Radiation Therapy Planning with Functional Imaging Segmentation
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Solution Overview
Problem
Current radiation therapy planning techniques are inadequate due to insufficient incorporation of functional imaging information, leading to inaccurate contour demarcation, lack of automation, and failure to account for noisy and low-contrast images, resulting in non-optimal treatment plans and increased patient side effects.
Innovation Solution
A biology-based segmentation method that co-registers anatomical and functional imaging data to create parametric maps, clusters tissue regions by radiation sensitivity, and prescribes tailored radiation doses, optimizing treatment plans through a device that integrates these maps with anatomical data and radiobiological models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If functional imaging information is incorporated to identify aggressive tumor areas, then radiation treatment precision is improved, but image noise and low contrast introduce arbitrariness and reduce measurement precision
Solution Approach 1:
The patent introduces physiological or biological models as intermediary components that process raw functional imaging data. These models (such as kinetic models for tracer uptake) serve as mediators between the noisy functional images and the treatment planning decision, transforming unreliable raw data into more reliable physiological parameters that can be confidently used for contour demarcation and dose prescription.
Solution Approach 2:
The patent replaces direct visual inspection and manual contouring based on noisy functional images with automated computational procedures. The optimization procedure automatically determines contour locations and dose distributions by processing functional imaging data through mathematical models, eliminating the arbitrariness introduced by manual interpretation of low-contrast images.
2Adaptability or versatility
If physicians manually review functional imaging information to draw contours, then treatment planning flexibility is improved, but processing time increases and automation is reduced
Solution Approach 1:
The patent implements self-service through automated optimization procedures that automatically determine contour locations and dose distributions without requiring manual physician intervention. The system uses mathematical optimization to automatically process functional imaging data, calculate dose distributions, and generate treatment plans, thereby increasing automation while maintaining adaptability through configurable optimization parameters.
Solution Approach 2:
The patent performs preliminary processing of functional imaging data through physiological modeling and optimization calculations before the physician reviews the final treatment plan. This preliminary automated analysis prepares optimized contour suggestions and dose distributions in advance, allowing the physician to review and adjust pre-processed results rather than manually creating contours from scratch.
3Productivity
If functional imaging data with noise is fed directly into optimization process, then processing speed is improved, but artifacts emerge and optimization stability deteriorates
Solution Approach 1:
The patent performs preliminary processing steps before the optimization procedure, including physiological modeling of functional imaging data and initial contour estimation. By pre-processing the noisy functional images through biological models to extract meaningful physiological parameters, the optimization process receives cleaner, more stable input data, preventing artifacts while maintaining processing efficiency.
Solution Approach 2:
The patent introduces physiological models as intermediary processing layers between the noisy functional imaging data and the optimization algorithm. These models act as filters that transform unstable, noisy image data into stable physiological parameters (such as tracer uptake rates), which then serve as reliable inputs for the optimization process, eliminating instability and artifacts.
4Ease of manufacture
If uniform radiation dose is prescribed to target region, then treatment simplicity is improved, but aggressive tumor areas receive insufficient dose
Solution Approach 1:
The patent applies local quality by transitioning from uniform dose prescription to spatially varying dose distributions. The optimization procedure calculates different radiation doses for different regions within the target volume based on functional imaging characteristics, allowing aggressive tumor areas to receive higher doses while less aggressive areas receive lower doses, thereby achieving precise dose tailoring to local tumor biology.
Solution Approach 2:
The patent changes the dose prescription parameter from a single uniform value to a spatially distributed set of values. By using optimization procedures that process functional imaging data, the system determines position-dependent dose values throughout the target volume, transforming the simple uniform dose parameter into a complex spatial dose distribution that reflects tumor heterogeneity.
Data Source
Figure 1
Figure 2~3(B)
AI summary
A radiation therapy planning procedure and device provides a model-based segmentation of co-registered anatomical and functional imaging information to provide a more precise radiation therapy plan. The biology-based segmentation models the imaging information to produce a parametric map, which is then clustered into regions of similar radiation sensitivity or other biological parameters relevant for treatment definition. Each clustered region is prescribed its own radiation prescription dose.