Tetris-inspired detector with neural network for radiation mapping
In recent years, radiation mapping has attracted widespread research
attention and increased public concerns on environmental monitoring. In terms
of both materials and their configurations, radiation detectors have been
developed to locate the directions and positions of the radiation sources. In
this process, algorithm is essential in converting detector signals to
radiation source information. However, due to the complex mechanisms of
radiation-matter interaction and the current limitation of data collection,
high-performance, low-cost radiation mapping is still challenging. Here we
present a computational framework using Tetris-inspired detector pixels and
machine learning for radiation mapping. Using inter-pixel padding to increase
the contrast between pixels and neural network to analyze the detector
readings, a detector with as few as four pixels can achieve high-resolution
directional mapping. By further imposing Maximum a Posteriori (MAP) with a
moving detector, further radiation position localization is achieved.
Non-square, Tetris-shaped detector can further improve performance beyond the
conventional grid-shaped detector. Our framework offers a new avenue for high
quality radiation mapping with least number of detector pixels possible, and is
anticipated to be capable to deploy for real-world radiation detection with
moderate validation.
Ryotaro Okabe, Shangjie Xue, Jiankai Yu, Tongtong Liu, Benoit Forget, Stefanie Jegelka, Gordon Kohse, Lin-wen Hu, Mingda Li
# Tetris-inspired detector with neural network for radiation mapping

###### Abstract

In recent years, radiation mapping has attracted widespread research attention and increased public concerns on environmental monitoring. In terms of both materials and their configurations, radiation detectors have been developed to locate the directions and positions of the radiation sources. In this process, algorithm is essential in converting detector signals to radiation source information. However, due to the complex mechanisms of radiation-matter interaction and the current limitation of data collection, high-performance, low-cost radiation mapping is still challenging. Here we present a computational framework using Tetris-inspired detector pixels and machine learning for radiation mapping. Using inter-pixel padding to increase the contrast between pixels and neural network to analyze the detector readings, a detector with as few as four pixels can achieve high-resolution directional mapping. By further imposing Maximum a Posteriori (MAP) with a moving detector, further radiation position localization is achieved. Non-square, Tetris-shaped detector can further improve performance beyond the conventional grid-shaped detector. Our framework offers a new avenue for high quality radiation mapping with least number of detector pixels possible, and is anticipated to be capable to deploy for real-world radiation detection with moderate validation.

## Introduction

Since the Fukushima nuclear accident in 2011 till the recent risk at Zaporizhzhia nuclear power plant, there is an increasing global need calling for improved radiation detection technology, aiming to achieve high-performance radiation detection mapping with minimum impact on detectors and reduced cost. Due to the simultaneous presence of multiple radiation-interaction mechanisms, radiation detection for ionizing radiation is considerably harder than visible light. The large penetration depth of radiation, such as hard X-ray, \(\gamma\)-ray, and neutron, reduces the angular sensitivity of detectors and limits the majority of radiation detection efforts to focus on counting or spectra acquisition rather than their directional information. The challenge on acquiring directional radiation information further triggers additional difficulties in performing source localization, that to determine the position distributions of radiation sources [1, 2]. In recent years, radiation localization has attracted increased interest with applications such as autonomous nuclear site inspection. Several prototypes of system configurations have been proposed including unmanned ground [3, 4, 5, 6, 7], aerial [3, 5, 7] and underwater vehicles [8, 9]. Despite the remarkable progress, the information extraction process of the radioactive environment are still at an early stage with further in-depth studies much needed.

In past decades, several approaches have been proposed for directional radiation detection. One approach is the High Efficiency Multimode Imager (HEMI), which can be used to detect and locate \(\gamma\)-ray and X-ray radioactive sources [10, 11, 12, 13]. A typical HEMI consists of two layers of CdZnTe (CZT) detectors, the first layer has a randomly arranged aperture for coded aperture imaging and the second layer is the conventional co-planar detector grid. This system requires the incident beam only come from a limited solid angle range in order to make sure the beam passes through the aperture of the first layer in order to interact with the second layer. The traditional reconstruction algorithm requires that all the incident beams should come within the field of view. If the radiation incident from another direction, the accuracy will be affected (especially for near-field radiation). Besides, this system can only conditionally detect multiple sources, usually in the case that the sources