New iterative cone beam CT reconstruction software: Parameter optimisation and convergence study
Cone beam computed tomography (CBCT) provides a volumetric image reconstruction from tomographic projection data. Image quality is the main concern for reconstruction in comparison to conventional CT. The reconstruction algorithm used is clearly important and should be carefully designed, developed and investigated before it can be applied clinically. The Multi-Instrument Data Analysis System (MIDAS) tomography software originally designed for geophysical applications has been modified to CBCT image reconstruction. In CBCT reconstruction algorithms, iterative methods offer the potential to generate high quality images and would be an advantage especially for down-sampling projection data. In this paper, studies of the CBCT iterative algorithms implemented in MIDAS are presented. Stability, convergence rate, quality of reconstructed image and edge recovery are suggested as the main criteria for monitoring reconstructive performance. Accordingly, the selection of relaxation parameter and number of iterations are studied in detail. Results are presented, where images are reconstructed from full and down-sampled cone beam CT projection data using iterative algorithms. Various iterative algorithms have been implemented and the best selection of the iteration number and relaxation parameters are investigated for ART. Optimal parameters are chosen where the errors in projected data as well as image errors are minimal.
cbct iterative algorithm edge recovery down-sampled data