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Novel methodology for coronary artery disease evaluation : from a new imaging technique to deep learning based quantification

Other Titles
 관상동맥 질환 평가를 위한 새로운 방법론 : 새로운 영상 획득 기법부터 심층학습기반 자동 정량화까지 
Authors
 홍영택 
Degree
박사
Issue Date
2018
Description
의과학
Abstract
Cardiovascular disease remains the leading cause of mortality in the world. Coronary computed tomographic angiography (CTA) has emerged as a reliable noninvasive modality for the diagnosis of coronary artery disease (CAD). However, on-site evaluation of CAD is still a challenging problem. To solve this problem, this dissertation covers methods ranging from a new imaging acquisition technique to deep learning based automatic quantification. To obtain quality CTA, catheter-directed selective CTA (S-CTA) was developed in the preclinical model, and the clinical feasibility of S-CTA was validated in patients who had diagnosed CAD. S-CTA successfully produced an optimal luminal enhancement with an extremely low-dose of iodine. Automatic quantification was developed using convolutional neural networks (CNN). We successfully measured vascular minimal lumen area, diameter stenosis, and plaque volume with the proposed CNN model. When S-CTA was used for automatic quantification, the proposed CNN successfully captured intrinsic features of the contrast-enhanced lumen and calcified plaque better than C-CTA. S-CTA can be understood as an intraprocedural CTA modality under the combined-system that incorporates the coronary angiography system and a 320-detector row CT scanner. S-CTA enables a strategic stepwise approach for coronary catheterization and on-site evaluation for coronary stenosis.
Files in This Item:
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Appears in Collections:
1. College of Medicine (의과대학) > Others (기타) > 3. Dissertation
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/166394
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