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Published online before print October 19, 2005, 10.1148/radiol.2373041639
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(Radiology 2005;237:1048-1055.)
© RSNA, 2005

Technical Developments

Hepatic Fat Fraction: MR Imaging for Quantitative Measurement and Display—Early Experience1

Hero K. Hussain, MD, Thomas L. Chenevert, PhD, Frank J. Londy, RT, Vikas Gulani, MD, PhD, Scott D. Swanson, PhD, Barbara J. McKenna, MD, Henry D. Appelman, MD, Saroja Adusumilli, MD, Joel K. Greenson, MD and Hari S. Conjeevaram, MD

1 From the Departments of Radiology/MRI (H.K.H., T.L.C., F.J.L., V.G., S.D.S., S.A.), Pathology (B.J.M., H.D.A., J.K.G.), and Internal Medicine (H.S.C.), University of Michigan Health System, 1500 E Medical Center Dr, MRI UHB2A209, Ann Arbor, MI 48109-0030. From the 2004 RSNA Annual Meeting. Received September 23, 2004; revision requested November 29; revision received December 27; accepted January 21, 2005. Address correspondence to T.L.C. (e-mail: tlchenev{at}umich.edu).


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 Materials and Methods
 Results
 Discussion
 References
 
The institutional review board approved this HIPAA-compliant study. After all five patients with nonalcoholic fatty liver disease signed a consent, they underwent magnetic resonance (MR) imaging for hepatic fat quantification. The purpose of this study was to develop a fast and accurate method to acquire and display quantitative maps of the percentage of hepatic fat. In-phase and out-of-phase gradient-echo MR imaging was performed with dual flip angles (70°, 20°) to resolve ambiguity of the dominant constituent. T2* corrections were also estimated and applied to generate color-coded maps of the estimated percentage of hepatic fat. MR imaging results were compared with biopsy results in two of five patients, and the technique was validated qualitatively and quantitatively with a water-oil phantom. Results of the phantom study confirmed that the dual–flip angle algorithm can be used to correctly identify the dominant constituent, allowing depiction of 0%–100% of fat content. The estimated liver fat fraction was comparable to quantitative fat measurements at biopsy in both patients (MR imaging, 18.3% ± 2.8 [standard deviation] and 28.6% ± 2.4, vs quantitative histopathologic analysis, 11.2% and 28.5%, respectively).

Supplemental material: radiology.rsnajnls.org/cgi/content/full/2373041639/DC1

© RSNA, 2005


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
Materials and Methods
 Results
 Discussion
 References
 
Water and lipid protons contribute essentially to all native tissue signal in magnetic resonance (MR) imaging (1,2). These constituents have slightly different precessional frequencies, as dictated by their chemical environment. This frequency difference, or chemical shift, is often exploited to generate in-phase (IP) and out-of-phase (OP) images in which the water net magnetization vector is aligned with (IP image) or opposed to (OP image) the fat net magnetization vector, respectively. Signal intensity loss on OP images relative to IP images indicates a mixture of water and lipid within tissues, and this signal intensity loss is a feature used for the diagnosis of fatty infiltration of the liver and other organs (3–6) and for the characterization of some benign and malignant tumors (7–25).

Nonalcoholic fatty liver disease is a term that incorporates a spectrum of histologic findings ranging from simple steatosis (fat only) to cirrhosis (26,27). Nonalcoholic steatohepatitis is an intermediate stage characterized by steatosis, hepatic cell inflammation, and death. These changes can progress to cirrhosis. The definitive diagnosis of nonalcoholic fatty liver disease is determined with a liver biopsy, which shows fat, with or without inflammation or fibrosis (27).

MR imaging with IP and OP imaging is commonly used to assess fatty infiltration of the liver. With the original two-point Dixon method, a slight timing offset of the 180° refocusing pulse is used in a spin-echo sequence to create an OP condition without altering echo time (28). IP and OP spin-echo images are then combined to estimate fat-only and water-only images. The IP and OP images usually are provided in magnitude format, wherein all phase information is lost, but it efficiently reduces other adverse effects, such as those caused by magnet inhomogeneity. This, however, leads to a serious ambiguity as to which constituent (water or fat) is dominant in a given voxel. Several authors have proposed solutions to remove the ambiguity by using phase-sensitive processing. These methods require the acquisition of additional data (eg, three-point Dixon method) and specialized phase-correction algorithms to yield true fat-only and true water-only images (29–36). Unfortunately, the acquisition times of methods that are based on a spin-echo sequence are relatively long (ie, several minutes). Moreover, the resultant fat-only and water-only images still contain T1 and T2 relaxation effects, which confound quantification of fat content. Certainly, relaxation times are measurable and can be incorporated into fat content estimates. Alternatively, intermediate-weighted IP and OP images and phase-correction algorithms can mitigate relaxation and ambiguity effects, but these result in increased imaging time. Moreover, true fat-only and water-only images often exhibit other systematic inhomogeneities (eg, surface coil) that hinder display of quantitative fat content unless additional processing is performed.

