CME
JOURNAL OF MAGNETIC RESONANCE IMAGING 32:1141–1148 (2010)
Original Research
Diffusion Tensor Imaging of Liver Fibrosis
in an Experimental Model
Jerry S. Cheung, PhD,1–3 Shu Juan Fan, MSc,1,2 Darwin S. Gao, BEng,1,2
April M. Chow, PhD,1,2 Kwan Man, PhD,4 and Ed X. Wu, PhD1,2,5*
Purpose: To characterize changes in diffusion properties
of liver using diffusion tensor imaging (DTI) in an experimental model of liver fibrosis.
characterizing liver fibrosis and to determine its role in
clinical settings.
Key Words: diffusion tensor imaging; diffusion-weighted
imaging; liver fibrosis; liver cirrhosis; apparent diffusion
coefficient; fractional anisotropy; MRI; CCl4
J. Magn. Reson. Imaging 2010;32:1141–1148.
C 2010 Wiley-Liss, Inc.
V
Materials and Methods: Liver fibrosis was induced in
Sprague–Dawley rats (n ¼ 12) by repetitive dosing of carbon tetrachloride (CCl4). The animals were examined with
a respiratory-gated single-shot spin-echo echo-planar DTI
protocol at 7 T before, 2 weeks after, and 4 weeks after
CCl4 insult. Apparent diffusion coefficient (ADC), directional diffusivities (ADC// and ADC?), and fractional anisotropy (FA) were measured. Liver histology was performed with hematoxylin-eosin staining and Masson’s
trichrome staining.
Results: Significant decrease (P < 0.01) in ADC was found
at 2 weeks (0.86 6 0.09 103 mm2/s) and 4 weeks (0.74
6 0.09 103 mm2/s) following CCl4 insult, as compared
with that before insult (0.97 6 0.08 103 mm2/s). Meanwhile, FA at 2 weeks (0.18 6 0.03) after CCl4 insult was
significantly lower (P < 0.01) than that before insult (0.26
6 0.05), and subsequently normalized at 4 weeks (0.26 6
0.07) after the insult. Histology showed collagen deposition, presence of intracellular fat vacuoles, and cell necrosis/apoptosis in livers with CCl4 insult.
Conclusion: DTI detected the progressive changes in
water diffusivities and diffusion anisotropy of liver tissue
in this liver fibrosis model. ADC and FA are potentially
valuable in detecting liver fibrosis at early stages and
monitoring its progression. Future human studies are
warranted to further verify the applicability of DTI in
1
Laboratory of Biomedical Imaging and Signal Processing, University of
Hong Kong, Pokfulam, Hong Kong SAR, China.
2
Department of Electrical and Electronic Engineering, University of
Hong Kong, Pokfulam, Hong Kong SAR, China.
3
Athinoula A. Martinos Center for Biomedical Imaging, Department of
Radiology, Massachusetts General Hospital and Harvard Medical
School, Charlestown, MA 02129.
4
Department of Surgery, University of Hong Kong, Pokfulam, Hong
Kong SAR, China.
5
Department of Anatomy, University of Hong Kong, Pokfulam, Hong
Kong SAR, China.
Contract grant sponsor: Hong Kong Grant Council; Contract grant
number: GRF HKU7808/09M.
*Address reprint requests to: E.X.W., Laboratory of Biomedical Imaging and Signal Processing, Departments of Electrical and Electronic
Engineering, Medicine and Anatomy, University of Hong Kong, Hong
Kong SAR, China. E-mail: [email protected]
Received December 19, 2009; Accepted August 6, 2010.
DOI 10.1002/jmri.22367
View this article online at wileyonlinelibrary.com.
C 2010 Wiley-Liss, Inc.
V
LIVER FIBROSIS is a typical complication of chronic
liver diseases resulting in cirrhosis and increased risk of
hepatocellular carcinoma (1–5). More than 400 and 170
million individuals are chronically infected with hepatitis B and hepatitis C virus, respectively, worldwide (6).
Early diagnosis and characterization of liver fibrosis
could facilitate early interventions and thus prevent its
progression to cirrhosis (7–9). However, the efficacy of
the current noninvasive techniques in assessing liver fibrosis such as routine liver function tests, serological
tests of specific serum makers, and liver stiffness measurement using ultrasound transient elastography has
yet to be established (6,10–13). At present, percutaneous liver biopsy has been considered the gold standard,
yet it is highly invasive. Liver biopsy is also associated
with potential risks of complications and repeated biopsies are not practicable clinically, thus its utility as a tool
for longitudinal monitoring has been limited. In addition, it is prone to sampling error and interobserver variation (14,15), leading to erroneous staging. Therefore, a
noninvasive magnetic resonance imaging (MRI) technique for detecting liver fibrosis at early stages and monitoring the disease progression or regression in response
to treatment is highly desirable.
