Liu Dawei, Dangi Shusil, Schwarz Karl Q, Linte Cristian A
Center for Imaging Science, Rochester Institute of Technology, Rochester, NY, USA.
Medicine, Cardiology, University of Rochester Medical Center, Rochester, NY, USA.
Proc SPIE Int Soc Opt Eng. 2020 Feb;11319. doi: 10.1117/12.2550650. Epub 2020 Mar 16.
Left ventricular ejection fraction (LVEF) assessment is instrumental for cardiac health diagnosis, patient management, and patient eligibility for participation in clinical studies. Due to its non-invasiveness and low operational cost, ultrasound (US) imaging is the most commonly used imaging modality to image the heart and assess LVEF. Even though 3D US imaging technology is becoming more available, cardiologists dominantly use 2D US imaging to visualize the LV blood pool and interpret its area changes between end-systole and end-diastole. Our previous work showed that LVEF estimates based on area changes are significantly lower than the true volume-based estimates by as much as 13%, which could lead to unnecessary and costly therapeutic decisions. Acquiring volumetric information about the LV blood pool necessitates either time-consuming 3D reconstruction or 3D US image acquisition. Here, we propose a method that leverages on a statistical shape model (SSM) constructed from 13 landmarks depicting the LV endocardial border to estimate a new patient's LV volume and LVEF. Two methods to estimate the 3D LV geometry with and without size normalization were employed. The SSM was built using the 13 landmarks from 50 training patient image datasets. Subsequently, the Mahalanobis distance (with size normalization) or the vector distance (without size normalization) between an incoming patient's LV landmarks and each shape in the SSM were used to determine the weights each training patient contributed to describing the new, incoming patient's LV geometry and associated blood pool volume. We tested the proposed method to estimate the LV volumes and LVEF for 16 new test patients. The estimated LVEFs based on Mahalanobis distance and vector distance were within 2.9% and 1.1%, respectively, of the ground truth LVEFs calculated from the 3D reconstructed LV volumes. Furthermore, the viability of using fewer principal components (PCs) to estimate the LV volume was explored by reducing the number of PCs retained when projecting landmarks onto PCA space. LVEF estimated based on 3 PCs, 5 PCs, and 10 PCs are within 6.6%, 5.4%, and 3.3%, respectively, of LVEF estimates using the full set of 39 PCs.
左心室射血分数（LVEF）评估对于心脏健康诊断、患者管理以及患者参与临床研究的资格判定至关重要。由于其非侵入性和低操作成本，超声（US）成像成为最常用于心脏成像和评估LVEF的成像方式。尽管三维超声成像技术越来越普及，但心脏病专家主要使用二维超声成像来观察左心室血池，并解读其在收缩末期和舒张末期之间的面积变化。我们之前的研究表明，基于面积变化的LVEF估计值比基于真实体积的估计值显著低达13%，这可能导致不必要且昂贵的治疗决策。获取左心室血池的容积信息需要耗时的三维重建或三维超声图像采集。在此，我们提出一种方法，该方法利用从描绘左心室内膜边界的13个地标构建的统计形状模型（SSM）来估计新患者的左心室容积和LVEF。采用了两种估计三维左心室几何形状的方法，一种进行了尺寸归一化，另一种未进行尺寸归一化。SSM是使用来自50个训练患者图像数据集的13个地标构建的。随后，将新患者左心室地标与SSM中每个形状之间的马氏距离（进行了尺寸归一化）或向量距离（未进行尺寸归一化）用于确定每个训练患者对描述新的、即将到来的患者左心室几何形状和相关血池容积所贡献的权重。我们测试了所提出的方法对16名新测试患者的左心室容积和LVEF进行估计。基于马氏距离和向量距离估计的LVEF分别在从三维重建左心室容积计算出的真实LVEF的2.9%和1.1%以内。此外，通过减少将地标投影到主成分分析（PCA）空间时保留的主成分数量，探索了使用较少主成分（PC）来估计左心室容积的可行性。基于3个PC、5个PC和10个PC估计的LVEF分别在使用全套39个PC估计的LVEF的6.6%、5.4%和3.3%以内。
