Applying deep learning and Tensorflow to improve brain MRI images quality

Taking brain MRI images is complicated procedure as the orientation, location, and coverage needs to be correct in all three spatial dimentsions. The quality and consistency of positioning and orientation of the slices relies heavily on the skill and experience of the scan operator. This process can be time-consuming and difficult, especially for complex anatomies. As a result, there can be inconsistencies from scan operator to scan operator. This lack of consistency can make the job of the radiologist in interpreting these images more difficult especially when a patient is being scanned as a follow up to previous MRI exam and they are trying to identify subtle changes in anatomy or disease progression over time.

The researchers from GE Healthcare Magnetic Resonance Imaging team developed an approach to aid the scan operator. The approach is based on 3 deep neural networks, can be adopted to take MRI images of the other body parts and achieves 99.2% accuracy score. The researchers notice that Tensorflow significantly helped them to develop and deliver the approach to the production.

Medium article: https://medium.com/tensorflow/intelligent-scanning-using-deep-learning-for-mri-36dd620882c4
GE Helthcare website: https://www.gehealthcare.com

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