Morphologic analysis using conventional MRI for
the assessment of liver fibrosis and cirrhosis has been
subjected to interobserver variability and limited in
sensitivity and specificity (16–18). Several novel MRI
techniques including contrast-enhanced MRI, MR
elastography, and MR diffusion imaging have been
proposed as noninvasive alternatives to characterize
liver fibrosis and cirrhosis (18,19). Double-contrast
MRI using both gadolinium chelates and superparamagnetic iron oxides (SPIOs) was suggested to provide
synergistic effects in visualizing liver fibrosis directly
based on the hepatic texture alterations. Specifically,
SPIOs preferentially accumulated and darkened
1141
1142
nonfibrous liver tissue, while gadolinium chelates
caused signal enhancement in fibrous tissues which
appeared as bright reticulations (20,21). MR elastography has been recently shown to be sensitive in
assessing liver fibrosis by measuring the mechanical
properties of liver tissue, in particular, tissue elasticity. Liver stiffness measured by MR elastography was
found to increase systematically with the stage of liver
fibrosis (22,23). However, more studies remain to be
performed to fully evaluate the clinical utility of these
two MRI techniques for staging liver fibrosis.
MR diffusion imaging in liver, which can be easily
incorporated into routine MRI examination on most
clinical scanners, is a sensitive tool for characterizing
the microscopic motion of water molecules (24–26).
Molecular diffusion of water molecules arises from
their random motion at a microscopic level. Diffusionweighted imaging (DWI) and diffusion tensor imaging
(DTI) have been widely used to characterize normal
and diseased tissues the in central nervous system
since its introduction (24–27). With the advent of the
single-shot echo-planar imaging (EPI) technique in
combination with respiratory triggering or breathholding (28–31), MR diffusion imaging has become
possible in abdominal organs regardless of the effect
of gross physiologic motion. Apparent diffusion coefficient (ADC) measured by DWI has been used to characterize focal hepatic lesions (32–36) and diffuse liver
diseases including liver fibrosis (37–46). Liver fibrosis
is a nonspecific response to chronic liver disease
which leads to excess synthesis of extracellular matrix
(ECM), especially collagen fibers, in which the protons
are less abundant and are tightly bound (47,48). Several studies hypothesized that molecular water diffusion in fibrotic liver would be restricted by the presence of collagen fibers in the distorted lobular
structure and their results showed that ADC values
decreased in fibrotic and cirrhotic liver as compared
with normal liver (38–45).
DTI has the potential to provide both functional (ie,
water diffusion in tissue) and microstructural information in liver tissue by means of water diffusivity
and diffusion anisotropy quantitation, and thus may
contribute to better characterization of liver fibrosis.
The aim of this study was to characterize changes in
diffusion properties of liver using DTI in an experimental model of liver fibrosis.
MATERIALS AND METHODS
Animal Preparation
All animal experiments were approved by the institutional animal ethics committee. Liver fibrosis was
induced in male adult Sprague–Dawley rats (220–260
g; n ¼ 12) by subcutaneous injection of 1:1 volume
mixture of carbon tetrachloride (CCl4) in olive oil at a
dose of 0.2 mL / 100 g of body weight twice a week
for 4 weeks (49–51). Intermittent administration of
CCl4 has been widely used to experimentally induce
liver fibrosis in rodents by evoking a marked infiltration of inflammatory cells, thus mimicking the
changes in chronic viral hepatitis-associated fibrosis
(49,52). The twice-weekly dosing can induce early
Cheung et al.
Figure 1. Schedule of CCl4 administration (twice a week) for
induction of liver fibrosis in adult Sprague–Dawley rats, diffusion tensor imaging (DTI) experiments, and liver histology.
stages of liver fibrosis and established fibrosis after 2
and 4 weeks of CCl4 administration, respectively, in
rodents (49,51). This well-controlled CCl4-induced
liver fibrosis model allows the study of a homogeneous population of liver fibrosis. Prior to MRI experiments, animals were fasted to reduce motion artifact
due to peristalsis (53). MRI was performed in animals
at 1 day before, 2 weeks after, and 4 weeks following
CCl4 administration. Note that animals were imaged
at about 48 hours after the last CCl4 administration
to avoid any acute toxic or inflammatory responses of
CCl4 on MRI measurements (54). The overall schedule
of the animal experiment is shown in Fig. 1.
In Vivo DTI Experiments
All MRI experiments were performed on a 7 T MRI
scanner with a maximum gradient of 360 mT/m (70/
16 PharmaScan, Bruker Biospin, Germany), using a
60 mm quadrature resonator for both radiofrequency
(RF) transmission and receiving. During imaging,
each animal was anesthetized with isoflurane/air
using 1.0%–1.5% for maintenance via a nose cone.
