Comput Math Methods Med - A CT reconstruction algorithm based on non-aliasing Contourlet transform and compressive sensing.

Tópicos

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{ method(2212) result(1239) propos(1039) }
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Resumo

Compressive sensing (CS) theory has great potential for reconstructing CT images from sparse-views projection data. Currently, total variation (TV-) based CT reconstruction method is a hot research point in medical CT field, which uses the gradient operator as the sparse representation approach during the iteration process. However, the images reconstructed by this method often suffer the smoothing problem; to improve the quality of reconstructed images, this paper proposed a hybrid reconstruction method combining TV and non-aliasing Contourlet transform (NACT) and using the Split-Bregman method to solve the optimization problem. Finally, the simulation results show that the proposed algorithm can reconstruct high-quality CT images from few-views projection using less iteration numbers, which is more effective in suppressing noise and artefacts than algebraic reconstruction technique (ART) and TV-based reconstruction method.

Resumo Limpo

compress sens cs theori great potenti reconstruct ct imag sparseview project data current total variat tv base ct reconstruct method hot research point medic ct field use gradient oper spars represent approach iter process howev imag reconstruct method often suffer smooth problem improv qualiti reconstruct imag paper propos hybrid reconstruct method combin tv nonalias contourlet transform nact use splitbregman method solv optim problem final simul result show propos algorithm can reconstruct highqual ct imag fewview project use less iter number effect suppress nois artefact algebra reconstruct techniqu art tvbase reconstruct method

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