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Effects of image normalization on the statistical analysis of perfusion MRI in elderly brains
Author(s) -
Dai Weiying,
Carmichael Owen T.,
Lopez Oscar L.,
Becker James T.,
Kuller Lewis H.,
Gach H. Michael
Publication year - 2008
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.21590
Subject(s) - spatial normalization , normalization (sociology) , statistical parametric mapping , voxel , artificial intelligence , pattern recognition (psychology) , smoothing , parametric statistics , magnetic resonance imaging , computer science , nuclear medicine , mathematics , medicine , statistics , radiology , computer vision , sociology , anthropology
Purpose To fully understand the effects of an image processing methodology on the comparisons of regional patterns of brain perfusion over time and between subject groups. Materials and Methods Two brain normalization methods were compared using images of elderly controls and subjects with MCI and AD: the normalization package of statistical parametric mapping (SPM2), and a fully deformable model (FDM). The performance of these two normalization methods was quantitatively evaluated based on two criteria: (a) the alignment accuracy of five brain structures to the colin27 reference volume, and (b) impact of spatial normalization methods on the sensitivity of perfusion magnetic resonance imaging (pMRI). Results The delineations of all five brain structures had significantly higher overlap with expert manual tracings using FDM compared to SPM (two‐tailed, P < 0.025). When applied to the biostatistical analysis of CBF maps, a larger number of statistically significant voxels was identified from FDM compared with SPM2 regardless of the effects of the threshold and smoothing kernel. Conclusion The greater degree of deformation freedom associated with FDM may yield more accurate region matching and higher statistical sensitivity in identifying regions of CBF differences between elderly groups with prevalent late‐life neurodegenerative conditions. J. Magn. Reson. Imaging 2008;28:1351–1360. © 2008 Wiley‐Liss, Inc.