Importance Weighted Variational Cardiac MRI Registration Using Transformer and Implicit Prior

Abstract

The variational registration model takes advantage of explaining uncertainties of registration results. However, most existing variational registration models are based on convolutional neural networks (CNNs), which cannot capture distant information in images. Besides, the evidence lower bound (ELBO) and the commonly used standard prior cannot close the gap between the real posterior and the variational posterior in the vanilla variational registration model. This paper proposes a network in a variational image registration model for cardiac motion estimation to effectively capture the spatial correspondence of long-distance images and solve the shortcomings of CNNs. Our proposed network comprises a Transformer with a T2T module and the cross attention between the moving and the fixed images. To close the gap between the real posterior and the variational posterior, the importance-weighted evidence lower bound (iwELBO) is introduced into the variational registration model with an implicit prior. The coefficients of a parametric transformation using multi-supports CSRBFs are latent variables in our variational registration model, which improve registration accuracy significantly. Experimental results show that the proposed method outperforms state-of-arts research on public cardiac datasets.