MRI Motion Correction Using Interleaved Scout Images
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Solution Overview
Problem
Existing retrospective motion correction methods in magnetic resonance imaging (MRI) are computationally expensive and prone to convergence towards local minima due to large patient motion, especially when using parallel imaging techniques like SENSE+motion, which are not robust enough for clinical applications.
Innovation Solution
Incorporate multiple low-resolution scout images into the MRI sequence to improve the stability and robustness of retrospective motion correction by using these scout images to estimate and correct patient motion, thereby stabilizing the minimization process and reducing computational costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If retrospective motion correction methods are used to correct patient motion in MRI, then motion artefacts are reduced, but computational cost becomes excessively high
Solution Approach 1:
The patent divides the motion correction process into two separate stages: (1) motion estimation using a simplified model, and (2) image reconstruction using the estimated motion parameters. This segmentation avoids the computationally expensive alternating optimization by decoupling motion estimation from image reconstruction, thereby reducing overall computational cost while maintaining correction quality.
Solution Approach 2:
The patent performs motion estimation as a preliminary step before final image reconstruction. By estimating motion parameters first using a simplified forward model and then applying these parameters in the reconstruction stage, the method avoids repeated updates during alternating optimization, significantly reducing computational burden while preserving motion correction effectiveness.
2Reliability
If alternating optimization is used to estimate motion and image parameters, then motion correction is achieved, but computation time becomes prohibitive for clinical use
Solution Approach 1:
The patent segments the optimization process into distinct phases: motion estimation phase using a simplified model, and image reconstruction phase using the pre-estimated motion parameters. This eliminates the need for alternating updates between motion and image parameters, reducing computation time from hours to minutes while maintaining correction accuracy.
Solution Approach 2:
Motion parameters are estimated in advance as a preliminary step before performing the final image reconstruction. This preliminary motion estimation allows the reconstruction stage to focus solely on image quality optimization with fixed motion parameters, avoiding the iterative back-and-forth updates of alternating optimization and dramatically reducing computation time.
3Reliability
If large patient motion occurs during scanning, then motion artefacts increase, but retrospective correction methods converge towards local minima
Solution Approach 1:
The patent changes the parameters used in motion estimation by employing a simplified forward model with fewer optimization variables compared to the full SENSE+motion model. This parameter simplification makes the optimization landscape more favorable, reducing the likelihood of converging to local minima even when large patient motion occurs, while still achieving accurate motion correction.
Data Source
AI summary
A method for generating a motion-corrected MR image dataset of a subject includes: acquiring k-space data of an MR image of a subject in an imaging sequence; acquiring at least two low-resolution scout images of the subject interleaved with the k-space data of the imaging sequence; comparing the scout images with one another in order to detect and/or to estimate subject motion between the scout images; and reconstructing a motion-corrected MR image dataset from the k-space data acquired in the imaging sequence. The reconstruction process includes: estimating the motion trajectory of the subject by comparing the k-space data with at least one of the low-resolution scout images; and estimating the motion-corrected image dataset using the estimated motion trajectory, wherein the estimations involve minimizing the data consistency error between the acquired k-space data and a forward model described by an encoding operator.