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

Improved Image Guidance in TACE Procedures

Purpose of the work in this thesis is to improve the image guidance in TACE procedures. More specifically, we intend to develop and evaluate technology that permits dynamic roadmapping based on a 3D model of the liver vasculature

3D reconstruction of coronary artery using Feldkamp-Davis-Kress algorithm

An important cause of death in industrialized countries is coronary heart diseases. To treat those pathologies, a percutaneous intervention that consists in inserting a catheter in the femoral artery is performed. The instrument is directed to the affected arteries, and coronary angiography is used to lead the surgeon in an interventional context. However, 2D angiography which is frequently used during an intervention, does not consider depth, resulting in high doses of contrast agent and an extended exposure to X-ray. To mitigate the impact of these problems, medical imaging techniques such as 3D coronary artery imaging are used to assist surgeons during the intervention. Many imaging modalities are used to acquire the sequences, but the rotational angiography is favored due to its lower contrast agent use and its ease of use in an interventional context. This imaging technique allows the surgeon to guide the catheter in 3D in a clear manner, and limit the use of X-rays and contrast agent by reducing the duration of the intervention. In this thesis, we present a flexible algorithm, Feldkamp-Davis-Kress (FDK), to reconstruct 3D model of coronary artery in multiple angle views. The dual-axis rotational coronary artery angiography is proposed to use along with this algorithm. The cameras parameters are first calibrated by a nonlinear optimization where the reprojection error is minimized. Then the optimal working view is calculated to avoiding the vessel overlap and foreshortening effects. To reduce the cardiac motion effect, ECG-gated is applied into the reconstruction algorithm. The proposed method can be used in the framework to improve 3D navigation guidance in surgery. It could be a good tool for clinicians in coronary artery disease

Context-aware learning for robot-assisted endovascular catheterization

Endovascular intervention has become a mainstream treatment of cardiovascular diseases. However, multiple challenges remain such as unwanted radiation exposures, limited two-dimensional image guidance, insufficient force perception and haptic cues. Fast evolving robot-assisted platforms improve the stability and accuracy of instrument manipulation. The master-slave system also removes radiation to the operator. However, the integration of robotic systems into the current surgical workflow is still debatable since repetitive, easy tasks have little value to be executed by the robotic teleoperation. Current systems offer very low autonomy, potential autonomous features could bring more benefits such as reduced cognitive workloads and human error, safer and more consistent instrument manipulation, ability to incorporate various medical imaging and sensing modalities. This research proposes frameworks for automated catheterisation with different machine learning-based algorithms, includes Learning-from-Demonstration, Reinforcement Learning, and Imitation Learning. Those frameworks focused on integrating context for tasks in the process of skill learning, hence achieving better adaptation to different situations and safer tool-tissue interactions. Furthermore, the autonomous feature was applied to next-generation, MR-safe robotic catheterisation platform. The results provide important insights into improving catheter navigation in the form of autonomous task planning, self-optimization with clinical relevant factors, and motivate the design of intelligent, intuitive, and collaborative robots under non-ionizing image modalities.Open Acces