用4D心脏数据生成动态全息影像，帮医生术前更准规划搭桥手术。
Innovative Integration of 4D Cardiovascular Reconstruction and Hologram: A New Visualization Tool for Coronary Artery Bypass Grafting Planning
- 基于4D心梗造影数据，自动分割心脏结构与钙化血管。
- 全息影像使医生评分高达4.57/5，贴合实际手术发现。
- 适合心血管外科医生术前评估粘连与血管深度。
背景：冠状动脉搭桥术（CABG）规划需精准空间可视化，考量血管深度、钙化及心包粘连。目标：开发并评估一种用于术前CABG规划的动态心血管全息可视化工具。方法：利用14例候选患者的心脏4D计算机断层扫描血管造影数据，构建半自动化流程，实现心脏结构、心外膜脂肪组织（EAT）及冠状动脉的时序分割，并量化冠状动脉钙化，可视化血管在EAT中的深度，通过运动分析评估心包粘连。使用Looking Glass平台呈现动态心血管全息影像。13名心脏外科医生采用李克特量表评价该工具。此外，对21例患者（含7例再次心脏手术者）的全息影像所测心包粘连评分，与术中所见进行对比。结果：医生对该工具在术前规划中的实用性评价极高（平均分4.57/5.0）。全息影像评分与术中发现高度相关（r=0.786，P<0.001）。结论：本研究建立了一种从患者特异性数据生成临床相关动态全息影像的可视化框架，临床反馈证实其在术前规划中的有效性。
原文摘要 · Abstract (English)
Background: Coronary artery bypass grafting (CABG) planning requires advanced spatial visualization and consideration of coronary artery depth, calcification, and pericardial adhesions. Objective: To develop and evaluate a dynamic cardiovascular holographic visualization tool for preoperative CABG planning. Methods: Using 4D cardiac computed tomography angiography data from 14 CABG candidates, we developed a semi-automated workflow for time-resolved segmentation of cardiac structures, epicardial adipose tissue (EAT), and coronary arteries with calcium scoring. The workflow incorporated methods for cardiac segmentation, coronary calcification quantification, visualization of coronary depth within EAT, and pericardial adhesion assessment through motion analysis. Dynamic cardiovascular holograms were displayed using the Looking Glass platform. Thirteen cardiac surgeons evaluated the tool using a Likert scale. Additionally, pericardial adhesion scores from holograms of 21 patients (including seven undergoing secondary cardiac surgeries) were compared with intraoperative findings. Results: Surgeons rated the visualization tool highly for preoperative planning utility (mean Likert score: 4.57/5.0). Hologram-based pericardial adhesion scoring strongly correlated with intraoperative findings (r=0.786, P<0.001). Conclusion: This study establishes a visualization framework for CABG planning that produces clinically relevant dynamic holograms from patient-specific data, with clinical feedback confirming its effectiveness for preoperative planning.
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