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.

翻译：图像到图像的转换是计算机视觉中的常见任务，并且在医学成像领域的影响力正迅速增长。基于条件生成对抗网络（cGANs）的深度学习方法，如Pix2PixGAN，已被广泛探索用于执行图像到图像的转换任务。然而，当考虑含噪医学图像数据时，此类方法无法直接应用于生成清晰图像。近期，一种名为AmbientGAN的增强型GAN架构被提出，它能够利用含噪测量数据进行训练，从而合成高质量的无噪医学图像。受AmbientGAN启发，本工作提出了一种新的cGAN架构——Ambient-Pix2PixGAN，用于利用含噪测量数据执行医学图像到图像的转换任务。我们开展了考虑MRI到PET转换的数值研究，采用传统图像质量指标与基于任务的图像质量指标对提出的Ambient-Pix2PixGAN进行评估。实验证明，我们提出的Ambient-Pix2PixGAN能够成功地在含噪测量数据上训练，从而生成目标成像模态下的高质量转换图像。