Body temperature was maintained at 36.5 C by circulating warm water in a heating pad. A respiratory
belt was placed around the abdomen to monitor respiration and synchronize MR acquisition. T1-weighted
scout scans were acquired in three orthogonal directions to ensure similar slice localization for DTI in different animals at different timepoints. DTI was performed in one axial slice covering a large portion of
liver while avoiding inclusion of the lung. The respiratory-gated single-shot spin-echo EPI (SE-EPI) DTI protocol was employed using repetition time (TR) ¼ 2 respiratory cycles (2.0–2.5 sec), echo time (TE) ¼ 32
msec, duration of gradient pulse/diffusion time (d/D)
¼ 2.6/20 msec, two b-values (0 and 1000 s/mm2), six
diffusion gradient directions, field of view (FOV) ¼
5.12 5.12 cm2, slice thickness ¼ 2 mm, acquisition
matrix ¼ 64 64, voxel size ¼ 0.8 0.8 2 mm3,
sampling bandwidth (BW) ¼ 221 kHz, number of signal averages (NEX) ¼ 10, and scan time of 3 minutes
(30,31). Note that a high b-value (1000 s/mm2) was
used to decrease the effect of blood perfusion on diffusion measurements (30,32,55). The DTI acquisition
was repeated twice. A T2-weighted image at the same
slice location was acquired with a 2D rapid acquisition with relaxation enhancement (RARE) protocol
using TR ¼ 2000 msec, effective TE ¼ 40 msec, FOV
¼ 5.12 5.12 cm2, slice thickness ¼ 2.0 mm, acquisition matrix ¼ 128 128, RARE factor ¼ 8, and with
DTI Assessment of Liver Fibrosis
1143
respiratory gating. Note that a relatively small matrix
size was chosen in single-shot SE-EPI DTI to maintain
sufficient signal-to-noise ratio (SNR) and acceptable
level of EPI-related artifacts such as geometric distortion and N/2 ghosting.
Image and Statistical Analysis
Image analysis was performed on a blind basis with
respect to the status of the animals. Diffusionweighted EPI images were coregistered with the nondiffusion-weighted (B0) EPI image using Automated
Image Registration (AIR 5.2.5) (56) for correcting any
misregistration caused by the body motions during
DTI and the gradient eddy current related image distortions. ADC, axial diffusivity (ADC//), radial diffusivity (ADC?), fractional anisotropy (FA), and color-coded
FA (CFA) direction maps were generated using DTIStudio (v. 2.4; JHU, Baltimore, MD) (57). In brief, three
principal diffusivities (l1, l2, and l3) were derived
from diagonalizating the 3 3 diffusion tensor matrix. Afterwards, ADC//, ADC?, ADC, and FA were
computed as follows (25,26,30,31):
ADC== ¼ l1
½1
ADC? ¼ ðl2 þ l3 Þ=2
ADC ¼ ðl1 þ l2 þ l3 Þ=3
½2
½3
ffi
rffiffiffi sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
3 ðl1 ADCÞ2 þ ðl2 ADCÞ2 þ ðl3 ADCÞ2
FA ¼
:
2
l1 2 þ l2 2 þ l3 2
½4
Note that water diffusivities parallel and perpendicular to the principal diffusion direction are represented by ADC// and ADC?, respectively. Moreover,
ADC is a rotationally invariant measure of the overall
water diffusivity, while FA is a measure of diffusion
directionality. A region of interest (ROI) was first
defined in the axial anatomical scout image acquired
before CCl4 insult by encompassing a large homogeneous region in liver parenchyma while excluding
major blood vessels. It was used for ADC, l//, l?, and
FA measurements. For measurements at subsequent
timepoints the same ROI mask was placed onto the
anatomical scout image with slight manual adjustments to account for misregistration. The measurements of repeated DTI acquisitions were averaged for
each animal and one-way analysis of variance
(ANOVA) with Tukey’s multiple comparison test was
employed to compare the ADC, ADC//, ADC?, and FA
measurements among different timepoints in animals.
Results were expressed as mean 6 standard deviation
(SD). A P-value of less than 0.05 was considered statistically significant.
Histology
After the MRI scan following 2 weeks of CCl4 administration, 4 out of 12 animals were sacrificed for
histological evaluation. Furthermore, four out of the
remaining eight animals were sacrificed after MRI fol-
lowing 4 weeks of CCl4 insult as shown in Fig. 1. One
additional normal animal was sacrificed as a control.
Liver specimens were fixed in formalin, embedded in
paraffin, sectioned, and examined by light microscopy
after standard hematoxylin-eosin (H&E) staining and
Masson’s trichrome staining (58–60). Degree of liver
fibrosis in the samples was evaluated semiquantitatively according to the METAVIR classification using a
five-point scale with F0, F1, F2, F3, and F4 for no
fibrosis, portal fibrosis without septa, portal fibrosis
with few septa, numerous septa without cirrhosis,
and cirrhosis, respectively (61).
