Download Stimulated Echo Diffusion Weighted Imaging of the Liver at 3 Tesla

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Magnetic Resonance in Medicine 00:00–00 (2016)
Stimulated Echo Diffusion Weighted Imaging
of the Liver at 3 Tesla
Hui Zhang,1 Aiqi Sun,1 Hongjun Li,2 Pairash Saiviroonporn,3
Ed X. Wu,4,5 and Hua Guo1*
to microscopic motion, which is due to Brownian motion
of water molecules and blood microcirculation in biological tissue (1). The DW signal depends not only on bvalues but also the diffusion time (D) (2). This is because
water molecule diffusion is hindered or restricted by cellular microstructures, such as the cell membrane, cytoskeleton, and macromolecules in the tissue environment (3).
Theoretically, a longer diffusion time results in a larger
mean squared diffusion displacement per unit time and
stronger interactions between diffusing molecules and the
surrounding environment. Studies in human brain tissue
(4–6), skeletal muscle (7), and rat livers (8) have shown
that the diffusion time dependency of measured diffusion
coefficients may be used to characterize the restricted diffusion in the microenvironment. Specifically, this kind of
dependency may be sensitive to the pathological alterations in fibrotic livers due to the changes of collagen deposition (9–19).
Usually, the diffusion measurements are performed
using a spin-echo (SE) based imaging sequence with Stejskal and Tanner diffusion gradients (20). The significantly increased echo times (TE) with a desired D lead to
a strong T2 decay and thus decrease the signal-to-noise
ratio (SNR). Therefore, the diffusion time D is limited to
a small range. Compared with the traditional spin echo
(SE) based method, stimulated echo acquisition mode
(STEAM) (21) DWI (22–24) is a better candidate for diffusion time variation for several reasons. First, the diffusion mainly occurs during the spin deposition period
between the 2nd and 3rd radiofrequency (RF) pulses, so
echo time (TE) values are only affected by the gradient
lobes and RF pulse durations which can be kept to a
minimum value that hardware allows. Second, during
the mixing time (TM) between the 2nd and 3rd RF pulses,
longitudinal relaxation is predominant over transverse
relaxation (25). Thus signals can be well maintained
even with a long D. In general, STEAM DWI has half of
the SNR of the SE counterpart (26,27). However, assuming the liver T2 ¼ 34 6 4 ms and T1 ¼ 809 6 71 ms at 3T
(28), STEAM DWI can provide a higher SNR when D is
greater than 54.8 ms according to theoretical calculation.
The following equations were used for the SE DWI and
STEAM DWI sequences, respectively.
Purpose: Diffusion time (D) effect in diffusion measurements
has been validated as a sensitive biomarker in liver fibrosis by
rat models. To extend this finding to clinical study, a reliable
imaging technique is highly desirable. This study aimed to
develop an optimal stimulated echo acquisition mode (STEAM)
diffusion-weighted imaging (DWI) method dedicated to human
liver imaging on 3 Tesla (T) and preliminarily investigate the
dependence effect in healthy volunteers.
Methods: STEAM DWI with single-shot echo planar imaging
readout was used as it provided better signal-to-noise ratio (SNR)
than spin echo DWI methods when a long D was needed for liver
imaging. Additionally, a slice-selection gradient reversal method
was used for fat suppression. Motion compensation and SNR
improvement strategies were used to further improve the image
quality. Five b-values with three Ds were tested in 10 volunteers.
Results: Effective fat suppression and motion compensation
were reproducibly achieved in the optimized sequence. The
signal decay generally became slower when the Ds increased.
Obvious reduction of diffusion coefficients was observed with
increasing Ds in the liver.
Conclusion: The results verified the D dependence in diffusion
measurements, indicating restricted diffusion in healthy human
livers for the first time at 3T. This prepared STEAM DWI a
potential technique for liver fibrotic studies in clinical practice.
C 2016 Wiley PeriodiMagn Reson Med 000:000–000, 2016. V
cals, Inc.
