Directional fast-marching and multi-model strategy to extract coronary artery centerlines

Abstract

Background: 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. Method: 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. Results: 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. Conclusion: 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.