In this abstract, we describe a fast and robust methodology to highlight on-console, the diagnostic quality of acquired MRI imaging data. Specifically, using convolutional neural networks we flag the MRI volumes affected by motion and consequently hinder the diagnosis by clinician at the time of reading the exam. By prospectively flagging such exams at acquisition console itself and re-acquiring them with improved protocol will obviate the need for costly patient recall and re-scan in clinical setting.
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