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ab
3 Imaging ofCommon Biliary Tract Diseases
93
c
d
Fig. 3.69 Branch-like dilatation of the intrahepatic bile duct. (a) Plain
CT scan shows branch-like dilatation of the intrahepatic bile duct;
(b~c) CT Contrast-enhanced scan shows branch-like dilatation of the
3.5.3.2 Changes oftheDistal End oftheDilated
Extrahepatic Bile Duct
intrahepatic bile duct; (d) MRCP shows branch-like dilatation of the
intrahepatic bile duct, and a circular lling defect (choledocholithiasis)
can be seen in the middle and upper segment of the common bile duct
diagnostic signicance (Saluja et al. 2007) (Figs. 3.71 and
3.72). MRCP reveals that the edge of the obstruction terminal
is irregularly narrowed. Centripetal and transverse stenoses
Abrupt Interruption andIrregular Tapering
otheDilated Extrahepatic Ducts
CT images revealed tapering or disappearance of the dilated
extrahepatic duct. The occurrence of this phenomenon with
no positive stone shadow observed in the obstructed end is
highly suggestive of malignancy; the association with an
obstructive terminal mass or irregular wall thickening more
than 4mm also suggests malignancy, which has differential
are often malignant (Suthar et al. 2015; Park et al. 2004),
while eccentric or cup-shaped stenoses are presumed to cor-
respond to cholangiocarcinoma in most cases, and also, this
condition can easily happen when there are calculi (Fig.3.73).
In this case, with or without enhancement in enhanced scan
and thickening of the wall became the distinguishing feature.
The stones were not strengthened, while cholangiocarcinoma
had different degrees of enhancement at the obstructive end.
94
ab
X. Quan et al.
c
Fig. 3.70 Dilatation of the intrahepatic bile duct in the form of soft rattan. (a~b) T2WI: Soft rattan dilatation of intrahepatic bile duct; (c) MRCP
shows soft rattan dilatation of intrahepatic bile duct and enlarged gallbladder
3 Imaging ofCommon Biliary Tract Diseases
ab
d
c
95
Fig. 3.71 Ductal adenocarcinoma of the head of the pancreas. (a)
MRCP shows abrupt truncation of the pancreatic segment of the common bile duct and dilation of the intrahepatic and extrahepatic bile
ducts; (b) T2WI indicates intrahepatic bile duct dilation; (c) T2WI
shows enlarged gallbladder, and dilatation of the upper segment of the
common bile duct; (d) No calculi are found at the obstruction end
96
a b
c
X. Quan et al.
d
e
Fig. 3.72 Ampullary adenocarcinoma involving the head of pancreas.
(a) MRCP shows irregular narrowing and thinning of the lower segment
of the common bile duct, with irregular lling defects, intrahepatic and
extrahepatic bile duct dilation, and enlarged gallbladder; (b~c) T2WI
shows gallbladder enlargement, dilated common duct, and sudden
tapering of the lower part of the common bile duct; (d) Contrast-
enhanced CT scan shows enlargement of the head of pancreas, uneven
enhancement, and small aky low enhancement area; (e) Contrast-
enhanced CT scan at different levels shows masses at the head of
pancreas
3 Imaging ofCommon Biliary Tract Diseases
97
a
c
b
Fig. 3.73 CT and MRI manifestations of calculi in the lower common
bile duct. (a) MRCP shows abrupt truncation of the lower segment of
common bile duct, and the above intrahepatic and extrahepatic bile
Gradual Tapering oftheDilated Extrahepatic Bile
Ducts
On CT images, dilated bile ducts gradually tapered, with a
range above 3 cm. This is the characteristic of benign
obstruction (Fig.3.74), such as that caused by inammation
(Katabathina etal. 2014).
