There are a number of powerful total variation (TV) regularization methods with great promises in limited data cone-beam CT reconstruction with an enhancement of image quality. These promising TV methods require careful selection of image reconstruction parameters for which there is no well established criteria. This pa- per presents a comprehensive valuation of parameter selection in a number of major TV-based reconstruction algorithms. The appropriate way of selecting the values for each individual parameter has been suggested. Finally, the new adaptive-weighted projection-controlled steepest descent (AwPCSD) algorithm is presented which imple- ments the edge-preserving function for CBCT reconstruction with limited data. The proposed algorithm shows signicant robustness compared to other three existing al- gorithms: ASD-POCS, AwASD-POCS and PCSD. The proposed AwPCSD algorithm is able to preserve the edges of the reconstructed images better with less sensitive pa- rameters to tune.
- Department of Electronic & Electrical Engineering - Professor
- EPSRC Centre for Doctoral Training in Statistical Applied Mathematics (SAMBa)
- Centre for Biosensors, Bioelectronics and Biodevices (C3Bio)
- Centre for Autonomous Robotics (CENTAUR)
- Electronics Materials, Circuits & Systems Research Unit (EMaCS)
Person: Research & Teaching