Key words: STEAM DWI;
restricted diffusion; liver DWI
diffusion
time
dependence;
INTRODUCTION
Diffusion-weighted imaging (DWI) has been increasingly
used in clinical applications as a result of its sensitivity
1
Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, Beijing, China.
2
Department of Medical Imaging Center, Beijing You An Hospital, Capital
Medical University, Beijing, China.
3
Department of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol
University, Bangkok, Thailand.
4
Laboratory of Biomedical Imaging and Signal Processing, The University
of Hong Kong, Hong Kong SAR, China.
5
Department of Electrical and Electronic Engineering, The University of
Hong Kong, Hong Kong SAR, China.
Grant sponsor: National Natural Science Foundation of China; Grant
numbers: 61271132, 61571258; Grant sponsor: Beijing Natural Science
Foundation; Grant numbers: 7142091, 7132108.
*Correspondence to: Hua Guo, Ph.D., Center for Biomedical Imaging
Research, Department of Biomedical Engineering, Tsinghua University,
Beijing, China. E-mail: [email protected]
MSE ¼ M0 expðTESE =T2 Þ expðb ADCÞ
MSTEAM ¼ M0 expðTESTEAM =T2 Þ expðTM =T1 Þ
expðb ADCÞ=2
Received 6 July 2015; revised 19 December 2015; accepted 23 December
2015
DOI 10.1002/mrm.26128
Published online 00 Month 2016 in Wiley Online Library (wileyonlinelibrary.
com).
C 2016 Wiley Periodicals, Inc.
V
[1]
[2]
where ADC is apparent diffusion coefficient. For example,
when D¼80 ms, on our scanner, TE SE is 104 ms, TESTEAM
is 53 ms and TM is 53.58 ms. Thus, the SNR of the
1
2
STEAM DWI sequence increases by 47.6% compared
with the SE DWI sequence when the same diffusion gradient duration is used.
According to the previous studies on rat models at 7
Tesla (T) (8), STEAM DWI demonstrated the sensitivity
of different Ds for detecting the pathological alterations
during liver fibrogenesis. It is desirable to validate this
diffusion time dependency biomarker in human livers to
get one step closer to clinical practice. However, due to
tremendous challenges (29–31) in liver imaging at 3T, to
our knowledge, the study of the dependency of measured diffusion coefficients on diffusion time has not
been reported so far.
The main challenges include the following: (a) Fast
single-shot echo planar imaging (EPI) is very sensitive to
chemical-shift artifacts because of the low bandwidth
along the phase-encoding direction (32). Due to increased
field inhomogeneity in body imaging (33,34), fat suppression is particularly challenging for liver DWI at 3T and
traditional fat suppression methods, such as spectral
inversion-recovery (SPIR) and spectrally selective attenuated inversion recovery (SPAIR), cannot suppress the fat
signals effectively. Additionally, these methods can also
prolong the acquisition time or increase the specific
absorption rate (SAR). The slice-selection gradient reversal (SSGR) method, proposed by Gomori et al (35), used
slice-selection gradients with opposite polarities during
the application of two sequential RF pulses to suppress
fat signals. Nagy and Weiskopf (36) showed this to be
quite effective in SE DWI even for in vivo experiments.
Intuitively, it is possible to incorporate this scheme into the
STEAM DWI sequence straightforwardly. (b) For liver imaging, respiratory and cardiac motion is an inevitable issue,
and is also a major source of artifacts in abdominal imaging.
Strategies have been proposed to decrease these effects,
such as breath hold techniques and respiratory triggering.
There exists a trade-off between the image quality and
acquisition time. (c) According to basic MRI signal equations (26,32), the STEAM based sequences only provide
half the SNR of SE based sequences when the same acquisition parameters are used. Thus, a relatively long acquisition
time or a greater number of averages are needed for a sufficiently high SNR, which inevitably causes more motion
artifacts. (d) The very low liver T2 value at 3T, approximately 34 6 4 ms in healthy human livers (28), also limits
the signal acquisition duration and TE value.
