image based arterial wall thickness estimation for

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Dec 12, 2012 - given slice of a DICOM image to a value of around 6.8 pixels per mm. ... finding the actual thickness, we used 'Radiant DICOM Viewer'.
International Congress on Computational Mechanics and Simulation (ICCMS), IIT Hyderabad, 10-12 December 2012

IMAGE BASED ARTERIAL WALL THICKNESS ESTIMATION FOR ABDOMINAL AORTIC ANEURYSM RUPTURE RISK ANALYSIS Maheshkumar H. Kolekar, Lloyds Raja, Himanshu Rai, Anamika Prasad Indian Institute of Technology, Patna, Bihar, 800013 Presenting author email: [email protected] Corresponding author email: [email protected]

Abstract Cardiovascular disease (CVD) is one of the leading causes of deaths in many countries with CVD mortality rate projected to double in the next decade. Arterial wall thickness is considered a key biomechanical determinate of CVD risk and disease progression. In this study, we have developed image processing algorithms for wall thickness estimation in computed tomography (CT) angiography images of a patient suffering from aortic aneurysm. Direct measurement results show that the estimated thickness closely resembles the actual thickness in the CT data. The 3D computer models demonstrate the heterogeneous nature of arterial wall which can significantly impact the prediction of its rupture potential and is critical in better understanding of CVD risks and disease progression in the given context.

1. Introduction and Related Work Currently Cardiovascular disease (CVD) is responsible for 24% of all deaths in India [1] with CVD related mortality projected to double within the next decade. CVD exhibits itself both as narrowing of arterial lumen (coronary artery disease, peripheral artery disease) and ballooning of artery wall (abdominal aortic aneurysm, cerebral aneurysm). CVD incidence and disease progression is strongly depended upon factors such as race/ethnicity, gender, and age [2, 3]. Hence, understanding CVD risks, identifying prevention methods, and planning treatment options requires strong coupling between epidemiological data and underlying biomechanics analysis. Such studies are currently limited in Indian context. In this work we investigate arterial wall thickness as one of the factors of CVD risk assessment and disease progression, with a focus on aortic aneurysms. Arterial wall thickness is considered a key biomechanical determinate of aneurysm rupture potential [2] and may vary significantly among different ethnicities [3]. Vorp et al [6] have followed a biomechanics-based approach to predict Abdominal Aortic Aneurysm (AAA) rupture on a patient-specific basis which proved to be more effective than maximum diameter criterion. Giampaolo et al [7] have evaluated a series of 1D size, 2D shape, 3D size, 3D shape, and second-order curvature-based indices to quantify AAA geometry, as well as the geometry of a size-matched idealized fusiform aneurysm and a patient-specific normal abdominal aorta used as controls. Additionally, an estimation of local variation and distribution of AAA wall thickness is provided by means of an adhoc image processing suite developed for cardiovascular structures. Judy et.al. [8] formulated an algorithm for estimating wall thickness in AAAs based on intensity histograms and neural networks involving segmentation of contrast enhanced abdominal computed tomography images. Two vascular surgeons manually segmented the lumen, inner wall, and outer wall of each data set and a reference standard was defined as the average of their segmentations. Reproducibility was determined by comparing the reference standard to lumen contours generated automatically by the algorithm and a commercially available software package. Repeatability was assessed by comparing the lumen, outer wall, and inner wall contours, as well as wall thickness, made by the two surgeons using this algorithm. In spite of its accuracy, existence of computational complexity is a major concern [10], [11] and these methods are not available for general use. It is thus important ot have image segmentation and model building

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International Congress on Computational Mechanics and Simulation (ICCMS), IIT Hyderabad, 10-12 December 2012 techniques for lumen and wall thickness which can build on existing open source software and be made available for use

(a) Normal Aorta

(b) Swollen Aorta (Diameter > 4.5cm) (c) Ruptured Aorta [9] Figure-1: CT images of three stages of AAA

