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Advanced MRI Methods for Assessment of Chronic Liver Disease

¹ Department of Radiology, MRI, New York University Medical Center, 530 First Ave., New York, NY 10016.
² Department of Radiology, Mayo Clinic, Rochester, MN.
³ Departments of Radiology, Medical Physics, and Biomedical Engineering, University of Wisconsin, Madison, WI.

Received February 18, 2009; accepted after revision February 25, 2009.

 
R. L. Ehman was supported by grant EB001981 from the National Institutes of Health.

B. Taouli is supported by Radiological Society of North America Scholarship Grant RSCH 0710.

The Mayo Clinic and R. L. Ehman hold patents related to the subject matter of this article and have a potential financial interest.

Address correspondence to B. Taouli (bachir.taouli{at}nyumc.org).

Abstract

Top Abstract Introduction Role of Liver Biopsy... Limitations of Liver Biopsy Noninvasive Diagnosis of Liver... Diagnosis and Quantification of... Limitations of MRI Methods Future Directions Conclusions References

 
OBJECTIVE. With recent advances in technology, advanced MRI methods such as diffusion-weighted and perfusion-weighted MRI, MR elastography, chemical shift-based fat-water separation, and MR spectroscopy can now be applied to liver imaging. We will review the respective roles of these techniques for assessment of chronic liver disease.

CONCLUSION. MRI plays an increasingly important role in assessment of patients with chronic liver disease because of the lack of ionizing radiation and the possibility of performing multiparametric imaging.

Keywords: chronic viral hepatitis • cirrhosis • fat • fibrosis • iron • liver disease • MRI

Introduction

Top Abstract Introduction Role of Liver Biopsy... Limitations of Liver Biopsy Noninvasive Diagnosis of Liver... Diagnosis and Quantification of... Limitations of MRI Methods Future Directions Conclusions References

 
Chronic liver diseases encompass many different causes, including mainly viral infections, nonalcoholic fatty liver disease, alcohol abuse, primary sclerosing cholangitis, primary hemochromatosis, and autoimmune disease. Chronic liver diseases can lead to hepatic fibrosis, cirrhosis, end-stage liver disease, portal hypertension, and hepatocellular carcinoma (HCC) and constitute an important cause of morbidity, mortality, and health care costs [1]. In the United States, two emerging causes of chronic liver disease will be discussed in this review: chronic hepatitis C virus (HCV) infection and nonalcoholic fatty liver disease.

HCV infection accounts for approximately 40% of all chronic liver disease, results in an estimated 8,000-10,000 deaths annually, and is the most frequent indication for liver transplantation [2-4]. Simulations for the years 2010-2019 suggest that morbidity and mortality associated with HCV will increase dramatically, resulting in 165,900 deaths from chronic liver disease, 27,200 deaths from HCC, and $10.7 billion in direct medical costs [5-7]. Progression to cirrhosis occurs in 20-30% of patients infected with HCV, with disease duration up to 20 years [8]. The early detection of fibrosis and cirrhosis has important clinical implications in these patients. Antiviral treatment of chronic HCV can eradicate the infection, increase patient survival, and reduce the need for liver transplantation [8, 9].

The prevalence of nonalcoholic fatty liver disease also has increased dramatically, reflecting the obesity epidemic [10, 11]. It afflicts an estimated 90-100 million (> 30%) people in the United States [12, 13], including 10% of children [14-18]. With the current epidemic of obesity and diabetes, nonalcoholic fatty liver disease is widely expected to overtake chronic HCV infection as the leading indication for liver transplantation in the next decade. Nonalcoholic fatty liver disease includes a spectrum of liver abnormalities ranging from liver steatosis to nonalcoholic steatohepatitis [19]. Up to 25% of patients with nonalcoholic fatty liver disease can progress to inflammation and liver injury, leading to fibrosis and cirrhosis, with the ultimate risk of developing HCC [20, 21].

Nonalcoholic fatty liver disease is closely linked to the "metabolic syndrome," a constellation of conditions that includes obesity, diabetes, and insulin resistance. As shown in Figure 1, insulin resistance leads to an intracellular accumulation of triglycerides and fatty acids (steatosis) [22, 23]. Fatty acids are known to cause oxidative stress that can injure the liver, activating stellate cells that are responsible for hepatic injury and fibrosis. Why some patients with steatosis develop inflammatory and fibrotic changes while others do not is not well understood. Day and James [24] proposed a "two-hit" model, hypothesizing that steatosis is the first hit. An unknown form of oxidative stress (e.g., iron overload, infection, genetic predisposition) forms the second hit and is necessary for progression to fibrosis. A vicious self-perpetuating cycle of steatosis, insulin resistance, and fibrosis ensues, eventually leading to cirrhosis [21].

View larger version (11K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 1 —Flowchart shows pathophysiology of nonalcholic fatty liver disease with possibility of preemptive diagnosis (using MRI) and intervention. Although pathophysiology of nonalcoholic fatty liver disease is not entirely understood, it is generally thought that insulin resistance leads to intracellular accumulation of triglycerides and fatty acids, which are known to cause oxidative stress. Presence of unknown second "hit" leads to development of inflammation (nonalcoholic steatohepatitis [NASH]), injury and fibrosis, and ultimately cirrhosis. Like all patients with cirrhosis, those with NASH are at increased risk of liver failure or hepatocellular carcinoma (HCC).

Steatosis is the earliest biomarker of nonalcoholic fatty liver disease and is a necessary feature of disease for the development of fibrosis. It is also essential to quantify liver injury through the measurement of fibrosis to differentiate simple steatosis from nonalcoholic steatohepatitis. Early detection of steatosis, the hallmark and earliest feature of nonalcoholic fatty liver disease, would facilitate early diagnosis and intervention before liver damage is irreversible. Thus, there has been great interest in the development of accurate quantitative biomarkers of steatosis with MRI. Steatosis is also an important disease feature in other types of chronic liver diseases [25-27].

