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

Paper Details

Date Published: 27 March 2019
PDF: 4 pages
Proc. SPIE 11050, International Forum on Medical Imaging in Asia 2019, 110500C (27 March 2019); doi: 10.1117/12.2521445
Show Author Affiliations
Dahim Choi, Ewha Womans Univ. (Korea, Republic of)
Juhee Kim, Ewha Womans Univ. (Korea, Republic of)
Seung-Hoon Chae, Electronics and Telecommunications Research Institute (Korea, Republic of)
Jongduk Baek, Yonsei Univ. (Korea, Republic of)
Andreas Maier, Friedrich-Alexander-Univ. Erlangen-Nürnberg (Germany)
Rebecca Fahrig, Siemens Healthineers (Germany)
Hyun-Seok Park, Ewha Womans Univ. (Korea, Republic of)
Jang-Hwan Choi, Ewha Womans Univ. (Korea, Republic of)


Published in SPIE Proceedings Vol. 11050:
International Forum on Medical Imaging in Asia 2019
Feng Lin; Hiroshi Fujita; Jong Hyo Kim, Editor(s)

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