Limited-Angle Computed Tomography Reconstruction using Combined FDK-Based Neural Network and U-Net

Limited-Angle Computed Tomography Reconstruction using Combined FDK-Based Neural Network and U-Net
Title:
Limited-Angle Computed Tomography Reconstruction using Combined FDK-Based Neural Network and U-Net
Other Titles:
42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society
DOI:
10.1109/EMBC44109.2020.9176040
Publication Date:
20 July 2020
Citation:
Abstract:
The limited-angle cone-beam Computed Tomography (CT) is often used in C-arm for clinical diagnosis with the advantages of cheap cost and radiation dose reduction. However, due to incomplete projection data, the 3-dimensional CT images reconstructed by conventional methods, such as the Feldkamp, Davis and Kres (FDK) algorithm [1], suffer from heavy artifacts and missing features. In this paper, we propose a novel pipeline of neural networks jointly by a FDK-based neural network revisited from W¨urfl et al.’s work [2] and an image domain U-Net to enhance the 3-dimensional reconstruction quality for limited projection sinogram less than 180 degrees, i.e. 145 degrees in our work. Experimental results, on simulated projections of real-scan CTs, show that the proposed pipeline can reduce some of the major artifacts caused by the limited views while keep the key features, with a 16:60% improvement than W¨urfl et al.’s work on peak signal-to-noise ratio.
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Funding Info:
The work is partially supported by grant 152 187 0014/IAF311014N and SIPGA.
Description:
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
2694-0604
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