Abstract #1373

A Deep Learning Method to Remove Motion Artifacts in Fetal MRI

Adam Lim^1,2, Justin Lo^1,2, Matthias Wagner³, Birgit Ertl-Wagner^3,4, and Dafna Sussman^1,2,5

¹Department of Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON, Canada, ²Institute for Biomedical Engineering, Science and Technology (iBEST), Toronto Metropolitan University and St. Michael’s Hospital, Toronto, ON, Canada, ³Division of Neuroradiology, The Hospital for Sick Children, Toronto, ON, Canada, ⁴Department of Obstetrics and Gynecology, University of Toronto, Toronto, ON, Canada, ⁵Department of Medical Imaging, University of Toronto, Toronto, ON, Canada

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Artifacts, Deep Learning, Generative Adversarial Network, Image DenoisingMotion artifacts are a common issue in fetal MR imaging that limit the visibility of essential fetal anatomy. In such cases, the sequence acquisition must be repeated in order for an accurate diagnosis. This study introduces a deep learning approach utilizing a Generative Adversarial Network (GAN) framework for removing motion artifacts in fetal MRIs. Results exceeded current state-of-the-art methods by achieving an average SSIM of 93.7%, and PSNR of 33.5dB. The presented network demonstrates rapid and accurate results that can be advantageous in clinical use.

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