Coronary CT angiography – IVUS image fusion for quantitative plaque and stenosis analyses

Henk A. Marquering a, Jouke Dijkstra a*, Quentin J. A. Besnehard b, Julien P.M. Duthéb, Joanne D.

Schuijf c, Jeroen J. Bax c, Johan H.C. Reiber a

a Division of Image Processing, Dept. of Radiology, Leiden University Medical Center, PO Box 9600, 2300RC Leiden, The Netherlands; b Department of Bioengineering and Computer Science,

University of Poitiers, 40 Avenue du Recteur Pinau, 86022 Poitiers, France; c Dept. of Cardiology, Leiden University Medical Center, PO Box 9600, 2300RC Leiden, The Netherlands

ABSTRACT

Rationale and Objective: Due to the limited temporal and spatial resolution, coronary CT angiographic image quality is not optimal for robust and accurate stenosis quantification, and plaque differentiation and quantification. By combining the high-resolution IVUS images with CT images, a detailed representation of the coronary arteries can be provided in the CT images. Methods: The two vessel data sets are matched using three steps. First, vessel segments are matched using anatomical landmarks. Second, the landmarks are aligned in cross-sectional vessel images. Third, the semi-automatically detected IVUS lumen contours are matched to the CTA data, using manual interaction and automatic registration methods. Results: The IVUS-CTA fusion tool facilitates the unique combined view of the high-resolution IVUS segmentation of the outer vessel wall and lumen-intima transitions on the CT images. The cylindrical projection of the CMPR image decreases the analysis time with 50 percent. The automatic registration of the cross-vessel views decreases the analyses time with 85 percent. Conclusions: The fusion of IVUS images and their segmentation results with coronary CT angiographic images provide a detailed view of the lumen and vessel wall of coronary arteries. The automatic fusion tool makes such a registration feasible for the development and validation of analysis tools.

Keywords: Cardiac procedures, disease characterization, multimodality display, registration, segmentation.

*jouke.dijkstra@lumc.nl; phone +31 71 526-2270; fax +31 71 526-6801; www.lkeb.nl

1. INTRODUCTION During the past few years, Multislice Computed Tomography (MSCT) imaging has emerged as a new non-invasive imaging modality to evaluate the presence of coronary artery disease (CAD). Coronary MSCT angiography has the potential to become a first in line imaging procedure for expected CAD. CT angiography (CTA) has a number of advantages compared to the traditional coronary angiography; CTA provides a 3D impression of the coronary arteries, it is minimal invasive and is more cost efficient. CTA may also be performed in patients with a lower pre-test likelihood of CAD because of its lower risk of complications compared to invasive imaging with cardiac catheterization. This may allow identification of those patients with previously unknown elevated risk at a much earlier stage, thereby providing the opportunity to initiate treatment before damaging coronary events may occur. CTA can detect both non-obstructive and obstructive lesions and allows imaging of atherosclerotic plaques. Despite the advantages of MSCT, its use in practice is commonly hampered by the lack of dedicated analysis tools for the accurate quantification of CAD. This lack of dedicated and validated tools is related to problems with the image quality, which is affected by motion artifacts, low vessel opacification, calcium deposits or opacified adjacent structures, and reconstruction inaccuracies [1]. Because of these artifacts, the human eye is also mislead and may erroneously interpret the images.

For the development and validation of the segmentation and quantification algorithms, we therefore consciously avoid manually drawn contours on CT data as ground truth. Instead, we will use the current gold standard for quantification of coronary atherosclerosis: intravascular ultrasound (IVUS) for the anatomical plaque quantification. However, IVUS is an

Medical Imaging 2008: Visualization, Image-guided Procedures, and Modeling, edited byMichael I. Miga, Kevin Robert Cleary, Proc. of SPIE Vol. 6918, 69181G, (2008)

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invasive procedure. Recently, observations in IVUS and MSCT images have been compared in various publications [2, 3]. In [4] a strong correspondence of the MSCT density and IVUS echogenity for three types of plaques has been demonstrated. In [5] it has been shown that even for mildly stenotic coronary segments good correspondence can be obtained. It should be noted that these studies were all performed by visual comparison. In our reported approach, we aim to exploit the quantitative analysis results of IVUS images to improve classification of the stenosis degrees and plaque composition. This paper describes our semi-automated method for merging the IVUS and CTA data.

