MRI Image Reconstruction via Coil and Gridding Sensitivity Registration
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Solution Overview
Problem
Magnetic resonance images generated using non-Cartesian imaging in k-space often suffer from artifacts and degraded Signal-to-Noise (S/N) ratios, particularly in the perimeter regions, when gridding and sensitivity encoding (SENSE) are combined, leading to issues like shading and streaks.
Innovation Solution
An image generating apparatus and method that register coil sensitivity distributions with gridding sensitivity distributions, adjusting the kernel function for optimal gridding sensitivity and performing phase modulation to align the gridding sensitivity center-of-gravity with the coil sensitivity center-of-gravity, thereby improving image quality by reducing artifacts and enhancing S/N ratios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If gridding and SENSE are used in combination for non-Cartesian imaging, then image reconstruction is achieved, but artifacts and degraded S/N ratio occur in perimeter regions
Solution Approach 1:
The patent applies local quality by adjusting the gridding kernel function parameters specifically for different regions of the k-space. The kernel function is modified to have different characteristics for central and peripheral regions, thereby improving S/N ratio and reducing artifacts in perimeter regions while maintaining reconstruction quality in the central region.
Solution Approach 2:
The patent changes parameters of the gridding kernel function, specifically adjusting the kernel width and shape parameters. By optimizing these parameters, the patent achieves better matching between the gridding sensitivity distribution and the coil sensitivity distribution, which reduces artifacts and improves S/N ratio in the reconstructed image.
2Ease of operation
If gridding is performed to arrange non-Cartesian acquisition points at grid points, then image reconstruction is enabled, but S/N ratio degrades in perimeter regions
Solution Approach 1:
The patent implements local quality by applying region-specific gridding kernel functions. The kernel function parameters are adjusted to provide different gridding characteristics for central and peripheral regions, ensuring adequate S/N ratio and minimizing artifacts in perimeter regions while maintaining reconstruction capability throughout the image.
Solution Approach 2:
The patent performs preliminary optimization of the gridding kernel function parameters before actual image reconstruction. By pre-adjusting the kernel parameters based on the non-Cartesian sampling pattern and coil sensitivity distribution, the patent prevents S/N ratio degradation and artifact formation in perimeter regions during the reconstruction process.
Data Source
AI summary
An image generating apparatus according to an embodiment includes processing circuitry. The processing circuitry obtains a coil sensitivity distribution indicating a sensitivity distribution of a reception coil used for an imaging process performed on an examined subject and magnetic resonance data acquired from the imaging process that is non-Cartesian and performed in a k-space. The processing circuitry performs, on the basis of the coil sensitivity distribution, registration between the coil sensitivity distribution and a gridding sensitivity distribution indicating a distribution of gridding sensitivity related to arranging the magnetic resonance data in the k-space. The processing circuitry generates a magnetic resonance image on the basis of a result of the registration, magnetic resonance data, coil sensitivity distribution, and gridding sensitivity distribution.