Algorithmic solutions for live device-to-vessel match

An overlay of diagnostic angiograms and interventional fluoroscopy during minimally invasive cathlab interventions can support navigation but suffers from artifacts due to mismatch of vessels and interventional devices. Here, weak image features and strict real-time constraints do not allow for standard multi-modality registration techniques. In the presented method, diagnostic angiograms are filtered to extract the imaged vessel structure. A distance-transform of the extracted vessels allows for fast matching, with interventionally imaged devices which are extracted with fast local filters only. Competing vessel and object filters are tested on 10 diagnostic angiograms and 25 fluoroscopic frames showing a guidewire. Their performance is tested in comparison to manual segmentations. A newly presented directional stamping-filter based on anisotropic diffusion of local image patches offers the best results for vessel extraction and also improves the guidewire detection. Using these filters, the device-to-vessel match succeeds in 92% of the tested frames. This rate decreases to 75% for an initial mismatch of 16 pixels.