Hovgaardkure3905

Recently, we introduced a bi-objective optimization approach based on dose-volume indices to automatically create clinically good HDR prostate brachytherapy plans. To calculate dose-volume indices, a reconstruction algorithm is used to determine the 3D organ shape from 2D contours, inevitably containing settings that influence the result. We augment the optimization approach to quickly find plans that are robust to differences in 3D reconstruction.

Studied reconstruction settings were interpolation between delineated organ contours, overlap between contours, and organ shape at the top and bottom contour. Two options for each setting yields 8 possible 3D organ reconstructions per patient, over which the robust model defines minimax optimization. selleck chemical For the original model, settings were based on our treatment planning system. Both models were tested on data of 26 patients and compared by re-evaluating selected optimized plans both in the original model (1 organ reconstruction, the difference determines the cosf 3D organ reconstruction settings, can be obtained using our robust optimization approach. With its limited effect on total runtime, our approach therefore offers a way to account for dosimetry uncertainties that result from choices in organ reconstruction settings that is viable in clinical practice.We performed a theoretical study of the chemical ordering and surface segregation of Pt-Ag nanoalloys in the range of size from 976 to 9879 atoms (3.12 to 6.76 nm). We used an original many-body potential able to stabilize the L1$_1$ ordered phase at equiconcentration leading to a strong silver surface segregation. Based on a recent experimental study where nanoparticles up to 2.5 nm have been characterized by high transmission electron microscopy with the L1$_1$ ordered phase in the core and a silver surface shell, we predict in our model via Monte Carlo simulations that the lower energy configuration is more complicated with a three-shell alternance of Ag/Pt/Ag from the surface surrounding the L1$_1$ ordered phase in the core. The stress analysis demonstrates that this structure softens the local stress distribution inside the nanoparticle which contributes to reduce the internal energy.We report on the innovative approaches we developed for the fabrication of site-controlled semiconductor nanostructures [e.g. quantum dots (QDs), nanowires], based on the spatially selective incorporation and/or removal of hydrogen in dilute nitride semiconductor alloys [e.g. Ga(AsN) and (InGa)(AsN)]. In such systems, the formation of stable nitrogen-hydrogen complexes removes the effects nitrogen has on the alloy properties, which in turn paves the way to the direct engineering of the material's electronic-and, thus, optical-properties not only the bandgap energy, but also the refractive index and the polarization properties of the system can indeed be tailored with high precision and in a reversible manner. Here, lithographic approaches and/or plasmon-assisted optical irradiation-coupled to the ultra-sharp diffusion profile of hydrogen in dilute nitrides-are employed to control the hydrogen implantation and/or removal process at a nanometer scale. This results in a highly deterministic control of the spatial and spectral properties of the fabricated nanostructures, eventually obtaining semiconductor nanowires with controlled polarization properties, as well as site-controlled QDs with an extremely high control on their spatial and spectral properties. The nanostructures fabricated with these techniques, whose optical properties have also been simulated by finite-element-method calculations, are naturally suited for a deterministic coupling in optical nanocavities (i.e. photonic crystal cavities and circular Bragg resonators) and are therefore of potential interest for emerging quantum technologies.Recently, artificial intelligence technologies and algorithms have become a major focus for advancements in treatment planning for radiation therapy. As these are starting to become incorporated into the clinical workflow, a major concern from clinicians is not whether the model is accurate, but whether the model can express to a human operator when it does not know if its answer is correct. We propose to use Monte Carlo Dropout (MCDO) and the bootstrap aggregation (bagging) technique on deep learning (DL) models to produce uncertainty estimations for radiation therapy dose prediction. We show that both models are capable of generating a reasonable uncertainty map, and, with our proposed scaling technique, creating interpretable uncertainties and bounds on the prediction and any relevant metrics. Performance-wise, bagging provides statistically significant reduced loss value and errors in most of the metrics investigated in this study. The addition of bagging was able to further reduce errors by another 0.34% for [Formula see text] and 0.19% for [Formula see text] on average, when compared to the baseline model. Overall, the bagging framework provided significantly lower mean absolute error (MAE) of 2.62, as opposed to the baseline model's MAE of 2.87. The usefulness of bagging, from solely a performance standpoint, does highly depend on the problem and the acceptable predictive error, and its high upfront computational cost during training should be factored in to deciding whether it is advantageous to use it. In terms of deployment with uncertainty estimations turned on, both methods offer the same performance time of about 12 s. As an ensemble-based metaheuristic, bagging can be used with existing machine learning architectures to improve stability and performance, and MCDO can be applied to any DL models that have dropout as part of their architecture.The aim of the present study was to investigate the accuracy of localization and rotational orientation detection of a directional deep brain stimulation (DBS) electrode using a state-of-the-art magnetoencephalography (MEG) scanner. A directional DBS electrode along with its stimulator was integrated into a head phantom and placed inside the MEG sensor array. The electrode was comprised of six directional and two omnidirectional contacts. Measurements were performed while stimulating with different contacts and parameters in the phantom. Finite element modeling and fitting approach were used to compute electrode position and orientation. The electrode was localized with a mean accuracy of 2.2 mm while orientation was determined with a mean accuracy of 11°. The limitation in detection accuracy was due to the lower measurement precision of the MEG system. Considering an ideal measurement condition, these values represent the lower bound of accuracy that can be achieved in patients. However, a future magnetic measuring system with higher precision will potentially detect location and orientation of a DBS electrode with an even increased accuracy.