MRI Quantification via Complex Summation of Concentric Voxels
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Solution Overview
Problem
Magnetic resonance imaging (MRI) faces challenges in quantifying small objects, such as sub-voxel objects, due to limited signal-to-noise ratio and spatial resolution, making it difficult to accurately determine their magnetic moment, susceptibility, or volume.
Innovation Solution
A method involving complex summations of real and imaginary values from MRI data is used to calculate the magnetic moment and susceptibility of objects, employing concentric regions and error propagation to minimize uncertainty, allowing for precise volume determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional MRI quantification methods are used, then the measurement process is simple, but the measurement precision is poor due to limited spatial resolution and signal-to-noise ratio
Solution Approach 1:
The patent divides the voxel into multiple concentric regions (first region, second region, third region) and calculates separate complex summations for each region. This segmentation allows the method to isolate and quantify the magnetic moment contribution of sub-voxel objects by analyzing signal variations across different spatial zones, thereby improving measurement precision without requiring higher spatial resolution imaging.
Solution Approach 2:
The patent transforms the problem from simple scalar signal measurement to complex dimensional analysis by calculating complex summations that incorporate both real and imaginary components of the MRI signal. This dimensional transformation enables the extraction of magnetic moment information that cannot be obtained from conventional single-value measurements, improving quantification precision through multi-dimensional data utilization.
2Measurement precision
If higher spatial resolution is used to detect sub-voxel objects, then the measurement precision improves, but the imaging duration and signal-to-noise ratio deteriorate
Solution Approach 1:
The patent performs preliminary calculations of complex summations for multiple concentric regions during the standard MRI acquisition process, utilizing the signal data already collected. By preparing and processing this data in advance through systematic summation and analysis, the method extracts precise object characteristics without requiring additional imaging time or higher resolution scans, thus avoiding the trade-off between imaging duration and detection precision.
3Reliability
If curve-fitting approaches are used to quantify objects, then the calculation process is simple, but the reliability is reduced due to uncertainty and noise sensitivity
Solution Approach 1:
The patent employs a self-service approach by using the MRI signal data itself to calculate the magnetic moment through direct complex summation of real and imaginary values across concentric regions. This method eliminates the need for external curve-fitting procedures or model-based inference, allowing the data to speak for itself through systematic mathematical processing. The result is improved reliability through direct measurement while maintaining computational efficiency through structured summation algorithms.
Data Source
AI summary
A method comprises digitally representing a volume of space as a plurality of voxels and assigning real and imaginary values derived from magnetic resonance imaging data of the space to each of the voxels. Furthermore, the method comprises a steps of calculating a first complex summation of the real and imaginary values of a first set of the voxels, and calculating a second complex summation of the real and imaginary values of a second set of the voxels. Each set of voxels represents a different region of the volume of space. The regions are concentric. The method also comprises steps of using the first and second summations, along with another value quantitatively calculated from the magnetic resonance imaging data, to calculate a value that is dependent upon the approximate magnetic moment of an object within the volume of space, and digitally representing and storing said value.