Three-Dimensional Coronary Visualization, Part 1: Modeling

Automated three-dimensional vessel reconstruction based on deep segmentation and bi-plane angiographic projections

Automated three-dimensional (3D) blood vessel reconstruction to improve vascular diagnosis and therapeutics is a challenging task in which the real-time implementation of automatic segmentation and specific vessel tracking for matching artery sequences is essential. Recently, a deep learning-based segmentation technique has been proposed; however, existing state-of-the-art deep architectures exhibit reduced performance when they are employed using real in-vivo imaging because of serious issues such as low contrast and noise contamination of the X-ray images. To overcome these limitations, we propose a novel methodology composed of the de-haze image enhancement technique as pre-processing and multi-level thresholding as post-processing to be applied to the lightweight multi-resolution U-shaped architecture. Specifically, (1) bi-plane two-dimensional (2D) vessel images were extracted simultaneously using the deep architecture, (2) skeletons of the vessels were computed via a morphology operation, (3) the corresponding skeleton structure between image sequences was matched using the shape-context technique, and (4) the 3D centerline was reconstructed using stereo geometry. The method was validated using both in-vivo and in-vitro models. The results show that the proposed technique could improve the segmentation quality, reduce computation time, and reconstruct the 3D skeleton automatically. The algorithm accurately reconstructed the phantom model and the real mouse vessel in 3D in 2 s. Our proposed technique has the potential to allow therapeutic micro-agent navigation in clinical practice, thereby providing the 3D position and orientation of the vessel.

Point-Cloud Method for Automated 3D Coronary Tree Reconstruction From Multiple Non-Simultaneous Angiographic Projections

X-ray angiography is the most commonly used imaging modality for the detection of coronary stenoses due to its high spatial and temporal resolution of lumen contour and its utility to guide coronary interventions in real time. However, the high inter- and intra-observer variability in interpreting the geometry of 3D vascular structure based on multiple 2D image projections is a limitation in the accurate determination of lesion severity. This could be addressed by the 3D reconstruction of the coronary arterial (CA) tree. The automated reconstruction of 3D CA tree from 2D projections is challenging due to the existence of several imaging artifacts, such as vessel overlap, foreshortening, and most importantly respiratory and cardiac motion. Along with these artifacts, the acquisition geometry introduces the possibility of generating false vessel segments in the reconstruction. Our approach aims to reduce the motion artifacts in angiographic projections by developing a new method for rigid and non-rigid motion correction. A novel point-cloud based approach is subsequently introduced for reconstruction of 3D vessel centerlines by iteratively minimizing the reconstruction error. The performance of the proposed 3D reconstruction is evaluated using angiographic projections from 45 patients, producing average reprojection errors of 0.092 ±0.055 mm and 0.910 ±0.352 mm for 3D centerlines reconstruction, when co-registered with the parent vessels on projection planes that were/were not used to derive the 3D reconstruction, respectively. A comparison of the reconstructed 3D lumen surface with optical coherence tomography (OCT) measurements has been performed, showing no statistically significant difference in the luminal cross-sections reconstructed with our method, compared to OCT.

Reconstruction of coronary arteries from X-ray angiography: A review

Despite continuous progress in X-ray angiography systems, X-ray coronary angiography is fundamentally limited by its 2D representation of moving coronary arterial trees, which can negatively impact assessment of coronary artery disease and guidance of percutaneous coronary intervention. To provide clinicians with 3D/3D+time information of coronary arteries, methods computing reconstructions of coronary arteries from X-ray angiography are required. Because of several aspects (e.g. cardiac and respiratory motion, type of X-ray system), reconstruction from X-ray coronary angiography has led to vast amount of research and it still remains as a challenging and dynamic research area. In this paper, we review the state-of-the-art approaches on reconstruction of high-contrast coronary arteries from X-ray angiography. We mainly focus on the theoretical features in model-based (modelling) and tomographic reconstruction of coronary arteries, and discuss the evaluation strategies. We also discuss the potential role of reconstructions in clinical decision making and interventional guidance, and highlight areas for future research.