TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Respiratory Motion Correction on PET Images Based on 3D Convolutional Neural Network |
| 영문제목(English Title) |
Respiratory Motion Correction on PET Images Based on 3D Convolutional Neural Network |
| 저자(Author) |
Yibo Hou
Jianfeng He
Bo She
|
| 원문수록처(Citation) |
VOL 16 NO. 07 PP. 2191 ~ 2208 (2022. 07) |
한글내용
(Korean Abstract) |
|
영문내용
(English Abstract) |
Motion blur in PET (Positron emission tomography) images induced by respiratory motion will reduce the quality of imaging. Although exiting methods have positive performance for respiratory motion correction in medical practice, there are still many aspects that can be improved. In this paper, an improved 3D unsupervised framework, Res-Voxel based on U-Net network was proposed for the motion correction. The Res-Voxel with multiple residual structure may improve the ability of predicting deformation field, and use a smaller convolution kernel to reduce the parameters of the model and decrease the amount of computation required. The proposed is tested on the simulated PET imaging data and the clinical data. Experimental results demonstrate that the proposed achieved Dice indices 93.81%, 81.75% and 75.10% on the simulated geometric phantom data, voxel phantom data and the clinical data respectively. It is demonstrated that the proposed method can improve the registration and correction performance of PET image. |
| 키워드(Keyword) |
PET images
Respiratory motion correction
image registration
Deep Learning
|
| 파일첨부 |
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