Quantitative Assessment of Liver Fat with Magnetic Resonance Imaging and Spectroscopy

Abstract

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 non-alcoholic fatty liver disease (NAFLD). Other conditions associated with steatosis include alcoholic liver disease, viral hepatitis, 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. Non-invasive 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 magnetic resonance (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.

Figures

Figure 1

Fatty liver spectrum in a 44 year-old woman with fatty liver disease. An MR spectrum was acquired from a single 20×20×20 mm3 voxel in the right lobe during a single breath-hold without averaging using the STEAM sequence at 3T (TR/TE = 3500/10 ms; mixing time = 5 ms). Six distinct fat peaks can be resolved in vivo at clinical field strengths, as labeled (1–6), corresponding to the peaks in table 1.

Figure 2

Signal fat-fraction using fat-suppressed MRI technique, acquired in a 32 year-old man with fatty liver disease. Axial T2-weighted single shot fast spin echo images of the liver acquired with no fat saturation (NFS, left) and with fat saturation (FS, right). The signal in a co-localized region of interest (oval) is 640 arbitrary units (a.u.) in the NFS image and 250 a.u. in the FS image. Using Equation 2, the calculated signal fat-fraction is (640–250)/640 = 61%. The true fat-fraction by MR spectroscopy (not shown) is 17%, illustrating that the fat-suppressed MRI techniques may produce erroneous estimates of hepatic fat-content. Incomplete fat saturation and inadvertent water suppression (arrows) are potential pitfalls with fat-suppressed MRI techniques.

Figure 3

Signal fat-fraction maps can be calculated on a pixel-by-pixel basis using dual-echo (in-phase and opposed phase) imaging using equation 3. Signal fat-fraction maps may not accurately reflect the concentration of fat within the liver unless all confounding factors are addressed. Note that the dynamic range of signal fat-fraction calculated from dual-echo imaging, as well as all magnitude-based methods, is limited to 0–50%. Fortunately, fat-fractions greater than 50% are uncommon in the liver, although can occur (e.g., Figure 13). Fat-fraction maps can be displayed in gray-scale or in color.

Figure 4

Signal fat-fraction maps with full dynamic range (0–100%) can be calculated on a pixel-by-pixel basis using equation 1, when complex-based fat-water separation methods are used to provide separate fat-only and water-only images. The signal fat-fraction map may not accurately reflect the concentration of fat within the liver unless all confounding factors are addressed. Fat-fraction maps can be displayed in gray-scale or in color.

Figure 5

The effect of T1 weighting on fat-fraction estimation is demonstrated in this 39 year-old woman with fatty liver disease. Signal fat-fraction parametric maps were generated from magnitude images at fixed TR (=150ms) and flip angles of 70, 50, 30, and 10 degrees, and the apparent signal fat-fractions are shown. Reducing T1 weighting by decreasing flip angle can minimize T1 bias, although at a cost of reduced SNR performance. Avoiding T1 bias is important – otherwise the apparent fat-fraction depends on image parameters such as TR and flip angle, and comparisons of results using different protocols or different scanners may be invalid.

Figure 6

Use of low flip angle is a common strategy to reduce T1 bias using chemical shift methods but does not suffice for accurate PDFF estimation. 12 year-old boy with fatty liver disease (same subject as in Figures 11 and 12) 2D out-of-phase (TE = 1.15ms, left) and in-phase (TE = 2.3ms, center) magnitude gradient echo images were acquired at 3T. To reduce T1 bias, images were acquired with a 10-degree flip angle and TR of 150ms. The corresponding signal fat-fraction parametric map (right) was calculated using equation 3. Shown on the map are signal fat-fractions in five regions of interest demonstrating heterogeneity of fat across the liver. The circled region of interest is co-localized to an MRS voxel, where the MRS-determined fat fraction was 39.5%. Thus, while use of low flip angle relative to TR effectively reduces T1 bias, it is not sufficient for accurate fat fraction estimation, as discussed in the text.

Figure 7

T2* correction is necessary for accurate fat-fraction estimation. 54 year-old man with concomitant hepatic iron overload and fatty liver. Shown are signal fat-fraction maps generated without T2* correction (left) from a pair of out-of-phase and in-phase magnitude images at 3T (TE = 1.15 and 2.3 ms), and with T2* correction (middle) from nine serial out-of-phase and in-phase images. An R2* map (=1/T2*) is generated as part of the T2* correction process (right) (63,65). Compared to the T2*-corrected fat-fraction values, the T2*-uncorrected fat-fraction values are underestimated, particularly in the left lobe. The underestimation of fat-fraction in the left lobe is explained by the R2* map, because the R2* in the left lobe of the liver is higher (ie: higher iron concentration). .

Figure 8

Accurate spectral modeling of fat is necessary to accurately measure liver fat. Complex-based quantitative fat-fraction imaging was performed in a 16 year-old girl with fatty liver, using corrections for all confounding factors, including spectral modeling (left). T2-corrected STEAM MRS (circle) demonstrated 30% fat-fraction by imaging and 29% by MRS, indicating close agreement. The same data were reconstructed using a single-peak model for fat (right), resulting in underestimation of fat-fraction in the liver (24%) and subcutaneous adipose tissue (85%). The fat-fraction in subcutaneous adipose tissue normally ranges between 90–100%.

