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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