Dose Estimation Model Incorporating Planning Trade-offs
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current radiotherapy treatment planning faces challenges in accurately delivering radiation doses to tumors while minimizing exposure to healthy structures, as existing dose estimation models struggle with variability in treatment planning trade-offs and require manual exclusion of outlier plans, leading to reduced model scope and less accurate dose estimations.
Innovation Solution
A dose estimation model that includes treatment planning trade-offs, allowing for a more flexible approach by training on multiple treatment plans with varying objectives, transforming patient geometry and trade-off data into radiation dose data, and using regression algorithms to estimate radiation doses for improved target coverage and healthy structure sparing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing dose estimation models are used, then radiation dose can be estimated, but the models struggle with variability in treatment planning trade-offs and require manual exclusion of outlier plans, leading to reduced model scope and less accurate dose estimations
Solution Approach 1:
The patent transforms treatment planning trade-offs into quantifiable parameters that can be incorporated into the dose estimation model. By converting subjective planning decisions into objective parameters, the model can accommodate diverse treatment approaches without requiring manual exclusion of outlier plans, thereby maintaining both accuracy and adaptability
Solution Approach 2:
The patent creates a unified dose estimation model that handles multiple types of treatment plans (including previously excluded outliers) through a single framework. This universal model eliminates the need for separate handling of different plan types while maintaining dose estimation accuracy across diverse scenarios
2Measurement precision
If manual exclusion of outlier plans is performed, then dose estimation accuracy may be maintained for common cases, but model scope is reduced and the process becomes less automated
Solution Approach 1:
The patent enables the dose estimation model to automatically handle and incorporate outlier plans through its own parameter transformation mechanism, eliminating the need for external manual intervention. The model self-adjusts to accommodate diverse treatment plans by transforming their unique characteristics into usable parameters
Solution Approach 2:
The patent incorporates treatment planning trade-off parameters into the dose estimation model, creating a feedback loop where planning decisions directly influence dose estimates. This automated feedback mechanism allows the model to adapt to various plan types without manual exclusion while maintaining accuracy
Data Source
AI summary
One example method for generating a dose estimation model for radiotherapy treatment planning may include obtaining training data that includes multiple treatment plans associated with respective multiple past patients. The method may also include processing the training data to determine, from each of the multiple treatment plans, first data that includes one or more features associated with a particular past patient, second data associated with treatment planning trade-off selected for the particular past patient and third data associated with radiation dose for delivery to the particular past patient. The method may further include generating the dose estimation model by training, based on the first data, second data and third data from the multiple treatment plans, the dose estimation model to estimate a relationship that transforms the first data and second data to the third data.