As shown in Figure-1, the average diameter of swollen aorta is about 1.5 times higher than the normal aorta. Figure-1 (c) shows the ruptured aorta. The risk of rupture of small aneurysms (smaller than 4.0 centimeters) is much less than the risk of rupture of large aneurysms (larger than 6.0 centimeters) [5]. In addition to size, the risk of rupture of an abdominal aortic aneurysm depends on the rate at which the aneurysm is expanding. The evidence suggests that aneurysms expand at an average rate of 0.3 to 0.4 centimeters per year (1 inch = 2.5 cm). We combine medical image analysis technique to estimate variable arterial wall thickness with the aim of building 3D computer model of variable thickness aortic wall that can be used in detailed stress analysis [12]. We use clinical CT data from patients suffering from aortic aneurysm. Using ITK-SNAP [3] and in-house developed semi-automatic wall thickness detection method, we build 3D computer models of the aneurysm wall. The wall thickness of the segmented aorta was measured by proposed algorithm. A 3D model of the segmented lumen, outer and inner wall regions was obtained using ITK snap which can be opened in Paraview [4].

2. Wall Thickness Estimation Figure-2 presents the technique we have used for wall thickness estimation. Using ITK Snap, we zoomed the given slice of a DICOM image to a value of around 6.8 pixels per mm. The region containing the lumen outer and inner aortic wall is also segmented using ITK snap. In the segmented image, red region represents lumen and the yellow region is the region enclosed between inner and outer wall whose average thickness we seek. From the colored segmented image, a binary image is obtained such that only the region between the outer and inner walls is white and the remaining are black. Threshold for obtaining the binary image is decided by the opacity value used during segmentation. Then the proposed wall-thickness estimation algorithm is applied to get the average wall thickness in each slice.

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International Congress on Computational Mechanics and Simulation (ICCMS), IIT Hyderabad, 10-12 December 2012

Figure 2: Steps involved in the Aortic wall thickness measurement scheme. Algorithm: Wall Thickness Estimation Step-1: Zoom the given slice of DICOM image by value Z and crop the region containing lumen, inner and outer wall. We have selected Z=6.8 pixels/mm in ITK snap. Step-2: Starting from the centre, draw radial lines separated by Θ degree as shown in Figure-3. We have used Θ=4 degree. Step-3: Traverse along any radial line. If three consecutive pixels are white then estimate the thickness as the number of white pixels (D) along the line. Step-4: Measure the thickness as the ratio of D and zoom value (Z). Step-5: Compute average thickness of the selected slice. Step-6: Repeat steps 1 to 5 for all the slices of DICOM image.

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International Congress on Computational Mechanics and Simulation (ICCMS), IIT Hyderabad, 10-12 December 2012

Figure 3: Selecting the points for thickness measurement. 3. Results and Discussions The 3D computer model of the aneurysm wall given in Figure-1 shows the heterogeneous nature of vessel wall thickness. The data used for experimentation consists of about 563 and 540 axial DICOM slices collected from patient-1and 2 respectively. From the original data, 23 slices were chosen for wall thickness estimation. The estimated wall thickness and actual thickness computed from those 23 slices are given in Table-1 and 2. For finding the actual thickness, we used ‘Radiant DICOM Viewer’. Using this software, we selected 20 points for each slice and computed the average thickness as shown in Figure 4.

Figure 4: Computing the actual average thickness using Radiant DICOM viewer Figure-5 shows the comparison of actual and estimated data for patient-1 and 2. It can be observed that percentage of error resulting due to our algorithm is well within the tolerable limit. The percentage error is computes as follows: %𝐸𝑟𝑟𝑜𝑟 =

𝐴𝑐𝑡𝑢𝑎𝑙 𝑇ℎ𝑖𝑐𝑘𝑛𝑒𝑠𝑠 − 𝐸𝑠𝑡𝑖𝑚𝑎𝑡𝑒𝑑 𝑇ℎ𝑖𝑐𝑘𝑛𝑒𝑠 𝑥100 𝐴𝑐𝑡𝑢𝑎𝑙 𝑇ℎ𝑖𝑐𝑘𝑛𝑒𝑠𝑠

Slices whose average wall thickness is very less compared to that of others can be potential candidates for aneurism.

