Motion-Compensated MRI Reconstruction via Non-Cartesian K-Space Segmentation
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Solution Overview
Problem
Magnetic resonance imaging (MRI) systems face challenges in producing high-quality images due to motion artifacts, particularly in three-dimensional imaging, where prolonged scan durations and assumptions of two-dimensional rigid body motion compensation techniques break down, leading to reduced diagnostic value and the need for additional navigator data or external motion estimation schemes.
Innovation Solution
A method for reconstructing motion-compensated images using MRI systems that acquire k-space data along non-Cartesian trajectories, segmenting the data into subsets based on identified motion frames, determining and applying motion correction parameters to correct the k-space data subsets, and combining them to form a corrected k-space data set for image reconstruction, without requiring additional navigators or external motion estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If three-dimensional imaging is used to improve diagnostic information, then image quality and diagnostic value are improved, but scan duration is prolonged leading to increased motion artifacts
Solution Approach 1:
The patent segments the k-space data into multiple subsets based on temporal information and motion detection. Each subset corresponds to a specific time period or motion state, allowing independent processing and motion correction. This segmentation enables the system to handle prolonged scan durations by treating different temporal segments separately, thereby maintaining image quality despite extended acquisition times.
Solution Approach 2:
The patent implements dynamic motion correction by continuously tracking motion parameters throughout the scan and adapting correction strategies in real-time. The system dynamically adjusts motion compensation based on detected motion patterns, making the imaging process adaptable to patient movement rather than relying on static assumptions. This dynamic approach maintains diagnostic image quality even during prolonged three-dimensional scans.
2Reliability
If two-dimensional rigid body motion compensation techniques are used to reduce motion artifacts, then motion correction is achieved, but the techniques break down in three-dimensional imaging
Solution Approach 1:
The patent extends motion compensation from two-dimensional to three-dimensional space by incorporating z-direction motion parameters and adapting the correction algorithms to handle volumetric data. The system processes three-dimensional k-space data and applies motion correction in all three spatial dimensions, making the technique versatile for three-dimensional imaging while maintaining reliability of motion correction.
Solution Approach 2:
The patent creates a universal motion compensation framework that can handle both two-dimensional and three-dimensional imaging scenarios. The system uses non-Cartesian trajectories and generic motion correction algorithms that are not limited to specific imaging dimensions or types, making the technique broadly applicable across different MRI protocols while maintaining reliable motion correction performance.
3Reliability
If additional navigator data or external motion estimation schemes are used to improve motion correction, then motion artifact reduction is improved, but device complexity and acquisition time increase
Solution Approach 1:
The patent implements self-service motion correction by using the main imaging data itself to estimate and correct motion, rather than requiring separate navigator echoes or external motion tracking systems. The system extracts motion information from the k-space data during routine image acquisition and applies correction automatically, making the process self-sufficient. This approach maintains high motion correction accuracy while avoiding additional hardware or protocol complexity.
Solution Approach 2:
The patent merges motion estimation and image reconstruction into a unified process. Instead of separately acquiring navigator data and then using it to correct images, the system combines motion parameter estimation with the main imaging sequence, using the same k-space data for both purposes. This merging eliminates redundant data acquisition and processing steps, maintaining motion correction reliability while reducing overall system complexity and scan time.
Data Source
AI summary
A method for reconstructing a motion-compensated image depicting a subject with a magnetic resonance imaging (MRI) system is provided. An MRI system is used to acquire a time series of k-space data from the subject by sampling k-space along non-Cartesian trajectories, such as radial, spiral, or other trajectories at a plurality of time frames. Those time frames at which motion occurred are identified and this information used to segment the time series into a plurality of k-space data subsets. For example, the k-space data subsets contain k-space data acquired at temporally adjacent time frames that occur between those identified time frames at which motion occurred. Motion correction parameters are determined from the k-space data subsets. Using the determined motion correction parameters, the k-space data subsets are corrected for motion. The corrected data subsets are combined to form a corrected k-space data set, from which a motion-compensated image is reconstructed.