RESULTS
Figure 2 shows the representative B0 EPI images, T2weighted images, ADC maps, FA maps, and CFA
direction maps of liver from one animal before, 2
weeks after, and 4 weeks after CCl4 insult. Typical
ROIs used for ADC, ADC//, ADC?, and FA are illustrated in the B0 EPI images with size of 95 6 27 mm2
in all animals at different timepoints. Note that FA in
the liver was seen to be relatively inhomogeneous
without any directional dominance, while the dominant diffusion direction in the spinal cord was consistently observed to be along the cranial–caudal direction (blue in the CFA maps) as expected. ADC, FA,
ADC//, and ADC? values at different timepoints of
CCl4 insult are shown in Fig. 3. A significant decrease
(P < 0.01) in ADC was found at 2 weeks (0.86 6 0.09
103 mm2/s) and 4 weeks (0.74 6 0.09 103
mm2/s) following CCl4 insult, as compared with that
before insult (0.97 6 0.08 103 mm2/s). Meanwhile,
FA at 2 weeks (0.18 6 0.03) after CCl4 insult was significantly lower (P < 0.01) than that before (0.26 6
0.05) and 4 weeks after (0.26 6 0.07) the insult. FA at
4 weeks after CCl4 insult was not significantly different from that before insult. Moreover, ADC// at 2
weeks (1.01 6 0.09 103 mm2/s) and 4 weeks (0.95
6 0.09 103 mm2/s) after insult was found to be
significantly lower (P < 0.01) than that before insult
(1.24 6 0.15 103 mm2/s), whereas ADC? at 4
weeks (0.64 6 0.09 103 mm2/s) after insult was
significantly lower (P < 0.01) than that before (0.84 6
0.06 103 mm2/s) and 2 weeks after (0.78 6 0.09 103 mm2/s) insult.
Figure 4 shows the typical H&E and Masson’s trichrome staining of normal liver and livers at 2 weeks
and 4 weeks after CCl4 insult. Collagen deposition
was stained blue by Masson’s trichrome staining in
fibrotic livers. Compared with normal liver (Fig. 4a),
collagen deposition and intracellular fat vacuoles were
consistently observed in livers with CCl4 insult (Fig.
4b,c). Cell necrosis/apoptosis was evident in liver
with 2-week CCl4 insult (Fig. 4b), while collagen deposition was more pronounced in liver with 4-week CCl4
insult (Fig. 4c). Similar histological findings were
observed in all liver samples collected, and they were
largely consistent with those from the earlier studies
of CCl4-induced liver fibrosis in rodent models (54).
Specifically, the liver specimens collected at 2 weeks
after fibrosis induction showed scattered collagen
1144
Cheung et al.
Figure 2. Nondiffusion-weighted (B0) EPI
images, T2-weighted images, ADC maps,
FA maps, and color-coded FA (CFA) direction maps of liver from one animal before,
2 weeks after, and 4 weeks after CC4
insult. ADC (in mm2/s) and FA maps are
displayed in the same scale for different
timepoints. Typical measurement ROIs are
shown in the B0 EPI images encompassing
a large homogeneous region in liver parenchyma. CFA maps have the color coding of
red for left–right, green for dorsal–ventral,
and blue for cranial–caudal direction.
deposition in the sinusoids of the pericentral lobular
area without septa formation (Fig. 4b), representing
mild fibrosis. The specimens collected at 4 weeks
exhibited clear septa formation with dense collagen
deposition forming portal–portal and portal–central
bridges (Fig. 4c), indicating advanced fibrosis. Based
on the semiquantitative METAVIR staging, week-2
liver specimens were classified as F1 (portal fibrosis
without septa, n ¼ 2) and F2 (portal fibrosis with few
septa, n ¼ 2). Week-4 specimens were staged as F2 (n
¼ 1) and F3 (numerous septa without cirrhosis, n ¼
3). Normal liver yielded F0 (no fibrosis, n ¼ 1) as
expected.
DISCUSSION
Liver fibrosis is an important factor of hepatic dysfunction, yet its progression can be reversed by sup-
pressing the underlying cause (7–9). The standard diagnosis and staging of liver fibrosis currently rely on
histological analysis through liver biopsy. However, it
is considerably invasive and often associated with
potential complications, sampling errors, and interobserver variability (14,15). Accordingly, the development of alternative noninvasive techniques to characterize liver fibrosis is urgently needed. MR diffusion
imaging, a routine MRI protocol in studying brains in
both humans and animal models (24–26), is a sensitive and noninvasive technique for characterizing the
random microscopic motion of water molecules. Thus,
DTI may provide information on the microstructural
environments in liver tissue and reveal the pathological alterations in tissue microstructure during liver
fibrogenesis. In this study we characterized the
changes in diffusion properties of liver using DTI in a
well-controlled experimental model of liver fibrosis.
Note that the CCl4-intoxication fibrosis model
DTI Assessment of Liver Fibrosis
Figure 3. ADC (a), FA (b), ADC// (c), and ADC? (d) values of
animals at 0 (before injury), 2 and 4 weeks after CCl4 insult.
One-way ANOVA with Tukey’s multiple comparison test was
performed with **P < 0.01, *P < 0.05, and n.s. for insignificance.
employed in the current study is the most commonly
recognized and employed experimental model to
investigate the cellular and molecular mediators
involved in fibrosis that are highly relevant to those in
human (49).
1145
The histological observations revealed the collagen
deposition and thus confirmed liver fibrogenesis in
the animals studied. Despite the potential water diffusivity increase associated with cell necrosis/apoptosis, ADC was observed to decrease gradually with
CCl4 insult, likely due to the increased extracellular
collagen deposition and increased intracellular fat
droplets during the progression of liver fibrosis (Fig.