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International Congress on Computational Mechanics and Simulation (ICCMS), IIT Hyderabad, 10-12 December 2012 Sl. No 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Average thickness (mm) for Patient-1 Slice Estimated Actual Value No. using proposed (Computed algorithm manually) 250 2.3482 2.28 255 2.8092 2.59 257 2.4539 2.52 260 2.8531 2.79 263 2.0789 2.34 265 2.6137 2.66 267 2.4887 2.37 270 3.1817 2.92 273 2.5966 2.39 275 2.5504 2.75 277 2.8474 2.63 280 2.6455 2.65 283 2.8228 2.73 285 3.0991 2.94 287 2.8679 2.75 290 3.2738 3.54 295 3.1434 3.05 297 2.5219 2.38 300 2.2443 2.15 305 2.8277 2.67 310 2.9500 2.93 315 2.5938 2.73 320 4.2678 4.13

% Error (e) 2.04 8.46 2.62 2.26 11.15 1.74 5.01 8.96 8.64 7.26 8.27 0.17 3.40 5.4 4.29 7.52 3.06 5.96 4.38 5.90 0.68 4.99 3.34

Table-1: Average wall thickness of various slices for patient-1

Sl. No

Slice No.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

250 255 260 265 270 275 280 285 290 295 300 305 310 315 320 325 330 335 340 345 350 355 360

Average thickness (mm) Patient-2 Estimated using Actual Value proposed (Computed algorithm manually) 3.0161 2.98 3.5323 3.64 5.5257 5.42 3.8801 3.72 3.6711 3.78 4.1156 4.20 3.6762 3.35 2.8753 3.12 2.3849 2.25 3.2624 3.12 3.4594 3.62 3.0483 3.21 2.9409 2.81 3.5282 3.12 3.2613 2.87 3.0013 3.20 2.7674 2.59 3.1546 3.31 3.8186 3.72 3.3379 3.42 2.9245 3.01 2.7696 2.91 2.5103 2.43

% Error (e) 1.09 2.96 1.95 4.30 2.88 2.01 9.74 7.84 5.99 4.56 4.45 5.04 4.66 13.08 13.63 6.21 6.85 4.69 2.65 2.40 2.84 4.82 3.30

Table-2: Average wall thickness of various slices patient-2

(a)

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International Congress on Computational Mechanics and Simulation (ICCMS), IIT Hyderabad, 10-12 December 2012

(b) Figure-5: Comparison of estimated thickness and actual thickness of (a) patient-1 (b) patient-2

Figure-6: Comparison of wall thickness distribution of various points along the outer wall of a normal slice and slice with aneurism. Figure-6 shows the drastic difference in wall thickness a normal slice and a slice with aneurism. It can be observed that the average wall thickness of the aneurism slice is less compared to that of a normal slice. This indicates that there is a risk of rupture. The slices used for experiments on patient-1 data are marked with Indigo color in Figure-7. By studying the average wall thickness of various patients with AAA and in follow-up studies, we can provide better prediction about the average wall thickness below which surgery is required.

