MRI Motion Artifact Compensation via K-Space Extrapolation
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Solution Overview
Problem
Current magnetic resonance imaging (MRI) techniques face challenges in efficiently correcting for translational and rotational motion artifacts, particularly in fast or high-resolution imaging, where additional data acquisition is not feasible and existing post-processing methods are computationally expensive and sub-optimal for complex motion.
Innovation Solution
The k-space extrapolation and correlation (EXTRACT) technique generates a motion-free reference region in k-space and uses correlation processes to estimate motion, employing edge enhancement and finite-support solution extrapolation techniques to correct for both translational and rotational motion without requiring additional data acquisition, thereby reducing computational costs and processing time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If navigator echoes are used for motion compensation, then motion correction accuracy is improved, but scan time is prolonged
Solution Approach 1:
The patent extracts motion information from the existing k-space data itself, rather than acquiring separate navigator echo data. By using the actual imaging data to estimate motion parameters through correlation and optimization techniques, the method eliminates the need for additional navigator echo acquisitions, thus avoiding scan time prolongation while maintaining motion correction accuracy
Solution Approach 2:
The patent makes the imaging data serve dual purposes: both for image formation and for motion estimation. The k-space data is used simultaneously to reconstruct the image and to estimate motion parameters through autocorrelation and optimization, eliminating the need for separate motion monitoring systems and reducing overall scan time
2Measurement precision
If autofocusing technique is used for motion correction, then motion correction capability is improved, but computational cost increases
Solution Approach 1:
The patent segments the k-space data into different regions and processes them separately using correlation techniques. By dividing the data processing into manageable segments and using efficient correlation algorithms, the method reduces the overall computational burden compared to applying autofocusing to the entire k-space simultaneously
Solution Approach 2:
The patent applies motion correction selectively to regions where motion artifacts are most prominent, rather than uniformly processing all k-space data with full autofocusing complexity. This partial action approach maintains correction effectiveness while reducing computational requirements
3Use of energy by moving object
If one-dimensional successive approach is used for motion correction, then computation load is reduced, but correction optimality decreases for complex motion
Solution Approach 1:
The patent extends motion correction from one-dimensional translational correction to two-dimensional correction by incorporating rotational motion estimation. By adding the rotational dimension to the correction process and using correlation techniques that account for both translation and rotation, the method maintains computational efficiency while improving correction optimality for complex motion patterns
Data Source
AI summary
A technique for correcting translational and rotational motion of an object, such as an object in a magnetic field, utilizes the k-space representation of the object. An initial region in the k-space representation is used as a motion-free reference region, indicative of the motion-free object. Motion-free data adjacent to the initial region are then estimated by extrapolating from the initial region, and the extrapolated data are subsequently used to estimate motion by correlating it with actual data. Segments adjacent to the initial region are then motion corrected and incorporated into an expanding base region. The expanded base region is used in subsequent correction steps. This process is continued until the entire k-space is motion-corrected. Two different extrapolation methods were used for the purpose of motion estimation: edge enhancement and finite-support solution. One technique is utilized near the k-space center and the other is utilized in the outer k-space regions.