Algorithm-driven Advances for Scientific CT Instruments: From Model-based to Deep Learning-based Approaches

TL;DR

This paper reviews recent advances in reconstruction algorithms for scientific CT instruments, highlighting improvements from model-based to deep learning approaches that enable faster, more accurate, and novel 3D imaging capabilities.

Contribution

It provides a comprehensive overview of the transition from traditional model-based to deep learning-based reconstruction algorithms in scientific CT imaging.

Findings

Model-based algorithms formulate reconstruction as high-dimensional optimization problems.

Deep learning approaches have recently enhanced CT reconstruction performance.

Advances enable faster, more accurate, and innovative 3D imaging in materials science.

Abstract

Multi-scale 3D characterization is widely used by materials scientists to further their understanding of the relationships between microscopic structure and macroscopic function. Scientific computed tomography (CT) instruments are one of the most popular choices for 3D non-destructive characterization of materials at length scales ranging from the angstrom-scale to the micron-scale. These instruments typically have a source of radiation that interacts with the sample to be studied and a detector assembly to capture the result of this interaction. A collection of such high-resolution measurements are made by re-orienting the sample which is mounted on a specially designed stage/holder after which reconstruction algorithms are used to produce the final 3D volume of interest. The end goal of scientific CT scans include determining the morphology,chemical composition or dynamic behavior of materials when subjected to external stimuli. In this article, we will present an overview of recent advances in reconstruction algorithms that have enabled significant improvements in the performance of scientific CT instruments - enabling faster, more accurate and novel imaging capabilities. In the first part, we will focus on model-based image reconstruction algorithms that formulate the inversion as solving a high-dimensional optimization problem involving a data-fidelity term and a regularization term. In the last part of the article, we will present an overview of recent approaches using deep-learning based algorithms for improving scientific CT instruments.