Artificial Neural Network NMR Processing for Low-SNR Metabolite Quantification
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Solution Overview
Problem
Magnetic Resonance Spectroscopy (MRS) faces challenges in metabolite quantitative analysis due to low signal-to-noise ratio, line-broadening, and spectral baseline issues, limiting its accuracy and reliability in disease diagnosis.
Innovation Solution
An artificial neural network-based method and apparatus for processing NMR and MRS data that enhances SNR, performs line-narrowing, removes spectral baselines, and reconstructs high-quality spectra, while providing algorithms for reliable data processing and disease diagnosis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional NMR/MRS processing methods are used, then the basic metabolite detection is possible, but the signal-to-noise ratio is low and quantification accuracy is limited
Solution Approach 1:
The patent replaces conventional signal processing methods (Fourier transform-based) with an artificial neural network-based system. The neural network directly processes time-domain NMR signals to produce metabolite concentration estimates, substituting the traditional mechanical/mathematical processing chain with an intelligent system that can handle noise and extract meaningful signals more effectively.
Solution Approach 2:
The patent changes the fundamental parameters of signal processing by training neural networks on simulated NMR data with varying conditions (different metabolite concentrations, noise levels, spectral overlaps). This allows the system to learn optimal extraction parameters for each metabolite under different scanning conditions, improving quantification accuracy without requiring manual parameter adjustment.
2Adaptability or versatility
If the magnetic field strength is weak, then the equipment is more accessible, but line-broadening occurs and spectral resolution is reduced
Solution Approach 1:
The patent substitutes traditional spectral deconvolution methods with neural network-based signal processing. The neural network learns to distinguish overlapping metabolite signals and baseline variations directly from the raw NMR data, effectively resolving spectral overlaps without requiring high magnetic field strengths to achieve narrow linewidths.
Solution Approach 2:
The artificial neural network acts as an intermediary between the raw NMR signal and the final metabolite quantification. It processes the time-domain signal, separating metabolite peaks from baseline and noise, thereby mediating the impact of poor spectral resolution caused by weak magnetic fields.
3Reliability
If the acquisition time is extended to improve SNR, then the signal quality improves, but the scanning time increases and productivity decreases
Solution Approach 1:
The patent performs preliminary action by training the neural network on extensive simulated data representing various signal conditions before actual scanning. This pre-training allows the system to efficiently process real NMR data without requiring extended acquisition times, as the network already knows how to extract meaningful signals from noisy data.
Solution Approach 2:
The patent replaces the conventional approach of improving SNR through extended acquisition time with an intelligent signal extraction system. The neural network processes shorter, noisier signals more effectively than traditional methods, achieving good metabolite quantification without requiring long scanning times.
4Measurement precision
If conventional processing algorithms are used, then the basic analysis is possible, but CRLB only shows precision not accuracy and may cause prejudice in results interpretation
Solution Approach 1:
The patent substitutes the CRLB-based evaluation system with a neural network-based quantification approach that directly estimates metabolite concentrations. The system provides both concentration values and uncertainty estimates, replacing the indirect precision measure (CRLB) with direct accuracy measures that reflect true metabolite levels.
Solution Approach 2:
The patent implements feedback mechanisms where the neural network continuously refines its estimates based on the input signal characteristics and comparison with reference data. This feedback loop allows the system to correct for biases and provide more accurate quantification results, improving the reliability of diagnostic interpretations.
Data Source
AI summary
An apparatus for processing nuclear magnetic resonance and magnetic resonance spectroscopy data may include: a data input unit configured to receive input data from a magnetic resonator, define incomplete data from the input data based on a sampling time, and classify, based on a preset criterion, remaining data of the input data except the incomplete data as ground truth data satisfying the preset criterion or bad data not satisfying the preset criterion; a data recovery unit configured to obtain recovered data by recovering the incomplete data and the bad data which are received from the data input unit; and a disease diagnosis unit configured to generate metabolite quantification data based on a metabolite concentration range by using the ground truth data received from the data input unit and the recovered data received from the data recovery unit.