Research Papers:

An efficient iterative CBCT reconstruction approach using gradient projection sparse reconstruction algorithm

Heui Chang Lee _, Bongyong Song, Jin Sung Kim, James J. Jung, H. Harold Li, Sasa Mutic and Justin C. Park

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Oncotarget. 2016; 7:87342-87350. https://doi.org/10.18632/oncotarget.13567

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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

Correspondence to:

Jin Sung Kim, email: [email protected]

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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