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Abstract #3733

Robust and Generalizable Quality Control of Structural MRI images

Ben A Duffy1, Srivathsa Pasumarthi Venkata1, Long Wang1, Sara Dupont1, Lei Xiang1, Greg Zaharchuk1, and Tao Zhang1
1Subtle Medical Inc., Menlo Park, CA, United States

We present an automated deep learning-based quality control system that generalizes to images of different orientations, images with and without contrast as well as those from different acquisition sites. Because the same model was able to classify images with different orientations, test-time augmentation substantially improved performance. Images that were moderately affected by artifacts were able to be identified with 95% accuracy. Furthermore, robustness to different data types (and potentially artifact types) was ensured by using an out-of-distribution detection procedure. This was able to discriminate spine MRI images from T1 brain images with an AUC of 0.98.

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