Mathias Unberath
Signal Processing for Interventional X-ray-based Coronary Angiography
Abstract
Rotational angiography using C-arm scanners enables intra-operative 3-D imaging that has proved beneficial for diagnostic assessment and interventional guidance. Despite previous efforts, rotational angiography was not yet successfully established in clinical practice for coronary artery imaging but remains subject of intensive academic research. 3-D reconstruction of the coronary vasculature is impeded by severe lateral truncation of the thorax, as well as substantial intra-scan respiratory and cardiac motion. Reliable and fully automated solutions to all of the aforementioned problems are required to pave the way for clinical application of rotational angiography and, hence, sustainably change the state-of-care.
Within this thesis, we identify shortcomings of existing approaches and devise algorithms that effectively address non-recurrent object motion, severe angular undersampling, and the dependency on projection domain segmentations. The proposed methods build upon virtual digital subtraction angiography (vDSA) that voids image truncation and enables prior-reconstruction-free respiratory motion compensation using both Epipolar consistency conditions (ECC) and auto-focus measures (AFMs). The motion-corrected geometry is then used in conjunction with a novel 4-D iterative algorithm that reconstructs images at multiple cardiac phases simultaneously. The method allows for communication among 3-D volumes by regularizing the temporal total variation (tTV) and thus implicitly addresses the problem of insufficient data very effectively. Finally, we consider symbolic coronary artery reconstruction from very few observations and develop generic extensions that consist of symmetrization, outlier removal, and projection domain-informed topology recovery. When applied to two state-of-the-art reconstruction algorithms, the proposed methods substantially reduce problems due to incorrect 2-D centerlines, promoting improved performance. Given that all methods proved effective on the same in silico and in vivo data sets, we are confident that the proposed algorithms bring rotational coronary angiography one step closer to clinical applicability.