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The primary goal of our research activities in this project is the development of methods for estimating and correcting cardiac motion in order to increase the image quality of cardiac vasculature reconstruction. In interventional environments patients often do have arrhythmic heart signals or cannot hold breath during the complete data acquisition. This important group of patients cannot be reconstructed with current approaches that do strongly depend on a high degree of cardiac motion periodicity for working properly. In this project we try to develop novel algorithmic approaches to cardiac vasculature reconstruction and therefore address the following questions:For more information please visit www.cavarev.com.
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In this project we consider the problem of evaluating hardware acceleration schemes for 3-D cone beam reconstruction algorithms. Researchers and industry work hard on hardware-optimized 3D reconstruction which is crucial for a seamless workflow integration. A crucial limitation, however, of these publications is that the presented results are not comparable to each other. This is mainly due to variations in data acquisitions, preprocessing, and chosen geometries and the lack of a common publicly available test dataset. With RabbitCT we provide such a standardized dataset that allows for substantial comparison of hardware accelerated backprojection methods. In summary, it is an online platform for worldwide comparison in reconstruction performance and ranking on different architectures using a specific high resolution C-arm CT dataset of a rabbit. This includes a sophisticated benchmark interface, a prototype implementation in C++, and image quality measures.For more information please visit www.rabbitct.com.