Browsing by Author "Jin, Shih-Chun"

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Development of LabVIEW Based Micro Computed Tomography System on Vertical Rotary Gantry
(2015-01-01)
Tu, Shih-Huan
;
Chen, Ya-Chen
;
Hsiao, Tzu-Chien
;
Jin, Shih-Chun
;
Chen, Jyh-Cheng
Construction of a micro computed tomography (micro-CT) system has been proposed and developed in recent years. For the need of exploring and investigating a novel computed tomography (CT) image reconstruction method, custom-built micro-CT system offers a corresponding laboratory research environment. To develop a controllable program capable of incorporating with different devices efficiently, we use Laboratory Virtual Instrumentation Engineering Workbench (LabVIEW), a graphical programming language, as our programming environment to implement micro-CT system on vertical rotary gantry. We scanned and reconstructed the 360 projections derived from full 360 degrees scan with angular stepping of 1 degrees. Total scan time is about 9.1 minutes. We will combine micro fluorescence tomography (micro-FT) and micro-CT on uni-platform in the future.
Development of Limited-Angle Iterative Reconstruction Algorithms with Context Encoder-Based Sinogram Completion for Micro-CT Applications
(2018-12-01)
Jin, Shih-Chun
;
Hsieh, Chia-Jui
;
Chen, Jyh-Cheng
;
Tu, Shih-Huan
;
Chen, Ya-Chen
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.