4b,c). In addition, the ADC decrease could also be
associated with the decreased blood perfusion as suggested by several other studies (39,43,44) despite the
relatively high b-value used. FA decrease at 2 weeks
after CCl4 insult resulted from the significant
decrease of ADC// (P < 0.01). This was likely due to
the prominent cell necrosis/apoptosis that occurred
(Fig. 4b), perturbing water diffusion along the radially
oriented cellular structures in hepatic plates. The
subsequent FA increase at 4 weeks after CCl4 insult
arose from the significant decrease of ADC? (P < 0.01)
thereafter. This might be caused by the pronounced
extracellular collagen deposition (Fig. 4c), leading to
decreased water diffusivity in the more isotropic
extracellular compartment. Note that the reduction
and subsequent normalization of FA were the direct
consequence of the distinct changes of ADC// and
ADC? at 2 and 4 weeks after fibrosis induction (as
shown in Fig. 3), respectively. It is possible that such
Figure 4. Typical H&E staining (400; left column) and Masson’s trichrome staining (200 and 40; middle and right column, respectively) of normal liver (a), and livers subjected to 2-week (b) and 4-week (c) CCl4 insult. Collagen deposition
(green arrows), fat vacuoles (blue arrows), and cell necrosis/apoptosis (black arrows) were observed in the insulted livers.
[Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]
1146
differential changes in ADC// and ADC? could arise
from a number of concomitant alterations in the tissue microenvironment, including collagen deposition,
fatty infiltration, hepatitis, cell necrosis/apoptosis,
inflammatory cell infiltration, and fibroblast proliferation with different degrees, leading to the observed FA
changes. Consistent with our results, several groups
have reported that ADC values were lower in fibrotic
and cirrhotic liver as compared with normal liver in
both human and animal studies (38–45) despite of the
different b-values used. To the best of our knowledge,
there has been no study investigating the diffusion
anisotropy (eg, FA values) in liver fibrosis, although
the DTI protocol has been employed (45). Nonetheless,
the degree of diffusion anisotropy may change with
the disturbed microstructure in fibrotic liver, as
observed in the current study. It is worth noting that
the percentage change in FA at 2 weeks after CCl4
insult (32 6 11%) was higher than that in ADC (12 6
3%) at 2 weeks after insult, indicating that FA could
provide higher sensitivity in detecting early liver
fibrosis.
The combined effects of water diffusion and capillary blood perfusion in tissues have been reported to
be measured by MR diffusion imaging (55). ADC could
be overestimated at low b-values due to the increased
signal attenuation from intravoxel spin dephasing
caused by the pseudorandom blood flow in presence
of diffusion gradient. On the other hand, the true
diffusion measurement would be affected less at high
b-values where the diffusion attenuation would be
mainly a result of molecular water diffusion because
the blood signal will be mostly suppressed by the
large diffusion gradients (32,55). In this study, the
ADC value measured in normal rat livers using a bvalue of 1000 s/mm2 was 0.97 6 0.08 103 mm2/
s, which was lower than those reported previously
(1.54 6 0.29 103 mm2/s) measured at a b-value of
500 s/mm2 (39). This discrepancy likely resulted from
the lesser contribution of the pseudorandom blood
perfusion to the apparent diffusion measurement in
the current study due to the higher b-value used.
Note that the actual imaging sequences and imaging
parameters, such as the effects of blood flow under a
specific sequence setting and voxel size, could also
influence the extent of perfusion contribution to the
apparent diffusion measurement. In addition, several
studies suggested that microvascular perfusion may
be important in assessing liver fibrosis (39,43). In this
regard, an intravoxel incoherent motion (IVIM) model
(55) has been proposed to separate the effect of blood
perfusion from water diffusion (44). Further studies
using IVIM analysis are needed to assess the relative
contribution of these two effects on ADC measurement in fibrotic liver.
Several limitations exist in the current study. First,
comprehensive histopathological correlation was not
performed to examine and validate the mechanisms
underlying the DTI index changes observed in this
study. In fact, as in DTI of neural tissue, the direct
correlation between DTI indices and specific tissue
morphological characteristics can be complex and
problematic, if not impossible, because the water dif-
Cheung et al.
fusion process in vivo is affected by numerous and
complex determinants, including cellular microstructures, membrane permeability or water exchange, and
possibly other biophysical properties associated with
different water populations. Nonetheless, such validation or correlation study is highly desired in future
investigation. Second, the development and progression of liver fibrosis in the CCl4-intoxication fibrosis
model differs from those in human due to different
etiology of the disease. In particular, the repetitive
dosing of CCl4 in animals can induce fibrosis development within weeks, while the development of fibrosis
in patients with liver diseases is more gradual and
may take several months or years, depending on the
underlying causes. Despite different etiology, the
underlying pathophysiologic processes of fibrogenesis
(eg, inflammatory cell infiltration, cell necrosis, fibroblast proliferation, pronounced ECM/collagen deposition, and potential progression to cirrhosis) are largely
similar (62,63). In addition, the current finding of
ADC reduction in the experimental fibrosis model is
in accordance with those reported in patients with
liver fibrosis (38,41,42,45,46), suggesting that the
liver tissue microstructural alterations may act on the
diffusion parameters in a similar manner. Third, since
a low b-value of 0 s/mm2 was used along with a high
b-value in the current DTI acquisition, the ADC quantitation could be affected by the pseudodiffusion effect
of blood perfusion (30,55,64). Acquiring more DWIs
with multiple and higher b-values can improve the accuracy of the true ADC measurements at the cost of
substantially longer scan time. Furthermore, the spatial resolution was relatively low in the single-shot
EPI-DTI acquisition. It can be improved by increasing
acquisition matrix via shimming improvement or/and
susceptibility reduction. Lastly, the findings from the
current study were preliminary, given the limited
sample sizes (n ¼ 12). Statistical power could be further increased by larger sample sizes in future
studies.
