  We propose a novel approach to translate unpaired contrast computed
tomography (CT) scans to non-contrast CT scans and the other way around.
Solving this task has two important applications: (i) to automatically generate
contrast CT scans for patients for whom injecting contrast substance is not an
option, and (ii) to enhance the alignment between contrast and non-contrast CT
by reducing the differences induced by the contrast substance before
registration. Our approach is based on cycle-consistent generative adversarial
convolutional transformers, for short, CyTran. Our neural model can be trained
on unpaired images, due to the integration of a multi-level cycle-consistency
loss. Aside from the standard cycle-consistency loss applied at the image
level, we propose to apply additional cycle-consistency losses between
intermediate feature representations, which enforces the model to be
cycle-consistent at multiple representations levels, leading to superior
results. To deal with high-resolution images, we design a hybrid architecture
based on convolutional and multi-head attention layers. In addition, we
introduce a novel data set, Coltea-Lung-CT-100W, containing 100 3D triphasic
lung CT scans (with a total of 37,290 images) collected from 100 female
patients (there is one examination per patient). Each scan contains three
phases (non-contrast, early portal venous, and late arterial), allowing us to
perform experiments to compare our novel approach with state-of-the-art methods
for image style transfer. Our empirical results show that CyTran outperforms
all competing methods. Moreover, we show that CyTran can be employed as a
preliminary step to improve a state-of-the-art medical image alignment method.
We release our novel model and data set as open source at
https://github.com/ristea/cycle-transformer.
