Effects of image normalization on the statistical analysis of perfusion MRI in elderly brains

Year: 2008

Type: article

Abstract: 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.

Source: Journal of Magnetic Resonance Imaging

DOI: 10.1002/jmri.21590

Authors: Weiying Dai, Owen T. Carmichael, Oscar L. Lopez, James T. Becker, Lewis H. Kuller, H. Michael Gach

Institutions: University of Pittsburgh, University of California, Davis, University of Nevada, Las Vegas, University of Nevada, Reno

Language: English

Topic: Medical Image Segmentation Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: bronze

Funders: National Institutes of Health, National Institute on Aging, National Heart, Lung, and Blood Institute

OpenAlex ID: W2045701379