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Prof. Dr. Martin Reuter
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Title: Sensitive Longitudinal NeuroImaging? and Morphometry
Abstract: Structural magnetic resonance imaging data are frequently analyzed to reveal morphological changes of the human brain in dementia. Most contemporary imaging biomarkers are scalar values, such as the volume of a structure, and may miss the localized morphological variation of early presymptomatic disease progression. Neuroanatomical shape descriptors, however, can represent complex geometric information of individual anatomical regions and may demonstrate increased sensitivity in association studies. Yet, they remain largely unexplored. In this talk we will describe sensitive computational methods for the automated quantification of neuroanatomical morphometric estimates and their longitudinal changes. We will, furthermore, show that neuroanatomical shape features, such as lateral shape asymmetry, demonstrate increased sensitivity to detect early disease effects and can improve computer-aided diagnosis of dementia.
Biography:
Prof. Reuter is Director of Image Analysis at the German Center for Neurodegenerative Diseases (DZNE) in Bonn. He is Assistant Professor of Radiology and Assistant Professor of Neurology at the Harvard Medical School in Boston, USA. He, furthermore, directs the Laboratory for Computational Longitudinal Neuroimaging at the Martinos Center for Biomedical Imaging, Massachusetts General Hospital, and is Research Affiliate at the Computer Science and Artificial Intelligence Lab (CSAIL) of the Massachusetts Institute of Technology. Prof. Reuter obtained a degree in Mathematics and Computer Science in 2001 and a Ph.D. in Computational Geometry in 2005 from the Leibniz University Hanover, Germany. He pursued his postdoctoral studies with a Feodor Lynen Fellowship of the Alexander von Humboldt Foundation at the Massachusetts Institute of Technology until 2008 and subsequently moved to the Martinos Center for Biomedical Imaging at the Massachusetts General Hospital. He has received prizes and awards from international journals, workshops, competitions, industry and funding bodies, including a competitive career award of the National Institute of Health (NIH). Prof. Reuter’s research contributions include advanced non-rigid shape analysis and highly sensitive computational methods for longitudinal neuroimaging and computer-aided diagnosis. His methods are widely employed as part of the FreeSurfer? software suite, for example, to uncover risk and preserving factors of neurodegenerative diseases or to assess disease modifying therapies. Furthermore, various neuroimaging studies, such as the Alzheimer’s Disease Neuroimaging Initiative (ADNI), rely on his contributions for the automated quantification of relevant biomarkers from large imaging data sets.
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Title: | Talk on Sensitive Longitudinal NeuroImaging? and Morphometry |
Date: | 2 August 2017 |
Location: | FMI 00.13.009A |
Abstract: | Structural magnetic resonance imaging data are frequently analyzed to reveal morphological changes of the human brain in dementia. Most contemporary imaging biomarkers are scalar values, such as the volume of a structure, and may miss the localized morphological variation of early presymptomatic disease progression. Neuroanatomical shape descriptors, however, can represent complex geometric information of individual anatomical regions and may demonstrate increased sensitivity in association studies. Yet, they remain largely unexplored. In this talk we will describe sensitive computational methods for the automated quantification of neuroanatomical morphometric estimates and their longitudinal changes. We will, furthermore, show that neuroanatomical shape features, such as lateral shape asymmetry, demonstrate increased sensitivity to detect early disease effects and can improve computer-aided diagnosis of dementia. |
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Type: | News |
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