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In this paper, we systematically evaluate the performance of adaptiveadjustment of the relaxation parameters of various iterative algorithms forX-ray CT reconstruction relying on sparsity priors. Sparsity prior has beenfound to be an efficient strategy in CT reconstruction where significantlyfewer attenuation measurements are available. Sparsity prior CT reconstructionrelies on iterative algorithms such as the algebraic reconstruction technique(ART) to produce a crude reconstruction based on which a sparse approximationis performed. Data driven adjustment of relaxation has been found to ensurebetter convergence than traditional relaxation for ART. In this paper, we studythe performance of such data driven relaxation on a (CS) compressed sensingenvironment. State-of-the-art algorithms are implemented and their performanceanalyzed in regard to conventional and data-driven relaxation. Experiments areperformed both on simulated and real environments. For the simulated case,experiments are conducted with and without the presence of noise. Correlationcoefficients, root mean square error, structural similarity index andperceptual dissimilarity metric were used for the quantitative comparisons ofthe results. Experiments reveal that data driven relaxation also ensuresoverall better quality reconstruction in a CS environment compared totraditional relaxation. However, when the data are corrupted by noise,inconsistencies emerge in the convergence unless a threshold is imposed on themaximum amendments. Data driven relaxation seems a logical choice to morerapidly reach the solution. In a compressed sensing environment, especiallywhen the data are corrupted by noise, a threshold to specify the maximumamendments needs to be specified. Experimentally, we have set the threshold as20% of the previous value and thus have ensured more consistency in theconvergence.

Adaptive Adjustment of Relaxation Parameters for Algebraic  Reconstruction Technique and its Possible Application to Sparsity Prior X-ray  CT Reconstruction