Tetris-inspired detector with neural network for radiation mapping

Ryotaro Okabe (), Shangjie Xue, Jayson R. Vavrek, Jiankai Yu, Ryan Pavlovsky, Victor Negut, Brian J. Quiter, Joshua W. Cates, Tongtong Liu, Benoit Forget, Stefanie Jegelka, Gordon Kohse, Lin-wen Hu () and Mingda Li ()
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Ryotaro Okabe: Massachusetts Institute of Technology
Shangjie Xue: Massachusetts Institute of Technology
Jayson R. Vavrek: Lawrence Berkeley National Laboratory
Jiankai Yu: Massachusetts Institute of Technology
Ryan Pavlovsky: Lawrence Berkeley National Laboratory
Victor Negut: Lawrence Berkeley National Laboratory
Brian J. Quiter: Lawrence Berkeley National Laboratory
Joshua W. Cates: Lawrence Berkeley National Laboratory
Tongtong Liu: Massachusetts Institute of Technology
Benoit Forget: Massachusetts Institute of Technology
Stefanie Jegelka: Massachusetts Institute of Technology
Gordon Kohse: Massachusetts Institute of Technology
Lin-wen Hu: Massachusetts Institute of Technology
Mingda Li: Massachusetts Institute of Technology

Nature Communications, 2024, vol. 15, issue 1, 1-13

Abstract: Abstract Radiation mapping has attracted widespread research attention and increased public concerns on environmental monitoring. Regarding materials and their configurations, radiation detectors have been developed to identify the position and strength of the radioactive sources. However, due to the complex mechanisms of radiation-matter interaction and data limitation, high-performance and low-cost radiation mapping is still challenging. Here, we present a radiation mapping framework using Tetris-inspired detector pixels. Applying inter-pixel padding for enhancing contrast between pixels and neural networks trained with Monte Carlo (MC) simulation data, a detector with as few as four pixels can achieve high-resolution directional prediction. A moving detector with Maximum a Posteriori (MAP) further achieved radiation position localization. Field testing with a simple detector has verified the capability of the MAP method for source localization. Our framework offers an avenue for high-quality radiation mapping with simple detector configurations and is anticipated to be deployed for real-world radiation detection.

Date: 2024
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DOI: 10.1038/s41467-024-47338-w

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