Monte Carlo Dosimetry Validation for X-Ray Guided Endovascular Procedures.

BACKGROUND: Endovascular treatment is continuously gaining ground in vascular surgery procedures. However, current patient radiation dose estimation does not take into account the exact patient morphology and organs' composition. Monte Carlo (MC) simulation can accurately estimate the dose by recreating the irradiation process generated during X-ray-guided interventions. This study aimed to validate the MC simulation models by comparing simulated and measured dose distributions in endovascular aortic aneurysm repair (EVAR) procedures. METHODS: We conducted a clinical study in patients treated for EVAR. Patient dose measurements were taken with passive dosimeters using Optically Stimulated Luminescence technology in 4 specific anatomical points on the skin: xiphoid process, pubic symphysis, right and left iliac crest. Dose measurements were compared to the corresponding simulated doses with the Geant4 Application for Emission Tomography (GATE) and GPU Geant4-based Monte Carlo Simulations (GGEMS) MC simulations softwares. The MC simulation took as input the computed tomography scan of the patient and the parameters of the imaging system (orientation angles, tube voltage, and aluminum filtration) and gives as output the three-dimensional (3D) dose map for each patient and angulation. RESULTS: A good agreement with real doses was found for doses simulated by the MC GATE method (P < 0.0001; r = 0.97; 95% confidence interval [CI] [0.96-0.98]), as well as for doses simulated by the GGEMS method (P < 0.0001; r = 0.96; 95% CI [0.94-0.97]). The mean relative error for all measurements was 5 ± 5% in the MC GATE group and 6 ± 5% in the GGEMS group. Process execution on GGEMS (6 sec) was faster than the GATE MC simulation (5 hr). CONCLUSION: Considering the current imaging settings, this study shows the potential of using the GATE and GGEMS MC simulations platforms to model the 3D dose distributions during EVAR procedures.

Fast dose calculation in x-ray guided interventions by using deep learning.

Objective.Patient dose estimation in x-ray-guided interventions is essential to prevent radiation-induced biological side effects. Current dose monitoring systems estimate the skin dose based in dose metrics such as the reference air kerma. However, these approximations do not take into account the exact patient morphology and organs composition. Furthermore, accurate organ dose estimation has not been proposed for these procedures. Monte Carlo simulation can accurately estimate the dose by recreating the irradiation process generated during the x-ray imaging, but at a high computation time, limiting an intra-operative application. This work presents a fast deep convolutional neural network trained with MC simulations for patient dose estimation during x-ray-guided interventions.Approach.We introduced a modified 3D U-Net that utilizes a patient's CT scan and the numerical values of imaging settings as input to produce a Monte Carlo dose map. To create a dataset of dose maps, we simulated the x-ray irradiation process for the abdominal region using a publicly available dataset of 82 patient CT scans. The simulation involved varying the angulation, position, and tube voltage of the x-ray source for each scan. We additionally conducted a clinical study during endovascular abdominal aortic repairs to validate the reliability of our Monte Carlo simulation dose maps. Dose measurements were taken at four specific anatomical points on the skin and compared to the corresponding simulated doses. The proposed network was trained using a 4-fold cross-validation approach with 65 patients, and evaluating the performance on the remaining 17 patients during testing.Main results.The clinical validation demonstrated a average error within the anatomical points of 5.1%. The network yielded test errors of 11.5 ± 4.6% and 6.2 ± 1.5% for peak and average skin doses, respectively. Furthermore, the mean errors for the abdominal region and pancreas doses were 5.0 ± 1.4% and 13.1 ± 2.7%, respectively.Significance.Our network can accurately predict a personalized 3D dose map considering the current imaging settings. A short computation time was achieved, making our approach a potential solution for dose monitoring and reporting commercial systems.

Genomic Organization of Repetitive DNA Elements and Its Implications for the Chromosomal Evolution of Channid Fishes (Actinopterygii, Perciformes).

Channid fishes, commonly referred to as "snakeheads", are currently very important in Asian fishery and aquaculture due to the substantial decline in natural populations because of overexploitation. A large degree of chromosomal variation has been found in this family, mainly through the use of conventional cytogenetic investigations. In this study, we analyzed the karyotype structure and the distribution of 7 repetitive DNA sequences in several Channa species from different Thailand river basins. The aim of this study was to investigate the chromosomal differentiation among species and populations to improve upon the knowledge of its biodiversity and evolutionary history. Rearrangements, such as pericentric inversions, fusions and polyploidization, appear to be important events during the karyotypic evolution of this genus, resulting in the chromosomal diversity observed among the distinct species and even among populations of the same species. In addition, such variability is also increased by the genomic dynamism of repetitive elements, particularly by the differential distribution and accumulation of rDNA sequences on chromosomes. This marked diversity is likely linked to the lifestyle of the snakehead fishes and their population fragmentation, as already identified for other fish species. The karyotypic features highlight the biodiversity of the channid fishes and justify a taxonomic revision of the genus Channa, as well as of the Channidae family as a whole, as some nominal species may actually constitute species complexes.