Deep Learning Beam Modeling for Faster Radiotherapy Planning
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Solution Overview
Problem
Conventional manual beam modeling in radiation therapy is time-consuming, inefficient, and prone to inconsistencies, especially for advanced techniques like IMRT and VMAT, due to the need for extensive manual tuning and complex dose calculations.
Innovation Solution
An AI-based approach using a deep learning model to automatically generate beam models by establishing a relationship between machine scanning data and beam model parameters, reducing the need for manual intervention and improving efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual beam modeling is used to ensure accurate dose calculation, then model precision is improved, but time consumption and complexity increase significantly
Solution Approach 1:
The patent replaces the manual mechanical process of beam modeling with an automated deep learning system. The neural network automatically extracts beam model parameters from machine scanning data, eliminating the need for manual parameter tuning and dose calculations while maintaining high accuracy. This substitution of manual mechanical operations with automated intelligent systems directly resolves the contradiction between accuracy and time consumption.
Solution Approach 2:
The patent creates a virtual copy of the physical beam modeling process through deep learning. Instead of manually measuring and adjusting physical parameters, the system uses machine learning models to replicate and predict beam characteristics from scanning data, significantly reducing the time required while preserving model accuracy.
2Reliability
If manual tuning is performed to adapt beam model to specific machine characteristics, then model reliability is improved, but operational complexity increases
Solution Approach 1:
The patent enables the beam modeling system to perform self-service by automatically adapting to specific machine characteristics without human intervention. The deep learning model autonomously processes machine scanning data and extracts parameters tailored to each machine's unique characteristics, eliminating the need for manual tuning while maintaining reliability.
Solution Approach 2:
The patent automatically adjusts beam model parameters based on machine-specific scanning data through deep learning. The system dynamically changes parameters to match the specific characteristics of each radiation therapy machine, ensuring reliability while reducing the complexity of manual parameter adjustment processes.
3Manufacturing precision
If extensive manual tuning is conducted for advanced techniques like IMRT and VMAT, then treatment precision is improved, but productivity decreases
Solution Approach 1:
The patent replaces manual tuning operations with automated deep learning systems for advanced techniques like IMRT and VMAT. The neural network automatically processes complex treatment parameters and generates optimized beam models, maintaining high treatment precision while dramatically improving productivity by eliminating time-consuming manual processes.
Solution Approach 2:
The patent performs preliminary beam model generation and parameter extraction automatically before treatment planning begins. By pre-processing and preparing accurate beam models through deep learning, the system eliminates the need for extensive manual tuning during the treatment planning phase, thereby improving overall productivity without sacrificing precision.
Data Source
AI summary
Systems and methods for generating a beam model for radiotherapy treatment planning are discussed. An exemplary system includes a memory to store a trained deep learning model, and a processor circuit to generate a beam model. The deep learning model can be trained to establish a relationship between machine scanning data and values of beam model parameters, and validated for accuracy. The processor circuit can receive machine scanning data indicative of a configuration or an operation status of the radiation therapy device, apply the machine scanning data to the trained deep learning model to determine values for the beam model parameters, and generate a beam model based on the determined values of the plurality of beam model parameters. The beam model may be provided to a user, or a treatment planning system.