End-To-End Procedure For IORT in Brain Metastases and Film Dosimetry.

Purpose: The study is intended to perform an end-to-end test of the entire intraoperative process using cadaver heads. A simulation of tumor removal was performed, followed by irradiation of the bed and measurement of absorbed doses with radiochromic films. Materials and Methods: Low-energy X-ray intraoperative radiotherapy (IORT) was used for irradiation. A computed tomography study was performed at each site and the absorbed doses calculated by the treatment planning system, as well as absorbed doses with radiochromic films, were studied. Results: The absorbed doses in the organs at risk (OAR) were evaluated in each case, obtaining maximum doses within the tolerance limits. The absorbed doses in the target were verified and the deviations were <1%. Conclusions: These tests demonstrated that this comprehensive procedure is a reproducible quality assurance tool which allows continuous assessment of the dosimetric and geometric accuracy of clinical brain IORT treatments. Furthermore, the absorbed doses measured in both target and OAR are optimal for these treatments.

Neural network assisted design of plasmonic nanostructures on superconducting transition-edge-sensors for single photon detectors.

Transition edge sensors (TESs) are extremely sensitive thermometers made of superconducting materials operating at their transition temperature, where small variations in temperature give rise to a measurable increase in electrical resistance. Coupled to suitable absorbers, they are used as radiation detectors with very good energy resolution in several experiments. Particularly interesting are the applications that TESs may bring to single photon detection in the visible and infrared regimes. In this work, we propose a method to enhance absorption efficiency at these wavelengths. The operation principle exploits the generation of highly absorbing plasmons on the metallic surface. Following this approach, we report nanostructures featuring theoretical values of absorption reaching 98%, at the telecom design frequency (λ = 1550 nm). The optimization process takes into account the TES requirements in terms of heat capacity, critical temperature and energy resolution leading to a promising design for an operating device. Neural networks were first trained and then used as solvers of the optical properties of the nanostructures. The neural network topology takes the geometrical parameters, the properties of materials and the wavelength of light as input, predicting the absorption spectrum at single wavelength as output. The incorporation of the material properties and the dependence with frequency was crucial to reduce the number of required spectra for training. The results are almost indistinguishable from those calculated with a commonly used numerical method in computational electromagnetism, the finite-difference time-domain algorithm, but up to 106 times faster than the numerical simulation.