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Constantine Lyketsos

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2 papers

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YNIMG Journal 2014 Journal Article

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

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.

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YNIMG Journal 2009 Journal Article

Multi-contrast large deformation diffeomorphic metric mapping for diffusion tensor imaging

Can Ceritoglu
Kenichi Oishi
Xin Li
Ming-Chung Chou
Laurent Younes
Marilyn Albert
Constantine Lyketsos
Peter C.M. van Zijl

Diffusion tensor imaging (DTI) can reveal detailed white matter anatomy and has the potential to detect abnormalities in specific white matter structures. Such detection and quantification are, however, not straightforward. The voxel-based analysis after image normalization is one of the most widely used methods for quantitative image analyses. To apply this approach to DTI, it is important to examine if structures in the white matter are well registered among subjects, which would be highly dependent on employed algorithms for normalization. In this paper, we evaluate the accuracy of normalization of DTI data using a highly elastic transformation algorithm, called large deformation diffeomorphic metric mapping. After simulation-based validation of the algorithm, DTI data from normal subjects were used to measure the registration accuracy. To examine the impact of morphological abnormalities on the accuracy, the algorithm was also tested using data from Alzheimer's disease (AD) patients with severe brain atrophy. The accuracy level was measured by using manual landmark-based white matter matching and surface-based brain and ventricle matching as gold standard. To improve the accuracy level, cascading and multi-contrast approaches were developed. The accuracy level for the white matter was 1. 88±0. 55 and 2. 19±0. 84 mm for the measured locations in the controls and patients, respectively.

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