2. METHOD The semi-automated fusion of the CTA and IVUS images is performed in three steps, which are described below. In our approach, we have chosen to merge the images using the automatically determined contours of the lumen.

2.1 Patient selection

We studied 10 patients who have undergone MSCT, IVUS and coronary angiography. MSCT has been performed after informed consent was obtained and prior to conventional coronary angiography in combination with IVUS imaging. The MSCT studies have been performed on a Toshiba Multi-Slice Aquilion 64 system. In patients with heart rates higher than 65 beats per minute, beta-blocking medication was administered to lower the heart rate.

In all patients, conventional coronary angiography has been performed according to standard clinical protocol. For each IVUS examination, a 20 MHz, 2.9 F, phased-array IVUS catheter (Eagle Eye, Volcano Corporation, Rancho Cordova, USA) is used with an automated continuous pullback speed of 0.5 mm/s.

We want to match the IVUS images with the CTA segmented images. We assume that the centerline as it is detected by the quantitative CTA software corresponds with the IVUS catheter trajectory. A deviation of these trajectories may potentially introduce difference in slice orientation. We, therefore, restrict ourselves to using IVUS data from reasonable straight vessel segments, without pullback artifacts and no severe cardiac motion.

2.2 CTA contour detection

Vessel segments in the coronary CTA data are extracted using automated vessel segmentation software (QAngio-CT, Medis, Leiden, the Netherlands), which is based upon a level set approach [7]. The detected centerline is used to compute a CMPR image of the vessel from the CT data by stretching the vessel centerline to a straight line. The resulting 3D image stack contains 2D images (slices) perpendicular to the centerline, and allows for the visualization of the entire length of the vessel in a single image by removing its curvature. Four longitudinal cut planes, each rotated 45 degrees apart, of the CMPR image passing through its center are determined to perform the longitudinal contour detection. The lumen borders are detected using the model-guided minimal cost approach [8]. The contour is defined as the minimal cost path that travels from top to bottom of a cost function of the cut plane and contains a combination of spatial first- and second order derivatives. To exclude calcified plaques from the lumen, an intensity-based detection of calcified plaques is applied. Depending on the distance between the lumen and calcified plaques, the gradient-based penalty function is adjusted.

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The longitudinal contours produce a series of intersection points for each transversal slice, which are used to guide the transversal contour detection. These points specify the region of interest for the transversal minimal cost contour detection and they function as attraction points with an adjustable strength to attract the contour detection to areas close to these points. The contours in the transversal images can be manually corrected by adjusting the contour or by moving an attraction point.

2.3 IVUS contour detection

The IVUS contour detection is performed similar to the CT contour detection, a combination of the longitudinal and transversal contour detection using IVUS segmentation software (QIvus, Medis, Leiden the Netherlands). The contour detection itself is based the Minimal Cost Algorithm using a combination of spatial first- and second order derivatives and intensity information. As mentioned before, IVUS allows the detection of both the lumen-intima (lumen) and the media-adventitia (vessel) interface. Additional information is added to the cost matrix with respect to calcified plaque, shadows, stent struts, and side-branches. To compensate for the translational and rotational movement of the catheter during the pullback, a cardiac motion correction procedure is applied based upon image cross-correlation techniques. This results in smoother pullback datasets, resulting in a more automatic contour detection.

