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

• Main Authors: Shih-Chun Jin, Chia-Jui Hsieh, Jyh-Cheng Chen, Shih-Huan Tu, Ya-Chen Chen, Tzu-Chien Hsiao, Angela Liu, Wen-Hsiang Chou, Woei-Chyn Chu, Chih-Wei Kuo
• Format: Article
• Language: English
• Published: MDPI AG 2018-12-01
• Series: Sensors
• Subjects: context encoder (CE); limited-angle iterative reconstruction (LAIR); generative adversarial network (GAN)
• Online Access: https://www.mdpi.com/1424-8220/18/12/4458

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.