Model-Based Fusion of Multi-modal Volumetric Images: Application to Transcatheter Valve Procedures

摘要

Minimal invasive procedures such as transcatheter valve in-terventions are substituting conventional surgical techniques. Thus, novel operating rooms have been designed to augment traditional surgical equipment with advanced imaging systems to guide the procedures. We propose a novel method to fuse pre-operative and intra-operative in-formation by jointly estimating anatomical models from multiple image modalities. Thereby high-quality patient-specific models are integrated into the imaging environment of operating rooms to guide cardiac in-terventions. Robust and fast machine learning techniques are utilized to guide the estimation process. Our method integrates both the redundant and complementary multimodal information to achieve a comprehensive modeling and simultaneously reduce the estimation uncertainty. Exper-iments performed on 28 patients with pairs of multimodal volumetric data are used to demonstrate high quality intra-operative patient-specific modeling of the aortic valve with a precision of 1.09mm in TEE and 1.73mm in 3D C-arm CT. Within a processing time of 10 seconds we additionally obtain model sensitive mapping between the pre- and intra-operative images.