Complex MRI Noise Analysis for Accurate SNR Estimation
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Solution Overview
Problem
Noise analysis in medical imaging, particularly in magnetic resonance imaging (MRI), is challenging due to the complexity of signals and the difficulty in accurately calculating signal-to-noise ratio (SNR) when signal intensities vary spatially or temporally, leading to inaccuracies in noise analysis.
Innovation Solution
A method and system for noise analysis that directly processes complex signals by obtaining multiple signal representations, determining reciprocal values using multi-dimensional integration, and calculating a noise parameter such as SNR without converting to real signals, simplifying the analysis process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If complex signals are converted to real signals for noise analysis, then noise analysis can be performed using conventional methods, but the analysis process becomes more complex and accuracy decreases
Solution Approach 1:
Instead of converting complex signals to real signals and then performing noise analysis, the patent inverts the approach by directly performing noise analysis on complex signals using complex statistical methods. This allows simultaneous analysis of both real and imaginary components, maintaining signal integrity while simplifying the overall process.
Solution Approach 2:
The patent extends noise analysis from the traditional real-number domain to the complex-number domain, adding an imaginary dimension to the analysis. This enables comprehensive noise characterization that captures both magnitude and phase information, improving accuracy while providing a more direct analysis pathway.
2Measurement precision
If statistical analysis is performed on multiple spatial points to calculate noise variance, then noise can be estimated, but signal intensity variations lead to inaccurate results
Solution Approach 1:
The patent changes the fundamental parameter domain from real numbers to complex numbers, allowing noise variance to be calculated in the complex domain. This enables the use of complex statistical moments that account for both real and imaginary components, providing accurate noise estimation even when signal intensities vary across spatial points.
3Measurement precision
If repeated signal acquisitions are performed to obtain signal values at different time points, then noise analysis can be conducted with equivalent signal intensity, but the acquisition process becomes time-consuming
Solution Approach 1:
The patent enables continuous noise analysis by performing calculations on complex signals directly without requiring repeated acquisitions. By utilizing the full complex information available in each acquisition and applying complex statistical methods, the system achieves accurate noise estimation from a single set of measurements, eliminating time loss.
4Productivity
If mathematical operations are performed on signals, then signal processing can be achieved, but noise performance changes and analysis difficulty increases
Solution Approach 1:
The patent introduces complex statistical moments as intermediary quantities that bridge signal processing and noise analysis. By calculating these complex moments directly from the complex signals, the method provides a unified framework that handles both signal processing and noise characterization simultaneously, reducing overall complexity.
Data Source
AI summary
A method and a system for noise analysis may be provided. A plurality of signals of a target object may be obtained. A first value of a first signal representation and a second value of a second signal representation of the target object may be determined based on the plurality of signals. A value of a noise parameter may be determined based on the first value of the first signal representation and the second value of the second signal representation.