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Xingde Li

Researcher at Johns Hopkins University

Publications -  291
Citations -  18785

Xingde Li is an academic researcher from Johns Hopkins University. The author has contributed to research in topics: Optical coherence tomography & Endomicroscopy. The author has an hindex of 60, co-authored 280 publications receiving 17610 citations. Previous affiliations of Xingde Li include Kennedy Krieger Institute & Institute for Systems Biology.

Papers
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Proceedings ArticleDOI

Ultrahigh resolution OCT using continuum generation in an air-silica microstructure optical fiber

TL;DR: In this paper, the authors demonstrate ultra-high-resolution optical coherence tomography using continuum generation in an air-silica microstructure fiber using bandwidths near 1.3 µm, longitudinal resolutions of 4.0 µm.
Proceedings ArticleDOI

Fiber-optic endomicroscopy for intrinsic two-photon fluorescence imaging of epithelial cells and tissues

TL;DR: An endomicroscope with enhanced signal collection efficiency was developed using customized double-clad fiber and aspherical compound-lens and ex vivo two-photon fluorescence imaging of epithelial tissues was demonstrated for the first time with an all-fiber-optic scanning endomicrobialscope.
Proceedings ArticleDOI

In vivo imaging of osteoarthritic changes with optical coherence tomography

TL;DR: Optical coherence tomography (OCT) is a powerful medical imaging technology because it permits the high resolution imaging of cross sectional microstructure in situ and in real time as discussed by the authors.
Proceedings Article

Rapid-scanning miniature endoscope for real-time forward-imaging optical coherence tomography coherence tomography

TL;DR: In this article, a 2.4mm diameter PZT-actuated endoscope capable of rapid lateral scanning at 1.2 kHz was developed, and a new lateral-priority image acquisition sequence was demonstrated to achieve forward OCT imaging in real time.
Journal ArticleDOI

Deep learning-based optical coherence tomography image analysis of human brain cancer.

TL;DR: In this article , a deep convolutional neural network (CNN) was trained on labeled OCT images and co-occurrence matrix features extracted from these images to synergize attenuation characteristics and texture features.