High-resolution X-ray luminescence extension imaging

Xiangyu Ou, Xian Qin, Bolong Huang, Jie Zan, Qinxia Wu, Zhongzhu Hong, Lili Xie, Hongyu Bian, Zhigao Yi, Xiaofeng Chen, Yiming Wu, Xiaorong Song, Juan Li, Qiushui Chen (), Huanghao Yang () and Xiaogang Liu ()
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Xiangyu Ou: Fuzhou University
Xian Qin: National University of Singapore
Bolong Huang: The Hong Kong Polytechnic University
Jie Zan: Fuzhou University
Qinxia Wu: Fuzhou University
Zhongzhu Hong: Fuzhou University
Lili Xie: Fuzhou University
Hongyu Bian: National University of Singapore
Zhigao Yi: National University of Singapore
Xiaofeng Chen: Fuzhou University
Yiming Wu: National University of Singapore
Xiaorong Song: Fuzhou University
Juan Li: Fuzhou University
Qiushui Chen: Fuzhou University
Huanghao Yang: Fuzhou University
Xiaogang Liu: National University of Singapore

Nature, 2021, vol. 590, issue 7846, 410-415

Abstract: Abstract Current X-ray imaging technologies involving flat-panel detectors have difficulty in imaging three-dimensional objects because fabrication of large-area, flexible, silicon-based photodetectors on highly curved surfaces remains a challenge1–3. Here we demonstrate ultralong-lived X-ray trapping for flat-panel-free, high-resolution, three-dimensional imaging using a series of solution-processable, lanthanide-doped nanoscintillators. Corroborated by quantum mechanical simulations of defect formation and electronic structures, our experimental characterizations reveal that slow hopping of trapped electrons due to radiation-triggered anionic migration in host lattices can induce more than 30 days of persistent radioluminescence. We further demonstrate X-ray luminescence extension imaging with resolution greater than 20 line pairs per millimetre and optical memory longer than 15 days. These findings provide insight into mechanisms underlying X-ray energy conversion through enduring electron trapping and offer a paradigm to motivate future research in wearable X-ray detectors for patient-centred radiography and mammography, imaging-guided therapeutics, high-energy physics and deep learning in radiology.

Date: 2021
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DOI: 10.1038/s41586-021-03251-6

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