Dual-echo gradient-recalled echo imaging allows rapid acquisition of IP and OP images of the entire liver in a few breath holds (15). Since the IP and OP images are acquired simultaneously with dual echoes, gross misregistration errors are reduced. In clinical practice, simple visual comparison of OP with IP images, or calculation of signal intensity loss (24) is used to assess the presence of lipid. Again, the standard magnitude format of the images introduces ambiguity of the dominant constituent, and relaxation (T1 and T2*) affects the contribution of water and lipid signals to the images. Therefore, unless relaxation weighting is negligible, relaxation times are measured or known a priori, and a phase-sensitive combination of phase-corrected images is performed, estimates of fat content determined with conventional dual-echo gradient-recalled echo imaging are inaccurate.

The purpose of our study, therefore, was to develop a fast and accurate method to acquire and display quantitative maps of the percentage of liver fat in patients with nonalcoholic fatty liver disease.


   Materials and Methods
TOP
 ABSTRACT
 INTRODUCTION
 Materials and Methods
Results
 Discussion
 References
 
Theory and Fat Quantification Algorithm
Imaging of the entire liver is performed by using a breath-hold dual-echo spoiled gradient-recalled echo sequence with repetition time of 155 msec and echo time of 2.3 msec for OP images and 4.6 msec for IP images (155/2.3, 4.6) acquired with flip angles of 70° and then 20° to provide T1-weighted and intermediate-weighted images, respectively. A third dual-echo gradient-echo breath-hold gradient-recalled echo sequence with two IP echoes (4.5 and 18 msec) is also performed to calculate T2*. The percentage of hepatic fat is estimated from both sets of images, and T2* correction is applied as shown in the computer simulations (Figs 1, 2). Since fat exhibits shorter native T1 than does water, this dual–flip angle algorithm is used to identify whether water or fat is the dominant constituent on the basis of the increase or decrease in the estimated percentage of fat with increased T1 weighting. A detailed explanation of the theory and technique of the fat quantification algorithm is provided in Appendix E1 (radiology.rsnajnls.org/cgi/content/full/2373041639/DC1). Software was developed to reconstruct maps of the hepatic fat fraction for each section of the imaged anatomy and to display the percentage of hepatic fat in color maps for subsequent quantitative region-of-interest and volume-of-interest analysis.



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  		Figure 1. Graph shows the estimated percentage of fat, as given with Equation (E4) (radiology.rsnajnls.org/cgi/content/full/2373041639/DC1), as a function of true fat fraction at low T1 weighting (intermediate weighting) (flip angle, 20°) and high T1 weighting (flip angle, 70°). As expected, the lower flip angle data more faithfully match the true fat fraction because of reduced T1 relaxation contamination. Note ambiguity in fat fraction that originates from the magnitude format of IP and OP data. For example, both 30% and 70% true fat content yield apparent fat content around 30%. T2* correction has been applied to this simulation, which also demonstrates the performance of the algorithm for combined intermediate and T1 weighting. The technique yields quantitative fat estimates over a substantially greater dynamic range (ie, 0%–100%) without the need for phase-correction reconstruction algorithms or T1 measurement. Parameters used for this computer simulation were as follows: 150/2.3, 4.6; flip angle 1, 70°; flip angle 2, 20°; T1 of fat, 600 msec; T1 of water, 300 msec; and T2* of water and fat, 20 msec.

 


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  		Figure 2. Graph shows estimated percentage of fat, as given with Equation (E4) (radiology.rsnajnls.org/cgi/content/full/2373041639/DC1), as a function of true fat fraction at low T1 weighting (flip angle, 20°) and high T1 weighting (flip angle, 70°). The T2* (assumed to be 4 msec) effect leads to substantial underestimation of percentage of fat at a level of true fat content of less than 50%. Parameters used for this computer simulation were as follows: 150/2.3, 4.6; flip angle 1, 70°; flip angle 2, 20°; T1 of fat, 600 msec; T1 of water, 300 msec; and T2* of water and fat, 4 msec.

