Learning a model-driven variational network for deformable image registration

Xi Jia, Alexander Thorley, Wei Chen, Huaqi Qiu, Linlin Shen, Iain B. Styles, Hyung Jin Chang, Ales Leonardis, Antonio De Marvao, Declan P O'Regan, Daniel Rueckert, Jinming Duan

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)


Data-driven deep learning approaches to image registration can be less accurate than conventional iterative approaches, especially when training data is limited. To address this issue and meanwhile retain the fast inference speed of deep learning, we propose VR-Net, a novel cascaded variational network for unsupervised deformable image registration. Using a variable splitting optimization scheme, we first convert the image registration problem, established in a generic variational framework, into two sub-problems, one with a point-wise, closed-form solution and the other one being a denoising problem. We then propose two neural layers (i.e. warping layer and intensity consistency layer) to model the analytical solution and a residual U-Net (termed generalized denoising layer) to formulate the denoising problem. Finally, we cascade the three neural layers multiple times to form our VR-Net. Extensive experiments on three (two 2D and one 3D) cardiac magnetic resonance imaging datasets show that VR-Net outperforms state-of-the-art deep learning methods on registration accuracy, whilst maintaining the fast inference speed of deep learning and the data-efficiency of variational models.

Original languageEnglish
Pages (from-to)199 - 212
Number of pages14
JournalIEEE Transactions on Medical Imaging
Issue number1
Publication statusPublished - Jan 2022
Externally publishedYes


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