MRI Neuronal Resonance Detection via Multi-TR Sampling
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Solution Overview
Problem
Current MRI techniques, including fMRI, struggle to directly measure neuronal activity due to the small size of MRI signals generated by neuronal currents and the indirect measurement through hemodynamic responses, which limits the ability to detect frequency-selective communication between brain regions.
Innovation Solution
A method involving the acquisition of MRI data using multiple repetition times to identify the magnitude and frequency components of neuronal resonance signals, allowing for the detection of frequency-selective inter-brain region communication by calculating correlation between digital sequences transformed into frequency bands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional MRI techniques are used to measure neuronal activity, then the measurement can be performed, but the signal-to-noise ratio is insufficient due to the small size of MRI signals generated by neuronal currents
Solution Approach 1:
The patent applies periodic action by using multiple repetition times (TR) in the MRI pulse sequence to repeatedly acquire signals at different sampling intervals. This periodic sampling allows the neuronal resonance signals to be captured at multiple phases, enabling coherent integration to enhance the signal-to-noise ratio while maintaining measurement precision.
Solution Approach 2:
The patent changes the repetition time (TR) parameter across multiple acquisitions to sample the neuronal resonance signals at different temporal phases. By varying this critical timing parameter, the method enables frequency domain analysis to distinguish neuronal signals from noise, thereby improving both measurement precision and signal-to-noise ratio.
2Measurement precision
If fMRI is used to indirectly measure brain activity through hemodynamic responses, then neuronal activity can be mapped, but the temporal resolution is insufficient due to the slow hemodynamic response with time delay of about 4 seconds
Solution Approach 1:
The patent extracts the neuronal resonance signals from the complex MRI data by using frequency domain analysis. By transforming the time-domain signals obtained through multiple repetition times into the frequency domain, the method isolates the neuronal activity components from the slow hemodynamic background, achieving high temporal resolution measurement independent of hemodynamic response delays.
3Reliability
If existing MRI imaging methods are used to detect neuronal currents, then direct measurement can be attempted, but the detection reliability is insufficient because the current signal is too small to be consistently measured in vivo
Solution Approach 1:
The patent merges multiple MRI signals acquired at different repetition times into a single enhanced signal through coherent integration in the frequency domain. By combining the information from multiple acquisitions, the method amplifies the weak neuronal current signals while suppressing random noise, thereby improving both detection reliability and measurement precision simultaneously.
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 approach enables the identification of frequency components of neuronal resonance signals even when the signal cannot be predicted, providing a detailed communication map between brain regions and improving signal-to-noise ratio, thereby enhancing the understanding of brain functions and diseases.
Implementation Method 1
measuring a magnitude of a neuronal resonance signal by sampling a magnetic resonance signal of a neuron resonance signal according to a plurality of different repetition periods
Data Source
AI summary
Disclosed are a method of detecting a neuron resonance signal and an MRI signal processing apparatus. The method of detecting a neuron resonance signal includes acquiring a plurality of different digital sequences respectively corresponding to a plurality of different repetition periods by sampling a magnetic resonance signal of a neuron resonance signal according to each of the plurality of different repetition periods and calculating correlation between the plurality of different digital sequences in a frequency band based on the plurality of different digital sequences.