SMS‐HSL: Simultaneous multislice aliasing separation exploiting hankel subspace learning

Purpose To develop a novel, simultaneous multislice reconstruction method that exploits Hankel subspace learning (SMS‐HSL) for aliasing separation in the slice direction. Methods An SMS signal model with the Hankel‐structured matrix was proposed. To efficiently suppress interslice leakage artifacts from a signal subspace perspective, a null space was learned from the reference data combined over all slices other than a slice of interest using singular value decomposition. Given the fact that the Hankel‐structured matrix is rank‐deficient while the magnitude between the reference and its estimate is similar in k‐space, the SMS‐HSL was reformulated as a constrained optimization problem with both low‐rank and magnitude priors. SMS signals were projected onto a slice‐specific subspace while undesired signals were eliminated using the null space operator. The simulations and experiments were performed with increasing multiband factors up to 6 using the SMS‐HSL and the split slice‐GRAPPA. Results Compared with the split slice‐GRAPPA, the SMS‐HSL shows superior performance in suppressing aliasing artifacts and noises at high multiband factors even with: insufficient reference signals, a small number of coils, and a short distance between aliasing voxels. Conclusion We successfully demonstrated the effectiveness of the SMS‐HSL over the split slice‐GRAPPA for aliasing separation in the slice direction. Magn Reson Med 78:1392–1404, 2017. © 2016 International Society for Magnetic Resonance in Medicine.