Registration on DCE-MRI images via multi-domain image-to-image translation

Registration of dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is challenging as rapid intensity changes caused by a contrast agent lead to large registration errors. To address this problem, we propose a novel multi-domain image-to-image translation (MDIT) network based on image disentangling for separating motion from contrast changes before registration. In particular, the DCE images are disentangled into a domain-invariant content space (motion) and a domain-specific attribute space (contrast changes). The disentangled representations are then used to generate images, where the contrast changes have been removed from the motion. After that the resulting deformations can be directly derived from the generated images using an FFD registration. The method is tested on 10 lung DCE-MRI cases. The proposed method reaches an average root mean squared error of 0.3 ± 0.41 and the separation time is about 2.4 s for each case. Results show that the proposed method improves the registration efficiency without losing the registration accuracy compared with several state-of-the-art registration methods. •We formulate the problem of separation of motion and contrast changes as an image-to-image translation problem.•We propose a novel I2I network, namely MDIT, for translating DCE-MRI images cross multiple domains.•Experimental results demonstrate the superiority of our method compared with some state-of-the-art registration methods.