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Abstract #0356

REconstruction of MR images acquired in highly inhOmogeneous fields using DEep Learning (REMODEL)

Punith B Venkate Gowda1, Asha K Kumara Swamy1, Sachin Jambawalikar2, Sairam Geethanath1,2, and Thomas Vaughan2

1Medical Imaging Research Centre, Dayananda Sagar College of Engineering, Bangalore, India, 2Dept. of Radiology, Columbia University Medical Center, New York, NY, United States

The aim of this study was to develop and demonstrate a supervised learning algorithm to reconstruct MR images acquired in highly in-homogeneous magnetic fields. Brain images were used to train a deep neural network. This was performed for image sizes of 32 x 32 and 64 x 64. Results obtained demonstrate REMODEL’s ability to reconstruct the images obtained in in-homogeneous magnetic fields of up to ±50 kHz with high fidelity. The root-mean-square-error for these reconstructions compared to the uncorrupted ground truth was lesser than 0.15 and significantly lesser than the corrupted images.

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