 
Phantom Study
A phantom model that consisted of a bottle (approximately 700 mL) filled with equal volumes of water and mineral oil (to simulate fat) was used to test the proposed method qualitatively and quantitatively by two of the authors (T.L.C. and F.J.L.).

Qualitative analysis.—A single oblique section was acquired to intersect the oil-water interface such that there was a continuous transition from pure water to pure oil. The signal cancellation effect in voxels that have a comparable mix of fat and water was clearly apparent on the OP image. MR images of the phantom were obtained in the coronal plane by using a head coil. The dual-echo spoiled gradient-recalled acquisition in the steady state sequence (155/2.3, 4.6) was acquired with a 70° flip angle in the first acquisition and a 20° flip angle in the second acquisition. The data obtained were used to generate fat fraction maps by using the algorithm described previously.

Quantitative analysis.—Imaging of the water-oil cylindrical phantom was repeated with the phantom in a vertical position. Sixty-two sections that were each 3 mm thick were obtained parallel to the oil-water interface by using the dual-echo spoiled gradient-recalled echo sequence (200/2.2, 4.25). Ten sections were combined on a pixel-by-pixel basis to create a 3-cm-thick slab. The sections were summed as complex numbers to emulate how actual fat and water constituents combine as vectors within voxels. The location of the 10-section group (ie, 3-cm slab) was then processed in increments of 3 mm at a time to create a stepwise quantifiable transition across the water-oil interface (ie, from 0% to 100% oil [in increments of 10%] in each slab, respectively).

Clinical Studies
Patients.—Five consecutive patients (four men, one woman; mean age, 41 years; range, 35–50 years;) who were suspected of having nonalcoholic fatty liver disease were referred for MR imaging between October 2003 and February 2004 for estimation of the percentage of hepatic fat. Three of these patients were asymptomatic, and two complained of fatigue. All patients were suspected of having nonalcoholic fatty liver disease because of unexplained elevation of liver enzyme levels (alanine aminotransferase level, 57–133 IU/L [normal level, 0–45 IU/L]; aspartate aminotransferase level, 39–124 IU/L [normal level, 2–35 IU/L]), with no history of excessive alcohol intake, negative results of viral screening, no liver mass at ultrasonography, and exclusion of other likely causes of liver disease, such as autoimmune hepatitis, hemochromatosis, Wilson disease, and {alpha}1-antitrypsin deficiency. Our institutional review board approved this study. All patients signed an institutional review board–approved, Health Insurance Portability and Accountability Act–compliant consent form prior to MR imaging for fat quantification.

MR imaging.—All MR imaging studies were performed by one operator (H.K.H.), and results were interpreted by the operator and another author (S.A.) who had 6 and 4 years of experience in liver imaging, respectively. The operator and the interpreter were blinded to the biopsy results (if available) but not to the patient history. Imaging was performed with a 1.5-T magnet (Signa Echospeed, LX version 8.3; GE Medical Systems, Milwaukee, Wis) and a torso phased-array coil. A respiratory belt was applied to monitor the breathing cycle. Patients were instructed to suspend respiration at end inspiration and to be consistent in their breath holds. Sequences were used as follows: The first sequence used was a coronal breath-hold T2-weighted single-shot fast spin-echo localizer sequence. The second sequence was a transverse breath-hold T1-weighted two-dimensional dual-echo spoiled gradient-recalled acquisition in the steady state sequence (150/2.3, 4.6; flip angle, 70°; section thickness, 6; section gap, 0 mm; matrix, 256 x 160; number of sections, 20–30; and acquisition time, 24–32 seconds) through the liver. The third sequence was a transverse breath-hold intermediate-weighted dual-echo spoiled gradient-recalled echo sequence (150/2.3, 4.5; flip angle, 20°; section thickness, 6 mm; section gap, 0 mm; matrix, 256 x 160; number of sections, 20–30; and acquisition time, 24–32 seconds) that was performed without retuning of the imager between the second and the third sequences. The fourth sequence was a transverse breath-hold T1-weighted dual-echo gradient-echo sequence (approximately 230/4.5, 18.2; flip angle, 70°; section thickness, 6 mm; section gap, 0 mm; matrix, 256 x 160; number of sections, 24–32; and acquisition time, 30 seconds for each acquisition). Because of breath-hold limitations, two separate breath holds were required to cover the entire liver. The overall imaging time, starting from the time that the patient entered the imaging room, ranged 30–40 minutes. Most of this time was spent preparing the patient and the sequences. The total sequence performance time was approximately 10 minutes.

