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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