A hybrid domain approach to reduce streak artifacts of sparse view ct image via convolutional neural network

Seongjun Kim, Byeongjoon Kim, Jongduk Baek

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In this study, we propose a method to reduce streak artifacts of sparse view CT images via convolutional neural network (CNN). The main idea of the proposed method is to utilize both image and sinogram domain data for CNN training. To generate datasets, projection data were acquired from 512 (128) views using Siddon's ray-driven algorithm, and full (sparse) view CT images were reconstructed by filtered back projection with a Ram-Lak filter. We first trained U-net based CNN_img, which was designed to reduce the streak artifacts of sparse view CT in image domain. Then, the output images of CNN_img were used as prior images to conduct pseudo full view sinogram. Before upsampling, sparse view sinogram was normalized by the prior images, and then linear interpolation was employed to estimate the missing view data compared to full view sinogram. The upsampled data were denormalized using prior images. To reduce the residual errors in pseudo full view sinogram data, we trained CNN_hybrid with residual encoder-decoder CNN, which is known to be effective in reducing the residual errors while preserving structural details. In order to increase the learning efficiency, the dynamic range of the pseudo full view sinogram data was converted via exponential function. The results show that the CNN_hybrid provides better performance in streak artifacts reduction than CNN_img, which is also confirmed by quantitative assessment.

Original languageEnglish
Title of host publicationMedical Imaging 2021
Subtitle of host publicationPhysics of Medical Imaging
EditorsHilde Bosmans, Wei Zhao, Lifeng Yu
ISBN (Electronic)9781510640191
Publication statusPublished - 2021
EventMedical Imaging 2021: Physics of Medical Imaging - Virtual, Online, United States
Duration: 2021 Feb 152021 Feb 19

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


ConferenceMedical Imaging 2021: Physics of Medical Imaging
Country/TerritoryUnited States
CityVirtual, Online

Bibliographical note

Funding Information:
This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science and ICT (NRF-2019R1A2C2084936, 2020R1A4A1016619)

Publisher Copyright:
© COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging


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