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

项目总结/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）的病因 局部功能障碍，使心脏的偏远区域处于机械不利地位，导致长期 不利的左心室（LV）重塑和并发的充血性心力衰竭（CHF）。MI的过程和 MI后重塑是复杂的，包括血管和肌细胞损伤、急性和慢性炎症、细胞外基质（ECM）的改变和血管生成。负荷超声心动图是临床上建立的， 通过在静息和运动或药物诱导的应激后对LV成像以显示缺血和/或瘢痕，检测和表征冠状动脉疾病和心肌损伤的成本效益技术。 在我们之前的工作中，我们开发了定量的三维差异变形措施， 静息和多巴酚丁胺负荷后4DE衍生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范围和严重程度，并显示缺血区域。我们现在 我建议，这些相同措施的改进版本可以用于治疗和结果的靶向 评估MI后不良局部心肌重塑的治疗。我们选择一个特定的向上， 即将到来的治疗策略作为范例：在MI区域内局部递送可注射水凝胶， 旨在改变LV心肌的生物力学特性以及炎症，从而 有助于减少不良重塑。我们的新的，强大的方法来估计改进的密集位移 和差异变形措施是基于一个创新的数据驱动，深度前馈，神经网络 一种架构，在来自标记的、精心构建的合成模型的数据之间采用域自适应 生理学和超声心动图图像形成（即具有地面实况）的数据，以及来自未标记噪声的数据， 体内猪或人超声心动图（缺失或非常有限的基础事实）。训练是基于来自这两个域的数万个四维（4D）图像导出的块，最初基于位移 分别从传统B模式数据和块模式的基于形状的处理中导出，斑点跟踪 原始射频（RF）数据的处理;随后基于直接从B模式和RF图像中学习 强度信息。在静息和应力4DE图像序列的非刚性配准后，将从猪和人体超声心动图测试数据中导出定量4D微分变形参数。这些 参数将在基线时以及在将可注射水凝胶递送到患者体内后的几个时间点获得。 MI区域。从4D负荷超声心动图导出的微分变形参数的能力， 指导MI区域的可注射水凝胶局部输送，然后确定评估/预测结局 在MI和MI后重塑的混合急性/慢性猪模型中。该技术将被翻译到人类，并通过测量可重复性和与我们新的强大的，深的重建的关系进行评估。 基于学习的微分变形参数在一小群受试者。

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.