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

Cryogenic electron tomography (cryo-ET) is a technique for imaging biological samples such as viruses, cells, and proteins in 3D. A microscope collects a series of 2D projections of the sample, and the goal is to reconstruct the 3D density of the sample called the tomogram. This is difficult as the 2D projections have a missing wedge of information and are noisy. Tomograms reconstructed with conventional methods, such as filtered back-projection, suffer from the noise, and from artifacts and anisotropic resolution due to the missing wedge of information. To improve the visual quality and resolution of such tomograms, we propose a deep-learning approach for simultaneous denoising and missing wedge reconstruction called DeepDeWedge. DeepDeWedge is based on fitting a neural network to the 2D projections with a self-supervised loss inspired by noise2noise-like methods. The algorithm requires no training or ground truth data. Experiments on synthetic and real cryo-ET data show that DeepDeWedge achieves competitive performance for deep learning-based denoising and missing wedge reconstruction of cryo-ET tomograms.