Search Results (2)

Real-time tumor-tracked radiotherapy with a linear accelerator-magnetic resonance (linac-MR) hybrid system requires accurate tumor delineation at a fast MR imaging rate. Various autocontouring methods have been previously evaluated against “gold standard” manual contours by experts. However, manually drawn contours have inherent intra- and inter-observer variations. We aim to quantify these variations and evaluate our tumor-autocontouring algorithm against the manual contours. Ten liver, ten prostate, and ten lung cancer patients were scanned using a 3 tesla (T) magnetic resonance imaging (MRI) scanner with a 2D balanced steady-state free precession (bSSFP) sequence at 4 frames/s. Three experts manually contoured the tumor in two sessions. For autocontouring, an in-house built U-Net-based autocontouring algorithm was used, whose hyperparameters were optimized for each patient, expert, and session (PES). For evaluation, (A) Automatic vs. Manual and (B) Manual vs. Manual contour comparisons were performed. For (A) and (B), three types of comparisons were performed: (a) same expert same session, (b) same expert different session, and (c) different experts, using Dice coefficient (DC), centroid displacement (CD), and the Hausdorff distance (HD). For (A), the algorithm was trained using one expert’s contours and its autocontours were compared to contours from (a)–(c). For Automatic vs. Manual evaluations (Aa–Ac), DC = 0.91, 0.86, 0.78, CD = 1.3, 1.8, 2.7 mm, and HD = 3.1, 4.6, 7.0 mm averaged over 30 patients were achieved, respectively. For Manual vs. Manual evaluations (Ba–Bc), DC = 1.00, 0.85, 0.77, CD = 0.0, 2.1, 2.8 mm, and HD = 0.0, 4.9, 7.2 mm were achieved, respectively. We have quantified the intra- and inter-observer variations in manual contouring of liver, prostate, and lung patients. Our PES-specific optimized algorithm generated autocontours with agreement levels comparable to these manual variations, but with high efficiency (54 ms/autocontour vs. 9 s/manual contour). Full article

The ongoing diffusion of solid-state DC/DC converters makes possible a partial migration of electric power systems from the present AC paradigm to a future DC scenario. In addition, the power demand in the domestic environment is expected to grow considerably, for example, due to the progressive diffusion of electric vehicles, induction cooking and heat pumps. To face this evolution, the paper introduces a novel electric topology for a hybrid AC/DC smart house, based on the solid-state transformer technology. The electric scheme, voltage levels and converters types are thoroughly discussed to better integrate the spread of electric appliances, which are frequently based on internal DC buses, within the present AC distribution networks. Voltage levels are determined to guarantee high safety zones with negligible electric risk in the most exposed areas of the house. At the same time, the developed control schemes assure high power quality (voltage stability in the case of both load variations and network perturbations), manage power flows and local resources according to ancillary services requirements and increase the domestic network overall efficiency. Dynamic simulations are performed, making use of DIgSILENT PowerFactory software, to demonstrate the feasibility of the proposed distribution scheme for next-generation smart houses under different operating conditions. Full article