Abstract Introduction Radiation damage to the central nervous system (CNS) has been a persistent problem for decades, owing to difficulties such as brain radiotherapy and astronaut radiation protection during space flight. Hippocampus is the most radiation-sensitive structure of the central nervous system. The present study aims to investigate damage induced by 12 C ions and proton radiation in pyramidal neurons of cornu ammonis regions and dentate granule neurons using Geant4 toolkit. Materials and Methods The Geant4/Geant4-DNA Monte Carlo toolkits were used to simulate the neuron shape and particle track structures. The computations were done for various energy proton beams and 12 C particles with different linear energy transfers from a few to hundreds of keV/µm. Damage to pyramidal and dentate granule neurons of hippocampus as well as length reduction of the dendrites were studied by the dose absorbed in dendrite and D TH (dendrites threshold dose). Also, in the present study, using Spearman’s correlation test, we examined the correlation between the morphological characteristics of neurons and the reduction in the length of their dendrites. Results According to the results obtained in this study, the apical dendrites of CA3 and the basal dendrites of CA1 were more vulnerable following proton irradiation and carbon ions with different energies under proton and carbon radiation. But, the pyramidal neurons of CA2 were more resistant to radiation than those of CA1and CA3 regions. Furthermore, the dentate granule neurons were more resistant to radiation than the pyramidal neurons. According to the results of Spearman’s correlation test, there was a statistical significance correlation (p-value< 0.05) between some of the morphological characteristics of neurons and the reduction in the length of their dendrites. Conclusion The results indicate that the neuron morphology is an important factor determining the accumulation of absorbed dose and length reduction of neurons. By evaluating the neurons with different morphology from the hippocampus, it was found that the dentate granule and pyramidal neurons had different vulnerabilities to radiation. The pyramidal neurons with less densely packed are more resistant to radiation.

Abstract Introduction Contrast-enhanced spectral mammography (CEM) is based on dual-energy breast exposure following intravenous administration of an iodinated contrast agent. Our study quantitatively assessed radiation dose from CEM for different breast thicknesses and compared it with doses from full-field digital mammography (FFDM) and with the dose limits set in the European Guidelines. Materials and Methods We studied 200,976 exposures from 59,438 examinations performed between January 2019 and December 2023 at the National Research Institute of Oncology, using a GE Healthcare Pristina Senographe mammography unit. Radiation exposure–related data, including average glandular dose (AGD) values, were obtained from the GE DoseWatch monitoring system. Doses for CEM exposure pairs (low- and high-energy exposure) were summed. Results Out of 176,700 FFDM exposures, the acceptable dose level was exceeded only in 11 cases (less than 0.01%). For combined CEM exposures, the acceptable dose level was exceeded in 965 out of 5481 cases (17.6%). The doses in FFDM exposures and low-energy CEM exposures are similar. Average glandular dose for FFDM exposures and low-energy CEM exposures is clearly dependent on the breast thickness. In contrast, the dose for a single high-energy CEM exposure is about 0.7 mGy and shows no clear dependency on breast thickness. Acceptable dose levels for combined CEM exposures were exceeded primarily with breasts with small thicknesses, most often (70% of cases) in patients whose breasts were less than 4 cm thick. For breasts from 6 cm and larger, no CEM exposures exceeded acceptable dose levels. Conclusions Although CEM exposures are a combination of two exposures (low- and high-energy), in most cases, the doses associated with CEM exposure do not exceed limits that were originally set for single FFDM exposures.

