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Abstract #2189

Low-field MR imaging using multiplicative regularization

Merel de Leeuw den Bouter1, Martin van Gijzen1, and Rob Remis2
1Delft Institute of Applied Mathematics, Delft University of Technology, Delft, Netherlands, 2Circuits and Systems, Delft University of Technology, Delft, Netherlands

We present an image reconstruction approach that incorporates regularization by multiplying the data-fidelity term by a total variation functional. Usually, regularization is carried out in an additive manner, with a regularization parameter balancing out the two terms. Such a parameter often needs to be tuned for each dataset through extensive numerical experimentation. Our approach does not require such a parameter. We applied the method to in-vivo data acquired using a low-field MR scanner. Our results show that the method successfully denoises low-field MR images.

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