MRI Phase-Difference Statistics for Magnetic Susceptibility Analysis
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Solution Overview
Problem
Existing techniques for determining magnetic susceptibility in magnetic resonance image data face challenges with poor convergence in nonlinear approximation and high calculation burden, lacking efficient methods for analyzing such data.
Innovation Solution
An image processing method that includes acquiring phase difference image signals, performing statistical processing to calculate statistics like average, standard deviation, kurtosis, and skewness, and evaluating target materials using these statistics, with options for single-echo or multi-echo methods to refine calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If nonlinear approximation is used to determine magnetic susceptibility, then measurement precision may be improved, but calculation complexity and convergence reliability deteriorate
Solution Approach 1:
The patent transforms the nonlinear magnetic susceptibility determination problem into a linear statistical analysis problem by changing the parameter space. Instead of directly solving for magnetic susceptibility through nonlinear approximation, the method calculates statistical parameters (mean, standard deviation, skewness, kurtosis) of phase difference distributions, which linearly correlate with magnetic susceptibility. This parameter transformation resolves the contradiction by maintaining measurement precision while eliminating calculation complexity and convergence issues.
2Measurement precision
If nonlinear approximation is used for magnetic susceptibility determination, then measurement precision may be improved, but reliability of results deteriorates due to poor convergence
Solution Approach 1:
The patent replaces the mechanical iterative optimization process of nonlinear approximation with a direct statistical calculation approach. By substituting the iterative numerical method with closed-form statistical formulas (calculating mean, standard deviation, skewness, and kurtosis of phase distributions), the method eliminates convergence reliability issues while maintaining the ability to accurately determine magnetic susceptibility through the statistical properties of the data.
3Measurement precision
If detailed statistical processing is performed for each region, then measurement precision is improved, but calculation burden increases
Solution Approach 1:
The patent extracts only the essential statistical features (mean, standard deviation, skewness, kurtosis) from the phase difference distributions in each region, rather than performing comprehensive detailed analysis. By taking out only these four key statistical parameters that directly correlate with target material characteristics, the method achieves high measurement precision while maintaining calculation efficiency, avoiding unnecessary computational burden from more detailed processing.
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
Enables simple and accurate analysis of magnetic resonance image data, reducing calculation complexity and providing reliable evaluations of target materials like iron bound to proteins, correlating with clinical indices for conditions like Alzheimer's disease.
Implementation Method 1
magnetic resonance image data acquired by scanning a plurality of regions of a living body
Data Source
AI summary
An image processing apparatus processes magnetic resonance image data acquired by scanning multiple regions of a living body. An MRI imaging apparatus scans multiple regions of the living body to acquire the magnetic resonance image data. An image processing unit generates phase difference image data from the magnetic resonance image data. A signal acquisition unit acquires a phase difference image signal from the phase difference image data. A statistic calculation unit performs statistical processing of the distribution of the phase difference image signal with respect to the phase difference for each region to calculate a statistic, for example. A target material evaluation unit evaluates the amount of the target material included in multiple regions using the statistic for each region.