Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:4209-4212. doi: 10.1109/EMBC46164.2021.9630376.
Carotid atherosclerotic plaque growth leads to the progressive luminal stenosis of the vessel, which may erode or rupture causing thromboembolism and cerebral infarction, manifested as stroke. Carotid atherosclerosis is considered the major cause of ischemic stroke in Europe and thus new imaging-based computational tools that can improve risk stratification and management of carotid artery disease patients are needed. In this work, we present a new computational approach for modeling atherosclerotic plaque progression in real patient-carotid lesions, with moderate to severe degree of stenosis (>50%). The model incorporates for the first time, the baseline 3D geometry of the plaque tissue components (e.g. Lipid Core) identified by MR imaging, in which the major biological processes of atherosclerosis are simulated in time. The simulated plaque tissue production results in the inward remodeling of the vessel wall promoting luminal stenosis which in turn predicts the region of the actual stenosis progression observed at the follow-up visit. The model aims to support clinical decision making, by identifying regions prone to plaque formation, predict carotid stenosis and plaque burden progression, and provide advice on the optimal time for patient follow-up screening.
颈动脉粥样硬化斑块的生长导致血管腔的进行性狭窄，这可能导致血栓形成和脑梗死，表现为中风。颈动脉粥样硬化被认为是欧洲缺血性中风的主要原因，因此需要新的基于影像学的计算工具来改善颈动脉疾病患者的风险分层和管理。在这项工作中，我们提出了一种新的计算方法，用于模拟中重度狭窄（>50%）的真实患者颈动脉病变中动脉粥样硬化斑块的进展。该模型首次将由磁共振成像识别的斑块组织成分（如脂质核心）的基线 3D 几何结构纳入其中，其中动脉粥样硬化的主要生物学过程随时间被模拟。模拟的斑块组织产生导致血管壁的内向重塑，促进管腔狭窄，进而预测在随访时观察到的实际狭窄进展区域。该模型旨在通过识别易于发生斑块形成的区域，预测颈动脉狭窄和斑块负荷进展，并提供有关患者随访筛查的最佳时间的建议，从而支持临床决策。
