Accelerating separable footprint (SF) forward and back projection on GPU

Xiaobin Xie; Madison G. McGaffin; Yong Long; Jeffrey A. Fessler; Minhua Wen; James Lin

doi:10.1117/12.2252010

9 March 2017 Accelerating separable footprint (SF) forward and back projection on GPU

Xiaobin Xie, Madison G. McGaffin, Yong Long, Jeffrey A. Fessler, Minhua Wen, James Lin

Proceedings Volume 10132, Medical Imaging 2017: Physics of Medical Imaging; 101322S (2017) https://doi.org/10.1117/12.2252010
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

Statistical image reconstruction (SIR) methods for X-ray CT can improve image quality and reduce radiation dosages over conventional reconstruction methods, such as filtered back projection (FBP). However, SIR methods require much longer computation time. The separable footprint (SF) forward and back projection technique simplifies the calculation of intersecting volumes of image voxels and finite-size beams in a way that is both accurate and efficient for parallel implementation. We propose a new method to accelerate the SF forward and back projection on GPU with NVIDIA’s CUDA environment. For the forward projection, we parallelize over all detector cells. For the back projection, we parallelize over all 3D image voxels. The simulation results show that the proposed method is faster than the acceleration method of the SF projectors proposed by Wu and Fessler.13 We further accelerate the proposed method using multiple GPUs. The results show that the computation time is reduced approximately proportional to the number of GPUs.

Citation Download Citation

Xiaobin Xie, Madison G. McGaffin, Yong Long, Jeffrey A. Fessler, Minhua Wen, and James Lin "Accelerating separable footprint (SF) forward and back projection on GPU", Proc. SPIE 10132, Medical Imaging 2017: Physics of Medical Imaging, 101322S (9 March 2017); https://doi.org/10.1117/12.2252010

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