Radiation dose estimation with multiple artificial neural networks in dicentric chromosome assay.

PURPOSE: The dicentric chromosome assay (DCA), often referred to as the 'gold standard' in radiation dose estimation, exhibits significant challenges as a consequence of its labor-intensive nature and dependency on expert knowledge. Existing automated technologies face limitations in accurately identifying dicentric chromosomes (DCs), resulting in decreased precision for radiation dose estimation. Furthermore, in the process of identifying DCs through automatic or semi-automatic methods, the resulting distribution could demonstrate under-dispersion or over-dispersion, which results in significant deviations from the Poisson distribution. In response to these issues, we developed an algorithm that employs deep learning to automatically identify chromosomes and perform fully automatic and accurate estimation of diverse radiation doses, adhering to a Poisson distribution. MATERIALS AND METHODS: The dataset utilized for the dose estimation algorithm was generated from 30 healthy donors, with samples created across seven doses, ranging from 0 to 4 Gy. The procedure encompasses several steps: extracting images for dose estimation, counting chromosomes, and detecting DC and fragments. To accomplish these tasks, we utilize a diverse array of artificial neural networks (ANNs). The identification of DCs was accomplished using a detection mechanism that integrates both deep learning-based object detection and classification methods. Based on these detection results, dose-response curves were constructed. A dose estimation was carried out by combining a regression-based ANN with the Monte-Carlo method. RESULTS: In the process of extracting images for dose analysis and identifying DCs, an under-dispersion tendency was observed. To rectify the discrepancy, classification ANN was employed to identify the results of DC detection. This approach led to satisfaction of Poisson distribution criteria by 32 out of the initial pool of 35 data points. In the subsequent stage, dose-response curves were constructed using data from 25 donors. Data provided by the remaining five donors served in performing dose estimations, which were subsequently calibrated by incorporating a regression-based ANN. Of the 23 points, 22 fell within their respective confidence intervals at p < .05 (95%), except for those associated with doses at levels below 0.5 Gy, where accurate calculation was obstructed by numerical issues. The accuracy of dose estimation has been improved for all radiation levels, with the exception of 1 Gy. CONCLUSIONS: This study successfully demonstrates a high-precision dose estimation method across a general range up to 4 Gy through fully automated detection of DCs, adhering strictly to Poisson distribution. Incorporating multiple ANNs confirms the ability to perform fully automated radiation dose estimation. This approach is particularly advantageous in scenarios such as large-scale radiological incidents, improving operational efficiency and speeding up procedures while maintaining consistency in assessments. Moreover, it reduces potential human error and enhances the reliability of results.

Feasibility Study on Automatic Interpretation of Radiation Dose Using Deep Learning Technique for Dicentric Chromosome Assay.

The interpretation of radiation dose is an important procedure for both radiological operators and persons who are exposed to background or artificial radiations. Dicentric chromosome assay (DCA) is one of the representative methods of dose estimation that discriminates the aberration in chromosomes modified by radiation. Despite the DCA-based automated radiation dose estimation methods proposed in previous studies, there are still limitations to the accuracy of dose estimation. In this study, a DCA-based automated dose estimation system using deep learning methods is proposed. The system is comprised of three stages. In the first stage, a classifier based on a deep learning technique is used for filtering the chromosome images that are not appropriate for use in distinguishing the chromosome; 99% filtering accuracy was achieved with 2,040 test images. In the second stage, the dicentric rate is evaluated by counting and identifying chromosomes based on the Feature Pyramid Network, which is one of the object detection algorithms based on deep learning architecture. The accuracies of the neural networks for counting and identifying chromosomes were estimated at over 97% and 90%, respectively. In the third stage, dose estimation is conducted using the dicentric rate and the dose-response curve. The accuracies of the system were estimated using two independent samples; absorbed doses ranging from 1- 4 Gy agreed well within a 99% confidential interval showing highest accuracy compared to those in previous studies. The goal of this study was to provide insights towards achieving complete automation of the radiation dose estimation, especially in the event of a large-scale radiation exposure incident.

Measurement of isomeric yield ratios of 99m,g;101m,g;102m,gRh in the natPd(γ,pxn) reactions with the bremsstrahlung end-point energies of 50-70MeV.

The isomeric yield ratios of 99m,gRh, 101m,ggRh, and 102m,gRh isomeric pairs produced from the natPd(γ,pxn) reactions were measured with the bremsstrahlung end-point energies of 50-, 55-, 60-, 65-, and 70-MeV at the 100MeV electron linac of the Pohang Accelerator Laboratory, Korea. The measurements were carried out with the activation method in combination with off-line γ-ray spectrometry. In order to improve the accuracy of the activity measurements, the separation of the overlapping γ-rays and the necessary corrections for the counting losses were made. The new experimental results are compared with the theoretical values of the TALYS-1.6 code.