Radiotherapy Planning Fluence Map Generation Using Deep Learning
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Solution Overview
Problem
Conventional radiotherapy treatment planning techniques require significant computational resources, are time-consuming, and prone to human errors due to the need for iterative optimization of preliminary fluence maps and manual adjustments, which can affect treatment accuracy and efficiency.
Innovation Solution
A system and method for radiotherapy planning that utilizes a fluence map generation model, such as a convolutional neural network (CNN) or generative adversarial network (GAN), to automatically generate deliverable fluence maps directly from planning information, reducing the need for preliminary map optimization and manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional iterative optimization methods are used to generate fluence maps, then treatment planning accuracy can be achieved, but the process becomes time-consuming and computationally intensive
Solution Approach 1:
The system performs preliminary action by pre-training deep learning models (CNNs or GANs) on extensive datasets of planning information and corresponding optimized fluence maps. This pre-computed knowledge is then applied during actual treatment planning to generate deliverable fluence maps rapidly without requiring iterative optimization at the time of use, thus achieving both high accuracy and fast planning times
Solution Approach 2:
The invention uses copying by training the deep learning model to learn the mapping from planning information to optimized fluence maps from training data. The model essentially copies the optimization patterns and solutions from the training dataset, enabling it to generate accurate fluence maps without performing the actual iterative optimization process during treatment planning
2Manufacturing precision
If manual adjustments and iterative optimization are performed on fluence maps, then treatment accuracy can be improved, but the process becomes complex and prone to human errors
Solution Approach 1:
The system applies self-service by enabling the deep learning model to automatically generate deliverable fluence maps directly from planning information without requiring manual adjustments or iterative optimization steps. The model independently performs the entire optimization task, eliminating human intervention and associated errors while maintaining high treatment accuracy
Solution Approach 2:
The invention substitutes the mechanical system of manual调整和iterative optimization algorithms with an intelligent system based on deep learning. The trained neural network model replaces the traditional step-by-step mechanical optimization process, automatically generating accurate fluence maps in a single pass without requiring manual intervention or complex iterative procedures
3Productivity
If automated fluence map generation using deep learning models is implemented, then planning efficiency and accuracy are improved, but computational resources are required for model training and deployment
Solution Approach 1:
The system resolves this contradiction by performing the computationally intensive model training in advance as a preliminary action. Once the deep learning model is trained on extensive datasets, it can be deployed to generate fluence maps rapidly during actual treatment planning with minimal computational resources required at the time of use, thus achieving high planning efficiency while managing computational resource usage
Data Source
AI summary
The present disclosure may provide a system for radiotherapy planning. The system may obtain planning information relating to at least one beam to be delivered to a subject in a treatment of the subject. The system may also generate an input of a fluence map generation model based on the planning information. For each of the at least one beam, the system may further generate at least one deliverable fluence map relating to at least one segment of the beam based on the input and the fluence map generation model.