Radiotherapy Treatment Planning System Using Multiple Calculation Models
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Solution Overview
Problem
Medical practitioners face difficulties in selecting the appropriate calculation models and parameter sets for creating treatment plans in radiotherapy, as different models yield varying biological effect indices, making it challenging to find a compromise plan.
Innovation Solution
A treatment planning system that utilizes multiple calculation models to calculate biological effect indices and searches for conditions where these indices approach predetermined target values, allowing for the creation of a robust treatment plan independent of a single model or parameter set.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a single calculation model and parameter set are used, then the treatment planning process is simple, but the reliability of the treatment plan is reduced due to model-specific biases and uncertainties
Solution Approach 1:
The patent combines multiple calculation models (e.g., Poisson statistical model, LQ model, EUD model) and parameter sets into a unified treatment planning system. The system integrates these diverse models to calculate biological effect indices, allowing the medical practitioner to evaluate treatment plans using multiple perspectives simultaneously, thereby improving reliability through model ensembling.
Solution Approach 2:
The treatment planning system is designed to be universal by supporting multiple calculation models and parameter sets within a single platform. The system can switch between different models (Poisson, LQ, EUD) and parameter combinations based on clinical needs, making it adaptable to various treatment scenarios and tumor types while maintaining a consistent user interface and workflow.
2Measurement precision
If multiple calculation models are used, then the accuracy and robustness of treatment planning is improved, but the ease of operation decreases due to the need to select and manage multiple models
Solution Approach 1:
The system implements self-service functionality by automatically calculating biological effect indices using multiple calculation models without requiring the medical practitioner to manually configure each model. The system pre-configures multiple parameter sets and models, and automatically executes calculations across all of them, allowing the practitioner to simply input treatment parameters and receive comprehensive results.
Solution Approach 2:
The system segments the complex task of treatment planning into distinct calculation modules, each handling a specific calculation model (Poisson, LQ, EUD). This segmentation allows the system to process different models independently and present results in an organized manner, making it easier for the medical practitioner to review and compare outcomes from different models without being overwhelmed by complexity.
3Adaptability or versatility
If multiple parameter sets are evaluated, then the comprehensiveness of treatment evaluation is improved, but the time required for treatment planning increases
Solution Approach 1:
The system maintains continuous calculation across multiple parameter sets by executing all model evaluations in an automated sequence. Once treatment parameters are input, the system continuously processes calculations through all configured models and parameter combinations without requiring manual intervention or waiting periods, thereby reducing the perceived time loss despite the comprehensive evaluation.
Solution Approach 2:
The system performs preliminary configuration of multiple calculation models and parameter sets before the actual treatment planning process. By pre-setting up the computational framework with multiple models (Poisson, LQ, EUD) and their associated parameters, the system eliminates the need for time-consuming model selection and configuration during the treatment planning session, allowing rapid evaluation across all models.
Data Source
AI summary
A treatment planning system is configured to create a treatment planning for radiotherapy and includes a processing device and a memory. The memory stores a plurality of calculation models, and the processing device uses at least two of the plurality of calculation models to calculate calculation values of biological effect indices representing an effect of radiotherapy with respect to a condition for radiotherapy, and searches for the condition so that at least two of the calculation values to be calculated approach a predetermined target value.