J. Biomedical Science and Engineering, 2008, 1, 116-120
Published Online August 2008 in SciRes. http://www.srpublishing.org/journal/jbise JBiSE
An image analysis method for quantification of he-
patic perfusion based on contrast-enhanced ultra-
sound imaging
Yi-xin Li, Fang Yang & Ning Gu*
State Key Laboratory of Bioelectronics, Jiangsu Laboratory for Biomaterials and Devices, School of Biological Science and Medical Engineering, Southeast
University, Nanjing 210096, PR China. *Correspondence should be addressed to N. Gu (guning@seu.edu.cn).
Information about hepatic perfusion is used in
clinical liver disease diagnosis. An image analy-
sis system can help physicians make efficient
and accurate diagnosis. The objective of this
study is to propose an image analysis method
for the quantification of the hepatic perfusion
based on contrast-enhanced ultrasound imaging
(CEUI). The proposed method contains frame
selection, image registration, digital subtraction
and grey-scale calculation. Then, by processing
an image sequence, a time-intensity curve (TIC)
for hepatic perfusion is derived. The kernel of
this image analysis technology is digital subtrac-
tion and its accuracy is improved by frame selec-
tion and image registration. The advantage of
this method is that it can obtain the perfusion
information of the whole liver w hich is rarely ob-
tained by traditional image analysis technology;
therefore, it is a supplement of the traditional
image analysis method. This method is applied
on the quantification of a rabbit’s hepatic perfu-
sion and the result shows the efficiency of it.
Keywords: Hepatic perfusion quantification,
Image analysis, Image registration, Digital sub-
Blood perfusion describes the amount of blood delivered
to the capillary beds of a block of tissue in a certain pe-
riod of time and its quantity determines whether the en-
ergy status of the tissue is likely to become compromised
[1]. Thus, when blood perfusion of the tissue is abnormal,
many diseases will be caused and on the contrary, when
diseases occur, abnormal blood perfusion will be present.
Various liver diseases lead to significant alterations of
hepatic perfusion and quantification of hepatic perfusion
has the potential to improve the assessment and manage-
ment of liver diseases [2]. Consequently, much effort has
been done to quantify hepatic perfusion with the help of
different imaging modalities, such as MRI, CT and so on
As a versatile, non-invasive, low risk, low cost and
portable real-time imaging technique, ultrasound is the
most frequently used imaging modality [4]. CEUI is a
new ultrasonic imaging modality developed with the in-
troduction of ultrasonic contrast agent (UCA) and the
evolution of corresponding imaging technologies [5].
Since CEUI can image the blood flow, it has been exten-
sively used in clinical diagnosis [4,6,7]. One of the most
important possible applications in the clinic is that it is
more and more used to assess and quantify the blood flow
and blood perfusion of various tissues and organs [8--12].
Efficient image analysis technology is needed during
the quantification of hepatic perfusion using CEUI. The
conventional analysis method is to measure mean signal
intensity (mean grey-scale) of the pixels in a manual
placed, equally sized and shaped region of interest (ROI)
in each of the image frame, and then the TIC of the ROI
is obtained [1,3,11]. Some important perfusion parame-
ters, for example the mean transit time (MTT) and blood
volume fraction (BVF), can be obtained easily using this
analysis method. Despite its efficiency, there are still is-
sues to be addressed. Since the manual placed ROI is usu-
ally local, the perfusion information of the whole liver
such as the three vascular phases of the liver perfusion, as
an important perfusion parameter, can hardly be derived
directly from the TIC of the local ROI.
As a supplement of the traditional image analysis
method, to reflect the three vascular phases of the liver
perfusion directly, the proposed image analysis method in
this paper focuses on the derivation of the TIC of all the
automatically segmented contrast-enhanced areas rather
than that of the local ROI. The segmentation is imple-
mented by subtracting the baseline image (background)
from the contrast-enhanced image. After the subtraction,
all the contrast-enhanced areas (all the pixels with a grey-
scale above zero in the subtraction image) are obtained.
To verify the method, a rabbit’s hepatic perfusion video is
acquired using CEUI and is analyzed using the proposed
method. The result of the application shows the efficiency
SciRes Copyright © 2008
Y. X. Li et al. / J. Biomedical Science and Engineering 1 (2008) 116-120 117
SciRes Copyright © 2008 JBiSE
of the proposed method.
The layout of the paper is as follows: Section 2 pre-
sents the theoretical framework of the image analysis
method. The experiment is described in section 3 along
with results and discussion in section 4. The paper con-
cludes in section 5.
