MRI Residual Map for Signal Model Consistency
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Solution Overview
Problem
Conventional magnetic resonance imaging (MRI) techniques assume signal model constants as known, leading to errors in quantitative parameter determination due to deviations from true values, making the fitting process less numerically stable and noisy.
Innovation Solution
A method and apparatus for MRI that evaluates the quality of fit of signal models by using multiple models with different combinations of free and fixed parameters, incorporating goodness-of-fit analysis and region of interest (ROI) analysis to optimize parameter fitting, allowing for the testing and adjustment of parameters like noise and temperature, and generating residual maps to assess model accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If signal model constants are assumed to be known, then the fitting process is simpler, but errors in quantitative parameter determination occur due to deviations from true values
Solution Approach 1:
The patent changes the status of signal model constants from fixed assumed values to variable parameters that are iteratively refined. By allowing constants to vary and updating them based on residual analysis and goodness-of-fit metrics, the system resolves the contradiction between simple fitting and accurate quantitative parameter determination.
2Measurement precision
If signal model constants are treated as unknowns and solved for, then measurement precision improves, but the fitting operation becomes less numerically stable and parameters become noisier
Solution Approach 1:
The patent implements feedback by calculating residuals between observed and modeled signals, then using these residuals to iteratively update signal model constants. This feedback loop allows the system to refine parameters based on actual data quality, improving measurement precision while maintaining numerical stability through controlled iteration rather than simultaneous solving of all unknowns.
Solution Approach 2:
The patent performs preliminary actions by first estimating signal model constants using initial assumptions, then systematically refining them through residual analysis and goodness-of-fit evaluation. This staged approach separates the fitting process into preliminary estimation and iterative refinement phases, improving both precision and stability.
3Adaptability or versatility
If multiple signal models with different parameter combinations are used, then adaptability improves, but device complexity increases
Solution Approach 1:
The patent applies dynamics by making the signal model parameters adaptive rather than static. The system dynamically adjusts which parameters are treated as constants versus variables based on residual analysis and goodness-of-fit metrics, allowing flexible adaptation to different imaging conditions while managing complexity through automated model selection.
Data Source
AI summary
In a magnetic resonance (MR) apparatus and a method for operating an MR apparatus, MR data are acquired and evaluated with regard to multiple tentative signal models for producing a parameter map based on one of those signal models. The parameter map shows multiple parameters that have respective effects on the MR data. Each tentative signal model is initially analyzed to determine whether any of the parameters used therein can be assumed to be at least locally constant, and the initially analyzed tentative signal model is then subjected at least to a quality of fit analysis. The tentative signal model having at least the best quality of fit analysis result is then used to generate a parameter map that is displayed at a display monitor.