Intraprocedural fusion of electroanatomical maps (EAM) with imaging data based on rapidly-sampled volumetric point clouds from continuous EAM catheter tracking

摘要

Image-guided therapy for electrophysiology applications requires integration of pre-procedural volumetric imaging data with intra-procedural electroanatomical mapping (EAM) information. Existing methods for fusion of EAM and imaging data are based on fiducial landmark identification or point-to-surface distance minimization algorithms, both of which require detailed EAM mapping. This mapping procedure requires specific selection of points on the endocardial surface and this point acquisition process is skill-dependent, time-consuming and labor-intensive. The mapping catheter tip must first be navigated to a landmark on the endocardium, tip contact must be verified, and finally the tip location must be explicitly annotated within the EAM data record. This process of individual landmark identification and annotation must be repeated carefully > 50 times to define endocardial and other vascular surfaces with sufficient detail for iterated-closest-point (ICP)-based registration. To achieve this, 30-45 minutes of mapping just for the registration procedure can be necessary before the inter-ventional component of the patient study begins. Any acquired EAM point location that is not in contact with the chamber surface can adversely impact the quality of registration. Significantly faster point acquisition can be achieved by recording catheter tip locations automatically and continuously without requiring explicit navigation to and annotation of fiducial landmarks. We present a novel registration framework in which EAM locations are rapidly acquired and recorded in a continuous, untriggered fashion while the electrophysiologist manipulates the catheter tip within the heart. Results from simulation indicate that mean registration errors are on the order of 3-4mm, comparable in magnitude to conventional registration procedures which take significantly longer to perform. Qualitative assessment in clinical data also reflects good agreement with physician expectations.