# Simultaneous Sensing Error Recovery and Tomographic Inversion Using an Optimization-based Approach

## TL;DR

This paper introduces an optimization-based method to simultaneously recover sensing errors and perform tomographic inversion, improving image accuracy despite experimental perturbations.

## Contribution

It develops new physical models and optimization techniques specifically for correcting center of rotation errors in tomography.

## Key findings

- Effective error correction demonstrated on synthetic data
- Improved image reconstruction accuracy
- Robustness to known rotational perturbations

## Abstract

Tomography can be used to reveal internal properties of a 3D object using any penetrating wave. Advanced tomographic imaging techniques, however, are vulnerable to both systematic and random errors associated with the experimental conditions, which are often beyond the capabilities of the state-of-the-art reconstruction techniques such as regularizations. Because they can lead to reduced spatial resolution and even misinterpretation of the underlying sample structures, these errors present a fundamental obstacle to full realization of the capabilities of next-generation physical imaging. In this work, we develop efficient and explicit recovery schemes of the most common experimental error: movement of the center of rotation during the experiment. We formulate new physical models to capture the experimental setup, and we devise new mathematical optimization formulations for reliable inversion of complex samples. We demonstrate and validate the efficacy of our approach on synthetic data under known perturbations of the center of rotation.