Textural feature calculated from segmental fluences as a modulation index for VMAT.

Purpose: Textural features calculated from various segmental fluences of volumetric modulated arc therapy (VMAT) plans were optimized to enhance its performance to predict plan delivery accuracy.

Methods: Twenty prostate and twenty head and neck VMAT plans were selected retrospectively. Fluences were generated for each VMAT plan by summations of segments at sequential groups of control points.

Modulation index for VMAT considering both mechanical and dose calculation uncertainties.

The aim of this study is to present a modulation index considering both mechanical and dose calculation uncertainties for volumetric modulated arc therapy (VMAT). As a modulation index considering only mechanical uncertainty of VMAT, MIt has been previously suggested. In this study, we developed a weighting factor which represents dose calculation uncertainty based on the aperture shapes of fluence maps at every control point of VMAT plans.

Modulation indices for volumetric modulated arc therapy.

The aim of this study is to present a modulation index (MI) for volumetric modulated arc therapy (VMAT) based on the speed and acceleration analysis of modulating-parameters such as multi-leaf collimator (MLC) movements, gantry rotation and dose-rate, comprehensively. The performance of the presented MI (MIt) was evaluated with correlation analyses to the pre-treatment quality assurance (QA) results, differences in modulating-parameters between VMAT plans versus dynamic log files, and differences in dose-volumetric parameters between VMAT plans versus reconstructed plans using dynamic log files. For comparison, the same correlation analyses were performed for the previously suggested modulation complexity score (MCS(v)), leaf travel modulation complexity score (LTMCS) and MI by Li and Xing (MI Li&Xing).

Hypergraph-based recognition memory model for lifelong experience.

Cognitive agents are expected to interact with and adapt to a nonstationary dynamic environment. As an initial process of decision making in a real-world agent interaction, familiarity judgment leads the following processes for intelligence. Familiarity judgment includes knowing previously encoded data as well as completing original patterns from partial information, which are fundamental functions of recognition memory.