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.

双休uspid主动脉瓣（BAV）修复是主动脉浮肿的年轻人的有前途的外科手术治疗 （ar）。然而，BAV维修手术仍未得到充分利用，并且在机构之间可变地应用 缺乏标准化方法进行BAV维修计划的一部分。目前，BAV维修计划退休 在心脏是直接观察的术中术中手动测量的主要手动测量 处于被捕状态，使外科医生难以在生理学下识别瓣膜动力学缺陷 状况。为了应对这一挑战，长期目标是开发多模式4D图像分析和 阀门建模平台，该平台系统地特征了术前的BAV形态和动态，并且 启用特定于患者的手术计划。该提案的总体目标是（i）填写知识 在主动脉尖，环和根之间的精确解剖学关系中差距，使BAV成为BAV 在功能上胜任，（ii）开发计算图像分析，以精确确定患者 - 引起AR的具体，解剖和动态扭曲，以便将这些缺陷确定为风险 BAV维修手术的分层和计划。这项工作将通过追求三个具体目标来进行： （1）设计和评估BAV 4D重建的自动分割和建模算法 来自多种临床成像方式的设备； （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bavs; （3）使用从阀维修中获取的图像评估BAV维修计划系统 三个机构的程序。提议的项目利用了两个的完全好处 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， 它既捕获具有高空间分辨率的主动脉尖的形态学细节 具有高临时分辨率的3D BAV设备。该项目的创新是拟议的工具 可以改变BAV维修计划的制定方式。而不是依靠术中检查 阀门未经压力时，外科医生可以交互可视化图像衍生的BAV模型并量化 当阀门处于术前4D物理状态时，AR的动态机制。的意义 这项研究是，它可以促进机构跨机构的阀门维修计划的一致性，下降 外科医生对直觉和反复试验的缓解，从而增加了年轻人的BAV修复的利用率 成年人。相对于传统阀替换，这将具有生活质量优势，这需要 终身抗凝治疗（机械阀）或由于耐用性有限而导致多次重新置换 （生物假体阀）。最终，计算机辅助阀的多模式图像数据的系统分析 缺陷检测将广泛受益于手术治疗的进步和先天性心脏 疾病。