High-resolution whole-brain diffusion MRI at 3T using simultaneous multi-slab (SMSlab) acquisition

NeuroImage

Simin Liu¹  Erpeng Dai²  Hua Guo² 

[1] Department of Radiology, Stanford University, Stanford, CA, United States;Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China;

关键词: High resolution;    3D diffusion MRI;    Simultaneous multi-slab;    RF encoding;    Synthesized 3D navigator;   

DOI  :  

来源: DOAJ

【 摘 要 】

High-resolution diffusion MRI (dMRI) is a crucial tool in neuroscience studies to detect fine fiber structure, depict complex fiber architecture and analyze cortical anisotropy. However, high-resolution dMRI is limited by its intrinsically low SNR due to diffusion attenuation. A series of techniques have been proposed to improve the SNR performance, but most of them are at the cost of long scan time, which in turn sacrifice the SNR efficiency, especially for large FOV imaging, such as whole-brain imaging. Recently, a combination of 3D multi-slab acquisition and simultaneous multi-slice (SMS) excitation, namely simultaneous multi-slab (SMSlab), has been demonstrated to have potential for high-resolution diffusion imaging with high SNR and SNR efficiency. In our previous work, we have proposed a 3D Fourier encoding and reconstruction framework for SMSlab acquisition. In this study, we extend this 3D k-space framework to diffusion imaging, by developing a novel navigator acquisition strategy and exploring a k-space-based phase correction method. In vivo brain data are acquired using the proposed SMSlab method and compared with a series of different acquisitions, including the traditional 3D multi-slab, 2D SMS and 2D single-shot EPI (ss-EPI) acquisitions. The results demonstrate that SMSlab has a better SNR performance compared with 3D multi-slab and 2D SMS. The detection capacity of fine fiber structures is improved using SMSlab, compared with the low-resolution diffusion imaging using conventional 2D ss-EPI.

【 授权许可】

Unknown