Neural Network Radiotherapy Dose Prediction
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Solution Overview
Problem
Current radiation therapy treatment planning is computationally intensive and often relies on trial-and-error methods, leading to inefficiencies and potential errors in creating optimal treatment plans that balance tumor irradiation and organ protection, particularly in clinics with limited expertise or resources.
Innovation Solution
A system utilizing deep convolutional neural networks (DCNNs) to predict fluence and dose maps based on three-dimensional medical images and anatomy maps, enabling the generation of optimized radiation therapy treatment plans through supervised learning and reducing the subjectivity in plan design.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional trial-and-error treatment planning methods are used, then treatment plans can be created with manual adjustment, but the process becomes computationally intensive and time-consuming
Solution Approach 1:
The patent uses neural networks to copy and learn from existing high-quality treatment plans to generate new plans. The system trains on a database of approved treatment plans and uses this learned knowledge to rapidly generate initial treatment plans, avoiding the need to recreate optimal plans from scratch through trial-and-error methods.
Solution Approach 2:
The patent replaces the manual mechanical process of trial-and-error plan creation with an automated neural network system. The neural network uses deep learning algorithms to process patient anatomy data and directly generate optimized treatment plans, substituting the iterative manual calculation process with a computational model that learns from historical data.
2Reliability
If manual treatment planning is performed by practitioners, then subject judgment and expertise can be applied, but subjectivity and potential errors remain in the process
Solution Approach 1:
The system incorporates feedback mechanisms where the neural network continuously learns from feedback data. This includes feedback from treatment outcomes, practitioner evaluations, and comparisons against gold standard plans. The feedback loop enables the model to refine its predictions and improve consistency over time while maintaining high accuracy.
Solution Approach 2:
The patent transforms the planning process by changing key parameters from manual adjustment to automated prediction. Instead of relying on practitioner experience and subjective judgment, the system uses learned parameters from the neural network that are derived from large datasets of treatment plans and outcomes, ensuring consistent and reproducible results.
3Reliability
If deep local expertise is available in a clinic, then high-quality treatment plans can be created, but clinics lacking expertise struggle to produce optimized plans
Solution Approach 1:
The neural network system provides universal capability across different clinics regardless of local expertise level. The system can be deployed in any clinic and maintains consistent plan quality by drawing from its trained knowledge base, which includes patterns from diverse treatment cases. This universal model ensures that clinics without deep local expertise can still produce high-quality plans.
Solution Approach 2:
The neural network acts as an intermediary between patient data and treatment plan generation. Instead of requiring direct practitioner expertise to create plans, the system uses the neural network as an intermediate layer that processes patient information and generates optimized plans based on learned patterns, bridging the gap between available data and quality plan production.
4Manufacturing precision
If treatment planning is performed manually with iterative adjustments, then optimization can be achieved, but the process becomes complex and difficult to standardize
Solution Approach 1:
The system copies the optimization patterns from existing high-quality treatment plans directly into new plan generation. By learning from the database of approved plans, the neural network captures the essential optimization patterns and applies them automatically, simplifying the complex iterative adjustment process into a single prediction step while maintaining high dose distribution precision.
Data Source
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AI summary
The present disclosure relates to systems and methods for developing radiotherapy treatment plans though the use of machine learning approaches and neural network components. A neural network is trained using one or more three-dimensional medical images, one or more three-dimensional anatomy maps, and one or more dose distributions to predict a fluence map or a dose map. During training the neural network receives a predicted dose distribution determined by the neural network that is compared to an expected dose distribution. Iteratively the comparison is performed until a predetermined threshold is achieved. The trained neural network is then utilized to provide a three-dimensional dose distribution.