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

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 threedimensional information.

Functional imaging and endoscopy

The emergence of endoscopy for the diagnosis of gastrointestinal diseases and the treatment of gastrointestinal diseases has brought great changes.The mere observation of anatomy with the imaging mode using modern endoscopy has played a significant role in this regard.However,increasing numbers of endoscopies have exposed additional deficiencies and defects such as anatomically similar diseases.Endoscopy can be used to examine lesions that are difficult to identify and diagnose.Early disease detection requires that substantive changes in biological function should be observed,but in the absence of marked morphological changes,endoscopic detection and diagnosis are difficult.Disease detection requires not only anatomic but also functional imaging to achieve a comprehensive interpretation and understanding.Therefore,we must ask if endoscopic examination can be integrated with both anatomic imaging and functional imaging.In recent years,as molecular biology and medical imaging technology have further developed,more functional imaging methods have emerged.This paper is a review of the literature related to endoscopic optical imaging methods in the hopes of initiating integration of functional imaging and anatomical imaging to yield a new and more effective type of endoscopy.