Radiotherapy Treatment Planning via Machine Learning Parameter Prediction
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Solution Overview
Problem
The existing radiotherapy treatment plan optimization processes are time-consuming and prone to inaccuracies due to the cumbersome conversion of fluence maps and initial machine parameter settings, particularly in photon and ion therapies, which hinders efficient and accurate dose distribution.
Innovation Solution
A machine learning system is trained to determine initial machine parameter settings based on reference dose distributions, enabling direct output of optimized machine parameter settings for radiotherapy treatment planning, reducing the need for iterative conversion steps and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional fluence map optimization and conversion methods are used to determine initial machine parameter settings, then the process follows established procedures, but the optimization time is long and accuracy is reduced due to multiple conversion steps
Solution Approach 1:
The patent replaces the traditional mechanical conversion process (fluence map optimization followed by conversion to machine parameters) with a machine learning-based system. The ML model directly predicts optimal machine parameter settings from treatment plan inputs, eliminating the need for iterative conversion steps and significantly reducing both time and accuracy losses.
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between the treatment plan inputs and the final machine parameter settings. This ML intermediary learns the complex mapping relationships from training data and provides accurate initial parameter estimates without requiring the traditional multi-step conversion process.
2Reliability
If multiple iterative conversion steps are performed to convert fluence maps to machine parameters, then feasibility is improved, but the process becomes time-consuming and introduces accumulation of errors
Solution Approach 1:
The patent performs preliminary action by training the machine learning model in advance on large datasets of treatment plans and their corresponding optimal machine parameters. This pre-trained model can then quickly provide reliable initial parameter settings without requiring multiple iterative conversions during the actual treatment planning process.
Solution Approach 2:
The patent substitutes the iterative mechanical conversion process with a direct machine learning prediction approach. The ML model has learned the feasibility constraints and optimal parameter relationships during training, allowing it to generate reliable settings in a single step rather than through multiple conversions.
3Ease of manufacture
If conventional initialization methods are used for machine parameters, then the process is simple to implement, but the initial values are less accurate leading to longer optimization iterations
Solution Approach 1:
The patent applies preliminary action by pre-training the machine learning model on comprehensive datasets before deployment. This allows the system to achieve high accuracy in initial parameter estimation while maintaining ease of use during actual treatment planning, as the complex training phase is completed beforehand.
Solution Approach 2:
The patent changes the approach from using simple mathematical formulas or heuristic methods for parameter initialization to using a data-driven machine learning model. This parameter change enables the system to learn complex relationships from training data and provide more accurate initial values without significantly increasing operational complexity.
Data Source
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AI summary
The present disclosure relates to the use of machine learning for determining initial machine setting parameters for radiotherapy treatment planning. A machine-learning system is trained on data sets including a dose distribution and a set of machine parameter settings resulting from that dose distribution. The trained system can be used for determining machine parameter settings based on a desired dose distribution, which may be used as initial machine parameter settings for radiation treatment optimization.