Motion-Corrected MRI Using Sequential K-Space Navigators
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Solution Overview
Problem
Magnetic resonance imaging (MRI) scans are susceptible to motion artifacts due to subject movement during the extended time required to acquire k-space data, which can degrade image quality and necessitate repeated scans, increasing costs.
Innovation Solution
A method and system that utilizes duplicated lines of k-space data as navigators to detect motion between sequential groups, calculating a motion detection metric, combining groups below a threshold, and reconstructing a motion-corrected MRI using a joint optimization algorithm, thereby reducing scan time and artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If k-space data acquisition time is extended to improve image quality, then image quality improves, but subject motion increases causing motion artifacts
Solution Approach 1:
The patent divides the k-space data acquisition into sequential groups with duplicate navigator lines inserted between them. This segmentation allows motion detection and correction at intermediate points during the acquisition process, enabling longer scans to maintain image quality while correcting for subject motion through the use of these segmented navigator measurements.
Solution Approach 2:
The patent introduces duplicate navigator lines as intermediary elements between sequential groups of k-space data. These navigator lines act as mediators that detect motion without being part of the final image reconstruction, allowing the system to monitor and correct for subject motion during the extended acquisition time needed for high-quality imaging.
2Measurement precision
If duplicate navigator lines are inserted between sequential groups, then motion detection capability improves, but data processing complexity increases
Solution Approach 1:
The patent extracts the motion detection function by separating duplicate navigator lines from the final image reconstruction process. These navigator lines are acquired during scanning but are taken out and used exclusively for motion detection and correction calculations, while the main image reconstruction uses only the primary k-space data, thereby improving motion detection without proportionally increasing reconstruction complexity.
Solution Approach 2:
The patent performs motion detection using duplicate navigator lines as a preliminary action before final image reconstruction. By calculating motion parameters from these navigator measurements first, the system can pre-correct the main k-space data, simplifying the overall processing by separating motion correction from the computationally intensive reconstruction step.
3Loss of time
If sequential groups of k-space data are combined based on motion threshold, then scan time is reduced, but motion correction accuracy may decrease
Solution Approach 1:
The patent implements dynamic grouping of sequential k-space data based on motion thresholds. Adjacent groups are combined when motion between them falls below a predetermined threshold, allowing the acquisition to adapt to actual motion conditions. This dynamic approach reduces scan time by skipping redundant acquisitions during low-motion periods while maintaining motion correction accuracy by using duplicate navigator lines to detect and correct larger motions.
Data Source
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AI summary
Disclosed herein is a method of medical imaging and an associated medical system. The method comprises: receiving (200) k-space data (114, 114'), wherein the k-space data comprises sequential groups (500, 502, 504), wherein the sequential groups comprise lines of k-space data, wherein adjacent pairs of the sequential groups comprise duplicated lines (510, 512, 514) of k-space data unique to each of the adjacent pairs; calculating (202) a motion detection metric (116) for the adjacent pairs of the sequential groups using the respective duplicated lines of k-space data; combining (204) adjacent pairs of the sequential groups if the respectively calculated motion detection metric is below a predetermined threshold to reform the sequential groups; and reconstructing (206) a motion corrected magnetic resonance image (122) from the reformed sequential groups (118) of k-space data using a joint optimization algorithm (120) that simultaneously provides the motion corrected magnetic resonance image and motion transformations between the reformed sequential groups, and wherein the motion corrected magnetic resonance image is at least partially reconstructed using the duplicated lines of k-space data.