Noise reduction method in low-dose CT data combining neural networks and an iterative reconstruction technique

Dahim Choi, Juhee Kim, Seung Hoon Chae, Jongduk Baek, Andreas Maier, Rebecca Fahrig, Hyun Seok Park, Jang Hwan Choi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations


Improving image quality from low-dose CT image and keeping diagnostic features is integral to lowering the amount of exposure to radiation and its potential risks. Noise reduction methods using deep neural network have been developed and displayed impressive performance, but there are limitations on noise remnants, blurring on high-frequency edge, and artifacts occurrence. To increase noise reduction performance and deal with those issues simultaneously, we have implemented block-based REDCNN model and applied patch-based Landweber-type iteration to images passed through REDCNN model. The model successfully smooths noise on CT images which are imposed Gaussian and Poisson noise, and outperforms noise reduction by other state-of-the-art deep neural network models. We also have tested the effect of repetition of an iterative reconstruction, changing a step size and the number of iteration.

Original languageEnglish
Title of host publicationInternational Forum on Medical Imaging in Asia 2019
EditorsFeng Lin, Hiroshi Fujita, Jong Hyo Kim
ISBN (Electronic)9781510627758
StatePublished - 2019
EventInternational Forum on Medical Imaging in Asia 2019 - Singapore, Singapore
Duration: 7 Jan 20199 Jan 2019

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


ConferenceInternational Forum on Medical Imaging in Asia 2019

Bibliographical note

Funding Information:
This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government, MSIP (grant no: NRF-2017R1C1B5018287, NRF-2015M3A9A7029725, and NRF-2017M2A2A6A02070522, URL: The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Publisher Copyright:
© 2019 SPIE.


  • Deep Neural Networks
  • Landweber iteration
  • Low-dose CT
  • Noise Reduction


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