Abstract Introduction Low-level laser therapy (LLLT), also known as photobiomodulation, has emerged as a promising therapeutic option for various medical applications, including pain management and wound healing. This study aims to investigate the dose parameters of LLLT to optimize therapeutic efficacy. Materials and Methods We utilized Finite Element Analysis within the COMSOL Multiphysics software package to model light-tissue interactions and refine dosing protocols. Lasers with wavelengths of 660 nm, 780 nm, and 808 nm were selected due to their widespread use in therapy. Additionally, we examined several factors that impact the effectiveness of the treatment. Key parameters considered include energy, energy density, power, power density, irradiation time, and tissue penetration depth. Results The recommended stimulation time should not exceed six minutes (480 seconds) at a power density of 15.62 mW/cm². However, if the power density is reduced to a maximum of 3.10 mW/cm 2 , the stimulation time can be safely extended to 10 minutes (600 seconds) without causing undesirable thermal effects, as long as the tissue temperature does not exceed 40°C during the extended stimulation. It is important to note that the dose applied to the surface of the tissue significantly decreases as it penetrates deeper. The average energy loss is approximately 11% per millimetre of tissue. Our simulations indicate that effective doses range from 0.38 J/cm² to 9.37 J/cm² while maintaining safe tissue temperatures, which are consistent with WALT recommendations. Conclusion Our findings help identify factors influencing stimulation, guiding therapists to standardize treatment parameters such as wavelength, exposure time, and dosages measured in joules, watts, W/cm², and J/cm² for consistency and safety across studies.

Abstract Introduction A special type of radiotherapy is Intraoperative Electron Radiation Therapy (IOERT). The electron applicator is the element forming the final shape of the electron beam delivered to the postoperative bed. This work presents the new design of an electron applicator made from the biocompatible material MED610. The applicators will form an electron beam in the AQURE intraoperative accelerator. Material and methods The main problem to be solved during the construction work was to propose a design solution for the electron applicator that would be light, transparent, and made of a biocompatible material, and at the same time sufficiently shield the patient against radiation scattered outside its walls. Moreover, the applicator used in intraoperative therapy should have the thinnest possible walls. Results The calculations and design work performed allowed the lower part of the applicator to be made of a biocompatible, material. This element allows the beam to be formed in accordance with the normative recommendations, it is light and transparent. In this case, PolyJet 3D printing was selected as the most appropriate method. Conclusions Thanks to the use of a new solution for the shape of the walls, the applicator limits the leakage radiation by the standard, without the need for thicker outer walls. This material is light, thanks to which the doctor keeps this part of the applicator in the right position until it is fastened with the part on the head that does not require additional, dedicated fastening. The resin MED610 is suitable for 3D printing, it can be easily used to create any shape, and thus the possibilities of forming the lower part of the applicator are not limited. In the future, for individual purposes, the shape of the beam formed by the lower part of the applicator may be dedicated to special applications.

Abstract Introduction In this article, the effectiveness of two commercial metal artifact reduction algorithms, Siemens iMAR and GE MARS, in computed tomography (CT) imaging is evaluated. Metal artifacts, which arise primarily due to the presence of high atomic number metals in clinical imaging, significantly degrade image quality and impede accurate diagnostics. Material and methods The study compares monoenergetic and dual-energy CT reconstruction algorithms by examining their performance on phantom models, including a Gammex Tissue Characterization Phantom and a custom-made spine stabilization system phantom. Quantitative assessments, such as Hounsfield unit analysis were performed. Results The results show that the iterative reconstruction algorithm (iMAR) from Siemens offers superior artifact suppression and image clarity compared to GE’s dual-energy algorithm (MARS), particularly in cases involving titanium implants. Quantitative assessments, such as Hounsfield unit measurements and visual image analysis, confirm that iMAR produces images with reduced artifacts and more consistent tissue characterization. Conclusions These findings suggest that the choice of artifact reduction algorithm has a profound impact on the diagnostic and planning accuracy of CT scans in patients with metal implants.

Abstract Introduction Radiotherapy aims to precisely target tumors while sparing healthy tissue, traditionally relying on CT imaging for accurate dose planning. However, CT has limitations in soft tissue contrast and exposes patients to ionizing radiation. MRI offers superior soft tissue contrast without radiation but lacks electron density information, restricting its use in dose planning. This study addresses this gap by developing deep learning models to generate pseudo-CT images from MRI, enabling MRI-only workflows in radiotherapy. Methodology Paired MRI and CT scans from 12 subjects were processed using normalization, alignment, and masking. Four deep learning architectures (U-Net, Pix2Pix, CycleGAN, and conditional GAN (cGAN)) were trained to generate synthetic CT images from MRI data. Model performance was evaluated using metrics including mean absolute error (MAE), mean squared error (MSE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and Pearson correlation coefficient (PCC). Results Pix2Pix achieved the highest SSIM and PSNR, indicating strong structural preservation and reduced noise. It also had the lowest MAE and MSE, showing high accuracy in synthetic-CT generation. The cGAN model scored highest in PCC, highlighting its effective intensity alignment with real CT data. Statistical tests confirmed Pix2Pix’s superior performance, though CycleGAN and cGAN also showed notable results in alignment accuracy. Conclusion Deep learning models, particularly Pix2Pix, can generate reliable pseudo-CT images from MRI, supporting MRI-only radiotherapy planning. This approach reduces radiation exposure and may streamline radiotherapy work-flows, offering a promising advance for patient-centered cancer care and MRI-only radiotherapy workflows.

