Reconstruction with magnetic resonance compressed sensing

In one approach, VDAMP is improved to allow multiple coils. The aliasing is modeled in the wavelet domain with spatial modulation for each of the frequency subbands. The spatial modulation uses the coil sensitivities. As a result of the spatial modulation, the aliasing modeling more closely models t...

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Bibliographic Details
Main Authors Nadar, Mariappan S, Mailhe, Boris, Millard, Charles
Format Patent
LanguageEnglish
Published 10.09.2024
Subjects
CALCULATING
COMPUTING
COUNTING
IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
MEASURING
MEASURING ELECTRIC VARIABLES
MEASURING MAGNETIC VARIABLES
PHYSICS
TESTING
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Summary:In one approach, VDAMP is improved to allow multiple coils. The aliasing is modeled in the wavelet domain with spatial modulation for each of the frequency subbands. The spatial modulation uses the coil sensitivities. As a result of the spatial modulation, the aliasing modeling more closely models the variance allowing the regularization to use denoising operations. In another approach, the regularization computation may be simplified by using a machine-learned network in VDAMP. To account for the aliasing modeling of VDAMP, a convolutional neural network is trained with input of both the noisy image and the covariances of the aliasing model.
Bibliography:Application Number: US202117303790