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Innovative Integration of 4D Cardiovascular Reconstruction and Hologram: A New Visualization Tool for Coronary Artery Bypass Grafting Planning

28 April 2025
S. Wang
Tong Ren
Nan Cheng
Li Zhang
Rong Wang
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Abstract

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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@article{wang2025_2504.19401,
  title={ Innovative Integration of 4D Cardiovascular Reconstruction and Hologram: A New Visualization Tool for Coronary Artery Bypass Grafting Planning },
  author={ Shuo Wang and Tong Ren and Nan Cheng and Li Zhang and Rong Wang },
  journal={arXiv preprint arXiv:2504.19401},
  year={ 2025 }
}
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