The images were transferred to a personal computer (Dell, Round Rock, Tex) and analyzed by using the program described. All fat quantification analyses were performed by one author (T.L.C.) who was blinded to the clinical history, image interpretation, and biopsy results if available. The time required to process each data set and produce the final color maps of the hepatic fat fraction was less than 5 minutes.

Pathologic analysis.—Verification of MR imaging results was performed at liver biopsy prior to (n = 1) or immediately after (n = 1) MR imaging. All biopsies were performed by one operator (H.S.C.); the tissue sample was obtained from the right lobe of the liver without imaging guidance. Only two of five patients underwent liver biopsy. This is because one patient refused biopsy at the time of MR imaging, but because of continued symptoms and increased levels of liver enzymes, the patient underwent a liver biopsy 9 months later. In the other two patients, who were asymptomatic, it was the decision of the hepatologist not to perform the liver biopsy and to conduct follow-up in these patients with serial liver enzyme measurements. The biopsy specimens were analyzed qualitatively and independently by three experienced liver pathologists (B.J.M., H.D.A., J.K.G.) who had 19, 37, and 16 years of experience in liver pathology, respectively. The pathologists were blinded to the MR imaging results.

Qualitative assessment of the percentage of fat in the liver was based on the pathologist's visual estimation of the total percentage of fat relative to nonfatty tissue in the biopsy specimen. The estimated percentage of fat was graded on a scale of 0–3 (37) as follows: grade 0, none; grade 1, less than 33%; grade 2, 33%–66%; and grade 3, greater than 66%.

The slides stained with hematoxylin-eosin also were quantitatively analyzed with image analysis software (Image J, version 1.3; National Institutes of Health, Bethesda, Md). This method of analysis generates a histogram of pixels according to their color intensity on the histopathologic slide. Fat dissolves during preparation of the specimen, and the dissolution results in the appearance of white lobules at the sites where fat is present. The slide area for analysis was selected manually and excluded artifactual areas. Two major populations were seen in the histogram analysis, and these populations represented the white tissue (fat) and nonwhite-stained tissue (nonfat). The pixel intensity level at a notch point in the histogram was chosen by visual inspection and used to segment bright from dark pixels. These were used as surrogates for fat (brighter) and nonfat (darker) regions in calculation of fat content, according to histogram area at histopathologic analysis.


   Results
TOP
 ABSTRACT
 INTRODUCTION
 Materials and Methods
 Results
Discussion
 References
 
Phantom Study
Qualitative analysis.—Apparent fat fraction maps were generated for the phantom by using the MR imaging algorithm described (Fig 3 ). This algorithm successfully adds substantial dynamic range greater than the 50% apparent fat level, as illustrated by the quantitative color scale and graphic plots of pixel values along a left-to-right line through the center of the phantom.



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  		Figure 3a. (a) Image shows phantom model that consisted of a bottle (approximately 700 mL) filled with equal volumes of water and light mineral oil (to simulate fat). A single oblique section was acquired to intersect the oil-water interface such that there was a continuous transition from pure water to pure oil. (b) Images of the phantom obtained by using a dual-echo gradient-recalled echo sequence at low T1 weighting (flip angle, 20°) and high T1 weighting (flip angle, 70°) to test the proposed algorithm. The signal cancellation effect in voxels that have comparable mix of fat and oil is clearly apparent on the OP image (*). (c) Image shows color maps of the estimated percentage of oil. The color scale represents the estimated percentage of oil. Note ambiguity in the estimated percentage of oil at a level of true oil concentration of greater than 50% with low and high T1-weighted dual-echo spoiled gradient-recalled acquisition in the steady state sequences. The combined algorithm clearly addresses this issue and yields quantitative oil estimates over a substantially greater dynamic range (ie, 0%–100%). (d) Graph of percentage of oil along a line though the center of the phantom that shows transition from pure water (on left side of image) to pure oil (on right side of image).

