4D Multimodal Image-Based Modeling for Bicuspid Aortic Valve Repair Surgery

二叶式主动脉瓣修复手术的 4D 多模态基于图像的建模

• 批准号: 10420584
• 负责人: Alison Marie Pouch
• 金额: $ 74.54万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-01 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10420584
• 关键词: 3-Dimensional; 4D Imaging; Address; Algorithms; Anatomic Models; Anatomy; Angiography; Anticoagulation; Aortic Valve Insufficiency; Artificial Heart; Bioprosthesis device; Cessation of life; Characteristics; Competence; Complex; Computer Assisted; Control Groups; Data; Defect; Detection; Devices; Engineering; Evaluation; Four-dimensional; Geometry; Goals; Heart; Heart Valve Diseases; Heart Valves; Heart failure; Hemorrhage; Image; Image Analysis; Institution; Intervention; Intuition; Knowledge; Manuals; Maryland; Measurement; Mechanics; Methods; Modality; Modeling; Morphology; Motion; Multimodal Imaging; Operative Surgical Procedures; Outcome; Patients; Pennsylvania; Phenotype; Physiological; Plant Roots; Procedures; Progressive Disease; Quality of life; Reproducibility; Research; Resolution; Risk; Standardization; Statistical Models; Surgeon; System; Technology; Testing; Time; Tissues; Transesophageal Echocardiography; Universities; Ursidae Family; Variant; Visualization; Work; X-Ray Computed Tomography; alternative treatment; aortic valve replacement; automated algorithm; automated segmentation; base; bicuspid aortic valve; clinical imaging; comorbidity; congenital heart disorder; conventional therapy; dashboard; design; diagnostic tool; emerging adult; imaging modality; in silico; innovation; machine learning algorithm; machine learning method; multi-atlas segmentation; multidisciplinary; multimodality; premature; preservation; prevent; reconstruction; repair model; repair strategy; repaired; risk stratification; statistics; temporal measurement; tool; touchscreen; two-dimensional; valve replacement; young adult

Bicuspid aortic valve (BAV) repair is a promising surgical treatment for young adults with aortic regurgitation (AR). However, BAV repair surgery remains underutilized and variably applied across institutions, owing in part to the lack of a standardized approach to BAV repair planning. Currently, BAV repair planning relies primarily on intraoperative manual measurements of the valve made by direct observation while the heart is in an arrested state, making it difficult for the surgeon to identify defects in valve dynamics under physiological conditions. To address this challenge, the long-term goal is to develop a multimodal 4D image analytics and valve modeling platform that systematically characterizes pre-operative BAV morphology and dynamics and enables patient-specific surgical planning. The overall objectives of this proposal are to (i) fill a knowledge gap in the precise anatomical relationships between the aortic cusps, annulus, and root that make a BAV functionally competent, and (ii) develop computational image analytics to precisely identify the patient- specific, anatomical and dynamic distortions that cause AR so that these defects can be prioritized for risk stratification and planning of BAV repair surgery. This work will be carried out by pursuing three specific aims: (1) Design and assess an automated segmentation and modeling algorithm for 4D reconstruction of the BAV apparatus from multiple clinical imaging modalities; (2) characterize the morphological and dynamic features of BAV competence and create a machine learning method for comprehensive anomaly detection in regurgitant BAVs; (3) evaluate a BAV repair planning system using images acquired from valve repair procedures at three institutions. The proposed project leverages the complementary benefits of two modalities: real-time 3D transesophageal echocardiography and 4D computed tomography angiography, which capture both the morphological detail of the aortic cusps with high spatial resolution and the motion of the 3D BAV apparatus with high temporal resolution. The innovation of this project is that the proposed tools could change how BAV repair planning is carried out. Instead of relying on intraoperative inspection of the valve while it is unpressurized, the surgeon can interactively visualize image-derived BAV models and quantify dynamic mechanisms of AR when the valve is in a pre-operative 4D physiological state. The significance of this research is that it could promote consistency in valve repair planning across institutions, decrease surgeons’ reliance on intuition and trial-and-error, and thereby increase the utilization of BAV repair in young adults. This would have quality of life advantages relative to conventional valve replacement, which requires lifelong anticoagulation therapy (mechanical valves) or multiple re-replacements due to limited durability (bioprosthetic valves). Ultimately, the systematic analysis of multimodal image data for computer-aided valve defect detection will broadly benefit advancement of surgical treatments for acquired and congenital heart disease.

二叶式主动脉瓣 (BAV) 修复术是治疗年轻主动脉瓣反流的一种有前途的手术治疗方法 (AR) 然而，BAV 修复手术仍未得到充分利用，并且各机构的应用情况各不相同。 部分原因是缺乏 BAV 维修计划的标准化方法 目前，BAV 维修计划依赖于 BAV 维修计划。 主要是在心脏处于手术状态时通过直接观察对瓣膜进行术中手动测量 处于停滞状态，使得外科医生很难在生理条件下识别瓣膜动力学缺陷 为了应对这一挑战，长期目标是开发多模态 4D 图像分析和方法。 瓣膜建模平台，系统地描述术前 BAV 形态和动力学特征， 该提案的总体目标是（i）充实知识。 构成 BAV 的主动脉瓣尖、瓣环和根部之间的精确解剖关系存在间隙 功能胜任，并且（ii）开发计算图像分析以精确识别患者 导致 AR 的特定的、解剖学和动态的扭曲，以便可以对这些缺陷进行风险优先排序 BAV 修复手术的分层和规划将通过实现三个具体目标来进行： (1) 设计和评估用于 BAV 4D 重建的自动分割和建模算法 (2)表征形态和动态特征 BAV 能力并创建一种用于全面异常检测的机器学习方法 反流 BAV；(3) 使用瓣膜修复获取的图像评估 BAV 修复计划系统 拟议的项目利用了两个机构的互补优势。 方式：实时 3D 经食管超声心动图和 4D 计算机断层扫描血管造影， 它以高空间分辨率捕捉主动脉瓣的形态细节和运动 该项目的创新之处在于所提出的工具。 可以改变 BAV 修复计划的执行方式，而不是依赖术中检查。 在未加压的情况下，外科医生可以交互式地可视化图像衍生的 BAV 模型并量化 当瓣膜处于术前 4D 生理状态时 AR 的动态机制的意义。 这项研究表明，它可以促进各机构阀门维修计划的一致性，减少 外科医生对直觉和反复试验的依赖，从而提高了年轻患者中 BAV 修复的利用率 与传统的瓣膜置换术相比，成人的生活质量相对有优势。 终身抗凝治疗（机械瓣膜）或由于耐用性有限而多次重新更换 （生物瓣膜）最终，计算机辅助瓣膜的多模态图像数据的系统分析。 缺陷检测将广泛有利于后天性和先天性心脏外科治疗的进步 疾病。