|
KMID : 1151820200140070991
|
|
Journal of the Korean Society of Radiology
2020 Volume.14 No. 7 p.991 ~ p.1001
|
|
|
Development of Convolutional Network-based Denoising Technique using Deep Reinforcement Learning in Computed Tomography
|
|
Cho Jenong-Hyo
Yim Do-Bin
Nam Ki-Bok
Lee Da-Hye
Lee Seung-Wan
|
|
|
Abstract
|
|
|
|
Supervised deep learning technologies for improving the image quality of computed tomography (CT) need a lot of training data. When input images have different characteristics with training images, the technologies cause structural distortion in output images. In this study, an imaging model based on the deep reinforcement learning (DRL) was developed for overcoming the drawbacks of the supervised deep learning technologies and reducing noise in CT images. The DRL model was consisted of shared, value and policy networks, and the networks included convolutional layers, rectified linear unit (ReLU), dilation factors and gate rotation unit (GRU) in order to extract noise features from CT images and improve the performance of the DRL model. Also, the quality of the CT images obtained by using the DRL model was compared to that obtained by using the supervised deep learning model. The results showed that the image accuracy for the DRL model was higher than that for the supervised deep learning model, and the image noise for the DRL model was smaller than that for the supervised deep learning model. Also, the DRL model reduced the noise of the CT images, which had different characteristics with training images. Therefore, the DRL model is able to reduce image noise as well as maintain the structural information of CT images.
|
|
|
KEYWORD
|
|
|
Deep reinforcement learning, Denoising, Computed tomography, Image quality
|
|
|
FullTexts / Linksout information
|
|
|
|
|
|
Listed journal information
|
|
|
|