Role of Liver Biopsy for Assessment of Chronic Liver Disease

Top Abstract Introduction Role of Liver Biopsy... Limitations of Liver Biopsy Noninvasive Diagnosis of Liver... Diagnosis and Quantification of... Limitations of MRI Methods Future Directions Conclusions References

 
The typical histologic features of chronic HCV are variable degrees of hepatocellular necrosis and inflammation (activity or grade of disease) and fibrosis (stage of disease), with possible associated fat or iron deposition [28, 29]. Several semiquantitative methods have been proposed to assess fibrotic changes and histologic activity in chronic hepatitis [30-33], but the most generally accepted one is the METAVIR classification [33]. The histopathologic findings have a role in assessing prognosis, guiding antiviral therapy, and predicting treatment efficacy in viral hepatitis [29, 34]. Clinically significant fibrosis is generally defined by fibrosis stage of F2 or higher (on the METAVIR scale from F0 to F4 with F4 being cirrhosis) [33].

Advanced fibrosis or cirrhosis (F3 or F4) on initial liver biopsy is associated with a decreased likelihood of sustained virologic response to treatment [35, 36]. Repeat liver biopsies can also be useful to evaluate the progression of the disease in patients who have opted against treatment or in patients who did not respond to their initial therapy and are considering another course of therapy. In nonalcoholic fatty liver disease, in addition to the fat grading, liver biopsy can assess for the presence of inflammation and fibrosis [37].

Limitations of Liver Biopsy

Top Abstract Introduction Role of Liver Biopsy... Limitations of Liver Biopsy Noninvasive Diagnosis of Liver... Diagnosis and Quantification of... Limitations of MRI Methods Future Directions Conclusions References

 
Although liver biopsy is a relatively safe procedure when performed by experienced clinicians, it has poor patient acceptance, is not risk free, and is difficult to repeat. Prior studies have suggested a risk of hospitalization of 1-5%, with up to 0.57% risk of severe complications, and reported mortality rates of one in 1,000 to one in 10,000 [38-42] with liver biopsy. In addition, liver biopsy is prone to interobserver variability and sampling errors [43-46] and is relatively expensive compared with MRI. In contrast, MRI is relatively inexpensive, noninvasive, and safe and avoids the use of ionizing radiation, making it a very attractive and cost-effective alternative for early diagnosis and subsequent disease monitoring during therapy. In this review, we discuss the acquisition, results, and limitations of advanced MRI methods for assessment of chronic liver disease. Focal liver lesions will not be discussed.

Noninvasive Diagnosis of Liver Fibrosis and Cirrhosis

Top Abstract Introduction Role of Liver Biopsy... Limitations of Liver Biopsy Noninvasive Diagnosis of Liver... Diagnosis and Quantification of... Limitations of MRI Methods Future Directions Conclusions References

 
Hepatic fibrogenesis is a complex dynamic process, which is mediated by necroinflammation and activation of stellate cells [47], with abnormal collagen deposition resulting from increased collagen synthesis and decreased collagen degradation. A reliable and reproducible noninvasive marker of hepatic fibrosis is strongly needed, and such a tool would reduce biopsy-related risks and costs and could be useful for guiding antiviral treatment and monitoring treatment efficacy, and for clinical evaluation of new types of antiviral and antifibrotic drugs. Furthermore, identification of occult advanced fibrosis or cirrhosis may direct further management and has essential prognostic implications.

Serologic Markers
Liver function tests are known to be poorly correlated with the degree of fibrosis. For example, a study showed that up to 40% of patients with advanced fibrosis have persistently normal alanine aminotransferase (ALT) levels [48]. Serologic markers of hepatic fibrosis, such as aspartate aminotransferase (AST)/ALT ratio, platelet count, and prothrombin index, have a variable accuracy [1]. Imbert-Bismut and colleagues [49] have developed a score based on a combination of basic serum markers, known as FibroTest (combination of ₂-macroglobulin, ₂-globulin, -globulin, apolipoprotein A-1, glutamyl transpeptidase, and total bilirubin). This test panel performed with 75% sensitivity and 85% specificity for diagnosis of stage F2 and higher. In a subsequent study, the same group reported a lower performance of the FibroTest and Actitest (activity index, which incorporates ALT) in patients treated for HCV, with an area under the curve (AUC) of 0.76 before treatment [50].

Transient Elastography
Sonographic elastography (FibroScan, Echosens) [51-54] recently has been developed to measure liver stiffness in chronic hepatitis, showing strong correlation between measured stiffness and increasing degrees of fibrosis. For example, an AUC of 0.83 was reported for diagnosis of fibrosis of stage F2 or higher using this method [53], which still does not have Food and Drug Administration approval in the United States.

View larger version (104K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 2A —52-year-old man with chronic hepatitis C without evidence of fibrosis at liver biopsy (stage F0; top) and 67-year-old woman with cirrhosis secondary to chronic hepatitis C (stage F4; bottom). Breath-hold fat-suppressed turbo spin-echo T2-weighted (A) and breath-hold fat-suppressed single-shot echo-planar diffusion-weighted images for b = 0 (B) and b = 700 (C) mm2/s and apparent diffusion coefficient (ADC) map (D) (using b = 0 and 700 mm2/s) are shown. In patient without fibrosis, hepatic ADC was within normal range, measuring 1.6 x 10-3 s/mm2, with liver appearing brighter than spleen (which is known to have low ADC). In cirrhotic patient, T2-weighted image shows no clear morphologic changes in cirrhosis. However, hepatic ADC was decreased (reaching spleen ADC), measuring 1.0 x 10-3 s/mm2 (according to ADC values described by Taouli et al. [77]).

View larger version (96K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 2B —52-year-old man with chronic hepatitis C without evidence of fibrosis at liver biopsy (stage F0; top) and 67-year-old woman with cirrhosis secondary to chronic hepatitis C (stage F4; bottom). Breath-hold fat-suppressed turbo spin-echo T2-weighted (A) and breath-hold fat-suppressed single-shot echo-planar diffusion-weighted images for b = 0 (B) and b = 700 (C) mm2/s and apparent diffusion coefficient (ADC) map (D) (using b = 0 and 700 mm2/s) are shown. In patient without fibrosis, hepatic ADC was within normal range, measuring 1.6 x 10-3 s/mm2, with liver appearing brighter than spleen (which is known to have low ADC). In cirrhotic patient, T2-weighted image shows no clear morphologic changes in cirrhosis. However, hepatic ADC was decreased (reaching spleen ADC), measuring 1.0 x 10-3 s/mm2 (according to ADC values described by Taouli et al. [77]).

