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GPU-Based Iterative Medical CT Image Reconstructions

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Abstract

The algebraic reconstruction technique (ART) is an iterative algorithm for CT (i.e., computed tomography) image reconstruction that delivers better image quality with less radiation dosage than the industry-standard filtered back projection (FBP). However, the high computational cost of ART requires researchers to turn to high-performance computing to accelerate the algorithm. Alas, existing approaches for ART suffer from inefficient design of compressed data structures and computational kernels on GPUs. Thus, this paper presents our CUDA-based CT image reconstruction tool based on the algebraic reconstruction technique (ART) or cuART. It delivers a compression and parallelization solution for ART-based image reconstruction on GPUs. We address the under-performing, but popular, GPU libraries, e.g., cuSPARSE, BRC, and CSR5, on the ART algorithm and propose a symmetry-based CSR format (SCSR) to further compress the CSR data structure and optimize data access for both SpMV and SpMV_T via a column-indices permutation. We also propose sorting-based global-level and sorting-free view-level blocking techniques to optimize the kernel computation by leveraging different sparsity patterns of the system matrix. The end result is that cuART can reduce the memory footprint significantly and enable practical CT datasets to fit into a single GPU. The experimental results on a NVIDIA Tesla K80 GPU illustrate that our approach can achieve up to 6.8x, 7.2x, and 5.4x speedups over counterparts that use cuSPARSE, BRC, and CSR5, respectively.

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Notes

  1. The sorting-based global-level blocking is easy to be implemented, while the sorting-free view-level blocking delivers faster preprocessing time and less data padding and can also enable the adapted algorithm to converge faster.

  2. This kernel leverages the merits of our SCSR format and blocking techniques to provide significant performance improvements.

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Authors and Affiliations

  1. Department of Computer Science, Virginia Tech, Blacksburg, VA, USA

    Xiaodong Yu, Hao Wang & Wu-chun Feng

  2. Department of Biomedical Engineering and Mechanics, Virginia Tech, Blacksburg, VA, USA

    Hao Gong & Guohua Cao

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  1. Xiaodong Yu
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  2. Hao Wang
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  4. Hao Gong
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  5. Guohua Cao
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Correspondence to Xiaodong Yu.

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Yu, X., Wang, H., Feng, Wc. et al. GPU-Based Iterative Medical CT Image Reconstructions. J Sign Process Syst 91, 321–338 (2019). https://doi.org/10.1007/s11265-018-1352-0

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