Innovative MRI-based Characterization of Cardiac Dyssynchrony

 描述（由申请人提供）：心脏不同步使心脏功能恶化，通常无法有效治疗。该提案的目标是开发和提供一种新的分析技术，用于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，特别是在心脏再同步治疗方面（CRT）。CRT是治疗不同步性心力衰竭恶化的最有效治疗方法，根据ECG标准，仅约66%选择接受治疗的心力衰竭患者的结局得到显著改善。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（最流行的心脏成像技术）的选择标准的反应率并不高。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壁间运动的空间分辨率比超声心动图高得多。然而，CMR的定量评估主要限于全局体积测量，其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）的研究仅限于定性评估，限制了对它们改善CRT治疗的潜在效用的推断。为了通过CMR准确量化心脏功能，我们开发了用于描述心脏功能的生物力学模型和用于识别健康心脏非典型形态和功能模式的机器学习技术。我们建议将这两种技术联合收割机用于准确量化左心室（LV）内的心脏不同步。具体来说，我们的方法将提取LV运动的丰富描述， 一组回顾性选择的同步或不同步LV运动受试者的CMR应变。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，我们将这些模式与现有的临床评分相关联，以找到CRT结局的潜在预测标志物。我们假设这些标记物与CRT结果的相关性高于当前单独的临床标记物。识别这些标志物将有可能进一步对CRT的预期结果进行疾病分层，然后可以用于导出新的选择标准，从而提高成功率。该项目还将传播我们新颖的、数据驱动的量化运动的方法。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LV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.