1. Honours Project
REGISTRATION STUDY OF CENTERLINES
EXTRACTED FROM 3D CT CORONARY
ANGIOGRAPHY IMAGES AND 2D X-RAY
ANGIOGRAPHY IMAGES
Gaurav Kaila
LUMC Supervisor: Ir. Pieter Kitslaar
TU Delft Supervisor: Dr. Ir. Emile Hendriks
Date: 15th
December, 2014
Delft University of Technology Division of Image Processing
Department of Biomedical Engineering Leiden University Medical Center

2. Abstract
Coronary Artery Disease (CAD) is one of the most commonly occurring heart diseases
worldwide. It results from the buildup of plaque below the intima layer inside the vessel
wall. This obstruction in the vessel walls hinders the blood flow to the heart muscle.
Furthermore, the rupture of plaques might cause blood clot formation resulting into
obstruction of the blood flow.
The most commonly used treatment techniques for CAD is Percutaneous Coronary
Intervention (PCI). It involves introduction of a guide wire through the groin which is
moved towards the ostium of the coronary artery. Once the guide wire is in place, a
balloon at the tip of the guide wire is inflated at the lesion area which helps open up the
vessel. The image guidance of the guide wire is usually done using intraoperative X-ray
images which give the cardiologist a fair idea of the position of the guide wire and the
vessel. Though this process yields a good success rate in less complicated occlusions, it
still is difficult to obtain good results for complex vascular anatomies, bifurcating lesions
and chronically totally occluded vessels. X-ray angiography only visualizes the lumen of
the vessels and suffers from problems like foreshortening and vessel overlap due to the
projection nature of the modality.
In order to circumvent this problem, preoperative coronary Computed Tomography
Angiography (CTA) images may be combined with intraoperative X-ray images. CTA gives
valuable information on not only the lumen but also the plaque in the coronary vessels
which is not visible on the X-ray images. This additional information helps the
cardiologist to decide on the best strategy during the intervention. To easily present this
information from CTA along with the X-ray images an alignment or registration between
these two data sources is needed.
In this report, we describe three registration algorithms for aligning 3D CTA coronary
centerlines with reconstructed 3D centerlines obtained from two X-ray angiography
projections. We explore the Iterative Closest Point (ICP) registration algorithm, a pre-
processed version of the ICP and the 3D version of the scale invariant curvature
signature registration technique, suggested by Cui et al, that we will refer to Cui’s
algorithm in this report. [7] Furthermore, a GUI is developed to analyze the point wise
correspondence between the CTA and X-ray angio centerlines. Finally, a comparison of
the three registration algorithms is made to assess the advantages and the
disadvantages of the techniques.

3. Contents
Abstract
1 Introduction 1
2 Materials and Methods 3
2.1 Materials 3
2.2 Methods 4
2.2.1 Scale Invariant Curvature Signature technique 5
2.2.2 Iterative Closest Point algorithm 6
2.2.3 Iterative Closest Point algorithm with pre-
processed centerlines 6
2.3 GUI Development 8
3 Results 9
3.1 Centerline Registration 9
3.1.1 Patient 1 9
3.1.2 Patient 2 10
3.2 Error Analysis 11
3.3 Validation 11
3.4 Graphical User Interface 12
4 Discussion
4.1 Comparison of Registration Algorithms 16
4.2 Validation Discussion 17
4.3 Usability of the GUI 18
Conclusion and Future Work 18
References 20

