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Full-Text Articles in Engineering

A Comparative Study Of Two Prediction Models For Brain Tumor Progression, Deqi Zhou, Loc Tran, Jihong Wang, Jiang Li, Karen O. Egiazarian (Ed.), Sos S. Agaian (Ed.), Atanas P. Gotchev (Ed.) Jan 2015

A Comparative Study Of Two Prediction Models For Brain Tumor Progression, Deqi Zhou, Loc Tran, Jihong Wang, Jiang Li, Karen O. Egiazarian (Ed.), Sos S. Agaian (Ed.), Atanas P. Gotchev (Ed.)

Electrical & Computer Engineering Faculty Publications

MR diffusion tensor imaging (DTI) technique together with traditional T1 or T2 weighted MRI scans supplies rich information sources for brain cancer diagnoses. These images form large-scale, high-dimensional data sets. Due to the fact that significant correlations exist among these images, we assume low-dimensional geometry data structures (manifolds) are embedded in the high-dimensional space. Those manifolds might be hidden from radiologists because it is challenging for human experts to interpret high-dimensional data. Identification of the manifold is a critical step for successfully analyzing multimodal MR images.

We have developed various manifold learning algorithms (Tran et al. 2011; Tran et al. …


Frontal White Matter Integrity In Adults With Down Syndrome With And Without Dementia, David K. Powell, Allison Caban-Holt, Greg A. Jicha, William C. Robertson, Roberta Davis, Brian T. Gold, Frederick A. Schmitt, Elizabeth Head Jul 2014

Frontal White Matter Integrity In Adults With Down Syndrome With And Without Dementia, David K. Powell, Allison Caban-Holt, Greg A. Jicha, William C. Robertson, Roberta Davis, Brian T. Gold, Frederick A. Schmitt, Elizabeth Head

Magnetic Resonance Imaging and Spectroscopy Center Faculty Publications

Adults with Down syndrome (DS) are at high risk for developing Alzheimer's disease after the age of 40 years. To detect white matter (WM) changes in the brain linked to dementia, fractional anisotropy (FA) from diffusion tensor imaging was used. We hypothesized that adults with DS without dementia (DS n = 10), DS with dementia (DSAD n = 10) and age matched non-DS subjects (CTL n = 10) would show differential levels of FA and an association with scores from the Brief Praxis Test and the Severe Impairment Battery. WM integrity differences in DS compared with CTL were found predominantly …


Multimodal Imaging Evidence For Axonal And Myelin Deterioration In Amnestic Mild Cognitive Impairment, Brian T. Gold, Yang Jiang, David K. Powell, Charles D. Smith Jan 2012

Multimodal Imaging Evidence For Axonal And Myelin Deterioration In Amnestic Mild Cognitive Impairment, Brian T. Gold, Yang Jiang, David K. Powell, Charles D. Smith

Neuroscience Faculty Publications

White matter (WM) microstructural declines have been demonstrated in Alzheimer's disease and amnestic mild cognitive impairment (aMCI). However, the pattern of WM microstructural changes in aMCI after controlling for WM atrophy is unknown. Here, we address this issue through joint consideration of aMCI alterations in fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity, as well as macrostructural volume in WM and gray matter compartments. Participants were 18 individuals with aMCI and 24 healthy seniors. Voxelwise analyses of diffusion tensor imaging data was carried out using tract-based spatial statistics (TBSS) and voxelwise analyses of high-resolution structural data was conducted using …


Prediction Of Brain Tumor Progression Using Multiple Histogram Matched Mri Scans, Debrup Banerjee, Loc Tran, Jiang Li, Yuzhong Shen, Frederic Mckenzie, Jihong Wang, Ronald M. Summers (Ed.), Bram Van Ginneken (Ed.) Jan 2011

Prediction Of Brain Tumor Progression Using Multiple Histogram Matched Mri Scans, Debrup Banerjee, Loc Tran, Jiang Li, Yuzhong Shen, Frederic Mckenzie, Jihong Wang, Ronald M. Summers (Ed.), Bram Van Ginneken (Ed.)

Electrical & Computer Engineering Faculty Publications

In a recent study [1], we investigated the feasibility of predicting brain tumor progression based on multiple MRI series and we tested our methods on seven patients' MRI images scanned at three consecutive visits A, B and C. Experimental results showed that it is feasible to predict tumor progression from visit A to visit C using a model trained by the information from visit A to visit B. However, the trained model failed when we tried to predict tumor progression from visit B to visit C, though it is clinically more important. Upon a closer look at the MRI scans …