Enhanced Cerenkov Luminescence Tomography Based On Sparse Bayesian Learning And Hybrid Transmission Model

Cerenkov Luminescence Tomography(CLT)can detect the Cerenkov Luminescence produced by the decay of radionuclides using optical imaging instruments,reflecting the three-dimensional spatial information of radionuclide probes in vivo,and has a large number of Clinically applicable radionuclide probes have received extensive attention from scholars in the field of optical molecular imaging.However,Cerenkov imaging has the disadvantages of weak fluorescence signal,poor tissue penetration,and serious erroneous reverse problems,which severely limits the application and development of this technology.In this thesis,we focus on the complex transmission of Cerenkov luminescence in biological tissues and the sparseness of fluorescence signals.We have conducted preliminary research on the enhancement methods of CLT Cerenkov luminescence signal and the three-dimensional reconstruction methods of CLT.The specific research work is as follows:(1)Due to the weak CLT signal intensity and poor tissue penetration,the Cerenkov Luminescence signal enhancement method based on radioluminescence microparticles was adopts.The radioluminescence microparticles was bombarded by high-energy rays generated during the decay of radionuclides to emit RL.And finally,the CCD camera acquires a mixture of Cerenkov fluorescence and radiation fluorescence.The experiment uses rare-earth nanoparticles Gd2O2S:Tb and radionuclide 68 Ga Cl3.The results show that the Cerenkov Luminescence signal enhancement method based on radiofluorescence material makes the total surface light signal increase,increase the detection sensitivity,and accelerate the conversion of CLT technology to clinical application.(2)The transmission law and numerical model of Cerenkov luminescence in biological tissues are the basis for accurate reconstruction in the later period.Taking into consideration the spectral characteristics of Cerenkov luminescence and the applicability of the diffusion approximation equation and the third-order simplified spherical harmonic approximation,this thesis combines multi-spectral CLT techniques to construct a hybrid transmission model that uses a diffusion approximation equation for high-scatter regions and three for low-scatter regions.Order simplified spherical harmonic approximation model.Digital mouse simulation and real-life simulation experiments also verify that the method can significantly improve the reconstruction speed and have good performance under the condition of ensuring the reconstruction accuracy.(3)For the ill-posedness inverse problem of single-view CLT reconstruction,a novel single-view enhanced Cerenkov luminescence tomography reconstruction method was proposed.In this method,single-view data acquisition is used,and sparse Bayesian learning(SBL)reconstruction algorithm combining with the strategy of iterative-shrinking permissible region is adopted to solve the inverse problem.Non-homogeneous cylinder simulation and physical phantom experiments are designed and conducted to verify the accuracy and stability of the proposed method.The results indicate that the proposed method can improve the reconstruction accuracy and speed,and the ill-posedness of the inverse problem can be mitigate effectively.