 


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  		Figure 3b. (a) Image shows phantom model that consisted of a bottle (approximately 700 mL) filled with equal volumes of water and light mineral oil (to simulate fat). A single oblique section was acquired to intersect the oil-water interface such that there was a continuous transition from pure water to pure oil. (b) Images of the phantom obtained by using a dual-echo gradient-recalled echo sequence at low T1 weighting (flip angle, 20°) and high T1 weighting (flip angle, 70°) to test the proposed algorithm. The signal cancellation effect in voxels that have comparable mix of fat and oil is clearly apparent on the OP image (*). (c) Image shows color maps of the estimated percentage of oil. The color scale represents the estimated percentage of oil. Note ambiguity in the estimated percentage of oil at a level of true oil concentration of greater than 50% with low and high T1-weighted dual-echo spoiled gradient-recalled acquisition in the steady state sequences. The combined algorithm clearly addresses this issue and yields quantitative oil estimates over a substantially greater dynamic range (ie, 0%–100%). (d) Graph of percentage of oil along a line though the center of the phantom that shows transition from pure water (on left side of image) to pure oil (on right side of image).

 


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  		Figure 3c. (a) Image shows phantom model that consisted of a bottle (approximately 700 mL) filled with equal volumes of water and light mineral oil (to simulate fat). A single oblique section was acquired to intersect the oil-water interface such that there was a continuous transition from pure water to pure oil. (b) Images of the phantom obtained by using a dual-echo gradient-recalled echo sequence at low T1 weighting (flip angle, 20°) and high T1 weighting (flip angle, 70°) to test the proposed algorithm. The signal cancellation effect in voxels that have comparable mix of fat and oil is clearly apparent on the OP image (*). (c) Image shows color maps of the estimated percentage of oil. The color scale represents the estimated percentage of oil. Note ambiguity in the estimated percentage of oil at a level of true oil concentration of greater than 50% with low and high T1-weighted dual-echo spoiled gradient-recalled acquisition in the steady state sequences. The combined algorithm clearly addresses this issue and yields quantitative oil estimates over a substantially greater dynamic range (ie, 0%–100%). (d) Graph of percentage of oil along a line though the center of the phantom that shows transition from pure water (on left side of image) to pure oil (on right side of image).

 


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  		Figure 3d. (a) Image shows phantom model that consisted of a bottle (approximately 700 mL) filled with equal volumes of water and light mineral oil (to simulate fat). A single oblique section was acquired to intersect the oil-water interface such that there was a continuous transition from pure water to pure oil. (b) Images of the phantom obtained by using a dual-echo gradient-recalled echo sequence at low T1 weighting (flip angle, 20°) and high T1 weighting (flip angle, 70°) to test the proposed algorithm. The signal cancellation effect in voxels that have comparable mix of fat and oil is clearly apparent on the OP image (*). (c) Image shows color maps of the estimated percentage of oil. The color scale represents the estimated percentage of oil. Note ambiguity in the estimated percentage of oil at a level of true oil concentration of greater than 50% with low and high T1-weighted dual-echo spoiled gradient-recalled acquisition in the steady state sequences. The combined algorithm clearly addresses this issue and yields quantitative oil estimates over a substantially greater dynamic range (ie, 0%–100%). (d) Graph of percentage of oil along a line though the center of the phantom that shows transition from pure water (on left side of image) to pure oil (on right side of image).

 
Quantitative analysis.—The percentage of oil in the stepwise slabs, which was estimated by using the combined algorithm, tracks well with the line of unity (Fig 4). There was 5% overestimation of the apparent fat content in pure water. This overestimation resulted from the anomalously long T2* of the pure water content of the phantom, which does not mimic water in tissues.



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  		Figure 4. Graph of percentage of oil in the quantitative stepwise water-oil phantom. The oil content of the 3-mm slab is graduated in increments of 10% as it demonstrates transition from pure water to pure oil. Note that the estimated percentage of oil, measured in the center of the phantom with the combined algorithm, tracks well with the line of unity.