Figure 9

Noise-related bias can result using complex-based MRI techniques when the fat-fraction is close to zero. This occurs because the separated fat and water signals are converted into magnitude images to calculate the fat-fraction. In regions with no fat, the complex fat data has zero mean signal; due to noise, however, about half the pixels in the regions have positive values and about half have negative values (upper left). After the magnitude operation, the negative values are converted into positive values and the mean signal becomes non-zero (upper right). Consequently, the apparent fat-fraction is overestimated (bottom left). Liu et al has described the use of either phase constrained or magnitude discrimination methods (bottom right) to minimize noise related bias(59). T2-corrected STEAM MRS demonstrated a true fat-fraction of 1.0%. Images courtesy Diego Hernando, PhD.

Figure 10

Unexpected phase shifts such as eddy currents can occur echoes acquired at different echo times. In this example, 6 echoes are acquired in one TR with fly-back gradients between echoes. A schematic diagram (top) demonstrates that the phase of the first echo in a voxel containing only water deviates from a linear trajectory. This deviation in phase leads to over estimation of the fat concentration, because the phase variation mimics signal oscillation from fat-water interference. A hybrid magnitude fitting method applied after complex-based fat-water separation can correct for such errors by discarding phase information(64). In this example, T2-corrected STEAM MRS demonstrated a true fat-fraction of 1.3%, close to the corrected value of 1.2% (right). Images courtesy Diego Hernando, PhD.

Figure 11

Estimation of PDFF using single-breathhold T1-independent, T2-corrected, spectrally corrected spectroscopy. 12 year-old boy with fatty liver disease (same subject as in Figures 6 and 12). After a single pre-acquisition excitation pulse, five STEAM spectra were acquired at echo times (TEs) of 10, 15, 20, 25, and 30 ms in a single 21-second breath-hold from a single 20×20×20 mm3 voxel without averaging at 3T. Notice the water peak decays more rapidly as a function of TE than the main fat peaks, indicating that liver water has shorter T2 than liver fat. TR of 3500 ms and mixing time of 5 ms are used to minimize T1 effects. The MRS voxel is co-localized with the circled region of interest shown in Figures 6 and 12. Using the nomenclature in Table 1, the water peak and the six resolvable fat peaks are labeled. To estimate the proton density fat-fraction (PDFF), the areas of the water (4.7 ppm) and three major fat spectral peaks (0.9, 1.3, 2.1 ppm) are measured at each TE. For each frequency, the peak area is corrected for T2 decay using nonlinear least-square fitting to determine its relative proton density. The relative proton densities of the two fat peaks (4.2, 5.3 ppm) obscured by the water peak are determined from those of the measurable fat peaks using prior knowledge of the NMR spectrum of liver fat (Table 1)(84), ie: spectrally corrected. The PDFF (39.5% in this case) is then calculated by dividing the fat proton density (sum of all fat peaks) by the sum of the fat and water proton densities.. ppm = parts per million.

Figure 12

Estimation of PDFF Using Single-Breath-hold T1-independent, T2*-corrected, spectrally modeled magnitude and complex chemical shift-based MRI techniques. 12 year-old boy with fatty liver disease (same subject as in Figures 6 and 11). Proton density fat-fraction (PDFF) parametric maps were calculated using magnitude (left) and complex (right) chemical shift-based fat-water separation techniques. The magnitude-based technique uses magnitude source images (not shown) to generate a PDFF image with dynamic range of 0–50%, while the complex-based technique uses complex (magnitude and phase) source images (not shown) to generate a PDFF image with dynamic range of 0–100%. The two techniques demonstrate excellent agreement both qualitatively and quantitatively for fat within the liver. The two techniques also agree closely with MR spectroscopy. The calculated PDFF within the circled region of interest is 39% using the magnitude-based technique, 40% using the complex-based technique, and 39.5% using MR spectroscopy (Figure 10).

Figure 13

Serial liver MRI exams can be used to demonstrate changes in liver fat during therapy. Serial PDFF maps obtained with complex-based MRI were acquired in a 41 year-old man with hypertriglyceridemia and insulin resistance undergoing plasmapheresis and multi-drug therapy to lower serum triglycerides. Follow-up MRI demonstrates reduction in hepatic PDFF (from 53% to 33%). Notice the corresponding reduction in liver size. This example demonstrates that fat-fractions greater than 50% may occur, and it also illustrates the use of gray-scale and color-scale PDFF maps.

Figure 14

Quantitative MRI can be used to assess the concentration of fat in fat-containing liver masses such as hepatic adenomas. The estimated proton density fat-fraction is 19% in this pathology-proven lipid rich adenoma. Conventional T1-weighted dual-echo images are shown for comparison; notice signal loss on the opposed-phase image. Quantifying fat in such lesions currently has unclear clinical utility but potentially may offer insight into pathophysiology.

Figure 15

Quantitative proton density fat-fraction imaging performed in a 16-year-old girl with polycystic ovary syndrome (PCOS), demonstrates severe steatosis. PCOS is a condition associated with insulin resistance and NAFLD. Quantitative fat-fraction imaging in PCOS may be an emerging application in an adolescent population that is often reticent to liver biopsy. In this patient, aminotransferases were mildly elevated, raising concern for steatohepatisis. Biopsy demonstrated severe steatohepatitis. Complex-based PDFF maps acquired at 1.5T (PDFF = 33%) and 10 days later at 3T (PDFF = 32%) demonstrate close qualitative and quantitative agreement. Conventional T1 weighted dual-echo images (1.5T) demonstrating marked signal drop-out on opposed phase imaging are shown for comparison.