title: Sensitivity of voxel-based morphometry analysis to choice of imaging protocol at 3 T.
abstract: The objective of this study was to determine which 3D T(1)-weighted acquisition protocol at 3 T is best suited to voxel-based morphometry (VBM), and to characterize the sensitivity of VBM to choice of acquisition. First, image quality of three commonly used protocols, FLASH, MP-RAGE and MDEFT, was evaluated in terms of SNR, CNR, image uniformity and point spread function. These image metrics were estimated from simulations, phantom imaging and human studies. We then performed a VBM study on nine subjects scanned twice using the three protocols to evaluate differences in grey matter (GM) density and scan-rescan variability between the protocols. These results reveal the relative bias and precision of the tissue classification obtained using the different protocols. MDEFT achieved the highest CNR between white and grey matter, and the lowest GM density variability of the three sequences. Each protocol is also characterized by a distinct regional bias in GM density due to the effect of transmission field inhomogeneity on image uniformity combined with spatially variant GM T(1) values and the sequence's T(1) contrast function. The required population sample size estimates to detect a difference in GM density in longitudinal VBM studies, i.e. based only on methodological variance, were lowest for MDEFT. Although MP-RAGE requires more subjects than FLASH, its higher cortical CNR improves the accuracy of the tissue classification results, particularly in the motor cortex. For cross-sectional VBM studies, the variance in morphology across the population is likely to be the primary source of variability in the power analysis.
authors.affiliation: McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada. tardif@bic.mni.mcgill.ca
authors.forename: Christine L
authors.initials: CL
authors.lastname: Tardif
authors.forename: D Louis
authors.initials: DL
authors.lastname: Collins
authors.forename: G Bruce
authors.initials: GB
authors.lastname: Pike
mesh_terms: D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000033:anatomy & histology*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D009474:Neurons / Q000166:cytology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity