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Abstract #3979

Total Deep Variation Regularization for Improved Iterative Quantitative Susceptibility Mapping (TDV-QSM)

Carlos Milovic1, Jose Manuel Larrain2,3, and Karin Shmueli1
1Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom, 2Department of Electrical Engineering, Pontificia Universidad Catolica de Chile, Santiago, Chile, 3Biomedical Imaging Center, Pontificia Universidad Catolica de Chile, Santiago, Chile

Quantitative Susceptibility Mapping (QSM) is an ill-posed inverse problem. Traditionally, it is solved by minimization of a functional. Regularization terms may be interpreted as a denoising process. Many state-of-the-art methods are based on Total Variation regularization terms, with great success. With the advent of Deep Learning, new regularization strategies have been derived from training datasets. Total Deep Variation (TDV) is a recently proposed technique, showing impressive results. We applied a pre-trained TDV network as a denoising step in an iterative QSM solver. Results show improved error metrics for synthetic brain phantoms and enhanced in-vivo reconstructions, compared to Total-Variation-based algorithms.

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