Deep learning technologies have dramatically reshaped the field of medical image
registration over the past decade. The initial developments, such as regression-based and U …

Due to their extreme long-range modeling capability, vision transformer-based networks
have become increasingly popular in deformable image registration. We believe, however …

Unsupervised image registration commonly adopts U-Net style networks to predict dense
displacement fields in the full-resolution spatial domain. For high-resolution volumetric …

U-Net style networks are commonly utilized in unsupervised image registration to predict
dense displacement fields, which for high-resolution volumetric image data is a resource …

In this work, we investigate image registration in a variational framework and focus on
regularization generality and solver efficiency. We first propose a variational model …

Medical images are often characterized by their structured anatomical representations and
spatially inhomogeneous contrasts. Leveraging anatomical priors in neural networks can …

A dual autoencoder employing separable convolutional layers for image denoising and
deblurring is represented. Combining two autoencoders is presented to gain higher …

Deep image registration has demonstrated exceptional accuracy and fast inference. Recent
advances have adopted either multiple cascades or pyramid architectures to estimate dense …

Image registration is fundamental in medical imaging applications, such as disease
progression analysis or radiation therapy planning. The primary objective of image …

Objective: To propose a 3D nonrigid registration method that accurately estimates the 3D
displacement field from artifact-corrupted Coronary Magnetic Resonance Angiography …