AI Dose Deposition Correction for Fast Radiation Planning
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Solution Overview
Problem
Existing radiation therapy dose calculation methods, particularly for small tumors, suffer from inaccuracies due to suboptimal modeling of radiation behavior, leading to difficulties in creating precise treatment plans that minimize damage to healthy tissue while effectively targeting cancerous tumors.
Innovation Solution
An artificial intelligence model, such as a neural network, is trained to enhance the accuracy of dose deposition calculations by learning the relationships between different dosing algorithms, allowing it to mimic the results of more computationally intensive methods like Monte Carlo simulations without the need for their full execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Monte Carlo simulations are used to calculate dose deposition, then measurement precision is improved, but productivity deteriorates due to slow computation speed
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing correction factors through Monte Carlo simulations during an offline training phase. These pre-computed correction factors are then applied during clinical dose calculations, eliminating the need to run full Monte Carlo simulations for each patient while maintaining high accuracy.
Solution Approach 2:
The system creates a simplified copy or approximation of the complex Monte Carlo simulation by training a neural network model to replicate its results. The neural network learns the relationship between input parameters and Monte Carlo output, enabling fast dose calculations that mimic Monte Carlo accuracy without the computational burden.
2Productivity
If deterministic methods are used to calculate dose deposition, then productivity is improved due to faster computation, but measurement precision deteriorates
Solution Approach 1:
The system introduces an intermediary neural network model that bridges deterministic methods and Monte Carlo simulations. The neural network takes deterministic calculation results as input and applies learned corrections to achieve Monte Carlo-level accuracy, acting as a mediator between the two approaches.
Solution Approach 2:
The system changes the parameters of deterministic calculations by applying correction factors that adjust the results toward Monte Carlo accuracy. These correction factors are derived from Monte Carlo data and modify key parameters such as dose values and distribution patterns to improve precision.
3Ease of operation
If empirical models are used for small tumors, then ease of operation is improved, but measurement precision deteriorates due to model generality
Solution Approach 1:
The system applies local quality by training separate neural network models for different tumor sizes, anatomical locations, and treatment types. Each model is specialized for specific conditions, providing locally optimized accuracy rather than relying on a single general-purpose empirical model.
Data Source
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AI summary
Embodiments described herein provide for training an artificial intelligence model (200, 300) to boost dose depositions. The artificial intelligence model (200, 300) receives medical images and a dose deposition determined according to a first dose deposition model. The artificial intelligence model (200, 300) modifies the received dose deposition determined according to the first dose deposition model such that the dose deposition simulates a dose deposition determined by a second dose deposition model.