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Abstract #0657

Efficient Super-Resolution in Intracranial Vessel Wall Magnetic Resonance Imaging using 3D Deep Densely Connected Neural Networks

Yuhua Chen1,2, Zhaoyang Fan2, Feng Shi2, Zixiao Tian2, Anthony Christodoulou2, Yibin Xie2, and Debiao Li2

1Department of Bioengineering, UCLA, Los Angeles, CA, United States, 2Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States

Spatial resolution is of paramount importance for intracranial vessel wall (IVW) MR imaging because the vessel wall is submillimeter thin. However, high spatial resolution typically comes at the expense of long scan time, small spatial coverage and low signal-to-noise ratio (SNR). If we can reconstruct a high-resolution (HR) image from a low-resolution (LR) input, we can potentially achieve larger spatial coverage, higher SNR and better spatial resolution in a shorter scan. In this work, we propose a new Single Image Super-Resolution(SISR) technique which recovers an HR image from an LR image using 3D Densely Connected Super-Resolution Networks (DCSRN). We compared our network with state-of-art deep learning network in restoring 4x down-graded images and ours are faster and better.

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