Radiation Treatment Plan Optimization for Time Efficiency
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Solution Overview
Problem
Current radiation therapy treatment plans are inefficient, leading to longer treatment times due to complex transitions between treatment fields, requiring manual adjustments and prolonged beam-off times, which can expose healthy tissues to unnecessary radiation and increase patient discomfort.
Innovation Solution
An optimized spatial point sequence is determined to minimize total treatment time by interleaving and intermixing treatment fields, with an optimization algorithm that considers beam-on and beam-off times, clinical protocols, and hardware constraints, while penalizing excessive monitor unit (MU) counts to ensure collision-free and time-efficient trajectories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If complex treatment fields (IMRT, VMAT, 3D conformal) are used to deliver radiation precisely to the target while avoiding healthy tissue, then treatment precision and dose distribution are improved, but treatment time increases due to long transitions between consecutive fields
Solution Approach 1:
The treatment plan is segmented into multiple treatment fields (IMRT, VMAT, 3D conformal) with specific spatial points and trajectories. Each field is defined by control points including source-to-surface distance, source-to-axis distance, gantry angle, couch angle, and MLC positions. This segmentation allows independent optimization of each field's precision while enabling efficient sequencing to minimize total transition time.
Solution Approach 2:
The patent applies dynamic optimization to determine the optimal sequence and trajectory of treatment fields. The system dynamically adjusts the order of fields and their corresponding spatial points to minimize total treatment time while maintaining precision. This includes optimizing the transition paths between fields and determining when to switch between different treatment modalities based on real-time constraints and objectives.
2Manufacturing precision
If longer travel distances for treatment axes are used to access different spatial points for treatment fields, then treatment coverage and precision are improved, but transition time between fields increases
Solution Approach 1:
The system performs preliminary optimization to pre-determine the optimal sequence of treatment fields and their corresponding spatial points. By calculating the best trajectory path in advance, the system minimizes unnecessary travel distances and positions fields to reduce transition time. The optimization algorithm considers all constraints (clearance margins, mechanical limits) beforehand to create an efficient treatment sequence.
Solution Approach 2:
The optimization process incorporates feedback mechanisms that continuously monitor treatment parameters and adjust the sequence accordingly. The system evaluates transition times, spatial point requirements, and field constraints to dynamically refine the treatment trajectory. This feedback loop ensures that the optimal sequence is maintained while adapting to any variations in treatment requirements or machine capabilities.
3Reliability
If manual adjustment and human supervision are used during transitions between treatment fields, then treatment safety and precision are improved, but treatment time increases
Solution Approach 1:
The system performs self-service optimization by automatically determining the optimal treatment sequence and trajectory without requiring manual intervention during execution. The optimization algorithm independently calculates the best field sequence, transition paths, and timing, eliminating the need for real-time manual adjustments. This automated self-optimization maintains safety through built-in constraint checking while significantly reducing beam-off time.
Solution Approach 2:
The patent replaces manual mechanical adjustment with computational optimization algorithms. Instead of relying on human operators to manually adjust fields and monitor transitions, the system uses computer-based optimization to automatically determine the optimal treatment sequence. This substitution of mechanical/manual processes with computational methods maintains precision and safety while eliminating time-consuming manual interventions.
4Reliability
If excessive monitor unit (MU) counts are used to ensure adequate radiation dose delivery, then treatment completeness is improved, but treatment time increases
Solution Approach 1:
The system optimizes treatment parameters including monitor unit (MU) counts, dose rates, and field sequencing to achieve the required dose delivery in minimal time. The optimization algorithm adjusts MU counts dynamically based on the specific treatment requirements, spatial points, and transition constraints. By changing these parameters optimally, the system ensures complete dose delivery without excessive MU accumulation that would increase treatment time.
Data Source
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AI summary
An optimized radiation treatment plan may be developed in which the total monitor unit (MU) count is taken into account, A planner may specify a maximum treatment time. An optimization algorithm may convert the specified maximum treatment time to a maximum total MU count, which is then used as a constraint in the optimization process. A cost function for the optimization algorithm may include a term that penalizes any violation of the upper constraint for the MU count.