Therefore, the aim of this study is to develop and optimize a reliable STEAM DWI method at 3T and use it to
investigate the dependence of diffusion measurement on
D in healthy human livers. Results show that the new
STEAM DWI sequence performs superiorly and the
restricted diffusion behavior is well observed in the liver
at 3T.
METHODS
Sequence Optimization
Based on the aforementioned problems, we optimized a
sequence based on a traditional single-shot STEAM DWI
sequence shown in Figure 1. We refer to this figure for
the following definitions. The sequence consists of diffusion signal preparation followed by a typical single-shot
Zhang et al.
FIG. 1. Pulse sequence diagram for STEAM single-shot EPI DWI.
EPI acquisition scheme. All gradients are labeled in Figure 1. The detailed optimizations include the following:
Basic STEAM DWI sequence: The stimulated echo is
encoded by the diffusion gradients (Gdiff ) and the residual transverse magnetization is spoiled by the crusher
gradient (GTM ) during the TM duration. Note that in this
STEAM DWI, the actual first b-value is 12 s/mm2 but not
zero due to the presence of crusher gradients (Gcrush ) and
slice-select gradients (37).
Fat suppression: We incorporated the SSGR technique
(35) into the STEAM DWI sequence for fat suppression.
The sequence is ideally suited for introducing opposite
polarity gradients. Specifically, the 2nd and 3rd sliceselection gradients (Gss ) were reversed compared with
the 1st one, through which only on-resonance water signals in a slice experience both excitation and refocusing
pulses. For a multislice acquisition mode, the slice gap
and thickness should satisfy two basic conditions: (i) the
chemical shift distance of fat should be at most half of
the slice thickness (35); (ii) the frequency differences
between slices should be equal to or greater than the
sum of Dvoffset and DvRF , where Dvoffset is the frequency
offset between water and fat, and DvRF is the bandwidth
of the excitation RF or refocusing pulses (38). Only then
can this method separate the fat and water signals in the
slice direction or suppress the fat signals in every slice
successfully.
TE control: To achieve minimum TE values, the total b
values was assigned in multiple directions simultaneously. SENSE ¼ 2 was used to shorten the EPI acquisition window. Moreover, we compressed the duration of
diffusion gradients as much as possible by using the
maximum gradient strength with slewrate ¼ 200 mT/m/
ms, multidirection.
Respiratory motion control: Due to the limited SNR of
STEAM DWI acquisitions, multiple averages are used,
especially in the liver with short T2 values. By using the
respiratory triggering method rather than the breath-hold
scheme, measurements can be taken over a longer time
scale with more b-values for precise curve fitting and diffusion coefficient quantification. Additionally, the respiratory scheme relies less on patient cooperation to achieve
suitable results. It should be noted that all subjects had the
MRI scans several hours after meals with empty stomachs
to avoid severe gastrointestinal peristalsis.
Data rejection: Free breathing respiratory triggered
scans with multiple dynamic repetitions were recorded
individually for postprocessing. Before data analysis,
corrupted data with severe signal voids in the liver
STEAM DWI of the Liver at 3T
parenchyma were rejected semiautomatically based on
the uniformity of the images from the repeated scans to
avoid over estimation of diffusion coefficients. First, for
each image acquisition with a different TM, a region of
interest (ROI) was drawn manually to encompass the
whole liver parenchyma without large blood vessels
based on an image with a lowest b value, which was 12
s/mm2 in this study. Then this ROI was applied to
images with the left b-values and different repetition
numbers. Different slices used different ROIs. Within the
prescribed ROIs for each b value, a coefficient of variations (CV ¼ SD/mean 100%) of the signal intensities
from different repetition scans was calculated. Using an
averaged CV as a threshold, scans with larger CV values
were rejected because they included severer signal loss
or intensity inhomogeneity. After this, qualified data
were magnitude averaged for further data analysis. The
process was applied for both the SE and STEAM DWI
acquisitions. This helps to further compensate for the
respiratory and cardiac motions compared with the direct
averaging. By this means, the precision of diffusion coefficients can be improved for tissue characterization.
