Magnetic Resonance Data Homogenization Across Frequency Bands
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Solution Overview
Problem
Conventional homogenization of magnetic resonance data can adversely affect noise levels, leading to inconsistent signal-to-noise ratios across different regions of the examination object, particularly affecting areas further from the body surface.
Innovation Solution
A method involving spectral decomposition of magnetic resonance data into multiple frequency ranges, applying distinct homogenization fields to each range, and combining them to generate homogenized data, using a mapping rule to adjust correction values based on signal intensity and frequency, thereby reducing noise and enhancing signal uniformity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional homogenization is applied to magnetic resonance data, then signal intensity uniformity is improved, but noise levels are adversely affected
Solution Approach 1:
The patent segments the magnetic resonance data into multiple frequency ranges (low-frequency range and high-frequency range) and applies different homogenization fields to each range. The first homogenization field is applied to low-frequency data while the second homogenization field is applied to high-frequency data, allowing selective noise management while maintaining signal uniformity across different frequency components.
Solution Approach 2:
The patent applies different homogenization fields (first homogenization field and second homogenization field) to different frequency ranges of the magnetic resonance data. This local differentiation allows the system to optimize homogenization parameters for specific frequency characteristics, improving signal uniformity while preserving noise characteristics in specific frequency bands.
2Manufacturing precision
If conventional homogenization is applied to magnetic resonance data, then signal uniformity across examination regions is improved, but signal-to-noise ratio consistency is worsened
Solution Approach 1:
The patent divides the frequency spectrum into multiple ranges and processes each range separately with appropriate homogenization fields. This segmentation allows the system to maintain signal uniformity across examination regions while preserving the natural signal-to-noise ratio characteristics of different frequency components, thereby improving overall consistency.
Solution Approach 2:
The patent changes the homogenization parameters by applying different homogenization fields (first and second homogenization fields) to different frequency ranges. This parameter differentiation enables optimization of both signal uniformity and signal-to-noise ratio consistency by tailoring homogenization strength to frequency-specific characteristics.
Data Source
AI summary
Method for generating homogenized magnetic resonance data, including: providing magnetic resonance data of an examination object; providing a first homogenization field specific to the examination object; providing a frequency spectrum having frequency ranges; spectral decomposition of the magnetic resonance data into the frequency ranges, wherein first magnetic resonance data is associated with a first frequency range of the frequency ranges and second magnetic resonance data is associated with a second frequency range of the frequency ranges; determining a second homogenization field by taking into account the first homogenization field; applying the first homogenization field to the first magnetic resonance data and generating first homogenized magnetic resonance data; applying the second homogenization field to the second magnetic resonance data and generating second homogenized magnetic resonance data; and generating homogenized magnetic resonance data by combining the first homogenized magnetic resonance data with the second homogenized magnetic resonance data.