Prospective Motion Correction in Parallel Imaging MRI
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Solution Overview
Problem
Conventional parallel imaging techniques fail to effectively combine with prospective motion correction methods in MRI systems, leading to aliasing and artifacts due to changes in coil sensitivity caused by subject motion during long 3D scans.
Innovation Solution
A method that involves acquiring MR data from multiple RF coils, applying prospective motion correction by determining motion changes and dividing data into scan plane orientation groups, generating unaliasing coefficients for each group, and synthesizing data to correct for motion, ultimately combining the data to form a complete k-space dataset for image generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If parallel imaging is used to accelerate data acquisition in 3D MRI scans, then scan time is reduced and productivity is improved, but motion artifacts increase and image quality deteriorates due to subject motion during long scans
Solution Approach 1:
The patent divides the 3D MRI scan into multiple smaller 2D slices that can be acquired sequentially. Each slice is processed independently with its own parallel imaging reconstruction, allowing motion correction to be applied between slices while maintaining the speed benefits of parallel imaging within each slice.
Solution Approach 2:
The system performs preliminary motion correction calculations and updates scan coordinates before acquiring each slice. By predicting and compensating for motion in advance, the system prevents motion artifacts from degrading image quality while maintaining accelerated scan times through parallel imaging.
2Reliability
If prospective motion correction is applied to adjust scan coordinates for moving subjects, then motion artifacts are reduced and image quality improves, but coil sensitivity weightings change and lead to aliasing artifacts in parallel imaging reconstruction
Solution Approach 1:
The patent implements dynamic recalculation of unaliasing coefficients after each motion correction update. As the subject moves and scan coordinates are adjusted, the system dynamically computes new coil sensitivity weightings and corresponding unaliasing coefficients to match the updated geometry, preventing aliasing artifacts while maintaining motion correction benefits.
Solution Approach 2:
The system continuously monitors subject motion and provides feedback by updating scan coordinates and recalculating reconstruction parameters. This closed-loop approach ensures that motion correction and parallel imaging reconstruction remain synchronized, eliminating aliasing artifacts caused by mismatched coil sensitivity weightings.
3Productivity
If conventional parallel imaging techniques are used with motion corrected data, then data acquisition is accelerated, but erroneous image reconstruction occurs due to mismatched unaliasing coefficients and moving subject geometry
Solution Approach 1:
The system performs preliminary calculation of unaliasing coefficients based on predicted subject position for each slice before data acquisition. This pre-computation ensures that the reconstruction parameters are ready and correctly matched to the motion-corrected geometry, enabling both fast parallel imaging reconstruction and accurate image formation.
Solution Approach 2:
The patent dynamically changes reconstruction parameters including unaliasing coefficients and coil sensitivity weightings to match the motion-corrected scan geometry. By adapting these parameters in real-time according to subject motion, the system maintains accurate image reconstruction while preserving the speed advantages of parallel imaging.
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 enables accurate image reconstruction by aligning scan coordinates with the target volume, reducing motion-related artifacts and improving image quality in 3D MRI scans by effectively integrating prospective motion correction with parallel imaging.
Implementation Method 1
Radio frequency (RF) coils are used to create pulses of RF energy at or near the resonance frequency of the hydrogen nuclei. These coils are used to add energy to the nuclear spin system in a controlled fashion.
Implementation Method 2
When a human body, or part of a human body, is placed in the main magnetic field, the nuclear spins that are associated with the hydrogen nuclei in tissue water become polarized. This means that the magnetic moments that are associated with these spins become preferentially aligned along the direction of the main magnetic field, resulting in a small net tissue magnetization along that axis
Implementation Method 3
gradient coils that produce smaller amplitude, spatially varying magnetic fields when current is applied to them. Typically, gradient coils are designed to produce a magnetic field component that is aligned along the z axis and that varies linearly in amplitude with position along one of the x, y or z axes. The effect of a gradient coil is to create a small ramp on the magnetic field strength, and concomitantly on the resonance frequency of the nuclear spins, along a single axis.
Implementation Method 4
As the nuclear spins then relax back to their rest energy state, they give up energy in the form of an RF signal. This signal is detected by the MRI system and is transformed into an image using a computer and known reconstruction algorithms.
Data Source
AI summary
A method for generating a magnetic resonance (MR) image includes acquiring MR data from each of a plurality of RF coils and applying a prospective motion correction method to the MR data for each RF coil including determining a set of motion measurements that include a scan plane orientation associated with each data point in the MR data. The MR data for each RF coil is divided into a plurality of scan plane orientation groups based on motion changes. A set of unaliasing coefficients is generated for each scan plan orientation group and applied to the MR data to synthesize data for each RF coil. The acquired MR data and synthesized data for each RF coil is combined to generate a scan plane orientation data set. Each scan plane orientation data set is combined to generate a complete k-space data set.