Real-Time Radiation Treatment Planning Using Genetic Optimization
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Solution Overview
Problem
Current radiation treatment planning systems for brachytherapy are inefficient due to reliance on trial-and-error methods, single-objective optimization algorithms, and manual adjustment of importance factors, which lead to lengthy calculation times and suboptimal treatment plans, especially in multi-objective scenarios where multiple clinical cases require different parameter settings.
Innovation Solution
A real-time radiation treatment planning system utilizing three-dimensional imaging and segmentation algorithms combined with genetic optimization techniques to determine the optimal placement and dwell times of energy-emitting sources within hollow needles, allowing for immediate generation and implementation of treatment plans without pre-defined objectives, and employing Fast Fourier Transform-based convolution for dose distribution calculation to reduce computational time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If trial-and-error methods with manual adjustment of importance factors are used, then treatment plans can be generated, but calculation time becomes excessively long and optimization efficiency deteriorates
Solution Approach 1:
The patent replaces manual trial-and-error adjustment of importance factors with an automated genetic algorithm that evolves optimal importance factors through iterative selection, crossover, and mutation operations. This substitution of manual mechanical adjustment with an automated evolutionary computational system resolves the contradiction by maintaining optimization quality while eliminating excessive calculation time and manual intervention.
Solution Approach 2:
The genetic algorithm enables the system to self-optimize importance factors without external manual intervention. The algorithm automatically adjusts importance factors across multiple generations based on treatment plan quality metrics, allowing the system to serve itself in optimizing treatment plans rather than requiring continuous manual tuning, thus reducing both time loss and maintaining precision.
2Adaptability or versatility
If single-objective optimization algorithms are used, then calculation is simpler, but multi-objective treatment scenarios cannot be properly addressed
Solution Approach 1:
The genetic algorithm framework provides a universal optimization mechanism that can handle multiple objectives simultaneously by evaluating treatment plans against multiple criteria (e.g., dose coverage, organ at risk protection, dose homogeneity) and evolving importance factors that balance all objectives. This multi-functional capability resolves the contradiction by enabling multi-objective optimization without requiring separate algorithms for each objective, thus increasing adaptability while managing complexity through a unified approach.
3Adaptability or versatility
If manual adjustment of importance factors is performed for each clinical case, then optimization can be tailored, but the process becomes excessively time-consuming and productivity decreases
Solution Approach 1:
The genetic algorithm automatically performs case-specific optimization by evolving importance factors tailored to each clinical scenario without requiring manual intervention. The system self-adjusts parameters based on patient-specific anatomy and treatment requirements, maintaining adaptability while dramatically improving productivity by eliminating time-consuming manual adjustment processes for each case.
Solution Approach 2:
The genetic algorithm performs preliminary automated optimization of importance factors before treatment plan finalization, preparing optimized parameters in advance without manual intervention. This preliminary automated action resolves the contradiction by enabling case-specific tailoring to occur automatically before the actual treatment planning, thus maintaining adaptability while freeing up clinical time for other productive activities.
4Measurement precision
If a larger number of sampling points are used for dose calculation, then accuracy increases, but computational cost increases
Solution Approach 1:
The genetic algorithm optimizes the importance factors that weight different sampling points and dose calculation parameters, allowing the system to achieve high accuracy with an optimized subset of sampling points rather than uniformly processing all points. This parameter optimization resolves the contradiction by changing the computational parameters (importance factors) to maximize accuracy per unit computational cost, enabling high precision dose calculation with reduced overall computational burden.
Data Source
AI summary
A real time radiation treatment planning system for use in effecting radiation therapy of a pre-selected anatomical portion of an animal body. A processor is provided with a three-dimensional imaging algorithm and a three-dimensional image segmentation algorithm for at least the specific organs within the anatomical portion and the needles. The processor converts the image data into a three-dimensional image of the anatomical portion using at least one single or multi-objective anatomy based genetic optimization algorithm. The processor determines in real time the optimal number and position of the hollow needles, the position of the energy emitting source within each hollow needle and the dwell times of the energy emitting source at each position. For post planning purposes, the processor determines, based on three-dimensional image information in real time, the real needle positions and the dwell times of the energy emitting source for each position.