MRI Denoising via Wavelet Phase-Amplitude Filtering
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Solution Overview
Problem
Magnetic resonance imaging (MRI) faces challenges with low signal-to-noise ratio (SNR) due to inherent limitations, leading to reduced image quality and loss of information, particularly in quantitative and functional imaging applications, where noise reduction techniques often compromise image details.
Innovation Solution
The method involves wavelet decomposition of MRI images, followed by statistical amplitude and phase processing, using a transfer function with combined amplitude and phase filters to differentiate and suppress noise while preserving signal contributions, thereby enhancing image quality without repeated acquisitions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional noise reduction techniques are applied to improve SNR, then signal-to-noise ratio is improved, but image details and information are lost
Solution Approach 1:
The patent segments the image processing into wavelet decomposition into multiple frequency sub-bands, allowing selective processing of different frequency components. Noise is primarily present in high-frequency sub-bands while image details exist across multiple scales, enabling targeted noise reduction that preserves structural information.
Solution Approach 2:
The patent applies local quality by using thresholding operations that adapt to local image characteristics in each wavelet sub-band. The thresholding parameters are adjusted based on local signal strength and noise estimates, preserving edges and fine details in high-signal regions while aggressively denoising in low-signal regions.
2Measurement precision
If repeated acquisitions are performed to improve SNR through averaging, then signal-to-noise ratio improves proportionally to square root of number of experiments, but time consumption and productivity decrease
Solution Approach 1:
The patent performs preliminary wavelet decomposition and noise characterization on a single acquisition before applying the denoising algorithm. This preliminary analysis of the noise statistics and signal distribution enables effective denoising without requiring multiple repeated acquisitions for statistical averaging.
Solution Approach 2:
The patent replaces the mechanical approach of repeated physical acquisitions with a computational approach using wavelet-based signal processing. Instead of physically repeating the measurement process multiple times, the system uses mathematical transformations and algorithms to achieve equivalent or superior noise reduction from a single acquisition.
3Object-generated harmful factors
If thresholding techniques are applied in wavelet domain for noise reduction, then noise is suppressed, but signal phase coherence is lost and image quality deteriorates
Solution Approach 1:
The patent changes the parameter representation from standard wavelet coefficients to log-polar coordinates, where the magnitude and phase are separated and processed differently. This parameter transformation allows independent optimization of noise suppression in the magnitude domain while preserving phase information, maintaining signal phase coherence after inverse transformation.
Data Source
AI summary
A method of image processing of magnetic resonance (MR) images for creating de-noised MR images, comprises the steps of providing image data sets including multiple complex MR images (S7), subjecting the MR images to a wavelet decomposition (S12) for creating coefficient data sets of wavelet coefficients (Sn,m) representing the MR images in a wavelet frequency domain, calculating normalized coefficient data sets of wavelet coefficients Formula (I) (S17), wherein the coefficient data sets are normalized with a quantitative amount of variation, in particular standard deviation Formula (II), of noise contributions included in the coefficient data sets (Sn,m), averaging the wavelet coefficients of each coefficient data set (S18) for providing averaged wavelet coefficients Formula (III) of the coefficient data sets, calculating phase difference maps (Δϕn,m) for all coefficient data sets (S20), wherein the phase difference maps provide phase differences between the phase of each wavelet coefficient and the phase of the averaged wavelet coefficients Formula (III), calculating scaled averaged coefficient data sets of wavelet coefficients by scaling the averaged wavelet coefficients Formula (III) with scaling factors (Cn,m), which are obtained by comparing parts of the normalized wavelet coefficients of the normalized coefficient data sets Formula (I) that are in phase with the averaged wavelet coefficients Formula (III) (S22), calculating rescaled coefficient data sets of wavelet coefficients Formula (IV) (S24) by applying a transfer function Formula (V) on the coefficient data sets (Sn,m) and on the scaled averaged coefficient data sets, wherein the transfer function includes combined amplitude and phase filters, each depending on the normalized coefficient data sets Formula (I) and me phase difference maps (Δϕn,m), resp., and subjecting the rescaled coefficient data sets to a wavelet reconstruction Formula (IV) (S25) for providing the denoised MR images.