Translational Rapid Ultraviolet-Excited Sectioning Tomography Assisted with Deep Learning

Translational rapid ultraviolet-excited sectioning tomography (TRUST) applies ultraviolet (UV) excitation to a sample and images fluorescence and autofluorescence emission for tomographically imaging the sample. Deep-learning neural networks are used to achieve higher imaging speed and imaging resolution. In one use, fluorescence images acquired with relatively low imaging resolution can be transformed into high-resolution images through the first conditional generative adversarial network (cGAN), a super-resolution neural network (e.g., ESRGAN), which is also helpful for reducing the image scanning time. In another use, the second cGAN, such as Pix2Pix, is used to realize virtual optical sectioning to enhance the axial resolution of the imaging system. Compared to the conventional pattern illumination methods (e.g., HiLo microscopy), which need at least two shots for each field of view, the imaging speed is also times higher because only one shot under the uniform-illumination condition of UV irradiation is required.