Phase Estimation for Retrospective Motion Correction in MRI
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Solution Overview
Problem
Conventional MRI motion correction techniques fail to account for orientation-dependent phase variations caused by patient motion, leading to poor reconstruction quality and imaging artifacts, especially in gradient echo sequences susceptible to main magnetic field inhomogeneities.
Innovation Solution
The use of a SENSE plus motion model that detects motion without real-time tracking, incorporating phase information from projection onto convex sets (POCS) algorithms to refine images and minimize data consistency errors across multiple motion states, thereby compensating for patient motion and reducing artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional motion correction techniques are used, then the imaging process is simplified, but phase variations caused by motion are not accurately compensated, leading to poor reconstruction quality
Solution Approach 1:
The patent changes the parameter model from simple rigid motion to include orientation-dependent phase variations. By introducing phase correction terms that account for B0 field inhomogeneities and tissue susceptibility differences, the system accurately compensates for motion-induced phase errors while maintaining operational simplicity through automated parameter estimation.
Solution Approach 2:
The patent replaces complex mechanical motion tracking systems with a computational approach using SENSE plus motion model. Instead of using external cameras or navigators, the system uses MRI data itself to estimate motion parameters and phase variations through iterative reconstruction algorithms, substituting mechanical tracking with computational modeling.
2Device complexity
If SENSE plus motion model is used without phase correction, then the reconstruction process is simplified, but motion artifacts remain due to unaccounted phase variations
Solution Approach 1:
The patent implements an iterative feedback mechanism where phase corrections are estimated from the current image reconstruction, used to correct the k-space data, and the process repeats until convergence. This feedback loop continuously refines the phase correction until motion artifacts are minimized, balancing complexity with artifact reduction.
Solution Approach 2:
The patent performs preliminary phase correction by estimating phase variations from the initial image reconstruction before final image generation. This preliminary action identifies and corrects phase errors early in the process, preventing them from propagating into final artifacts while keeping the overall process manageable through structured iteration.
3Measurement precision
If phase information is incorporated through multiple iterations, then motion correction accuracy is improved, but computational time increases
Solution Approach 1:
The patent applies partial action by performing phase correction iterations only when and where necessary. The iterative process continues until convergence criteria are met or a maximum number of iterations is reached, avoiding unnecessary computational effort in cases where motion is minimal or phase variations are already accounted for, thus balancing accuracy with time efficiency.
Data Source
AI summary
Techniques are disclosed related to the compensation of phase variations introduced into k-space lines, which cause imaging artifacts. The techniques utilize the detection of motion via an encoding plus motion model, which does not require the use of additional prospective or retrospective motion detection techniques. The techniques described herein use the encoding plus motion model to reconstruct an initial image from a set of motion states, and then calculate phase information from images that are projected form the initial reconstructed image using a projection onto convex sets (POCS). The phase information is incorporated into the encoding plus motion model over several iterations to minimize data consistency error, thereby generating a refined image that compensates for patient motion over the set of motion states.