Research On Dual-Modality Optical-Raman Projection Tomography System And Its Sparse Reconstruction Method

Optical projection tomography(OPT)technology can obtain a three-dimensional(3D)structural image(transmission OPT)or fluorescence image(emission OPT)of a sample by collecting the projection images from multiple angles and combining them with a reconstruction algorithm.It enables micro-level spatial resolution at millimeter-scale samples,and it has the advantages of dynamic imaging,no radiation,low cost,and ease to use,providing an effective 3D imaging method for embryo,tissue,and organ research.However,the emission OPT requires fluorescence labeling of the sample,which will induce the issues such as phototoxicity and photobleaching.The transmission OPT can provide structural images of the sample at micro-or sub-micron resolution.However,this technique cannot obtain the specific molecular composition information of samples.Raman spectroscopic imaging technology can provide fingerprint information of molecular chemical bonds of samples.Spontaneous Raman scattering-based wide-field imaging is a better choice for realizing a large 3D field of view and high-resolution imaging in a labelfree manner.However,this technique cannot determine the microstructure of samples and specific spatial distribution information of chemical components.Given this,we integrated these two optical molecular imaging technologies to develop a dual-modality optical-Raman projection tomography technique and method to acquire 3D sample microstructural and molecular components fusion images.In this dissertation,we investigated the technology and applications of dual-modality optical-Raman projection tomography imaging.The main contributions of this dissertation can be summarized as follows:1.We explored the feasibility of the dual-modality optical-Raman projection tomography technique.Firstly,we built an optical projection tomography system and verified the system performance comprehensively.Secondly,a wide-field Raman imaging system was built,and the feasibility of the system was verified by detecting the Raman signal of different samples,which laid a foundation for the further construction of the dual-modality optical-Raman projection tomography system.Further,considering the effect of sample scattering on signal generation and detection,we built a Bessel beam-based Raman spectroscopic imaging system by using the long focusing and self-healing properties of the Bessel beam.The feasibility of the system was verified by investigating the Raman spectral signals of dimethyl sulfoxide and acetaminophen.We then explored the potential of the system in detecting the signals at weakly scattering samples by testing the Raman spectra of different concentrations of acetaminophen in the scattering medium,which provided a new idea for optical-Raman projection imaging of the weakly scattering samples.2.A dual-modality optical-Raman projection tomography system was developed to obtain the fusion information of 3D sample microstructure and molecular components.The system adopts the architecture of “dual excitation,common-path detection”,that is,the optical projection imaging and Raman projection imaging used an intensity-tunable surface light source and a solid-state laser as the excitation sources to generate uniform plane beam,respectively,while sharing the same CCD camera to obtain the projection images of samples.Firstly,the proposed system,including spatial resolution and sensitivity,was detected,and these results showed that the system had a cross-scale spatial resolution of 5.12-36.42 μm and a lipid molecule detection capability better than 70 m M.Second,the feasibility and effectiveness of the dual-modality system and its reconstruction algorithm were investigated by single and multi-microsphere experiments.Finally,we validated the imaging capability and potential of the system in biological samples by dual-modality imaging and 3D reconstruction of model biological samples such as zebrafish,Arabidopsis,and Drosophila.3.To address the problems of large amount of projection data and long acquisition time required by traditional analytical algorithm-based projection tomography imaging,this dissertation constructed a framework of iterative algorithm and deep learning network-based sparse-view reconstruction scheme,which can reduce the amount of data required for reconstruction to 1/20 of the traditional algorithm.We first established the pixel vertexdriven model-based total variation regularization simultaneous algebra reconstruction technique and φ-net deep learning network-based sparse-view reconstruction scheme.The performance of these two reconstruction methods was then compared and evaluated by using simulation data.Second,the dual-modality optical-Raman projection images of biological samples acquired in the previous chapter were reconstructed based on these two reconstruction algorithms to verify the application capability of the spare-view reconstruction schemes.These results demonstrated that the iterative algorithm can reconstruct acceptable images when the number of projections was reduced to 30,and the data required for reconstruction was reduced to 1/6 of the traditional algorithm.When the number of projections was decreased to 9,the reconstructed images still have high accuracy,the data desired for reconstruction based on the φ-net-based deep learning algorithm was decreased to 1/20 of the conventional algorithm.4.Considering the conventional sample fixation method of traditional tomography is not conducive to in vivo imaging,a new sample fixation method is designed to abandon an imaging cavity,which will bring the problem of missing projection angle of data acquisition,this dissertation investigated the accuracy and speed of dual-modality optical-Raman projection tomography under limited-angle data acquisition.First,the pixel vertex-driven model-based total variation regularization algebra reconstruction technique and two-stage deep learning network-based limited-angle reconstruction schemes were established.The performance of these two algorithms was compared and evaluated using the simulation data to determine the minimum acquisition angle that satisfied the best reconstruction results.Second,to further reduce the data acquisition time,combined with the sparse-view reconstruction strategy,we used the above-mentioned limited-angle reconstruction schemes to reconstruct the experimental data,so as to determine the minimum number of projection data within the limited-angle.Finally,this dissertation continued to explore a novel sample fixation method for limited-angle data acquisition and validated the performance by acquiring different biological sample data from the dual-modality projection tomography sub-system.Combined with the evaluation indexes such as structural similarity and root mean square error,we demonstrated the application capability of this novel fixation method and limited-angle reconstruction algorithm.