TOWARDS COMPUTER ASSISTED CARDIAC CATHETERIZATION

How 3D Visualization Supports It

Klaus Drechsler, Cristina Oyarzun Laura

Department of Cognitive Computing & Medical Imaging

Fraunhofer Institute for Computer Graphics Research, Darmstadt, Germany

Georgios Sakas

Interactive Graphics Systems Group (GRIS), Darmstadt University of Technology, Germany

Keywords:

Cardiology, Catheterization, 3D Visualization, Segmentation, Vessel analysis, 3D-2D registration.

Abstract:

Although cardiac catheterization procedures take place under x-ray guidance, the doctor is almost blind. Ves-

sels are almost invisible until he injects a contrast agent and looking only at 2D x-ray images and reconstructing

a 3D image in his head makes it error prone and tedious. Only experienced doctors are able to accomplish

this procedure with the expected results. This paper describes our preliminary work and work in progress to

support doctors during cardiac catheterizations using 3D visualization.

1 INTRODUCTION

In cardiac catheterization procedures the doctor in-

serts a catheter into a vessel and positions it under

x-ray guidance within the coronary arteries to inject

a contrast agent, place a stent or a balloon to widen

the vessel. The contrast agent helps him to make

the coronary arteries and their potential pathologies

(stenosis, calciﬁcations) visible. Although the proce-

dure takes place under x-ray guidance the doctor is

almost blind. The vessels are almost invisible until he

injects the contrast agent and looking only at 2D x-ray

images and reconstructing a 3D image in real-time in

his head makes it error prone and tedious. Only doc-

tors with many years of experience are able to accom-

plish this procedure with the expected results. During

a catheterization a doctor with less years of experi-

ence can need computer assistance before he injects

the contrast agent to navigate the catheter in 3D to the

destination and after he injected the contrast agent to

see the arteries in 3D.

The gold standard for detecting stenosis and

plaques so far is an invasive cardiac catheterization to

inject a contrast agent and inspect the gathered x-ray

images, called coronary angiography. We assume that

in the near future the diagnostic of the vessels will be

done using non-invasive computer tomography (CT)

and that only the intervention, if necessary, will be

done in a catheter laboratory. This assumption is not

unrealistic. A steadily increasing number of hospi-

tals are already using CT for diagnostic purposes and

papers successfully investigating CT for this use case

were published (e.g. (Hoffmann et al., 2005), (Mieres

et al., 2007)). A combination of preoperatively gath-

ered CT data and operatively gathered x-ray images

(so called coronary angiograms) will further enhance

the trust in CT for the assessment of the coronary ar-

teries.

Our goal is to develop a sophisticated system that

provides doctors preoperatively an automated quanti-

tative analysis of the coronary arteries using CT data

whose result (the found pathologies) can be directly

displayed in the x-ray images gathered during the in-

tervention. Furthermore we want to provide naviga-

tion support by displaying the position of the catheter

in a 3D visualization of the heart (respectively the

coronary arteries). In a second step the system could

be extended to a complete training system for resi-

dents and doctors-in-training.

In this paper we present our preliminary work and

work in progress to support doctors during cardiac

catheterizations using a 3D heart of the patient. We

use an algorithm based on raytracing in 2D space

(thus on a slice by slice basis) to extract the heart from

a CT image which is acquired before the catheteri-

zation procedure takes place. A tracking based ves-

sel segmentation algorithm is used to visualize the

coronary arteries and to provide an automated quan-

159

Drechsler K., Oyarzun Laura C. and Sakas G. (2009).

TOWARDS COMPUTER ASSISTED CARDIAC CATHETERIZATION - How 3D Visualization Supports It.

In Proceedings of the First International Conference on Computer Imaging Theory and Applications, pages 159-165

DOI: 10.5220/0001771801590165

 SciTePress

titative analysis of the vessels. This allows us to

show pathologies to the doctor before and during the

catheterization. A further processing takes place to

generate a 2D view of the 3D heart similar to a coro-

nary angiogram which is needed for a 2D-3D regis-

tration of the two modalities. As a result our system

will support the doctor during the catheterization pro-

cedure by visualizing a 3D heart that corresponds to

the coronary angiogram. In addition we want to dis-

play the current catheter position in the 3D visualiza-

tion by using a magnetic tracking system. This will

help especially doctors with less experience to safely

perform a cardiac catheterization.

