A deep learning method for simultaneous denoising and missing wedge reconstruction in cryogenic electron tomography
Year: 2024
Type: article
Abstract: 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 artefacts 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 is simpler than current state-of-the-art approaches for denoising and missing wedge reconstruction, performs competitively and produces more denoised tomograms with higher overall contrast.
Source: Nature Communications
Authors: Simon Wiedemann, Reinhard Heckel
Institution: Technical University of Munich
Language: English
Subfield: Structural Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $6,790
Awards: G1305463392, Theorie und Praxis von nicht Trainierten Neuronalen Netzwerken, Verbundprojekt: Digitale Transformation und Souveränität künftiger Kommunikationsnetze - 6G-life -, Teilvorhaben: Grundlagen und Systemlösungen für Post Shannon Kommunikation, In-Network Computing, eingebettete Sicherheit und Resilienz, Lösungsverfahren für inverse Probleme basierend auf neuronalen Netzen: Expressivität, Generalisierung und Robustheit, G7472567491
OpenAlex ID: W4402748059