MRI K-space Motion Correction via Non-linear Estimation
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Solution Overview
Problem
Magnetic resonance imaging (MRI) is affected by motion artifacts due to changes in object orientation and position within the Field of View during data acquisition, leading to image inconsistencies and reduced quality, which existing methods like faster imaging or motion correction using navigators may not adequately address without compromising spatial resolution or requiring additional hardware.
Innovation Solution
A computer-implemented method that receives and samples k-space data subsets, selects a base subset as a motion-free reference, estimates motion parameters using a non-linear motion estimating function, and corrects the second subset based on these parameters, allowing for improved image alignment without additional hardware, using techniques like artificial neural networks for efficient motion parameter extraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If faster imaging is used to reduce motion artifacts, then motion artifacts are reduced, but spatial resolution is limited
Solution Approach 1:
The patent segments the k-space data into multiple subsets acquired at different time points, then applies individual motion correction to each subset before combining them. This allows motion artifacts to be corrected without requiring faster imaging that would compromise spatial resolution.
Solution Approach 2:
The patent performs motion correction as a preliminary step before final image reconstruction. By estimating motion parameters and correcting k-space subsets beforehand, the method prevents motion artifacts from degrading the final image quality without requiring accelerated imaging sequences.
2Object-affected harmful factors
If navigators are used during acquisition to track motions, then prospective motion correction is achieved, but device complexity increases
Solution Approach 1:
The patent uses the existing MRI system's own hardware components (gradient coils, RF system, and standard imaging sequences) to perform motion correction. The method extracts motion information from the acquired k-space data itself without requiring external navigator equipment or additional tracking hardware.
Solution Approach 2:
The patent replaces the mechanical/physical approach of using external navigators and tracking hardware with a computational approach. Motion parameters are estimated through signal processing and optimization algorithms applied to the acquired data, substituting complex hardware systems with software-based solutions.
3Reliability
If k-space is sampled redundantly to average out motion effects, then motion robustness is improved, but acquisition time increases
Solution Approach 1:
The patent changes the parameter of motion correction from temporal averaging (requiring redundant sampling) to parameter estimation and correction. By estimating motion parameters for each k-space subset and applying corrective transformations, the method achieves motion robustness without requiring redundant data acquisition, thus maintaining efficient scan times.
4Object-affected harmful factors
If motion parameters are estimated using optimization problems, then motion correction is achieved, but convergence speed is slow
Solution Approach 1:
The patent performs preliminary actions to accelerate convergence: using a reduced model for faster initial estimation, applying smart initialization strategies, and using hierarchical optimization approaches. These preliminary steps provide good initial guesses that enable the optimization to converge quickly to accurate motion parameters.
Solution Approach 2:
The patent segments the motion estimation problem into multiple independent optimizations for different k-space subsets rather than solving one large optimization problem. This segmentation allows parallel processing and reduces the computational burden of each individual optimization, significantly improving overall convergence speed.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach significantly improves image quality by reducing motion artifacts, decreases the need for repeated scans, and is economically attractive by integrating with conventional MRI systems without additional hardware, while allowing for efficient motion parameter estimation and correction.
Implementation Method 1
Magnetic resonance imaging (MRI) is affected by motion artifacts
Implementation Method 2
receiving at least a first and a second subset of k-space data as radio frequency signals emitted from excited hydrogen atoms of a subject
Data Source
AI summary
The disclosure relates to a computer implemented method for magnetic resonance imaging. The method includes: receiving at least a first and a second subset of k-space data as radio frequency signals emitted from excited hydrogen atoms of a subject; sampling the first and second subset of k-space data; choosing the first subset of k-space data as a base subset of k-space data; estimating motion parameters of the second subset of k-space data against the base subset of k-space data; and correcting the second subset of k-space data based on the estimated motion parameters of the second subset of k-space data. The motion parameters of the second subset of k-space data are parameters of a non-linear motion estimating function representing a motion of the subject between receiving the first subset of k-space data and receiving the second subset of k-space data.