F
Fei Liu
Researcher at University of Science and Technology Beijing
Publications - 121
Citations - 1645
Fei Liu is an academic researcher from University of Science and Technology Beijing. The author has contributed to research in topics: Iterative reconstruction & Optical fiber. The author has an hindex of 19, co-authored 112 publications receiving 1256 citations. Previous affiliations of Fei Liu include Chinese Academy of Sciences & Southwest University of Science and Technology.
Papers
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Journal ArticleDOI
CC-KF: Enhanced TOA Performance in Multipath and NLOS Indoor Extreme Environment
TL;DR: A new distance mitigation algorithm based on channel classification and Kalman filter to enhanced TOA performance in multipath and NLOS indoor extreme environment is presented, which could significantly reduce the ranging error caused by the extreme channel condition in indoor area.
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Multi-event waveform-retrieved distributed optical fiber acoustic sensor using dual-pulse heterodyne phase-sensitive OTDR.
TL;DR: A novel type of distributed optical fiber acoustic sensor, with the ability to detect and retrieve actual temporal waveforms of multiple vibration events that occur simultaneously at different positions along the fiber, is demonstrated.
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Salt-induced aggregation of gold nanoparticles for photoacoustic imaging and photothermal therapy of cancer
Mengmeng Sun,Fei Liu,Yukun Zhu,Wansheng Wang,Jin Hu,Jing Liu,Zhifei Dai,Kun Wang,Yen Wei,Jing Bai,Weiping Gao +10 more
TL;DR: The GNP depots in situ formed by salt-induced aggregation of gold nanoparticles in biological media show strong NIR absorption induced by plasmonic coupling between adjacent GNPs and very high photothermal conversion efficiency, enabling photothermal destruction of tumor cells.
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A Combined Fluorescence and Microcomputed Tomography System for Small Animal Imaging
TL;DR: A hybrid full-angle free-space FMT and X-ray micro-cone-beam computed tomography (CT) (micro-CBCT) prototype system, providing both functional and anatomical images is proposed, preliminarily validate the accuracy and performance of the integrated system.
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Accurate prediction of responses to transarterial chemoembolization for patients with hepatocellular carcinoma by using artificial intelligence in contrast-enhanced ultrasound.
Dan Liu,Dan Liu,Fei Liu,Xiaoyan Xie,Liya Su,Ming Liu,Xiaohua Xie,Ming Kuang,Guangliang Huang,Yuqi Wang,Hui Zhou,Kun Wang,Manxia Lin,Jie Tian,Jie Tian +14 more
TL;DR: Deep learning (DL) radiomics-based CEUS model can accurately predict responses of HCC patients to their first TACE session by quantitatively analyzing their pre-operative CEUS cines.