Quantitative evaluation of deep convolutional neural network-based image denoising for low-dose computed tomography

Quantitative evaluation of deep convolutional neural network-based image denoising for low-dose computed tomography

Abstract To minimize radiation risk, dose reduction is important in the diagnostic and therapeutic applications of computed tomography (CT). However, image noise degrades image quality owing to the reduced X-ray dose and a possible unacceptably reduced diagnostic performance. Deep learning approache...

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Bibliographic Details
Main Authors: Keisuke Usui, Koichi Ogawa, Masami Goto, Yasuaki Sakano, Shinsuke Kyougoku, Hiroyuki Daida
Format: Article
Language:English
Published: SpringerOpen 2021-07-01
Series:Visual Computing for Industry, Biomedicine, and Art
Subjects:
Deep learning
Convolutional neural network
Low-dose computed tomography
Denoising
Image quality
Online Access:https://doi.org/10.1186/s42492-021-00087-9

Internet

https://doi.org/10.1186/s42492-021-00087-9

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