Sparsity Prior Computed Tomography Reconstruction Using a Nonstandard Simultaneous X-ray Acquisition Model |
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| Affiliation: | 1. Department of Radiation Therapy, Abbotsford Centre, BC Cancer Agency, Abbotsford, BC, Canada;2. Department of Medical Physics, Vancouver Centre, BC Cancer Agency, Vancouver, BC, Canada;3. Department of Radiation Therapy, Vancouver Centre, Vancouver, BC, Canada;4. Department of Radiation Therapy, Vancouver Centre, BC Cancer Agency, Vancouver, BC, Canada;5. Department of Radiation Therapy, Centre for the Southern Interior, BC Cancer Agency, Kelowna, BC, Canada;6. Department of Radiation Therapy, Vancouver Centre, British Columbia Cancer Agency, Vancouver, BC, Canada;7. Department of Surgery, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada;1. School of Chemistry, Physics and Mechanical Engineering, Queensland University of Technology, Brisbane, Queensland, Australia;2. Department of Radiation Therapy, Radiation Oncology Mater Centre, Brisbane, Queensland, Australia;1. Directorate of Radiography, Centre for Health Sciences Research, University of Salford, Salford, UK;2. Faculty of Health Sciences, The University of Sydney, Sydney, New South Wales, Australia;1. Department of Radiological Science, Konyang University, Daejeon, Republic of Korea;2. Department of Radiological Science and Convergence Engineering, Yonsei University, Wonju-si, Gangwon-do, Republic of Korea;3. Department of Radiological Science, Eulji University, Seongnam-si, Gyeonggi-do, Republic of Korea |
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| Abstract: | In this article we systematically evaluate the performance of several state-of-the-art, sparsity prior computed tomography (CT) reconstruction algorithms, using a nonstandard simultaneous x-ray acquisition method. Sparsity prior is an efficient strategy in CT reconstruction, relying on iterative algorithms such as the algebraic reconstruction technique to produce a crude reconstruction, based on which sparse approximation is performed. The simultaneous x-ray acquisition model ensures rapid capture of x-rays; however, it captures a significantly fewer number of attenuation measurements, and the projections are nonuniform. We propose a weighted average filter in the reconstruction framework to ensure better quality reconstruction by minimizing the effect of nonuniform projections. The performance of the state-of-the-art algorithms is analyzed with and without weighted averaging before sparse approximation, in simulated and real environments. Experiments in the simulated environment are conducted with and without the presence of noise. From the results, it is evident that sparsity prior algorithms are capable of producing cross-sectional reconstruction using the simultaneous x-ray acquisition model, and better reconstruction quality is achievable with the incorporation of weighted averaging in the reconstruction framework. |
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| Keywords: | computed tomography CT X-ray CT iterative reconstruction compressed sensing algebraic reconstruction technique |
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