Radioprotection issues in uraniferous minerals collections with reference to an actual case.

Our work investigated the radioprotection implications associated with the possession of a collection of uraniferous minerals. Considering different scenarios, we developed (and applied to an actual collection) specific formulas for radiation doses evaluation. We discussed the shielding necessary to reduce the gamma irradiation down to the required values. A mathematical model was developed to estimate the minimum air flow rate to reduce the radon air concentration below the reference values. The radiation risks associated to the handling of single specimens was also addressed, including hand skin irradiation and shielding capabilities of surgical lead gloves. Finally, we discussed the radiation risks associated to the exhibition of a single specimen. The results, compared to the safety standards of the EU Directive 13/59, show that the exhibition of uraniferous samples with activity of a few MBq do not need specific radioprotection requirements nor for the involved personnel nor for visitors.

UNet and MobileNet CNN-based model observers for CT protocol optimization: comparative performance evaluation by means of phantom CT images.

Purpose: The aim of this work is the development and characterization of a model observer (MO) based on convolutional neural networks (CNNs), trained to mimic human observers in image evaluation in terms of detection and localization of low-contrast objects in CT scans acquired on a reference phantom. The final goal is automatic image quality evaluation and CT protocol optimization to fulfill the ALARA principle. Approach: Preliminary work was carried out to collect localization confidence ratings of human observers for signal presence/absence from a dataset of 30,000 CT images acquired on a PolyMethyl MethAcrylate phantom containing inserts filled with iodinated contrast media at different concentrations. The collected data were used to generate the labels for the training of the artificial neural networks. We developed and compared two CNN architectures based respectively on Unet and MobileNetV2, specifically adapted to achieve the double tasks of classification and localization. The CNN evaluation was performed by computing the area under localization-ROC curve (LAUC) and accuracy metrics on the test dataset. Results: The mean of absolute percentage error between the LAUC of the human observer and MO was found to be below 5% for the most significative test data subsets. An elevated inter-rater agreement was achieved in terms of S-statistics and other common statistical indices. Conclusions: Very good agreement was measured between the human observer and MO, as well as between the performance of the two algorithms. Therefore, this work is highly supportive of the feasibility of employing CNN-MO combined with a specifically designed phantom for CT protocol optimization programs.

ACTIVITY ESTIMATION WHEN DEALING WITH COLLECTIONS OF URANIFEROUS MINERALS.

The activity estimation of hand-size specimens of uraniferous minerals is not a trivial issue due to the manipulation difficulty caused by the emitted ionising radiation and the dependence of radiometric quantities from several parameters. Sample modelling requires approximations, leading to large uncertainty in the evaluation of the activity. In this work, a new procedure to evaluate uraniferous specimens activity, including a detailed description of measured parameters, the instrumentation and the mathematical formulation of the process, is presented. The proposed methodology takes into consideration sample size, ore composition and measured radiation. The procedure was used to measure the activity of a group of uraniferous mineral specimens belonging to Natural History Museum of the University of Florence, Italy. The experimental set-up was designed to reduce the measurement uncertainty. The aim of this work is to propose a methodology that can be easily applied to the specimens manipulation, conservation and exhibition.