Best Hansson (mindmoon7)

The head circumference-for-age from WHO was extrapolated for male and females individuals up to 18 years-old and their respective water equivalent diameter were estimated. Finally, the SSDE was calculated for all the CT head scans performed in a 9-years period in patients aged from 0 to 18 years old. Typical values of CTDIvol,16cmand DLP were also defined. SSDE varied from 0.80 up to 1.16 of the CTDIvol,16cm, depending on sex and age of the patient. WHO-estimated water equivalent diameter-for-age differed less than 20% from the measured water equivalent diameter-for-age. Typical values of SSDE varied from 28.5 up to 38.9 mGy, while typical values ranged from 30.9 up to 47.6 mGy for the CTDIvol,16cmand from 417.6 up to 861.1 mGy*cm for the DLP. SSDE can be directly calculated for head CT scans once the age of the patient is known.Target volume delineation is a vital but time-consuming and challenging part of radiotherapy, where the goal is to deliver sufficient dose to the target while reducing risks of side effects. For head and neck cancer (HNC) this is complicated by the complex anatomy of the head and neck region and the proximity of target volumes to organs at risk. The purpose of this study was to compare and evaluate conventional PET thresholding methods, six classical machine learning algorithms and a 2D U-Net convolutional neural network (CNN) for automatic gross tumor volume (GTV) segmentation of HNC in PET/CT images. For the latter two approaches the impact of single vs. multimodality input on segmentation quality was also assesed. 197 patients were included in the study. The cohort was split into training and test sets (157 and 40 patients, respectively). Five-fold cross-validation was used on the training set for model comparison and selection. Manual GTV delineations represented the ground truth. Tresholding, classical machine learning and CNN segmentation models were ranked separately according to the cross-validation Sørensen-Dice similarity coefficient (Dice). PET thresholding gave a maximum mean Dice of 0.62, whereas classical machine learning resulted in maximum mean Dice scores of 0.24 (CT) and 0.66 (PET; PET/CT). CNN models obtained maximum mean Dice scores of 0.66 (CT), 0.68 (PET) and 0.74 (PET/CT). The difference in cross-validation Dice between multimodality PET/CT and single-modality CNN models was significant (p ≤ 0.0001). The top-ranked PET/CT-based CNN model outperformed the best-performing thresholding and classical machine learning models, giving significantly better segmentations in terms of cross-valdiation and test set Dice, true positive rate, positive predictive value and surface distance-based metrics (p ≤ 0.0001). Thus, deep learning based on multimodality PET/CT input resulted in superior target coverage and less inclusion of surrounding normal tissue.To further improve the understanding ofinvitrobiological effects of incorporated radionuclides, it is essential to accurately determine cellular absorbed doses. In the case of β-emitters, the cross-dose is a major contribution, and can involve up to millions of cells. Realistic and efficient computational models are needed for that purpose. Conventionally, distances between each cell are calculated and the related dose contributions are cumulated to get the total cross-dose (standard method). In this work, we developed a novel approach for the calculation of the cross-absorbed dose, based on the use of the radial distribution function (rdf) that describes the spatial properties of the cellular model considered. The dynamic molecular tool LAMMPS was used to create 3D cellular models and compute \textitrdfs for various conditions of cell density, volume size, and configuration type (lattice and randomized geometry). The novel method is suitable for any radionuclide of nuclear medicine. Here, the model was applied for the labelling of cells with18F-FDG used for PET imaging, and first validated by comparison with other reference methods. sellec