View larger version (71K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 2C —52-year-old man with chronic hepatitis C without evidence of fibrosis at liver biopsy (stage F0; top) and 67-year-old woman with cirrhosis secondary to chronic hepatitis C (stage F4; bottom). Breath-hold fat-suppressed turbo spin-echo T2-weighted (A) and breath-hold fat-suppressed single-shot echo-planar diffusion-weighted images for b = 0 (B) and b = 700 (C) mm2/s and apparent diffusion coefficient (ADC) map (D) (using b = 0 and 700 mm2/s) are shown. In patient without fibrosis, hepatic ADC was within normal range, measuring 1.6 x 10-3 s/mm2, with liver appearing brighter than spleen (which is known to have low ADC). In cirrhotic patient, T2-weighted image shows no clear morphologic changes in cirrhosis. However, hepatic ADC was decreased (reaching spleen ADC), measuring 1.0 x 10-3 s/mm2 (according to ADC values described by Taouli et al. [77]).

View larger version (131K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 2D —52-year-old man with chronic hepatitis C without evidence of fibrosis at liver biopsy (stage F0; top) and 67-year-old woman with cirrhosis secondary to chronic hepatitis C (stage F4; bottom). Breath-hold fat-suppressed turbo spin-echo T2-weighted (A) and breath-hold fat-suppressed single-shot echo-planar diffusion-weighted images for b = 0 (B) and b = 700 (C) mm2/s and apparent diffusion coefficient (ADC) map (D) (using b = 0 and 700 mm2/s) are shown. In patient without fibrosis, hepatic ADC was within normal range, measuring 1.6 x 10-3 s/mm2, with liver appearing brighter than spleen (which is known to have low ADC). In cirrhotic patient, T2-weighted image shows no clear morphologic changes in cirrhosis. However, hepatic ADC was decreased (reaching spleen ADC), measuring 1.0 x 10-3 s/mm2 (according to ADC values described by Taouli et al. [77]).

Functional MRI Methods
Recent advances in MRI have led to a growing interest in optimizing and applying functional MRI methods for assessment of liver disease. These methods include—but are not limited to—diffusion-weighted imaging (DWI), perfusion-weighted MRI, MR elastography (MRE), and MR spectroscopy (MRS).

DWI—DWI is based on intravoxel incoherent motion and provides noninvasive quantification of water diffusion and micro-capillary-blood perfusion [60]. DWI does not require gadolinium contrast material, which is attractive in patients with renal dysfunction at risk for nephrogenic systemic fibrosis [61-63].

DWI acquisition—DWI is performed optimally on systems with high-performance gradients usually using a single-shot echoplanar imaging (EPI) sequence with diffusion gradients applied in three orthogonal directions: frequency-encoding (x), phase-encoding (y), and section-select (z) directions. Breath-hold, free-breathing, or respiratory-triggered EPI sequences can be performed in conjunction with parallel imaging to improve image quality [64, 65]. A free-breathing or respiratory-triggered acquisition allows the use of multiple levels of diffusion weighting (b values) in a single acquisition, with improved image quality compared with breath-hold acquisition; however, this image quality is at the expense of longer acquisition time [66]. The selection of b values is based on a compromise between image quality and adequate diffusion-weighted contrast [67]. At least three b values should be used to obtain a good fit for apparent diffusion coefficient (ADC) calculation.

DWI processing—The process of ADC calculation is usually automated on most clinical systems. This is achieved by performing a monoexponential fit between the liver signal intensity (in logarithmic scale) and the b values as follows: ADC = ln (SI₀ / SI) / b (in which SI₀ is signal intensity for b = 0, and SI is signal intensity for the higher b value). The slope of the line that describes this relationship in each voxel represents the ADC. In diffuse liver disease, ADC values should be calculated in multiple locations within the liver (excluding the lateral left lobe, which could be affected by cardiac-related artifacts) by placing regions of interest (ROIs) to measure ADC values.

DWI results in liver fibrosis and cirrhosis—Several studies have shown that the ADC of cirrhotic liver is lower than that of normal liver [67-72] (Figs. 2A, 2B, 2C, and 2D). Koinuma et al. [73] showed a significant negative correlation between hepatic ADC and fibrosis score in a large population of patients (n = 163) using a low b value (128 s/mm²). However, their results showed no correlation between ADC and inflammation grades. Lewin et al. [74] investigated the role of DWI (using b values of 400-800 s/mm²) compared with FibroScan and serum markers in a large series of patients with HCV (n = 54 plus 20 healthy volunteers) and showed an excellent performance of DWI for prediction of moderate and severe fibrosis and prediction of severe fibrosis and cirrhosis. Patients with moderate to severe fibrosis (F2-F4) had hepatic ADC values lower than those without or with mild fibrosis (F0 or F1) and healthy volunteers: 1.10 ± 0.11 versus 1.30 ± 0.12 versus 1.44 ± 0.02 x 10^-3 mm²/s, respectively. For the discrimination of patients with fibrosis stage F3 or F4 from those with F0 or F2, the AUCs were 0.92 for DWI, 0.92 for FibroScan, 0.79 for FibroTest, and 0.86-0.87 for other blood tests. Sensitivity, specificity, positive predictive value, and negative predictive value were 87%, 87%, 72%, and 94%, respectively, for the diagnosis of advanced fibrosis and cirrhosis (F3-F4) using an ADC cutoff of 1.21 x 10^-3 mm²/s. In addition, they found a significant relationship between ADC and inflammation scores, and suspected a possible associated influence of steatosis on ADC values. Girometti et al. [75] reported lower ADC in cirrhotic livers compared with healthy control subjects and showed an AUC of 0.93, with sensitivity of 89.7% and specificity of 100% for diagnosing cirrhosis (using b values of 0-150 to 250-400 s/mm²). In a recent study, ADC was also found to be a significant predictor of fibrosis stage of 1 or higher (sensitivity 88.5% and specificity 73.3%) and inflammation grade of 1 or higher (sensitivity 75% and specificity 78.6%) [76]. Another study showed a decrease in liver ADC in significant and severe fibrosis using b values of 500 s/mm² or higher [77], with the best correlation shown with b = 700 s/mm².

