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Deformation field estimation is an important and challenging issue in manymedical image registration applications. In recent years, deep learningtechnique has become a promising approach for simplifying registrationproblems, and has been gradually applied to medical image registration.However, most existing deep learning registrations do not consider the problemthat when the receptive field cannot cover the corresponding features in themoving image and the fixed image, it cannot output accurate displacementvalues. In fact, due to the limitation of the receptive field, the 3 x 3 kernelhas difficulty in covering the corresponding features at high/originalresolution. Multi-resolution and multi-convolution techniques can improve butfail to avoid this problem. In this study, we constructed pyramidal featuresets on moving and fixed images and used the warped moving and fixed featuresto estimate their "residual" deformation field at each scale, called thePyramidal Residual Deformation Field Estimation module (PRDFE-Module). The"total" deformation field at each scale was computed by upsampling and weightedsumming all the "residual" deformation fields at all its previous scales, whichcan effectively and accurately transfer the deformation fields from lowresolution to high resolution and is used for warping the moving features ateach scale. Simulation and real brain data results show that our methodimproves the accuracy of the registration and the rationality of thedeformation field.

Unsupervised Deformable Medical Image Registration via Pyramidal Residual Deformation Fields Estimation