Data Acquisition
All MRI experiments were performed on a Philips
Achieva 3.0T TX scanner (Philips, Best, The Netherlands) with a 2-channel transmit RF coil (39) and a 32channel receive RF coil. Volume shimming and adaptive
RF shimming was used to minimize B0 inhomogeneity
and transmit field inhomogeneity, respectively. The
STEAM DWI sequences were first carefully tuned on a
water phantom to optimize image banding artifact control. For nondiffusion weighted acquisitions with the
smallest TMs, the diffusion encoding gradient does not
exist and image banding artifacts always exist. Thus a
minimum b value of 12 s/mm2 was used to suppress free
induction decay signals derived from the 1st and 3rd RF
pulses, which can remove the banding artifacts effectively. In vivo imaging was then optimized on healthy
volunteers. The reliability, artifact levels, and quantification precision were examined. The study was approved
by our local ethics board and written informed consent
was obtained from each volunteer.
During in vivo imaging optimization, three fat suppression methods, SPIR, SPAIR, and SSGR were initially
compared using the STEAM DWI sequence in 10 volunteers. To compare the motion control effects between the
breath hold and respiratory triggering schemes, two sets
of scans were performed on seven subjects. One set was
acquired by triggering with nine dynamically repeated
scans and five b-values (12, 200, 300, 400, and 600 s/
mm2), trigger delay ¼ 500 ms (the end of the inspiratory
phase for less respiratory motion). The other set used the
breath-holding scheme. Due to the scan time limitation
of the breath hold method, the tests only included three
b-values (b ¼ 12, 200, and 600 s/mm2) and the number of
the repetitions was three. The total acquisition time for
triggered sampling was around 130 s, while it was 21 s
for breath-holding sequence.
After an optimized STEAM sequence was found based
on the above tests, 10 healthy volunteers were recruited
3
for the final quantification study. Three different diffusion times were used in the STEAM DWI sequence: 80,
106, and 186 ms. The maximum gradient amplitude that
the hardware can provide, 29.5 mT/m, was used to
achieve minimum TE values for the maximum b factor
and the minimum diffusion time. The maximum b factor,
600 s/mm2, determined the diffusion gradient duration.
For the lower b factors, only the gradient amplitudes
changed and other parameters remained the same. Different diffusion times shared the same TE because TM was
used to change the b factors. For each diffusion time,
five b-values (12, 200, 300, 400, and 600 s/mm2) were
used in each scan session. SE DWI was also acquired as
a reference with six b-values (0, 12, 200, 300, 400, and
600 s/mm2). In that, the first b-value was used for ROI
selection and other b-values were used for quantification. It has an equivalent diffusion time of 33 ms.
Other imaging parameters shared by both sequences
were as follows: field of view (FOV) ¼ 290 mm 206 mm,
phase encoding direction ¼ anterior/posterior (A/P), inplane resolution ¼ 3 mm 3 mm, slice thickness ¼ 5 mm,
diffusion gradient duration d ¼ 8 ms, TR=TE ¼ 1600=
(STE: 53 ms, SE: 63 ms), SENSE ¼ 2. Three slices were
acquired in the central liver with a slice gap of 9 mm.
Nine scans were dynamically repeated for STEAM DWI
and 3 scans for SE DWI, respectively. Here this FOV fit
well without any oversampling or reduced FOV technique. Meanwhile, for the reproducibility assessment,
intra- and interscans of the sequence were also tested
with repeated measurements on 10 subjects by using the
same protocol twice during one scan session. Then these
protocols were repeated with an interexamination time
of more than 1 day. The corresponding diffusion coefficients of every slice for every different Ds were calculated to examine the intra- and interscan reproducibility.