Masses at the Obstruction End Masses at the obstruction
end were mostly malignant tumors, and a few were chronic
pancreatitis. The former are associated with necrosis in the
mass and blurring of peripancreatic fat planes, while the latter are associated with pancreatic calcication and beading
of the pancreatic duct.
ducts are dilated; (b) T2WI shows a short T2 signal calculus shadow at
the obstruction end; (c) Target signs (high-density stones in the dilated
common bile duct lled with low-density bile)
3.5.4 Analysis ofObstructive Jaundice by CT
andMRI
3.5.4.1 Obstruction intheEarly Phases
Normally, bile duct dilatation can occur after obstruction
of the common bile duct. Therefore, when obstructive
jaundice and corresponding biochemical changes have
occurred clinically, no bile duct dilatation may have
formed yet. At this time, follow-up observation should be
paid attention to. It has been reported in the literature that
biliary dilatation can be observed 2weeks after obstructive jaundice.
98
ab
d
c
X. Quan et al.
e
Fig. 3.74 Cholangitis. (a) MRCP shows that the dilated pancreas in
the upper segment of the common bile duct gradually becomes thinned
into beak shape. The intrahepatic bile duct is slightly dilated, and stones
can be seen in the intrahepatic bile duct and gallbladder; (b) T2WI
3.5.4.2 Biliary Obstruction Associated withLiver
Disease such asDiuse Cirrhosis
Due to liver inammation, tissue brosis or extensive inltration of tumor tissue, diffuse cirrhosis, and liver cancer can
cause inhibition of intrahepatic bile duct dilatation.
shows dilation of the upper segment of common bile duct; (c) T2WI
shows gradual tapering of the middle segment of the common bile duct;
(d, e) T2WI shows gradual tapering of the lower and middle segments
of the common bile duct
Therefore, CT and MRI do not show the obvious dilation of
intrahepatic bile duct when it is associated with extrahepatic
bile duct obstruction; especially in the early phases of the
disease, it is difcult to judge whether jaundice is obstructive
at this stage.
3 Imaging ofCommon Biliary Tract Diseases
99
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Introduction to3D Visualization
ofAbdominal CT Images
SusuBao, FengpingPeng, andChihuaFang
4
4.1 Introduction
This chapter introduces the development, procedures, and
characteristics of the Medical Images Three-Dimensional
Visualization System (MI-3DVS), a system that has independent intellectual property rights in China.
It includes:
• Procedures for data acquisition.
• Data pre-processing.
• Medical image segmentation.
• 3D realization and reconstruction.
4.1.1 Basic Procedures for3D Visualization
ofAbdominal CT Images
As 3D visualization of abdominal CT images is an emerging
eld of research, it cannot be studied using traditional optical
imaging research methods based on light intensity; therefore,
new and targeted approaches are needed.
The research content of 3D Visualization of Abdominal
CT Images includes: medical CT data acquisition, data
preprocessing, medical image segmentation, and 3D visualization. The basic processing procedure is shown in
Fig.4.1.
CT image
acquisition
Fig. 4.1 Flow chart of 3D reconstruction and visualization of CT data
S. Bao · F. Peng
South China Normal University, Guangzhou, China
C. Fang (
Zhujiang Hospital, Southern Medical University,
Guangzhou, China
*)
Data
preprocessing
Image analysis
3D
visualization
4.1.2 Basic Techniques for3D Visualization
ofAbdominal CT Images
4.1.2.1 CT Acquisition Protocols
CT data acquisition is different from acquiring general optical data. At present, CT data is acquired by ray tomography
technology. Therefore, high-quality CT data can be achieved
only through effective post-processing.
4.1.2.2 Data Preprocessing
Compared with ordinary images, medical images are characteristically ambiguous and heterogenous in nature. Therefore,
image preprocessing of CT data is required to obtain a better
display effect, and the target area can be highlighted to prepare for the next segmentation. Common image preprocessing operations in CT include: grayscale windowing of CT
images enhancement, and image format conversion.
4.1.2.3 Medical Image Segmentation
The structure of medical images is complex, and the gray
scale between different tissues is of high ambiguity and
uncertainty. For some tissues and organs, their boundaries
can hardly be distinguished by the naked eye. In order to
compensate for these weaknesses and to accurately differentiate normal from abnormal tissues in medical imaging, it is
necessary to perform image segmentation. Image segmentation plays an important role in medical applications; it is an
indispensable means to extract quantitative information of
distinctive structure from images; furthermore, it plays a key
role in the realization of visualization.
