Diffusion Probabilistic Models for Missing-Wedge Correction in Cryo-Electron Tomography

Abstract Interpretation of 3D cryo-electron tomography (cryo-ET) reconstructions (tomograms) is hampered by the so-called missing-wedge (MW) distortions, which arise because tilt image series used for the reconstructions are acquired in a limited angular range. While many deep-learning approaches address the correction of the MW artifacts on the level of tomograms (3D volumes), the correction at the level of 2D tilt images (generation of unacquired images) remains underexplored. We propose MW-RaMViD, a 2D tilt-image generation method for MW correction, based on Random-Mask Video Diffusion (RaMViD) method for prediction of frames in natural videos. To adapt RaMViD for cryo-ET, we add MRC image-format support, floating-point pixel intensity representation, and a controlled inference protocol enabling both one-run and progressive MW completion (generating a small number of missing tilts per step using a sliding window). We evaluate the method on a synthetic noisy tilt-series dataset and study the effects of MW completion step size and conditioning sequence length. Results show that smaller step sizes and larger conditioning windows reduce error accumulation at higher tilt angles and improve reconstruction fidelity, which was measured by Root Mean Square Error on the image level and by Fourier Shell Correlation on the tomogram level.