With recent advances in fast imaging techniques,
especially single-shot EPI, DWI and DTI have been
increasingly used in imaging abdominal organs
including liver at clinical field strength of 1.5 and 3 T
(33–35,65,66). At present, DWI and DTI are implemented on most clinical scanners and can be readily
incorporated into routine liver examinations to study
liver fibrosis. In contrast, the MR elastography that
has been recently proposed for liver fibrosis assessment requires additional hardware and software,
consequently limiting its availability in most clinical
settings (22,23). Our results suggest that the microstructural alterations in fibrotic liver may vary the
degree of water molecules diffusion anisotropy. As
such, ADC values measured by conventional DWI can
depend on the direction of diffusion gradient applied,
and thus are less reproducible and more difficult for
comparative studies. DTI should provide better ADC
quantitation because DTI-derived ADC is rotationally
invariant.
In conclusion, the experimental results in this study
demonstrated that DTI could detect longitudinal
changes in diffusion properties of liver in an
DTI Assessment of Liver Fibrosis
experimental model of liver fibrosis. Changes in directional diffusivities (ADC// and ADC?) and FA may
reveal functional (ie, water diffusivity) and microstructural changes, respectively, during the progression of
liver fibrosis. Therefore, DTI is potentially valuable in
detecting and characterizing liver fibrosis at early
stages and monitoring its progression and related
interventions in a noninvasive manner. Future human
studies are warranted to further verify the applicability of DTI in characterizing liver fibrosis and to determine its role in clinical settings.
REFERENCES
1. Moradpour D, Wands JR. The molecular pathogenesis of hepatocellular carcinoma. J Viral Hepat 1994;1:17–31.
2. Colombo M. The role of hepatitis C virus in hepatocellular carcinoma. Recent Results Cancer Res 1998;154:337–344.
3. Brechot C. Pathogenesis of hepatitis B virus-related hepatocellular carcinoma: old and new paradigms. Gastroenterology 2004;
127:S56–61.
4. Cuadrado A, Orive A, Garcia-Suarez C, et al. Non-alcoholic steatohepatitis (NASH) and hepatocellular carcinoma. Obes Surg
2005;15:442–446.
5. Voigt MD. Alcohol in hepatocellular cancer. Clin Liver Dis 2005;9:
151–169.
6. Zhou K, Lu LG. Assessment of fibrosis in chronic liver diseases.
J Dig Dis 2009;10:7–14.
7. Wanless IR, Nakashima E, Sherman M. Regression of human cirrhosis. Morphologic features and the genesis of incomplete septal
cirrhosis. Arch Pathol Lab Med 2000;124:1599–1607.
8. Arthur MJ. Reversibility of liver fibrosis and cirrhosis following
treatment for hepatitis C. Gastroenterology 2002;122:1525–1528.
9. Veldhuijzen IK, Toy M, Hahne SJ, et al. Screening and early treatment of migrants for chronic hepatitis B virus infection is costeffective. Gastroenterology 2010;138:522–530.
10. Fontana RJ, Lok AS. Noninvasive monitoring of patients with
chronic hepatitis C. Hepatology 2002;36:S57–64.
11. Bataller R, Brenner DA. Liver fibrosis. J Clin Invest 2005;115:
209–218.
12. Lackner C, Struber G, Liegl B, et al. Comparison and validation
of simple noninvasive tests for prediction of fibrosis in chronic
hepatitis C. Hepatology 2005;41:1376–1382.
13. Parkes J, Guha IN, Roderick P, Rosenberg W. Performance of serum marker panels for liver fibrosis in chronic hepatitis C. J Hepatol 2006;44:462–474.
14. Afdhal NH, Nunes D. Evaluation of liver fibrosis: a concise review.
Am J Gastroenterol 2004;99:1160–1174.
15. Manning DS, Afdhal NH. Diagnosis and quantitation of fibrosis.
Gastroenterology 2008;134:1670–1681.
16. Ito K, Mitchell DG. Hepatic morphologic changes in cirrhosis: MR
imaging findings. Abdom Imaging 2000;25:456–461.
17. Awaya H, Mitchell DG, Kamishima T, Holland G, Ito K, Matsumoto T. Cirrhosis: modified caudate-right lobe ratio. Radiology
2002;224:769–774.
18. Taouli B, Ehman RL, Reeder SB. Advanced MRI methods for
assessment of chronic liver disease. AJR Am J Roentgenol 2009;
193:14–27.
