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Abstract #1790

Convolutional Neuronal Network Inception-v3 detects Partial Volume Artifacts on 2D MR-Images of the Lung for Automated Quality Control

Andreas Voskrebenzev1,2, Cristian Crisosto1,2, Maximilian Zubke1,2, Frank Wacker1,2, and Jens Vogel-Claussen1,2
1Institute of Diagnostic and Interventional Radiology, Hannover Medical School, Hannover, Germany, 2German Center for Lung Research (DZL), Biomedical Research in Endstage and Obstructive Lung Disease Hannover (BREATH), Hannover, Germany


The partial volume effect (PVE) is an often-observed artifact in MR imaging. Especially images with a low spatial resolution, will show an averaged voxel signal of multiple tissue components. These artifacts can be so substantial that a further image analysis can be omitted. This is e.g. the case for phase-resolved functional lung imaging (PREFUL), which is based on the 2D acquisition of coronal image-time-series to assess ventilation and perfusion dynamics. In this study the pretrained convolutional neural network Inception-v3 was trained via transfer-learning to detect images, which show substantial PVE with a classification accuracy of 91%.

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