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Cryogenic electron tomography (cryo-ET) is a technique for imaging biologicalsamples such as viruses, cells, and proteins in 3D. A microscope collects aseries of 2D projections of the sample, and the goal is to reconstruct the 3Ddensity of the sample called the tomogram. This is difficult as the 2Dprojections have a missing wedge of information and are noisy. Tomogramsreconstructed with conventional methods, such as filtered back-projection,suffer from the noise, and from artifacts and anisotropic resolution due to themissing wedge of information. To improve the visual quality and resolution ofsuch tomograms, we propose a deep-learning approach for simultaneous denoisingand missing wedge reconstruction called DeepDeWedge. DeepDeWedge is based onfitting a neural network to the 2D projections with a self-supervised lossinspired by noise2noise-like methods. The algorithm requires no training orground truth data. Experiments on synthetic and real cryo-ET data show thatDeepDeWedge achieves competitive performance for deep learning-based denoisingand missing wedge reconstruction of cryo-ET tomograms.

A Deep Learning Method for Simultaneous Denoising and Missing Wedge Reconstruction in Cryogenic Electron Tomography