MRI Motion Correction Using Weighted Data for Non-Rigid Motion
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Solution Overview
Problem
Existing retrospective motion correction techniques in MRI suffer from computational inefficiencies and inaccuracies due to the rigid-body assumption, particularly when dealing with non-rigid and independent motion of body parts, leading to motion artifacts in magnetic resonance images.
Innovation Solution
A method that weights magnetic resonance data to reduce signal from non-rigidly moving body parts, estimates motion parameters from this weighted data, and uses a separable optimization approach to improve motion correction accuracy, employing techniques like SAMER and SENSE+motion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If retrospective motion correction techniques are used to estimate motion trajectory from acquired magnetic resonance data, then motion artifacts are reduced, but computation time increases significantly
Solution Approach 1:
The patent segments the magnetic resonance data by identifying and separating data contributions from different anatomical regions. By weighting or masking data from regions prone to non-rigid motion (e.g., abdomen, thorax) differently from stable regions (e.g., head, pelvis), the method enables more accurate motion estimation without requiring computationally expensive full-volume optimization, thus resolving the contradiction between correction accuracy and computation time.
Solution Approach 2:
The patent applies local quality by treating different spatial regions of the body with different motion correction strategies. Regions with high motion variability are handled with specialized weighting or masking, while regions with stable anatomy use standard correction approaches. This localized differentiation improves overall motion correction accuracy while maintaining computational efficiency by avoiding uniform high-cost processing across the entire volume.
2Device complexity
If rigid-body motion model is assumed for motion estimation, then computational complexity is reduced, but accuracy deteriorates when non-rigid motion occurs
Solution Approach 1:
The patent divides the body into segments with different motion characteristics. By identifying regions that exhibit non-rigid motion and applying region-specific weighting or masking to their data contributions, the method allows the overall system to use a computationally efficient rigid-body model for most regions while accounting for non-rigid effects in specific areas, thus balancing complexity and accuracy.
Solution Approach 2:
The patent dynamically adjusts the weighting parameters applied to different spatial regions based on their motion characteristics. By changing the weight values assigned to data from different body parts during the optimization process, the method effectively adapts the rigid-body model to accommodate non-rigid motion in specific regions without increasing overall computational complexity, thereby maintaining both efficiency and accuracy.
Data Source
AI summary
A method for generating a motion-corrected magnetic resonance image dataset of a body region of a subject, the method comprising receiving magnetic resonance data acquired of the body region; receiving information on the body region covered by the magnetic resonance image dataset; weighting at least part of the received magnetic resonance data by reducing the signal originating from parts of the body region that are expected to have undergone non-rigid and/or independent motion during the acquisition, thereby producing weighted magnetic resonance data; and estimating the motion-corrected image dataset by minimizing the data consistency error between the magnetic resonance data acquired in the imaging protocol and a forward model described by an encoding matrix, wherein the encoding matrix includes motion parameters, Fourier encoding, and optionally subsampling and/or coil sensitivities of a multi-channel coil array, wherein the estimation includes at least one step of estimating motion parameters from the weighted magnetic resonance data.