Innovative MRI-based Characterization of Cardiac Dyssynchrony

 描述(由申请人提供)：心脏不同步会恶化心脏功能，通常无法有效治疗。这项建议的目的是开发和提供一种新的分析技术，以了解心脏不同步患者复杂的心脏运动模式(在时间和空间上)，希望改善其治疗结果，特别是心脏再同步治疗(CRT)。CRT是治疗与恶化的心力衰竭不同步的最有效的治疗方法，仅有约66%的心力衰竭患者选择接受治疗，根据心电图标准，CRT显著改善了预后。基于成像的选择标准对于提高成功率至关重要。不幸的是，基于超声心动图的选择标准的应答率并不高，超声心动图是最流行的心脏成像技术。电影心血管磁共振(CMR)有可能更好地表征不同步，因为它显示心脏力学和室壁运动比超声心动图高得多的空间分辨率。然而，CMR的定量评估大多局限于全局体积测量，忽略了图像捕获的大部分运动信息。例如，对一些不同步的独特运动特征(如间隔闪光和心尖摇摆)的研究一直局限于定性评估，限制了对它们改善CRT治疗的潜在有效性的推断。为了通过CMR准确地量化心脏功能，我们开发了描述心脏功能的生物力学模型和识别健康心脏不典型的形态和功能模式的机器学习技术。我们建议将这两种技术结合起来，以准确量化左心室(LV)内的心脏不同步。具体地说，我们的方法将提取丰富的LV运动描述和 来自一组具有同步或不同步LV运动的回顾性选择的受试者的CMR的菌株。然后，我们将使用机器学习方法来识别特定于不同步的局部和全局运动模式。最后，我们将把这些模式与已经存在的临床评分相关联，以找到关于CRT结果的潜在预测标记物。我们假设这些标记物与CRT结果的相关性将比目前的临床标记物更高。识别这些标志物将有可能根据CRT的预期结果进一步对疾病进行分层，然后可以用来得出新的选择标准，从而导致更高的成功率。该项目还将传播我们用于量化该运动的新颖的、数据驱动的方法。其他研究小组可以应用我们的工具来专门研究不同步，以及其他影响左室运动的心脏疾病。

 DESCRIPTION (provided by applicant): Cardiac dyssynchrony deteriorates cardiac function and often cannot be treated effectively. The goal of this proposal is to develop and provide a new analysis technique for understanding the complex cardiac motion patterns (in time and space) of patients with cardiac dyssynchrony, with the hope of improving its treatment outcomes, specifically with respect to cardiac resynchronization therapy (CRT). CRT, the most effective treatment for dyssynchrony with worsening heart failure, significantly improves outcome in only ~66% of heart failure patients selected for the treatment, which is based on ECG criteria. Selection criteria based on imaging are essential to improving the success rate. Unfortunately, response rates are not higher with selection criteria based on echocardiography, the most popular cardiac imaging technique. Cine cardiovascular magnetic resonance (CMR) has the potential to better characterize dyssynchrony, as it shows cardiac mechanics and intramural wall motion with much higher spatial resolution than echocardiography. However, quantitative assessments of CMR have been mostly limited to global volumetric measures, which ignore most of the motion information captured by the images. For example, studies of a number of distinctive motion features of dyssynchrony (such as septal flash and apical rocking) have been confined to qualitative assessment, limiting inference of their potential utility for improving CRT treatment. To accurately quantify cardiac function through CMR, we have developed biomechanical models for describing cardiac function and machine learning technology for identifying morphological and functional patterns atypical for healthy hearts. We propose to combine these two technologies to accurately quantify cardiac dyssynchrony within the Left Ventricle (LV). Specifically, our methods will extract a rich description of LV motion and strain from the CMRs of a set of retrospectively selected subjects with synchronous or dyssynchronous LV motion. We will then use machine learning methods to identify local and global motion patterns specific to dyssynchrony. Finally, we will correlate these patterns to already existing clinical scores to find potentially predictive markers with respect to CRT outcome. We hypothesize that these markers will have a higher correlation to CRT outcome than current clinical markers alone. Identifying these markers will have the potential to further stratify the disease with respect to the expected outcome of CRT, which then can be used to derive new selection criteria that lead to higher success rates. The project will also disseminate our novel, data-driven methodology for quantifying that motion. Other research groups can apply our tools to specifically study dyssynchrony, as well as other cardiac diseases impacting LV motion in general.