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Abstract #3410

QC-Aautomator: A deep learning based automated artifact detection in dMRI data

Zahra Riahi Samani1, Jacob Alappatt1, Parker Drew1, and Ragini Verma1

1Penn Patho-Connectomics Lab, Radiology, University of Pennsylvania, Philadelphia, PA, United States

We have developed a deep learning based automated Quality Control (QC) tool, QC-Automator, for diffusion weighted MRI data, that will detect different artifacts. This will ensure that appropriate steps can be taken at the pre-processing stage to improve data quality and ensure that these artifacts do not affect the results of subsequent image analysis. Our tool based on convolutional neural nets has 94 – 98% accuracy in detecting the various artifacts including motion, multiband interleaving artifact, ghosting, susceptibility, herringbone and chemical shift. It is robust and fast and paves the way for efficient and effective artifact detection in large datasets.

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