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Dr. Tammy Riklin Raviv

Dr. Tammy Riklin Raviv

Senior Lecturer
Department of Electrical and Computer Engineering
Ben-Gurion University of the Negev


Tammy Riklin Raviv is a Senior Lecturer in the Department of Electrical and Computer Engineering of Ben-Gurion University of the Negev. Her research focuses on the development of computational tools for medical and biomedical logical imaging. She holds a B.Sc. in Physics (magna cum laude) and an M.Sc. in Computer Science (magna cum laude) from the Hebrew University of Jerusalem. She received her Ph.D. from the School of Electrical Engineering of Tel-Aviv University. During 2010-2012 she was a research fellow at Harvard Medical School and the Broad Institute. Prior to this (2008-2010) she was a post-doctorate associate at the Computer Science and Artificial Intelligence Laboratory (CSAIL) at the Massachusetts Institute of Technology (MIT).


Accelerated Magnetic Resonance Imaging by Generative Adversarial Neural Networks


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 acquisition, while maintaining anatomically meaningful imaging. This is accomplished by partial MRI sampling, while using an adversarial neural network to directly estimate the missing k-space 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 3T and 1.5T brain MRI, from large publicly available dataset, where only 40%, 25% and 16.6% of the raw samples of each scan are used. To assess the clinical usability of the reconstructed images we also performed tissue segmentation and compared the results to those obtained by using the original fully-sampled images.

Segmentation compatibility, measured in terms of Dice scores and Hausdorff distances, demonstrate the quality of the proposed MRI reconstruction with respect to other methods, including the widely-used Compressed Sensing.