# A Cone-Beam X-Ray CT Data Collection designed for Machine Learning

## TL;DR

This paper introduces an open dataset of cone-beam X-ray CT scans of walnuts, designed for machine learning tasks like artefact reduction, reconstruction, super-resolution, and segmentation, with comprehensive reconstruction tools provided.

## Contribution

It provides a unique, annotated dataset with multiple source orbits and ground truth images, facilitating research in high cone-angle artefact reduction and other image reconstruction tasks.

## Key findings

- Dataset includes 42 walnuts with varied natural features.
- Provides raw data, geometry, and reconstruction scripts for open use.
- Enables development of ML algorithms for artefact reduction and limited-angle reconstruction.

## Abstract

Unlike previous works, this open data collection consists of X-ray cone-beam (CB) computed tomography (CT) datasets specifically designed for machine learning applications and high cone-angle artefact reduction. Forty-two walnuts were scanned with a laboratory X-ray set-up to provide not only data from a single object but from a class of objects with natural variability. For each walnut, CB projections on three different source orbits were acquired to provide CB data with different cone angles as well as being able to compute artefact-free, high-quality ground truth images from the combined data that can be used for supervised learning. We provide the complete image reconstruction pipeline: raw projection data, a description of the scanning geometry, pre-processing and reconstruction scripts using open software, and the reconstructed volumes. Due to this, the dataset can not only be used for high cone-angle artefact reduction but also for algorithm development and evaluation for other tasks, such as image reconstruction from limited or sparse-angle (low-dose) scanning, super resolution, or segmentation.