The mechanism of diffusion restriction in patients with chronic liver disease is not clearly understood and is likely multifactorial, possibly related to the presence of increased connective tissue in the liver (which is proton poor) and from decreased blood flow. A recent animal study [78] showed that rats with hepatic fibrosis have reduced ADC values in vivo but not when DWI was performed ex vivo, which suggests that decreased perfusion had the primary effect on decreased apparent diffusion. In addition, a recent study by Luciani et al. [79], based on intravoxel incoherent motion MRI [80-84], also has suggested that restricted diffusion observed in patients with cirrhosis reflects diminished capillary perfusion and, to a much lesser extent, pure molecular diffusion. Their analysis of 37 patients showed lower ADC values between cirrhotic and normal livers (ADC = 1.23 ± 0.4 vs 1.39 ± 0.2 x 10^-3 mm²/s), which they attributed primarily to reduced perfusion in cirrhotic livers.

Perfusion-Weighted MRI
Liver perfusion can be assessed by monitoring the uptake and washout of gadolinium-based contrast agents using high-temporal-resolution T1-weighted imaging. Because of the lack of ionizing radiation, perfusion-weighted MRI can be used to quantify perfusion of the whole liver, with the possibility of repeating the study after treatment. However, with the recent recognition of the risk of nephrogenic systemic fibrosis [61-63], gadolinium-based contrast agents should be avoided in patients with severe renal dysfunction.

Perfusion-weighted MRI acquisition— With state-of-the-art systems, it is possible to cover the entire liver with good spatial and temporal resolution. The majority of prior liver perfusion-weighted MRI studies have relied on a 2D acquisition limited to a single axial slice to preserve high spatial and temporal resolution [85-87]. The initial experimental work on perfusion-weighted MRI by Scharf et al. [85] in 1999 on pigs (at 1 T) showed a good correlation between MR perfusion parameters and a reference thermal diffusion probe in the setting of partial portal vein occlusion. Materne et al. [86] and Annet et al. [87] performed perfusion-weighted MRI at 1.5 T with a single axial slice at the level of the portal vein using cardiac triggering, which enabled high temporal resolution. We suggest the use of whole-liver perfusion imaging at 1.5 T or 3 T using a 3D interpolated spoiled gradient-recalled echo sequence in the coronal plane with an acceleration factor of 3 [88]. One to three volume acquisitions are performed before IV contrast administration of 10 mL of gadopentetate dimeglumine (Magnevist, Bayer HealthCare) or 10 mL of gadobenate dimeglumine (MultiHance, Bracco Diagnostics). Approximately 36-40 coronal slices are acquired every 3-5 seconds (depending on the liver size). The images are acquired first during a breath-hold and then during quiet free breathing, for a total acquisition time of 3-5 minutes.

Perfusion-weighted MRI processing—By placing ROIs in the tissue of interest, signal intensity (SI) versus time curves are obtained (Figs. 3A, and 3B). ROIs are placed in the main portal vein, proximal abdominal aorta (used as a surrogate for the hepatic artery), and liver parenchyma to measure SI. To simplify the perfusion quantification, a linear relationship between SI and gadolinium concentration can be assumed for the range of expected concentrations in the liver and blood on the basis of prior work on measurement of gadopentetate dimeglumine concentration in vivo and in vitro [89]. However, it is also possible to perform a potentially more accurate nonlinear conversion of SI to gadolinium concentration using either analytic expressions [90], calibration curves from phantom studies, or unenhanced T1 measurements [89, 91]. A dual-input single-compartment model, which has been validated previously using radiolabeled microspheres in rabbits [86], can be used to fit the resulting time-activity curves in the liver. This model reflects the dual blood supply from the portal vein and hepatic artery received by the liver. Details on the model can be obtained elsewhere [87, 88]. The following parameters can be obtained: absolute arterial blood flow (F_a, in mL/min), absolute portal venous blood flow (F_p, in mL/min), arterial fraction (ART, in percent) = 100 x F_a / (F_a + F_p), portal venous fraction (PV in percent) = 100 - ART, distribution volume (in percent) of gadolinium through the liver, and mean transit time (MTT in seconds, the average time it takes a gadolinium molecule to traverse the liver from arterial or portal venous entry to the hepatic venous exit).

View larger version (139K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 3A —54-year-old woman with cirrhosis secondary to HCV infection. Perfusion MR images of liver obtained using coronal 3D interpolated spoiled gradient-recalled echo sequence covering entire liver before and after injection of 10 mL of gadopentetate dimeglumine with temporal resolution of approximately 3 seconds. Selected time points from 35 measures are shown in chronologic order (top to bottom from left to right). Progressive opacification of hepatic artery, portal vein, and liver parenchyma is observed.

View larger version (9K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 3B —54-year-old woman with cirrhosis secondary to HCV infection. Signal intensity-time curve obtained by placing regions of interest in aorta (used as surrogate for hepatic artery) (), portal vein (), and liver parenchyma () to measure signal intensity at each time point (in arbitrary units). Visually, there is evidence of delayed enhancement of liver parenchyma. Dual-input single-compartment model [87, 88] was used to compute perfusion parameters using signal intensity measurement converted to gadolinium concentration, which showed increased arterial fraction (36%), hepatic arterial flow (14.5 mL/100 g/min), distribution volume (27%), and mean transit time (40 seconds) and decreased hepatic portal venous flow (29 mL/100 g/min), consistent with advanced fibrosis or cirrhosis [88].

A prospective study of liver perfusion parameters showed increased arterial flow, MTT, and distribution volume and decreased portal venous flow in patients with advanced fibrosis and cirrhosis (n = 27) [88]. Distribution volume, MTT, and arterial flow had the best sensitivity (76.9-84.6%) and specificity (71.4-78.5%) for the diagnosis of advanced fibrosis as determined by histologic examination. The increased distribution volume in patients with cirrhosis may be related to increased interstitial volume. The increase in MTT may be explained by collagen deposition in the extracellular spaces of Disse restricting diffusion of small particles.

