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Abstract #3131

Automatic quality assessment of high-resolution T2-weighted images used in hippocampus volumetry and functional studies – a tool developed as part of DZNE-DELCODE study.

Arturo Cardenas-Blanco1,2, Yi Chen3, Jose Pedro Valdes-Herrera4, Laura Dobisch1, Renat Yakupov1, Klaus Fliessbach5,6, Michael Wagner5,6, Annika Spottke6, Stefan Teipel7,8, Katharina Buerger9,10, Anja Schneider5,6, Oliver Peters11,12, Peter Nestor1, Josef Priller11,12, Jens Wiltfang13,14, Christoph Laske15,16, Frank Jessen6,17, and Emrah Duezel1,3,18

1German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany, 2IKND, Magdeburg, Germany, 3Institute of cognitive neurology and dementia research, Magdeburg, Germany, 4Aging & Cognition Research Group, German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany, 5Department of Psychiatry, University Hospital Bonn, Bonn, Germany, 6German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany, 7German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany, 8Department of Psychosomatic Medicine, University Medicine Rostock, Rostock, Germany, 9German Center for Neurodegenerative Diseases (DZNE), Munich, Germany, 10Institute for Stroke and Dementia Research, Ludwig-Maximillian-Universitaets, Munich, Germany, 11Department of Psychiatry, Charité-Universitätsmedizin Berlin, Berlin, Germany, 12German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany, 13Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, Goettingen, Germany, 14German Center for Neurodegenerative Diseases (DZNE), Goettingen, Germany, 15Department of Psychiatry and Psychotherapy, Eberhard Karls University, Tuebingen, Germany, 16Aging & Cognition Research Group, German Center for Neurodegenerative Diseases (DZNE), Tuebingen, Germany, 17Department of Psychiatry, University of Cologne, Cologne, Germany, 18Institute of Cognitive Neuroscience, University College London, London, United Kingdom

This abstract presents a processing pipeline developed to automatically assess the quality of specific structural T2-weighted images typically acquired in the study of the hippocampus. By combining existing neuroimaging tools, the presented pipeline generates descriptive information about the signal properties in different tissue classes of the T2-weighted image. This information could subsequently be used to detect sub-optimal volumes due to noise or motion artifacts. Similarly as it measures the angulation of the T2-weighted slices with respect to the HC, it could also be used to automatically determine whether the field of view angulation follows protocol.

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