All agreements were evaluated by Lin’s concordance correlation coefficient (CCC) (40) and Bland-Altman analysis (41). A CCC value of 1 indicated perfect agreement,
while values >0:75 were considered to represent superb
agreement, 0:75 0:5 were considered fair, and <0:5 was
considered poor. In concordance correlation graphs, the
points are closer to the x ¼ y line, the higher level of
agreement they represent. All subjects provided written
informed consent.
Data Analysis
First, all DWI images from different repeated scans were
preprocessed to reject motion corrupted data. This step
can avoid the overestimation of ADCs, caused by the
contaminated images. Then, a region-of-interest (ROI),
encompassing most of the liver lobe, was prescribed on
the lowest b-value image of every slice by a radiologist
with 10-year experience. To ensure both STEAM DWI
with different Ds and SE DWI images share the same
ROI, we used the intersection region of all their corresponding ROIs as the final ROI for the following calculation. Given the influence of perfusion on the signal decay
is quite small when b-value is greater than 200 s/mm2
(16,42), the perfusion ADC (ADCp) map was generated
from two DW images with b ¼ 12, 200 s/mm2 using the
following equation,
4
Zhang et al.
FIG. 2. Illustration of perfusionfree ROI generation. First, basic
ROIs were drawn on both SE
and STEAM images with minimum b values, shown in (a) and
(b), respectively. Then the ROI
intersection between (a) and
(b) was used for ADCp calculation (c). Next, based on the histogram of ADCp in (d), ADCp
with extraordinarily high values
(with frequency lower than 8%)
was excluded and the final
ROI for further analysis was
generated (e).
SIb =SI0 ¼ exp ðb ADCp Þ
[3]
where SI represents signal intensity at different b values.
Using ADCp, perfusion-free ROIs were generated for
the final quantification. First, separate ROIs were first
drawn in the SE (Fig. 2a) and STEAM (Fig. 2b) images
with the lowest b values. Then, the intersection was chosen as the ROI for ADCp computation (Fig. 2c). From the
histogram of the ADCp map (Fig. 2d), which estimating
the frequency distribution that depicts the variation of
ADCp within the ROI caused by blood microcirculation,
a new perfusion-free ROI (Fig. 2e) was then determined
for further ADC and true diffusion coefficient (Dtrue )
analyses by excluding pixels with extraordinarily high
ADCp values (with frequency lower than 8%). The
threshold was selected to maximally exclude pixels
dominated by large blood vessels while preserving liver
parenchyma regions. ADC values were then calculated in
the perfusion-free regions from all five b-values according to the equation:
SIb =SI0 ¼ expðb ADCÞ
RESULTS
Fat Suppression
Representative results for one slice using three different
types of fat suppression techniques from one subject are
shown in Figure 3. The images with different b-values
(columns) and fat suppression methods (rows) are shown
in the same windowing level. There were obvious chemical shift artifacts in the first two rows along the phase
encoding direction (indicated by white arrows), which
were acquired using SPIR and SPAIR. In comparison, the
SSGR approach eliminated the fat signals and chemical
shift artifacts more effectively. For the DWI images using
different fat suppression methods, consistent results
were obtained in all subjects. Both SPIR and SPAIR
methods showed severe fat artifacts in all slices for all b
values with different diffusion times.
[4]
Dtrue was estimated from the large b-values (300, 400,
and 600 s/mm2) using,
SIb =SI0 ¼ ð1 f Þexp ðb Dtrue Þ
Note that here the measured ADCs were calculated with
the actual b-value (12 s/mm2) for the lowest diffusion
weighting.
[5]
where f is perfusion fraction. All pixel-wise least square
nonlinear fittings were conducted in Matlab (The Mathworks Inc., Natick, MA). The averaged ADC and Dtrue
were calculated from the three slices. The differences of
the diffusion measurements from the three Ds among all
volunteers were tested using one-way analysis of variance (ANOVA) with Tukey’s multiple comparison posttests, with P < 0:05 considered statistically significant.
