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Abstract #3413

Rectal Cancer MRI Motion Quality Assessment using a Convolutional Neural Network

Avishkar Sharma1, Ke Lei2, Shreyas Vasanawala1, and Vipul Sheth1
1Radiology, Stanford University, Stanford, CA, United States, 2Electric Engineering, Stanford University, Stanford, CA, United States


Magnetic resonance (MR) imaging plays a pivotal role in the staging and treatment planning of rectal cancer. Accurate staging depends on good-quality high-resolution axial T2-weighted images orthogonal to the rectal tumor. Rectal MRI is often confounded by motion artifacts secondary to bowel peristalsis and patient movement. We propose a CNN model that automatically assesses image quality instantaneously after a scan is finished to reduce the frequency of patient recalls and non-diagnostic images. Our model achieves high accuracy in identifying motion degradation on an individual slice basis and perfect accuracy when classifying the entire sequence.

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