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Automatic Segmentation of Rotational X-Ray Images for Anatomic Intra-Procedural Surface Generation in Atrial Fibrillation Ablation Procedures

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9 Author(s)
Robert Manzke ; Tomographic Imaging Syst., Philips Res. Eur., Hamburg, Germany ; Carsten Meyer ; Olivier Ecabert ; Jochen Peters
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Since the introduction of 3-D rotational X-ray imaging, protocols for 3-D rotational coronary artery imaging have become widely available in routine clinical practice. Intra-procedural cardiac imaging in a computed tomography (CT)-like fashion has been particularly compelling due to the reduction of clinical overhead and ability to characterize anatomy at the time of intervention. We previously introduced a clinically feasible approach for imaging the left atrium and pulmonary veins (LAPVs) with short contrast bolus injections and scan times of ~ 4-10 s. The resulting data have sufficient image quality for intra-procedural use during electro-anatomic mapping (EAM) and interventional guidance in atrial fibrillation (AF) ablation procedures. In this paper, we present a novel technique to intra-procedural surface generation which integrates fully-automated segmentation of the LAPVs for guidance in AF ablation interventions. Contrast-enhanced rotational X-ray angiography (3-D RA) acquisitions in combination with filtered-back-projection-based reconstruction allows for volumetric interrogation of LAPV anatomy in near-real-time. An automatic model-based segmentation algorithm allows for fast and accurate LAPV mesh generation despite the challenges posed by image quality; relative to pre-procedural cardiac CT/MR, 3-D RA images suffer from more artifacts and reduced signal-to-noise. We validate our integrated method by comparing (1) automatic and manual segmentations of intra-procedural 3-D RA data, (2) automatic segmentations of intra-procedural 3-D RA and pre-procedural CT/MR data, and (3) intra-procedural EAM point cloud data with automatic segmentations of 3-D RA and CT/MR data. Our validation results for automatically segmented intra-procedural 3-D RA data show average segmentation errors of (1) ~ 1.3 mm compared with manual 3-D RA segmentations (2) ~ 2.3 mm compared with automatic segmentation of pre-procedural CT/MR data and (3) ~ 2.1 mm compared with registered int- a-procedural EAM point clouds. The overall experiments indicate that LAPV surfaces can be automatically segmented intra-procedurally from 3-D RA data with comparable quality relative to meshes derived from pre-procedural CT/MR.

Published in:

IEEE Transactions on Medical Imaging  (Volume:29 ,  Issue: 2 )