The commonly used segmentation approaches include
threshold-based image segmentation, interactive image segmentation, and image segmentation based on active contour
models or deformation models (Kang etal. 2020). Different
segmentation techniques can be selected according to the
varying characteristics of different medical target tissues and
the image to be segmented.
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021
C. Fang, W. Y. Lau (eds.), Biliary Tract Surgery, https://doi.org/10.1007/978-981-33-6769-2_4
101
102
4.1.2.4 3D Visualization
There are two major approaches for 3D visualization of medical image data: surface rendering and volume rendering.
Surface rendering rstly extracts the value of the object
contour from the three-dimensional data eld, then constructs
the intermediate geometric elements (such as surface and
plane) from the three-dimensional data eld according to the
values, and nally realizes the drawing by traditional computer graphic technology. Marching cubes is one of the most
typical methods of such algorithms (Wang etal. 2020), and
this method can extract relatively clear isosurface (Cirne and
Pedrini 2013). When the image is rotated, it does not need
to retraverse the volume data. Moreover, the existing graphics
hardware can be used to realize the rendering function, which
accelerates the image generation and transformation process.
However, the visualized graphics constructed by this method
can only provide a thin outer shell of the object; it can neither
reect the full picture and details of the whole original data
eld, nor solve the issue of blurred boundary. Additionally,
with the increasing size of the volume data, a large number
of intermediate geometric primitives are generated, which
requires a large memory space and slow drawing speed.
Volume rendering does not require the construction of
intermediate geometric primitives, and the volume data are
directly projected onto the image plane to obtain the full picture and details of the volume data. The typical method is ray
casting (Santos etal. 2020), which is a popular technique of
volume visualization for the generation of high-quality and
realistic images. It is particularly suitable for unshaped volumetric datasets such as clouds, fog, uid, brain soft tissue,
and gas; however, it is rather time consuming for each image
to be generated to traverse the volume data.
4.1.3 Composition ofMI-3DVS
MI-3DVS consists of an abdominal medical center database,
a medical image processing center, and a computer-aided
surgical simulation platform (Fig.4.2).
4.1.4 Advantages ofMI-3DVS
• The source data is from the current advanced 64-slice spi-
ral CT.
• Image data processing and simulation surgery are closely
connected through STL les.
• The simulation surgery platform which is based on the
PHANTOM force feedback device with property rights
for secondary development can form mechanical haptic
feedback.
• The image storage center can be used to conduct data
mining and pattern recognition research on patient data.
S. Bao et al.
Data acquisition
computer-aided
surgical simulation
platform
Fig. 4.2 Diagram showing MI-3DVS operation
Data Center
image processing
center
4.2 Image Registration, Segmentation,
and3D Reconstruction
4.2.1 Image Registration
Image registration refers to geometrically alignment of one
image with another (Nicolas etal. 2020). After image registration, the two registered images should achieve spatial
consistency; moreover, the different sets of data should be
transformed into one coordinate system. The primary goal
of image registration is to eliminate or suppress geometric
discrepancies between the registered image and the reference image by applying a linear combination of translation,
rotation, scaling, and shearing. Image registration is a crucial
step in all image analysis and processing tasks; moreover, it
is the prerequisite for image contrast, image fusion, change
detection, and target recognition.
In this chapter, triphasic CT scan of the liver was adopted
(venous, portal, and arterial phases). Although the number
of layers scanned is the same, the scan sequences are different. Thus, it is necessary to carry out three-stage registration
so as to achieve complete fusion of the liver and its internal
conduit.
The image matching algorithms can be commonly divided
into two categories: matching algorithm based on geometric
pattern value and pixel gray value. On the basis of fully utilizing the characteristics of CT image data, combined with
the existing template matching algorithm, a three-stage liver
data registration algorithm based on the similarity of CT
images is proposed, which effectively realizes the registration and fusion of three stages of liver data.
4.2.1.1 Template Matching Algorithm
Template matching is the process of searching for small parts
of a source image that match a template image. It is basically
an approach for searching and nding the location of a tem-