 
Patients
The estimated percentage of fat at MR imaging for all five patients and the estimates from qualitative and quantitative analysis of the liver biopsy slides in the two patients who underwent biopsy are shown in the Table. In correspondence to biopsy location, the percentage of fat was estimated by using 6.1-cm2 (patient 1) and 5.5-cm2 (patient 2) regions of interest placed over the right lobe of the liver, with care to avoid vascular structures. The IP and OP images, quantitative maps of the estimated percentage of fat by using the T1-weighted and intermediate-weighted dual-echo spoiled gradient-recalled acquisition in the steady state, the combined algorithm, and the pathologic specimen for one of two patients who underwent liver biopsy of the right lobe are shown in Figure 5. The color displays (Fig 5b, 5c) provide an efficient visual assessment of fat content and its heterogeneous distribution (only one section obtained through the right lobe and the medial segment of the left lobe of the liver is shown on the images).


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  		Mean, Qualitative, and Quantitative Estimated Percentages of Fat

 


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  		Figure 5a. (a) Transverse IP and OP images (155/2.3 [OP], 4.6 [IP]) of the liver in a patient suspected of having nonalcoholic steatohepatitis, obtained at low T1 weighting (flip angle, 20°) and high T1 weighting (flip angle, 70°). (b, c) Estimated percentage of fat maps obtained with the combined algorithm displayed on a (b) 100% scale and (c) 50% scale. The mean T2* value of the liver is 11 msec. The mean percentage of fat in the right lobe is 28.6% ± 2.4 (obtained with 5.5-cm2 region of interest). (d) Hematoxylin-eosin–stained slide of the biopsy specimen from the right lobe on medium power. (e) Graph shows results at quantitative histopathologic analysis with pixel intensity values, which yielded an estimate of 28.5% of fat.

 


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  		Figure 5b. (a) Transverse IP and OP images (155/2.3 [OP], 4.6 [IP]) of the liver in a patient suspected of having nonalcoholic steatohepatitis, obtained at low T1 weighting (flip angle, 20°) and high T1 weighting (flip angle, 70°). (b, c) Estimated percentage of fat maps obtained with the combined algorithm displayed on a (b) 100% scale and (c) 50% scale. The mean T2* value of the liver is 11 msec. The mean percentage of fat in the right lobe is 28.6% ± 2.4 (obtained with 5.5-cm2 region of interest). (d) Hematoxylin-eosin–stained slide of the biopsy specimen from the right lobe on medium power. (e) Graph shows results at quantitative histopathologic analysis with pixel intensity values, which yielded an estimate of 28.5% of fat.

 


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  		Figure 5c. (a) Transverse IP and OP images (155/2.3 [OP], 4.6 [IP]) of the liver in a patient suspected of having nonalcoholic steatohepatitis, obtained at low T1 weighting (flip angle, 20°) and high T1 weighting (flip angle, 70°). (b, c) Estimated percentage of fat maps obtained with the combined algorithm displayed on a (b) 100% scale and (c) 50% scale. The mean T2* value of the liver is 11 msec. The mean percentage of fat in the right lobe is 28.6% ± 2.4 (obtained with 5.5-cm2 region of interest). (d) Hematoxylin-eosin–stained slide of the biopsy specimen from the right lobe on medium power. (e) Graph shows results at quantitative histopathologic analysis with pixel intensity values, which yielded an estimate of 28.5% of fat.

 


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  		Figure 5d. (a) Transverse IP and OP images (155/2.3 [OP], 4.6 [IP]) of the liver in a patient suspected of having nonalcoholic steatohepatitis, obtained at low T1 weighting (flip angle, 20°) and high T1 weighting (flip angle, 70°). (b, c) Estimated percentage of fat maps obtained with the combined algorithm displayed on a (b) 100% scale and (c) 50% scale. The mean T2* value of the liver is 11 msec. The mean percentage of fat in the right lobe is 28.6% ± 2.4 (obtained with 5.5-cm2 region of interest). (d) Hematoxylin-eosin–stained slide of the biopsy specimen from the right lobe on medium power. (e) Graph shows results at quantitative histopathologic analysis with pixel intensity values, which yielded an estimate of 28.5% of fat.

 


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  		Figure 5e. (a) Transverse IP and OP images (155/2.3 [OP], 4.6 [IP]) of the liver in a patient suspected of having nonalcoholic steatohepatitis, obtained at low T1 weighting (flip angle, 20°) and high T1 weighting (flip angle, 70°). (b, c) Estimated percentage of fat maps obtained with the combined algorithm displayed on a (b) 100% scale and (c) 50% scale. The mean T2* value of the liver is 11 msec. The mean percentage of fat in the right lobe is 28.6% ± 2.4 (obtained with 5.5-cm2 region of interest). (d) Hematoxylin-eosin–stained slide of the biopsy specimen from the right lobe on medium power. (e) Graph shows results at quantitative histopathologic analysis with pixel intensity values, which yielded an estimate of 28.5% of fat.