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C H APT ER 1
Introduction
Coronary Artery Disease (CAD) is a leading cause of heart related deaths worldwide.
With changing lifestyles, increasing stress and poor dietary habits, heart has become
more vulnerable than ever before. CAD occurs due to the buildup of plaque below the
intima layer inside the vessel wall. This plaque formation restricts proper flow of blood
to the myocardium causing weakening of the muscles and leading to fatal consequences
[1]. There are multiple treatment methods for CAD, namely, medication, percutaneous
coronary intervention (PCI) and coronary artery bypass surgery. Medication involves
intake of medicines that reduce heart rate and blood pressure, prevent blood clotting
and lower cholesterol. Bypass surgery is an invasive treatment technique in which blood
flow is restored to the heart by grafting arteries from other parts of the body. PCI is a
minimally invasive technique in which a guide wire is introduced into the affected artery
and a balloon is inflated at the lesion area to open up the vessel [2]. Out of the three
techniques described, medication is the preferred method for curing CAD and has a high
success rate in less complicated artery anatomies.
Efficiency of PCI can be improved by providing image guidance to the cardiologist during
PCI. With access to only 2D projection information generated from the intraoperative X-
ray system, the cardiologist has to depend on a mental 3D reconstruction of the
coronary artery and the guide wire.
Recently new techniques are presented that allow to reconstruct a 3D model of the
coronary arteries from two projections (3DQCA) with high accuracy [3] However, lack of
information on the plaque position and composition might still lead to an inefficient PCI
procedure. Due to insufficient plaque information, the type of stent, stent placement or
the balloon inflation might not be perfect. For instance, the presence of heavy
calcifications might prevent a stent from fully expanding. Hence, In order to provide a
better understanding and image guidance during PCI, alignment of the 3D preoperative
coronary CTA data with intraoperative 2D X-ray angiography images will be benificial.
Previously, several dynamic road mapping methods have been discussed to align the
2D/3D coronary data to achieve high success rate in PCI. Turgeon et al [3], proposed a
static registration technique to align preoperative angiography data (CT, MR or
rotational angiography) to intraoperative X-ray angiography. Their approach is based on
segmentation of the coronary arteries from the preoperative imaging data and
comparison of the intraoperative angiography images with projections of the resulting
coronary model. The segmentation is needed to aligning the data as it involves different
contrast injection practices for the 2D/3D image acquisition. For the CTA, the contrast is
injected intravenously through the arm and hence results into contrast flow through the
cardiac chambers, coronary arteries and aorta. On the other hand, contrast injection
during X-ray image acquisition is done directly into the coronary artery which results
Introduction

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into illumination of only the coronary artery. Without pre-processing this results into
poor projection images which are not very suitable for intensity based registration
approach.
Based on the possibility of generating a 3D model of the coronary arteries from two x-
ray projects using the 3DQCA technique, we propose to use this as a basis to find the
correspondence between the information in the CTA data with the information in the x-
ray images directly in the 3D domain. For now we will focus only on the correspondence
between the luminal center lines for single vessels obtained from CTA and the 3DQCA
output.
For this we investigated three different registration algorithms. We align the centerlines
extracted from the 2D/3D data and align them as precisely as possible. Two of the three
registration techniques are based on the concept of Iterative Closest Point technique
that minimizes the cost factor between a collection of points. A position of the best
match is determined by the minima of the cost function (mean squared error). The third
technique employed to align the centerlines is based on the scale invariant curvature
signature of the centerlines. As coronary arteries have high curvature values, utilizing
their curvature properties for registration might serve as a good registration criterion.
Finally, a GUI is developed to compare the three registration methods and to show point
wise correlation between the 2D/3D data.
The following chapter describes the materials and methods used for our study. Chapter
3 describes the results and gives an overview of the GUI. Chapter 4 discusses the results,
presents the conclusion and future work in this area.

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C H APT ER 2
Materials and Methods
This chapter describes the datasets considered for our study. The method of
centerline extraction is briefly explained followed by the description of the
registration algorithms employed.
2.1 Materials
Coronary artery images from two modalities are considered: Computed Tomography
Angiography and X-Ray Angiography.
For extracting the CTA centerlines, a vesselness based skeletonization process is
used [5]. These algorithms are implemented inside a software package called
QAngioCT RE (Medis medical imaging systems bv, Leiden, The Netherlands) which
provides the 3D centerlines as output for further processing (Figure 1).
To obtain the X-ray angiography centerlines, a fast marching wave propagation
algorithm is applied [4] in two projections followed by 3D reconstruction [6]. These
algorithms are implemented inside the software package QAngioXA 3D RE (Medis
medical imaging systems bv, Leiden, The Netherlands) which was used to obtain the
resulting 2D and 3D centerlines (Figure 2).
We evaluate our registration algorithms on data extracted from four patients.

