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Abstract #3439

MR Image Reconstruction using GRAPPA with Total Generalized Variation

Rida Zainab1, Muhammad Haseeb Hassan1, Omair Inam1, Ibtisam Aslam1,2, and Hammad Omer1
1Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad, Pakistan, 2Department of Radiology and Medical Informatics, Hospital University of Geneva, Geneva, Switzerland

GRAPPA reconstructed images may exhibit noise modulated by the receiver coil sensitivities. Total variation (TV) regularization has been recently used to solve the image de-noising problem. However, conventional TV fails to remove staircase artifacts in the reconstructed MR images due to inhomogeneities in field strength and receiver coils. In this abstract, total generalized variation (TGV) regularization is used to de-noise the GRAPPA reconstructed images, while eliminating the limitations posed by TV. Experiments are performed on 8-channel in-vivo human-head data set. The results show that the proposed method successfully removes the noise and preserve fine details in the GRAPPA reconstructed images.

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