Data Rejection
Figure 4 compares the differences between the direct average (the upper) and the average with data rejection (the
lower) with different diffusion weightings. We can see the
significant signal loss and intensity inhomogeneity, especially in the left lobe of the liver adjacent to the heart. The
rejection rate rose with the increase of diffusion time and
the diffusion weighting, as shown in Figure 5. The average
rejection rates for different b values of 12, 200, 300, 400,
600 s/mm2 (Fig. 5) were 0.46%, 15.8%, 22.9%, 26.5%, and
34.7%, respectively. The overall rejection rate was approximately 20.1% for all b values and all diffusion times.
Even for the SE sequence and nonweighted images (the
STEAM DWI of the Liver at 3T
5
FIG. 3. Comparison of three types of fat suppression methods under different diffusion weightings. From top to bottom, the fat suppression methods are SPAIR (a), SPIR (b), and SSGR (c), respectively.
first b-value) of STE, the data may be contaminated by
motion. Therefore, the method using data rejection shows
the advantages in getting homogeneous images and is
more useful when there are severe respiratory and other
unexpected motions.
Motion Control
Figure 6 shows the representative final diffusion weighted
images after data rejection with two different motion control modes: breath-hold (the upper) and respiratory triggering (the lower). It is very obvious that the SNR was much
lower in the breath-hold mode than the trigger based
approach. For the used case here, after data rejection, only
two NSAs (the number of signal averages) were left, while
for free respiratory triggering, seven NSAs were used. As
the representative results shown, images with high bvalues with long TMs by breath-holding have lower SNR
compared with those by respiratory triggering in all seven
subjects, which is due to limited averages (no more than
three) for the breath-hold scans. Consequently, the respiratory triggering is superior in achieving a high SNR for
accurate diffusion measurements.
Reproducibility Test
The scatter and Bland-Altman plots for the intra- and interscan reproducibility evaluation are shown in Figure 7. Figures 7a and c are the concordance correlations of ADCs for
the intrascans and interscans, respectively, which represent
the agreement for the 10 subjects in all three slices with
three different diffusion times. Comparison of the
FIG. 4. Comparison of DW images with either direct average or data rejection. Three slices with different diffusion weightings for each
method are shown.
6
FIG. 5. The rejection rate for different Ds with different diffusion
weightings. Note that data were presented as the mean value of
three slices for all subjects.
measurements’ reliability for this technique produced a
CCC of 0.934 (95% CI: 0.867, 0.968) and a CCC of 0.894
(95% CI: 0.802, 0.946) for the intra- and interscans, respectively, which indicates excellent agreement between intraand interscans. In Figures 7b and 7d, the Bland-Altman
method was used to present the corresponding statistical
results of these measurements. For the Bland-Altman analysis, the difference percentage of two repeated measurements is shown on the y-axis. The lower and upper limits
of the concordance interval are marked in the graph which
are from -5.9% to 8.4% for intrascans and from -4.4% to
10.7% for interscans.
Zhang et al.
cate the standard deviation among these subjects. It can be
observed that the signal decay became much slower as the
diffusion time increased. Figure 8b shows the comparison
of the averaged ADCs of all slices from the STEAM
sequences with different diffusion times and the SE
sequences. The ADC values for SE, D ¼ 80, 106 and 186
ms were 1.64 6 0.13, 1.37 6 0.08, 1.26 6 0.05, and
1.06 6 0.07 (10-3 mm2/s), respectively. Note that the
ADC was measured using the first b-value b ¼ 12 s/mm2
for both the STEAM and SE DWI sequences. The comparison demonstrated that ADC generally decreased as diffusion time increased, especially for D ¼ 33 or 80 versus 186
ms. Figure 8c shows the comparison of another diffusion
quantification parameter Dtrue from the sequences with 4
diffusion times. The Dtrue for SE, D¼80, 106 and 186 ms
were 1.09 6 0.05, 1.01 6 0.05, 0.95 6 0.04, and 0.85 6 0.04
(10-3 mm2/s), respectively. Again, the reduction of Dtrue
supported that Dtrue generally decreased as diffusion time
increased, especially for D ¼ 33 or 80 versus 186 ms. Both
the ADC and Dtrue change confirms the statistically significant dependency of diffusion coefficients on diffusion
times in the liver. The f for SE, D ¼ 80, 106, and 186 ms
were 0.347 6 0.05, 0.338 6 0.04, 0.332 6 0.05 and
0.305 6 0.03, respectively, which were consistent with the
previous report (43). The reduction of f with increasing D
indicated the contribution from perfusion became smaller
for longer D, as previously reported (8).
