Dose Volume Histogram Feedback for Radiation Therapy Accuracy
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Solution Overview
Problem
Current radiation therapy techniques face challenges in ensuring accurate and consistent delivery of treatment plans due to uncertainties such as patient setup errors, physiological changes, and motion, which can lead to incorrect dose distribution and potential harm to healthy tissues.
Innovation Solution
The implementation of adaptive radiation therapy with a feedback loop for quality assurance, utilizing dose volume histograms to validate treatment delivery and adjust treatment plans based on real-time patient data, ensuring accurate dose distribution and machine performance verification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If adaptive radiation therapy with feedback loop is implemented, then treatment accuracy and reliability are improved, but device complexity and resource requirements increase
Solution Approach 1:
The patent implements a feedback loop where dose volume histograms are generated from treatment delivery data and compared against planned dose distributions. This feedback mechanism enables automatic detection of delivery errors and triggers alerts for quality assurance review, thereby improving treatment reliability without requiring continuous manual monitoring
Solution Approach 2:
The system performs self-verification by automatically generating dose volume histograms from treatment delivery data and comparing them against planned distributions. The quality assurance process is partially automated, allowing the system to self-diagnose delivery errors and trigger appropriate alerts without requiring constant human intervention
2Measurement precision
If dose volume histogram analysis is performed for quality assurance, then error detection capability is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs partial verification by generating dose volume histograms for specific dose thresholds and regions of interest rather than analyzing the entire dose distribution. This selective analysis approach maintains high error detection capability while reducing computational processing time and resource requirements
3Reliability
If margins are increased to account for uncertainties, then target coverage is improved, but healthy tissue exposure increases
Solution Approach 1:
The feedback loop compares actual delivered dose distributions against planned distributions using dose volume histogram analysis. This enables detection of setup errors and delivery deviations that would otherwise require larger safety margins, allowing for more precise target coverage with reduced margins and consequently less healthy tissue exposure
Solution Approach 2:
The patent replaces the mechanical approach of using fixed geometric margins to account for uncertainties with an information-based quality assurance system. By using dose volume histogram analysis and feedback loops to detect and correct delivery errors, the system substitutes computational verification for physical margin expansion, thereby reducing healthy tissue exposure
Data Source
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AI summary
A system and method of defining a new region of interest for an existing region of interest using a dose volume histogram. The method includes the acts of generating a dose volume histogram of radiation dose for a pre-existing region of interest (200), selecting a subset of the dose volume histogram (204), and defining a new region of interest that corresponds to the selected subset of the dose volume histogram (212).