Radiotherapy Planning System Optimizing Dose Distribution and Delivery Time
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Solution Overview
Problem
Conventional radiotherapy treatment planning often fails to balance dosimetric objectives with treatment delivery efficiency, leading to inefficient treatment plans that are difficult to execute, particularly in terms of duration and resource utilization.
Innovation Solution
A computer-based system that generates multiple treatment plan variants by considering both dosimetric and non-dosimetric planning objectives, such as treatment duration and machine parameters, to identify Pareto-optimal solutions that improve delivery efficiency while maintaining clinical quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional radiotherapy treatment planning focuses only on dosimetric objectives, then dosimetric quality is improved, but treatment delivery efficiency deteriorates
Solution Approach 1:
The system changes the parameters considered in treatment planning by incorporating non-dosimetric parameters (treatment duration, machine parameters, resource utilization) alongside traditional dosimetric parameters. This multi-parameter optimization approach transforms the planning process to simultaneously evaluate both dosimetric quality and delivery efficiency, resolving the contradiction by expanding the parameter space rather than sacrificing one for the other.
Solution Approach 2:
The invention adds another dimension to treatment planning by introducing delivery efficiency metrics as a separate optimization dimension. Instead of single-objective dosimetric optimization, the system creates a multi-dimensional optimization framework that evaluates treatment plans across both dosimetric quality and delivery efficiency dimensions, allowing clinicians to navigate trade-offs in a expanded solution space.
2Manufacturing precision
If treatment plans are optimized for dosimetric quality alone, then radiation dose distribution is improved, but treatment duration increases
Solution Approach 1:
The system modifies the optimization parameters to include treatment duration as a explicit constraint or objective function alongside dosimetric criteria. By parameterizing both dose distribution quality and treatment time, the system generates treatment plans that explicitly balance these competing requirements, preventing excessive treatment durations while maintaining dosimetric standards.
Solution Approach 2:
The system introduces dynamic optimization by allowing treatment plans to adapt between different dosimetric and efficiency priorities. The multi-objective framework enables dynamic trade-off adjustment where treatment duration can be extended or reduced based on clinical priorities, machine availability, and patient-specific factors, creating a more flexible and realistic planning approach.
3Manufacturing precision
If complex treatment plans are generated to achieve dosimetric objectives, then dose conformity is improved, but ease of operation deteriorates
Solution Approach 1:
The system changes the evaluation parameters to include operational simplicity metrics alongside dosimetric quality. By incorporating ease of execution as a quantifiable parameter in the multi-objective optimization, the system automatically balances dose conformity requirements with treatment execution simplicity, preventing overly complex plans that would be difficult to deliver in practice.
Data Source
AI summary
Example methods and systems for radiotherapy treatment planning based on treatment delivery efficiency are described. One example method may comprise a computer system configuring dosimetric planning objective(s) and non-dosimetric planning objective(s) associated with efficiency of treatment delivery. A set of multiple treatment plan variants may be generated based on the dosimetric planning objective(s) and non-dosimetric planning objective(s). A first treatment plan associated with a first tradeoff and a second treatment plan associated with a second tradeoff may then be identified from the set of multiple treatment plan variants. The second treatment plan may be associated with improved efficiency of treatment delivery compared to the first treatment plan.