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Activity Number:
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33
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Type:
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Contributed
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Date/Time:
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Sunday, August 6, 2006 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Nonparametric Statistics
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| Abstract - #306877 |
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Title:
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Evaluation of Spatial Normalization Parameters for SPM: Application to Type 2 Diabetes Data
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Author(s):
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Bedda Rosario*+ and Scott Ziolko and Lisa Weissfeld and Julie Price
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Companies:
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University of Pittsburgh and University of Pittsburgh and University of Pittsburgh and University of Pittsburgh
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Address:
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130 DeSoto Street, Pittsburgh, PA, 15261,
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Keywords:
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type 2 diabetes ; statistical parametric mapping ; spatial normalization ; registration ; total entropy
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Abstract:
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Normalization is the process of warping MRI and PET images of different subjects into a standard anatomical space. Statistical parametric mapping (SPM) software is widely used to perform this process; however, the impact of parameter choice within SPM has not been well studied. We assessed the effects of different parameter value choices (e.g. degree of regularization, elderly or MNI template and number of nonlinear basis functions) on the analysis of MRI and PET image data. Images were spatially normalized for two groups of subjects: Control and Type 2 Diabetics. The effect of parameter choice for SPM t-maps was assessed by location and magnitude of local maxima. Descriptive statistics and paired t tests were used to examine differences in total entropy.
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