The remainder of this paper is organized as fol-

lows. The next section gives an overview of related

work. The following section describes our prelimi-

nary work and explains what we are currently doing.

After that we conclude our paper with a critically dis-

cussion of our system.

2 RELATED WORK

In this section we give an overview of registration,

heart extraction and coronary artery segmentation

techniques.

2.1 Registration

When developing applications that combine and use

information gathered from different modalities, regis-

tration plays an important role. The goal of the sys-

tem described in (Filippatos, 2006) is to use Com-

puter Assisted Surgery to support the implantation of

a stent in the case of aortic aneurysms. They use a

ﬁducial based registration to match the 3D CT vol-

ume to the intraoperative x-ray image. For that they

adhere external markers on the chest. Then the mark-

ers are detected both in the 3D CT volume and in the

2D image using image thresholding, region growing

and contour detection techniques. Finally they get the

transformation matrix that matches the obtained 3D

points with the corresponding 2D x-ray points and

use it to initialize the 3D volume in an adequate po-

sition. For the determination of the position of the

stent they need to segment the Aorta. To achieve this

they use a canny edge detector. After that they obtain

the centerline of the segmented aorta and use back

projection of the 2D points into the 3D volume. Tak-

ing into account that the Stent will always be in the

aorta they get the searched position. The work pre-

sented in (Turgeon et al., 2005) deals with similarity-

based 2D-3D registration of coronary angiograms. It

is worth mentioning that the comparison for the regis-

tration is made between binary images instead of gray

scale images. In a ﬁrst step a 4D model of the heart

is obtained to create a simulation environment com-

posed of realistic 2D and 3D images. They are using

3D computed rotational angiography comprising sep-

arate left and right coronary arteries (LCA, RCA) an-

imated with cine-angiograms to create the simulation

environment. The 3D coronary tree of the 3D volume

is semi-manually segmented from one time frame of

the 4D model. Once this is done the intraoperative

angiograms (DRR) are generated from both the LCA

and RCA 4D model. They are further processed us-

ing multiscale segmentation and then merged to get

a binary image. To carry out the registration, the bi-

nary projection of the extracted coronary tree is com-

pared with two binary angiographies by means of the

entropy correlation coefﬁcient. Finally, the downhill

simplex method is used for the optimization of the

translation and rotation parameters that will be ap-

plied to the 3D volume. In (Lau and Chung, 2006)

the authors study how to avoid the problem of local

minimas that occurs in high-dimensional image regis-

tration. They use the vessels centerline as feature for

a feature based registration. For the segmentation of

the vessels they use a global thresholding method to

obtain a skeleton and represent it by a set of spheres

with the centers set to the coordinates of the skele-

ton points and radii equal to their distance transform

values which represents the distance to the closest

boundary. After creating a DRR (Digitally Recon-

structed Radiograph) of the 3D volume, the sum of

squared differences is used to get the transformation

matrix necessary to carry out the registration. The op-

timization method consist of calculating the result of

the cost function in a low resolution environment, op-

timizing it with Powells method, and doing a ﬁnal op-

timization with the best obtained transformation ma-

trix in the high resolution environment.

