q4DE: A Biomarker for Image-Guided, Post-MI Hydrogel Therapy

项目摘要/摘要 缺血性心脏病仍然是世界上最主要的死亡原因。急性心肌梗死(MI)原因 局部功能障碍，使心脏偏远区域处于机械劣势，导致长期 不良的左心室(LV)重构和并发充血性心力衰竭(CHF)。心梗和心绞痛的过程 心肌梗死后的重塑是复杂的，包括血管和心肌细胞损伤、急性和慢性炎症、细胞外基质(ECM)的改变和血管生成。负荷超声心动图是一种经临床证实的， 一种经济高效的技术，通过对静息和运动后或药物诱导的应激反应后的左室成像来检测和表征冠状动脉疾病和心肌损伤，以显示缺血和/或疤痕。 在这个项目之前的工作中，我们开发了应力的定量3D差异变形测量方法 多巴酚丁胺负荷后和静息状态下4DE测得的LV应变图的超声心动图这些措施 可以定位和量化左室心肌损伤的程度和严重程度，并显示缺血区。我们现在 建议这些相同措施的改进版本可以用于治疗和结果的定向 心肌梗死后局部不良心肌重塑的治疗评价。我们选择一个特定的UP和 即将到来的治疗策略作为范例：在心肌梗塞区域内局部注射水凝胶， 旨在改变左室心肌的生物力学特性以及炎症，从而 有助于最大限度地减少不利的改建。我们新的、稳健的方法来估计改进的密集位移 而差异变形测量是基于创新的数据驱动、深度前馈、神经网络 在来自已标记的、精心构建的合成模型的数据之间采用领域适配的体系结构 生理学和超声心动图图像形成(即地面真实)，以及来自未标记噪声的数据 活体猪或人体超声心动图(遗漏或非常有限的地面真相)。训练基于来自这两个域的数万个四维(4D)图像派生的块，最初基于位移 独立于传统B模式数据的基于形状的处理和斑点跟踪的块模式处理 原始射频(RF)数据的处理；后来基于直接从B型和RF图像学习 强度信息。在对静息和应力4DE图像序列进行非刚性配准后，将从猪和人体超声心动图测试数据中提取定量的4D微分变形参数。这些 参数将在基线和将可注射水凝胶输送到 密歇根地区。由四维负荷超声心动图得到的不同变形参数的能力 指导可注射水凝胶在心肌梗死区域的局部输送，并评估/预测结果 在急性和慢性心肌梗死和心肌梗死后重塑的混合猪模型中。这项技术将被翻译到人类身上，并通过测量重复性以及与重建的关系来评估我们新的坚固、深入的 在一小群受试者中基于学习的差异变形参数。

Project Summary/Abstract Ischemic heart disease remains the top cause of death in the world. Acute myocardial infarction (MI) causes regional dysfunction which places remote areas of the heart at a mechanical disadvantage resulting in long term adverse left ventricular (LV) remodeling and complicating congestive heart failure (CHF). The course of MI and post-MI remodeling is complex and includes vascular and myocellular injury, acute and chronic inflammation, alterations of the extracellular matrix (ECM) and angiogenesis. Stress echocardiography is a clinically established, cost-effective technique for detecting and characterizing coronary artery disease and myocardial injury by imaging the LV at rest and after either exercise or pharmacologically-induced stress to reveal ischemia and/or scar. In our previous effort on this project, we developed quantitative 3D differential deformation measures for stress echocardiography from 4DE-derived LV strain maps taken at rest and after dobutamine stress. These measures can localize and quantify the extent and severity of LV myocardial injury and reveal ischemic regions. We now propose that improved versions of these same measures can be used for both targeting of therapy and outcomes assessment in the treatment of adverse local myocardial remodeling following MI. We choose a particular up and coming therapeutic strategy as an exemplar: the local delivery of injectable hydrogels within the MI region that are intended to alter the biomechanical properties of the LV myocardium, as well as inflammation, and thereby help to minimize adverse remodeling. Our new, robust approach for estimating improved dense displacement and differential deformation measures is based on an innovative data-driven, deep feed-forward, neural network architecture that employs domain adaptation between data from labeled, carefully-constructed synthetic models of physiology and echocardiographic image formation (i.e. with ground truth), and data from unlabeled noisy in vivo porcine or human echocardiography (missing or very limited ground truth). Training is based on tens of thousands of four-dimensional (4D) image-derived patches from these two domains, initially based on displacements derived separately from shape-based processing of conventional B-mode data and block-mode, speckle-tracked processing of raw radio-frequency (RF) data; and later based on learning directly from B-mode and RF image intensity information. After non-rigid registration of rest and stress 4DE image sequences, quantitative 4D differential deformation parameters will be derived from porcine and human echocardiographic test data. These parameters will be derived at baseline, and at several timepoints after delivery of injectable hydrogels into the MI region. The ability of the differential deformation parameters derived from 4D stress echocardiography to guide local delivery of injectable hydrogels in a MI region and assess/predict outcomes will then be determined in a hybrid acute/chronic porcine model of MI and post-MI remodeling. The technique will be translated to humans and evaluated by measuring the reproducibility and the relationship to remodeling of our new robust, deep learning-based differential deformation parameters in a small cohort of subjects.