Comput Math Methods Med - A CT Reconstruction Algorithm Based on L1/2 Regularization.

Tópicos

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Resumo

Computed tomography (CT) reconstruction with low radiation dose is a significant research point in current medical CT field. Compressed sensing has shown great potential reconstruct high-quality CT images from few-view or sparse-view data. In this paper, we use the sparser L1/2 regularization operator to replace the traditional L1 regularization and combine the Split Bregman method to reconstruct CT images, which has good unbiasedness and can accelerate iterative convergence. In the reconstruction experiments with simulation and real projection data, we analyze the quality of reconstructed images using different reconstruction methods in different projection angles and iteration numbers. Compared with algebraic reconstruction technique (ART) and total variance (TV) based approaches, the proposed reconstruction algorithm can not only get better images with higher quality from few-view data but also need less iteration numbers.

Resumo Limpo

comput tomographi ct reconstruct low radiat dose signific research point current medic ct field compress sens shown great potenti reconstruct highqual ct imag fewview sparseview data paper use sparser l regular oper replac tradit l regular combin split bregman method reconstruct ct imag good unbiased can acceler iter converg reconstruct experi simul real project data analyz qualiti reconstruct imag use differ reconstruct method differ project angl iter number compar algebra reconstruct techniqu art total varianc tv base approach propos reconstruct algorithm can get better imag higher qualiti fewview data also need less iter number

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