An Effective CUDA Parallelization of Projection in Iterative Tomography Reconstruction

Projection and back-projection are the most computationally intensive parts in Computed Tomography (CT) reconstruction, and are essential to acceleration of CT reconstruction algorithms. Compared to back-projection, parallelization efficiency in projection is highly limited by racing condition and thread unsynchronization. In this paper, a strategy of Fixed Sampling Number Projection (FSNP) is proposed to ensure the operation synchronization in the ray-driven projection with Graphical Processing Unit (GPU). Texture fetching is also used utilized to further accelerate the interpolations in both projection and back-projection. We validate the performance of this FSNP approach using both simulated and real cone-beam CT data. Experimental results show that compare to the conventional approach, the proposed FSNP method together with texture fetching is 10~16 times faster than the conventional approach based on global memory, and thus leads to more efficient iterative algorithm in CT reconstruction.

[1]  B. De Man,et al.  Distance-driven projection and backprojection , 2002, 2002 IEEE Nuclear Science Symposium Conference Record.

[2]  Hao Gao Fast parallel algorithms for the x-ray transform and its adjoint. , 2012, Medical physics.

[3]  Zhiqiang Hu,et al.  An LOR-based fully-3D PET image reconstruction using a blob-basis function , 2007, 2007 IEEE Nuclear Science Symposium Conference Record.

[4]  Jingyan Xu,et al.  Is iterative reconstruction ready for MDCT? , 2009, Journal of the American College of Radiology : JACR.

[5]  Laurent D. Cohen,et al.  Sparse reconstruction from a limited projection number of the coronary artery tree in X-ray rotational imaging , 2012, 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI).

[6]  Jelle Vlassenbroeck,et al.  A Multiresolution Approach to Iterative Reconstruction Algorithms in X-Ray Computed Tomography , 2010, IEEE Transactions on Image Processing.

[7]  Gumersindo Verdú Martín,et al.  CT Image Reconstruction Based on GPUs , 2013, ICCS.

[8]  R. Siddon Fast calculation of the exact radiological path for a three-dimensional CT array. , 1985, Medical physics.

[9]  Wufan Chen,et al.  Joint-MAP Tomographic Reconstruction with Patch Similarity Based Mixture Prior Model , 2011, Multiscale Model. Simul..

[10]  R M Lewitt,et al.  Multidimensional digital image representations using generalized Kaiser-Bessel window functions. , 1990, Journal of the Optical Society of America. A, Optics and image science.

[11]  Zhengrong Liang,et al.  Iterative image reconstruction for cerebral perfusion CT using a pre-contrast scan induced edge-preserving prior , 2012, Physics in medicine and biology.

[12]  L. Xing,et al.  Iterative image reconstruction for CBCT using edge-preserving prior. , 2008, Medical physics.

[13]  Bin Zhang,et al.  An immediate after-backprojection filtering method with blob-shaped window functions for voxel-based iterative reconstruction , 2006, 2006 IEEE Nuclear Science Symposium Conference Record.

[14]  Cong Nie,et al.  Bayesian statistical reconstruction for low-dose X-ray computed tomography using an adaptive-weighting nonlocal prior , 2009, Comput. Medical Imaging Graph..

[15]  P. Mayo,et al.  Parallel CT image reconstruction based on GPUs , 2014 .

[16]  B. De Man,et al.  Distance-driven projection and backprojection in three dimensions. , 2004, Physics in medicine and biology.

[17]  Steve B. Jiang,et al.  GPU-based iterative cone-beam CT reconstruction using tight frame regularization , 2010, Physics in medicine and biology.

[18]  Xiaochuan Pan,et al.  Investigation of iterative image reconstruction in low-dose breast CT , 2014, Physics in medicine and biology.

[19]  Michael Knaup,et al.  GPU-based parallel-beam and cone-beam forward- and backprojection using CUDA , 2008, 2008 IEEE Nuclear Science Symposium Conference Record.

[20]  Christine Toumoulin,et al.  L0 constrained sparse reconstruction for multi-slice helical CT reconstruction , 2011, Physics in medicine and biology.

[21]  Jianhua Ma,et al.  Nonlocal Prior Bayesian Tomographic Reconstruction , 2008, Journal of Mathematical Imaging and Vision.

[22]  Wang LI-Fang,et al.  Optimization of Cone Beam CT Reconstruction Algorithm Based on CUDA , 2013 .

[23]  Peter B. Noël,et al.  GPU-based cone beam computed tomography , 2010, Comput. Methods Programs Biomed..

[24]  Tao Yang,et al.  GPU based iterative cone-beam CT reconstruction using empty space skipping technique. , 2013, Journal of X-ray science and technology.

[25]  Qianjin Feng,et al.  Low-dose computed tomography image restoration using previous normal-dose scan. , 2011, Medical physics.

[26]  Fumihiko Ino,et al.  High-performance cone beam reconstruction using CUDA compatible GPUs , 2010, Parallel Comput..

[27]  E. Sidky,et al.  Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization , 2008, Physics in medicine and biology.

[28]  Weichung Wang,et al.  A fast forward projection using multithreads for multirays on GPUs in medical image reconstruction. , 2011, Medical physics.

[29]  T. J. Hebert,et al.  Numerical evaluation of methods for computing tomographic projections , 1994 .

[30]  Craig S. Levin,et al.  Fast, Accurate and Shift-Varying Line Projections for Iterative Reconstruction Using the GPU , 2009, IEEE Transactions on Medical Imaging.

[31]  Gengsheng Lawrence Zeng,et al.  Unmatched projector/backprojector pairs in an iterative reconstruction algorithm , 2000, IEEE Transactions on Medical Imaging.