@incollection{eprints3595,
           title = {Whole Image Synthesis Using a Deep Encoder-Decoder Network},
          number = {9968},
       publisher = {Springer International Publishing},
            year = {2016},
          author = {Vasileios Sevetlidis and Mario Valerio Giuffrida and Sotirios A. Tsaftaris},
       booktitle = {Simulation and Synthesis in Medical Imaging. First International Workshop, SASHIMI 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 21, 2016, Proceedings},
          series = {Lecture Notes in Computer Science},
           pages = {127--137},
             url = {http://eprints.imtlucca.it/3595/},
        abstract = {The synthesis of medical images is an intensity transformation of a given modality in a way that represents an acquisition with a different modality (in the context of MRI this represents the synthesis of images originating from different MR sequences). Most methods follow a patch-based approach, which is computationally inefficient during synthesis and requires some sort of ?fusion? to synthesize a whole image from patch-level results. In this paper, we present a whole image synthesis approach that relies on deep neural networks. Our architecture resembles those of encoder-decoder networks, which aims to synthesize a source MRI modality to an other target MRI modality. The proposed method is computationally fast, it doesn?t require extensive amounts of memory, and produces comparable results to recent patch-based approaches.}
}