Multi-Resolution Field Map Estimation for Chemical Shift Encoding
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Solution Overview
Problem
Traditional multi-echo chemical shift encoding imaging methods converge to incorrect local minimum values when estimating B0 field deviations, leading to fat-water swapping issues, especially in non-uniform B0 fields, and fail to accurately separate water and fat signals.
Innovation Solution
A method involving multi-resolution safest path local growth and self-checking field map estimation, where seed points are selected and local growth performed at different resolutions, and high-resolution field maps are merged using a self-checking mechanism to ensure accurate field map estimation and separation of components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional multi-echo chemical shift encoding imaging is used to estimate B0 field deviation, then water-fat separation can be achieved, but the method converges to incorrect local minimum values when B0 field non-uniformity is large, causing fat-water swapping
Solution Approach 1:
The patent performs preliminary field map estimation at low resolution to obtain an initial field map, which serves as a reliable starting point for subsequent high-resolution estimation. This preliminary action prevents the optimization algorithm from converging to incorrect local minima by providing a globally accurate initial guess before detailed local refinement.
Solution Approach 2:
The patent divides the field map estimation process into multiple resolution levels (low resolution and high resolution). At each level, the estimation is performed separately and then refined in the next level, allowing the algorithm to first capture global field variations accurately and then add local details without being trapped by local minima.
2Measurement precision
If multi-resolution field map estimation is performed to increase seed point distribution, then phase factor value accuracy improves, but computational complexity increases
Solution Approach 1:
The patent segments the computational domain into multiple resolution levels, performing field map estimation at coarse resolution first to identify seed points and establish initial field maps. This segmentation allows computationally intensive operations to be performed on smaller, lower-resolution data first, reducing overall computational complexity while maintaining accuracy through progressive refinement.
Solution Approach 2:
The patent adds a resolution dimension to the field map estimation process by performing calculations at multiple scales. This dimensional approach allows the algorithm to efficiently explore the solution space at coarse resolution and then refine results at finer resolutions, achieving high accuracy without proportionally increasing computational complexity at all levels simultaneously.
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 effectively identifies seed points at low resolution, increases the number and distribution range of seed points, and corrects phase factor value deviations, ensuring accurate separation of water and fat signals by eliminating errors caused by phase factor value jumps at highest resolution.
Implementation Method 1
Magnetic resonance chemical shift encoding imaging is an imaging method based on the difference in chemical shifts between components in the tissue
Implementation Method 2
Magnetic resonance chemical shift encoding imaging is an imaging method based on the difference in chemical shifts between components in the tissue
Data Source
AI summary
A method, an apparatus, and a device for magnetic resonance chemical shift encoding imaging are provided. The method includes in a phasor-error spectrum established based on a simplified multi-point magnetic resonance signal model, determining a pixel point having a unique phase factor value and causing the phasor-error spectrum to reach a local minimum value as an initial seed point; estimating a phase factor value of a pixel point to be estimated according to the initial seed point to obtain a field map; mapping and merging the field maps at the highest resolution to obtain a reconstructed field map; determining a reconstructed seed point from the reconstructed field map, and estimating the reconstructed seed point to obtain a phase factor value of a reconstructed pixel point to be estimated.