Glowing plastic rod scintillator with neural network pattern, symbolizing AI-enhanced radiation detection.

Can AI Revolutionize Radiation Detection? A Fast, New Technique Unveiled

Lena Kashyap in Tech & Innovation December 2025 • 4 min read.

"Researchers are using artificial neural networks to evaluate plastic rod scintillators, offering a faster, more efficient way to detect gamma rays with potential applications in medical imaging and industrial safety."

Radiation detection is a cornerstone of numerous critical fields, from medical diagnostics and astronomical observations to industrial safety and security. Traditional methods often rely on complex and time-consuming processes, creating a demand for faster, more efficient solutions. Recent innovations in artificial intelligence are now offering promising alternatives, capable of revolutionizing how we detect and interpret radiation.

For years, scientists have explored various materials and techniques to improve radiation detection. Among these, plastic rod scintillators have emerged as valuable tools, particularly for detecting gamma rays. These scintillators emit light when struck by radiation, allowing researchers to measure the intensity and location of the source. However, accurately evaluating the performance of these scintillators has remained a challenge – until now.

A groundbreaking study has introduced a novel approach that leverages the power of artificial neural networks (ANNs) to evaluate plastic rod scintillators. This method promises to significantly accelerate the evaluation process while maintaining high levels of accuracy. This article delves into the details of this innovative technique, exploring its potential applications and implications for the future of radiation detection.

How Does AI Enhance Gamma-Ray Detection?

Glowing plastic rod scintillator with neural network pattern, symbolizing AI-enhanced radiation detection.

The core of this new technique lies in the use of artificial neural networks. ANNs are computational models inspired by the structure and function of the human brain, capable of learning complex patterns from data. In this case, the ANN is trained to analyze the light emitted by a plastic rod scintillator when it interacts with gamma rays.

Here's a breakdown of the key components and steps involved:

Plastic Rod Scintillator: This is the primary detector material. When gamma rays strike the scintillator, it emits photons of light.
Photomultiplier Tube (PMT): A PMT is attached to one end of the scintillator rod. It detects the light emitted and converts it into an electrical signal.
Data Acquisition: The electrical signals from the PMT are recorded and processed to create a spectrum, which represents the intensity of light at different energy levels.
Artificial Neural Network (ANN): The ANN is trained on a dataset of spectra obtained from various known positions of a gamma-ray source. It learns to associate specific spectral patterns with the location of the radiation interaction.
Position Prediction: Once trained, the ANN can accurately predict the position of a gamma-ray interaction based solely on the spectrum produced by the scintillator.

The researchers used a relatively simple setup, coupling a plastic scintillator (BC400) with a single PMT. They trained the ANN using data acquired from 35 different source positions. The results demonstrated remarkable accuracy, with the ANN able to predict the interaction position with a mean absolute error of less than 0.8 cm for Cesium-137 (137Cs) and 0.5 cm for Cobalt-60 (60Co) sources. This level of precision opens up exciting possibilities for various applications.

The Future of Radiation Detection is Here

This innovative technique has the potential to transform radiation detection across numerous sectors. By combining plastic rod scintillators with the power of AI, researchers have created a fast, accurate, and cost-effective solution that surpasses traditional methods. This approach paves the way for more advanced medical imaging, enhanced industrial safety protocols, and improved security measures. As AI continues to evolve, we can expect even more groundbreaking applications in the field of radiation detection, further enhancing our ability to understand and harness the power of the invisible world around us.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.1016/j.radphyschem.2018.12.005, Alternate LINK

Title: A Novel And Fast Technique For Evaluation Of Plastic Rod Scintillators As Position Sensitive Gamma-Ray Detectors Using Artificial Neural Networks

Subject: Radiation

Journal: Radiation Physics and Chemistry

Publisher: Elsevier BV

Authors: S.Z. Islami Rad, R. Gholipour Peyvandi

Published: 2019-04-01

Everything You Need To Know

How does the new technique actually work?

The new technique uses a combination of a Plastic Rod Scintillator and an Artificial Neural Network (ANN). The Plastic Rod Scintillator detects gamma rays by emitting light when struck. This light is then measured by a Photomultiplier Tube (PMT), which converts the light into an electrical signal. The Artificial Neural Network (ANN) analyzes these signals to determine the position of the gamma-ray source. The significance lies in its potential to speed up and improve the accuracy of radiation detection compared to older methods. This approach promises faster results and enhanced precision, leading to advancements in several areas.

What is a Plastic Rod Scintillator and why is it important?

A Plastic Rod Scintillator is a material that emits light when it interacts with radiation, especially gamma rays. This light emission is then used to detect and measure the radiation. In this context, they are crucial for detecting gamma rays. By measuring the light emitted by the Plastic Rod Scintillator, scientists can determine the intensity and location of the radiation source. The implications involve the ability to more effectively monitor and understand radiation levels in different environments, improving safety and advancing scientific research.

What is an Artificial Neural Network (ANN) and how is it used here?

An Artificial Neural Network (ANN) is a computational model inspired by the human brain's structure and function. It learns complex patterns from data, making it useful for tasks like image recognition and, in this case, analyzing data from radiation detectors. The ANN is trained to analyze the light patterns produced by a Plastic Rod Scintillator when exposed to gamma rays. The ANN's role here is to analyze the signals from the Plastic Rod Scintillator to determine the position of the gamma-ray source. The implications are faster and more accurate evaluation processes in radiation detection.

What is a Photomultiplier Tube (PMT) and what is its role in this process?

Photomultiplier Tubes (PMTs) are used in the new technique to detect the light emitted by the Plastic Rod Scintillator. A PMT is attached to the Plastic Rod Scintillator and converts the light emitted into an electrical signal, which can then be analyzed. The importance is in its ability to detect and amplify the faint light signals produced when the scintillator interacts with radiation, which is essential for accurate detection. PMTs enable the use of ANNs in determining the location of the gamma rays. This process is critical for creating accurate maps of radiation sources.

What are some potential applications of this new radiation detection method?

This technique can be used in medical imaging to create more detailed and accurate scans, and in industrial safety to improve radiation monitoring. In medical imaging, the improved precision of the detector allows for the creation of clearer images, which can lead to better diagnoses. In industrial settings, the increased accuracy in radiation detection can ensure the safety of workers and the environment. The implications include improved accuracy, faster results, and the possibility of cheaper and more effective detection methods.