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Abstract #3414

Unsupervised Deep Learning Method for EPI Distortion Correction using Dual-Polarity Phase-Encoding Gradients

Jee Won Kim1, Kinam Kwon2, Byungjai Kim1, Sunho Kim1, and Hyunwook Park1
1Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea, Republic of, 2Samsung Advanced Institute of Technology (SAIT), Suwon, Korea, Republic of

We propose a new scheme for EPI distortion correction, which implements unsupervised learning, trained with readily available images, such as ImageNet2012 dataset. The distortion-corrected image is obtained by the MR image generation function using the input distorted images and the frequency-shift maps that are the outputs of the network. Two distorted images obtained with dual-polarity phase-encoding gradients are the inputs of the neural network. The neural network estimates the frequency-shift maps from the distorted images. To train the neural network, unsupervised learning was conducted by minimizing the L1 loss between input distorted images and the estimated distorted images.

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