Accelerated Magnetic Resonance Imaging by Adversarial Neural Network

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10553)

Abstract

A main challenge in Magnetic Resonance Imaging (MRI) for clinical applications is speeding up scan time. Beyond the improvement of patient experience and the reduction of operational costs, faster scans are essential for time-sensitive imaging, where target movement is unavoidable, yet must be significantly lessened, e.g., fetal MRI, cardiac cine, and lungs imaging. Moreover, short scan time can enhance temporal resolution in dynamic scans, such as functional MRI or dynamic contrast enhanced MRI. Current imaging methods facilitate MRI acquisition at the price of lower spatial resolution and costly hardware solutions.

We introduce a practical, software-only framework, based on deep learning, for accelerating MRI scan time allows maintaining good quality imaging. This is accomplished by partial MRI sampling, while using an adversarial neural network to estimate the missing samples. The inter-play between the generator and the discriminator networks enables the introduction of an adversarial cost in addition to a fidelity loss used for optimizing the peak signal-to-noise ratio (PSNR). Promising image reconstruction results are obtained for 1.5T MRI where only 52% of the original data are used.

Notes

Acknowledgment

This research is partially supported by the Israel Science Foundation (T.R.R. 1638/16) and the IDF Medical Corps (T.R.R.).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Electrical EngineeringThe Zlotowski Center for Neuroscience, Ben-Gurion University of the NegevBeershebaIsrael

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