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International Congress on Computational Mechanics and Simulation (ICCMS), IIT Hyderabad, 10-12 December 2012

Figure-7: 3-D model showing the lumen and the outer and inner walls of slices for Patient-1. Slices marked in indigo are those for which we have estimated the wall thickness in Table-1. The slices are in ascending order from bottom to top. 4. Summary and Conclusion We have proposed wall thickness estimation algorithm for abdominal aortic aneurysm rupture risk analysis. Such development are necessary in building techniques that will allow for rapid and accurate wall thickness analysis, which can be extended to larger patient base for better understanding CVD risks and disease progression in Indian population. The wall thickness estimation algorithm has the edge that we have the pixel location values for the points on inner and outer wall boundaries and their corresponding thickness values thus allowing us to locate the wall thickness values for any given region which will be helpful in developing the 3-D models which can be used in preoperative procedure planning. In future, we will focus on better segmentation technique which will allow us to have smooth edges as compared to the jagged boundaries that we have obtained now and automation of the method that can allow for building 3D model with least user intervention.

5. References [1] WHO Statistics on India, Mortality and burden of disease (http://www.who.int/countries/ind/en/), 2010. [2] Rosero EB, Peshock RM, Khera, A, Clagett P, Lo H, Timaran, “Sex, race, and age distributions of mean aortic wall thickness in a multiethnic population-based sample”, Journal of Vascular Surgery 53(4), 950-957, 2011. [3] Burke, GL, Evans GW, et al., “Arterial wall thickness is associated with prevalent cardiovascular disease in middle-aged adults: the Atherosclerosis Risk in Communities (ARIC) Study”, Stroke 26(3), 386-391, 1995. [4] http://paraview.org/. [5] Brewster DC, Cronenwett JL, Hallett JW Jr, Johnston KW, Krupski WC, Matsumura JS, “Guidelines for the treatment of abdominal aortic aneurisms”, Journal of vascular surgery, Vol.37(5), pp.1106-1117, 2003.

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International Congress on Computational Mechanics and Simulation (ICCMS), IIT Hyderabad, 10-12 December 2012 [6] Vorp, DA, Geest JPV, “Biomechanical determinants of abdominal aortic aneurysm rupture”, Arteriosclerosis, thrombosis, and vascular biology 25(8), 1558-1566, 2005. [7] Giampaolo Martufi, Elena S. Di Martino, Cristina H. Amon, Satish C. Muluk, Ender A. Finol, “Normalization Three-Dimensional Geometrical Characterization of Abdominal Aortic Aneurysms: Image-Based Wall Thickness Distribution”, Journal of Biomechanical Engineering, vol. 131, June 2009. [8] Judy Shum, Elena S., DiMartino, Adam Goldhammer, Daniel H. Goldmanb, Leah C. Acker, Gopal Patel, Ender A. Finol “Semiautomatic vessel wall detection and quantification of wall thickness in computed tomography images of human abdominal aortic aneurysms”, Medical Phys, vol.37(2), Feb 2010. [9] http://en.wikipedia.org/wiki/File:RupturedAAA.png http://en.wikipedia.org/wiki/Abdominal_aortic_aneurysm [10] M. Auer and T. Christian Gasser, “Reconstruction and Finite Element Mesh Generation of Abdominal Aortic Aneurisms from Computerized Tomography Angiography Data with Minimal User Interactions”, IEEE Transactions on Medical Imaging, Vol. 29(4), 2010. [11] Sílvia D. Olabarriaga, Jean-Michel Rouet, Maxim Fradkin, Marcel Breeuwer, and Wiro J. Niessen “Segmentation of Thrombus in Abdominal Aortic Aneurysms from CTA with Non-parametric Statistical Grey Level Appearance Modeling” , IEEE Transactions on Medical Imaging, Vol. 24(4), 2005. [12] Prasad A, LK To, ML Gorreparti, CK Zarins, CA Figueroa. “Computational Analysis of Stresses Acting on Inter-Modular Junctions in Thoracic Aortic End grafts” Journal of Endovascular Therapy, Vol. 18, No. 4, Pages. 559-568, 2011. [13] Prasad A, Xiao N, Gong XY, Zarins CK, Figueroa CA (2012). A Computational Framework for Investigating the Positional Stability of Abdominal Endografts, Biomechanics in Modeling and Mechanobiology, 2012 (in press)

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