Adaptive differentiable grids for cryo-electron tomography reconstruction and denoising.

ABSTRACT:

Motivation

Tilt-series cryo-electron tomography is a powerful tool widely used in structural biology to study 3D structures of micro-organisms, macromolecular complexes, etc. Still, the reconstruction process remains an arduous task due to several challenges: The missing-wedge acquisition, sample misalignment and motion, the need to process large data, and, especially, a low signal-to-noise ratio.

Results

Inspired by the recently introduced neural representations, we propose an adaptive learning-based representation of the density field of the captured sample. This representation consists of an octree structure, where each node represents a 3D density grid optimized from the captured projections during the training process. This optimization is performed using a loss that co

SUBMITTER: Wang Y 

PROVIDER: S-EPMC10560095 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature