This study developed a patient-specific cardiac motion-based virtual reality cardiovascular intervention simulator for training purposes. Personalized 3D cardiac models were generated from medical images using AI-based nnU-Net segmentation, and ECG-synchronized motion was integrated to reproduce physiological cardiac cycles through P-QRS-T waveform analysis. In particular, this study went beyond simple simulation by designing and implementing serious game elements including difficulty adjustment by user skill level, real-time haptic feedback, and a quantitative scoring system to maximize educational effectiveness. Real-time stent insertion was implemented at performance exceeding 60fps through the Extended Position-Based Dynamics (XPBD) algorithm. Experimental results showed that the segmentation model achieved high accuracy with an average Dice Similarity Coefficient (DSC) above 0.90, and the dynamic model demonstrated biomechanical behavior similar to clinical data, showing 12.3% coronary artery diameter change and 3.2mm positional displacement during the cardiac cycle.

ABSTRACT
1. 서론
2. 관련 연구 분석
    2.1 의료 데이터에 기반한 세그멘테이션과 메쉬 생성
    2.2 심전도 심장 움직임에 따른 스텐트 시뮬레이션
3. 가상현실 시뮬레이터 구현
    3.1 연구 내용 전개
    3.2 메쉬 재구성을 통한 최적화
    3.3 심전도 신호 연동 움직임 이벤트 모델링
    3.4 확장된 위치기반 동역학을 이용한 스텐트 시뮬레이션
    3.5 훈련 기능성 게임(Serious Game) 요소 설계 및 구현
4. 연구 결과
5. 결론 및 제언
사사표기
참고문헌
<저자소개>

• Taesoo Jang(Biomedical Engineering Research Center, Asan Medical Center, Seoul 05505, Korea) | 장태수
• Jae-Soon Choi(University of Ulsan College of Medicine, Asan Medical Center, Seoul 05505, Korea) | 최재순 Corresponding author