2.2 Heart Extraction

Cardiac CT data normally contains non-cardiac struc-

tures such as ribs, lungs or the sternum. These struc-

tures obscure the view to the heart, but an isolated

heart is necessary to make a visualization of the coro-

nary arteries on the surface of the heart possible. The

authors of (Lorenz et al., 2004) developed a method

to extract the heart from Computed Tomography An-

giography (CTA) datasets using active contours. They

locate the chest and the descending aorta in all slices

of the CT data in order to roughly estimate the lo-

cation of the heart. Afterwards they use active con-

tours to outline the border of the heart in a slice-by-

slice manner. In (Funka-Lea et al., 2006) the authors

IMAGAPP 2009 - International Conference on Imaging Theory and Applications

160

isolate the heart from CTA scans using graph-cuts

(Boykov and Jolly, 2001). First they automatically de-

termine a seed-region within the heart by computing

the volumetric barycenter weighted by intensity. In a

second step they do some kind of pre-segmentation by

determining the ellipsoid of maximum volume con-

tained within the heart. The ellipsoid is used as initial-

ization for the graph-cut algorithm. To prevent leak-

ing into the aorta or pulmonary vessels the authors

introduce a what they call ‘blob’-constraint to prefer

a shape whose edges are oriented perpendicular to the

direction toward the center of the seed region.

2.3 Coronary Artery Segmentation

The segmentation of the coronary tree is a necessary

step before an advanced quantitative analysis can take

place. Several vessel segmentation algorithms have

been developed in the past. A very comprehensive

overview can be found in (Kirbas and Quek, 2004).

It follows a description of three of the more recent

work in this ﬁeld. In (Hennemuth et al., 2005) the

authors developed a method to segment the coronary

tree in CTA datasets with one user-deﬁned point in

the middle of the aorta. Starting with this point, the

authors segment and mask the aorta by using a semi-

3D region growing combined with a moment-based

shape analysis to ﬁt an ellipse into the segmented re-

gion. The algorithm continues to examine connected

voxel clusters around the aorta and starts a 3D re-

gion growing from those clusters which contain ori-

gins of coronary arteries. The origin of a coronary

artery is detected by considering size, the center of

gravity, the eccentricity and the orientation. Branches

that are not automatically segmented by this algo-

rithm can be manually added by interactively placing

additional seed points. The authors of (Florin et al.,

2005) propose a particle-based approach to segment

the coronary arteries. They generate hypothesis (state

vectors or particles) of a vessel being at a certain lo-

cation, having certain orientation, referring to a cer-

tain shape with certain irregular appearance charac-

teristics. For the latter they use a Gaussian mixture

model that consists of two components to model the

contrast enhanced blood and high density components

like calciﬁcations or stents. Given a starting point and

a number of particles one performs random perturba-

tions and the corresponding particles are visually and

statistically evaluated. A segmentation is a weighted

linear combination of the particles. In (Luengo-Oroz

et al., 2007) an algorithm based on morphological

grayscale reconstruction is presented to segment the

coronary tree in CTA datasets. First, the user has to

provide an initial point as a marker in which slice the

artery is ﬁrst found. Then their proposed algorithm is

performed from the mark in that slice to segment the

artery in it. The algorithm performs a reconstruction

by dilation followed by a top-hat opening by recon-

struction to extract only the bright areas of tubular-

like structures. Afterwards a set of potential marks is

automatically generated for the following slice. The

authors use pre-knowledge from the characteristics of

tubular structures to generate new marks for the next

slice by searching an area of higher probability ob-

tained from the segmentation of the previous slice.

These steps are repeated for subsequent slices until

there are no more potential marks.

3 GENERAL ARCHITECTURE

The general architecture of our system is shown in ﬁg-

ure 1. All blocks were implemented at our department

using VTK and ITK and recently adopted to make use

of MITK (Wolf et al., 2005) which provides conve-

nient methods to add user interaction to medical ap-

plications. The registration is currently under active

development.

Figure 1: General architecture.

3.1 Heart Extraction

The automatic extraction of the heart from contrast

agent enhanced CT data is described in (Jaehne et al.,

2008). Basically a partition (labels) of the anatom-

ical structures by automatically selecting thresholds

using Otsus method (Otsu, 1979) is obtained. Then

the center of gravity of the two brightest gray levels,

which lies in the middle of the heart, is calculated for

every axial slice. From this point a radial search ray

pattern is send out in order to ﬁnd the outer bound-

ary of the heart. Overlapping structures like the aorta

or the sternum prevent this method to function prop-

erly; the search rays are to long. These parts are han-

dled in a subsequent step. On each side of the aorta

TOWARDS COMPUTER ASSISTED CARDIAC CATHETERIZATION - How 3D Visualization Supports It

161

and the sternum the last rays which hit on lung tis-

sue and therefore have the correct length are automat-

ically detected. Interpolation is then used to correct

the rays between them. Afterwards the end points are

connected and a binary mask is generated which is ap-

plied to the original CT data to extract the heart. The

result is shown in ﬁgure 2.