2.4 Vessel segment matching

Vessel segments in the coronary CTA data are extracted using automated vessel segmentation software (QAngio-CT, Medis, Leiden the Netherlands). The CMPR image of the vessel segment looks very similar to the longitudinal cross section of an IVUS pullback series (Fig. 1). Next, the vessel segments are matched by visual comparison of the CTA and IVUS images using landmarks, such as bifurcations, calcified plaques and stents, which are clearly visible in both CTA and IVUS (Fig 2). For this step, we have developed a “Cylindrical Maximal Intensity Projection” (CMIP) method, which determines the maximum intensity at a given distance from the vessel centerline over a range of azimuthal angle. For CTA data the range is 0 to 360o. The CMPR image can be represented in cylindrical coordinates:

),,(),,( zrCMPRzyxCMPR ϕ→ (1)

With 22 )()( cc yyxxr −+−= , which is the distance from the vessel’s centerline and ϕ =

))/()((tan 1cc xxyy −−− . Where ( cx , cy ) is the center of the transversal slices of the CMPR image. The CMIP is

subsequently defined as

)3600|),,(max(),( oozrCMPRzrCMIP <≤= ϕϕ . (2)

Figure 1: Determining a stretched CMPR image. (a) The centerline calculated in the 3D CTA volume data set. By stretching

the volume in the vicinity of the centerline such that the centerline is a straight line, the CMPR image is calculated. (b) Volume rendering expression of the CMPR image. (c) and (d) 2D longitudinal expressions of the CMPR image. (e) Transversal slice of the CMPR image representing the vessel perpendicular to the centerline.

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I

An example of the CMIP is shown in Fig. 3.

Figure 2: Anatomical Landmarks (bifurcations) in an IVUS image (left) and a CTA CMPR image (right). The landmarks are

depicted with the arrows.

Figure 3: Illustration of the Cylindrical MIP display option. The top three images are 2D slices through the regular CMPR

image at different azimuth showing landmarks (branches) at different distances. The lower image shows the cylindrical MIP image in which all the landmarks are shown in a single image.

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IIr

We can use a similar approach to view the landmarks in the IVUS images in a single image. However, because the IVUS data is noisier, we generally use a variable, smaller azimuthal range and take the minimal intensity instead of the maximum. In Figure 4 an impression of the interface for the placement of the landmarks for the vessel segment matching is shown.

Figure 4: User interface for detecting landmarks in IVUS and CTA images. On the left pane transversal views of the IVUS

images (top) and CTA images (bottom) are shown. The second pane on the left shows the longitudinal view of the IVUS stack. The third pane on the left shows a CMIP of the IVUS image stack. The pane on the right shows the CMIP of the CTA CMPR image. The markers can be used to place the position of the landmarks. The application calculates the distances between the markers for the IVUS and CTA images. The transversal slices can directly be positioned on the landmarks in the longitudinal views, and the markers can also directly be placed at the position of the transversal slice.

2.5 Transversal global orientation matching

We have two options to align the anatomical landmarks; first, an offset can be added to align the position of a single landmark. Second, all landmarks can be aligned such that we correct for the differences in the length between landmarks in the two modalities. At this point, we can simultaneously run through the stack of IVUS and CTA images. See Fig. 5 for an illustration of the interface.

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II

Th

Figure 5: Illustration of the interface of the IVUS CTA fusion software after the vessel segment matching. The left top panel

shows the CTA image together with the CTA lumen contour and IVUS lumen and media-adventitia contours. The top right pane shows the IVUS image also with the IVUS contours. In the lower left pane both the IVUS and CTA images are shown. The IVUS image is rotated and translated according to the manual- or automatic matching. The lower right pane shows the lumen area measurements along the vessel for both image modalities. The positions of the vessel-segment matching landmarks are depicted with the vertical broken lines.

Since IVUS cross-sectional images may rotate relative to each other due to catheter twist during the pullback, it is possible that the proximal part of the IVUS segment needs to be twisted with respect to the distal part. The global, azimuthal orientation of the data is performed by matching corresponding landmarks in the cross-sectional images of the CTA and IVUS image stacks. This is performed by manually putting seed points at landmarks such as the center of calcified plaques and bifurcations. Using this manual tool, a first step in the azimuthal fitting of the 3D “pipe” of IVUS images compared to the CTA data is realized (Fig. 6).

AA BB CC DD EEAA BB CC DD EEAA BB CC DD EE

Figure 6: Illustration of the fusion approach. We start with an IVUS cross-sectional image (A) and CTA image (B) of the

same part of the coronary artery. (C) Illustration of the use of an anatomical landmark (calcified plaque) to align the two slices. The position of the calcified plaques is depicted with a yellow arrow. (D) Illustration of the rigid registration using the initial contours. (E) Classified CT pixels based upon the registered IVUS contour. The lumen pixels are here shown with a red overlay.

