| Summary: | 碩士 === 國立臺灣大學 === 醫學工程學研究所 === 106 === Heart disease is the second leading cause of death in Taiwan, and coronary artery disease is the most common type of heart disease. Bad eating habits and lifestyle such as smoking, high blood pressure, high cholesterol level, diabetes or lack of exercise are risk factors for coronary artery disease. Atherosclerosis is a chronic condition in which arteries harden through build-up of plaques, and often occurs at bifurcations of coronary arteries. Different treatments are taken depending on the severity of disease. Patient with mild symptoms are suggested to take medicines while revascularization is recommended in severe cases. Percutaneous coronary intervention with stent implantation is less invasive and recovers more rapidly, and gradually becomes the preferred revascularization modality in treating coronary artery disease. However, physicians often perform stent placement through some conventional projection angles based on personal experience or clinical guideline, of which the view may be inappropriate during the surgery. Restenosis or stent embolism may occur if a stent doesn’t completely cover the lesion. Besides, bifurcation angles impact the selection of stent technology, so the research is proposed to develop a computer-aided stent placement system based on CTA. The information on optimal projection angles and bifurcation angles helps physicians to choose the suitable stent before surgery, and to place stent more precisely by having an optimal view for the visualization of coronary artery lesions during surgery.
Four main bifurcation regions of the left and right coronary arteries were analyzed in our research, including LAD/LCX, LAD/diagonal, LCX/OM, and PDA/PLA. First, multiple hypothesis tracking was used to extract coronary artery trees and vessel centerline from an image, and a deep learning algorithm was proposed to improve the result of vessel segmentation. A bifurcation angle could be calculated after a bifurcation point and two branch points were defined in a bifurcation plane. Parameters of angle discrepancy and foreshortening ratio were proposed to be minimized to acquire an optimal projection angle. Result showed that the deep learning algorithm had a good performance in coronary artery segmentation. Besides, Angle discrepancy and foreshortening ratio of the (obtainable) optimal projection angle were smaller than those of conventional projection angles, implying a better visualization in surgical view which was further validated by conventional angiography. In addition, the statistical analysis showed that (obtainable) optimal projection angles were not densely distributed among 43 cases. The foreshortening ratio was slightly improved if the tolerance of angle discrepancy changed from two to ten degrees.
A computer-aided stent placement system based on CTA is proposed in the research. Physicians are able to make pre-clinical planning by getting information on bifurcation angle to choose the suitable stent, and can place stents precisely under the view of optimal projection angle, which shortens the time spent during the surgery and improves the clinical outcome.
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