GPU-based cone beam computed tomography

Authors:
Peter B. Noël;Alan M. Walczak;Jinhui Xu;Jason J. Corso;Kenneth R. Hoffmann;Sebastian Schafer
Affiliations:
The State University of New York at Buffalo, USA and Department of Computer Science and Engineering, USA;The State University of New York at Buffalo, USA and Toshiba Stroke Research Center, USA;The State University of New York at Buffalo, USA and Department of Computer Science and Engineering, USA;The State University of New York at Buffalo, USA and Department of Computer Science and Engineering, USA;The State University of New York at Buffalo, USA and Toshiba Stroke Research Center, USA;The State University of New York at Buffalo, USA and Toshiba Stroke Research Center, USA
Venue:
Computer Methods and Programs in Biomedicine
Year:
2010

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Abstract

The use of cone beam computed tomography (CBCT) is growing in the clinical arena due to its ability to provide 3D information during interventions, its high diagnostic quality (sub-millimeter resolution), and its short scanning times (60s). In many situations, the short scanning time of CBCT is followed by a time-consuming 3D reconstruction. The standard reconstruction algorithm for CBCT data is the filtered backprojection, which for a volume of size 256^3 takes up to 25min on a standard system. Recent developments in the area of Graphic Processing Units (GPUs) make it possible to have access to high-performance computing solutions at a low cost, allowing their use in many scientific problems. We have implemented an algorithm for 3D reconstruction of CBCT data using the Compute Unified Device Architecture (CUDA) provided by NVIDIA (NVIDIA Corporation, Santa Clara, California), which was executed on a NVIDIA GeForce GTX 280. Our implementation results in improved reconstruction times from minutes, and perhaps hours, to a matter of seconds, while also giving the clinician the ability to view 3D volumetric data at higher resolutions. We evaluated our implementation on ten clinical data sets and one phantom data set to observe if differences occur between CPU and GPU-based reconstructions. By using our approach, the computation time for 256^3 is reduced from 25min on the CPU to 3.2s on the GPU. The GPU reconstruction time for 512^3 volumes is 8.5s.