Self-Justification Fitting for MRI Relaxation Quantification
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Solution Overview
Problem
Conventional MRI systems face challenges in producing objective, quantitative data for T1 and T2 relaxation parameter quantification due to system imperfections, leading to subjective diagnoses and reliance on skilled interpreters, especially in remote areas where radiologists may not be available.
Innovation Solution
The implementation of self-justification fitting methods that predict and analyze measurable quantification ranges to exclude compromised data points, improving the accuracy and reliability of T1, T2, and other relaxation parameter quantifications by selectively excluding or weighting outlying points to maintain acceptable error ranges.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional MRI pulse sequences are used to acquire T1 and T2 weighted signals, then qualitative images with various weightings can be produced, but the process requires numerous repetitions and lengthy acquisition time
Solution Approach 1:
The patent changes the approach from acquiring multiple qualitative images with different weightings to directly acquiring quantitative relaxation parameter data by modifying the pulse sequence parameters and analysis methodology to extract T1 and T2 values directly from the signal decay curves
Solution Approach 2:
The patent replaces the conventional image-based qualitative analysis with an automated quantitative analysis system that uses mathematical fitting of signal decay curves to extract relaxation parameters, substituting the radiologist's visual interpretation with computational analysis
2Reliability
If conventional qualitative image-based diagnosis is used, then diagnoses can be made by examining multiple image types, but the process requires skilled radiologists and is subjective
Solution Approach 1:
The patent replaces the radiologist's subjective visual interpretation with an automated computational system that objectively calculates relaxation parameters from the acquired signals, eliminating the need for skilled interpreters and reducing subjectivity in diagnosis
Solution Approach 2:
The system performs self-justification fitting where the data itself validates the fitted parameters, allowing the quantitative analysis to stand on its own without requiring expert verification, making the system self-sufficient and automatable
3Extent of automation
If quantitative relaxation parameter analysis is used, then objective diagnosis can be achieved, but system imperfections have substantial impact on quantified mapping values
Solution Approach 1:
The patent implements self-justification fitting where the fitted parameters are used to validate the fit quality, and the process iterates to ensure the extracted relaxation parameters are consistent with the acquired signal data, providing internal feedback to maintain accuracy despite system imperfections
Solution Approach 2:
The patent performs preliminary analysis to identify and exclude compromised data points before final parameter extraction, preventing system imperfections from substantially impacting the quantified values by removing erroneous measurements in advance
Data Source
AI summary
Apparatus, methods, and other embodiments associated with self-justification fitting for magnetic resonance imaging (MRI) relaxation parameter quantification are described. One example nuclear magnetic resonance (NMR) apparatus includes a self-justification fitting logic configured to selectively include and exclude data points from a set of data points associated with NMR signals based, at least in part, on their impact on a fit attribute (e.g., standard deviation). In one embodiment, the self-justification is configured to select a subset of data points from the set of data points as a function of values for a fit attribute computed from fitting at least two different subsets of data points from the set of data points to a known NMR signal evolution.