This project focuses on advanced modeling of the heart in the setting of cardiology applications, with a special focus on the aortic and mitral valve. In current clinical practice, minimally invasive procedures are starting to replace open heart surgeries. As there is no direct view or access to the affected anatomy, there is a high demand for predictive models, sophisticated planning and operating room (OR) integration tools. In the context of transcatheter therapies, such as the Transcatheter Aortic Valve Implantation (TAVI), biomarkers of the entire aortic-mitral apparatus must be extracted in order to perform patient selection and risk assessment as well as device selection and operation optimization. Current methods are able to extract basic biomarkers such as diameter, area measurements and C-arm angulations, but they fail to model the entire left-heart system and are neglecting the true volumetric physiology of the aortic root and leaflets. These features are mandatory for extracting advanced biomarkers as well as for computational modeling of biomechanics and fluid dynamics.

The main research focus lies on the development of novel segmentation algorithms for volumetric models of the subvalvular apparatus, in particular the aortic and mitral valve. Imaging modalities which offer high spatial resolution will be utilized to extract these volumetric models. Furthermore, to improve tracking performance, we will explore the value of biomechanical models.

This project is in cooperation with Siemens AG, Corporate Technology, Erlangen.

The modeling of three-dimensional scene geometry from temporal point cloud streams is of particular interest for a variety of computer vision applications. With the advent of RGB-D imaging devices that deliver dense, metric and textured 6-D data in real-time, on-the-fly reconstruction of static environments has come into reach. In this paper, we propose a system for real-time point cloud mapping based  n an efficient implementation of the iterative closest point (ICP) algorithm on the graphics processing unit (GPU). In order to achieve robust mappings at real-time performance, our nearest neighbor search evaluates both geometric and photometric information in a direct manner. For acceleration of the search space traversal, we exploit the inherent computing parallelism of GPUs. In this work, we have investigated the fitness of the random ball cover (RBC) data structure and search algorithm, originally proposed for high-dimensional problems, for 6-D data. In particular, we introduce a scheme that enables both fast RBC construction and queries. The proposed system is validated on an indoor scene modeling scenario. For dense data from the Microsoft Kinect sensor (640x480 px), our implementation achieved ICP runtimes of < 20 ms on an off-the-shelf consumer GPU.