Abstract This systematic review examines the establishment of Diagnostic Reference Levels (DRLs) in digital mammography across 18 European countries, based on studies from 2005-2025. A total of 353 articles were identified through the comprehensive search of academic networks: Google Scholar, PubMed, Research Gate, Academia. Only 18 peer-reviewed studies met inclusion criteria – reporting Mean Glandular Dose (MGD)-based DRLs from Finland to Malta. Eight studies used patient data, four used phantom measurements, and six used both. To overcome the challenging comparison of the variety of reported parameters, we undertook some data harmonisation procedures, focusing on a common Compressed Breast Thickness (CBT) range of 50-59 mm. The DRLs varied notably by country, with 75 th percentile MGDs ranging from 1.1 to 2.6 mGy and 95 th percentile from 1.6 to 2.9 mGy, averaging to 1.44 mGy, which is lower than the achievable European level (2 mGy). The harmonisation approach enabled the derivation of a comparable dataset of average MGDs, facilitating cross-country comparisons and insights into radiation dose optimisation in digital mammography across Europe.

Abstract Introduction With the increasing number of pediatric computed tomography (CT) examinations, there is a need to optimise protocols for children by adopting examination-specific protocols customised to the patient’s age, size, imaging region, and clinical indication. This study aimed to assess the radiation doses in pediatric CT examinations and compare them to international standards. Material and methods A cross-sectional retrospective study design was adopted to probe patient records at the radiology department of a teaching hospital in Ghana. Thus, scan parameters, volume computed tomography dose index (CTDI vol ), dose length product (DLP), as well as demographic data, were recorded from 496 pediatric patients (age 0-15 years) undergoing head, chest, and abdominopelvic CT examinations. Local Diagnostic Reference Levels (LDRLs) were established using the 75th percentile of patient dose values for each protocol and age group. These local levels were then compared with DRLs from other studies. Results Head CT was the most performed examination (35.0%) compared to chest (32.0%) and abdominopelvic (33.0%). The male group recorded the highest (59.1%) percentage of CT examinations compared to the female group. While LDRL values from this study were generally lower than data from other studies, the CTDI vol and DLP for head scans of patients between 11 and 15 years were found to be higher than the data from other studies. Conclusions Our study has established LDRLs for standard pediatric CT examinations in the teaching hospital. The LDRLs were generally lower than those reported in other studies, except for head scans in patients aged 11 to 15 years. These findings suggest that there are opportunities for further optimisation of pediatric CT imaging protocols at this facility.

Abstract Introduction This study aimed to evaluate the efficacy of TECAR therapy in treating MTrP using thermographic imaging as a diagnostic tool. Material and methods The study was conducted on a group of 13 volunteers aged 25 to 45 years (mean age 34.3 ± 6.4 years) who were diagnosed with active MTrP in the UT muscle. Thermography measured skin temperature changes in the treated areas before and after TECAR therapy. Results The study revealed a correlation between changes in skin temperature and clinical pain indicators, as measured by the NRS and PPT. Initial results showed no significant correlations between temperature parameters and pain scales. However, after seven days of TECAR therapy, a strong, positive correlation was observed between temperature and NRS scores (p = 0.004, r = 0.8), suggesting that TECAR therapy affects temperature in the MTrP area, resulting in a pain decrease. Conclusions The results obtained over 30 days of therapy showed variability in thermal response, indicating the need for further research to fully understand the mechanisms of action of TECAR therapy in the treatment of MTrP. Nevertheless, the observed strong correlation after seven days of therapy suggests promising possibilities for using thermography to monitor treatment effects.