Image-based biomechanical modeling for coronary atherosclerotic plaque progression and vulnerability prediction. (1st April 2022) - Lv, Rui; Wang, Liang; Maehara, Akiko; Guo, Xiaoya; Zheng, Jie; Samady, Habib; Giddens, Don P.; Mintz, Gary S.; Stone, Gregg W.; Tang, Dalin
Abstract: Atherosclerotic plaque progression and rupture play an important role in cardiovascular disease development and the final drastic events such as heart attack and stroke. Medical imaging and image-based computational modeling methods advanced considerably in recent years to quantify plaque morphology and biomechanical conditions and gain a better understanding of plaque evolution and rupture process. This article first briefly reviewed clinical imaging techniques for coronary thin-cap fibroatheroma (TCFA) plaques used in image-based computational modeling. This was followed by a summary of different types of biomechanical models for coronary plaques. Plaque progression and vulnerability prediction studies based on image-based computational modeling were reviewed and compared. Much progress has been made and a reasonable high prediction accuracy has been achieved. However, there are still some inconsistencies in existing literature on the impact of biomechanical and morphological factors on future plaque behavior, and it is very difficult to perform direct comparison analysis as differences like image modality, biomechanical factors selection, predictive models, and progression/vulnerability measures exist among these studies. Encouraging data and model sharing across the research community would partially resolve these differences, and possibly lead to clearer assertive conclusions. In vivo image-based computational modeling could be used as a powerful tool for quantitative assessment of coronary plaque vulnerability for potential clinical applications. Highlights: Review current progress in effectively predicting future coronary plaque behavior Predictive framework that combines medical imaging and biomechanical modeling Vast differences in image data, methods exist across published studies.