%0 Book Section
%A Sevetlidis, Vasileios
%A Giuffrida, Mario Valerio
%A Tsaftaris, Sotirios A.
%B Simulation and Synthesis in Medical Imaging. First International Workshop, SASHIMI 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 21, 2016, Proceedings
%D 2016
%F eprints:3595
%I Springer International Publishing
%N 9968
%P 127-137
%S Lecture Notes in Computer Science
%T Whole Image Synthesis Using a Deep Encoder-Decoder Network
%U http://eprints.imtlucca.it/3595/
%X 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.