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Figure 1: Example of CTA centerline extraction using QAngioCT (Medis medical imaging systems
bv, Leiden, The Netherlands)
Figure 2: Example of 2D angio centerline extraction and 3D reconstruction using QAngioXA 3D
(Medis medical imaging systems bv, Leiden, The Netherlands

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2.2 Methods
We use three registration algorithms in our study. 1) Scale invariant curvature signature
technique 2) Iterative closest point algorithm and 3) Iterative closest point algorithm
with centerlines pre-processed. Following is the brief description of each of the above
methods.
2.2.1 Scale Invariant Curvature Signature method [7]: The following steps
describe using the scale invariant curvature signature for the registration
process of the 3D curves (extracted centerlines).
a) Construct a cubic spline through the centerline.
b) Sample the splines with a given number of points (300 in our case).
c) Compute the curvature at the sample points.
d) Compute arc length for the spline curve and examine Curvature (K) vs Arc-Length.
(Figure 3)
e) Integrate the curvature over the arc length and examine Arc Length vs Integration of
Curvature (K). (Figure 4)
f) Compute curvature K of the curve at equal interval-sampled points and plot
curvature vs. integral of the curvature. This is the curvature signature. (Figure 5)
g) Do step a-f for both CTA and X-Ray angio data.
h) Compute the normalized cross correlation between the curvature signatures of both
the centerlines. (Figure 6)
i) Identify the point of maximum correlation. This point indicates the offset by
which the Angio curvature signature is moved over the CTA curvature
signature to get an optimal match.
j) Assuming CTA centerline as the fixed curve and X-Ray angio curve as moving, shift
the integral vs arc length curve of the X-Ray angio by the offset (computed in i) in
the integral domain. (Figure 7)
k) Extract points on the integral curve in the overlapping region.
l) Compute the landmark points using the points obtained in k. (Figure 8)
m) Register the original curves using these landmark points. (Figure 11)
Figure 3 Figure 4 Figure 5

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Figure 6 Figure 7 Figure 8
2.2.2 Iterative Closest Point algorithm (ICP) [8]: It is a point based registration
method that aligns two point clouds as closely as possible. Steps:
a.) Assign one point cloud (set of 300 points) as a target which is kept fixed while
the other point cloud as the source which is transformed.
b.) Find the closest corresponding points on the two centerlines by finding the
minimum Euclidean distance between each point on the angio centerline and all the
points of the CTA centerline.
d(p,A) = min d(p,ai) i Є {1,……..,Na}
p = point on the angio centerline; A = all the points on the CTA centerline
c.) Setup a mean squared error function that can be minimized in order to obtain
rotation and translation of the source point cloud with respect to the points
obtained in step 2.
MSE = (xi – yi)2
/n
(xi - yi) = distance between corresponding points on the centerline
n = number of points selected on the centerline
d.) Minimization of the error function is done using Singular Value Decomposition
[9]. This provides us with the rotation and translation of the moving centerline wrt
the fixed centerline.
e.) Reiterate from step b
f.) Stop when the MSE function cannot be reduced further or has reached a
threshold.
2.2.3 Iterative Closest Point algorithm with pre-processed centerlines: The
centerlines are pre-processed before ICP is applied..
Initialization steps are as follows:
a.) Rotate the Angio centerline by the angle formed between the tangents at
the median (center) point of both the centerlines. (Figure 9,10)
b.) Apply ICP as described above. (Figure 11)

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a b
Figure 9: a) CTA b) Angio Centerline Figure 10: Rotated Centerlines
Figure 11 Registered centerlines (Patient 4): a) X-Y view b) X-Z view and c) Y-Z view
(Red/Green: Angio, Blue: CTA)
Median
Tangent

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2.3 GUI development
The development of the graphical user interface (GUI) is done in MeVisLab (MeVis
Medical Solutions AG, Bremen Germany). Registration algorithms are written in MATLAB
R2013a (The Mathworks Inc, USA) using the image processing toolbox and interfaced
with MeVisLab by incorporating the MATLAB script wrapper. The Python scripting
language is used for the GUI code development.

