Reproducibility study of whole-brain 1H spectroscopic imaging with automated quantification.
A reproducibility study of proton MR spectroscopic imaging ((1)H-MRSI) of the human brain was conducted to evaluate the reliability of an automated 3D in vivo spectroscopic imaging acquisition and associated quantification algorithm. A PRESS-based pulse sequence was implemented using dualband spectral-spatial RF pulses designed to fully excite the singlet resonances of choline (Cho), creatine (Cre), and N-acetyl aspartate (NAA) while simultaneously suppressing water and lipids; 1% of the water signal was left to be used as a reference signal for robust data processing, and additional lipid suppression was obtained using adiabatic inversion recovery. Spiral k-space trajectories were used for fast spectral and spatial encoding yielding high-quality spectra from 1 cc voxels throughout the brain with a 13-min acquisition time. Data were acquired with an 8-channel phased-array coil and optimal signal-to-noise ratio (SNR) for the combined signals was achieved using a weighting based on the residual water signal. Automated quantification of the spectrum of each voxel was performed using LCModel. The complete study consisted of eight healthy adult subjects to assess intersubject variations and two subjects scanned six times each to assess intrasubject variations. The results demonstrate that reproducible whole-brain (1)H-MRSI data can be robustly obtained with the proposed methods.
Gu
Meng
M
Department of Electrical Engineering, Stanford University, Stanford, California 94305-5488, USA. mgu@stanford.edu
Kim
Dong-Hyun
DH
Mayer
Dirk
D
Sullivan
Edith V
EV
Pfefferbaum
Adolf
A
Spielman
Daniel M
DM
D000328:Adult; D001921:Brain / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods* / Q000592:standards*; D008297:Male; D011522:Protons; D011859:Radiography; D012015:Reference Standards; D015203:Reproducibility of Results
D011522:Protons