The main processes of this method can be described as
follows: 1) a frame before the injection of the UCA is
selected as the baseline image, and then one appropriate
frame for each second after the injection is selected as the
contrast-enhanced image, and all the contrast-enhanced
images construct the image sequence; 2) for each of the
contrast-enhanced image, the baseline image is registered
and subtracted to acquire all the contrast-enhanced areas
in the image; 3) the mean signal density of all the con-
trast-enhanced areas is calculated for every contrast-
enhanced image, and then the TIC can be derived at last.
2.1. Selection of appropriate frames
Tissue motion induced by respiration makes the frames at
different time point have lots of motion artefacts which
will heavily decrease the efficiency of digital subtraction.
Therefore, after the first frame is selected, all the other
selected frames should have the least motion with the first
frame. The purpose of this step is to reduce the global
motion between the frames. In this method, the energy of
the histogram of differences (EHD), proved to be compu-
tationally cheap and yield accurate results [13], is applied
as the similarity measurement criterion and the frame
with highest EHD has the greatest similarity with the first
frame. The theory of EHD can be briefly described as
() ()
Here MEHD (d ) is the energy of the histogram of differ-
ences, g[-G, G] [-255,255] Z. H(g) is defined as:
()( (,),)
= (2)
Here K, L are the width and height of the block to be
processed (the image frame in this study); Id (x, y) = I (x,
y) I0 (x+x, y+y) is the subtracted image, where I (x, y)
is the contrast-enhanced image and I0 (x+x, y+y) is the
aligned baseline image. δ (x, y) is the Kronecker delta
(, )0,
2.2. Image registration and digital subtraction
The global artefacts are reduced by selecting the appro-
priate frame; however, the local motion artefacts still exist
between the contrast-enhanced image and baseline image.
Figure 1. The schematic diagram for the selections of
control points.
To remove the local motion artefacts, the baseline image
is registered by finding the local motion vectors of the
pixels in the baseline image and then aligning them. The
most ideal method for registration is to align every pixel;
however the computational cost makes this hard to be
implemented. Given to the continuous motion of neigh-
bouring area, an alternative approach is to align the se-
lected control points first and then all the other pixels are
aligned by interpolation. To acquire the control points,
both the baseline image and the contrast-enhanced image
are divided into an array of rectangular tiles as shown in
Figure 1 and each vertex of the grid is selected as control
Based on the selection of control points, the whole
process of the registration and digital subtraction can be
described as follows:
Process 1:
Control points of both the images are selected as the
centres and R×R pixels around each centre are set to be
the ROIs. The ROI of baseline image is denoted as Ab(i, j),
and the corresponding area of the contrast-enhanced im-
age is denoted as Ac(i, j) (i, j = 1
Process 2:
Ab(i, j) is shifted according to the rules of three-step
search algorithm [14] and the position is located where
Ab(i+x, j+y) and Ac(i, j) had the largest MEHD (d). The
centre of Ab(i+x, j+y) is recorded and pixel unit (-x, -
y) is defined as the local motion vector of the control
point, where x and y are the number of pixels that have
been shifted in x (horizontal) and y (vertical) directions.
All the control points are processed by repeating this
Process 3:
Figure 2. The bilinear interpolation.
118 Y. X. Li et al. / J. Biomedical Science and Engineering 1 (2008) 116-120
SciRes Copyright © 2008 JBiSE
The interpolation of local motion vectors is done in this
step using bilinear interpolation, as shown in Figure 2.
The interpolated value v(x, y) at point (x, y) is calculated
from the values of the four points surrounding the point (x,
y) as follows:
{( 1) }()(,1)
vxykx lyvkl
xkylvk l
++− −+
+− +−+
+−−+ +
Where k = x and l = y (k = x denotes the largest
integer that do not exceed x). After the interpolation, all
the local motion vectors are obtained. Then, the registra-
tion of the baseline image is achieved by shifting the pix-
els according to the local motion vectors.
Process 4:
In this step, by subtracting the corrected baseline image
from the contrast-enhanced image, the subtraction image
is constructed and all the contrast-enhanced areas are pre-
2.3. Derivation of TIC
Since the subtraction image contained only the contrast-
enhanced areas, the mean signal intensity of the subtrac-
tion image is directly proportional to the amount of blood
flow at this point of time. Thus, the TIC is acquired by
performing following steps: (1) calculate the mean signal
intensity of every subtraction image by calculating the
average grey-scale of all the pixels in the contrast-
enhanced areas; (2) let the horizontal axis denote the time
while vertical axis the mean signal intensity.
