MR Coil Localization via Multi-Scale Power Spectrum Decomposition
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Solution Overview
Problem
In magnetic resonance (MR) guided interventions, the localization of RF coils is often inaccurate due to the offset of signal contribution from surrounding tissues and the ambiguity in peak signal width, leading to positional ambiguity in interventional devices.
Innovation Solution
A multi-scale decomposition method is used to analyze the power spectra of RF coil signals, identifying sharp transitions and calculating a likelihood distribution of the coil's position across different scales, thereby enhancing the accuracy and confidence of coil localization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If peak detection method is used to determine coil position, then localization speed is fast (few milliseconds), but measurement precision deteriorates due to offset from surrounding tissues and peak width ambiguity (3-5 mm)
Solution Approach 1:
The patent segments the power spectrum into multiple scales using wavelet decomposition. Instead of analyzing the entire spectrum at once, it divides the signal into different frequency bands and scales, allowing detection of sharp transitions at multiple resolutions. This segmentation enables precise identification of coil position by analyzing features at appropriate scales without being affected by surrounding tissue signals at other scales.
Solution Approach 2:
The patent transforms the one-dimensional peak detection problem into a multi-dimensional analysis by applying wavelet transformation across multiple scales. This adds a scale dimension to the analysis, allowing the system to distinguish between sharp transitions (coil signals) and broad variations (tissue signals) by examining the signal structure across different scale levels rather than relying solely on peak magnitude in a single frequency domain.
2Device complexity
If solenoid coils are used for tracking, then device complexity is reduced, but measurement precision deteriorates due to signal contribution from surrounding tissues causing offset
Solution Approach 1:
The patent extracts the characteristic sharp transition features from the power spectrum that are specific to coil signals, separating them from the broader tissue signal contributions. By using wavelet transformation to identify and isolate these sharp transitions at specific scales, the method effectively extracts the coil position information while filtering out the confounding tissue signals, maintaining the simplicity of solenoid coils while improving precision.
Solution Approach 2:
The patent changes the analysis parameters by applying multi-scale wavelet decomposition to the power spectrum. This transformation alters how the signal is represented, converting the problem from identifying peak magnitudes to detecting sharp transitions at specific scales. This parameter change in the analysis domain allows the system to distinguish coil signals from tissue signals based on their different temporal/frequency characteristics rather than relying on signal strength alone.
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 provides a more accurate and reliable localization of RF coils by reducing noise influence and improving positional determination, even in complex anatomical environments, with increased confidence in the detected position.
Implementation Method 1
magnetic resonance (MR) signal contribution is from surrounding tissues
Implementation Method 2
The coil connects with the medical device. An MR receiver connects with the coil. A processor connects with the MR receiver. The processor is configured to calculate X, Y, and Z data from information from the coil received in response to MR pulses
Data Source
AI summary
Localization of a coil is provided for magnetic resonance (MR)-guided intervention. A multi-scale decomposition and characteristic transitions in the power spectra for the coil are used to determine a distribution of likelihood of the coil being at each of various locations and/or to determine a confidence in the position determination. For example, the power spectra along each axis is used to generate a likelihood distribution of the location of the coil. The power spectra are decomposited at different scales. For each scale, the modulus maxima reflecting transitions in the power spectra are matched using various criteria. A likelihood is calculated for each of the matched candidates from characterizations of the matched candidates. The likelihood distribution is determined from a combination of the likelihoods from the various scales.