Automatic whole heart segmentation in static magnetic resonance image volumes
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
Automatic left atrium segmentation by cutting the blood pool at narrowings
MICCAI'05 Proceedings of the 8th international conference on Medical image computing and computer-assisted intervention - Volume Part II
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
STACOM'10/CESC'10 Proceedings of the First international conference on Statistical atlases and computational models of the heart, and international conference on Cardiac electrophysiological simulation challenge
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
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Pre-procedural imaging with cardiac CT or MR has become popular for guiding complex electrophysiology procedures such as those used for atrial fibrillation ablation therapy. Electroanatomical mapping and ablation within the left atrium and pulmonary veins (LAPV) is facilitated using such data, however the pre-procedural anatomy can be quite different from that at the time of intervention. Recently, a method for intra-procedural LAPV imaging has been developed based on contrast-enhanced 3-D rotational X-ray angiography (3-D RA). These intraprocedural data now create a compelling need for rapid and automated extraction of the LAPV geometry for catheter guidance. We present a new approach to automatic intra-procedural generation of LAPV surfaces from 3-D RA volumes. Using model-based segmentation, our technique is robust to imaging noise and artifacts typical of 3-D RA imaging, strongly minimizes the user interaction time required for segmentation, and eliminates inter-subject variability. Our findings in 33 patients indicate that intra-procedural LAPV surface models accurately represent the anatomy at the time of intervention and are comparable to pre-procedural models derived from CTA or MRA.