  Recently, more and more attention is drawn to the field of medical image
synthesis across modalities. Among them, the synthesis of computed tomography
(CT) image from T1-weighted magnetic resonance (MR) image is of great
importance, although the mapping between them is highly complex due to large
gaps of appearances of the two modalities. In this work, we aim to tackle this
MR-to-CT synthesis by a novel deep embedding convolutional neural network
(DECNN). Specifically, we generate the feature maps from MR images, and then
transform these feature maps forward through convolutional layers in the
network. We can further compute a tentative CT synthesis from the midway of the
flow of feature maps, and then embed this tentative CT synthesis back to the
feature maps. This embedding operation results in better feature maps, which
are further transformed forward in DECNN. After repeat-ing this embedding
procedure for several times in the network, we can eventually synthesize a
final CT image in the end of the DECNN. We have validated our proposed method
on both brain and prostate datasets, by also compar-ing with the
state-of-the-art methods. Experimental results suggest that our DECNN (with
repeated embedding op-erations) demonstrates its superior performances, in
terms of both the perceptive quality of the synthesized CT image and the
run-time cost for synthesizing a CT image.
