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Abstract #4658

A Reference-Free Convolutional Neural Network Model for Magnetic Resonance Imaging Reconstruction from Under-Sampled k-Space

Yang Song1, Yida Wang1, Xu Yan2, Minxiong Zhou3, Bingwen Hu1, and Guang Yang1

1Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China, 2MR Scientific Marketing, Siemens Healthcare, Shanghai, China, 3Shanghai University of Medicine & Health Sciences, Shanghai, China

We used a reference-free model based on convolutional neural network (RF-CNN) to reconstruct the under-sampled magnetic resonance images. The model was trained without fully sampled image (FS) as the reference. We compared our model with the traditional compressed sensing reconstruction (CS) and the CNN model trained by FS. Mean square error and structure similarity were used to evaluate the model. Our RF-CNN model performed better than CS, but did not perform as good as usual CNN model.

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