Automated MR Spectrum Analysis via Feature Vector Classification
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Solution Overview
Problem
The analysis of magnetic resonance spectra is complex and time-consuming due to variability in signal intensities influenced by multiple factors, leading to inherent uncertainty and inconsistent results, especially in diagnosing tissues and materials.
Innovation Solution
A method involving post-processing procedures such as baseline correction, apodization, zero padding, phase correction, and feature extraction using statistical methods to generate a feature vector for classification, which allows for automated and reproducible analysis and classification of spectra.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual spectral analysis methods are used to determine signal intensities and ratios, then diagnostic information can be obtained, but the analysis becomes extremely complex and time-consuming with inherent uncertainty
Solution Approach 1:
The patent replaces manual mechanical analysis methods with automated computer-based processing. The control unit automatically performs Fourier transformation, baseline correction, peak detection, and quantification, eliminating the need for manual spectral analysis while maintaining or improving accuracy and significantly reducing analysis time.
Solution Approach 2:
The system enables self-service analysis where the spectral data automatically undergoes processing, correction, and analysis without requiring manual intervention. The control unit independently performs all necessary operations from raw data acquisition through to diagnostic interpretation, making the analysis process autonomous and efficient.
2Reliability
If signal intensities are determined for specific resonances to examine metabolic processes, then diagnostic information is obtained, but signal intensity varies due to numerous factors including echo time, repetition interval, receiver gain, and measurement sequence
Solution Approach 1:
The patent applies baseline correction as a preprocessing step that adjusts the spectral baseline to a reference level, compensating for variations caused by different measurement parameters. This normalization process removes the influence of echo time, repetition interval, and receiver gain variations, allowing consistent comparison of signal intensities across different measurements.
Solution Approach 2:
The automated processing system replaces manual signal intensity determination with algorithm-based quantification that inherently compensates for measurement variations. The control unit applies standardized processing routines that normalize signals across different acquisition parameters, improving consistency and reliability.
3Reliability
If the same spectrum is analyzed by numerous persons, then different results are obtained, but automated analysis provides consistent results
Solution Approach 1:
The patent replaces variable human analysis with a standardized automated system. The control unit executes identical processing algorithms for all spectra, eliminating inter-observer variability. While the system complexity increases, it provides consistent, reproducible results that override the simplicity of manual methods.
Solution Approach 2:
The automated analysis system provides universal processing capabilities that handle all spectral data uniformly. The same control unit and algorithms process all spectra regardless of operator, patient, or measurement conditions, ensuring consistent application of analysis criteria across all cases.
4Reliability
If post-processing procedures are applied to improve spectral analysis capability, then analysis reliability is improved, but computational processing is required
Solution Approach 1:
The patent implements automated post-processing procedures including baseline correction, Fourier transformation, and peak detection that are executed by the control unit. These processing steps improve spectral analysis reliability by systematically correcting artifacts and enhancing signal quality, with the computational complexity managed through automated routing and processing.
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 simplifies spectral analysis, improves signal-to-noise ratio, and increases spectral resolution, enabling reliable and automated classification of spectra, reducing computational time and increasing the reliability of tissue and material diagnosis.
Implementation Method 1
magnetic resonance spectra offer the possibility of examining metabolic processes in human bodies
Implementation Method 2
If the processed or unprocessed FID is subjected to a Fourier transformation, a spectrum is obtained
Data Source
AI summary
In a method and magnetic resonance apparatus for automating the analysis of MR raw data representing a spectrum, at least one post-processing procedure is applied to the raw data, so as to obtain a processed spectrum. The number of numerical values depicted by the processed spectrum is lowered to a feature vector. The feature vector is allocated to one of numerous groups of known feature vectors.