Abstract #4383

Susceptibility-based Positive Contrast Imaging of MR Compatible Metallic Devices Using Model-based Deep Learning

Caiyun Shi^1,2, Jing Cheng¹, Xin Liu¹, Hairong Zheng¹, Yanjie Zhu¹, Dong Liang^1,3, and Haifeng Wang¹

¹Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, Shenzhen, China, ²Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, China, Shenzhen, China, ³Research Centre for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Science, Shenzhen, China, Shenzhen, China

Synopsis

Keywords: Interventional Devices, Machine Learning/Artificial IntelligenceSusceptibility-based positive contrast MR imaging exhibits excellent efficacy for visualizing the MR compatible metallic devices, by taking advantage of their high magnetic susceptibility. In this work, a model-based deep learning architecture with U-net is developed to realize the 3D susceptibility-based positive contrast MR imaging on real phantom experiments. We train the network on synthetic data to generate positive contrast images from magnetic field maps for localizing the seeds from their surroundings and demonstrate the potential of the deep learning implementation.

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