DISCUSSION
Diffusion Quantification
Figure 8a shows the normalized mean DWI signals averaged of all three slices from all volunteers. Error bars indi-
In this study, we proposed an optimized STEAM DWI
sequence to investigate the effect of D on the characterization of diffusion properties in healthy human livers at
FIG. 6. Comparison of DW images from either breath-hold (upper) or free breathing respiratory triggered scans (bottom) with different
diffusion times and diffusion weightings. For breath-hold, after data rejection, only two NSAs were used, while for respiratory triggered
scans, seven NSAs were used. A zoomed-in image is shown from the figure enclosed in the dashed line box for better visualization.
STEAM DWI of the Liver at 3T
7
FIG. 7. Reproducibility evaluation for the intrascans and interscans. a,c: The concordance
correlations. b,d: The BlandAltman plots. Individual ADC
measurements from different diffusion times of every slice in
every subject are represented
by gray dots.
3T. Compared with SE DWI sequences, the stimulated
echo based sequences are typically used to get large diffusion weighting which will not increase TE or incur
excessive T2-related signal loss (23,24). Furthermore, the
restriction diffusion study needs various diffusion measurements with different (from short to long) diffusion
times but sharing the same T2 weighting. Therefore,
STEAM DWI is a more suitable technique than the traditional SE DWI. Furthermore, to achieve long diffusion
times, the TE values in the SE DWI sequence will be
increased accordingly, but can remain unchanged in the
STEAM sequence by lengthening TM. Considering the
short T2 values of the liver at 3T, SE DWI is not able to
provide long diffusion times. Even though STEAM DWI
generally has half the SNR of SE DWI, it shows better
SNR performance due to the much reduced TE when T2
decay cannot be ignored. For instance, this applies in
the discussed case of liver imaging. Figure 9 shows a
FIG. 8. a: Normalized DWI signals calculated from the perfusion-free ROI as a function of b values for SE and STEAM DWI. Liver ADC
(b) and Dtrue (c) from the DWI signals of (a). One-way ANOVA with Tukey’s multiple comparison posttests was performed with *P < 0.05,
**P < 0.01, ***P < 0.001.
8
Zhang et al.
FIG. 9. Comparison between STEAM DWI (upper) and SE DWI (bottom) with the same diffusion time (䉭 ¼ 80 ms). Note that TE ¼ 107
ms for SE DWI, and TE ¼ 52 ms for STEAM DWI.
comparison between STEAM and SE DWI with the shortest diffusion time 䉭 ¼ 80 ms used in this study, in which
STEAM DWI has better SNR than SE DWI.
For liver imaging at 3T, the increased field inhomogeneity (34) and abdominal motion make the STEAM DWI
very challenging. For liver DWI in fibrosis diagnosis, EPI
as signal acquisition is a superior choice due to its high
efficiency and insensitivity to bulk motion (44). However, EPI has several drawbacks, notably the chemicalshift artifacts which make liver imaging extremely troublesome. Traditional fat suppression techniques are not
effective here, particularly for STEAM DWI. The efficient
fat suppression by the SSGR technique in SE DWI
inspired us to adopt it in the STEAM DWI sequence.
