Abstract #2077

Generative Adversarial Networks based Compressed Sensing for Multi-contrast Intracranial Vessel Wall Imaging Acceleration

Niranjan Balu¹, Long Wang², Tao Zhang², Zechen Zhou³, Enhao Gong², Kristi Pimentel¹, Mahmud Mossa-basha¹, Thomas Hatsukami^1,4, and Chun Yuan^1,5

¹Department of Radiology, University of Washington, Seattle, WA, United States, ²Subtle Medical Inc., Menlo Park, CA, United States, ³Philips Research North America, Cambridge, MA, United States, ⁴Department of Surgery, Division of Vascular Surgery, University of Washington, Seattle, WA, United States, ⁵Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China

Multi-contrast isotropic high resolution intracranial vessel wall imaging (VWI) can enable direct detection and follow-up surveillance of intracranial vessel wall pathologies. The overall scan time can be largely reduced by proper random undersampling. However, the image reconstruction process can be time consuming causing deployment difficulties of accelerated intracranial VWI for clinical use. In this study, a generative adversarial networks based compressed sensing method was developed for multi-contrast intracranial image reconstruction. The preliminary results demonstrate comparable/improved image quality for vessel wall delineation in comparison to the traditional image reconstruction method, while providing a significant reduction in reconstruction time.

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