Oncotarget

Research Papers:

Variable step size methods for solving simultaneous algebraic reconstruction technique (SART)-type CBCT reconstructions

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. 2017; 8:33827-33835. https://doi.org/10.18632/oncotarget.17385

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Abstract

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

Correspondence to:

Jin Sung Kim, email: jinsung@yuhs.ac

Keywords: SART, weighted least squares, image reconstruction, GPU, IGRT

Received: October 19, 2016     Accepted: March 20, 2017     Published: April 24, 2017

ABSTRACT

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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