This technique can be readily merged with STEAM DWI
without increasing the acquisition time or SAR. Additionally, diffusion scans are usually time consuming
with high duty cycle requirement of the gradient, which
can lead to drifts of the center frequency during scans
(45) in addition to the stronger field inhomogeneity at
3T. However, the SSGR method is rather insensitive to
these problems in EPI DWI at 3T because it relies on the
chemical shift effect along the slice direction for separation of water and fat, while the traditional methods are
based on the in-plane spectral effect and are less effective. Nevertheless, the fat signals have a distributed spectral shift and not a single peak. This means that the
small fraction of olefinic fatty acids with resonance frequency very close to that of water cannot be suppressed,
even by any frequency-sensitive imaging techniques,
including the gradient reversal-based technique.
The further strategies to address these challenges used
efficient motion controlling methods. In general, breathholding (33,46) was shown to be the best choice to minimize respiratory motions and achieve reproducible
results. However, limited acquisition time results in a
low SNR for the STEAM DWI sequence. In this study,
the number of average or repeated scan was limited to
three and the acquisition time was limited to 21 s, which
is not practical for clinical studies. The results show that
the SNR is much lower than the respiratory triggering
method. Therefore, the most appropriate method is respiratory triggering. To increase the reliability, only data without motion corruption were used. The image quality was
much increased and high reproducibility was achieved.
Thus, the method was proved optimal for the desired diffusion coefficient precision.
Based on the diffusion theory, the decrease of diffusion coefficient with increasing D is expected because
the diffusion of water molecules will change from random to restricted motion when encountering barriers
with a long enoughD. According to the results from the
rat liver model, the diffusion distance of water molecules
probed by D ¼ 186 ms will be comparable to the average
cell size of hepatocyte in the normal liver (8). The results
in this study show that the similarly restricted diffusion
behavior is reliably observed in human livers. This further validates the effect of D on liver diffusion quantification. Thus D could be a sensitive biomarker for
detection of pathological alterations in tissue microstructure during human liver firbrogenesis. This work has
prepared a feasible method for further clinical studies.
There are a couple of potential limitations in this
study. First, compared with the previous study on the
rat model (8) in which TM ranged from 15 ms to 200 ms
at a 7T animal scanner, the shortest TM with a maximum b ¼ 600 s/mm2 in this study is approximately 80
ms for STEAM DWI. It is limited by the gradient performance, bandwidth of the signal readout window and
other hardware factors of the clinical scanner. With this
parameter, we may not be able to find the transition
from free diffusion to restricted diffusion but only the
latter. Second, different from the motion control in the
rat model using anesthetization, to improve the image
quality, we used retrospective data rejection before ADC
computation. As Figure 5 shows, higher b factors and
longer diffusion times result in higher data rejection
rates. Because we used nine repetitions in all scans, the
SNR was affected by this process. Ideally, online data
reacquisition should be used such that the same number
of images is used in each case. Unfortunately, we cannot
program the image reconstruction on our system. In
addition, for further clinical application on patients,
there can be several limitations. On one hand, for fibrotic
patients with lower T2 and T2* values, the SNR and
acquisition time issues can be further exacerbated when
iron overload exists in fibrotic livers (47–49). More averages can improve the image quality; however, longer
scan time will be used. Other possible methods are to
use partial Fourier or a multishot mode to shorten the
acquisition window for further shorter TE. On the other
hand, irregular breathing will introduce more motion
which corrupts the image quality and prolongs the total
acquisition time.
STEAM DWI of the Liver at 3T
In conclusion, an STEAM DWI sequence was optimized to study the diffusion time dependency in the
liver for human subjects. For the first time, the SSGR
method was introduced into STEAM DWI for fat suppression. With careful motion control, homogeneous and
qualified images for the diffusion quantification can be
achieved. The results have shown the robustness and
reproducibility of the optimized method, especially in
fat suppression and qualitative motion artifact removal.
Additionally, the diffusion time dependency in restricted
diffusion was quantitatively demonstrated in human
liver tissue. From the previous animal model research
(8) to the current study, we have gradually prepared
STEAM DWI for further clinical study.
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