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Radioisotopes

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Comparison of machine learning approaches for radioisotope identification using NaI(TI) gamma-ray spectrum.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine
This research aims at comparing the performance of different machine learning algorithms used for NaI(TI) gamma-ray detector based radioisotope identification. Six machine learning algorithms were implemented, including support vector machine (SVM), ...

A new radionuclide identification method for low-count energy spectra with multiple radionuclides.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine
Radionuclide identification is to recognize the radionuclides in the environment by analyzing the energy spectrum. Rapid and accurate identification is important for nuclear security. Current radionuclide identification methods based on traditional p...

Tritium: Its relevance, sources and impacts on non-human biota.

The Science of the total environment
Tritium (H) is a radioactive isotope of hydrogen that is abundantly released from nuclear industries. It is extremely mobile in the environment and in all biological systems, representing an increasing concern for the health of both humans and non-hu...

Reducing scan time in Lu planar scintigraphy using convolutional neural network: A Monte Carlo simulation study.

Journal of applied clinical medical physics
PURPOSE: The aim of this study was to reduce scan time in Lu planar scintigraphy through the use of convolutional neural network (CNN) to facilitate personalized dosimetry for Lu-based peptide receptor radionuclide therapy.

Machine learning to predict environmental dose rates from a radionuclide therapy service - a proof of concept study.

Journal of radiological protection : official journal of the Society for Radiological Protection
The Ionising Radiation Regulations 2017 requires prior risk assessment calculations and regular environmental monitoring of radiation doses. However, the accuracy of prior risk assessments is limited by assumptions and monitoring only provides retros...

FPGA-based fast bin-ratio spiking ensemble network for radioisotope identification.

Neural networks : the official journal of the International Neural Network Society
In this work, we demonstrate the training, conversion, and implementation flow of an FPGA-based bin-ratio ensemble spiking neural network applied for radioisotope identification. The combination of techniques including learned step quantisation (LSQ)...

A Deep-Learning-Based Partial-Volume Correction Method for Quantitative Lu SPECT/CT Imaging.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine
With the development of new radiopharmaceutical therapies, quantitative SPECT/CT has progressively emerged as a crucial tool for dosimetry. One major obstacle of SPECT is its poor resolution, which results in blurring of the activity distribution. Es...

RAPTOR-AI: An open-source AI powered radiation protection toolkit for radioisotopes.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine
Artificial intelligence (AI) has gained significant attention in various scientific fields due to its ability to process large datasets. In nuclear radiation physics, while AI presents exciting opportunities, it cannot replace physics-based models es...