Automated segmentation and tracking of coronary ...

Automated segmentation and tracking of coronary arteries in ECG-gated cardiac CT scans Chuan Zhou, Heang-Ping Chan, Aamer Chughtai, Smita Patel, Prachi Agarwal, Lubomir M. Hadjiiski, Berkman Sahiner, Jun Wei, Jun Ge, Ella A. Kazerooni Department of Radiology, The University of Michigan, Ann Arbor, MI 48109-0904 ABSTRACT Cardiac CT has been reported to be an effective means for clinical diagnosis of coronary artery plaque disease. We are investigating the feasibility of developing a computer-assisted image analysis (CAA) system to assist radiologist in detection of coronary artery plaque disease in ECG-gated cardiac CT scans. The heart region was first extracted using morphological operations and an adaptive EM thresholding method. Vascular structures in the heart volume were enhanced by 3D multi-scale filtering and analysis of the eigenvalues of Hessian matrices using a vessel enhancement response function specially designed for coronary arteries. The enhanced vascular structures were then segmented by an EM estimation method. Finally, our newly developed 3D rolling balloon vessel tracking method (RBVT) was used to track the segmented coronary arteries. Starting at two manually identified points located at the origins of left and right coronary artery (LCA and RCA), the RBVT method moved a sphere of adaptive diameter along the vessels, tracking the vessels and identifying its branches automatically to generate the left and right coronary arterial trees. Ten cardiac CT scans that contained various degrees of coronary artery diseases were used as test data set for our vessel segmentation and tracking method. Two experienced thoracic radiologists visually examined the computer tracked coronary arteries on a graphical interface to count untracked false-negative (FN) branches (segments). A total of 27 artery segments were identified to be FNs in the 10 cases, ranging from 0 to 6 FN segments in each case. No FN artery segment was found in 2 cases. Keywords: Coronary artery, vessel segmentation, vessel tracking, ECG-gated cardiac CT scans, computer-aided diagnosis, image segmentation 1.

INTRODUCTION

Coronary artery disease (CAD) is the most common type of heart disease. It is the leading cause of death in the United States in both men and women1-3. CAD occurs when the coronary arteries become hardened and narrowed (atherosclerosis) due to the buildup of plaques from cholesterol and other materials on their inner walls. As the plaques grow, less blood can flow through the arteries. The heart muscle cannot get enough blood or oxygen it needs. This can lead to chest pain (angina) or a heart attack. Over time, CAD can also weaken the heart muscle and contribute to heart failure (cannot adequately pump blood to the body) and arrhythmias (changes in the normal beating rhythm of the heart). The coronary arteries consist of two main arteries: the right and left coronary arteries (LCA and RCA). LCA divides into the left anterior descending artery (LAD) and the left circumflex (LCx) branch, which supply blood to the left ventricle and left atrium. RCA divides into the posterior descending and acute marginal arteries, which supply blood to the right ventricle, right atrium, and sinoatrial node. The advent of multidetector row CT (MDCT) system has dramatically increased the quality and speed of CTA examinations4, 5. The increased temporal and spatial resolutions of 64-slice MDCT improve the robustness of ECG-gated cardiac CTA at relatively high heart rates. The reconstruction of cardiac CTA images requires the synchronization of the data to the cardiac cycle, i.e., to the motion of the heart. This can be accomplished by prospectively triggering the X-ray beam at selected phases of the cardiac cycle using the ECG trace, or by Medical Imaging 2008: Computer-Aided Diagnosis, edited by Maryellen L. Giger, Nico Karssemeijer Proc. of SPIE Vol. 6915, 69150O, (2008) · 1605-7422/08/$18 · doi: 10.1117/12.770362

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retrospectively selecting the desired cardiac phases from the acquired scan data based on simultaneously recorded ECG-signals. With retrospective ECG-gating employed in MDCT scan, the images can be reconstructed at any selected phases during the R-R interval. However, the same cardiac phase may not be optimal for all the coronary arteries. Reconstruction of multiple sets of images in different phases of the cardiac cycle is desirable to optimize vascular visualization. Unfortunately, the clinical diagnosis of cardiac artery disease is unreliable and difficult because the cardiac motion and other imaging sub-optimal conditions that lead to misdiagnosis far more often than correct detection. To detect cardiac artery diseases, readers have to visually track the arteries, adjusting the window setting and window level manually to maximize their visibility. Because of the cardiac motion, the same cardiac phase may not be optimal for all the coronary arteries, readers have to examine reconstructed images in different phases of the cardiac cycle, and detect suspicious diseases in phases with the best possible vascular visualization. False negatives (FN, missed diagnosis) are not uncommon because of the complexity of the images and the large number of arteries to be examined in different reconstructed cardiac phases in each case. Computer-assisted image analysis (CAA) may be a viable approach for assisting radiologists in this demanding task and reducing the chance of missing diagnosis. With advanced computer vision techniques, the computer is expected to be able to automatically trace the arteries in images reconstructed from different cardiac phases, and detect suspicious plaque locations by searching along the arteries, and finally alert the radiologists to the regions of interest (ROI) for suspicious plaques. Automatic and accurate segmentation and tracking of the coronary arteries in 3D ECG-gated cardiac computed tomographic images is an essential step for computerized detection of plaque diseases in coronary arteries. In this study, we are evaluating the feasibility of developing an automated method to segment and track the coronary arteries in 3D cardiac CT scans.

