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Open AccessArticle

Development of Limited-Angle Iterative Reconstruction Algorithms with Context Encoder-Based Sinogram Completion for Micro-CT Applications

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Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei 11221, Taiwan
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Department of Biomedical Engineering, National Yang-Ming University, Taipei 11221, Taiwan
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Institute of Biomedical Engineering, National Chiao-Tung University, Hsinchu 30010, Taiwan
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Institute of Computer Science and Engineering, National Chiao-Tung University, Hsinchu 30010, Taiwan
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Department of Computer Science and Engineering, National Chiao-Tung University, Hsinchu 30010, Taiwan
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Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA
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Materials & Electro-Optics Research Division, National Chung-Shan Institute of Science & Technology, Taoyuan 32599, Taiwan
*
Authors to whom correspondence should be addressed.
Sensors 2018, 18(12), 4458; https://doi.org/10.3390/s18124458
Received: 29 October 2018 / Revised: 11 December 2018 / Accepted: 13 December 2018 / Published: 16 December 2018
(This article belongs to the Special Issue Deep Learning-Based Image Sensors)
Limited-angle iterative reconstruction (LAIR) reduces the radiation dose required for computed tomography (CT) imaging by decreasing the range of the projection angle. We developed an image-quality-based stopping-criteria method with a flexible and innovative instrument design that, when combined with LAIR, provides the image quality of a conventional CT system. This study describes the construction of different scan acquisition protocols for micro-CT system applications. Fully-sampled Feldkamp (FDK)-reconstructed images were used as references for comparison to assess the image quality produced by these tested protocols. The insufficient portions of a sinogram were inpainted by applying a context encoder (CE), a type of generative adversarial network, to the LAIR process. The context image was passed through an encoder to identify features that were connected to the decoder using a channel-wise fully-connected layer. Our results evidence the excellent performance of this novel approach. Even when we reduce the radiation dose by 1/4, the iterative-based LAIR improved the full-width half-maximum, contrast-to-noise and signal-to-noise ratios by 20% to 40% compared to a fully-sampled FDK-based reconstruction. Our data support that this CE-based sinogram completion method enhances the efficacy and efficiency of LAIR and that would allow feasibility of limited angle reconstruction. View Full-Text
Keywords: context encoder (CE); limited-angle iterative reconstruction (LAIR); generative adversarial network (GAN) context encoder (CE); limited-angle iterative reconstruction (LAIR); generative adversarial network (GAN)
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MDPI and ACS Style

Jin, S.-C.; Hsieh, C.-J.; Chen, J.-C.; Tu, S.-H.; Chen, Y.-C.; Hsiao, T.-C.; Liu, A.; Chou, W.-H.; Chu, W.-C.; Kuo, C.-W. Development of Limited-Angle Iterative Reconstruction Algorithms with Context Encoder-Based Sinogram Completion for Micro-CT Applications. Sensors 2018, 18, 4458.

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