Diffeomorphic Image Registration

Diffeomorphic image registration (DIR) aims to find smooth, invertible transformations aligning two images while preserving their topology, crucial for applications like medical image analysis and atmospheric turbulence mitigation. Current research emphasizes developing deep learning-based methods, often employing convolutional neural networks (CNNs), transformers, or neural ordinary differential equations (NODEs), to efficiently estimate deformation fields, sometimes incorporating techniques like Riemannian optimization or implicit neural representations for improved accuracy and speed. These advancements are significantly impacting various fields by enabling more accurate and efficient analysis of medical images, improved image reconstruction in challenging conditions, and facilitating advanced modeling of dynamic systems.