  Robust and accurate alignment of multimodal medical images is a very
challenging task, which however is very useful for many clinical applications.
For example, magnetic resonance (MR) and transrectal ultrasound (TRUS) image
registration is a critical component in MR-TRUS fusion guided prostate
interventions. However, due to the huge difference between the image
appearances and the large variation in image correspondence, MR-TRUS image
registration is a very challenging problem. In this paper, an adversarial image
registration (AIR) framework is proposed. By training two deep neural networks
simultaneously, one being a generator and the other being a discriminator, we
can obtain not only a network for image registration, but also a metric network
which can help evaluate the quality of image registration. The developed
AIR-net is then evaluated using clinical datasets acquired through image-fusion
guided prostate biopsy procedures and promising results are demonstrated.
