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.
|Title of host publication||International Forum on Medical Imaging in Asia 2019|
|Editors||Feng Lin, Hiroshi Fujita, Jong Hyo Kim|
|Publication status||Published - 2019|
|Event||International Forum on Medical Imaging in Asia 2019 - Singapore, Singapore|
Duration: 2019 Jan 7 → 2019 Jan 9
|Name||Proceedings of SPIE - The International Society for Optical Engineering|
|Conference||International Forum on Medical Imaging in Asia 2019|
|Period||19/1/7 → 19/1/9|
Bibliographical noteFunding 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: http://nrf.re.kr). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
© 2019 SPIE.
All Science Journal Classification (ASJC) codes
- Electronic, Optical and Magnetic Materials
- Condensed Matter Physics
- Computer Science Applications
- Applied Mathematics
- Electrical and Electronic Engineering