Structured Cs3Cu2I5 scintillation screen based on oxidized silicon micropore array template for high-resolution X-ray imaging

Teng, Yunxue; Gu, Ming; Sun, Zhi‐Wei; Liu, Xiaolin; Liu, Bo; Zhang, Juannan; Huang, Shiming; Ni, Chen

doi:10.1016/j.jlumin.2023.120116

Cited by 1 publication

(1 citation statement)

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“…Scintillators, with their remarkable ability to transform ionizing radiation into visible photons, serve as the cornerstones of indirect X-ray imaging technology. This groundbreaking technology finds application in a myriad of fields, including medical imaging, nondestructive inspection, industrial testing, and material sciences. − Consequently, the pursuit of “better scintillators” to facilitate superior quality X-ray indirect imaging has garnered immense attention. Recent studies have unveiled that “nanophotonic scintillators”, achieved by two-dimensional periodic etching on scintillators or materials atop them, harbor the capacity to manipulate the optical field response on a scale commensurate with the wavelength of light at the scintillator exit surface. − This can engender substantial enhancements in scintillation yield and spectral response. − However, it must be accentuated that these studies have predominantly centered on bolstering the photon output of scintillators with the pivotal facet of improving signal reception quality warranting further meticulous exploration.…”

Section: Introductionmentioning

confidence: 99%

DA-HSFER: Empowering High-Performance Incoherent X-ray Scintillation Encoded Imaging with Deep Neural Networks

Shi,

Wu,

Wang

et al. 2024

ACS Photonics

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Fueled by research and applications in medical imaging, X-ray nondestructive testing, and high-energy particle detection, the demand for high-performance X-ray scintillator imagers is escalating. Yet, high-quality imaging is compromised due to the attenuation of the image details caused by the refractive index differences between the two sides of the emission interface. Although encoding high-frequency image information using periodic metasurfaces on the exit surface of the scintillators can mitigate considerable high-frequency information loss, current decoding solutions remain inchoate, even in principle. Addressing this, we present a universal framework that combines optical encoding with deep-learning-assisted image decoding in the X-ray incoherent imaging process. The targeted structured deep neural network (DNN), trained with a meticulously configured data set, successfully deciphers the physical mechanism within the X-ray scintillation encoded imaging process. This highlights the broad applicability across various samples, eliminating the need for additional transfer learning on the sample side. The results exhibit a striking 10-fold improvement in the signal-to-noise ratio (SNR) and a 6-fold increase in detection efficiency. We anticipate that our framework will augment the fundamental understanding of X-ray imaging as well as prove great significance for biological imaging applications.

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