Abstract #1970
EPI Nyquist Ghost Artifact Correction for Brain Diffusion Weighted Imaging (DWI) using Deep Learning
Fatima Sattar1, Sadia Ahsan1, Fariha Aamir1, Ibtisam Aslam1,2, Iram Shahzadi3,4, 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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