Substance discrimination imaging derived from switchable soft and hard x‐ray sensing in direct x‐ray detector

Abstract Substance discrimination beyond the shape feature is urgently desired for x‐ray imaging for enhancing target identification. With two x‐ray sources or stacked two detectors, the two‐energy‐channel x‐ray detection can discriminate substance density by normalizing the target thickness. Nevertheless, the artifacts, high radiation dose and difficulty in image alignment due to two sources or two detectors impede their widespread application. In this work, we report a single direct x‐ray detector with MAPbI 3 /MAPbBr 3 heterojunction for switchable soft x‐ray (<20 keV) and hard x‐ray (>20 keV) detection under one x‐ray source. Systematic characterizations confirm soft and hard x‐ray deposit their energy in MAPbI 3 and MAPbBr 3 layer, respectively, while working voltages can control the collection of generated charge carriers in each layer for selective soft/hard x‐ray detection. The switching rate between soft and hard x‐ray detection mode reaches 100 Hz. Moreover, the detector possesses a moderate performance with ~50 nGy s −1 in limit‐of‐detection, ~8000 μC Gy −1 cm −2 in sensitivity and ~7 lp/mm in imaging resolution. By defining the attenuation coefficient ratio ( 𝜇 L /𝜇 H ) as substance label, we effectively mitigate the influence of target thickness and successfully discriminate substances in the acquired x‐ray images. image

Authors

Institutions

Publication Details

Journal
InfoMat
Published
2024-10-13
DOI
https://doi.org/10.1002/inf2.12632
Citations
10
Primary Topic
Advanced X-ray and CT Imaging
Type
article
Field-Weighted Citation Impact
1.84

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Substance discrimination imaging derived from switchable soft and hard x‐ray sensing in direct x‐ray detector

Ying Jie Zhu, Damian Chinedu Onwudiwe, Byung Seong Bae, Jingda Zhao et al.
10 citations
InfoMat
Advanced X-ray and CT Imaging
1.84
article

Substance discrimination imaging derived from switchable soft and hard x‐ray sensing in direct x‐ray detector

Ying Jie Zhu, Damian Chinedu Onwudiwe, Byung Seong Bae, Jingda Zhao, Wei Lei, Mehmet Ertuğrul, Qi Cheng, Yuwei Li, Xin Wang, Xiaobao Xu
article en
10 citations

Abstract

Abstract Substance discrimination beyond the shape feature is urgently desired for x‐ray imaging for enhancing target identification. With two x‐ray sources or stacked two detectors, the two‐energy‐channel x‐ray detection can discriminate substance density by normalizing the target thickness. Nevertheless, the artifacts, high radiation dose and difficulty in image alignment due to two sources or two detectors impede their widespread application. In this work, we report a single direct x‐ray detector with MAPbI 3 /MAPbBr 3 heterojunction for switchable soft x‐ray (<20 keV) and hard x‐ray (>20 keV) detection under one x‐ray source. Systematic characterizations confirm soft and hard x‐ray deposit their energy in MAPbI 3 and MAPbBr 3 layer, respectively, while working voltages can control the collection of generated charge carriers in each layer for selective soft/hard x‐ray detection. The switching rate between soft and hard x‐ray detection mode reaches 100 Hz. Moreover, the detector possesses a moderate performance with ~50 nGy s −1 in limit‐of‐detection, ~8000 μC Gy −1 cm −2 in sensitivity and ~7 lp/mm in imaging resolution. By defining the attenuation coefficient ratio ( 𝜇 L /𝜇 H ) as substance label, we effectively mitigate the influence of target thickness and successfully discriminate substances in the acquired x‐ray images. image

InfoMatVol. 7(2)
Hoseo University (KR), Karadeniz Technical University (TR), North-West University (ZA), Southeast University (BD), The Fifth People’s Hospital of Suzhou (CN), Southeast University (CN)
National Natural Science Foundation of China, National Key Research and Development Program of China, Fundamental Research Funds for the Central Universities
Reduced inequalities
Openalex Percentile: Top 15%
Advanced X-ray and CT Imaging
1.84
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.