MR Elastography
MRE is an emerging diagnostic imaging technique for quantitatively assessing the mechanical properties of tissue [93]. Normal human liver tissue is very soft to palpation at surgery, similar to subcutaneous adipose tissue. In contrast, it is well known that the liver becomes very firm or even hard to palpation in patients with cirrhosis. On the basis of these considerations, investigators have developed techniques for applying MRE for evaluating the liver and have tested the usefulness of the technique for diagnosing hepatic fibrosis. MRE involves a three-step process: one, generating mechanical waves within the tissues of interest; two, imaging the micron-level displacements caused by propagating waves using a special MRI technique with oscillating motion-sensitizing gradients; and three, processing the wave images using an inversion algorithm to generate quantitative maps of mechanical properties.

MRE can be implemented on a standard MR system by installing a device for generating mechanical vibration in the liver under MR-scanner control, a special MRE pulse sequence, and processing software to generate the diagnostic MRE images, which are called "elastograms." In a typical implementation, a simple, drumlike passive acoustic driver is placed over the right anterior chest wall and coupled to a source of low-frequency sound waves by a flexible tube (Fig. 4). Vibrations at 40-90 Hz are generated in the abdomen with this device. The waves are imaged with a modified phase-contrast MRI pulse sequence. Imaging time can be as short as approximately 15 seconds using parallel acquisition techniques and is done during suspended respiration. Because the incremental imaging time is so small, MRE can readily be added to standard abdominal MRI protocols. MRE data are processed with a special inversion algorithm to generate a quantitative image showing the elasticity of the liver.

View larger version (14K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 4 —Drawing shows MR elastography as performed in standard scanner. Plastic drumlike passive driver device is placed over liver to generate shear waves that can be imaged with special phase-contrast MRI sequence. Abdominal acoustic driver is powered by speakerlike device (on left) that can be placed outside magnet room and connected to passive driver by flexible tubing. Propagating shear waves are imaged and information is processed to generate elastograms, which quantitatively depict stiffness of tissues.

View larger version (47K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 5A —Assessment of hepatic fibrosis with MR elastography (MRE) in three patients with chronic liver disease: T2-weighted anatomic images (top), MRE wave images (middle), and MR elastograms showing stiffness of liver tissue (bottom). In 36-year-old man with liver steatosis and no fibrosis, liver has normal appearance in anatomic image, and wave image shows that shear waves at 60 Hz have short wavelength, consistent with normally soft mechanical characteristics of normal liver tissue. Elastogram shows mean stiffness value of 2.1 kPa, well below upper limit of normal (2.9 kPa), indicating absence of hepatic fibrosis.

View larger version (50K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 5B —Assessment of hepatic fibrosis with MR elastography (MRE) in three patients with chronic liver disease: T2-weighted anatomic images (top), MRE wave images (middle), and MR elastograms showing stiffness of liver tissue (bottom). 29-year-old woman with hepatic steatosis and mild fibrosis also has normal-appearing liver, but wave images show relative prolongation of visualized shear waves. Elastogram shows abnormally high mean stiffness value of 4.8 kPa, consistent with moderate hepatic fibrosis. Biopsy showed stage 1 fibrosis.

View larger version (47K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 5C —Assessment of hepatic fibrosis with MR elastography (MRE) in three patients with chronic liver disease: T2-weighted anatomic images (top), MRE wave images (middle), and MR elastograms showing stiffness of liver tissue (bottom).43-year-old man with primary sclerosing cholangitis has abnormal hepatic contour and ascites, consistent with chronic liver disease. Wave image shows marked lengthening of visualized shear waves. Elastogram shows that liver stiffness is markedly heterogeneous, with many confluent areas measuring more than 8 kPa in stiffness. Biopsy confirmed presence of moderate to advanced fibrosis.

Diagnosis and Quantification of Hepatic Steatosis With MRI
For more than 20 years, MRI has been an established method for detecting the presence of hepatic steatosis [103]. MRI exploits the fact that fat resonates more slowly than water (by 210 Hz at 1.5 T) [103, 104].

MRS
Hydrogen-1 MRS is considered to be the noninvasive reference standard method for measuring hepatic fat content, with many studies showing strong correlations between MRS and histologic grade of steatosis [105-112]. Most methods use single-voxel spectroscopy approaches such as point-resolved spectroscopy (PRESS) [113] or stimulated-echo acquisition mode (STEAM) [114] and are easily performed within a few breath-holds. The main disadvantage of single-voxel spectroscopy is that a single, large voxel (typically 2.0 x 2.0 x 2.0 cm, or 8.0 cm³) is interrogated and cannot provide volumetric evaluation of the fat content of the liver. It is well known that hepatic steatosis is often heterogeneous, and thus single-voxel spectroscopy cannot provide a comprehensive evaluation of hepatic steatosis. However, 2D chemical shift imaging methods are being developed. In addition, MRS methods must use long TR values to avoid T1-related bias and, in theory, must correct for differential T2 decay between water and fat. Because MRS methods are spin-echo based, there is concern for the effects of J-coupling [115] that could influence the relative amplitudes of fat versus water signal and bias estimates of fat fraction. Finally, unlike imaging-based fat-quantification methods that automatically generate a fat-fraction image from which fat content can be assessed, MRS requires relatively complex postprocessing involving some user interaction to measure the fat fraction from the acquired spectrum.

View larger version (68K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 6A —In-phase (left) and out-of-phase (right) images in two patients with hepatic steatosis. Marked dropout on out-of-phase image is present in 51-year-old man, consistent with qualitatively severe steatosis.

View larger version (75K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 6B —In-phase (left) and out-of-phase (right) images in two patients with hepatic steatosis. Mild signal dropout (arrow) is seen on out-of-phase image of 47-year-old man, with focal fatty sparing near gallbladder fossa, consistent with mild steatosis.

These investigators have shown excellent correlation between fat fraction and the fat measured with MRS.

