MRI Susceptibility Map Calculation Using Phase Variation Weighting
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Solution Overview
Problem
In quantitative susceptibility mapping (QSM), the estimation accuracy of susceptibility maps is compromised due to phase variations and artifacts caused by low signal-to-noise ratios and partial volume effects, especially in regions with significant susceptibility changes, such as large veins, where the use of absolute image-based weighting leads to inaccurate weight assignment and increased artifacts.
Innovation Solution
A magnetic resonance imaging (MRI) technique that calculates a susceptibility map using a weighting image reflecting phase variations with high accuracy, where the phase variation is converted into a weight that monotonically decreases with increasing phase variation, allowing for more precise estimation and reduced artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If weighting is performed using the absolute image pixel values, then the weight decreases in regions with low signal-to-noise ratio, but the weighting does not accurately reflect phase variation leading to inaccurate susceptibility estimation
Solution Approach 1:
The patent changes the weighting parameter from absolute image pixel values to phase variation magnitude. By calculating the standard deviation of phase values in local regions and using this as the weighting factor, the system accurately reflects the actual phase variation characteristics, leading to improved susceptibility estimation accuracy while preserving phase variation information.
2Reliability
If weighting is performed using the absolute image, then calculation time is reduced, but artifacts increase in regions with significant susceptibility changes
Solution Approach 1:
The patent applies local quality by calculating phase variation-specific weighting for each local region rather than using a uniform absolute image-based weight. The standard deviation of phase values is computed in local windows, and weighting is applied independently to each region, allowing accurate artifact reduction in susceptibility change regions while maintaining calculation efficiency through localized processing.
3Measurement precision
If phase variation is calculated using large calculation regions, then phase variation is averaged out reducing noise, but spatial resolution is lost
Solution Approach 1:
The patent dynamically adjusts the calculation region size based on the specific imaging requirements and phase variation characteristics. The system calculates phase variation standard deviation in local regions with appropriate dimensions, balancing noise reduction through averaging with spatial resolution preservation. The dynamic adaptation allows optimization for different tissue types and imaging scenarios.
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 enhances the estimation accuracy of susceptibility maps and reduces artifacts by accurately reflecting phase variations in the weighting image, improving diagnostic precision, particularly in regions with significant susceptibility changes.
Implementation Method 1
applies a radio frequency magnetic field and a gradient magnetic field to a subject placed in a static magnetic field, measures a signal generated from the subject by magnetic resonance
Implementation Method 2
the slice gradient magnetic field, a phase encoding gradient magnetic field and a read-out gradient magnetic field that are perpendicular to each other on the imaging section are applied during the period from the excitation to the obtainment of the echo
Implementation Method 3
The measured echoes are arranged in a k-space having a kx axis, a ky axis, and a kz axis, and are subjected to inverse Fourier transform to perform image reconstruction
Implementation Method 4
a gray-scale image in which the pixel values are the phase values (phase image) is an image that reflects a magnetic field change due to unevenness of the static magnetic field, a susceptibility difference between biological tissues
Implementation Method 5
the susceptibility distribution is calculated from the phase distribution using the least squares method. Here, an error function is introduced, and a value that minimizes the error function is used as a solution
Implementation Method 6
weighting is performed for the error function according to the degree of the variation at each region in the phase image. The weighting is performed so that a weight of the region where the phase variation is large becomes small
Data Source
AI summary
Disclosed is a magnetic resonance imaging apparatus that calculates a susceptibility map using a weighting image that reflects a phase variation with high accuracy. The weighting image is calculated from a phase image obtained from a complex image obtained by MRI. First, a region used in calculation of the phase variation is set as a calculation region, and then, a standard deviation or a variance of pixel values of the phase image in the calculation region is set as the phase variation. Further, the phase variation is converted into a weight that monotonically decreases in a broad sense as the phase variation increases to obtain the weighting image.