@inproceedings{9a4178c0b2b8491e8a03e7158c3a1338,
title = "Patch-RegNet: A Hierarchical Deformable Registration Framework for Inter-/intra-modality head-and-neck image registration with ViT-Morph",
abstract = "Deformable image registration (DIR) between Computed Tomography (CT)/Magnetic Resonance (MR) or MR/MR images is fundamentally important for MR-guided adaptive radiotherapy. In this work, we propose a novel hierarchical DIR framework, Patch-RegNet, to achieve accurate and rapid CT/MR and MR/MR registration for head-and-neck cancer. Patch-RegNet includes three steps: a whole volume rigid registration, a patch-based rigid registration, and a patch-based DIR. An innovative deep-learning-based network, ViT-Morph, is developed for the patch-based DIR in Patch-RegNet, taking advantage of both CNN-based local features and long-range image relationships from Transformer. Our Patch-RegNet is demonstrated to achieve notably improved registration accuracy for both inter- and intra-modality registration.",
keywords = "CT/MR deformable registration, Modality independent neighborhood descriptor, Patch-based registration, Vision transformer",
author = "Yao Zhao and Xinru Chen and Brigid McDonald and Cenji Yu and Court, {Laurence E.} and Tinsu Pan and He Wang and Xin Wang and Jack Phan and Jinzhong Yang",
note = "Publisher Copyright: {\textcopyright} 2023 SPIE.; Medical Imaging 2023: Image Processing ; Conference date: 19-02-2023 Through 23-02-2023",
year = "2023",
doi = "10.1117/12.2653352",
language = "English (US)",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Olivier Colliot and Ivana Isgum",
booktitle = "Medical Imaging 2023",
}