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Abstract #0677

High-Performance Rapid Quantitative Imaging with Model-Based Deep Adversarial Learning

Fang Liu1,2 and Li Feng3
1Gordon Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States, 2Radiology, University of Wisconsin-Madison, Madison, WI, United States, 3Biomedical Engineering and Imaging Institute and Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States

The purpose of this work was to develop a novel deep learning-based reconstruction framework for rapid MR parameter mapping. Building upon our previously proposed Model-Augmented Neural neTwork with Incoherent k-space Sampling (MANTIS) technique combining efficient end-to-end CNN mapping and k-space consistency to enforce joint data and model fidelity, this new method further extends to incorporate the latest adversarial training (MANTIS-GAN), so that more realistic parameter maps can be directly estimated from highly-accelerated k-space data. The performance of MANTIS-GAN was demonstrated for fast T2 mapping. Our study showed that MANTIS-GAN represents a promising approach for efficient and accurate MR parameter mapping.

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