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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