| Autor: |
Jinhua Yu, Zhao Yao, Ting Luo, YiJie Dong, XiaoHong Jia, YinHui Deng, Ying Zhu, JingWen Zhang, Juan Liu, LiChun Yang, XiaoMao Luo, ZhiYao Li, YanJun Xu, Bin Hu, YunXia Huang, Cai Chang, JinFeng Xu, Hui Luo, Fajin Dong, XiaoNa Xia, ChengRong Wu, WenJia Hu, Gang Wu, QiaoYing Li, Qin Chen, WanYue Deng, QiongChao Jiang, YongLin Mou, HuanNan Yan, XiaoJing Xu, HongJu Yan, Ping Zhou, Yang Shao, LiGang Cui, Ping He, LinXue Qian, JinPing Liu, LiYing Shi, YaNan Zhao, YongYuan Xu, WeiWei Zhan, YuanYuan Wang, Jianqiao Zhou, GuoQing Wu |
| Rok vydání: |
2022 |
| DOI: |
10.21203/rs.3.rs-1702242/v1 |
| Popis: |
Elastography ultrasound (EUS) imaging is a vital ultrasound imaging modality. The current use of EUS faces many challenges, such as vulnerability to subjective manipulation, echo signal attenuation, and unknown risks of elastic pressure in certain delicate tissues. The hardware requirement of EUS also hinders the trend of miniaturization of ultrasound equipment. We therefore present a cost-efficient solution by designing a deep neural network to synthesize augmented reality EUS (AR-EUS) from conventional B-mode images. By using 4580 cases from 15 medical centers, we evaluate the performance of AR-EUS on breast cancer diagnosis. The quantitative metric and blind evaluation results show no significant difference between AR-EUS and real EUS in image authenticity and in clinical diagnosis. The performance of pocket-sized ultrasound in breast tumor diagnosis is also significantly improved after AR-EUS is equipped. These results highlight the potential of AR-EUS in clinical application. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
|
