利用深度学习扩展双光子成像视场

Abstract

Objective Twophoton microscopy (TPM) imaging has been widely used in many fields, such as in vivo tumor imaging, neuroimaging, and brain disease research. However, the small fieldofview (FOV) in twophoton imaging (typically within diameter of 1 mm) limits its further application. Although the FOV can be extended through adaptive optics technology, the complex optical paths, additional device costs, and cumbersome operating procedures limit its promotion. In this study, we propose the use of deep learning technology instead of adaptive optics technology to expand the FOV of twophoton imaging. The large FOV of TPM can be 0907107 -10 研究论文 第 50 卷 第 9 期/2023 年 5 月/中国激光 realized without additional hardware (such as a special objective lens or phase compensation device). In addition, a BNfree attention activation residual U -Net (nBRAnet) network framework is designed for this imaging method, which can efficiently correct aberrations without requiring wavefront detection.Methods Commercially available objectives have a nominal imaging FOV that has been calibrated by the manufacturer. Within the nominal FOV, the objective lens exhibits negligible aberrations. However, the aberrations increase dramatically beyond the nominal FOV. Therefore, the imaging FOV of the objective lens is limited to its nominal FOV . In this study, we improved the imaging of the FOV outside the nominal region by combining adaptive optics (AO) and deep learning. Aberrant and AOcorrected images were collected outside the nominal FOV. Thus, we obtained a paired dataset consisting of AOcorrected and uncorrected images. A supervised neural network was trained using the aberrated images as the input and the AOcorrected images as the output.After training, the images collected from regions outside the nominal FOV could be fed directly to the network. Aberrationcorrected images were produced, and the imaging system could be used without AO hardware. Results and DiscussionsThe experimental test results include the imaging results of samples such as fluorescent beads with diameter of 1 μm and Thy1 -GFP and CX3CR1 -GFP mouse brain slices, and the results of the corresponding network output. The high peak signaltonoise ratio (PSNR) values of the test output and ground truth demonstrate the feasibility of extending the FOV to TPM imaging using deep learning. At the same time, the intensity contrast between the nBRAnet network output image and the ground truth on the horizontal line is compared in detail (Figs. 3, 4, and 5). The extended FOV of different samples is randomly selected for analysis, and a high degree of coincidence is observed in the intensity comparison. The experimental results show that after using the network, both the resolution and fluorescence intensity can be restored to a level where there is almost no aberration , which is close to the result after correcting using AO hardware. To demonstrate the advantages of the network framework designed in this study, the traditional U -Net structure and the very deep superresolution (VDSR) model ar...