Parameter-free denoising of complex MR images by iterative multi-wavelet thresholding

A method for denoising Magnetic Resonance Imaging (MRI) data includes receiving a noisy image acquired using an MRI imaging device and determining a noise model comprising a non-diagonal covariance matrix based on the noisy image and calibration characteristics of the MRI imaging device. The noisy i...

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Main Authors Kannengiesser Stephan, Mailhe Boris, Nadar Mariappan S
Format Patent
LanguageEnglish
Published 14.02.2017
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Abstract A method for denoising Magnetic Resonance Imaging (MRI) data includes receiving a noisy image acquired using an MRI imaging device and determining a noise model comprising a non-diagonal covariance matrix based on the noisy image and calibration characteristics of the MRI imaging device. The noisy image is designated as the current best image. Then, an iterative denoising process is performed to remove noise from the noisy image. Each iteration of the iterative denoising process comprises (i) applying a bank of heterogeneous denoisers to the current best image to generate a plurality of filter outputs, (ii) creating an image matrix comprising the noisy image, the current best image, and the plurality of filter outputs, (iii) finding a linear combination of elements of the image matrix which minimizes a Stein Unbiased Risk Estimation (SURE) value for the linear combination and the noise model, (iv) designating the linear combination as the current best image, and (v) updating each respective denoiser in the bank of heterogeneous denoisers based on the SURE value. Following the iterative denoising process, the current best image is designated as a final denoised image.
AbstractList A method for denoising Magnetic Resonance Imaging (MRI) data includes receiving a noisy image acquired using an MRI imaging device and determining a noise model comprising a non-diagonal covariance matrix based on the noisy image and calibration characteristics of the MRI imaging device. The noisy image is designated as the current best image. Then, an iterative denoising process is performed to remove noise from the noisy image. Each iteration of the iterative denoising process comprises (i) applying a bank of heterogeneous denoisers to the current best image to generate a plurality of filter outputs, (ii) creating an image matrix comprising the noisy image, the current best image, and the plurality of filter outputs, (iii) finding a linear combination of elements of the image matrix which minimizes a Stein Unbiased Risk Estimation (SURE) value for the linear combination and the noise model, (iv) designating the linear combination as the current best image, and (v) updating each respective denoiser in the bank of heterogeneous denoisers based on the SURE value. Following the iterative denoising process, the current best image is designated as a final denoised image.
Author Nadar Mariappan S
Mailhe Boris
Kannengiesser Stephan
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Snippet A method for denoising Magnetic Resonance Imaging (MRI) data includes receiving a noisy image acquired using an MRI imaging device and determining a noise...
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SubjectTerms CALCULATING
COMPUTING
COUNTING
HANDLING RECORD CARRIERS
IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
PHYSICS
PRESENTATION OF DATA
RECOGNITION OF DATA
RECORD CARRIERS
Title Parameter-free denoising of complex MR images by iterative multi-wavelet thresholding
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