  Deformation field estimation is an important and challenging issue in many
medical image registration applications. In recent years, deep learning
technique has become a promising approach for simplifying registration
problems, and has been gradually applied to medical image registration.
However, most existing deep learning registrations do not consider the problem
that when the receptive field cannot cover the corresponding features in the
moving image and the fixed image, it cannot output accurate displacement
values. In fact, due to the limitation of the receptive field, the 3 x 3 kernel
has difficulty in covering the corresponding features at high/original
resolution. Multi-resolution and multi-convolution techniques can improve but
fail to avoid this problem. In this study, we constructed pyramidal feature
sets on moving and fixed images and used the warped moving and fixed features
to estimate their "residual" deformation field at each scale, called the
Pyramidal Residual Deformation Field Estimation module (PRDFE-Module). The
"total" deformation field at each scale was computed by upsampling and weighted
summing all the "residual" deformation fields at all its previous scales, which
can effectively and accurately transfer the deformation fields from low
resolution to high resolution and is used for warping the moving features at
each scale. Simulation and real brain data results show that our method
improves the accuracy of the registration and the rationality of the
deformation field.
