Image registration techniques usually assume that the images to be registered are of a certain type (e.g. single- vs. multi-modal, 2D vs. 3D, rigid vs. deformable) and there lacks a general method that can work for data under all conditions. We propose a registration method that utilizes neural networks for image representation. Our method uses untrained networks with limited representation capacity as an implicit prior to guide for a good registration. Unlike previous approaches that are specialized for specific data types, our method handles both rigid and non-rigid, as well as single- and multi-modal registration, without requiring changes to the model or objective function. We have performed a comprehensive evaluation study using a variety of datasets and demonstrated promising performance.
@article{arxiv.2411.02672,
title = {Multi-modal deformable image registration using untrained neural networks},
author = {Quang Luong Nhat Nguyen and Ruiming Cao and Laura Waller},
journal= {arXiv preprint arXiv:2411.02672},
year = {2025}
}