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      Stacked competitive networks for noise reduction in low-dose CT

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          Abstract

          Since absorption of X-ray radiation has the possibility of inducing cancerous, genetic and other diseases to patients, researches usually attempt to reduce the radiation dose. However, reduction of the radiation dose associated with CT scans will unavoidably increase the severity of noise and artifacts, which can seriously affect diagnostic confidence. Due to the outstanding performance of deep neural networks in image processing, in this paper, we proposed a Stacked Competitive Network (SCN) approach to noise reduction, which stacks several successive Competitive Blocks (CB). The carefully handcrafted design of the competitive blocks was inspired by the idea of multi-scale processing and improvement the network’s capacity. Qualitative and quantitative evaluations demonstrate the competitive performance of the proposed method in noise suppression, structural preservation, and lesion detection.

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

          Emil Y. Sidky, Xiaochuan Pan (2008)
          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.
          Article 0 comments Cited 294 times – based on 0 reviews     Review now
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            Low-Dose CT With a Residual Encoder-Decoder Convolutional Neural Network.

            Hu Chen, Yi Zhang, Mannudeep K Kalra … (2017)
            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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              Low-dose X-ray CT reconstruction via dictionary learning.

              Jiang Hsieh, Lei Zhang, Ge Wang … (2012)
              Although diagnostic medical imaging provides enormous benefits in the early detection and accuracy diagnosis of various diseases, there are growing concerns on the potential side effect of radiation induced genetic, cancerous and other diseases. How to reduce radiation dose while maintaining the diagnostic performance is a major challenge in the computed tomography (CT) field. Inspired by the compressive sensing theory, the sparse constraint in terms of total variation (TV) minimization has already led to promising results for low-dose CT reconstruction. Compared to the discrete gradient transform used in the TV method, dictionary learning is proven to be an effective way for sparse representation. On the other hand, it is important to consider the statistical property of projection data in the low-dose CT case. Recently, we have developed a dictionary learning based approach for low-dose X-ray CT. In this paper, we present this method in detail and evaluate it in experiments. In our method, the sparse constraint in terms of a redundant dictionary is incorporated into an objective function in a statistical iterative reconstruction framework. The dictionary can be either predetermined before an image reconstruction task or adaptively defined during the reconstruction process. An alternating minimization scheme is developed to minimize the objective function. Our approach is evaluated with low-dose X-ray projections collected in animal and human CT studies, and the improvement associated with dictionary learning is quantified relative to filtered backprojection and TV-based reconstructions. The results show that the proposed approach might produce better images with lower noise and more detailed structural features in our selected cases. However, there is no proof that this is true for all kinds of structures.
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                Author and article information

                Contributors
                Wenchao Du: Role: Data curationRole: MethodologyRole: Writing – original draft
                Hu Chen: Role: Funding acquisitionRole: Writing – review & editing
                Zhihong Wu: Role: Formal analysisRole: Resources
                Huaiqiang Sun: Role: Data curationRole: Validation
                Peixi Liao: Role: Formal analysisRole: Resources
                Yi Zhang:
                ORCID: http://orcid.org/0000-0001-7201-2092
                Role: Funding acquisitionRole: SupervisionRole: Writing – review & editing
                Yuanquan Wang: Role: Editor
                Journal
                Journal ID (nlm-ta): PLoS One
                Journal ID (iso-abbrev): PLoS ONE
                Journal ID (publisher-id): plos
                Journal ID (pmc): plosone
                Title: PLoS ONE
                Publisher: Public Library of Science (San Francisco, CA USA )
                ISSN (Electronic): 1932-6203
                Publication date (Electronic): 21 December 2017
                Publication date Collection: 2017
                Volume: 12
                Issue: 12
                Electronic Location Identifier: e0190069
                Affiliations
                [1 ] School of Computer Science, Sichuan University, Chengdu, China
                [2 ] National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University, Chengdu, China
                [3 ] Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
                [4 ] Department of Scientific Research and Education, The Sixth People’s Hospital of Chengdu, Chengdu, China
                Beijing University of Technology, CHINA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0001-7201-2092
                Article
                Publisher ID: PONE-D-17-36119
                DOI: 10.1371/journal.pone.0190069
                PMC ID: 5739486
                PubMed ID: 29267360
                SO-VID: 1c3c4d70-2024-42c3-ab61-1a00e13b3dd1
                Copyright © © 2017 Du et al
                License:

