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

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.

Highly robust reconstruction framework for three-dimensional optical imaging based on physical model constrained neural networks

“…Here, the regularization parameters need to be adjusted empirically, leading to artificial subjectivity and non-robustness of the reconstruction results. In recent years, deep learning technology has been applied to the field of 3D OI and exhibits great potential (Gao et al 2018, Li et al 2020, Meng et al 2020, Zhang et al 2021b, Mozumder et al 2022, Yedder et al 2022, Li et al 2023, such as the inverse problem simulation (IPS) (Gao et al 2018), graph convolution networks (GNN) (Li et al 2020), 3D fusion dual-sampling deep neural networks (Li et al 2023), etc. Such datadriven based deep learning methods which have been obtained spread applications in the field of photonics (Li et al 2021, Yun et al 2022, Zhao et al 2022 could learn the relationship between the surface light distribution and internal targets directly from a training dataset, which has the advantage of being fast compared to iterative methods.…”