19. Talwalkar JA, Yin M, Fidler JL, Sanderson SO, Kamath PS,
Ehman RL. Magnetic resonance imaging of hepatic fibrosis:
emerging clinical applications. Hepatology 2008;47:332–342.
20. Aguirre DA, Behling CA, Alpert E, Hassanein TI, Sirlin CB. Liver
fibrosis: noninvasive diagnosis with double contrast materialenhanced MR imaging. Radiology 2006;239:425–437.
21. Hughes-Cassidy F, Chavez AD, Schlang A, et al. Superparamagnetic iron oxides and low molecular weight gadolinium chelates
are synergistic for direct visualization of advanced liver fibrosis. J
Magn Reson Imaging 2007;26:728–737.
22. Huwart L, Peeters F, Sinkus R, et al. Liver fibrosis: non-invasive
assessment with MR elastography. NMR Biomed 2006;19:
173–179.
23. Rouviere O, Yin M, Dresner MA, et al. MR elastography of the
liver: preliminary results. Radiology 2006;240:440–448.
1147
24. Kucharczyk J, Mintorovitch J, Asgari HS, Moseley M. Diffusion/
perfusion MR imaging of acute cerebral ischemia. Magn Reson
Med 1991;19:311–315.
25. Basser PJ, Mattiello J, LeBihan D. MR diffusion tensor spectroscopy and imaging. Biophys J 1994;66:259–267.
26. Mori S, van Zijl PC. Diffusion weighting by the trace of the diffusion tensor within a single scan. Magn Reson Med 1995;33:
41–52.
27. Hui ES, Cheung MM, Qi L, Wu EX. Towards better MR characterization of neural tissues using directional diffusion kurtosis analysis. Neuroimage 2008;42:122–134.
28. Kwee TC, Takahara T, Koh DM, Nievelstein RA, Luijten PR. Comparison and reproducibility of ADC measurements in breathhold,
respiratory triggered, and free-breathing diffusion-weighted
MR imaging of the liver. J Magn Reson Imaging 2008;28:
1141–1148.
29. Taouli B, Sandberg A, Stemmer A, et al. Diffusion-weighted imaging of the liver: comparison of navigator triggered and breathhold
acquisitions. J Magn Reson Imaging 2009;30:561–568.
30. Cheung JS, Fan SJ, Chow AM, Hui ES, Wu EX. In vivo DTI
assessment of hepatic ischemia reperfusion injury in an experimental rat model. J Magn Reson Imaging 2009;30:890–895.
31. Cheung JS, Fan SJ, Chow AM, Zhang J, Man K, Wu EX. Diffusion tensor imaging of renal ischemia reperfusion injury in an experimental model. NMR Biomed 2010;23:496–502.
32. Yamada I, Aung W, Himeno Y, Nakagawa T, Shibuya H. Diffusion
coefficients in abdominal organs and hepatic lesions: evaluation
with intravoxel incoherent motion echo-planar MR imaging. Radiology 1999;210:617–623.
33. Taouli B, Vilgrain V, Dumont E, Daire JL, Fan B, Menu Y. Evaluation of liver diffusion isotropy and characterization of focal hepatic lesions with two single-shot echo-planar MR imaging
sequences: prospective study in 66 patients. Radiology 2003;226:
71–78.
34. Moteki T, Horikoshi H. Evaluation of hepatic lesions and hepatic
parenchyma using diffusion-weighted echo-planar MR with three
values of gradient b-factor. J Magn Reson Imaging 2006;24:
637–645.
35. Bruegel M, Holzapfel K, Gaa J, et al. Characterization of focal
liver lesions by ADC measurements using a respiratory triggered
diffusion-weighted single-shot echo-planar MR imaging technique. Eur Radiol 2008;18:477–485.
36. Goshima S, Kanematsu M, Kondo H, et al. Diffusion-weighted
imaging of the liver: optimizing b value for the detection and
characterization of benign and malignant hepatic lesions. J Magn
Reson Imaging 2008;28:691–697.
37. Boulanger Y, Amara M, Lepanto L, et al. Diffusion-weighted MR
imaging of the liver of hepatitis C patients. NMR Biomed 2003;
16:132–136.
38. Koinuma M, Ohashi I, Hanafusa K, Shibuya H. Apparent diffusion coefficient measurements with diffusion-weighted magnetic
resonance imaging for evaluation of hepatic fibrosis. J Magn
Reson Imaging 2005;22:80–85.
39. Annet L, Peeters F, Abarca-Quinones J, Leclercq I, Moulin P, Van
Beers BE. Assessment of diffusion-weighted MR imaging in liver
fibrosis. J Magn Reson Imaging 2007;25:122–128.
40. Girometti R, Furlan A, Bazzocchi M, et al. Diffusion-weighted
MRI in evaluating liver fibrosis: a feasibility study in cirrhotic
patients. Radiol Med 2007;112:394–408.
41. Lewin M, Poujol-Robert A, Boelle PY, et al. Diffusion-weighted
magnetic resonance imaging for the assessment of fibrosis in
chronic hepatitis C. Hepatology 2007;46:658–665.
