MRI Water-Fat Separation Using Error-Phasor Refinement
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Solution Overview
Problem
Current water-fat separation techniques in magnetic resonance imaging suffer from frequent fat-water swap artifacts due to limitations such as severe inhomogeneous main magnetic fields and eddy currents, reducing the accuracy of water-fat separation.
Innovation Solution
A method involving the determination of initial error phasors for water and fat constituents, followed by refinement using algorithms like smooth filtering and region growing, to accurately separate water and fat signals from echo images, reducing the probability of fat-water swap.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional water-fat separation techniques are used in MRI, then imaging speed and clinical applicability are improved, but fat-water swap artifacts occur frequently due to magnetic field inhomogeneity and eddy currents
Solution Approach 1:
The patent applies preliminary action by performing tissue segmentation and determining initial water-fat distribution before conducting the actual water-fat separation. This preliminary classification of tissues into water-dominated, fat-dominated, and mixed types provides a foundation for selecting appropriate error phasor candidates, thereby preventing fat-water swap artifacts before they occur during the separation process.
Solution Approach 2:
The patent implements local quality by determining separate error phasor candidates for water-dominated and fat-dominated tissues based on their respective initial water-fat distributions. Instead of using a uniform approach, the method tailors the error phasor selection to the local tissue characteristics, which improves separation accuracy in different tissue regions while maintaining overall imaging efficiency.
2Measurement precision
If refinement algorithms like smooth filtering and region growing are applied to error phasors, then water-fat separation accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the image into different tissue types (water-dominated, fat-dominated, and mixed tissues) based on initial water-fat distribution. This segmentation allows the application of refinement algorithms only where necessary, reducing overall computational complexity while maintaining high separation accuracy in critical regions.
Solution Approach 2:
The patent uses parameter changes by applying refinement algorithms selectively based on tissue type classification. Different processing parameters and algorithm intensities are applied to different tissue regions, optimizing the balance between separation accuracy and computational complexity rather than uniformly applying high-complexity processing to the entire image.
Data Source
AI summary
Method and device for water-fat separation of image, computer apparatus, and computer-readable storage medium. The method includes: obtaining plurality of echo images of a target object; obtaining an initial water and fat distribution image of the target object; determining a first error phasor candidate and a second error phasor candidate, the first error phasor candidate being used to characterize an error phasor of each element corresponding to water occupying a major constituent, and the second error phasor candidate being used to characterize an error phasor of each element corresponding to fat occupying a major constituent; determining an initial-guess of error phasor corresponding to each element in the plurality of echo images among the first error phasor candidate and the second error phasor candidate based on the initial water and fat distribution image; determining an optimal error phasor based on the initial-guess of the error phasor; and acquiring a water image and a fat image of the target object from the plurality of echo images according to the optimal error phasor.