Distributed optimization for nonrigid nano-tomography

TL;DR

This paper introduces a distributed optimization method for nano-CT that enhances resolution and reduces artifacts by joint reconstruction, projection alignment, and regularization, effectively handling noise and sample movement.

Contribution

It presents a novel joint solver integrating optical flow-based projection alignment and regularization for high-resolution nano-CT reconstruction.

Findings

Robustness to Poisson and background noise demonstrated

Effective alignment and unwarping of projection data

High-quality reconstructions on experimental datasets

Abstract

Resolution level and reconstruction quality in nano-computed tomography (nano-CT) are in part limited by the stability of microscopes, because the magnitude of mechanical vibrations during scanning becomes comparable to the imaging resolution, and the ability of the samples to resist beam damage during data acquisition. In such cases, there is no incentive in recovering the sample state at different time steps like in time-resolved reconstruction methods, but instead the goal is to retrieve a single reconstruction at the highest possible spatial resolution and without any imaging artifacts. Here we propose a joint solver for imaging samples at the nanoscale with projection alignment, unwarping and regularization. Projection data consistency is regulated by dense optical flow estimated by Farneback's algorithm, leading to sharp sample reconstructions with less artifacts. Synthetic data tests show robustness of the method to Poisson and low-frequency background noise. Applicability of the method is demonstrated on two large-scale nano-imaging experimental data sets.