NMR Signal Detection via System Function Variation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing NMR signal detection methods struggle to reliably detect all relevant signals, especially broad signals and those with low intensity, due to high noise interference and artefacts generated by derivation-based methods, leading to false positives and negatives.
Innovation Solution
A computer-implemented method that applies a predefined system function with varying parameter values to NMR signal data, distinguishing signal components from noise by generating base value centered spectra and identifying significant variations, using techniques such as deviation spectra or eigenspace matrices to extract signal intervals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If derivation-based methods are used to detect NMR signals, then narrow signals are preserved, but broad signals are lost and artefacts are generated
Solution Approach 1:
The patent applies systematic variation of signal properties through a system function with a variation parameter. By changing the variation parameter values, the system function can selectively amplify different signal components based on their properties (width, intensity), allowing both narrow and broad signals to be detected without generating artefacts that cause false positives and negatives.
Solution Approach 2:
The patent uses a dynamic approach by applying a system function with varying parameter values rather than a fixed processing method. The variation parameter allows the system to adaptively enhance different signal characteristics, making the detection process flexible enough to handle both narrow and broad signals while suppressing noise and artefacts.
2Reliability
If systematic variation of signal properties is applied using a system function, then all relevant signals can be detected regardless of intensity or width, but the processing complexity increases
Solution Approach 1:
The patent employs a system function with a variation parameter that can be systematically varied to detect different signal types. This parameter-based approach provides a unified framework for detecting both narrow and broad signals with different intensities, achieving high reliability without requiring multiple specialized processing paths, thus managing complexity through a single adaptable mechanism.
3Measurement precision
If the NMR signal data is processed with multiple variation parameter values, then the signal-to-noise ratio is improved, but the processing time increases
Solution Approach 1:
The patent systematically varies the variation parameter of the system function to enhance signal components while suppressing noise. By using a structured approach with multiple parameter values, the method improves the signal-to-noise ratio efficiently, as each parameter value targets specific signal characteristics and the systematic variation allows for optimized processing compared to trial-and-error approaches.
Data Source
Figure 1
Figure 2
Figure 3A~3B
AI summary
A system (100), method and computer program product for improved NMR signal detection. The system receives NMR signal data (202) produced by a sample (201) over time in response to an excitation pulse and selects a predefined system function (S, 121) for application to the NMR signal data (202) for systematic variation of signal properties The system function has a different influence on NMR signal components than on noise components of the sampled signal and has a variation parameter (VP) to control the systematic variation. A plurality of variation parameter values is provided with differing values to influence broad NMR signals as well as weak NMR signals. The system generates for each variation parameter value (VP-1 to VP-n) a corresponding intermediate data set (102-1 to 102-n) by applying the system function (121) with the respective variation parameter value (VP-1 to VP-n) to the NMR signal data (202). Further, from each intermediate data set (102-1 to 102-n) a respective base value centered spectrum (103-1 to 103-n) is generated in the frequency-domain. The respective base value centered spectra eliminate offsets from the intermediate data sets by approximating corresponding base values representing the actual offsets. The signal intervals (109) are detected by extracting from the base value centered spectra (103-1 to 103-n) for each frequency point variations induced by the system function and identifying frequency intervals with significant variation as signal intervals.