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

Choi D, Kim J, Chae SH, Baek J, Maier A, Fahrig R, Park HS, Choi JH (2019)

Publication Type: Conference contribution

Publication year: 2019

Journal

Proceedings of SPIE Society of Photo-optical Instrumentation Engineers (SPIE)

Publisher: SPIE

Book Volume: 11050

Conference Proceedings Title: Proceedings of SPIE - The International Society for Optical Engineering

Event location: Singapore SG

ISBN: 9781510627758

DOI: 10.1117/12.2521445

Abstract

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.

Authors with CRIS profile

Andreas Maier Lehrstuhl für Informatik 5 (Mustererkennung) Rebecca Fahrig Technische Fakultät

Involved external institutions

Ewha Womans University / 이화여자대학교 KR Korea, Republic of (KR) Yonsei University KR Korea, Republic of (KR) Electronics and Telecommunications Research Institute / 한국전자통신연구원 KR Korea, Republic of (KR)

How to cite

APA:

Choi, D., Kim, J., Chae, S.H., Baek, J., Maier, A., Fahrig, R.,... Choi, J.H. (2019). Noise reduction method in low-dose CT data combining neural networks and an iterative reconstruction technique. In Proceedings of SPIE - The International Society for Optical Engineering. Singapore, SG: SPIE.

MLA:

Choi, Dahim, et al. "Noise reduction method in low-dose CT data combining neural networks and an iterative reconstruction technique." Proceedings of the International Forum on Medical Imaging in Asia 2019, Singapore SPIE, 2019.

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