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Full-Text Articles in Engineering

Post-Acquisition Processing Confounds In Brain Volumetric Quantification Of White Matter Hyperintensities, Ahmed A. Bahrani, Omar M. Al-Janabi, Erin L. Abner, Shoshana H. Bardach, Richard J. Kryscio, Donna M. Wilcock, Charles D. Smith, Gregory A. Jicha Nov 2019

Post-Acquisition Processing Confounds In Brain Volumetric Quantification Of White Matter Hyperintensities, Ahmed A. Bahrani, Omar M. Al-Janabi, Erin L. Abner, Shoshana H. Bardach, Richard J. Kryscio, Donna M. Wilcock, Charles D. Smith, Gregory A. Jicha

Neurology Faculty Publications

BACKGROUND: Disparate research sites using identical or near-identical magnetic resonance imaging (MRI) acquisition techniques often produce results that demonstrate significant variability regarding volumetric quantification of white matter hyperintensities (WMH) in the aging population. The sources of such variability have not previously been fully explored.

NEW METHOD: 3D FLAIR sequences from a group of randomly selected aged subjects were analyzed to identify sources-of-variability in post-acquisition processing that can be problematic when comparing WMH volumetric data across disparate sites. The methods developed focused on standardizing post-acquisition protocol processing methods to develop a protocol with less than 0.5% inter-rater variance.

RESULTS: A ...


Imaging Of Glucose Metabolism By 13c-Mri Distinguishes Pancreatic Cancer Subtypes In Mice, Shun Kishimoto, Jeffrey R. Brender, Daniel R. Crooks, Shingo Matsumoto, Tomohiro Seki, Nobu Oshima, Hellmut Merkle, Penghui Lin, Galen Reed, Albert P. Chen, Jan Henrik Ardenkjaer-Larsen, Jeeva Munasinghe, Keita Saito, Kazutoshi Yamamoto, Peter L. Choyke, James Mitchell, Andrew N. Lane, Teresa W. M. Fan, W. Marston Linehan, Murali C. Krishna Aug 2019

Imaging Of Glucose Metabolism By 13c-Mri Distinguishes Pancreatic Cancer Subtypes In Mice, Shun Kishimoto, Jeffrey R. Brender, Daniel R. Crooks, Shingo Matsumoto, Tomohiro Seki, Nobu Oshima, Hellmut Merkle, Penghui Lin, Galen Reed, Albert P. Chen, Jan Henrik Ardenkjaer-Larsen, Jeeva Munasinghe, Keita Saito, Kazutoshi Yamamoto, Peter L. Choyke, James Mitchell, Andrew N. Lane, Teresa W. M. Fan, W. Marston Linehan, Murali C. Krishna

Center for Environmental and Systems Biochemistry Faculty Publications

Metabolic differences among and within tumors can be an important determinant in cancer treatment outcome. However, methods for determining these differences non-invasively in vivo is lacking. Using pancreatic ductal adenocarcinoma as a model, we demonstrate that tumor xenografts with a similar genetic background can be distinguished by their differing rates of the metabolism of 13C labeled glucose tracers, which can be imaged without hyperpolarization by using newly developed techniques for noise suppression. Using this method, cancer subtypes that appeared to have similar metabolic profiles based on steady state metabolic measurement can be distinguished from each other. The metabolic maps from ...


Distinct Patterns Of Default Mode And Executive Control Network Circuitry Contribute To Present And Future Executive Function In Older Adults, Christopher A. Brown, Frederick A. Schmitt, Charles D. Smith, Brian T. Gold Jul 2019

Distinct Patterns Of Default Mode And Executive Control Network Circuitry Contribute To Present And Future Executive Function In Older Adults, Christopher A. Brown, Frederick A. Schmitt, Charles D. Smith, Brian T. Gold

Neuroscience Faculty Publications

Executive function (EF) performance in older adults has been linked with functional and structural profiles within the executive control network (ECN) and default mode network (DMN), white matter hyperintensities (WMH) burden and levels of Alzheimer's disease (AD) pathology. Here, we simultaneously explored the unique contributions of these factors to baseline and longitudinal EF performance in older adults. Thirty-two cognitively normal (CN) older adults underwent neuropsychological testing at baseline and annually for three years. Neuroimaging and AD pathology measures were collected at baseline. Separate linear regression models were used to determine which of these variables predicted composite EF scores at ...