Tools for multiple granularity analysis of brain MRI data for individualized image analysis

被引：42

作者：

Djamanakova, Aigerim ^{[1
]}

Tang, Xiaoying ^{[1
]}

Li, Xin ^{[2
]}

Faria, Andreia V. ^{[2
]}

Ceritoglu, Can ^{[3
]}

Oishi, Kenichi ^{[2
]}

Hillis, Argye E. ^{[4
,5
,6
]}

Albert, Marilyn ^{[4
,7
]}

Lyketsos, Constantine ^{[7
,8
]}

Miller, Michael I. ^{[1
,3
]}

Mori, Susumu ^{[2
]}

机构：

[1] Johns Hopkins Univ, Sch Med, Dept Biomed Engn, Baltimore, MD 21205 USA

[2] Johns Hopkins Univ, Sch Med, Russell H Morgan Dept Radiol & Radiol Sci, Baltimore, MD 21205 USA

[3] Johns Hopkins Univ, Ctr Imaging Sci, Baltimore, MD 21205 USA

[4] Johns Hopkins Univ, Dept Neurol, Baltimore, MD 21205 USA

[5] Johns Hopkins Univ, Sch Med, Dept Phys Med & Rehabil, Baltimore, MD 21205 USA

[6] Johns Hopkins Univ, Sch Med, Dept Cognit Sci, Baltimore, MD 21205 USA

[7] Johns Hopkins Alzheimers Dis Res Ctr, Baltimore, MD USA

[8] Johns Hopkins Univ, Dept Psychiat & Behav Sci, Baltimore, MD 21205 USA

来源：

NEUROIMAGE | 2014年 / 101卷

关键词：

ATLAS-BASED SEGMENTATION; ALZHEIMERS-DISEASE; STRATEGIES; SELECTION; ACCURACY; FUSION;

D O I：

10.1016/j.neuroimage.2014.06.046

中图分类号：

Q189 [神经科学];

学科分类号：

071006 ;

摘要：

Voxel-based analysis is widely used for quantitative analysis of brain MRI. While this type of analysis provides the highest granularity level of spatial information (i.e., each voxel), the sheer number of voxels and noisy information from each voxel often lead to low sensitivity for detection of abnormalities. To ameliorate this issue, granularity reduction is commonly performed by applying isotropic spatial filtering. This study proposes a systematic reduction of the spatial information using ontology-based hierarchical structural relationships. The 254 brain structures were first defined in multiple (n = 29) geriatric atlases. The multiple atlases were then applied to T1-weighted MR images of each subject's data for automated brain parcellation and five levels of ontological relationships were established, which further reduced the spatial dimension to as few as 11 structures. At each ontology level, the amount of atrophy was evaluated, providing a unique view of low-granularity analysis. This reduction of spatial information allowed us to investigate the anatomical features of each patient, demonstrated in an Alzheimer's disease group. (C) 2014 Elsevier Inc. All rights reserved.

引用

页码：168 / 176

页数：9

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