  Image-to-image translation is a common task in computer vision and has been
rapidly increasing the impact on the field of medical imaging. Deep
learning-based methods that employ conditional generative adversarial networks
(cGANs), such as Pix2PixGAN, have been extensively explored to perform
image-to-image translation tasks. However, when noisy medical image data are
considered, such methods cannot be directly applied to produce clean images.
Recently, an augmented GAN architecture named AmbientGAN has been proposed that
can be trained on noisy measurement data to synthesize high-quality clean
medical images. Inspired by AmbientGAN, in this work, we propose a new cGAN
architecture, Ambient-Pix2PixGAN, for performing medical image-to-image
translation tasks by use of noisy measurement data. Numerical studies that
consider MRI-to-PET translation are conducted. Both traditional image quality
metrics and task-based image quality metrics are employed to assess the
proposed Ambient-Pix2PixGAN. It is demonstrated that our proposed
Ambient-Pix2PixGAN can be successfully trained on noisy measurement data to
produce high-quality translated images in target imaging modality.
