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.

项目摘要/摘要 心血管疾病是世界范围内死亡和发病的主要原因。尽管在以下方面取得了重大进展 生物医学成像，心律失常的成像仍然是一项重大的技术和科学 挑战。因此，心律失常的发生和发展的起源和机制 在很大程度上仍然没有得到充分的理解。患有心律失常的患者复发率高。 而且经常需要重复的治疗干预，部分原因是对这些过程进行了充分的成像 潜在的心律失常尚未形成。心律失常诊断的最新进展 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，导管标测是耗时和有创的，涉及将电极插入到 记录触发心脏不规则收缩的异常电活动的心腔 从表面上看。因为测量是肤浅的，它们不能充分地捕捉到完整的三个- 空间电生理动力学，它在表面下进化，通常有其起源 在心肌内。在这个项目中，申请者的目标是开发一种新的和完全不同的方法来 基于高分辨率4D(时间分辨3D)的心律失常深度透壁成像 超声波和人工智能(AI)。申请者将不是成像心脏的电活动，而是成像 心脏的4D形变和利用人工智能从形变中精确地预测电现象 高分辨率的测量结果。为了实现这一开创性的目标，申请者将产生广泛的 高分辨率数据集，捕获心律失常心脏的4D电和机械动力学，并训练 人工智能学习心脏变形和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疾病方面变得高度专业化 变形力学及其与相应的潜在电性心律失常的关联 形态学。这些数据将在超过最先进的电压敏感的体外荧光成像中产生 对完整、分离的心脏进行实验，以及在临床成像和计算机模拟中进行实验。快感-- 初步数据表明，风险方法是可以实现的，申请人的独特专业知识将使其成为可能 在体外成像方面，结合人工智能的最新进展，可能会导致重大突破。 基于超声的成像提供了对心律失常的实时透壁4D可视化 在心脏电生理学方面具有变革性，并为许多尚未见过的过程提供了新的见解 潜在的心律失常。如果成功，这种完全无创的成像技术将极大地 促进心律失常的诊断，并用于指导治疗，如导管消融， 更可靠、更有效。