Deep Learning in Neuroradiology.

G Zaharchuk,D Rubin,E Gong,C P Langlotz,M Wintermark

doi:10.3174/ajnr.a5543

G Zaharchuk, D Rubin + Show 3 more

Open Access

https://doi.org/10.3174/ajnr.a5543

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Abstract

Deep learning is a form of machine learning using a convolutional neural network architecture that shows tremendous promise for imaging applications. It is increasingly being adapted from its original demonstration in computer vision applications to medical imaging. Because of the high volume and wealth of multimodal imaging information acquired in typical studies, neuroradiology is poised to be an early adopter of deep learning. Compelling deep learning research applications have been demonstrated, and their use is likely to grow rapidly. This review article describes the reasons, outlines the basic methods used to train and test deep learning models, and presents a brief overview of current and potential clinical applications with an emphasis on how they are likely to change future neuroradiology practice. Facility with these methods among neuroimaging researchers and clinicians will be important to channel and harness the vast potential of this new method.

Highlights

While unsupervised learning holds great promise for medical imaging, this review focuses on supervised learning
A study using 3T input data and 7T output data showed that a deep network can be trained to create simulated “7T-like” images from 3T data.[16]
Often obtaining a certain imaging sequence can be very time-consuming; an example is DTI, in which the need for multiple angular directions lengthens the examination beyond what many patients can tolerate

Summary

Introduction

Much research in image-based deep learning has moved to using more computationally efficient structures, convolutional neural networks (CNNs). A study using 3T input data and 7T output data showed that a deep network can be trained to create simulated “7T-like” images from 3T data.[16] Often obtaining a certain imaging sequence can be very time-consuming; an example is DTI, in which the need for multiple angular directions lengthens the examination beyond what many patients can tolerate.

Results

Conclusion