  Deep learning (DL) image registration methods amortize the costly pair-wise
iterative optimization by training deep neural networks to predict the optimal
transformation in one fast forward-pass. In this work, we bridge the gap
between traditional iterative energy optimization-based registration and
network-based registration, and propose Gradient Descent Network for Image
Registration (GraDIRN). Our proposed approach trains a DL network that embeds
unrolled multiresolution gradient-based energy optimization in its forward
pass, which explicitly enforces image dissimilarity minimization in its update
steps. Extensive evaluations were performed on registration tasks using 2D
cardiac MR and 3D brain MR images. We demonstrate that our approach achieved
state-of-the-art registration performance while using fewer learned parameters,
with good data efficiency and domain robustness.
