Automatic Radiation Plan Adaptation via Preliminary Action
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Solution Overview
Problem
Radiation therapy treatment plans are computationally intensive and time-consuming to develop, requiring frequent recalculations due to dynamic patient and tissue changes, leading to patient discomfort, scheduling issues, and reduced treatment effectiveness.
Innovation Solution
An automatic adaptation process for radiation treatment plans that uses initially generated imaging information and allows for user corrections, enabling on-the-fly updates to existing plans, reducing the need for extensive human review and minimizing delays.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If treatment plans are recalculated frequently to account for dynamic patient and tissue changes, then treatment effectiveness is improved, but time consumption and patient discomfort increase
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing multiple alternative treatment plans with different parameters before they are needed. When patient conditions change, the system can quickly retrieve and adapt a pre-prepared plan rather than calculating from scratch, thus maintaining treatment effectiveness while reducing adaptation time.
Solution Approach 2:
The system implements dynamics by enabling real-time or near-real-time adaptation of treatment plans based on current patient data. The plan parameters can be dynamically adjusted without requiring complete recalculation, allowing the system to respond to changing patient conditions while minimizing time loss.
2Manufacturing precision
If comprehensive treatment planning processes are used to account for all patient and tissue attributes, then treatment precision is improved, but computational complexity and time requirements increase
Solution Approach 1:
The comprehensive treatment planning process is segmented into multiple independent modules, each handling specific aspects such as geometry calculation, dose calculation, and constraint optimization. This segmentation allows the system to maintain high precision by considering all attributes while reducing overall complexity through modular design, enabling parallel processing and faster computation.
Solution Approach 2:
The system uses parameter changes to simplify the planning process by adjusting key parameters such as beam angles, energy levels, and field shapes to achieve optimal treatment plans. By focusing on critical parameters rather than all possible variables, the system maintains treatment precision while reducing computational complexity.
3Reliability
If iterative calculation processes are used to optimize treatment plans, then plan quality is improved, but calculation time increases significantly
Solution Approach 1:
The system applies partial iterative optimization by performing a limited number of optimization cycles focused on the most critical plan parameters. Rather than exhaustively optimizing all parameters through multiple iterations, the system achieves sufficient plan quality with fewer iterations, thereby improving development speed while maintaining acceptable plan quality.
Solution Approach 2:
The system skips less critical optimization steps when time is constrained, rushing through the essential calculation phases to deliver a timely treatment plan. This approach allows the system to maintain core plan quality while reducing overall calculation time by selectively omitting or simplifying non-critical iterative steps.
Data Source
AI summary
An existing radiation treatment plan is accessed for a given patient as well as first information (such as automatically generated updated information) regarding at least one physical characteristic as corresponds to the radiation treatment of this patient. One then initiates, prior to receiving second information (such as user input) regarding the first information, an automatic adaptation process to adapt the treatment plan to accommodate the first information. Upon later receiving second information regarding the first information, one then modifies the automatic adaptation process itself to incorporate the second information regarding the first information.