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Abstract #4344

Automated quality control of MR images using deep convolutional neural networks

Michael C. Muelly1, Paul B. Stoddard1, and Shreyas S. Vasanwala1

1Department of Radiology, Stanford University, Stanford, CA, United States

The complexity of MR scanners results in significant variability in the quality of images produced, in some cases requiring clinical expertise to recognize suboptimal images. Deep convolutional neural networks are an emerging technique with potential clinical applications. We aim to investigate whether deep convolutional neural networks could be trained for three MR image quality control classification tasks: 1) Recognize adequacy of MR elastography wave propagation, 2) determine whether rectal gas susceptibility artifact obscuring the prostate is present, and 3) determine scan technique in unlabeled images. Using the Inception v4 deep convolutional neural network we found high classification accuracy for two out of these three problems suggesting the potential to automate certain aspects of MR quality control.

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