Physics-Informed Deep Learning for Image Distortion Correction from B0-inhomogeneities in Low-Field MRI
David Schote1, Lukas Winter1, Christoph Kolbitsch1, Felix Zimmermann1, Thomas O'Reilly2, Andrew Webb2, Frank Seifert1, and Andreas Kofler1
1Physikalisch-Technische Bundesastalt (PTB), Braunschweig and Berlin, Germany, 2C.J. Gorter Center for High Field MRI, Department of Radiology, Leiden University Medical Center, Leiden, Netherlands
A physics-informed deep neural network using a U-Net in combination with a multifrequency interpolation is presented to correct for B0-field image distortions in low-field MRI. Training data is based on 1.5T and 3.0T knee images and realistic measurement-based assumption of SNR and B0-field inhomogeneities from previously constructed Halbach magnets. Significant (p<0.05) improvements are demonstrated applying the suggested methodology to uncorrected images.
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