Machine Learning Radiation Therapy Planning for Faster Beam Optimization
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Solution Overview
Problem
Current radiation therapy planning methods are time-consuming and computationally intensive, relying on clinician expertise and brute-force methods to optimize energy delivery to tumors while minimizing healthy tissue damage, and lack consistency across varying patient cases.
Innovation Solution
Implementing machine learning-based systems that utilize reinforcement learning agents and models to optimize beam positions and strengths for radiation therapy planning, generating fluence maps, and determining leaf sequences, thereby improving precision and efficiency in radiation delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If brute-force methods are used to analyze CT scans and determine beam positions and intensities, then the planning process can be automated, but the process becomes time-consuming and highly computational-resource intensive
Solution Approach 1:
The patent changes the computational approach by transitioning from brute-force methods to machine learning-based methods. The system uses a machine learning model that takes CT scan data and clinical parameters as input and directly outputs optimized beam positions and intensities, fundamentally changing how the optimization problem is solved and reducing computational time and resource requirements.
Solution Approach 2:
The patent replaces the mechanical brute-force computational approach with an intelligent machine learning system. Instead of systematically testing all possible beam configurations through computational exhaustion, the system uses trained neural networks to directly determine optimal beam positions and intensities, substituting mechanical computation with intelligent pattern recognition and prediction.
2Extent of automation
If brute-force methods are used to analyze CT scans and determine beam positions and intensities, then the planning process can be automated, but the process becomes highly computational-resource intensive
Solution Approach 1:
The patent changes the computational approach by transitioning from brute-force methods to machine learning-based methods. The system uses a machine learning model that takes CT scan data and clinical parameters as input and directly outputs optimized beam positions and intensities, fundamentally changing how the optimization problem is solved and reducing computational time and resource requirements.
Solution Approach 2:
The patent replaces the mechanical brute-force computational approach with an intelligent machine learning system. Instead of systematically testing all possible beam configurations through computational exhaustion, the system uses trained neural networks to directly determine optimal beam positions and intensities, substituting mechanical computation with intelligent pattern recognition and prediction.
3Manufacturing precision
If clinicians manually configure treatment plans to maximize energy delivery to tumors while minimizing healthy tissue damage, then treatment optimization can be achieved, but the process requires extensive clinician expertise and is time-consuming
Solution Approach 1:
The patent implements self-service by enabling the system to automatically generate optimized treatment plans without requiring extensive manual configuration by clinicians. The machine learning model autonomously analyzes CT scans, identifies tumor locations, determines optimal beam positions and intensities, and generates complete treatment plans, allowing the system to serve itself rather than requiring continuous human intervention for optimization.
Solution Approach 2:
The patent replaces the mechanical manual configuration process with an intelligent machine learning system. Instead of requiring clinicians to manually adjust treatment parameters based on expertise and experience, the system uses trained neural networks to automatically determine optimal beam positions and intensities, substituting human expertise with automated intelligent decision-making.
4Extent of automation
If conventional automated approaches use brute-force methods for beam position and intensity determination, then automation is achieved, but the methods lack consistency across varying patient cases
Solution Approach 1:
The patent changes the computational approach by transitioning from brute-force methods to machine learning-based methods. The system uses a machine learning model that takes CT scan data and clinical parameters as input and directly outputs optimized beam positions and intensities, fundamentally changing how the optimization problem is solved and reducing computational time and resource requirements.
Solution Approach 2:
The patent replaces the mechanical brute-force computational approach with an intelligent machine learning system. Instead of systematically testing all possible beam configurations through computational exhaustion, the system uses trained neural networks to directly determine optimal beam positions and intensities, substituting mechanical computation with intelligent pattern recognition and prediction.
Data Source
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AI summary
Provided herein are methods and systems for planning radiation therapy. In examples, at least one processor can be programmed to: receive 202 data associated with a first planning target volume and a first radiation map, provide 204 the first planning target volume and the first radiation map to a first model to cause the first model to output data associated with at least one first beam position and least one first beam strength, generate 206 a second radiation map, and provide 208 the first planning target volume and the second radiation map to a second model to cause the second model to output data associated with at least one second beam position and least one second beam strength. At least one processor can be further programmed to: transmit 210 data associated with the at least one second beam position and the least one second beam strength to cause a linear accelerator to deliver radiation.