High-speed X-ray tomography for 4D imaging

• 成果类型: Article
• 署名作者: Grega, Ivan; Whitney, William; Deshpande, Vikram Sudhir
• 署名单位: University of Cambridge; Alphabet Inc.; DeepMind
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-8424; 1091-6490
• DOI: 10.1073/pnas.2521089122
• 发表日期: 2025-12-23
• 页码: e2521089122
• 关键词: tomography; dynamic; neural rendering

摘要

Capturing high-rate spatiotemporal deformation of materials in three dimensions (3D) remains a significant challenge with current X-ray imaging techniques. We present a methodology that combines advances in neural rendering techniques with volume correlation methods to accurately reconstruct complex, high-rate 3D spatiotemporal structural evolutions. The fidelity and versatility of the method, which requires no pretraining, are demonstrated for a diverse set of intricate 3D-printed microarchitected solids. Using laboratory-based X-ray tomography, we capture the 3D growth of a high-rate crush band on a timescale of less than 100 ms. By broadening this idea to a stereo X-ray concept, we eliminate the need to rotate the image object, thereby extending the technique to significantly faster timescales. Our neural rendering framework opens possibilities for 3D observations of viscoelastic responses of biological materials to 3D investigations of numerous poorly understood dynamic processes, such as the runaway failure of batteries, all using laboratory X-ray systems.