Simultaneous multi-slice reconstruction by Regularized Nonlinear Inversion

Abstract

Increasing imaging speed is of utmost importance in in-vivo magnetic resonance imaging (MRI). With simultaneous multi-slice (SMS) MRI we can simultaneously acquire several slices of an object, which allows for higher undersampling factors compared to single- or conventional multi-slice measurements by exploiting axial coil sensitivity information. In this thesis, we give a short introduction to the physical principles of MRI, cover the basics of a FLASH based SMS MRI sequence and perform tests to verify its accuracy: We check the fidelity of the slice distance as well as the flip angle and confirm the square-root-like signal-to-noise ratio benefit of SMS compared to conventional multi-slice experiments. A g-factor analysis is used to determine a favorable Cartesian undersampling scheme for multi-slice data. Common reconstruction strategies for SMS MRI make use of previously estimated coil sensitivities to solve a linear equation. Here, we propose a new method of SMS MRI based on Regularized Nonlinear Inversion (NLINV): SMS-NLINV. This method does not require a priori knowledge about the coil sensitivities and is attractive especially for real-time imaging where coil sensitivities may change due to motion or interactive changes to the slice position. We characterize the algorithm and present reconstructed phantom and in-vivo images.