An enhanced image reconstruction tool for computed tomography on GPUS

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 highperformance computing to accelerate the algorithm. Alas, existing approaches for ART suffer from in efficient design of compressed data structures and computational kernels on GPUS. Thus, this paper presents our enhanced 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 symmetrybased 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 and sorting-free blocking techniques to optimize the kernel computation by leveraging the 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)
Title of host publicationACM International Conference on Computing Frontiers 2017, CF 2017
PublisherAssociation for Computing Machinery, Inc
Pages97-106
Number of pages10
ISBN (Electronic)9781450344876
DOIs
StatePublished - May 15 2017
Event14th ACM International Conference on Computing Frontiers, CF 2017 - Siena, Italy
Duration: May 15 2017May 17 2017

Publication series

NameACM International Conference on Computing Frontiers 2017, CF 2017

Conference

Conference14th ACM International Conference on Computing Frontiers, CF 2017
Country/TerritoryItaly
CitySiena
Period5/15/175/17/17

Keywords

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

ASJC Scopus subject areas

  • Computer Science(all)

Fingerprint

Dive into the research topics of 'An enhanced image reconstruction tool for computed tomography on GPUS'. Together they form a unique fingerprint.

Cite this