2. MATERIALS AND METHODS 2.1 Materials The test data set in this study contained 10 ECG-gated cardiac CTA scans. The CTA scans were retrospectively collected from patient files at the University of Michigan Hospital with Institutional Review Boards (IRB) approval. The CTA scans were acquired with 16-slice or 4-slice CT scanners using 1.25 mm collimation. Two experienced thoracic radiologists provided gold standard for this study. The radiologists visually examined the computer tracked coronary arteries on a graphical interface to identify untracked branches (segments) for an evaluation of the false negative (FN) performance of the computerized vessel segmentation and tracking method. In a pilot test data set containing 10 ECG-gated cardiac CT scans, six of which were determined by an experienced thoracic radiologist as having sub-optimal image quality due to motion artifacts. Non-calcified soft plaques were clinically found in three cases, a mixture of calcified and non-calcified soft plaques was found in one case, and the remaining 6 cases contained only calcified plaques. 2.2 Computerized segmentation of coronary arteries in 3D CTA images Figure 1 shows the scheme of our computer coronary arteries segmentation method. To extract the heart region, morphological operations and an adaptive thresholding method based on expectation-maximization (EM) estimation are first applied to the whole volume of the CT scan. Vascular structures in the heart volume are enhanced by 3D multi-scale filtering and analysis of the eigenvalues of Hessian matrices using a vessel enhancement response function specially designed for coronary arteries. At each scale, the heart volume is convolved with a 3D Gaussian filter and the Hessian matrix is calculated at each voxel. A volume of interest (VOI) containing the response function value at each voxel is defined. In this VOI, voxels with a high response indicate an enhanced vessel whose size matches the given filter scale. An EM estimation is then applied to the VOI to segment the vessels by extracting the high response voxels at each scale. Finally, our newly developed 3D rolling balloon vessel tracking method (RBVT) is used to track the segmented coronary arteries. Starting at two

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manually identified points located at the origins of left and right coronary artery (LCA and RCA), the RBVT method moves a sphere of adaptive diameter along the vessels, tracking the vessels and identifying its branches automatically to generate the left and right coronary arterial trees.

CTA volume

Heart region extraction

Vascular structure enhancement

Vessel segmentation

Coronary arterial tree construction

Figure 1. The scheme of computerized coronary artery segmentation 2.2.1 Vascular structure enhancement To adaptively segment the coronary vessels, a 3D filter was used to enhance the vessel-like structures within the heart region based on the eigenvalues of the Hessian matrix at multiple scales. The conventional multiscale line filters6-8 based on Hessian matrix analysis that are used to enhance line structures shares a common approach: the images are first convolved with Gaussian filters at multiple scales and the eigenvalues of the Hessian matrix at each voxel are analyzed to determine the local shape of the structures in the images. The eigenvalues for the voxels that correspond to a linear structure will be different from those that correspond to a nonlinear structure, noise, or no structure. The response of the enhancement filter is maximal when the scale of the filter matches the size of the line structure. However, we found that the conventional multiscale line filter that is used to enhance tubular structures inside the image volume cannot enhance the vessel bifurcation which forms a blob-like structure when the vessel splits into two or more branches, thus causing a gap between the vessel branches. We therefore designed a new response function9 using the eigenvalues of the Hessian matrix to enhance all vascular structures including vessel bifurcations and to suppress non-vessel structures such as the soft tissue surrounding the vessels. At voxel r = (x,y,z), let the eigenvalues of the Hessian matrix be λ1 (r ) , λ2 (r ) and λ3 (r ) , where

| λ1 (r ) |>| λ2 (r ) |>| λ3 (r ) | , and their corresponding eigenvectors be e 1 (r ) , e2 ( r ) and e3 ( r ) . The eigenvector

ei (r ) represents the direction along which the second derivative represented by λi (r ) , i=1, 2, 3, is maximum. For a tubular structure in 3D volume:

| λ3 (r ) |≈ 0 | λ3 (r ) |