Metab Brain Dis. 2014 Jun;29(2):323-32 doi: 10.1007/s11011-014-9487-6. 2014 Feb 02.

A comparison of spectral quality in magnetic resonance spectroscopy data acquired with and without a novel EPI-navigated PRESS sequence in school-aged children with fetal alcohol spectrum disorders

Hess AT, Jacobson SW, Jacobson JL, Molteno CD, van der Kouwe AJ, Meintjes EM.

Abstract

Single voxel spectroscopy (SVS) can generate useful information regarding metabolite concentrations provided that the MR signal can be averaged over several minutes during which the subject remains stationary. This requirement can be particularly challenging for children who cannot otherwise be scanned without sedation. To address this problem we developed an EPI volume navigated (vNav) SVS PRESS sequence, which applies real-time head pose (location and orientation), frequency, and first-order B0 shim adjustments. A water-independent preprocessing algorithm removes residual frequency and phase shifts resulting from within-TR movements. We compare results and performance of the standard and vNav PRESS sequences in a sample of 9- to 10-year-olds from a South African cohort of children with fetal alcohol spectrum disorders (FASD) and healthy controls. Magnetic resonance spectroscopy (MRS) data in the deep cerebellar nuclei were initially acquired with the standard PRESS sequence. The children were re-scanned 1 year later with the vNav PRESS sequence. Good quality data were acquired in 73% using the vNav PRESS sequence, compared to only 50% for the standard PRESS sequence. Additionally, tighter linewidths and smaller variances in the measured concentrations were observed. These findings confirm previous reports demonstrating the efficacy of our innovative vNav sequence with healthy volunteers and young children with HIV and expand its application to a school-aged population with FASD-disorders often associated with attention problems and hyperactivity. This study provides the most direct evidence to date regarding degree to which these new methods can improve data quality in research studies employing MRS.

PMID: 24488204