3D MRI Motion Correction via Variable K-Space Sampling
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Solution Overview
Problem
Current magnetic resonance imaging (MRI) techniques face challenges in efficiently correcting for patient motion during scans, particularly in emergency settings, as existing methods are computationally expensive and time-consuming, limiting their clinical applicability.
Innovation Solution
A method for acquiring a three-dimensional MRI dataset using a modified k-space sampling order, where the central region of k-space is sampled differently than the periphery, allowing for more accurate and rapid retrospective motion correction, enabling clinically acceptable computation times without the need for navigators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard numerical methods are used for joint optimization of motion and image vectors, then motion correction accuracy is improved, but computation time becomes excessively long
Solution Approach 1:
The patent segments the k-space sampling into different regions (central region and peripheral region) with different sampling densities. The central region containing low-frequency information is sampled more densely, while the peripheral region is sampled less densely. This segmentation allows the optimization algorithm to focus computational resources on the most critical regions, reducing overall computation time while maintaining motion correction accuracy.
Solution Approach 2:
The patent applies different sampling strategies to different regions of k-space. The central region receives higher sampling density with multiple reads per phase encode step, while the peripheral region uses standard sampling. This local quality differentiation ensures that motion correction accuracy is maintained where it matters most (central region) while reducing computational burden in less critical areas.
2Productivity
If fast imaging protocols are used to reduce motion sensitivity, then scan time is reduced, but motion artifacts cannot be fully corrected in emergency settings
Solution Approach 1:
The patent performs motion estimation and correction during the image reconstruction process rather than requiring separate correction steps. By integrating motion parameter estimation into the reconstruction pipeline and using the reordered k-space sampling pattern, the system prepares and processes motion correction data concurrently with image formation, enabling effective motion correction without extending scan time.
3Reliability
If retrospective motion correction methods are implemented, then motion artifacts are reduced, but computational complexity increases
Solution Approach 1:
The patent segments the optimization problem by focusing computational efforts on estimating motion parameters from the centrally sampled k-space data rather than processing the entire k-space uniformly. This segmentation reduces the dimensional complexity of the optimization problem while maintaining the ability to correct motion artifacts effectively.
Solution Approach 2:
The patent changes the sampling parameters in k-space by using variable density sampling with reordering. This parameter change transforms the data acquisition strategy to provide sufficient information for motion estimation without requiring full sampling of all k-space points, thereby reducing computational complexity while maintaining motion correction capability.
Data Source
AI summary
A three-dimensional magnetic resonance image dataset of an object is acquired using a multi-shot imaging protocol in which several k-space lines are acquired in one shot. The three-dimensional k-space includes a central region and a periphery, wherein the sampling order of k-space lines differs between the central region and the periphery. At least one k-space line from each shot passes through the central region, whereas the periphery includes regions, which are sampled by k-space lines from a subset of the plurality of shots.