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Abstract #3669

A Fast Algorithm for Nonlinear QSM Reconstruction

Carlos Milovic1,2, Berkin Bilgic3, Bo Zhao3, Julio Acosta-Cabronero4, and Cristian Tejos1,2

1Electrical Engineering, Pontificia Universidad Catolica de Chile, Santiago, Chile, 2Biomedical Imaging Center, Pontificia Universidad Catolica de Chile, Santiago, Chile, 3Martinos Center for Biomedical Imaging, Harvard Medical School, MA, United States, 4German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany

This abstract presents a fast nonlinear solver for the QSM reconstruction using the total generalized variation regularization. The proposed method utilizes the alternating direction method of multipliers to obtain close-form solution to each sub-problem. To handle the non-linear data fidelity, a two-step algorithm is described, including a global optimum search and a local Newton-Raphson iteration. Compared to conventional linear solvers, nonlinear solutions reduce streaking artifacts and better handle noise in poor SNR regions. Reconstruction results are at least comparable to nonlinear MEDI in quality, but with an order of magnitude improvement in the computational efficiency.

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