Paper Info
Title
Modeling Longitudinal Voxelwise Feature Change in Normal Aging with Spatial-Anatomical Regularization.
Abstract
Image voxel/vertex-wise feature in the brain is widely used for automatic classification or significant region detection of various dementia syndromes. In these studies, the non-imaging variables, such as age, will affect the results, but may be uninterested to the clinical applications. Imaging data can be considered as a combination of the confound variable (e. g. age) and the variable of clinical interest (e. g. AD diagnosis). However, non-imaging confound variable is not well dealt in each voxel. In this paper, we proposed a spatial-anatomical regularized parametric function fitting approach that explicitly modeling the relationship between the voxelwise feature and the confound variable. By adding the spatial-anatomical regularization, our model not only obtains a better voxelwise feature estimation, but also generates a more interpretable parameter map to help understand the effect of confound variable on imaging features. Besides the commonly used linear model, we also develop a spatial-anatomical regularized voxelwise general logistic model to investigate deeper of the aging process in gray matter and white matter density map.
Year
DOI
Venue
2018
10.1007/978-3-030-00931-1_46
Lecture Notes in Computer Science
Keywords
Field
DocType
Voxelwise feature,Spatial-anatomical regularization,General logistic,Longitudinal model,Normal aging
Voxel,Parametric equation,Pattern recognition,White matter,Computer science,Linear model,Regularization (mathematics),Artificial intelligence,Region detection,Logistic regression
Conference
Volume
ISSN
Citations 
11072
0302-9743
0
PageRank 
References 
Authors
0.34
5
5
Name
Order
Citations
PageRank
Zhuo Sun1225.86
Wei Xu290.79
Shuhao Wang3202.54
Junhai Xu432.78
Yuchuan Qiao5155.55