Nuclear Medicine Brain Functional Imaging Templates for Age Differentiation
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Solution Overview
Problem
Conventional nuclear medicine brain functional imaging methods lack the ability to differentiate between age ranges due to non-continuous and cross-sectional statistical data, are susceptible to noise and imaging errors, and struggle with inconsistent standards across different hospitals and instruments, leading to inaccurate dementia diagnosis.
Innovation Solution
A calculation method using machine learning to generate expected value and standard deviation templates by aligning and normalizing images, employing a position-age function, and utilizing gradient descent and regularization to correct weight information, thereby establishing accurate brain area templates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If cross-sectional statistical data with segmented age ranges is used, then the calculation method is simple, but the ability to differentiate between ages in the same range is lost and continuity across intervals is lacking
Solution Approach 1:
The patent applies segmentation by dividing the brain area into multiple voxel sets (first voxel set, second voxel set, etc.) corresponding to different age ranges. Each voxel set has its own expected value and standard deviation calculated from images of that specific age group, enabling age-differentiated analysis while maintaining computational feasibility through focused segmentation rather than exhaustive fine-graining.
Solution Approach 2:
The patent introduces a new dimension by calculating Z-scores not just for individual voxels but for entire voxel sets representing different age ranges. This transforms the analysis from single-point measurements to regional comparisons across age groups, adding the dimension of age-range-based differentiation to the statistical analysis framework.
2Device complexity
If the entire brain area is divided by a single quantitative value, then the calculation is simplified, but the relationship between adjacent voxels and overall brain area changes is not considered, making it susceptible to noise
Solution Approach 1:
The patent segments the brain area into multiple voxel sets rather than treating it as a single unit. Each voxel set is processed separately with its own statistical parameters, allowing local variations and relationships between adjacent voxels to be preserved while reducing the impact of noise through localized analysis.
Solution Approach 2:
The patent applies local quality by calculating expected values and standard deviations specifically for each voxel set rather than using a global value for the entire brain. This allows the statistical properties to vary locally according to the specific characteristics of each age range and brain region, improving reliability by adapting to local variations while maintaining computational tractability.
3Ease of operation
If a uniform brain standard template is used across different hospitals and instruments, then the calculation method is standardized, but individual differences cannot be reflected and corrections across different institutions are difficult
Solution Approach 1:
The patent segments the standard template into multiple voxel sets corresponding to different age ranges and institutions. Each voxel set can be customized with institution-specific statistical parameters while maintaining the overall standardized framework. This allows different hospitals and instruments to have their own tailored templates that reflect their specific imaging characteristics and patient populations.
Data Source
AI summary
A calculation method for a nuclear medicine brain functional imaging template includes the following steps: selecting multiple sets of images from a known healthy human database; defining a position-age function by a position information in the set of images and an age information corresponding to the image; utilizing machine learning to compute the position-age function for obtaining a machine learning model and obtaining a weight information correspondingly; and calculating an expected value template function corresponding to the machine learning model based on the weight information and the age information.