 

   Discussion
TOP
 ABSTRACT
 INTRODUCTION
 Materials and Methods
 Results
 Discussion
References
 
Our data derived with the phantom and initial clinical results indicate that the proposed dual-echo dual–flip angle algorithm is an effective method for detection and quantification of the fat fraction in the liver.

To accurately quantify the hepatic fat fraction with IP- and OP-based methods, two major issues should be addressed: (a) the effect of tissue relaxation (T1 and T2*) on water and lipid signal and (b) the ambiguity as to which is the dominant constituent. T1 relaxation may be addressed by minimization of T1 weighting. In clinical practice, however, high-quality T1-weighted imaging (long repetition time [<200 msec] and large flip angle [>60°]) is desirable for diagnostic purposes (15). Moreover, disease can influence the T1 relaxation of nonfatty tissue; thus, corrections that are based on assumed T1 values would still be in error. Determination of T1 is possible but time consuming.

Our proposed method greatly reduces the T1 relaxation effect by means of acquisition of low T1-weighted images (intermediate-weighted) with the flip angle of 20° combined with an algorithm that obviates T1 calculations. As demonstrated with our results, the method is applicable over a large range of fat fractions. T2* shortening as a result of iron deposition, on the other hand, is not uncommon in the liver and may lead to substantial errors in hepatic fat estimates. Our proposed method reduces this error with calculation of T2* for correction of T2* decay between OP and IP images. A limitation of this approach is that a single T2* correction value is used for both water and fat constituents. Local field inhomogeneity affects both constituents, although their individual T2* values may still differ. For moderate to long T2* (>20 msec), even wide differences in T2* of water and fat constituents do not strongly affect fat estimates. By using simulation of conditions of T2* of more than 20 msec, the apparent percentage of fat yielded by our algorithm would be within 5% of the true value. If water has a low T2* of 10 msec relative to fat (T2* = 35 msec), the calculated percentage of fat according to our algorithm by using a single T2* correction value would still be within 10% of the true percentage of fat. Although shorter T2* more severely affects signal-to-noise ratio and fat quantification, as illustrated in Figure 2, our simulations indicate that single-valued T2* corrections reduce fat estimation error in most instances.

The addition of the intermediate-weighted sequence also serves to remove the ambiguity of the dominant constituent. Because fat has shorter T1 value than does water in almost all tissues, the percentage of fat is overestimated when it is calculated from the T1-weighted sequence compared with the intermediate-weighted sequence. This relationship is reversed when fat is the majority constituent. This simple concept is the basis of our algorithm to determine if fat is the majority or minority constituent. There are algorithmic miscalculations at approximately 45% fat content because of crossover of estimated fat curves, but this should have minimal clinical importance.

We verified our proposed method by using computer simulations and successfully tested it on a water-oil phantom. Although the results with the phantom confirmed the desirable features of the proposed algorithm, several technical problems may have influenced fat estimates. First, the application of a single T2* correction value when T2* is different for oil and for water phantom constituents (measured T2* for water and oil were >150 msec and 12 msec, respectively). As discussed previously, the net error was less than 10% by using a single T2* correction value. Second, the mineral oil spectrum indicates two prominent peaks that span a range of 3.3–3.8 ppm from water. This can result in incomplete signal cancellation at echo time values optimized for in vivo fat chemical shift values. Third, the spatial displacement of chemical shift as a result of reversing gradients in the readout direction has a complex effect on signal intensity measurements when they are performed near a fat-water interface. Fourth, the effect of chemical shift in the section-select direction displaces imperfect section profiles of oil relative to water, which complicates the transition from water to oil voxels. Last, the effect of flip angle inhomogeneity across the section excitation profile results in T1 saturation inhomogeneity across the thickness of the section. The last three effects are anticipated to be more severe in the "qualitative" ramp phantom relative to the "quantitative" phantom, which essentially resolves the issues by means of retrospective section combination.

