Plug-and-play advanced magnetic resonance spectroscopy
Dinesh K Deelchand1, Pierre-Gilles Henry1, James M Joers1
1Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA.
Purpose:
Advanced MRS protocols improve data quality and reproducibility relative to vendor-provided protocols; however, they are challenging to incorporate into the clinical workflow and require local MRS expertise for successful implementation. Here, we developed an automated advanced MRS acquisition protocol at 3T to facilitate acquisition of high-quality spectroscopic data without local MRS expertise.
Methods:
First, a B0 shimming protocol was selected for automation by comparing 3 widely used B0 algorithms (2 vendor protocols and FAST(EST)MAP). Next, voxel-based B0 and B1 calibrations were incorporated into the consensus-recommended semi-LASER sequence and combined with an automated VOI prescription tool, a recently developed method for automated voxel prescription. The efficiency of collecting single-voxel data from a clinical cohort (N = 40) with the automated protocol (calibration time and fraction of usable datasets) was compared with the nonautomated semi-LASER protocol (N = 35) whereby all prescan calibrations were executed manually in the academic hospital setting with rotating MR technologists in the neuroradiology unit.
Results:
A multi-iteration FAST(EST)MAP protocol resulted in narrower water linewidths than vendor's B0 shim protocols for data acquired from 6 brain locations (p < 1e-5) and was selected for automation. The automated B0 and B1 calibrations resulted in a time saving of ~4.5 minutes per voxel relative to the same advanced protocol executed manually. All spectra acquired with the automated protocol were usable, whereas only 86% of those collected with the manual protocol were usable and spectral quality was more variable.
Conclusion:
The plug-and-play advanced MRS protocol allows automated acquisition of high-quality MRS data with high success rate and consistency on a clinical 3T platform.
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