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Abstract #0614

SMS-HSL: Simultaneous Multi-Slice Aliasing Separation Exploiting Hankel Subspace Learning

Suhyung Park1 and Jaeseok Park2

1Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon, Korea, Republic of, 2Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Korea, Republic of

Simultaneous multi-slice (SMS) acquisition has recently gained attention in clinical and research applications. However, since the spatial variation of coil sensitivity along the slice direction is typically insufficient and thus SMS reconstruction including SENSE/GRAPPA and slice-GRAPPA is potentially ill-conditioned, it is challenging to separate the aliased slices in the presence of noise with increasing multi-band factors (MB). In this work, we propose a novel, SMS reconstruction method that exploits Hankel subspace learning (SMS-HSL) for aliasing separation in the slice direction, in which SMS signals are projected onto an individual subspace specific to each slice by incorporating the proposed SMS model into a constrained optimization with low rank and magnitude priors. Simulation and experiments were performed at high MB factors to demonstrate the effectiveness of the proposed SMS-HSL over conventional SMS methods.

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