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2.6 Azimuthal matching

Finally, the registration of the individual cross-sectional slices between the landmarks is guided by the luminal contour as determined by automatic contour detection methods for the IVUS images [8] and CTA [7]. The registration combines the contour information with the positioning of the corresponding landmarks. Before we start with an automatic matching, the transformation is set such that the center of gravity of the contours coincide. We implemented a number of different registration techniques:

1) Ellipse fit. With this approach the lumen contours are fitted with an ellipse. Subsequently, the orientation of the ellipse is aligned. This approach is fast, but is ambiguous since a rotation of 180 degrees gives the same solution and does not work for circular lumens.

2) Iterative point-set registration to minimize the contour mismatches.

3) Registration of 2D image representation of the contours. For this method, distance images to the contours are computed, which are subsequently registered.

For all the registration methods, the amount of rotation is constrained by the maximal physical rotation of the IVUS catheter. Furthermore, the IVUS analysis software already calculates the rotation and translation between the individual cross-sectional images based on rigid registration using cross-correlation. These numbers are also used to guide and limit the amount of rotation, especially in case of circular contours. After the automatic registration the tooling allows for manual correction of the contours by means of a graphical interface. For the registration the computation time and manually correction time is recorded.

For the point set registration (method 2) we initially started looking for a rigid transformation that consists of a rotation

matrix R and a 2D translation vector t . In this approach we consider a contour as a set of pointsΧ = { ix } for i = 1,…,

XN , and the transformed point set is denoted as tR,

Χ = { txR i +⋅ }: In the registration procedure we consider the

CTA contours as fixed and the IVUS contours moving and being mapped by transformation. The point set registration is optimized using Levenberg-Marquardt algorithm. The fitness is defined as the sum of the minimal Euclidean distances. When we represent the CTA lumen contour as the point set Y = { jy } for j =1,…, YN the distance is defined as

∑ = ∈−+⋅= X

Y

N

i jiNjytxRtRd

0 },...,1{||||min),( (3)

For the image registration we made use of the ITK imaging toolkit [9].

3. RESULTS Workflow

16 CT data sets of coronary vessels from 7 patients have been registered with IVUS scans. The automated tools resulted in significant reduction of analysis time: The CMIP technique for the CTA images gave a direct overview of all landmarks. The registration time of the vessel segments reduced with a factor up to 50 percent. Due to the noisy character of the IVUS data, only the CMIP projection over a limited range of 120 degrees resulted in significant reduction in analysis time. The automatic registration of the cross-sectional images reduced the analysis time with a factor up to 85 percent. In table 1 the results for the various registration methods are depicted. Overall, the registration gave good correspondence between the IVUS and CTA images for all registration methods. It should be noted that for healthy (circular) vessels, it is difficult to match the contours because of the lack of spatial information and anatomical landmarks like calcified plaque. In these cases, only the center of the lumen is registered and we used the global orientation determined by the landmarks and the IVUS software.

The data of 2 vessels were rejected because large differences in the luminal values. Furthermore, two data sets were rejected because no corresponding landmarks could be detected. In Figure 7, the result of the fusion of IVUS and CT is shown. This figure shows the great potential of the extra information that this fusion produces.

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The fused image provides a very helpful manner of interpreting CTA images. It is now possible to project the vessel borders on the CTA images. This border is commonly very difficult to determine due to the low contrast between the vessel wall and surrounding tissue. However, it is an important parameter in the quantification of plaque burden. Also the lumen-calcified plaque contours are difficult to determine due to the blooming effect of the calcified plaques. In the IVUS images, the lumen-calcified plaque border is well visualized. In Figure 8, the IVUS and CTA images of a vessel with a severe calcified plaque are merged. This figure clearly illustrates that the CTA contour detection underestimates the lumen diameter in the direction on the calcified plaques. Due to the blooming artifact, the intensity of the lumen is increased and these pixels are erroneously interpreted as calcified plaque. Furthermore, laterally to the calcified plaque the CTA contours include more pixels resulting in a “wider” shape of the contour. This is also caused by the blooming artifact: at these positions the blooming artifact results in an increase of the intensity of the tissue pixels. Therefore, these tissue pixels have the same intensity as the lumen pixels and are erroneously interpreted as lumen.

