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

Adversarial Image Registration with Application for MR and TRUS Image Fusion