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Abstract #2747

Deep learning based MR image diagnostic quality deduction to reduce patient recall

Arathi Sreekumari1, Ileana Hancu2, Dirk Beque3, Keith Park2, Uday Patil1, Desmond Teck Beng Yeo2, Thomas K Foo2, and Dattesh Shanbhag1

1GE Global Research, Bangalore, India, 2GE Global Research, Niskayuna, NY, United States, 3GE Global Research, Garching bei M√ľnchen, Germany

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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