Cross-Domain Synthesis of Medical Images Using Efficient Location-Sensitive Deep Network

Cross-modality image synthesis has recently gained significant interest in the medical imaging community. In this paper, we propose a novel architecture called location-sensitive deep network (LSDN) for synthesizing images across domains. Our network integrates intensity feature from image voxels and spatial information in a principled manner. Specifically, LSDN models hidden nodes as products of features and spatial responses. We then propose a novel method, called ShrinkConnect, for reducing the computations of LSDN without sacrificing synthesis accuracy. ShrinkConnect enforces simultaneous sparsity to find a compact set of functions that accurately approximates the responses of all hidden nodes. Experimental results demonstrate that LSDN+ ShrinkConnect outperforms the state of the art in cross-domain synthesis of MRI brain scans by a significant margin. Our approach is also computationally efficient, e.g. 26x faster than other sparse representation based methods.