Error Adaptive Functional Imaging Partitioning
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Solution Overview
Problem
Functional imaging data is prone to noise and uncertainty, leading to sub-optimal diagnosis and treatment due to inappropriate clustering and spatial resolution issues in medical imaging.
Innovation Solution
A method that adapts spatial resolution and cluster number based on error models to optimize partitioning of image data, allowing for varying spatial resolutions and dynamic adjustment of clusters to minimize error and improve visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If voxel-wise parameter estimates are used to maintain high spatial resolution, then spatial resolution is improved, but noise susceptibility increases
Solution Approach 1:
The patent applies local quality by adapting the spatial resolution and clustering parameters locally based on the error model. Different regions of the image are processed with different levels of smoothing and clustering based on their individual error characteristics, allowing high spatial resolution in low-noise regions while applying stronger noise reduction in high-noise regions.
Solution Approach 2:
The patent implements dynamics by making the clustering parameters and spatial resolution adaptive rather than fixed. The error model drives dynamic adjustment of the clustering granularity and smoothing intensity, allowing the system to optimize the trade-off between spatial resolution and noise susceptibility for each local region based on actual error conditions.
2Ease of manufacture
If a fixed number of clusters is specified for region division, then processing simplicity is maintained, but clustering accuracy deteriorates
Solution Approach 1:
The patent transforms the static, fixed cluster number approach into a dynamic system where the number and distribution of clusters are adapted based on the error model. The clustering process becomes adaptive, automatically adjusting the granularity and number of clusters in different regions to optimize accuracy while maintaining reasonable processing complexity.
Solution Approach 2:
The patent changes the clustering parameters (number of clusters, cluster granularity) based on the error model characteristics. Instead of using a fixed parameter set, the system adjusts clustering parameters dynamically according to local error conditions, improving clustering accuracy in high-error regions while maintaining simplicity in low-error regions.
3Stability of the object's composition
If uniform spatial resolution is applied across the entire region, then processing consistency is maintained, but error reduction efficiency decreases
Solution Approach 1:
The patent replaces uniform spatial resolution with local quality adaptation, where each region's spatial resolution and smoothing intensity are tailored to its specific error characteristics. This allows the system to maintain processing consistency through the error-driven adaptation framework while achieving superior error reduction efficiency by applying appropriate smoothing levels locally.
Data Source
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AI summary
A method for use in functional medical imaging includes adaptively partitioning functional imaging data as a function of a spatially varying error model. The functional image data is partitioned according to an optimization strategy. The data may be visualized or used to plan a course of treatment. In one implementation, the image data is partitioned so as to vary its spatial resolution. In another, the number of clusters is varied based on the error model.