Comput. Biol. Med. - Nonparametric optimization of constrained total variation for tomography reconstruction.

Tópicos

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Resumo

Iterative algorithms based on constrained total-variation (TV) optimization are effective for the reconstruction of limited data from X-ray computed tomography (CT). Such algorithms can be executed by implementing alternative operations projection onto convex sets (POCS) on the constraints, and a gradient descent approach for TV objective minimization. To balance TV-gradient descent with POCS, the adaptive-steepest-descent (ASD) method utilizes a set of complicated parameters to adjust the TV-gradient-descent step-size. The optimal parameters are difficult for users to select, and moreover, users have to empirically choose different parameters when reconstructing different types of images. To deal with these drawbacks, this paper proposes a nonparametric method for constrained TV optimization. The method automatically updates the step-size of TV iteration according to the changes in the consistency term defined by the constraints without introducing artificial parameters. The proposed method avoids the time-consuming parameter optimization, and can be conveniently implemented in various applications. Experimental results on phantom data demonstrate the flexibility and effectiveness of the proposed method.

Resumo Limpo

iter algorithm base constrain totalvari tv optim effect reconstruct limit data xray comput tomographi ct algorithm can execut implement altern oper project onto convex set poc constraint gradient descent approach tv object minim balanc tvgradient descent poc adaptivesteepestdesc asd method util set complic paramet adjust tvgradientdesc stepsiz optim paramet difficult user select moreov user empir choos differ paramet reconstruct differ type imag deal drawback paper propos nonparametr method constrain tv optim method automat updat stepsiz tv iter accord chang consist term defin constraint without introduc artifici paramet propos method avoid timeconsum paramet optim can conveni implement various applic experiment result phantom data demonstr flexibl effect propos method

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