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C H APT ER 3
Results
This section presents the results obtained for the three registration algorithms
applied on two of the four patient datasets considered. A visual comparison is done
between the registered centerlines. This is followed by the error analysis of the
registrations performed. Further, an overview of the graphical user interface (GUI) is
given.
3.1 Centerline Registration
Figures 12 and 13 show examples of the registration algorithms used in our study.
3.1.1 Patient 1
Figure 12: Registered centerlines (Patient 1): a) X-Y view b) X-Z view
and c) Y-Z view
(Red/Green: Angio, Blue: CTA)

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3.1.2 Patient 2
Figure 13: Registered centerlines (Patient 2): a) X-Y view b) X-Z view
and c) Y-Z view
(Red/Green: Angio, Blue: CTA)

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3.2 Error Analysis
The mean squared error (MSE) is evaluated for the registered centerlines.
Iterative closest point algorithm minimizes this error measure and reaches an
optimal match. For scale invariant curvature technique, MSE is evaluated
between landmark points on the two registered centerlines. Following bar graph
shows these findings (figure 14):
Figure 14: MSE Analysis for three registration techniques
The blue bar indicates the MSE for ICP, the orange bar is for MSE obtained from
pre-processed ICP and gray bar indicates MSE for Cui’s algorithm.
3.3 Validation
In order to validate the registration algorithms used, we compute mean square
error (MSE) on a selection of points. An independent observer selected multiple
corresponding points on the Angio and CTA centerline. These points
corresponded to clear visual landmarks in the data like the start/end of a stent or
at the bifurcations in the coronary arteries. No point wise correlation was
available for the observer and thus the points selected was unbiased. MSE was
computed between these manually selected points.
The bar graph in Figure 15 shows the results of the validation study by a single
observer:

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Figure 15: MSE error between manually selected validation points on the centerlines
3.4 Graphical User Interface
The GUI compares the three registration algorithms for different patients. The
centerlines are loaded along with the CTA volume and frontal/ lateral X-Ray angio
images. Curved MPR view renders image from the 3D CTA volume. The Curved
MPR plane can be defined in any direction and angle on the original dataset. This
plane is defined as a 3D Bezier path. The Curved MPR plane is rendered as a
stretched view and enables another view for the CTA centerline.
A snapshot of the GUI is shown in Figure 16.

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Figure 16: Screenshot of Graphical User Interface for registration of centerlines obtained from
3D CTA and 2D Angiography for Patient 2 using pre-processed ICP method
The registration algorithm can be interactively selected from the drop down menu at
the top left corner of the GUI. The user can select the 3D CTA centerline and the
corresponding CTA volume using the first two entry options. Angio centerline and the
frontal – lateral images can be selected using the 3rd entry option. This will enable the
user to view the registered centerlines. For validation purposes, the user can select the
landmark points on the angio frontal image and CMPR view of the CTA. This marks the
landmark points on the registered centerlines. These markers can be saved in the
directory and used again using the save/load buttons in the GUI. By using the “compute
validation error” button, MSE can be evaluated between the landmark points for each of
the registration methods. Following are GUI snapshots of a few patients (figure 17, 18):
Save Angio markers
Registration Method
CTA centerline CTA volume Angio Centerline
CTA Volume CMPR View Registered Centerlines Angio Lateral View Angio Frontal View
Load CTA markers Load Angio markers Save CTA markers

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Figure 17: Screenshot of Graphical User Interface for registration of centerlines
obtained from 3D CTA and 2D Angiography for Patient 1 using Cui’s algorithm

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Figure 18: Screenshot of Graphical User Interface for registration of centerlines obtained from
3D CTA and 2D Angiography for Patient 3 using pre-processed ICP method
The next chapter discusses the results given in Chapter 3. Conclusions and scope of
future work are also described.

