AC-PDP Algorithm for 3D Dose Error Detection

The patent introduces an intermediary computational model that translates raw dose measurement data into clinically relevant metrics. This intermediary layer processes the relationship between measured dose deviations and their impact on patient dose distributions, enabling accurate error detection without requiring direct complex analysis of raw data. The intermediary model acts as a bridge between simple measurements and complex clinical assessment.

The patent replaces traditional mechanical/physical QA metrics (passing rates, Gamma Index) with a computational approach based on dose perturbation analysis. Instead of relying on simple threshold-based metrics, the system uses algorithms that calculate and compare dose distributions under different scenarios, substituting physical measurement limitations with computational power to achieve higher sensitivity and specificity.

The patent performs preliminary calculations of dose perturbation effects before final QA assessment. By pre-computing the relationship between measurement deviations and patient dose impact, the system prepares clinical relevance information in advance, making it readily available during QA interpretation without requiring complex real-time analysis during the actual assessment process.

The patent creates a computational copy of the patient dose distribution and systematically perturbs it to simulate measurement errors. This copied model allows the system to evaluate the clinical impact of dose deviations without affecting the actual patient treatment plan, preserving information about error relevance while maintaining operational simplicity through virtual experimentation.