MRI Diffusion Tensor Imaging Quality Evaluation
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Solution Overview
Problem
Conventional diffusion tensor imaging (DTI-MRI) techniques are sensitive to tissue motion and other artifacts, leading to corrupted images and reduced signal-to-noise ratio, with existing methods requiring manual inspection or pixel-by-pixel evaluation, which is time-consuming and prone to errors.
Innovation Solution
A method for automatically evaluating the quality of acquired MRI data, identifying and removing corrupted images from tensor fitting, and re-acquiring data as needed, using least mean square error tensor estimation, residual error mapping, and quality map creation, allowing for real-time image quality assurance and correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional DTI-MRI techniques are used, then white matter tracts can be depicted, but the images are corrupted by tissue motion and artifacts
Solution Approach 1:
The patent applies preliminary action by performing quality evaluation and identifying corrupted images before they are used in tensor fitting. The system evaluates acquired diffusion-weighted images for quality metrics and flags corrupted images for exclusion from subsequent processing, preventing propagation of artifacts into final DTI maps
Solution Approach 2:
The patent extracts and removes corrupted image data from the dataset before tensor fitting. By identifying images that fail quality criteria (such as those with excessive motion artifacts or signal abnormalities) and excluding them from the fitting process, the system eliminates harmful factors that would otherwise degrade the reliability of DTI results
2Measurement precision
If manual inspection methods are used to evaluate image quality, then corrupted images can be identified, but the process is time-consuming and prone to errors
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated computational system. The implementation uses computer-executable instructions and algorithms to automatically evaluate image quality metrics, detect corrupted images, and generate quality reports, eliminating human time investment and subjectivity while maintaining or improving detection precision
Solution Approach 2:
The system performs self-service by automatically evaluating its own acquired images for quality without requiring external manual intervention. The automated quality evaluation system independently assesses each acquired image, identifies corrupted data, and determines which images should be excluded from tensor fitting, making the process efficient and objective
Data Source
AI summary
Featured is a method for automatically evaluating acquired MRI data, determining the quality of the acquired images and removing the image data when it is determined that an image is corrupted so the imaged data for the corrupted image is removed from the subsequent tensor fitting. In further embodiments, such determining includes judging the quality of the image data to determine if the image data satisfies a quality threshold criteria and if determined not to be satisfied adjudging the image to be corrupted. Such methods include performing said evaluating, determining and removing in real time and in the case where an image is determined to be corrupted, such methods further includes re-acquiring additional image data corresponding to each of the one or more images removed as being corrupted. Also featured are MRI systems embodying such methods.