Proton Therapy Planning With Dynamic Energy And Spot Optimization
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Solution Overview
Problem
Existing proton therapy treatment plans are constrained by discrete, predetermined increments, leading to uneven dose distributions and inefficient use of treatment system capabilities, necessitating time-consuming manual adjustments.
Innovation Solution
A method and system for proton therapy planning that discretizes layers and spots using an optimization algorithm to achieve an optimal distribution of layer energies and spots, allowing for fewer layers and potentially fewer spots, resulting in a smoother dose distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If discrete, predetermined increments are used for dose delivery, then treatment planning is simplified, but dose distribution becomes uneven and treatment efficiency decreases
Solution Approach 1:
The patent transforms the static, predetermined discrete energy increments into dynamic, continuously adjustable energy levels. The optimization algorithm dynamically determines the number of spots and energy levels based on patient-specific geometry and dosimetric criteria, allowing the system to adapt energy delivery parameters rather than being constrained by fixed predetermined increments. This resolves the contradiction by maintaining planning simplicity through automation while achieving precise, uniform dose distribution through dynamic parameter adjustment.
Solution Approach 2:
The patent changes the fundamental parameters of energy delivery by optimizing both the number of spots and energy levels rather than using fixed predetermined values. The system varies energy levels and spot intensities continuously to achieve smooth dose gradients, transforming the discrete parameter approach into a continuous optimization process that maintains simplicity through algorithmic control while achieving superior dose uniformity.
2Manufacturing precision
If manual trial-and-error modification of plan parameters is performed, then treatment plan quality can be improved, but treatment planning time increases significantly
Solution Approach 1:
The patent implements self-service by enabling the optimization algorithm to automatically adjust plan parameters including number of spots, energy levels, and spot intensities without requiring manual trial-and-error modification. The system independently optimizes these parameters based on dosimetric criteria and patient geometry, eliminating the time-consuming iterative manual adjustment process while maintaining or improving treatment plan quality through systematic computational optimization.
Solution Approach 2:
The patent replaces the manual mechanical adjustment process with an automated computational optimization system. Instead of physicists manually modifying plan parameters through iterative trials, the system uses algorithms to automatically determine optimal spot numbers, energy levels, and intensities, substituting human manual operations with automated computational processes that are both faster and more consistent.
3Productivity
If fewer spots and layers are used, then treatment delivery time is reduced, but dose distribution uniformity deteriorates
Solution Approach 1:
The patent resolves this contradiction by changing the parameter optimization approach - instead of using fixed predetermined energy increments, the system optimizes energy levels and spot intensities continuously. This allows achieving smooth dose distribution with fewer spots and layers by intelligently distributing proton fluence and selecting optimal energy levels, thereby reducing treatment delivery time while maintaining dose uniformity through parameter optimization rather than increasing the number of delivery elements.
Solution Approach 2:
The patent applies dynamics by making energy levels and spot intensities variable and optimized rather than fixed. The system dynamically determines the optimal number of spots and energy levels based on patient-specific requirements, allowing fewer delivery elements to achieve uniform dose distribution through intelligent, adaptive parameter selection rather than relying on a high number of fixed increments.
Data Source
AI summary
Embodiments of the present invention disclose methods and systems for proton therapy planning that includes proton energy and spot optimization that discretizes layers and spots using an optimization algorithm to produce an optimal distribution of layer energies and spots with a relatively smooth dose distribution. The treatment planning algorithms disclosed herein can freely choose the number of spots and the energy levels of the spots. In this way, each spot can be treated as its own layer and is not constrained by the requirements of other spots/layers. Thereafter, the spots defined by the algorithm can be sorted in a list according to energy levels/depth, and the spots can be grouped into blocks according to intensity and location. The blocks can be assigned energy levels based on the corresponding spots, such as an average of all the spots associated with the block. The blocks then are used as the energy layers applied by the proton therapy treatment system.