AI-assisted Imaging and Prediction of Cardiac Arrhythmia Origins using 4D Ultrasound

使用 4D 超声进行人工智能辅助成像和心律失常起源预测

• 批准号: 10473146
• 负责人: Jan Christoph
• 金额: $ 139.2万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10473146
• 关键词: 3-Dimensional; 3D ultrasound; Anatomy; Arrhythmia; Artificial Intelligence; Atrial Fibrillation; Cardiac; Cardiac Electrophysiologic Techniques; Cardiac ablation; Cardiovascular Diseases; Catheters; Complex; Computer Simulation; Consumption; Data; Data Set; Diagnosis; Disease; Electrodes; Electrophysiology (science); Goals; Heart; Image; Imaging Techniques; Lead; Learning; Life; Measurement; Mechanics; Morbidity - disease rate; Morphology; Myocardium; Patients; Process; Recurrence; Resolution; Surface; Therapeutic; Therapeutic Intervention; Time; Training; Ventricular Tachycardia; base; biomedical imaging; clinical imaging; ex vivo imaging; experimental study; fluorescence imaging; heart electrical activity; heart imaging; heart rhythm; heart visualization; high risk; insight; mortality; non-invasive imaging; novel; novel imaging technique; three-dimensional visualization; ultrasound; voltage

PROJECT SUMMARY / ABSTRACT Cardiovascular disease is the major cause of mortality and morbidity worldwide. Despite significant progress in biomedical imaging, the imaging of heart rhythm disorders remains a major technological and scientific challenge. Consequently, the origins and mechanisms for the onset and progression of cardiac arrhythmias remain largely insufficiently understood. Patients suffering from cardiac arrhythmias have high recurrence rates and often require repeated therapeutic interventions, in part because adequate imaging of the processes underlying heart rhythm disorders has yet to be developed. The state-of-the-art for the diagnosis of heart rhythm disorders, such as atrial fibrillation or ventricular tachycardia, is catheter-based electro-anatomic contact mapping. However, catheter mapping is time-consuming and invasive, involving the insertion of electrodes into the heart’s chambers, where abnormal electrical activity triggering the heart’s irregular contractions is recorded on its surface. Because the measurements are superficial, they do not adequately capture the full, three- dimensional electrophysiological dynamics, which evolve underneath the surface and often have their origin inside the heart muscle. In this project, the applicant aims to develop a novel and radically different approach for the in-depth transmural imaging of heart rhythm disorders based on high-resolution 4D (time-resolved 3D) ultrasound and artificial intelligence (AI). Instead of imaging the heart’s electrical activity, the applicant will image the heart’s 4D deformation and use AI to predict the electrical phenomena from the deformation with the precision of high-resolution measurements. To achieve this ground-breaking goal, the applicant will generate an extensive high-resolution dataset, capturing the 4D electrical and mechanical dynamics of arrhythmic hearts, and train an AI to learn the complex relationship between the heart’s deformations and the electrophysiological wave phenomena that cause these deformations. The AI will become highly specialized in recognizing cardiac deformation mechanics and associating them with the corresponding underlying electrical arrhythmia morphology. The data will be generated in beyond-state-of-the-art voltage-sensitive ex vivo fluorescence imaging experiments with intact, isolated hearts, as well as during clinical imaging and in computer simulations. The high- risk approach, which preliminary data suggests is achievable, will be enabled by the applicant’s unique expertise in ex vivo imaging, which, combined with recent advancements in AI, could lead to a major breakthrough. Ultrasound-based imaging providing transmural 4D visualizations of cardiac arrhythmias in real-time would be transformative in cardiac electrophysiology and provide novel insights into many of the yet unseen processes underlying heart rhythm disorders. If successful, the entirely non-invasive imaging technique could greatly advance the diagnosis of heart rhythm disorders and be used to guide therapeutics, such as catheter ablation, more reliably and effectively.

项目概要/摘要 心血管疾病是全世界死亡和发病的主要原因。尽管取得了重大进展 生物医学成像中，心律失常的成像仍然是一项主要的技术和科学 挑战。因此，心律失常发生和进展的起源和机制 在很大程度上仍然没有得到充分的了解。心律失常患者复发率高 并且经常需要重复的治疗干预，部分原因是对过程进行充分的成像 潜在的心律失常尚未发展。最先进的心律诊断 房颤或室性心动过速等疾病是基于导管的电解剖接触 映射。然而，导管标测非常耗时且具有侵入性，需要将电极插入体内 心室，记录触发心脏不规则收缩的异常电活动 在其表面。因为这些测量是肤浅的，所以它们不能充分捕捉完整的、三- 维度电生理动力学，在表面下演化并且通常有其起源 心肌内部。在这个项目中，申请人的目标是开发一种新颖且完全不同的方法 基于高分辨率 4D（时间分辨 3D）的心律失常的深度透壁成像 超声波和人工智能（AI）。申请人不会对心脏的电活动进行成像，而是对 心脏的 4D 变形，并使用 AI 精确预测变形中的电现象 的高分辨率测量。为了实现这一突破性目标，申请人将产生广泛的 高分辨率数据集，捕获心律失常心脏的 4D 电气和机械动力学，并训练 人工智能学习心脏变形与电生理波之间的复杂关系 导致这些变形的现象。人工智能将在识别心脏方面变得高度专业化 变形力学并将其与相应的潜在电心律失常相关联 形态学。数据将通过最先进的电压敏感离体荧光成像生成 使用完整的离体心脏进行实验，以及在临床成像和计算机模拟中进行实验。高- 初步数据表明，风险方法是可以实现的，它将通过申请人独特的专业知识来实现 离体成像，与人工智能的最新进展相结合，可能会带来重大突破。 基于超声的成像可实时提供心律失常的透壁 4D 可视化 心脏电生理学的变革，并为许多尚未见过的过程提供新的见解 潜在的心律失常。如果成功的话，完全非侵入性的成像技术将极大地促进 推进心律失常的诊断并用于指导治疗，例如导管消融、 更可靠、更有效。