Parameter selection in limited data cone-beam CT reconstruction using edge-preserving total variation algorithms. (21st November 2017)

• Record Type: Journal Article
• Authors: Lohvithee, Manasavee; Biguri, Ander; Soleimani, Manuchehr
• Is Part Of: Physics in medicine & biology. Volume 62:Number 24(2017:Dec.)
• Page Start: 9295
• Page End: 9321
• Publication Date: 2017-11-21
• Subjects: total variation (TV) -- edge preserving function -- parameter tuning -- iterative reconstruction -- limited data reconstruction; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/1361-6560/aa93d3
• Languages: English
• ISSNs: 0031-9155

Abstract

Abstract: There are a number of powerful total variation (TV) regularization methods that have great promise in limited data cone-beam CT reconstruction with an enhancement of image quality. These promising TV methods require careful selection of the image reconstruction parameters, for which there are no well-established criteria. This paper presents a comprehensive evaluation of parameter selection in a number of major TV-based reconstruction algorithms. An appropriate way of selecting the values for each individual parameter has been suggested. Finally, a new adaptive-weighted projection-controlled steepest descent (AwPCSD) algorithm is presented, which implements the edge-preserving function for CBCT reconstruction with limited data. The proposed algorithm shows significant robustness compared to three other existing algorithms: ASD-POCS, AwASD-POCS and PCSD. The proposed AwPCSD algorithm is able to preserve the edges of the reconstructed images better with fewer sensitive parameters to tune.