scholarly journals Use of polarization-sensitive optical coherence tomography to determine the directional polarization sensitivity of articular cartilage and meniscus

2006 ◽  
Vol 11 (6) ◽  
pp. 064001 ◽  
Author(s):  
Tuqiang Xie ◽  
Shuguang Guo ◽  
Jun Zhang ◽  
Zhongping Chen ◽  
George M. Peavy
Keyword(s):  
Optical Coherence Tomography ◽  
Articular Cartilage ◽  
Polarization Sensitivity ◽  
Optical Coherence

scholarly journals Directional polarization sensitivity of articular cartilage by optical coherence tomography

2006 ◽  
Author(s):  
Tuqiang Xie ◽  
Shouguang Guo ◽  
Jun Zhang ◽  
Zhongping Chen ◽  
George M. Peavy
Keyword(s):  
Optical Coherence Tomography ◽  
Articular Cartilage ◽  
Polarization Sensitivity ◽  
Optical Coherence

scholarly journals Synthetic polarization-sensitive optical coherence tomography by deep learning

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Yi Sun ◽  
Jianfeng Wang ◽  
Jindou Shi ◽  
Stephen A. Boppart
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Imaging System ◽  
Imaging Modality ◽  
Similarity Index ◽  
Polarization Sensitivity ◽  
Imaging Method ◽  
Optical Coherence ◽  
The Real ◽  
Oct Imaging

AbstractPolarization-sensitive optical coherence tomography (PS-OCT) is a high-resolution label-free optical biomedical imaging modality that is sensitive to the microstructural architecture in tissue that gives rise to form birefringence, such as collagen or muscle fibers. To enable polarization sensitivity in an OCT system, however, requires additional hardware and complexity. We developed a deep-learning method to synthesize PS-OCT images by training a generative adversarial network (GAN) on OCT intensity and PS-OCT images. The synthesis accuracy was first evaluated by the structural similarity index (SSIM) between the synthetic and real PS-OCT images. Furthermore, the effectiveness of the computational PS-OCT images was validated by separately training two image classifiers using the real and synthetic PS-OCT images for cancer/normal classification. The similar classification results of the two trained classifiers demonstrate that the predicted PS-OCT images can be potentially used interchangeably in cancer diagnosis applications. In addition, we applied the trained GAN models on OCT images collected from a separate OCT imaging system, and the synthetic PS-OCT images correlate well with the real PS-OCT image collected from the same sample sites using the PS-OCT imaging system. This computational PS-OCT imaging method has the potential to reduce the cost, complexity, and need for hardware-based PS-OCT imaging systems.

Quantitative articular cartilage sub-surface defect assessment using optical coherence tomography: An in-vitro study

2019 ◽  
Vol 221 ◽  
pp. 125-134 ◽  
Author(s):  
Roman Michalik ◽  
Thorn Pauer ◽  
Nicolai Brill ◽  
Matthias Knobe ◽  
Markus Tingart ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Articular Cartilage ◽  
Surface Defect ◽  
In Vitro Study ◽  
Vitro Study ◽  
Optical Coherence ◽  
Defect Assessment
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