CryoLithe: Rapid Cryo-ET Reconstruction via Transform-Localized Deep Learning
Cryo-electron tomography (cryo-ET) enables 3D visualization of cellular structures. Accurate reconstruction of high-resolution volumes is complicated by the very low signal-to-noise ratio and a restricted range of sample tilts. Recent self-supervised deep learning approaches, which post-process initial reconstructions by filtered backprojection (FBP), have significantly improved reconstruction quality with respect to signal processing iterative algorithms, but they are slow, taking dozens of hours for an expert to reconstruct a tomogram and demand large memory. We present CryoLithe, an end-to-end network that directly estimates the volume from an aligned tilt series. CryoLithe achieves denoising and missing wedge correction comparable or better than state-of-the-art self-supervised deep learning approaches such as Icecream, Cryo-CARE, IsoNet or DeepDeWedge, while being two orders of magnitude faster. To achieve this, we implement a local, memory-efficient reconstruction network. We demonstrate that leveraging transform-domain locality makes our network robust to distribution shifts, enabling effective supervised training and giving excellent results on real data$\unicode{x2013}$without retraining or fine-tuning. CryoLithe reconstructions facilitate downstream cryo-ET analysis, including segmentation and subtomogram averaging and is openly available: https://github.com/swing-research/CryoLithe.