This paper presents a novel deformable registration framework, leveraging an image prior specified through a denoising function, for severely noise-corrupted placental images. Recent work on plug-and-play (PnP) priors has shown the state-of-the-art performance of reconstruction algorithms under such priors in a range of imaging applications. Integration of powerful image denoisers into advanced registration methods provides our model with a flexibility to accommodate datasets that have low signal-to-noise ratios (SNRs). We demonstrate the performance of our method under a wide variety of denoising models in the context of diffeomorphic image registration. Experimental results show that our model substantially improves the accuracy of spatial alignment in applications of 3D in-utero diffusion-weighted MR images (DW-MRI) that suffer from low SNR and large spatial transformations.
@article{arxiv.1909.01170,
title = {Plug-and-Play Priors for Reconstruction-based Placental Image Registration},
author = {Jiarui Xing and Ulugbek Kamilov and Wenjie Wu and Yong Wang and Miaomiao Zhang},
journal= {arXiv preprint arXiv:1909.01170},
year = {2019}
}