An efficient iterative CBCT reconstruction approach using gradient projection sparse reconstruction algorithm
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Heui Chang Lee1,2, Bongyong Song3, Jin Sung Kim4, James J. Jung5, H. Harold Li6, Sasa Mutic6, Justin C. Park6
1Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA
2J Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, Florida, USA
3Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California, USA
4Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, Korea
5Department of Radiation Oncology, University of Florida, Gainesville, Florida, USA
6Department of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri, USA
Jin Sung Kim, email: firstname.lastname@example.org
Keywords: low-dose imaging, compressed sensing, cone-beam computed tomography (CBCT), gradient projection, backtracking line search
Received: September 27, 2016 Accepted: November 02, 2016 Published: November 24, 2016
The purpose of this study is to develop a fast and convergence proofed CBCT reconstruction framework based on the compressed sensing theory which not only lowers the imaging dose but also is computationally practicable in the busy clinic. We simplified the original mathematical formulation of gradient projection for sparse reconstruction (GPSR) to minimize the number of forward and backward projections for line search processes at each iteration. GPSR based algorithms generally showed improved image quality over the FDK algorithm especially when only a small number of projection data were available. When there were only 40 projections from 360 degree fan beam geometry, the quality of GPSR based algorithms surpassed FDK algorithm within 10 iterations in terms of the mean squared relative error. Our proposed GPSR algorithm converged as fast as the conventional GPSR with a reasonably low computational complexity. The outcomes demonstrate that the proposed GPSR algorithm is attractive for use in real time applications such as on-line IGRT.
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