Medical Image Analysis With Cross-Scan Morphology Calibration
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Solution Overview
Problem
Conventional medical image analysis technologies face limitations in providing personalized and accurate diagnostic information due to image artifacts and varying scan conditions, leading to inconsistencies in analysis results.
Innovation Solution
A method and device that utilize calibrating parameters based on correlations between morphological values from different scan conditions to segment and analyze medical images, allowing for personalized and accurate diagnostic information by considering the scan conditions and image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image segmentation technology is used to calculate auxiliary diagnostic indices, then it is possible to segment medical images and provide diagnostic information, but the results are affected by image artifacts and scan conditions leading to reduced accuracy and reliability
Solution Approach 1:
The patent transforms morphological values into standardized z-scores by changing the parameter representation from raw measurements to normalized statistical values. This allows comparison across different scan conditions and subjects, resolving the inconsistency caused by varying artifacts and acquisition parameters while maintaining measurement precision.
Solution Approach 2:
The patent introduces a standard brain model as an intermediary reference framework. By comparing individual subject morphology against this standardized model, the system mediates the differences caused by various scan conditions and artifacts, enabling reliable cross-subject and cross-scanner comparisons.
2Adaptability or versatility
If standardization to a standard brain model is performed, then comparison across subjects becomes possible, but completely personalized auxiliary diagnostic information cannot be provided
Solution Approach 1:
The patent segments the brain into multiple anatomical regions and calculates morphological values for each region separately. This allows the system to provide personalized diagnostic information for specific brain regions while maintaining the ability to compare across subjects through standardized z-score transformation, thus resolving the contradiction between personalization and comparability.
3Device complexity
If morphological values are calculated directly from medical images without calibration, then the analysis process is simple, but the values vary according to scan conditions reducing diagnostic reliability
Solution Approach 1:
The patent replaces complex physical calibration procedures with a computational standardization approach using z-scores. Instead of physically adjusting or calibrating images to a reference, the system uses statistical normalization to achieve comparable results across different scanners and protocols, maintaining simplicity while improving reliability.
Data Source
AI summary
A method of analyzing a medical image includes acquiring a correction parameter computed based on a correlation between a first morphological value that is acquired from a first medical image acquired under a first scan condition and is related to a target element, and a second morphological value that is acquired from a second medical image acquired under a second scan condition and is related to the target element. A target medical image is acquired under the second scan condition. A target region related to the target element is acquired by segmenting the target medical image into regions corresponding to elements including the target element. A target morphological value related to the target element is based on voxel data corresponding to the target region. A corrected morphological value is based on the target morphological value and the correction parameter. A morphological index is outputted based on the corrected morphological value.