School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, China.
School of Data Science, Fudan University, Shanghai, China.
Comput Biol Med. 2019 May;108:67-77. doi: 10.1016/j.compbiomed.2019.03.029. Epub 2019 Apr 5.
Computed tomography angiography (CTA) is a non-invasive technique to image coronary arteries and evaluate coronary artery diseases (CAD). The diagnosis of CAD requires modeling anatomical structures and analyzing the function and pathology of the coronary arteries. Therefore, a robust and automated method for extracting reliable coronary artery centerlines is valuable in clinical practice.
We extracted coronary centerlines using the directional fast marching (DFM) method and improved DFM with a multi-model strategy. The method comprises model guidance, the application of vessel direction, and a multi-model strategy: (1) coronary models are constructed using registration techniques and then used as prior knowledge of the vessels; (2) the vessel direction, modified from the eigenvectors of the Hessian matrix and vesselness, is used to guide the search for the vessel points during fast marching; and (3) the multi-model strategy is applied to identify suboptimal results from the overall outcome as in multi-atlas segmentation. Overlap and accuracy metrics are used to assess the segmentation. The authors evaluated the performance of the proposed method on 32 CT cardiac angiography datasets from the Rotterdam Coronary Artery Algorithm Evaluation Framework (RCAAEF). The authors also studied the effect of models on DFM.
For the quantitative evaluation, DFM improved the average overlap (OV) from 43.6% of a method without model information to 77.8%. In addition, with the ground truth delineated by experts, multi-model DFM (MM-DFM) obtained 83.5% average overlap (OV) in the training datasets and 86.6% in the test datasets.
The authors propose a novel approach to extract coronary centerlines from CTA using DFM and further extend DFM to a multi-model strategy. DFM effectively applies the prior shape of the coronary vessels and vascular features within the target image and has the potential to achieve clinically relevant results.
计算机断层血管造影（CTA）是一种非侵入性技术，用于成像冠状动脉并评估冠状动脉疾病（CAD）。CAD 的诊断需要对解剖结构进行建模，并分析冠状动脉的功能和病理学。因此，提取可靠的冠状动脉中心线的强大且自动化的方法在临床实践中具有价值。
我们使用定向快速行进（DFM）方法和改进的 DFM 与多模型策略提取冠状动脉中心线。该方法包括模型引导、血管方向的应用以及多模型策略：（1）使用配准技术构建冠状动脉模型，然后将其用作血管的先验知识；（2）修改自 Hessian 矩阵和血管特征的向量的血管方向用于指导快速行进中的血管点搜索；（3）多模型策略用于从整体结果中识别出次优结果，就像在多图谱分割中一样。重叠和准确性度量用于评估分割。作者在来自 Rotterdam Coronary Artery Algorithm Evaluation Framework（RCAAEF）的 32 个 CT 心脏血管造影数据集上评估了所提出方法的性能。作者还研究了模型对 DFM 的影响。
对于定量评估，DFM 将平均重叠（OV）从没有模型信息的方法的 43.6%提高到 77.8%。此外，使用专家划定的真实值，多模型 DFM（MM-DFM）在训练数据集中获得了 83.5%的平均重叠（OV），在测试数据集中获得了 86.6%的平均重叠（OV）。
作者提出了一种从 CTA 中提取冠状动脉中心线的新方法，使用 DFM 并进一步将 DFM 扩展到多模型策略。DFM 有效地应用了冠状动脉血管的先验形状和目标图像内的血管特征，并且有可能获得临床相关的结果。
