GPU-Based Iterative Medical CT Image Reconstructions

Xiaodong Yu, Hao Wang, Wu chun Feng, Hao Gong, Guohua Cao

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)321-338
Number of pages18
JournalJournal of Signal Processing Systems
Issue number3-4
StatePublished - Mar 1 2019


  • Algebraic reconstruction technique
  • Computed tomography
  • GPU
  • Image reconstruction
  • Sparse matrix-vector multiplication
  • Transposed SpMV

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Theoretical Computer Science
  • Signal Processing
  • Information Systems
  • Modeling and Simulation
  • Hardware and Architecture


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