42. Taouli B, Tolia AJ, Losada M, et al. Diffusion-weighted MRI for
quantification of liver fibrosis: preliminary experience. AJR Am J
Roentgenol 2007;189:799–806.
43. Girometti R, Furlan A, Esposito G, et al. Relevance of b-values in
evaluating liver fibrosis: a study in healthy and cirrhotic subjects
using two single-shot spin-echo echo-planar diffusion-weighted
sequences. J Magn Reson Imaging 2008;28:411–419.
44. Luciani A, Vignaud A, Cavet M, et al. Liver cirrhosis: intravoxel
incoherent motion MR imaging—pilot study. Radiology 2008;249:
891–899.
45. Taouli B, Chouli M, Martin AJ, Qayyum A, Coakley FV, Vilgrain
V. Chronic hepatitis: role of diffusion-weighted imaging and diffusion tensor imaging for the diagnosis of liver fibrosis and inflammation. J Magn Reson Imaging 2008;28:89–95.
1148
46. Sandrasegaran K, Akisik FM, Lin C, et al. Value of diffusionweighted MRI for assessing liver fibrosis and cirrhosis. AJR Am J
Roentgenol 2009;193:1556–1560.
47. Friedman SL. Molecular regulation of hepatic fibrosis, an integrated cellular response to tissue injury. J Biol Chem 2000;275:
2247–2250.
48. Wynn TA. Cellular and molecular mechanisms of fibrosis.
J Pathol 2008;214:199–210.
49. Constandinou C, Henderson N, Iredale JP. Modeling liver fibrosis
in rodents. Methods Mol Med 2005;117:237–250.
50. Man K, Ng KT, Lee TK, et al. FTY720 attenuates hepatic ischemia-reperfusion injury in normal and cirrhotic livers. Am J
Transplant 2005;5:40–49.
51. Zeisberg M, Yang C, Martino M, et al. Fibroblasts derive from hepatocytes in liver fibrosis via epithelial to mesenchymal transition. J Biol Chem 2007;282:23337–23347.
52. Armendariz-Borunda J, Seyer JM, Kang AH, Raghow R. Regulation of TGFb gene expression in rat liver intoxicated with carbon
tetrachloride. FASEB J 1990;4:215–221.
53. Cheung JS, Guo H, Leung JC, Man K, Lai KN, Wu EX. MRI visualization of rodent liver structure and peritoneal adhesion with
dialyzate enhancement. Magn Reson Med 2008;59:1170–1174.
54. Hazle JD, Narayana PA, Dunsford HA. Chronic carbon tetrachloride and phospholipase D hepatotoxicity in rat: in vivo 1H magnetic resonance, total lipid analysis, and histology. Magn Reson
Med 1990;15:211–228.
55. Le Bihan D, Breton E, Lallemand D, Aubin ML, Vignaud J, LavalJeantet M. Separation of diffusion and perfusion in intravoxel
incoherent motion MR imaging. Radiology 1988;168:497–505.
56. Woods RP, Grafton ST, Watson JD, Sicotte NL, Mazziotta JC. Automated image registration: II. Intersubject validation of linear and
nonlinear models. J Comput Assist Tomogr 1998;22:153–165.
Cheung et al.
57. Jiang H, van Zijl PC, Kim J, Pearlson GD, Mori S. DtiStudio:
resource program for diffusion tensor computation and fiber bundle tracking. Comput Methods Programs Biomed 2006;81:
106–116.
58. Kim H, Booth CJ, Pinus AB, et al. Induced hepatic fibrosis in
rats: hepatic steatosis, macromolecule content, perfusion parameters, and their correlations—preliminary MR imaging in rats.
Radiology 2008;247:696–705.
59. Wu EX, Wu Y, Nicholls JM, et al. MR diffusion tensor imaging
study of postinfarct myocardium structural remodeling in a porcine model. Magn Reson Med 2007;58:687–695.
60. Wu EX, Wu Y, Tang H, et al. Study of myocardial fiber pathway
using magnetic resonance diffusion tensor imaging. Magn Reson
Imaging 2007;25:1048–1057.
61. Bedossa P, Poynard T. An algorithm for the grading of activity in
chronic hepatitis C. The METAVIR Cooperative Study Group.
Hepatology 1996;24:289–293.
62. Tsukamoto H, Matsuoka M, French SW. Experimental models of hepatic fibrosis: a review. Semin Liver Dis 1990;10:
56–65.
63. Wu J, Norton PA. Animal models of liver fibrosis. Scand J Gastroenterol 1996;31:1137–1143.
64. Padhani AR, Liu G, Koh DM, et al. Diffusion-weighted magnetic
resonance imaging as a cancer biomarker: consensus and recommendations. Neoplasia 2009;11:102–125.
65. Naganawa S, Kawai H, Fukatsu H, et al. Diffusion-weighted
imaging of the liver: technical challenges and prospects for the
future. Magn Reson Med Sci 2005;4:175–186.
66. Shiehmorteza M, Sirlin CB, Wolfson T, et al. Effect of shot number on the calculated apparent diffusion coefficient in phantoms
and in human liver in diffusion-weighted echo-planar imaging.
J Magn Reson Imaging 2009;30:547–553.