn Reson Imaging 21; 32: Hines CD, Frydrychowicz A, Hamilton G, et al. 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. J Magn Reson Imaging 211; 33: Yokoo T, Shiehmorteza M, Hamilton G, et al. Estimation of hepatic proton-density fat fraction by using MR imaging at 3. T. Radiology 211; 258: Yu H, Shimakawa A, Hines CD, et al. Combination of complex-based and magnitude-based multiecho water-fat separation for accurate quantification of fat-fraction. Magn Reson Med 211; 66: Hernando D, Hines CD, Yu H, Reeder SB. Addressing phase errors in fat-water imaging using a mixed magnitude/complex fitting method. Magn Reson Med 212; 67: Reeder SB, Hu HH, Sirlin CB. Proton density fatfraction: a standardized MR-based biomarker of tissue fat concentration. J Magn Reson Imaging 212; 36: Sharma SD, Hu HH, Nayak KS. Accelerated water-fat imaging using restricted subspace field multi-echo GRE imaging. Magn Reson Med 213 [Epub ahead of print] 32. Breuer FA, Blaimer M, Mueller MF, et al. Controlled aliasing in volumetric parallel imaging (2D CAIPIR- INHA). Magn Reson Med 26; 55: Levin YS, Yokoo T, Wolfson T, et al. Effect of echo-sampling strategy on the accuracy of out-ofphase and in-phase multiecho gradient-echo MRI hepatic fat fraction estimation. J Magn Reson Imaging 214; 39: Johnson BL, Schroeder ME, Wolfson T, et al. Effect of flip angle on the accuracy and repeatability of hepatic proton density fat fraction estimation by complex data-based, T1-independent, T2- corrected, spectrum-modeled MRI. J Magn Reson Imaging 214; 39: Chebrolu VV, Hines CD, Yu H, et al. Independent estimation of T2 for water and fat for improved accuracy of fat quantification. Magn Reson Med 21; 63: Reeder SB, Robson PM, Yu H, et al. Quantification of hepatic steatosis with MRI: the effects of accurate fat spectral modeling. J Magn Reson Imaging 29; 29: Ren J, Dimitrov I, Sherry AD, Malloy CR. Composition of adipose tissue and marrow fat in humans by 1 H NMR at 7 Tesla. J Lipid Res 28; 49: Bydder M, Yokoo T, Hamilton G, et al. Relaxation effects in the quantification of fat using gradient echo imaging. Magn Reson Imaging 28; 26: Yu H, McKenzie CA, Shimakawa A, et al. Multiecho reconstruction for simultaneous water-fat decomposition and T2 estimation. J Magn Reson Imaging 27; 26: Hernando D, Kramer JH, Reeder SB. Multipeak fat-corrected complex R2 relaxometry: theory, optimization, and clinical validation. Magn Reson Med 213; 7: Pineda N, Sharma P, Xu Q, Hu X, Vos M, Martin DR. Measurement of hepatic lipid: high-speed T2-corrected multiecho acquisition at 1 H MR spectroscopy a rapid and accurate technique. Radiology 29; 252: Horng DE, Hernando D, Hines CD, Reeder SB. Comparison of R2 correction methods for accurate fat quantification in fatty liver. J Magn Reson Imaging 213; 37: Hussain HK, Chenevert TL, Londy FJ, et al. He- map estimation and compressed sensing. Magn 43. Kühn JP, Hernando D, Mensel B, et al. Quantita- patic fat fraction: MR imaging for quantitative Reson Med 212; 67: tive chemical shift-encoded MRI is an accurate measurement and display early experience. Ra- 3. Mashhood A, Railkar R, Yokoo T, et al. Repro- method to quantify hepatic steatosis. J Magn Re- diology 25; 237: ducibility of hepatic fat fraction measurement by son Imaging 214; 39: AJR:24, February

10 44. Kühn JP, Hernando D, Munoz del Rio A, et al. Effect of multipeak spectral modeling of fat for liver iron and fat quantification: correlation of biopsy with MR imaging results. Radiology 212; 265: Younossi ZM, Gramlich T, Bacon BR, et al. Hepatic iron and nonalcoholic fatty liver disease. Hepatology 1999; 3: Bonkovsky HL, Banner BF, Rothman AL. Iron and chronic viral hepatitis. Hepatology 1997; 25: Ruskó L, Bekes G. Liver segmentation for contrast-enhanced MR images using partitioned probabilistic model. Int J Comput Assist Radiol Surg 211; 6: Bashir MR, Dale BM, Merkle EM, Boll DT. Automated liver sampling using a gradient dual-echo Dixon-based technique. Magn Reson Med 212; 67: Chen G, Gu L, Qian L, Xu J. An improved level set for liver segmentation and perfusion analysis in MRIs. IEEE Trans Inf Technol Biomed 29; 13: Bashir MR, Zhong X, Dale BM, Gupta RT, Boll DT, Merkle EM. Automated patient-tailored screening of the liver for diffuse steatosis and iron overload using MRI. AJ13; 21: Bashir MR, Merkle EM, Smith AD, Boll DT. Hepatic MR imaging for in vivo differentiation of steatosis, iron deposition and combined storage disorder: single-ratio in/opposed phase analysis vs. dual-ratio Dixon discrimination. Eur J Radiol 212; 81:e11 e Yokoo T, Collins JM, Hanna RF, Bydder M, Middleton MS, Sirlin CB. Effects of intravenous gadolinium administration and flip angle on the assessment of liver fat signal fraction with opposed-phase and in-phase imaging. J Magn Reson Imaging 28; 28: FOR YOUR INFORMATION The comprehensive book based on the ARRS 214 annual meeting categorical course on The Radiology M and M Meeting: Misinterpretations, Misses, and Mimics is now available! For more information or to purchase a copy, see 36 AJR:24, February 215