Harmonic Background Phase Determination in MRI
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Solution Overview
Problem
Existing methods for determining the background phase in magnetic resonance imaging (MRI) data sets are prone to errors due to movement and external interferences, and require reference data sets or computationally expensive polynomial interpolations, which are not physically based and can fail in regions with fast spatial changes in susceptibility.
Innovation Solution
A method that uses a filter kernel to determine the background phase by assuming the spatial curve of the background phase is harmonic, iteratively applying the filter kernel to pixels within a closed or nearly closed contour in the phase image data set, and reconstructing the background phase without relying on reference data sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If reference data sets are used to determine background phase, then temperature changes can be detected, but movement and external interferences cause phase changes that are incorrectly interpreted as temperature changes
Solution Approach 1:
The invention extracts and removes the background phase component from the total phase signal by modeling it as a harmonic function. This separation allows the temperature-dependent phase changes to be isolated and measured accurately without contamination from movement or external interferences that affect the background phase.
Solution Approach 2:
The invention introduces a harmonic function model as an intermediary representation of the background phase. This mathematical model acts as a mediator between the raw phase data and the temperature measurement, allowing systematic removal of background phase effects while preserving the temperature-dependent signal.
2Area of stationary object
If polynomial interpolation is used to determine background phase in regions with fast spatial changes in susceptibility, then coverage of such regions is achieved, but the precision is insufficient and calculation becomes expensive
Solution Approach 1:
The invention changes the mathematical model parameter from polynomial functions to harmonic functions (sinusoidal functions). This parameter change in the functional form allows accurate representation of background phase even in regions with fast spatial susceptibility changes, while maintaining computational efficiency through iterative optimization.
Solution Approach 2:
The invention employs an iterative optimization process that dynamically adjusts the harmonic function parameters to best fit the measured phase data. This dynamic adaptation allows the model to accurately capture background phase variations in complex regions without requiring computationally expensive high-order polynomials.
3Ease of operation
If polynomial interpolation is used to determine background phase, then background phase can be estimated, but the determination is computationally expensive and can lead to calculation problems
Solution Approach 1:
The invention uses a computationally efficient harmonic function model that requires minimal computational resources compared to polynomial interpolation. The simple mathematical form of harmonic functions allows rapid calculation and iteration, making the background phase determination fast and suitable for real-time or near-real-time applications.
Solution Approach 2:
By changing from polynomial parameters to harmonic function parameters (amplitude, frequency, phase), the invention achieves a more efficient mathematical representation that is less computationally intensive while providing equivalent or superior accuracy in modeling background phase variations.
4Device complexity
If reference-less methods are used to determine temperature from measured phase values, then no reference data sets are needed, but information about system-dependent background phase changes must be present
Solution Approach 1:
The invention enables the system to self-determine the background phase by modeling it as a harmonic function that can be fitted to the measured phase data. This self-service approach eliminates the need for external reference data sets or additional hardware components, as the system uses its own measurements to characterize and remove the background phase.
Solution Approach 2:
The harmonic function model serves as an intermediary that bridges the gap between raw phase measurements and temperature determination in reference-less methods. It provides the necessary background phase information by modeling its spatial and temporal variations, allowing accurate temperature measurement without external references.
Data Source
AI summary
In a method to determine a background phase in phase values of a phase image data set that is acquired from an examination subject, wherein the background phase is determined in a partial region of the phase image data set, the phase image data set of the examination subject is acquired, and a substantially closed, planar contour is established in the phase image data set around the partial region, the planar contour having a contour area with a width of at least one pixel of the phase image data set. The phase values in the partial region are determined with the assumption that the spatial curve of the background phase is a harmonic or quasi-harmonic function, the phase values of the pixels in the partial region being determined based on the phase values in the planar contour.