Automated Motion Correction Evaluation in Dynamic Medical Images
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Solution Overview
Problem
Current methods for evaluating motion correction in dynamic medical images are largely qualitative and unsuitable for quantifying improvements, especially when contrast-related signal changes confound motion-related changes, making it difficult to assess the efficacy of motion correction across different sites or vendors.
Innovation Solution
An automated method and system that identify regions of interest, select valid voxels, compute similarity and dispersion metrics, and generate similarity and dispersion maps to quantify the efficacy of motion correction, providing a robust and quantitative evaluation of motion correction efficacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If contrast agent is used in dynamic MRI, then functional and metabolic aspects of disease can be understood, but detection of motion is adversely affected as contrast uptake confounds visual perception of motion
Solution Approach 1:
The patent extracts motion information from image data independent of contrast agent effects by using registration methods that compare anatomical structures across time points, separating motion detection from contrast uptake signals
Solution Approach 2:
The patent introduces intermediate processing steps including image registration and motion estimation algorithms that act as mediators between raw image data and motion correction, filtering out contrast-related signal changes while preserving motion information
2Ease of operation
If visual evaluation methods are used for motion correction, then qualitative assessment can be performed, but quantitative comparison across different sites or vendors is hindered
Solution Approach 1:
The patent replaces manual visual evaluation with automated computational algorithms that calculate objective motion correction metrics, substituting human subjective assessment with precise mathematical measurements
Solution Approach 2:
The patent transforms qualitative visual assessment into quantitative parameters by computing specific metrics such as registration accuracy, motion vector magnitudes, and correction efficacy scores that enable numerical comparison across different systems and sites
3Ease of operation
If difference images are used to evaluate motion correction, then visual comparison can be made, but quantification of improvement is unsuitable since contrast related signal changes confound motion related changes
Solution Approach 1:
The patent extracts pure motion-related signal changes from difference images by using registration-based methods that isolate anatomical displacement from contrast agent dynamics, separating the two effects that are confounded in simple difference image analysis
Data Source
AI summary
A method for automated evaluation of motion correction is presented. The method includes identifying one or more regions of interest in each of a plurality of images corresponding to a subject of interest. Furthermore, the method includes selecting valid voxels in each of the one or more regions of interest in each of the plurality of images. The method also includes computing a similarity metric, a dispersion metric, or both the similarity metric and the dispersion metric for each region of interest in each of the plurality of images. Additionally, the method includes generating a similarity map, a dispersion map, or both the similarity map and the dispersion map based on the similarity metrics and the dispersion metrics corresponding to the one or more regions of interest.