Implicit Reconstructions from Deformed Projections for CryoET

TL;DR

This paper introduces a novel method for reconstructing 3D biological structures from cryo-electron tomography data by modeling deformations as continuous operators and using implicit neural representations, enabling joint estimation without training data.

Contribution

It proposes a new framework that jointly estimates deformations and the 3D volume directly from cryoET images using implicit neural representations, without requiring training data.

Findings

Successfully models deformations as continuous operators.

Jointly estimates deformation parameters and 3D volume.

Does not require training data, leveraging standard priors.

Abstract

Cryo-electron tomography (cryoET) is a technique that captures images of biological samples at different tilts, preserving their native state as much as possible. Along with the partial tilt series and noise, one of the major challenges in estimating the accurate 3D structure of the sample is the deformations in the images incurred during the acquisition. We model these deformations as continuous operators and estimate the unknown 3D volume using implicit neural representations. This framework allows to easily incorporate the deformation and estimate jointly the deformation parameters and the volume using a standard optimization algorithm. This approach doesn't require training data and can benefit from standard prior in the optimization procedure.