Table 1: Analysis time for the IVUS CTA registration.

Registration Method: None Ellipse fit Point set registration

Distance image registration

Automatic registration processing time (sec per 100 slices).

0 17 36 230

Manual registration processing time (sec per 100 slices). 1243 120 60 70

Total processing time (sec per 100 slices) 1243 137 96 300

Figure 7: Results of the IVUS and CTA cross-sectional image fusion. The IVUS image is plotted with an opacity of 10

(left), 50 (middle), and 90 percent (right).

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Figure 8. Merged IVUS and CTA images of a vessel with a severe calcified plaque. (A) IVUS image with the CTA (in green) and IVUS (red) contours plotted on top. (B) CTA image with CTA and IVUS contours. The difference in shape between the CTA and IVUS contour is striking.

4. CONCLUSION The fusion of IVUS images and segmentation results with coronary CT angiographic images provide a unique and detailed overview of the lumen and vessel wall anatomy of coronary arteries. The semi-automatic analysis fusion tools reduce the processing time of such a registration and render its use feasible for clinical studies. Furthermore the IVUS-CTA fusion facilitates the development and validation of quantitative analyses of CTA data and may be used to improve manual CT interpretations and automated analysis tools. The success of the automatic registration indicates that the automatic contours in CT images correspond well with the IVUS contours. A point-set registration of the IVUS and CTA contours are the most suitable method for reducing processing time.

REFERENCES

1 Ropers D, Baum U, Pohle K, Anders K, Ulzheimer S, Ohnesorge B, Schlundt C, Bautz W, Daniel WG, Achenbach S. Detection of coronary artery stenoses with thin-slice multi-detector row spiral computed tomography and multiplanar reconstruction. Circulation 2003;107:664–666. 2 Achenbach S, Moselewski F, Ropers D, et al. Detection of calcified and noncalcified plaque by contrast-enhanced submillimeter multidetector spiral computed tomography. Circulation 2004; 109; 14-17. 3 Leber AW, Becker A, Knez A, et al. Accuracy of 64-Slice Computed Tomography to Classify and Quantify Plaque Volumes in the Proximal Coronary System. J. Am. Coll. Cardiol. 2006 Feb 7-47 (3): 672-677. 4 Schroeder S, Kopp AF, Baumbach A, et al. Noninvasive detection and evaluation of atherosclerotic coronary plaques with multislice computed tomography. J Am Coll Cardiol. 2001;37:1430-1435. 5 Schoenhagen P, Tuzcu EM, Stillman AE, Moliterno DJ, Halliburton SS, Kuzmiak SA, Kasper JM, Magyar WA, Lieber ML, Nissen SE, White RD. Non-invasive assessment of plaque morphology and remodeling in mildly stenotic coronary segments: comparison of 16-slice computed tomography and intravascular ultrasound. Coronary Artery Disease 2003 Sep;14(6):459-462. 6 Sonka M, M.D. Winniford, and S.M.Collins. Robust simultaneous detection of coronary borders in complex images. IEEE Trans Medical Imaging, 14:151161,1995.

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7 Marquering, H. Dijkstra J., de Koning et al. Towards quantitative CTA. 2004 Int J Cardiovasc. Imag.` 8 Dijkstra J, Koning G, Tuinenburg JC, et al. Automatic border detection in IntraVascular UltraSound images for quantitative measurements of the vessel, lumen and stent parameters. In: Computer assisted radiology and surgery. Lemke HU, Vannier MW, Inamura K, Farman AG, Doi K (eds), proceedings CARS 2001, Berlin, Excerpta Medica Int. Congress Series 1230. Amsterdam: Elsevier, 2001, 871-6. 9 www.itk.org.

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