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C H APT ER 4
Discussion
Earlier sections have described the methods, materials and results of our study. We
conclude the report by discussing the results obtained. We compare the three
registration techniques for the datasets considered and describe the usability of the
GUI.
4.1 Comparison of the registration algorithms
The Scale invariant curvature signature technique described in [7] is for 2D curves. We
extend this to 3D curves for our study. As this algorithm incorporates curvature
information, the X-ray angio centerline gets matched with respect to the curvature
information of the CTA centerline. This might be useful for coronary centerlines as they
offer high degree of curvature information. ICP on the other hand does not incorporate
curvature information and proceeds through the registration process by minimizing the
costs function between the centerlines. It is observed that curvature signature
registration process fails for patient 3. This is attributed to the fact that angio centerline
has a very low curvature with respect to arc length as compared to its CTA counterpart.
This leads to a smaller integral curve and constrained landmark points. Following figures
shows this graphically (figure 19):
a b

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C d
Figure 19: a: Lower curvature vs arc length for angio centerline; b: Smaller integral curve
for angio centerline; c: Constrained landmark points on CTA centerline;
d: Landmark points on angio centerline.
Incorporating pre-processing before applying ICP has an added advantage of better
initial error rates as compared to ICP. Though, the final error rates and visual
appearance of the registered centerlines are similar due to the similar registration
process, pre-processing provides a better starting position of the centerlines. ICP based
registration procedures are useful for centerlines having less curvature and almost a
linear orientation.
We can observe from figure 14 that pre –processed ICP leads to a lower error rate than
ICP. This was expected as we do an initial matching between the Angio centerline and
CTA centerline before implementing ICP. Cui’s method leads to a poor error rate as
compared to ICP because Cui’s method tries to match the curvature of the centerlines
whereas ICP tries to reduce the error between centerlines.
4.2 Validation discussion
It is observed from the validation study that MSE error of Cui’s algorithm is smaller than
ICP registration algorithm. This is attributed to the fact that Cui’s algorithms matches
the corresponding points on the centerline based on their curvature values. Simillar
points on the centerline will have similar curvature values and hence will be closer to
each other after Cui’s registration. On the other hand, ICP registration process is
motivated by the minimization of MSE between the two centerlines. This however does
not guarantee that similar points on the two centerlines will be close to each other.
Further, when the two centerlines are of unequal length, Cui’s method matches the
curvature of the overlapping segments and leads to a more realistic matching. Whereas,
ICP algorithm just minimizes the MSE between all the points on the centerlines and

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provides no realistic correlation. Thus, when similar points are selected on the two
centerlines for validation purposes, Cui’s method leads to a lower MSE value as
compared to ICP. Pre-processing of ICP also leads to low MSE values in 2 out of 4 cases.
This can be attributed to the fact that rotation of the moveable centerline with respect
to the fixed centerline can provide a better starting point to the ICP which further
minimizes the MSE to lead to a good result. This is observed in Figure 20.
a b c
Figure 20: a) Validation marker on Angio centerlines b) Mapped validation marker on CTA
centerline for Cui’s registration method c) Mapped validation marker on CTA centerline for ICP
algorithm
4.2 Usability of the GUI
The GUI provides a visual comparison of the three registration algorithms. This tool can
be primitively used to identify areas of plaque buildup in CTA and show the
corresponding location in the angio data, which can help the cardiologist with better
navigation of the guide wire. By selecting different registration techniques, flexibility is
available to select the best registration process for different datasets. The combined
information of CTA and X-Ray angio is important for better navigation of the guide wire
around the occlusion. The registered coronary artery centerlines provide this
information and by combining this with point correspondence, we get positional
information on the arteries.
Further, point wise correspondence helps to compare similar areas in both the
centerlines. This is especially helpful when we place a point on the frontal/lateral
images of the X-ray angio and get the corresponding position in the CTA volume.

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4.3 Conclusion and future work
In this study, we have evaluated and compared three registration algorithms for
matching 2D/3D coronary artery centerlines. This is followed by development of the GUI
that enables the user to select the best registration process based on the dataset.
Further, point wise correspondence is generated between the CTA and X-Ray angio
data.
Future work involves incorporating diameter and bifurcation information of the
centerlines into the registration process and visually overlaying the plaque position
information from the CTA data on the X-ray images. This is beneficial to aid the
cardiologist in navigating the guide wire during PCI.

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