A rabbit’s hepatic perfusion video was acquired using
CEUI. The rabbit was anaesthetized using isofluorane gas
(23%), and placed in dorsal recumbency on a heated
imaging table (37) under anesthesia. Ventral abdominal
hair (around the liver) was removed using a depilatory
cream. The imaging setup used in the experiment was
LOGIQ® 9 (produced by General Electric (GE) Co.) and
the scanning was carried out under a low power, low me-
chanical index (MI) contrast mode with the frequency of
the transducer 3.5MHz and the MI value 0.2 (which was
below the disruption threshold for the micro bubbles). All
the conditions were fixed during the imaging time includ-
ing the position of the ultrasound probe so that the motion
was mainly led by the respiration of the rabbit. The UCA
used in the experiment was provided by Jiangsu Labora-
tory for Biomaterials and Devices (China) and the synthe-
sis and characterization of the UCA were described in
another literature [15]. The UCA was injected by auricu-
lar vein bolus injection and the video began immediately
after the injection. 120 seconds later, the video was
stopped and each frame of the video was snapped and
stored. In this experiment, the region contained the whole
radial regions with the size 481×351 pixels were selected
and the size of the block around the control point was
31×31 pixels (which was determined by balancing the
computational cost and the accuracy of the registration).
Figure 3. The EHD of each frame within one second
(A) (B)
Figure 4. The result of frame selection: (A) the baseline image; (B) the contrast-enhanced image.
Y. X. Li et al. / J. Biomedical Science and Engineering 1 (2008) 116-120 119
SciRes Copyright © 2008 JBiSE
Figure 5. The results of registration and digital subtraction: (A) the subtraction image after registration and the selected
ROI (in the rectangular); (B) motion vector chart for the control points; (C) the histogram of the ROI with registration; (D)
the histogram of the ROI without registration.
The video acquired contained seven frames for each sec-
ond; therefore, in this study, the computer program cal-
culated the EHD of seven frames for each second and
then the frame with the highest EHD was selected to
construct the sequence. Figure 3 shows the EHD of the
frames within the 17 seconds after injection and it can be
found that the first frame has the largest EHD, thus, the
first frame is selected as the contrast-enhanced image for
the17 seconds and Figure 5 displays the contrast-
enhanced image that is selected.
Figure 5 is the result of registration and digital sub-
traction for the images shown in Figure 4. From Figure
5 (A), it can be found that the background is removed to
a large extent. The motion vector shown in Figure 5 (B)
illustrates that there is motion artefacts between the base-
line image and the contrast-enhanced image. Figure 5 (C)
and (D) show that after registration, the peak value
around zero is higher and the band concentrates better at
zero which means that the corresponding positions in the
baseline image and the contrast-enhanced image are
aligned efficiently, so the registration of the baseline
image improves the background subtraction. However,
Figure 5 (A) also shows that the background is not re-
moved completely and the reasons may include two main
aspects: First, there are still some issues with the regis-
tration algorithm, for example, the registration algorithm
only accounts for linear motion but not for rotation; Sec-
ondly, the imaging area and imaging direction changed
slightly between the baseline image and the contrast-
enhanced images which is inevitable because the probe
must touch the body of the rabbit whose respiration
means the probe will not be stationary.
It is reported that due to the specific blood supply to
the liver, three vascular phases can be defined. The arte-
rial phase (hepatic artery supply) starts 1020s after
intravenous injection and lasts for 1015 s. It is imme-
diately followed by the portal venous phase which ex-
tends from 3035 s post-injection to 120 s. Thereafter,
the late phase begins which ends with the disappearance
of the bubbles (about 5 min post-injection) [10]. In Fig-
ure 6 , the TIC begins to increase at about the 11 sec-
onds and then quickly reaches the peak value which last
until about 36 seconds; this indicates the arterial phase
very well. While keeping high signal intensity, the TIC
begins to decrease slightly at 36 seconds to 120 seconds,
and this shows the process of portal venous phase very
120 Y. X. Li et al. / J. Biomedical Science and Engineering 1 (2008) 116-120
SciRes Copyright © 2008 JBiSE
Figure 6. The TIC of the rabbit’s hepatic perfusion ob-
tained using the proposed method
In this study, as a supplement of the traditional image
analysis method, an image analysis method used to quan-
tify the total hepatic perfusion using CEUI is proposed.
The main contributions included in this method are
frame selection, image registration, digital subtraction
and grey-scale calculation. In the steps of frame selection
and registration, EHD is selected as the criterion of simi-
larity measure. The usefulness of the method is proved
by applying it on the quantification of a rabbit’s hepatic
perfusion. Since ultrasound is the most widely used im-
aging modality, and as the development of the technol-
ogy of CEUI, this method has great potential clinical
value. In addition, it can also be used to quantify the
perfusion of other organs such as the kidney. Although
this method shows great potential advantages, there are
still some issues to be addressed. First, the method is not
accurate enough to make the background removed com-
pletely which affects the efficiency of quantification;
secondly, the program for this method available now is
off-line, which hampers the real-time clinical application.
Further works are needed to address these issues so that
the method can be better used for clinical diagnosis.
The authors gratefully acknowledge the financial support given to
this research from National Important Science Research Pro-
gram of China (Nos. 2006CB933206)
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