Namimoto et al (15) attempted to derive fat fractions in adrenal masses from a signal intensity index (SII) obtained from IP and OP images acquired by using a dual-echo sequence, which is calculated thus: SII = (SIIP – SIOP)/SIIP, where SIIP and SIOP are signal intensity of IP and OP images, respectively. The authors did not apply quantitative corrections for the influence of T2* decay and assumed that the effect of T2* variability on measured fat to be small and used published T1 values of adrenal adenomas (38) and phantom solutions at these values to estimate the fat fraction (39). Since their model and analysis could not address fat fractions of more than 50%, they assumed the fat content to be less than 50%, which is a reasonable assumption for some tissues.

Fatty infiltration is an important constituent of nonalcoholic fatty liver disease, which is believed to be the most common liver disease in the western world, with an increasing prevalence (26,40–42). In nearly 20% of patients with nonalcoholic steatohepatitis, the disease progresses to cirrhosis over 5–10 years but rarely to hepatocellular carcinoma (41,43–49), and the definitive diagnosis of nonalcoholic steatohepatitis requires a liver biopsy (27,50).

Although it is considered the standard, liver biopsy is an invasive procedure with morbidity and occasional mortality, and hepatologists are reluctant to perform a biopsy on asymptomatic patients (51). Because of its patchy nature, hepatic steatosis may be missed or underestimated at biopsy, and missing such a diagnosis would result in a delay in diagnosis. Therefore, it has become necessary to develop a noninvasive method to quantify hepatic fat (52) and to determine how this method correlates with fat quantification and disease severity as measured at histologic analysis. At its minimum, this image-based method can guide biopsy sampling.

The reason for the differences between qualitative and quantitative analyses of the percentages of hepatic fat at histologic analysis most likely relates to the properties actually measured with the two methods. Pathologists use a subjective visual analysis, where some estimate the percentage of overall area on the microscopic field involved with steatosis (53), while others estimate the percentage of hepatocytes involved with macrovesicular steatosis (37). Recently, automated software has been applied to quantify the percentage of hepatic fat, and such quantification differs from the traditional visual estimates in that it is used to measure the surface area of the fat vacuoles in the microscopic field. Marsman et al (53) suggested a revised classification system for estimation of severity of fatty infiltration by using automated software, as the visual estimate in their study was consistently 1.4–2.9-fold greater than the automated software estimates. Similarly, our pathologists' qualitative estimation of the percentage of fat was twofold or more greater than were the estimations with MR imaging and quantitative analysis. Analogous to the measurement of the percentage of fat area in the microscopic field, at MR imaging, the percentage of signal that originates from fat protons, and not the percentage of cells that contain fat, is estimated.

Other applications of hepatic fat quantification include monitoring the toxic effect of some drugs and assessing livers prior to cadaveric and living-related liver transplantation or major hepatic resection. Fat quantification also can be used to characterize adrenal masses (54,55).

There are other limitations to our study: First, in the context of nonalcoholic fatty liver disease, the proposed method is used to measure only the hepatic fat and cannot be used to determine the presence of inflammatory changes or fibrosis. Quantification of hepatic fat, however, may become a useful tool in conjunction with other noninvasive tests in determination of disease severity (fibrosis) so as to avoid biopsy in patients with less severe disease. Second, the current method requires three separate acquisitions, which may have introduced misregistration artifacts. Third, the lipid fraction measured with this technique represents the percentage of total hepatic signal derived from the protons in fatty acid molecules; therefore, the estimated percentage of fat does not represent the weight per volume of lipid.

In conclusion, we propose a fast noninvasive MR imaging method to quantify and display the fat fraction in the liver. This method addresses the ambiguities associated with the dominant constituent and the effects of relaxation to yield maps of fat content throughout the liver for efficient visual and quantitative assessment.


   FOOTNOTES
 

Abbreviations: IP = in phase • OP = out of phase

Authors stated no financial relationship to disclose.

Author contributions: Guarantors of integrity of entire study, H.K.H., T.L.C., H.S.C.; study concepts/study design or data acquisition or data analysis/interpretation, all authors; manuscript drafting or manuscript revision for important intellectual content, all authors; approval of final version of submitted manuscript, all authors; literature research, H.K.H., V.G., H.S.C.; clinical studies, H.K.H., T.L.C., H.D.A., S.A., J.K.G., H.S.C.; experimental studies, H.K.H., T.L.C., V.G., S.D.S., B.J.K.; and manuscript editing, H.K.H., T.L.C., F.J.L., V.G., B.J.M., H.D.A., J.K.G., H.S.C.


   References
TOP
 ABSTRACT
 INTRODUCTION
 Materials and Methods
 Results
 Discussion
 References
 

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