公开三维多模态同步辐射数据，助力机器学习重建与融合算法研究。
Three-Dimensional, Multimodal Synchrotron Data for Machine Learning Applications
- 用同步辐射获取锌掺杂沸石13X的多分辨率三维数据
- 包含微观孔隙与钠锌相分布的精确空间信息
- 适合做超分辨、多模态融合与3D重建的研究者使用
机器学习在医学与物理科学成像中应用日益广泛，但高质量训练数据的缺乏是关键挑战。本文展示了一组独特的、多模态同步辐射数据，源自定制的锌掺杂沸石13X样品。通过多分辨率显微X射线计算机断层扫描表征其孔隙结构，再进行空间分辨的X射线衍射断层扫描，揭示钠与锌相的均匀分布。通过控制锌吸收，构建出空间隔离的双相材料。原始与处理后的数据均已在Zenodo发布。整体提供了一个空间分辨的三维、多模态、多分辨率数据集，可用于开发超分辨率、多模态数据融合及3D重建算法。
原文摘要 · Abstract (English)
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.
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