MRI Fat Characterization via Phase Unwrapping and Non-Linear Fitting
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Solution Overview
Problem
Current methods for determining the composition and properties of fat using magnetic resonance imaging (MRI) in clinical settings lack accuracy, particularly in quantifying the amount and composition of abdominal fat, which is crucial for diagnosing and monitoring obesity-related diseases.
Innovation Solution
A method involving post-processing of MRI images using a multiple-gradient echo sequence, which includes unwrapping phase images, extracting real signals, and calculating fat characterization parameters through a non-linear least-square fitting technique, allows for accurate quantification of unsaturated and saturated fatty acids in a region of interest.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional MRI techniques are used for fat characterization, then the method is simple and fast, but the measurement precision is insufficient for clinical accuracy
Solution Approach 1:
The patent applies preliminary phase unwrapping and field inhomogeneity correction before fat characterization parameter extraction. By correcting phase errors and inhomogeneities in advance, the method ensures accurate fat spectrum analysis without requiring complex real-time processing during acquisition.
Solution Approach 2:
The patent introduces an intermediary water signal model to separate and characterize fat and water signals. By modeling the water signal first and subtracting it, the method isolates the fat spectrum for accurate characterization parameters (chain length, double bonds) without direct interference from water resonance.
2Measurement precision
If detailed fat composition analysis is performed, then diagnostic accuracy improves, but acquisition time increases
Solution Approach 1:
The patent focuses the detailed spectral analysis only on regions containing fat (adipose tissue), rather than processing the entire image. By applying complex post-processing algorithms selectively to fat-containing regions identified through initial segmentation, the method achieves comprehensive fatty acid composition analysis without proportionally increasing overall acquisition time.
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 provides accurate fat characterization parameters, enabling effective prediction, diagnosis, and monitoring of obesity-related diseases, as well as screening for suitable compounds, with improved accuracy and reduced acquisition time in clinical MRI systems.
Implementation Method 1
images being acquired with a magnetic resonance imaging technique, the magnetic resonance imaging technique involving successive echoes of a multiple-gradient echo sequence, each image associating to each pixel of the image the amplitude of the measured signal in the magnetic resonance imaging technique and the phase of the measured signal in the magnetic resonance imaging technique
Data Source
AI summary
A method for post-processing images of a region of interest in a subject, the images being acquired with a magnetic resonance imaging technique, the method for post-processing comprising at least the step of:unwrapping the phase of each image,extracting a real signal over echo time for at least one pixel of the unwrapped images, andcalculating fat characterization parameters by using a fitting technique applied on a model,the model being a function which associates to a plurality of parameters each extracted real signal, the plurality of parameters comprising at least two fat characterization parameters and at least one parameter obtained by a measurement,the fitting technique being a non-linear least-square fitting technique using pseudo-random initial conditions.