25 research outputs found
Modelling small block aperture in an in-house developed GPU-accelerated Monte Carlo-based dose engine for pencil beam scanning proton therapy
Purpose: To enhance an in-house graphic-processing-unit (GPU) accelerated
virtual particle (VP)-based Monte Carlo (MC) proton dose engine (VPMC) to model
aperture blocks in both dose calculation and optimization for pencil beam
scanning proton therapy (PBSPT)-based stereotactic radiosurgery (SRS). Methods
and Materials: A block aperture module was integrated into VPMC. VPMC was
validated by an opensource code, MCsquare, in eight water phantom simulations
with 3cm thick brass apertures: four were with aperture openings of 1, 2, 3,
and 4cm without a range shifter, while the other four were with same aperture
opening configurations with a range shifter of 45mm water equivalent thickness.
VPMC was benchmarked with MCsquare and RayStation MC for 10 patients with small
targets (average volume 8.4 cc). Finally, 3 patients were selected for robust
optimization with aperture blocks using VPMC. Results: In the water phantoms,
3D gamma passing rate (2%/2mm/10%) between VPMC and MCsquare were
99.710.23%. In the patient geometries, 3D gamma passing rates (3%/2mm/10%)
between VPMC/MCsquare and RayStation MC were 97.792.21%/97.781.97%,
respectively. The calculation time was greatly decreased from 112.45114.08
seconds (MCsquare) to 8.206.42 seconds (VPMC), both having statistical
uncertainties of about 0.5%. The robustly optimized plans met all the
dose-volume-constraints (DVCs) for the targets and OARs per our institutional
protocols. The mean calculation time for 13 influence matrices in robust
optimization by VPMC was 41.6 seconds. Conclusion: VPMC has been successfully
enhanced to model aperture blocks in dose calculation and optimization for the
PBSPT-based SRS.Comment: 3 tables, 3 figure
Production Scheduling Requirements to Smart Manufacturing
The production scheduling has attracted a lot of researchers for many years, however most of the approaches are not targeted to deal with real manufacturing environments, and those that are, are very particular for the case study. It is crucial to consider important features related with the factories, such as products and machines characteristics and unexpected disturbances, but also information such as when the parts arrive to the factory and when should be delivered. So, the purpose of this paper is to identify some important characteristics that have been considered independently in a lot of studies and that should be considered together to develop a generic scheduling framework to be used in a real manufacturing environment.authorsversionpublishe
Objective comparison of particle tracking methods
Particle tracking is of key importance for quantitative analysis of intracellular dynamic processes from time-lapse microscopy image data. Because manually detecting and following large numbers of individual particles is not feasible, automated computational methods have been developed for these tasks by many groups. Aiming to perform an objective comparison of methods, we gathered the community and organized an open competition in which participating teams applied their own methods independently to a commonly defined data set including diverse scenarios. Performance was assessed using commonly defined measures. Although no single method performed best across all scenarios, the results revealed clear differences between the various approaches, leading to notable practical conclusions for users and developers