# Stereo X-ray Tomography

## TL;DR

This paper introduces a stereo X-ray tomography method that estimates 3D spatial locations of features from only two projections, enabling faster imaging for dynamic processes without full 3D reconstruction.

## Contribution

It develops a novel feature matching approach for X-ray images inspired by stereo vision, allowing 3D localization of points and lines with limited projections.

## Key findings

- Able to locate point features in 3D from two X-ray images
- Unique matching achieved with three observations from different angles
- Provides a method for rapid spatial information extraction in X-ray imaging

## Abstract

X-ray tomography is a powerful volumetric imaging technique, but detailed three dimensional (3D) imaging requires the acquisition of a large number of individual X-ray images, which is time consuming. For applications where spatial information needs to be collected quickly, for example, when studying dynamic processes, standard X-ray tomography is therefore not applicable. Inspired by stereo vision, in this paper, we develop X-ray imaging methods that work with two X-ray projection images. In this setting, without the use of additional strong prior information, we no longer have enough information to fully recover the 3D tomographic images. However, up to a point, we are nevertheless able to extract spatial locations of point and line features. From stereo vision, it is well known that, for a known imaging geometry, once the same point is identified in two images taken from different directions, then the point's location in 3D space is exactly specified. The challenge is the matching of points between images. As X-ray transmission images are fundamentally different from the surface reflection images used in standard computer vision, we here develop a different feature identification and matching approach. In fact, once point like features are identified, if there are limited points in the image, then they can often be matched exactly. In fact, by utilising a third observation from an appropriate direction, matching becomes unique. Once matched, point locations in 3D space are easily computed using geometric considerations. Linear features, with clear end points, can be located using a similar approach.