Improving robustness of 3D multi-shot EPI by structured low-rank reconstruction of segmented CAIPI sampling for fMRI at 7T

Three-dimensional (3D) encoding methods are increasingly being explored as alternatives to multi-slice two-dimensional (2D) acquisitions in fMRI, particularly in cases where high isotropic resolution is needed. 3D multi-shot EPI is the most popular 3D fMRI acquisition method, but is susceptible to physiological fluctuations which can induce inter-shot phase variations, and thus reducing the achievable tSNR, negating some of the benefit of 3D encoding. This issue can be particularly problematic at ultra-high fields like 7T, which have more severe off-resonance effects. In this work, we aim to improve the temporal stability of 3D multi-shot EPI at 7T by improving its robustness to inter-shot phase variations. We presented a 3D segmented CAIPI sampling trajectory ("seg-CAIPI") and an improved reconstruction method based on Hankel structured low-rank matrix recovery. Simulation and in-vivo results demonstrate that the combination of the seg-CAIPI sampling scheme and the proposed structured low-rank reconstruction is a promising way to effectively reduce the unwanted temporal variance induced by inter-shot physiological fluctuations, and thus improve the robustness of 3D multi-shot EPI for fMRI. HighlightsO_LIA 3D multi-shot EPI sampling trajectory using interleaved ordering along kz and a CAIPI blipping pattern improves robustness to inter-shot phase variations C_LIO_LIReconstruction based on Hankel structured low-rank matrix completion can significantly improve the temporal stability of 3D multi-shot acquisitions at 7T C_LIO_LI1.5mm resolution brain fMRI data show that ~60% improvement in mean tSNR can be obtained using the proposed method compared to the conventional method C_LIO_LI1.8mm resolution brain fMRI data demonstrate that the proposed method allows for 4-fold acceleration without loss of tSNR compared to conventional 3D EPI C_LIO_LIPreliminary brainstem fMRI data show that ~40% improvement in mean tSNR can be obtained using the proposed sampling and reconstruction C_LI