Nonconvex weighted variational metal artifacts removal via convergent primal-dual algorithms
arxiv(2024)
摘要
Direct reconstruction through filtered back projection engenders metal
artifacts in polychromatic computed tomography images, attributed to highly
attenuating implants, which further poses great challenges for subsequent image
analysis. Inpainting the metal trace directly in the Radon domain for the
extant variational method leads to strong edge diffusion and potential inherent
artifacts. With normalization based on pre-segmentation, the inpainted outcome
can be notably ameliorated. However, its reconstructive fidelity is heavily
contingent on the precision of the presegmentation, and highly accurate
segmentation of images with metal artifacts is non-trivial in actuality. In
this paper, we propose a nonconvex weighted variational approach for metal
artifact reduction. Specifically, in lieu of employing a binary function with
zeros in the metal trace, an adaptive weight function is designed in the Radon
domain, with zeros in the overlapping regions of multiple disjoint metals as
well as areas of highly attenuated projections, and the inverse square root of
the measured projection in other regions. A nonconvex L1-alpha L2
regularization term is incorporated to further enhance edge contrast, alongside
a box-constraint in the image domain. Efficient first-order primal-dual
algorithms, proven to be globally convergent and of low computational cost
owing to the closed-form solution of all subproblems, are devised to resolve
such a constrained nonconvex model. Both simulated and real experiments are
conducted with comparisons to other variational algorithms, validating the
superiority of the presented method. Especially in comparison to Reweighted
JSR, our proposed algorithm can curtail the total computational cost to at most
one-third, and for the case of inaccurate pre-segmentation, the recovery
outcomes by the proposed algorithms are notably enhanced.
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