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      Cycle-consistent adversarial denoising network for multiphase coronary CT angiography

      1 , 2 , 2 , 2 , 1

      Medical Physics

      Wiley

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          Image-to-Image Translation with Conditional Adversarial Networks

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            Least Squares Generative Adversarial Networks

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              Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization.

              An iterative algorithm, based on recent work in compressive sensing, is developed for volume image reconstruction from a circular cone-beam scan. The algorithm minimizes the total variation (TV) of the image subject to the constraint that the estimated projection data is within a specified tolerance of the available data and that the values of the volume image are non-negative. The constraints are enforced by the use of projection onto convex sets (POCS) and the TV objective is minimized by steepest descent with an adaptive step-size. The algorithm is referred to as adaptive-steepest-descent-POCS (ASD-POCS). It appears to be robust against cone-beam artifacts, and may be particularly useful when the angular range is limited or when the angular sampling rate is low. The ASD-POCS algorithm is tested with the Defrise disk and jaw computerized phantoms. Some comparisons are performed with the POCS and expectation-maximization (EM) algorithms. Although the algorithm is presented in the context of circular cone-beam image reconstruction, it can also be applied to scanning geometries involving other x-ray source trajectories.
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                Author and article information

                Journal
                Medical Physics
                Med. Phys.
                Wiley
                00942405
                February 2019
                February 2019
                December 26 2018
                : 46
                : 2
                : 550-562
                Affiliations
                [1 ]Bio Imaging and Signal Processing Laboratory; Department of Bio and Brain Engineering; KAIST; Daejeon Republic of Korea
                [2 ]Department of Radiology; University of Ulsan College of Medicine; Seoul Republic of Korea
                Article
                10.1002/mp.13284
                © 2018

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