Figure 2: Axial view of the extracted heart.

3.2 Coronary Artery Segmentation

The result of the heart extraction is used to seg-

ment the coronary arteries using the tracking based

corkscrew algorithm, which is described in greater de-

tail in (Wesarg and Firle, 2004). Basically it prepro-

cesses the data by using an adaptive threshold ﬁlter

that takes the gray values of three user provided seed

points (start, direction and end point) and the CT data

as inputs. It takes into account that the contrast agent

is not equally distributed in the vessel. It follows an

opening operation to remove connections to neighbor-

ing tissues and to the vessel wall behind hard plaques.

The actual corkscrew algorithm gets the results of the

opening operation, the original image and the output

of the adaptive threshold ﬁlter to calculate a path (cen-

terline and border) between a start and end point that

lies within the vessel. Afterwards a 3D model is gen-

erated using the marching cubes algorithm (Lorensen

and Cline, 1987). A three dimensional rendering of

the heart with one segmented branch of the coronary

artery is shown shown in ﬁgure 3.

The output of the corkscrew algorithm was suc-

cessfully used to implement an automated quantita-

tive analysis of the segmented vessel (Wesarg et al.,

2006) and evaluated its result in a clinical study (Khan

et al., 2006), (Wesarg et al., 2008).

Figure 3: A maximum intensitiy projection (MIP) of the

heart with one segmented branch of the coronary artery in

red.

3.3 Registration

There are mainly two possibilities how a computer

can assist a doctor during a cardiac catheterization,

namely before and after the injection of a contrast

agent. Before the contrast agent is injected, the

catheters position could be located and together with

the preoperatively found pathologies visualized in a

3D model of the heart. Therefore a registration of

the live x-ray with the CT data based on external

(e.g. ﬁducials) or internal (e.g. bones) markers is

necessary. After the contrast agent is injected, a 3D

model of the heart could be displayed (again together

with the preoperatively found pathologies) such that

it shows the heart from the position that x-ray gener-

ator looks at it. Therefore either a 2D-3D registration

using a 2D projection of the CT data or a 3D-3D regis-

tration using a 3D reconstruction of several 2D x-ray

images is necessary. Our current work concentrates

on the 2D-3D registration after the contrast agent has

been injected. Therefore we are currently implement-

ing a semi-automatic registration algorithm where the

doctor has to provide initially 2n equivalent points in

the ﬁrst generated 2D projection of the heart and the

coronary angiogram. These points are tracked during

the successive rotation of the 3D heart and genera-

tion of 2D projections to carry out an automatic point

based registration of the two modalities. To track the

selected points a method to project them back from

2D to 3D has to be realized. Our idea restricts the

potential coronary artery segmentation algorithms to

those which also provide the centerlines of the seg-

mented vessels. We create a 2D projection of the 3D

centerlines (this projection is called in the following

’2DC’) in addition to the normal 2D projection. If the

user clicks on a vessel in the 2D projection to mark

a point, it is aligned to match the underlying 2DC.

This information can then be used to ﬁnd the corre-

IMAGAPP 2009 - International Conference on Imaging Theory and Applications

162

sponding point(s) on the 3D centerline and thus in the

segmented vessel. To achieve this we store with ev-

ery pixel in the 2DC the coordinate(s) of its corre-

sponding 3D voxel(s) during the projection. A prob-

lem might arise if the user selects a point in the 2D

projection which represents overlapping vessels. In

this case it is not clear to which 3D centerline the se-

lected point belongs to. This situation is detected by

the fact that there are more then one 3D coordinates

stored together with the 2DC and used to inform the

user. We could also make use of the fact that the doc-

tor does not inject the contrast agent within both main

branches at the same time and thus ’turn off’ the cor-

responding 3D centerlines while calculating the bi-

nary projection.

