Clinical Target Volume Definition via Diffusion Tensor Imaging
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Solution Overview
Problem
Current methods for defining clinical target volumes in medical treatments, such as radiotherapy, often result in irradiation of healthy tissue and underdosage of tumor cell spread areas due to inaccurate assessment of tumor cell dissemination, leading to potential treatment inefficiencies and risks to healthy cells.
Innovation Solution
A method that acquires and co-registers first image data for anatomical structures and second image data indicating metabolic, molecular, or physical parameters related to tumor cell spread, allowing for the determination of an optimized clinical target volume by defining a safety margin based on these parameters, thereby improving the accuracy of tumor cell spread assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If generic safety margins are applied to the gross tumor volume for defining the clinical target volume, then the definition is simple and can be performed with standard imaging methods, but healthy tissue may be irradiated and treatment precision is reduced
Solution Approach 1:
The patent applies different margin sizes in different spatial directions around the gross tumor volume based on anisotropic diffusion characteristics. Instead of a uniform spherical margin, the system calculates direction-dependent margin widths according to the diffusion tensor imaging data, allowing smaller margins in directions with low diffusion probability and larger margins where tumor spread is more likely.
Solution Approach 2:
The system changes the margin parameter from a fixed generic value to a variable parameter that depends on the diffusion tensor characteristics at each spatial location. The margin width is dynamically adjusted based on the eigenvalues and eigenvectors of the diffusion tensor, transforming the CTV definition from a static geometric operation to a parameter-driven adaptive process.
2Reliability
If generic safety margins are applied to ensure complete tumor cell eradiction, then treatment safety is improved, but healthy tissue irradiation increases and treatment efficiency decreases
Solution Approach 1:
The patent implements local quality by applying different safety margins in different spatial directions based on the actual tumor cell diffusion probability. In directions where diffusion is unlikely, smaller margins are applied, reducing unnecessary treatment of healthy tissue. In directions where diffusion is more probable, larger margins are applied to ensure complete tumor cell eradiction, thus optimizing both safety and efficiency.
Solution Approach 2:
The system uses diffusion tensor imaging data as feedback to dynamically adjust the safety margin size. The diffusion characteristics provide information about the actual tumor cell spread probability, which feeds back into the CTV definition process to optimize the margin width, ensuring that safety is maintained where needed while reducing unnecessary treatment elsewhere.
3Ease of manufacture
If standard imaging methods are used to assess tumor volume, then the process is simple and accessible, but the assessment accuracy of tumor cell spread is insufficient
Solution Approach 1:
The patent merges diffusion tensor imaging data with conventional anatomical imaging (CT or MRI) to create a comprehensive CTV definition system. The diffusion tensor provides information about tumor cell spread probability while the anatomical imaging provides structural context, combining both data types to achieve more accurate tumor cell spread assessment while maintaining clinical accessibility.
Solution Approach 2:
The diffusion tensor imaging acts as an intermediary between the gross tumor volume and the final CTV definition. It provides a probabilistic map of tumor cell diffusion that mediates between the visible tumor boundaries and the actual spread risk, enabling more accurate assessment without requiring direct visualization of individual tumor cells.
Data Source
AI summary
Disclosed is a medical image data processing method for determining a clinical target volume for a medical treatment, wherein the method comprises executing, on at least one processor (3) of at least one computer (2), steps of: a) acquiring (S1) first image data describing at least one image of an anatomical structure of a patient; b) acquiring (S2) second image data describing an indicator for a preferred spreading direction or probability distribution of at least one target cell; c) determining (S3) registration data describing a registration of the first image data to the second image data by performing a co-registration between the first image data and the second image data using a registration algorithm; d) determining (S4) gross target region data describing a target region in the at least one image of the anatomical structure based on the first image data; e) determining (S5) margin region data describing a margin around the target region based on the gross target region data; f) determining (S6) clinical target volume data describing a volume in the anatomical structure for the medical treatment based on the registration data, the gross target region data and the margin region data.