MRI Image Reconstruction Using Local Weighting Regularization
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Solution Overview
Problem
In magnetic resonance imaging using the Parallel Imaging method, the image reconstructing process is sensitive to small changes in input data, leading to non-unique solutions and potential divergence, and regularizations using predetermined norms can degrade the quality of the output image.
Innovation Solution
A magnetic resonance imaging apparatus that applies a local weighting coefficient to the regularization term based on the likelihood of each pixel being in an observation target region, improving the stability and quality of the output image by suppressing artifacts in the background region.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a regularization process is performed using a predetermined norm on the output target image, then the robustness of the solution is improved, but the quality of the output target image is degraded
Solution Approach 1:
The patent applies different regularization strengths to different regions of the image by introducing a weighting coefficient that varies spatially. Regions with high likelihood of containing observation targets receive lower regularization weights to preserve detail, while background regions receive higher weights to suppress artifacts. This local differentiation resolves the contradiction by allowing robust regularization where needed while maintaining image quality where observation targets exist.
2Productivity
If the image reconstructing process is performed on undersampled k-space data, then the productivity is improved, but the solution becomes non-unique and may diverge
Solution Approach 1:
The patent modifies the objective function by adding a regularization term with a weighting coefficient that balances the trade-off between data fidelity and solution stability. This parameter adjustment allows the use of undersampled data for faster imaging while maintaining solution uniqueness through the carefully tuned regularization parameter that prevents divergence.
3Manufacturing precision
If a weighting coefficient is applied to the regularization term based on pixel likelihood, then the quality of the output image is improved, but the device complexity increases
Solution Approach 1:
The patent calculates the weighting coefficient map in advance based on the acquired data and coil sensitivity information, before performing the final image reconstruction. This preliminary calculation of the spatial weighting distribution simplifies the main reconstruction process while still achieving improved image quality, as the complex likelihood estimation is performed once rather than iteratively during reconstruction.
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
The approach enhances the robustness of the image reconstruction process and improves the quality of the output image by effectively using the local weighting coefficient to refine the regularization term, particularly in regions of interest like blood vessels.
Implementation Method 1
magnetic resonance imaging apparatus that acquires k-space data by executing a pulse sequence while performing undersampling
Data Source
AI summary
A magnetic resonance imaging apparatus according to an embodiment includes sequence controlling circuitry and processing circuitry. The sequence controlling circuitry is configured to acquire k-space data by executing a pulse sequence while performing undersampling. The processing circuitry is configured to generate an output target image by generating a folded image by applying a Fourier transform to the k-space data and further unfolding the folded image by performing a process that uses a regularization term. The processing circuitry applies a weight to the regularization term on the basis of whether or not each of the pixels in the output target image is included in an observation target region.