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

Aigerim Djamanakova; Xiaoying Tang; Xin Li; Andreia V Faria; Can Ceritoglu; Kenichi Oishi; Argye E Hillis; Marilyn Albert; Constantine Lyketsos; Michael I Miller; Susumu Mori

doi:10.1016/j.neuroimage.2014.06.046

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

Neuroimage. 2014 Nov 1:101:168-76. doi: 10.1016/j.neuroimage.2014.06.046. Epub 2014 Jun 27.

Authors

Affiliations

¹ Department of Biomedical Engineering, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
² The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
³ Center for Imaging Science, Johns Hopkins University, Baltimore, MD, USA.
⁴ Department of Neurology, Johns Hopkins University, Baltimore, MD, USA; Department of Physical Medicine and Rehabilitation, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Cognitive Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
⁵ Department of Neurology, Johns Hopkins University, Baltimore, MD, USA; The Johns Hopkins Alzheimer's Disease Research Center, Baltimore, MD, USA.
⁶ The Johns Hopkins Alzheimer's Disease Research Center, Baltimore, MD, USA; Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD, USA.
⁷ Department of Biomedical Engineering, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; Center for Imaging Science, Johns Hopkins University, Baltimore, MD, USA.
⁸ The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA. Electronic address: susumu@mri.jhu.edu.

Abstract

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.

MeSH terms

Adult
Aged
Aged, 80 and over
Aging / pathology*
Alzheimer Disease / pathology*
Brain / anatomy & histology*
Brain / pathology
Female
Humans
Image Interpretation, Computer-Assisted / methods*
Image Interpretation, Computer-Assisted / standards
Magnetic Resonance Imaging / methods*
Magnetic Resonance Imaging / standards
Male
Middle Aged
Reproducibility of Results
Sensitivity and Specificity
Young Adult

Abstract

MeSH terms

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