title: 3D pulse EPR imaging from sparse-view projections via constrained, total variation minimization.
abstract: Tumors and tumor portions with low oxygen concentrations (pO2) have been shown to be resistant to radiation therapy. As such, radiation therapy efficacy may be enhanced if delivered radiation dose is tailored based on the spatial distribution of pO2 within the tumor. A technique for accurate imaging of tumor oxygenation is critically important to guide radiation treatment that accounts for the effects of local pO2. Electron paramagnetic resonance imaging (EPRI) has been considered one of the leading methods for quantitatively imaging pO2 within tumors in vivo. However, current EPRI techniques require relatively long imaging times. Reducing the number of projection scan considerably reduce the imaging time. Conventional image reconstruction algorithms, such as filtered back projection (FBP), may produce severe artifacts in images reconstructed from sparse-view projections. This can lower the utility of these reconstructed images. In this work, an optimization based image reconstruction algorithm using constrained, total variation (TV) minimization, subject to data consistency, is developed and evaluated. The algorithm was evaluated using simulated phantom, physical phantom and pre-clinical EPRI data. The TV algorithm is compared with FBP using subjective and objective metrics. The results demonstrate the merits of the proposed reconstruction algorithm.
authors.affiliation: School of Computer and Information Technology, Shanxi University, Taiyuan, Shanxi 030006, China. Electronic address: zqiao@sxu.edu.cn.
authors.forename: Zhiwei
authors.initials: Z
authors.lastname: Qiao
authors.affiliation: Department of Radiation Oncology, Rush University Medical Center, Chicago, IL 60612, USA.
authors.forename: Gage
authors.initials: G
authors.lastname: Redler
authors.affiliation: Department of Radiation and Cellular Oncology, University of Chicago, Chicago, IL 60637, USA.
authors.forename: Boris
authors.initials: B
authors.lastname: Epel
authors.affiliation: School of Computer and Information Technology, Shanxi University, Taiyuan, Shanxi 030006, China.
authors.forename: Yuhua
authors.initials: Y
authors.lastname: Qian
authors.affiliation: Department of Radiation and Cellular Oncology, University of Chicago, Chicago, IL 60637, USA. Electronic address: h-halpern@uchicago.edu.
authors.forename: Howard
authors.initials: H
authors.lastname: Halpern
mesh_terms: D000465:Algorithms; D000818:Animals; D004578:Electron Spin Resonance Spectroscopy / Q000379:methods*; D007089:Image Enhancement / Q000379:methods; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods; D051379:Mice; D057054:Molecular Imaging / Q000379:methods*; D009374:Neoplasms, Experimental / Q000378:metabolism* / Q000473:pathology; D010092:Oximetry / Q000379:methods*; D010100:Oxygen / Q000378:metabolism*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D010100:Oxygen
keywords: Compressed sensing; EPR imaging; Image reconstruction; Optimization; Total variation minimization