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A GPU-based fiber tracking framework using geometry shaders

: Köhn, A.; Klein, J.; Weiler, F.; Peitgen, H.-O.


Miga, M.I. ; Society of Photo-Optical Instrumentation Engineers -SPIE-, Bellingham/Wash.:
Medical imaging 2009. Visualization, image-guided procedures, and modeling : 8 - 10 February 2009, Lake Buena Vista, Florida, United States
Bellingham, WA: SPIE, 2009 (Proceedings of SPIE 7261)
ISBN: 978-0-8194-7512-1
ISSN: 1605-7422
Paper 72611J
Medical Imaging Symposium <2009, Lake Buena Vista/Fla.>
Conference Paper
Fraunhofer MEVIS ()
GPU; diffusion tensor imaging; visualization; fiber tracking

The clinical application of fiber tracking becomes more widespread. Thus it is of high importance to be able to produce high quality results in a very short time. Additionally, research in this field would benefit from fast implementation and evaluation of new algorithms. In this paper we present a GPU-based fiber tracking framework using latest features of commodity graphics hardware such as geometry shaders. The implemented streamline algorithm performs fiber reconstruction of a whole brain using 30,000 seed points in less than 120 ms on a high-end GeForce GTX 280 graphics board. Seed points are sent to the GPU which emits up to a user-defined number of fiber points per seed vertex. These are recorded to a vertex buffer that can be rendered or downloaded to main memory for further processing. If the output limit of the geometry shader is reached before the stopping criteria are fulfilled, the last vertices generated are then used in a subsequent pass where the geometry shader continues the tracking. Since all the data resides on graphics memory the intermediate steps can be visualized in real-time. The fast reconstruction not only allows for an interactive change of tracking parameters but, since the tracking code is implemented using GPU shaders, even for a runtime change of the algorithm. Thus, rapid development and evaluation of different algorithms and parameter sets becomes possible, which is of high value for e.g. research on uncertainty in fiber tracking.