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Abstract #0601

Fast Reconstruction for Regularized Quantitative Susceptibility Mapping

Berkin Bilgic 1 , Audrey Fan 2 , Cornelius Eichner 1 , Stephen Cauley 1 , Jonathan Polimeni 1 , Marta Bianciardi 1 , Elfar Adalsteinsson 2 , Lawrence Wald 1 , and Kawin Setsompop 1

1 Martinos Center for Biomedical Imaging, Charlestown, MA, United States, 2 MIT, Cambridge, MA, United States

A high-resolution whole brain QSM reconstruction can take up to 20 min on a workstation, which poses a limit on QSM usability in clinical and research settings. Herein, we introduce an improved Split-Bregman (SB) L1-regularized dipole inversion algorithm that offers 20 faster reconstruction relative to the standard nonlinear conjugate gradient (NCG) solver. Additionally, we extend SB L1-regularization to admit magnitude-weighting that prevents smoothing across edges identified on the magnitude signal, and solve this more complicated problem 5 faster than the NCG approach. Further, we extend the previously proposed closed-form L2-based inversion to admit magnitude-weighting, and demonstrate 15 acceleration relative to NCG by employing a preconditioner that leads to faster convergence. Utility of the proposed methods is demonstrated in high-resolution (0.6 mm isotropic) 3D GRE data at 3T, as well as multi-echo Simultaneous Multi-Slice (SMS) EPI time-series at 7T.

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