  Cryogenic electron tomography is a technique for imaging biological samples
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.
Reconstruction is difficult as the 2D projections are noisy and can not be
recorded from all directions, resulting in a missing wedge of information.
Tomograms conventionally reconstructed with filtered back-projection suffer
from noise and strong artifacts due to the missing wedge. Here, we propose a
deep-learning approach for simultaneous denoising and missing wedge
reconstruction called DeepDeWedge. The algorithm requires no ground truth data
and is based on fitting a neural network to the 2D projections using a
self-supervised loss. DeepDeWedge performs better than CryoCARE and IsoNet,
which are state-of-the-art methods for denoising and missing wedge
reconstruction, and similarly and, in some cases, better than the combination
of the two methods. At the same time, DeepDeWedge is simpler than this two-step
approach, as it does denoising and missing wedge reconstruction simultaneously
rather than sequentially.
