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Abstract #0231

Simultaneous Reconstruction of Multiple b-Values DWI using a Joint Convolutional Neural Network

Chengyan Wang1, Yucheng Liang2, Yuan Wu1, Danni Yang2, and Yiping P. Du1

1Institute for Medical Imaging Technology (IMIT), School of Biomedical Engineering, Shanghai Jiao Tong university, Shanghai, China, 2Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States

This study presented a joint convolutional neural network (CNN) architecture for the reconstruction of multiple b-values diffusion-weighted (DW) images simultaneously. The proposed joint-net is able to extract high-level anatomical correlations among multi-contrast images and correct misalignment between images by adding a spatial transformation layer. Experimental results show that the proposed algorithm outperforms single image reconstruction network and compressed sensing algorithm with improved image quality. The training process of the joint-net is much more efficient compared to individual training for each b-value image. Besides, combination of data consistency and the joint-net enables precise characterization of brain tumor in a patient study.

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