Unfortunately, conventional in-phase and out-of-phase imaging suffers from three drawbacks. First, fat fractions over 50% cannot be assessed reliably [118, 121] because separation of water and fat signals is necessary to measure fat fractions from 0% to 100%. Hepatic fat fractions greater than 50% are very uncommon, however. As a result of this ambiguity, more recent approaches have used chemical shift-based water-fat separation methods to generate separate water and fat images that allow direct calculation of fat-fraction images with a complete dynamic range from 0% to 100% [112, 122, 123].

The second major drawback of conventional in-phase and out-of-phase imaging is that it is highly dependent on T1 and T2^*. Although fat fraction measurements made with in-phase and out-of-phase imaging show excellent correlation with other measures of hepatic steatosis (MRS, biopsy, phantoms, and so on), the estimates of fat fraction made with in-phase and out-of-phase imaging do not represent a biologically based estimate of the fat concentration in the liver. Fat-fraction estimates are highly dependent on acquisition parameters such as TR, TE, flip angle, and field strength, all of which alter the degree of T1 and T2^* weighting. Differences in T1 between fat and liver tissue lead to overestimation of fat if the acquisition is T1 weighted. T2^* decay corrupts the signal evolution of both water and fat as TE increases, leading to errors in fat-fraction estimation. The confounding effects of T1 and T2^* were recognized by Fishbein et al. [117] and Hussain et al. [118]. More recently, Liu et al. [124] and Bydder et al. [125] proposed a low flip angle approach that avoids T1-related bias by making the fat-fraction estimates T1 independent. Correction for T2^* decay has been addressed by Hussain et al. who used a second acquisition to measure and correct for T2^*. More recently, Yu et al. [126] and Bydder et al. have developed T2^* correction approaches in combination with six echoacquisition strategies. By directly estimating the T2^* as part of the acquisition itself, the effects of T2^* are removed and more accurate estimates of water and fat (and subsequently fat fraction) can be made. Lack of correction for T2^* decay can lead to very large errors. For example, in a liver with no fat and T2^* of 25 milliseconds, the apparent fat fraction with in-phase and out-of-phase imaging is 5%, which is an unacceptably high error, especially if we consider a 5.6% detection threshold. In addition, abnormally elevated intrahepatic stores of iron are common in nonalcoholic fatty liver disease. Hepatic iron overload is well known to create oxidative stress that leads to end-stage cirrhosis, liver failure, and development of HCC [127], which is a major cause of death in patients with primary hemochromatosis [127].

View larger version (88K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 7A —45-year-old woman with hepatic steatosis. Fat-fraction MR image (A) obtained using T1-independent, T2*-corrected acquisition with accurate spectral modeling using iterative decomposition of water and fat with echo asymmetry and least-squares estimation water-fat-separation methods [122, 124, 126, 132] (B) shows close agreement with single-voxel MR spectroscopy (MRS) B from 2 x 2 x 2 cm voxel from posterior segment of right lobe of liver (square, A). Fatty sparing in gallbladder fossa (arrow, A) is noted incidentally. At least five discrete fat spectral peaks (asterisks, B) can be seen in spectrum. MRI fat fraction was 25% ± 3%; MRS fat fraction was 27% ± 4%.

View larger version (7K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 7B —45-year-old woman with hepatic steatosis. Fat-fraction MR image (A) obtained using T1-independent, T2*-corrected acquisition with accurate spectral modeling using iterative decomposition of water and fat with echo asymmetry and least-squares estimation water-fat-separation methods [122, 124, 126, 132] (B) shows close agreement with single-voxel MR spectroscopy (MRS) B from 2 x 2 x 2 cm voxel from posterior segment of right lobe of liver (square, A). Fatty sparing in gallbladder fossa (arrow, A) is noted incidentally. At least five discrete fat spectral peaks (asterisks, B) can be seen in spectrum. MRI fat fraction was 25% ± 3%; MRS fat fraction was 27% ± 4%.

The final disadvantage of conventional in-phase and out-of-phase fat-fraction imaging as well as conventional chemical shift-based water separation methods is the spectral complexity of fat. Historically, in-phase and out-of-phase imaging and most chemical shift-based fat-water separation methods model water and fat both as single nuclear MR peaks. Although the spectral peak of water is a well defined single peak, fat is well known to contain at least six well-defined peaks, at least two of which are very close to the water peak [131]. As a result, it is not possible to accurately separate water and fat signals if fat is modeled as a single resonance. Recently, Bydder et al. [125] and Yu et al. [132] introduced methods that accurately describe spectral modeling of fat that accounts for the different chemical shifts of each fat peak as well as the relative amplitudes of these peaks. The use of accurate spectral modeling has been shown to greatly improve the agreement between the measured fat fraction with imaging and reference standards such as MRS [133, 134].

Methods that provide accurate estimation of fat content in the liver that are biologically based and have broad applicability across multiple platforms and field strengths must be T1 independent, correct for T2^* decay, and accurately model the nuclear MR spectrum of fat.

Figures 7A, and 7B shows a fat-fraction image obtained from a T1-independent acquisition with T2^* correction and accurate spectral modeling on the basis of a six-echo iterative decomposition of water and fat with echoasymmetry and least-squares estimation (IDEAL) acquisition [122, 126, 132]. This image shows close agreement with single-voxel MRS [133]. Figures 8A, and 8B shows fat-fraction images acquired with a T1-independent acquisition with T2^* correction and accurate spectral modeling on the basis of six magnitude images acquired on in-phase and out-of-phase images [125]. In this example, an approximately 20% decrease in liver fat concentration is nicely shown in a morbidly obese patient undergoing rapid weight loss. Of note, this method is a magnitude-based approach and therefore cannot resolve fat fractions greater than 50%, which is why subcutaneous fat appears dark in these fat-fraction images. However, this approach, as well as the IDEAL-based method, has been shown to have excellent agreement with MRS, indicating that measurements of liver fat are biologically based and directly measure the fat concentration in the liver [133, 134]. An interesting and important advantage of T2^*-correction methods such as those by Yu et al. [126] and Bydder et al. [125] is that R₂^* (that is, 1 / T2^*) images are estimated. As discussed later, T2^* measurements in the liver provide accurate measures of liver iron content, and thus T2^*-correction fat-quantification methods have the added benefit of simultaneous iron quantification in the liver.

