Multi-echo MRI Fat Iron Quantification via Adaptive Fitting
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Solution Overview
Problem
Current methods for quantifying fat and iron deposition in tissues using Magnetic Resonance Imaging (MRI) are limited by vendor-specific platforms and face challenges in accurately measuring iron due to the influence of fat, particularly in the presence of high fat levels.
Innovation Solution
A multi-step, adaptive fitting approach using multi-echo MRI that involves acquiring and processing multiple signal datasets to determine water and fat magnitude values and transverse relaxation rates, allowing for the calculation of proton density fat fraction (PDFF) and iron deposition values across various platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional PDFF calculation methods are used, then fat quantification can be performed, but the methods are limited to specific vendor/hardware platforms and lack cross-platform compatibility
Solution Approach 1:
The patent develops a PDFF calculation method that functions universally across different vendor platforms (GE, Siemens, Philips) by implementing a standardized multi-echo MRI signal processing approach. The method uses platform-independent mathematical models and fitting algorithms that can process multi-echo data regardless of the acquisition system, thereby achieving cross-platform compatibility while maintaining measurement precision through rigorous validation against vendor-specific methods.
2Measurement precision
If direct T2/T2* measurement methods are used to quantify iron, then iron deposition can be measured, but the measurements are problematic in the presence of fat due to fat signal influence
Solution Approach 1:
The patent extracts and separates the fat signal component from the total MRI signal before performing T2/T2* measurement for iron quantification. By using multi-echo data and signal modeling to identify and remove the fat contribution, the method isolates the water signal that contains the iron-related relaxation information, thereby eliminating the harmful influence of fat on iron measurement accuracy.
Solution Approach 2:
The patent performs preliminary fat signal characterization and removal before conducting the iron quantification measurement. The method first uses the multi-echo data to estimate fat fraction and fat T2* values, then applies these preliminary results to correct the water signal before calculating iron deposition, ensuring that fat influence is eliminated prior to the critical iron measurement step.
3Measurement precision
If multi-step adaptive fitting is performed to improve PDFF and iron quantification accuracy, then measurement precision improves, but computational complexity and processing time increase
Solution Approach 1:
The patent segments the PDFF and iron quantification process into distinct computational stages: (1) initial parameter estimation from multi-echo data, (2) fat signal characterization, (3) water signal extraction, and (4) final PDFF and iron calculation. Each stage processes specific aspects of the data independently, reducing the complexity of any single computational step while achieving high overall precision through the cumulative effect of multiple specialized processing stages.
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 method enables accurate and cross-platform compatible quantification of fat and iron deposition, improving diagnostic capabilities for conditions like hepatic steatosis and other tissue-related diseases by providing reliable PDFF and iron deposition values.
Implementation Method 1
Measuring iron with MRI is usually accomplished by measuring the tissue transverse relaxation values (T2 or T2*) or relaxation rates (R2=1/T2 or R2*=1/T2*) with MRI
Implementation Method 2
acquiring a plurality of multi-echo signal datasets representative of the anatomical tissue using a magnetic resonance (MR) pulse sequence
Data Source
AI summary
A computer-implemented method for quantifying fat and iron in anatomical tissue includes acquiring a plurality of multi-echo signal datasets representative of the anatomical tissue using a magnetic resonance (MR) pulse sequence. A plurality of multi-echo signal datasets are selected from the plurality of multi-echo signal datasets and used to determine a first water magnitude value and a first fat magnitude value. In response to determining that the multi-echo signal datasets include at least three multi-echo datasets, a first stage analysis is performed. This first stage analysis comprises selecting a first effective transverse relaxation rate value. Next, first algorithm inputs comprising the first water magnitude value, the first fat magnitude value, and the first effective transverse relation rate value are created. Then, a non-linear fitting algorithm is performed based on the first algorithm inputs to calculate a second water magnitude value, a second fat magnitude value, and a second effective transverse relaxation rate value. A first proton density fat fraction value is then determined based on the second water magnitude value and the second fat magnitude value.