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PROJECT TITLE :

Relaxed Linearized Algorithms for Faster X-Ray CT Image Reconstruction

ABSTRACT:

Statistical image reconstruction (SIR) strategies are studied extensively for X-ray computed tomography (CT) because of the potential of acquiring CT scans with reduced X-ray dose while maintaining image quality. But, the longer reconstruction time of SIR methods hinders their use in X-ray CT in practice. To accelerate statistical methods, several optimization techniques have been investigated. Over-relaxation may be a common technique to speed up convergence of iterative algorithms. For instance, employing a relaxation parameter that's close to 2 in alternating direction methodology of multipliers (ADMM) has been shown to hurry up convergence significantly. This paper proposes a relaxed linearized augmented Lagrangian (AL) methodology that shows theoretical faster convergence rate with over-relaxation and applies the proposed relaxed linearized AL technique to X-ray CT image reconstruction issues. Experimental results with both simulated and real CT scan information show that the proposed relaxed algorithm (with ordered-subsets [OS] acceleration) is about twice as quick as the prevailing unrelaxed fast algorithms, with negligible computation and memory overhead.


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