Abstract #1970

EPI Nyquist Ghost Artifact Correction for Brain Diffusion Weighted Imaging (DWI) using Deep Learning

Fatima Sattar¹, Sadia Ahsan¹, Fariha Aamir¹, Ibtisam Aslam^1,2, Iram Shahzadi^3,4, and Hammad Omer¹

¹Medical Image Processing Research Group (MIPRG), Department of Electrical and Computer Engineering, COMSATS University, Islamabad, Pakistan, ²Service of Radiology, Geneva University Hospitals and Faculty of Medicine, Hospital University of Geneva and University of Geneva, Geneva, Switzerland, ³OncoRay – National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden –Rossendorf, Dresden, Germany, Dresden, Germany, ⁴German Cancer Research Center (DKFZ), Heidelberg, Germany, Dresden, Germany

Synopsis

Echo-planar imaging suffers from Nyquist ghost (i.e., N/2 ghost) artifacts because of poor system gradients and delays. Many conventional methods have been used in literature to remove N/2 artifacts in Diffusion Weighted Imaging (DWI) but often produce erroneous results. This paper presents a deep learning approach to eliminate the phase error of k-space for removing the Nyquist ghost artifacts in DWI. Experimental results show successful removal of the ghost artifacts with improved SNR and reconstruction quality with the proposed method.

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