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Abstract #1516

7T-like MR Images Synthesis from 3T MRI using Auto-Context Convolutional Neural Network

khosro bahrami1, Islem Rekik1, Feng Shi1, and Dinggang Shen1,2

1University of North Carolina at Chapel Hill, Chapel Hill, NC, United States, 2Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea, Republic of

We propose a novel multi-step Convolutional Neural Network (CNN) architecture to cascade multiple CNNs, along with an Auto-Context Model (ACM), called Auto-Context CNN, to reconstruct 7T-like MR images from 3T MR images. Basically, we non-linearly map the input 3T MR images to their corresponding 7T MR images. To do so, in the training stage, we first partition the training 3T and 7T MR images into overlapping 3D patches, then we train the Auto-Context CNN to map each 3T patch to the center voxel in the corresponding 7T patch. In the testing step, we apply the trained Auto-Context CNN to generate the 7T-like MRI patch from each input 3T patch.

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