View larger version (134K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 8A —Fat-fraction MR images before and after weight loss. and B, MR images acquired using T1-independent, T2*-corrected method with accurate spectral modeling develped by Bydder et al. [125] in morbidly obese woman who weighed 159 kg before (A) and after (B) 12-kg weight loss in 27 days. Overall fat fraction was variable throughout liver but showed changes of approximately 3-5% from initial fat fraction of 16-24%. Fat in adipose tissue appears black because of inherent ambiguity of fat fractions greater than 50%; however, excellent quantification of hepatic steatosis can be achieved using this method (Courtesy of Shiehmorteza M, San Diego, CA).

View larger version (131K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 8B —Fat-fraction MR images before and after weight loss. MR images acquired using T1-independent, T2*-corrected method with accurate spectral modeling develped by Bydder et al. [125] in morbidly obese woman who weighed 159 kg before (A) and after (B) 12-kg weight loss in 27 days. Overall fat fraction was variable throughout liver but showed changes of approximately 3-5% from initial fat fraction of 16-24%. Fat in adipose tissue appears black because of inherent ambiguity of fat fractions greater than 50%; however, excellent quantification of hepatic steatosis can be achieved using this method (Courtesy of Shiehmorteza M, San Diego, CA).

View larger version (103K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 9A —Patterns of signal dropout. T2*-weighted (TE = 10 milliseconds) gradient-echo image of 27-year-old man with transfusional hemosiderosis qualitatively shows iron overload through decreased signal in spleen (short arrow) and bone marrow (arrowhead) in addition to liver. Note also sparing of pancreas (long arrow).

View larger version (82K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 9B —Patterns of signal dropout. In contrast, T2-weighted fast spin-echo (TE = 80 milliseconds) image from 22-year-old woman with genetic hemochromatosis shows decreased signal in pancreas (long arrow) in addition to liver, with sparing of spleen (short arrow) and bone marrow (arrowhead). Patterns of signal dropout are easily depicted on these conventional imaging methods, which are very helpful in distinguishing cause of these two forms of iron overload. However, assessing severity of disease requires quantitative methods.

Top Abstract Introduction Role of Liver Biopsy... Limitations of Liver Biopsy Noninvasive Diagnosis of Liver... Diagnosis and Quantification of... Limitations of MRI Methods Future Directions Conclusions References

Conventional T2- and T2^*-weighted imaging provides an excellent means for qualitative detection of hepatic iron overload. For example, Figures 9A, and 9B shows the effects of iron overload in two patients, one with transfusional hemosiderosis and the other with genetic hemochromatosis. In addition, iron overload also can be diagnosed with conventional inphase and out-of-phase imaging, as shown in Figures 10A, 10B, and 10C. Paradoxical signal dropout is seen on the in-phase image because this image was acquired at a longer TE than the out-of-phase image and iron accelerates T2^* and T2 decay. Of note, this paradoxical dropout explicitly shows why iron confounds the ability of in-phase and out-of-phase imaging to quantify fat: iron and fat have the opposite effect on signal dropout. The pattern of signal dropout in different organs can be used to distinguish the type of iron overload. For example, genetic hemochromatosis generally affects the liver and pancreas and spares the spleen and bone marrow, whereas hemosiderosis (e.g., from transfusional iron overload) affects the liver, bone marrow, and spleen while leaving the pancreas unaffected.

View larger version (117K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 10A —48-year-old man with genetic hemochromatosis. In-phase (A) and out-of-phase (B) images show paradoxical signal drop of liver on in-phase image. This occurs because TE of in-phase image (4.6 milliseconds) is longer than that of out-of-phase image (2.3 milliseconds), leading to signal dropout from accelerated T2* decay in presence of iron overload. Note lack of signal dropout in spleen, which is characteristic of genetic hemochromatosis.

View larger version (122K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 10B —48-year-old man with genetic hemochromatosis. In-phase (A) and out-of-phase (B) images show paradoxical signal drop of liver on in-phase image. This occurs because TE of in-phase image (4.6 milliseconds) is longer than that of out-of-phase image (2.3 milliseconds), leading to signal dropout from accelerated T2* decay in presence of iron overload. Note lack of signal dropout in spleen, which is characteristic of genetic hemochromatosis.

View larger version (91K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 10C —48-year-old man with genetic hemochromatosis. R2* (1 / T2*) map acquired using method of Yu et al. [126] shows markedly abnormal R2* of 240 seconds-1 (T2* = 4.1 milliseconds [circle]).

More recent approaches for iron quantification have focused on direct measurement of T2^* or R₂^* mapping. Using a 3D multiecho gradient-echo acquisition, Wood et al. [137] recently performed a study in 102 patients undergoing biopsy showing a linear correspondence between R₂^* and hepatic iron concentration. This study provides a useful calibration between R₂^* and hepatic iron concentration. The primary advantage of this approach is that a fundamental tissue property (R₂^*) is measured and is, in principle, independent of acquisition parameters such as TR, TE, and flip angle. The calibration between R₂^* and iron concentration will be dependent on field strength of course, and many investigators are currently evaluating the use of R₂^* measurements at 3 T for iron quantification [138].

Another primary advantage of R₂^* mapping is that rapid 3D multiecho gradient-echo sequences are now available for rapid R₂^* measurements within a single breath-hold. R₂^* values can be fit from the data in a fully automated manner, requiring no user input other than measuring R₂^* from the images and determining hepatic iron concentration from the calibration curve. Figure 10A, 10B, and 10C shows an R₂^* map from the same location calculated using the T2^* approach described by Yu et al. [126], explicitly showing the iron overload in the liver while sparing the spleen.

View larger version (75K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 11A —54-year-old woman with genetic hemochromatosis. R2* (1 / T2*) images measured using method of Yu et al. [126] before (A) and after (B) 1 year of phlebotomy therapy. Before therapy, R2* was 192 seconds-1 (T2* = 5.2 milliseconds [circle, A]) and after 1 year of phlebotomy, R2* was 106 seconds-1 (T2* = 9.4 milliseconds [circle, B]), showing treatment response that correlates with drop in serum ferritin from 184 to 91 ng/mL.

