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

doi:10.1117/12.2521445

27 March 2019 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

Author Affiliations +

Proceedings Volume 11050, International Forum on Medical Imaging in Asia 2019; 110500C (2019) https://doi.org/10.1117/12.2521445
Event: 2019 Joint International Workshop on Advanced Image Technology (IWAIT) and International Forum on Medical Imaging in Asia (IFMIA), 2019, Singapore, Singapore

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.

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Dahim Choi, Juhee Kim, Seung-Hoon Chae, Jongduk Baek, Andreas Maier, Rebecca Fahrig, Hyun-Seok Park, and Jang-Hwan Choi "Noise reduction method in low-dose CT data combining neural networks and an iterative reconstruction technique", Proc. SPIE 11050, International Forum on Medical Imaging in Asia 2019, 110500C (27 March 2019); https://doi.org/10.1117/12.2521445

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KEYWORDS

Denoising

3D modeling

Neural networks

Computed tomography

Image filtering

Image quality

3D image processing

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