MRI Motion Correction via Compressed Sensing and Preliminary Estimation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Magnetic resonance imaging methods face challenges in achieving high signal-to-noise ratio (SNR) and reducing image artifacts when reconstructing images from undersampled datasets, particularly in motion states where k-space coverage is incomplete.
Innovation Solution
The method employs compressed sensing for image reconstruction, incorporates motion correction using affine 3D translation and linear transforms, and averages motion-corrected images to enhance SNR, while utilizing full k-space sampling and correlations between motion states as prior information for improved reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If undersampled datasets are used for imaging, then scanning efficiency is improved, but signal-to-noise ratio deteriorates
Solution Approach 1:
A motion model is estimated in advance from the undersampled datasets before final image reconstruction. This preliminary motion estimation enables subsequent motion correction to be applied effectively, allowing the system to use undersampled data efficiently while maintaining high SNR through proper motion compensation during the reconstruction process.
2Loss of time
If undersampled datasets are used for imaging, then scanning time is reduced, but image quality deteriorates
Solution Approach 1:
The motion model is estimated preliminarily from the undersampled datasets, enabling accurate motion correction to be applied during image reconstruction. This preliminary estimation allows the system to achieve high image quality despite reduced scanning time and incomplete k-space coverage.
Solution Approach 2:
The system changes the sampling parameters dynamically, using variable density sampling in k-space where the center is fully sampled and peripheral regions are undersampled. This parameter optimization allows reduced scanning time while maintaining diagnostic image quality through the application of motion correction.
3Reliability
If motion correction is applied to compensate for undersampling differences, then signal-to-noise ratio is improved, but computational complexity increases
Solution Approach 1:
The motion model is estimated in advance from the undersampled datasets, which simplifies the subsequent reconstruction process. By performing motion estimation preliminarily, the system reduces the computational complexity of the final reconstruction while still achieving motion correction and high SNR.
Data Source
AI summary
A magnetic resonance imaging method includes acquisition of datasets of magnetic resonance data from an object. At least some of the datasets are undersampled in k-space. Each dataset relating to a motion state of the object. Images are reconstructed from each of the datasets by way of a compressed sensing reconstruction. Motion correction is applied to the reconstructed images relative to a selected motion state, so as to generate motion corrected images. A diagnostic image for the selected motion state is derived, e.g. by averaging from the motion corrected images.