Magnetic Resonance Chemical-Shift Imaging Seed Point Selection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing chemical-shift-encoded imaging methods struggle to accurately separate signals for more than two components and often experience field pattern selection contradictions, leading to incorrect results due to non-uniform magnetic fields and phase differences.
Innovation Solution
A computer-implemented magnetic resonance chemical-shift-encoded imaging method using a safest path-local growth strategy based on multiple resolutions, which establishes a phasor-error spectrum, determines unique seed points, and merges field patterns at high resolution to eliminate deviations and ensure correct component separation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional multi-echo chemical-shift-encoded imaging is used to correct B0 field bias, then water-fat separation images can be obtained, but the method easily converges to wrong local minimum values when B0 field bias is large or tissue space is separated, causing reverse water-fat separation
Solution Approach 1:
The patent divides the image into multiple non-overlapped subblocks and processes each subblock independently to obtain local field patterns. This segmentation approach prevents the algorithm from converging to wrong local minimums in regions with large B0 field bias or separated tissue spaces, as each subblock is processed with its own initial field pattern set selected based on smooth characteristics.
Solution Approach 2:
The patent applies different processing strategies to different regions by selecting initial field pattern sets based on local smooth characteristics in each subblock. This local quality approach ensures that the field pattern estimation adapts to local variations in B0 field bias and tissue distribution, improving both convergence reliability and separation accuracy.
2Productivity
If two-point water-fat separation technology is used with uniform field pattern assumption, then acquisition and imaging speed are improved, but the method cannot solve the affection caused by non-uniform B0 field
Solution Approach 1:
The patent changes the field pattern parameter from a uniform assumption to a spatially varying field pattern estimated through local growth algorithm. By allowing the field pattern parameter to vary across different subblocks and selecting initial sets based on smooth characteristics, the method maintains imaging speed while significantly improving field pattern accuracy in non-uniform B0 field conditions.
3Measurement precision
If local growth-based two-point water-fat separation is used to solve non-uniform B0 field, then field pattern estimation is improved, but additional information must be added to identify pure water and pure fat diagrams
Solution Approach 1:
The patent extracts the field pattern information from the signal model and processes it separately through the local growth algorithm in each subblock. By taking out the field pattern estimation as a distinct step and using smooth characteristics to select initial sets, the method improves field pattern estimation accuracy without significantly increasing overall processing complexity, as the extraction leverages existing signal characteristics.
4Measurement precision
If field pattern extraction algorithm is used with regional iteration, then separated water and fat images are obtained, but selection contradiction is presented causing jump in final field pattern
Solution Approach 1:
The patent segments the image into non-overlapped subblocks and performs field pattern extraction independently in each subblock. This segmentation eliminates selection contradiction and field pattern jumps by ensuring that each subblock's field pattern is determined locally based on its own smooth characteristics, without interference from adjacent regions that could cause contradictory selections.
Solution Approach 2:
Instead of iterating field patterns globally across the entire image which causes selection contradictions, the patent inverts the approach by processing each subblock independently and then combining results. This inversion from global iteration to local independent processing eliminates the selection contradiction problem and ensures field pattern consistency.
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 method effectively identifies and separates two components by increasing seed point quantity and distribution range, ensuring accurate field pattern estimation and eliminating signal deviations, thus providing correct water-fat separation images.
Implementation Method 1
The magnetic resonance chemical-shift-encoded imaging is an imaging method based on a chemical shift difference between components in a tissue
Implementation Method 2
magnetic resonance chemical-shift-encoded imaging
Data Source
Figure 1
Figure 2~3
Figure 4
AI summary
Provided are a magnetic resonance chemical-shift-encoded imaging method, apparatus, and device, belonging to the technical field of magnetic resonance imaging. The method comprises: in a phasor-error plot established on the basis of a two-point magnetic resonance signal model, determining to be an initial seed point a pixel having a unique phasor and causing said plot to reach a minimal local value; according to the initial seed point, estimating the phasor value of a to-be-estimated pixel to obtain a field map; mapping and merging the field map 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 the phasor value of the reconstructed to-be-estimated pixel; according to the reconstructed seed point and the phasor value of the reconstructed to-be-estimated pixel, obtaining two separate images having predetermined components. In the method, a region simultaneously containing two components is identified as a seed point, eliminating the deviation caused by phasor-value jump at high resolution and ensuring the correctness of the seed point ultimately selected.