본문 바로가기
KERD
논문KCI 등재

CCTA 영상에서 계층적 변형을 이용한 심혈관 비강체 정합

Non-rigid registration based on hierarchical deformation of coronary arteries in CCTA images

0

조회

0

다운로드

0

인용

0

참고문헌

키워드

주제분류컴퓨터학

초록

본 논문에서는 관상동맥 질환의 진단 및 치료에 활용 가능한 보조 정보 제공을 위해 동일 환자 시간차 3D 심장 컴퓨터단층촬영 혈관 조영술 영상에서 혈관 간 정확하고 빠른 비강체 정합 기법을 제안한다. 제안 방법은 다음과 같은 네 단계로 구성된다. 첫째, 시간차 3D 심장 컴퓨터단층촬영 혈관 조영술 영상으로부터 획득한 3D 혈관 중심선 간에 강체 정합을 통한 전역적 정렬을 수행한다. 둘째, 강체 정합 결과에서 3D 혈관 중심선 간 Point matching을 수행하며 대응 관계를 정의한다. 셋째, 혈관의 두께, 기울기 등의 정보를 이용하여 매칭된 대응 관계에서 Outlier를 제거한다. 넷째, 에너지 함수를 이용하여 계층적인 지역적 변형을 위한 비강체 변형 수행한다. 실험 결과, 제안 기법의 평균 정합 오차는 0.987mm이고, 평균 수행 시간은 2.137초로 정확하고 빠르게 정합을 수행한다.

Abstract

In this paper, we propose a accurate and fast non-rigid registration method between vessels using two 3D cardiac computed tomography angiography(CTA) images taken at different times to provide auxiliary information that can be used in the diagnosis and treatment of coronary artery disease. The proposed method consists of the following four steps. First, global alignment is performed through rigid registration between 3D vessel centerlines acquired from two 3D CTA images. Second, corresponding points is obtained through point matching between 3D vessel centerlines. Third, the outlier is removed from the corresponding points using the thickness and gradient of the vessel. Fourth, non-rigid deformation for hierarchical local transformation is performed using an energy function. As a result of the experiment, the average registration error is 0.987 mm, and the average execution time is 2.137 seconds. The proposed method shows that the registration is performed accurately and quickly.

참고문헌 (17)

  1. C. J. Murray, "Alternative Projections of Mortality and Disability by Cause 1990–2020: Global burden of disease study", The Lancet, 349(9064), pp.1498~1504 (1997)
  2. J. K. Min, "The Present State of Coronary Computed Tomography Angiography: A process in Evolution", Journal of the American College of Cardiology, 55(10), pp.957~965 (2010)
  3. G. Dibildox, "3D", 3D Registration of Coronary CTA and Biplane XA Reconstructions for Improved Image Guidance, 2014, pp.41(9), 919~928
  4. G. Kaila, "Fusion of CTA and XA data using 3D Centerline Registration for Plaque Visualization during Coronary Intervention", Proc. of Medical Imaging 2016:Image-Guided Procedures, Robotic Interventions, and Modeling, 9786, pp.978606~ (2016)
  5. D. P. Zhang, "Nonrigid Registration and Template Matching for Coronary Motion Modeling from 4D CTA", Lecture Notes in Computer Science, 6204, pp.210~221 (2010)
  6. L. Hadjiiski, "Automated Registration of Coronary Arterial Trees from Multiple Phases in Coronary CT Angiography (cCTA)", Proc. of SPIE Medical Imaging, 8670, pp.86703M~ (2013)
  7. Y. Luo, "Registration of Coronary Arteries in Computed Tomography Angiography Images using Hidden Markov Model", Proc. of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp.1993~1996 (2015)
  8. S. Zeng, "Towards Accurate and Complete Registration of Coronary Arteries in CTA Images", Proc. Medical Image Computing and Computer-Assisted Intervention, pp.419~427 (2018)
  9. Q. Cao, "Automatic Coronary Artery Plaque Thickness Comparison between Baseline and Follow‐up CCTA Images", Medical physics, 47(3), pp.1083~1093 (2020)
  10. 임선혜, "2D X-선 조영영상에서 정확한 혈관 구조 추출 기법", 한국차세대컴퓨팅학회 논문지, 13(1), pp.82~90 (2017), 한국차세대컴퓨팅학회
  11. 송영찬, "B-스플라인 기반 관심 영역 고정밀 비강체 정합 기법: 흉부 CT 영상에의 응용", 한국차세대컴퓨팅학회 논문지, 12(2), pp.87~96 (2016), 한국차세대컴퓨팅학회
  12. D. Han, "Automatic Coronary Artery Segmentation Using Active Search for Branches and Seemingly Disconnected Vessel Segments from Coronary CT Angiography", PLoS One, 11(8), pp.e0156837~ (2016)
  13. W. Lee, "Technical Aspect of Coronary CT Angiography : Imaging Tips and Safety Issues", Journal of the Korean Medical Association, 50(2), pp.104~108 (2007)
  14. 박태용, "3차원 시간차 심장 CTA 영상에서 고속의 정확한 심혈관 강체 정합 기법", 한국차세대컴퓨팅학회 논문지, 13(4), pp.59~67 (2017), 한국차세대컴퓨팅학회
  15. D. Rivest-Henault, "Nonrigid 2D", 3D Registration of Coronary Artery Models with Live Fluoroscopy for Guidance of Cardiac Interventions, 2012, pp.31(8), 1557~1572
  16. H. Hong, "Automatic Lung Nodule Matching on Sequential CT Images", Computers in Biology and Medicine, 38(5), pp.623~634 (2008)
  17. T. Y. Park, "Convolutional Neural Networkbased Segmentation and Non-rigid Registration in Multi-modality Images for Image-guided Intervention", Ph. D., pp.2019

관련 논문AI추천

이 간행물의 다른 논문 (7건)