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用于主动脉瓣狭窄个性化心脏病学的超声专用无创计算诊断框架。

An ultrasound-exclusive non-invasive computational diagnostic framework for personalized cardiology of aortic valve stenosis.

机构信息

Department of Mechanical Engineering, McMaster University Hamilton, ON, Canada.

School of Biomedical Engineering, McMaster University, Hamilton, ON, Canada.

出版信息

Med Image Anal. 2023 Jul;87:102795. doi: 10.1016/j.media.2023.102795. Epub 2023 Mar 21.

Abstract

Aortic stenosis (AS) is an acute and chronic cardiovascular disease and If left untreated, 50% of these patients will die within two years of developing symptoms. AS is characterized as the stiffening of the aortic valve leaflets which restricts their motion and prevents the proper opening under transvalvular pressure. Assessments of the valve dynamics, if available, would provide valuable information about the patient's state of cardiac deterioration as well as heart recovery and can have incredible impacts on patient care, planning interventions and making critical clinical decisions with life-threatening risks. Despite remarkable advancements in medical imaging, there are no clinical tools available to quantify valve dynamics invasively or noninvasively. In this study, we developed a highly innovative ultrasound-based non-invasive computational framework that can function as a diagnostic tool to assess valve dynamics (e.g. transient 3-D distribution of stress and displacement, 3-D deformed shape of leaflets, geometric orifice area and angular positions of leaflets) for patients with AS at no risk to the patients. Such a diagnostic tool considers the local valve dynamics and the global circulatory system to provide a platform for testing the intervention scenarios and evaluating their effects. We used clinical data of 12 patients with AS not only to validate the proposed framework but also to demonstrate its diagnostic abilities by providing novel analyses and interpretations of clinical data in both pre and post intervention states. We used transthoracic echocardiogram (TTE) data for the developments and transesophageal echocardiography (TEE) data for validation.

摘要

主动脉瓣狭窄 (AS) 是一种急性和慢性心血管疾病,如果不进行治疗,这些患者中有 50%会在出现症状后的两年内死亡。AS 的特征是主动脉瓣叶变硬,限制了它们的运动,并在跨瓣压下阻止了适当的打开。如果有瓣膜动力学评估,将为患者心脏恶化以及心脏恢复的状态提供有价值的信息,并对患者护理、干预计划制定和具有危及生命风险的关键临床决策产生巨大影响。尽管医学成像取得了显著进展,但仍没有临床工具可用于对瓣膜动力学进行侵入性或非侵入性的定量评估。在这项研究中,我们开发了一种高度创新的基于超声的非侵入性计算框架,可作为一种诊断工具,用于评估 AS 患者的瓣膜动力学(例如,应力和位移的瞬态 3D 分布、瓣叶的 3D 变形形状、几何瓣口面积和瓣叶的角度位置),且不会对患者造成任何风险。这种诊断工具考虑了局部瓣膜动力学和整体循环系统,为测试干预方案并评估其效果提供了一个平台。我们使用了 12 名 AS 患者的临床数据,不仅验证了所提出的框架,还通过提供干预前后的临床数据的新的分析和解释,展示了其诊断能力。我们使用经胸超声心动图 (TTE) 数据进行开发,并使用经食管超声心动图 (TEE) 数据进行验证。

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