@inproceedings{ca3fd8fbc3c842f6a30d237a01346249,
title = "Dual-Phase Deep Learning Motion Correction for Ultrasound Localization Microscopy",
abstract = "Ultrasound Localization Microscopy (ULM) holds great promise for a wide range of clinical applications. However, tissue motion can reduce localization accuracy, highlighting the need for fast and robust motion correction techniques. We propose a dual-phase deep learning motion estimation method and a simulation pipeline designed for carotid artery motion correction. Trained on simulated images and fine-tuned with in-vivo images, initial results suggest that our model outperforms conventional and baseline deep learning methods on simulated data and demonstrates fast correction on in-vivo data, indicating its potential for motion correction in ULM applications.",
keywords = "ULM, deep learning, motion correction, simulation",
author = "Haoxuan Yao and Gonzalez, \{Clara Rodrigo\} and Su Yan and Biao Huang and Jipeng Yan and Joseph Hansen-Shearer and Rifkat Zaydullin and Qingyuan Tan and Smith, \{Cameron A.B.\} and Mengjie Shi and Thomas Else and Tang, \{Meng Xing\}",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 2025 IEEE International Ultrasonics Symposium, IUS 2025 ; Conference date: 15-09-2025 Through 18-09-2025",
year = "2025",
doi = "10.1109/IUS62464.2025.11201559",
language = "英语",
series = "IEEE International Ultrasonics Symposium, IUS",
publisher = "IEEE Computer Society",
booktitle = "2025 IEEE International Ultrasonics Symposium, IUS 2025",
address = "美国",
}