Back to Search Start Over

GCR-Net: 3D Graph convolution-based residual network for robust reconstruction in cerenkov luminescence tomography

Authors :
Weitong Li
Mengfei Du
Yi Chen
Haolin Wang
Linzhi Su
Huangjian Yi
Fengjun Zhao
Kang Li
Lin Wang
Xin Cao
Source :
Journal of Innovative Optical Health Sciences, Vol 16, Iss 01 (2023)
Publication Year :
2023
Publisher :
World Scientific Publishing, 2023.

Abstract

Cerenkov Luminescence Tomography (CLT) is a novel and potential imaging modality which can display the three-dimensional distribution of radioactive probes. However, due to severe ill-posed inverse problem, obtaining accurate reconstruction results is still a challenge for traditional model-based methods. The recently emerged deep learning-based methods can directly learn the mapping relation between the surface photon intensity and the distribution of the radioactive source, which effectively improves the performance of CLT reconstruction. However, the previously proposed deep learning-based methods cannot work well when the order of input is disarranged. In this paper, a novel 3D graph convolution-based residual network, GCR-Net, is proposed, which can obtain a robust and accurate reconstruction result from the photon intensity of the surface. Additionally, it is proved that the network is insensitive to the order of input. The performance of this method was evaluated with numerical simulations and in vivo experiments. The results demonstrated that compared with the existing methods, the proposed method can achieve efficient and accurate reconstruction in localization and shape recovery by utilizing three-dimensional information.

Details

Language :
English
ISSN :
17935458 and 17937205
Volume :
16
Issue :
01
Database :
Directory of Open Access Journals
Journal :
Journal of Innovative Optical Health Sciences
Publication Type :
Academic Journal
Accession number :
edsdoj.25247c91f9fe4e8eba42530cb27c9785
Document Type :
article
Full Text :
https://doi.org/10.1142/S179354582245002X