Three-Dimensional, Multimodal Synchrotron Data for Machine Learning Applications
This provides a valuable resource for researchers in medical and physical sciences working on machine learning applications, though it is incremental as it focuses on data availability rather than new methods.
The authors tackled the lack of high-quality training data for machine learning in imaging by presenting a unique, multimodal synchrotron dataset of a zinc-doped Zeolite 13X sample, which includes raw and processed data for developing techniques like super-resolution and data fusion.
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.