Fast shadows and lighting effects using texture mapping
SIGGRAPH '92 Proceedings of the 19th annual conference on Computer graphics and interactive techniques
Accelerated volume rendering and tomographic reconstruction using texture mapping hardware
VVS '94 Proceedings of the 1994 symposium on Volume visualization
Mathematics for 3D game programming and computer graphics
Mathematics for 3D game programming and computer graphics
GPGPU: general purpose computation on graphics hardware
ACM SIGGRAPH 2004 Course Notes
A fast CT reconstruction scheme for a general multi-core PC
Journal of Biomedical Imaging
Out-of-core cone beam reconstruction using multiple GPUS
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Software architecture for multi-bed FDK-based reconstruction in X-ray CT scanners
Computer Methods and Programs in Biomedicine
Parallel implementation of a X-ray tomography reconstruction algorithm based on MPI and CUDA
Proceedings of the 20th European MPI Users' Group Meeting
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Currently, 3D cone-beam CT image reconstruction speed is still a severe limitation for clinical application. The computational power of modern graphics processing units (GPUs) has been harnessed to provide impressive acceleration of 3D volume image reconstruction. For extra large data volume exceeding the physical graphic memory of GPU, a straightforward compromise is to divide data volume into blocks. Different from the conventional Octree partition method, a new partition scheme is proposed in this paper. This method divides both projection data and reconstructed image volume into subsets according to geometric symmetries in circular cone-beam projection layout, and a fast reconstruction for large data volume can be implemented by packing the subsets of projection data into the RGBA channels of GPU, performing the reconstruction chunk by chunk and combining the individual results in the end. The method is evaluated by reconstructing 3D images from computer-simulation data and real micro-CT data. Our results indicate that the GPU implementation can maintain original precision and speed up the reconstruction process by 110-120 times for circular cone-beam scan, as compared to traditional CPU implementation.