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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