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      Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)

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          Abstract

          Given the potential risk of X-ray radiation to the patient, low-dose CT has attracted a considerable interest in the medical imaging field. Currently, the main stream low-dose CT methods include vendor-specific sinogram domain filtration and iterative reconstruction algorithms, but they need to access raw data whose formats are not transparent to most users. Due to the difficulty of modeling the statistical characteristics in the image domain, the existing methods for directly processing reconstructed images cannot eliminate image noise very well while keeping structural details. Inspired by the idea of deep learning, here we combine the autoencoder, deconvolution network, and shortcut connections into the residual encoder-decoder convolutional neural network (RED-CNN) for low-dose CT imaging. After patch-based training, the proposed RED-CNN achieves a competitive performance relative to the-state-of-art methods in both simulated and clinical cases. Especially, our method has been favorably evaluated in terms of noise suppression, structural preservation, and lesion detection.

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          Compressed sensing

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            $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation

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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

                Contributors
                Role: Member, IEEE
                Role: Senior Member, IEEE
                Role: Fellow, IEEE
                Journal
                8310780
                20511
                IEEE Trans Med Imaging
                IEEE Trans Med Imaging
                IEEE transactions on medical imaging
                0278-0062
                1558-254X
                7 December 2017
                13 June 2017
                December 2017
                01 December 2018
                : 36
                : 12
                : 2524-2535
                Affiliations
                College of Computer Science, Sichuan University, Chengdu 610065, China
                College of Computer Science, Sichuan University, Chengdu 610065, China
                Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA
                College of Computer Science, Sichuan University, Chengdu 610065, China
                Laboratory of Image Science and Technology, Southeast University, Nanjing 210096, China, and also with the Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, Nanjing 210096, China
                Department of Scientific Research and Education, The Sixth People’s Hospital of Chengdu, Chengdu 610065, China
                College of Computer Science, Sichuan University, Chengdu 610065, China
                Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180 USA
                Article
                PMC5727581 PMC5727581 5727581 nihpa925147
                10.1109/TMI.2017.2715284
                5727581
                28622671
                4a4c83d9-27e5-4ca5-826a-668bad83fcc5

                Personal use of this material is permitted. However, permission to use this material for any other purposes must be obtained from the IEEE by sending a request to pubs-permissions@ 123456ieee.org .

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                Categories
                Article

                residual neural network,deconvolutional,convolutional,auto-encoder,Low-dose CT,deep learning

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