Figure 4: General architecture.

In (Langs et al., 2004) the authors describe a simi-

lar registration method. In their registration approach

the user has to mark corresponding points in the 3D

model and the 2D x-ray image. However, if a doctor

has to select a point in 3D and a corresponding point

in 2D then he has either to reconstruct a 3D image

from the 2D x-ray or a 2D image from the 3D model

in his head to decide which points correspond. This

is what we would like to avoid and make this step as

easy as possible.

3.4 DRR Generation

In (Lacalli et al., 2008) the generation of 2D projec-

tions of the heart using DRRs is described. First a

preprocessing of the original CT data is necessary to

avoid insufﬁcient results due to non-cardiac structures

and large cardiac cavities (e.g. ventricles and atria).

The removal of non-cardiac structures has been de-

scribed above. The cavities are a problem, because

they are, like the coronary arteries, ﬁlled with the con-

trast agent and thus occlude the coronary arteries in

the generated DRRs. To remove the cavities from

the extracted heart a thresholding operation on the la-

beled CT data (that was generated in the heart extrac-

tion step) is ﬁrst applied to remove everything but the

highest label that correlates with both the cavities and

the arteries. Afterwards the coronary arteries are re-

moved by applying an erosion operation followed by

a neighborhood ﬁlter along all the three orthogonal

axes. Finally a dilation operation is applied to restore

the original size of the cavities. The result is used as a

mask to remove the cavities from the extracted heart.

A perspective projection is then carried out to gener-

ate the DRR to simulate a coronary angiogram. The

result is shown in ﬁg 5.

Figure 5: Digital Reconstructed Radiography of the heart.

The vessels are clearly visible.

4 CONCLUSIONS

AND DISCUSSION

This paper presented our preliminary work and

work in progress to support cardiac catheterizations

through 3D visualization. We described the build-

ing blocks of our proposed system and explained their

function. Though the heart extraction algorithm pro-

vides good results, it has some ﬂaws which can be

traced back to the way it works. It only uses ax-

ial slices to process the volume. While the heart is

fairly well extracted in the axial orientation, it still

needs some improvements for the other orientations.

6 shows the coronal view of the extracted heart. At

the bottom it can clearly be seen, that structures are

shown that do not belong to the heart.

Currently we are investigating an approach based

on deformable models to extract the heart from a CT

volume. Initial results are promising and are subject

for further publication. The corkscrew algorithm al-

ready proved its usefulness and robustness in a clin-

ical setup. However, currently it is only able to seg-

ment one branch of a vessel tree. It is not able to

detect bifurcations and automatically follow them in

order to segment the complete coronary tree. The

setting of three seed points is sufﬁcient to segment

one branch of the tree, but very tedious and time-

consuming if done for the whole tree. Recently we

started to investigate the possibilities to enhance the

corkscrew algorithm to automatically detect bifurca-

TOWARDS COMPUTER ASSISTED CARDIAC CATHETERIZATION - How 3D Visualization Supports It

163

Figure 6: Coronal view of the extracted heart.

tions. Furthermore our goal is to minimize user in-

teraction. Ideally the user only has to provide one

start point to segment the whole coronary tree. Direc-

tion and end point should be automatically detected

by taking anatomical knowledge of the heart into ac-

count. Another problem we found with the current

implementation of the corkscrew algorithm is that the

results slightly differ when the seed points are not set

at exactly the same coordinates. The reason for this

behavior has to be further investigated, but an auto-

mated seed point correction should take place to en-

sure reproducible results. An approach to align the

speciﬁed seed points towards the vessel center is de-

scribed in (Egger et al., 2007). Rays are sent out ra-

dially from the seed point with a user deﬁned length.

From the intersections with the vessel walls the direc-

tion to align the seed points can be computed.

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