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                Date received : 18 October 2017
                Date accepted : 7 December 2017
                Page count
                Figures: 12, Tables: 6, Pages: 15
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 61671312, 61302028, 61202160
                Award Recipient :
                ORCID: http://orcid.org/0000-0001-7201-2092
                Funded by: the National Key R&D Program of china
                Award ID: 2017YFB0802300
                Award Recipient :
                This work was supported in part by the National Natural Science Foundation of China under Grants 61671312, 61302028, 61202160, and the National Key R&D Program of china under Grants 2017YFB0802300.
                Categories
                Subject: Research Article
                Subject: Research and Analysis Methods
                Subject: Imaging Techniques
                Subject: Research and Analysis Methods
                Subject: Imaging Techniques
                Subject: Neuroimaging
                Subject: Computed Axial Tomography
                Subject: Biology and Life Sciences
                Subject: Neuroscience
                Subject: Neuroimaging
                Subject: Computed Axial Tomography
                Subject: Medicine and Health Sciences
                Subject: Diagnostic Medicine
                Subject: Diagnostic Radiology
                Subject: Tomography
                Subject: Computed Axial Tomography
                Subject: Research and Analysis Methods
                Subject: Imaging Techniques
                Subject: Diagnostic Radiology
                Subject: Tomography
                Subject: Computed Axial Tomography
                Subject: Medicine and Health Sciences
                Subject: Radiology and Imaging
                Subject: Diagnostic Radiology
                Subject: Tomography
                Subject: Computed Axial Tomography
                Subject: Engineering and Technology
                Subject: Signal Processing
                Subject: Noise Reduction
                Subject: Computer and Information Sciences
                Subject: Neural Networks
                Subject: Biology and Life Sciences
                Subject: Neuroscience
                Subject: Neural Networks
                Subject: Engineering and Technology
                Subject: Signal Processing
                Subject: Image Processing
                Subject: Medicine and Health Sciences
                Subject: Oncology
                Subject: Cancer Treatment
                Subject: Radiation Therapy
                Subject: Medicine and Health Sciences
                Subject: Clinical Medicine
                Subject: Clinical Oncology
                Subject: Radiation Therapy
                Subject: Medicine and Health Sciences
                Subject: Oncology
                Subject: Clinical Oncology
                Subject: Radiation Therapy
                Subject: Medicine and Health Sciences
                Subject: Diagnostic Medicine
                Subject: Diagnostic Radiology
                Subject: Bone Imaging
                Subject: X-Ray Radiography
                Subject: Research and Analysis Methods
                Subject: Imaging Techniques
                Subject: Diagnostic Radiology
                Subject: Bone Imaging
                Subject: X-Ray Radiography
                Subject: Medicine and Health Sciences
                Subject: Radiology and Imaging
                Subject: Diagnostic Radiology
                Subject: Bone Imaging
                Subject: X-Ray Radiography
                Subject: Medicine and Health Sciences
                Subject: Diagnostic Medicine
                Subject: Diagnostic Radiology
                Subject: X-Ray Radiography
                Subject: Research and Analysis Methods
                Subject: Imaging Techniques
                Subject: Diagnostic Radiology
                Subject: X-Ray Radiography
                Subject: Medicine and Health Sciences
                Subject: Radiology and Imaging
                Subject: Diagnostic Radiology
                Subject: X-Ray Radiography
                Subject: Physical Sciences
                Subject: Physics
                Subject: Particle Physics
                Subject: Elementary Particles
                Subject: Photons
                Custom metadata
                Data Availability The resource code of this work has been released with ′ https://github.com/Wenchao-Du/SCN-for-Image-Denoising′.

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