  As a powerful technique in medical imaging, image synthesis is widely used in
applications such as denoising, super resolution and modality transformation
etc. Recently, the revival of deep neural networks made immense progress in the
field of medical imaging. Although many deep leaning based models have been
proposed to improve the image synthesis accuracy, the evaluation of the model
uncertainty, which is highly important for medical applications, has been a
missing part. In this work, we propose to use Bayesian conditional generative
adversarial network (GAN) with concrete dropout to improve image synthesis
accuracy. Meanwhile, an uncertainty calibration approach is involved in the
whole pipeline to make the uncertainty generated by Bayesian network
interpretable. The method is validated with the T1w to T2w MR image translation
with a brain tumor dataset of 102 subjects. Compared with the conventional
Bayesian neural network with Monte Carlo dropout, results of the proposed
method reach a significant lower RMSE with a p-value of 0.0186. Improvement of
the calibration of the generated uncertainty by the uncertainty recalibration
method is also illustrated.
