2024/12/22 by Rudolf L. M. van Herten, van Herten, R. L. M., Josè P.S. Henriques +12
Engineering · Medicine · #Advanced X-ray and CT Imaging #Cardiac Imaging and Diagnostics #Coronary Interventions and Diagnostics #FOS: Electrical engineering #Image and Video Processing (eess.IV) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2412.17100
openalex publication_date 2024/12/22 · openalex created_date 2024/12/25 · openalex updated_date 2026/07/28
Registering coronary CT angiography (CCTA) and intravascular ultrasound (IVUS) enables comprehensive coronary analysis that neither modality can provide alone, yet their fusion remains challenging due to differences in imaging geometry, resolution, and artifact profiles. Existing methods depend on pre-computed lumen or vessel wall segmentations that are unreliable under IVUS acoustic shadowing from calcifications, limiting their clinical applicability. We propose TG-OT, a fully automatic CCTA-IVUS registration framework that eliminates this dependency by integrating trained feature detectors directly into the registration pipeline. Lightweight CNNs are trained to predict calcifications, bifurcations, and lumen radii on the topological (θ, z) cylinder, encouraging topologically coherent detections without requiring explicit segmentation. Registration is formulated as an optimization over centerline warping parameters, driven by an unbalanced Sinkhorn optimal transport loss on the cylindrical geometry that provides spatially informative gradients even for spatially disjoint predictions, complemented by a lumen matching term. Evaluated on N=47 paired CCTA-IVUS cases in a 5-fold cross-validation setup, TG-OT achieves strong longitudinal (Dicectl=0.99), rotational (Sc=0.96), and lumen alignment (DiceL=0.69) without manual interaction or prior segmentation, marking a meaningful step toward clinical integration of automatic CCTA-IVUS fusion.