# Three-Dimensional, Multimodal Synchrotron Data for Machine Learning Applications

arXiv: https://arxiv.org/abs/2409.07322 · HTML: https://mlsanity.com/paper/2409.07322/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 22 references that are in the map.
- First posted on arXiv 11 Sep 2024.

## Builds on (most cited first, 2 of 2)

- [MedSegDiff-V2: Diffusion based Medical Image Segmentation with Transformer](https://mlsanity.com/paper/2301.11798/) (2023)
- [Fusion of complementary 2D and 3D mesostructural datasets using generative adversarial networks](https://mlsanity.com/paper/2110.11281/) (2021)

## Built on by (most cited first, 1 of 1)

- [VoDaSuRe: A Large-Scale Dataset Revealing Domain Shift in Volumetric Super-Resolution](https://mlsanity.com/paper/2603.23153/) (2026)

## Abstract (arXiv)

Machine learning techniques are being increasingly applied in medical and physical sciences across a variety of imaging modalities; however, an important issue when developing these tools is the availability of good quality training data. Here we present a unique, multimodal synchrotron dataset of a bespoke zinc-doped Zeolite 13X sample that can be used to develop advanced deep learning and data fusion pipelines. Multi-resolution micro X-ray computed tomography was performed on a zinc-doped Zeolite 13X fragment to characterise its pores and features, before spatially resolved X-ray diffraction computed tomography was carried out to characterise the homogeneous distribution of sodium and zinc phases. Zinc absorption was controlled to create a simple, spatially isolated, two-phase material. Both raw and processed data is available as a series of Zenodo entries. Altogether we present a spatially resolved, three-dimensional, multimodal, multi-resolution dataset that can be used for the development of machine learning techniques. Such techniques include development of super-resolution, multimodal data fusion, and 3D reconstruction algorithm development.