View larger version (74K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 11B —54-year-old woman with genetic hemochromatosis. R2* (1 / T2*) images measured using method of Yu et al. [126] before (A) and after (B) 1 year of phlebotomy therapy. Before therapy, R2* was 192 seconds-1 (T2* = 5.2 milliseconds [circle, A]) and after 1 year of phlebotomy, R2* was 106 seconds-1 (T2* = 9.4 milliseconds [circle, B]), showing treatment response that correlates with drop in serum ferritin from 184 to 91 ng/mL.

View larger version (135K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 12A —47-year-old man with biopsy-proven hepatic iron overload and steatosis. In-phase (A) and out-of-phase (B) images show nearly identical signal intensities (846 and 851 AU, respectively), suggesting neither fat nor iron. However, simultaneous estimation of fat fraction (C) and R2* (1 / T2*) (D) using method of Yu et al. [126] indicates abnormal level of steatosis (9%, [circle, C]) and shortened T2* (15.1 milliseconds, normal = 25-30 milliseconds [left circle, D]). Also note shortened T2* in spleen (9.7 milliseconds [right circle, D]), which can also be inferred through signal dropout in spleen on in-phase image (arrow, A).

View larger version (141K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 12B —47-year-old man with biopsy-proven hepatic iron overload and steatosis. In-phase (A) and out-of-phase (B) images show nearly identical signal intensities (846 and 851 AU, respectively), suggesting neither fat nor iron. However, simultaneous estimation of fat fraction (C) and R2* (1 / T2*) (D) using method of Yu et al. [126] indicates abnormal level of steatosis (9%, [circle, C]) and shortened T2* (15.1 milliseconds, normal = 25-30 milliseconds [left circle, D]). Also note shortened T2* in spleen (9.7 milliseconds [right circle, D]), which can also be inferred through signal dropout in spleen on in-phase image (arrow, A).

View larger version (94K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 12C —47-year-old man with biopsy-proven hepatic iron overload and steatosis. In-phase (A) and out-of-phase (B) images show nearly identical signal intensities (846 and 851 AU, respectively), suggesting neither fat nor iron. However, simultaneous estimation of fat fraction (C) and R2* (1 / T2*) (D) using method of Yu et al. [126] indicates abnormal level of steatosis (9%, [circle, C]) and shortened T2* (15.1 milliseconds, normal = 25-30 milliseconds [left circle, D]). Also note shortened T2* in spleen (9.7 milliseconds [right circle, D]), which can also be inferred through signal dropout in spleen on in-phase image (arrow, A).

View larger version (95K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 12D —47-year-old man with biopsy-proven hepatic iron overload and steatosis. In-phase (A) and out-of-phase (B) images show nearly identical signal intensities (846 and 851 AU, respectively), suggesting neither fat nor iron. However, simultaneous estimation of fat fraction (C) and R2* (1 / T2*) (D) using method of Yu et al. [126] indicates abnormal level of steatosis (9%, [circle, C]) and shortened T2* (15.1 milliseconds, normal = 25-30 milliseconds [left circle, D]). Also note shortened T2* in spleen (9.7 milliseconds [right circle, D]), which can also be inferred through signal dropout in spleen on in-phase image (arrow, A).

Top Abstract Introduction Role of Liver Biopsy... Limitations of Liver Biopsy Noninvasive Diagnosis of Liver... Diagnosis and Quantification of... Limitations of MRI Methods Future Directions Conclusions References

DWI
Image quality needs to be improved, especially at higher field strengths. The use of different sequence parameters and hardware makes it difficult to make comparisons between studies, and DWI requires more standardization [139]. Liver fat and iron deposition may also alter diffusion measurements, and they should be assessed. Other technical factors, such as cardiac motion limiting evaluation of the left hepatic lobe and respiratory motion affecting ADC values in the right lobe, need to be addressed with respiratory-triggered techniques.

Perfusion-Weighted MRI
Similar to DWI, the selection of imaging parameters and perfusion models varies widely among studies and limits the comparison of perfusion-weighted MRI results from study to study. The intensive postprocessing required to obtain perfusion parameters is a barrier to the widespread clinical use of perfusion-weighted MRI. Automated perfusion analysis software may overcome this limitation.

MRE
The most common cause of technical failure of MRE is the presence of hepatic iron overload. The resulting low SI of the liver can prevent adequate visualization of mechanical waves. Alternative MRE pulse sequences can be developed to address this problem.

Future Directions

Top Abstract Introduction Role of Liver Biopsy... Limitations of Liver Biopsy Noninvasive Diagnosis of Liver... Diagnosis and Quantification of... Limitations of MRI Methods Future Directions Conclusions References

 
Multiparametric imaging combining conventional sequences with some of the previously discussed techniques (alone or in combination) could enable a comprehensive examination of the liver, including information on the presence of fat, iron, and fibrosis as well as HCC and portal hypertension and could represent the future of liver imaging, possibly replacing the liver biopsy, at least for follow-up studies. This development would constitute an important clinical tool that could be used as a noninvasive technique for prospective drug trials assessing antiviral and antifibrotic therapy.

The use of 3-T imaging provides higher signal-to-noise ratios and theoretically improved image quality. However, at higher field strengths, DWI using single-shot EPI is limited by increased susceptibility, which limits the use of higher b values. Optimized fat-water imaging at 3 T should also be standardized.

Conclusions

Top Abstract Introduction Role of Liver Biopsy... Limitations of Liver Biopsy Noninvasive Diagnosis of Liver... Diagnosis and Quantification of... Limitations of MRI Methods Future Directions Conclusions References

 
With the continued increased prevalence of liver disease (mostly due to nonalcoholic fatty liver disease and HCV infection), MRI will play an increasingly important role in the evaluation of patients with chronic liver disease because of the lack of ionizing radiation and the possibility of performing multiparametric imaging combining conventional and functional sequences. However, more clinical evidence is needed to determine which method or combination of methods achieves the best accuracy for assessment of fibrosis, fat, and iron deposition.

References

Top Abstract Introduction Role of Liver Biopsy... Limitations of Liver Biopsy Noninvasive Diagnosis of Liver... Diagnosis and Quantification of... Limitations of MRI Methods Future Directions Conclusions References

 

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