Variable step size methods for solving simultaneous algebraic reconstruction technique (SART)-type CBCT reconstructions
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Heui Chang Lee1,2, Bongyong Song3, Jin Sung Kim4, James J. Jung5, H. Harold Li6, Sasa Mutic6 and 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: SART, weighted least squares, image reconstruction, GPU, IGRT
Received: October 19, 2016 Accepted: March 20, 2017 Published: April 24, 2017
Compared to analytical reconstruction by Feldkamp-Davis-Kress (FDK), simultaneous algebraic reconstruction technique (SART) offers a higher degree of flexibility in input measurements and often produces superior quality images. Due to the iterative nature of the algorithm, however, SART requires intense computations which have prevented its use in clinical practice. In this paper, we developed a fast-converging SART-type algorithm and showed its clinical feasibility in CBCT reconstructions. Inspired by the quasi-orthogonal nature of the x-ray projections in CBCT, we implement a simple yet much faster algorithm by computing Barzilai and Borwein step size at each iteration. We applied this variable step-size (VS)-SART algorithm to numerical and physical phantoms as well as cancer patients for reconstruction. By connecting the SART algebraic problem to the statistical weighted least squares problem, we enhanced the reconstruction speed significantly (i.e., less number of iterations). We further accelerated the reconstruction speed of algorithms by using the parallel computing power of GPU.
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