Fast iterative image reconstruction using sparse matrix factorization with GPU acceleration

Jian Zhou, Jinyi Qi

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

1 Scopus citations

Abstract

Statistically based iterative approaches for image reconstruction have gained much attention in medical imaging. An accurate system matrix that defines the mapping from the image space to the data space is the key to high-resolution image reconstruction. However, an accurate system matrix is often associated with high computational cost and huge storage requirement. Here we present a method to address this problem by using sparse matrix factorization and parallel computing on a graphic processing unit (GPU).We factor the accurate system matrix into three sparse matrices: a sinogram blurring matrix, a geometric projection matrix, and an image blurring matrix. The sinogram blurring matrix models the detector response. The geometric projection matrix is based on a simple line integral model. The image blurring matrix is to compensate for the line-of-response (LOR) degradation due to the simplified geometric projection matrix. The geometric projection matrix is precomputed, while the sinogram and image blurring matrices are estimated by minimizing the difference between the factored system matrix and the original system matrix. The resulting factored system matrix has much less number of nonzero elements than the original system matrix and thus substantially reduces the storage and computation cost. The smaller size also allows an efficient implement of the forward and back projectors on GPUs, which have limited amount of memory. Our simulation studies show that the proposed method can dramatically reduce the computation cost of high-resolution iterative image reconstruction. The proposed technique is applicable to image reconstruction for different imaging modalities, including x-ray CT, PET, and SPECT.

Original languageEnglish (US)
Title of host publicationProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume7961
DOIs
StatePublished - 2011
EventMedical Imaging 2011: Physics of Medical Imaging - Lake Buena Vista, FL, United States
Duration: Feb 13 2011Feb 17 2011

Other

OtherMedical Imaging 2011: Physics of Medical Imaging
CountryUnited States
CityLake Buena Vista, FL
Period2/13/112/17/11

Keywords

  • graphics processing unit
  • Iterative image reconstruction
  • sparse matrix factorization
  • system modeling

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics
  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Radiology Nuclear Medicine and imaging

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    Zhou, J., & Qi, J. (2011). Fast iterative image reconstruction using sparse matrix factorization with GPU acceleration. In Progress in Biomedical Optics and Imaging - Proceedings of SPIE (Vol. 7961). [79610K] https://doi.org/10.1117/12.878799