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-Blanco^1,2, Yi Chen³, Jose Pedro Valdes-Herrera⁴, Laura Dobisch¹, Renat Yakupov¹, Klaus Fliessbach^5,6, Michael Wagner^5,6, Annika Spottke⁶, Stefan Teipel^7,8, Katharina Buerger^9,10, Anja Schneider^5,6, Oliver Peters^11,12, Peter Nestor¹, Josef Priller^11,12, Jens Wiltfang^13,14, Christoph Laske^15,16, Frank Jessen^6,17, and Emrah Duezel^1,3,18

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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