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Xu J, Zhu M, Tang P, Li J, Gao K, Qiu H, Zhao S, Lan G, Jia H, Yu B. Visualization enhancement by PCA-based image fusion for skin burns assessment in polarization-sensitive OCT. BIOMEDICAL OPTICS EXPRESS 2024; 15:4190-4205. [PMID: 39022536 PMCID: PMC11249677 DOI: 10.1364/boe.521399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Revised: 05/31/2024] [Accepted: 06/01/2024] [Indexed: 07/20/2024]
Abstract
Polarization-sensitive optical coherence tomography (PS-OCT) is a functional imaging tool for measuring tissue birefringence characteristics. It has been proposed as a potentially non-invasive technique for evaluating skin burns. However, the PS-OCT modality usually suffers from high system complexity and relatively low tissue-specific contrast, which makes assessing the extent of burns in skin tissue difficult. In this study, we employ an all-fiber-based PS-OCT system with single-state input, which is simple and efficient for skin burn assessment. Multiple parameters, such as phase retardation (PR), degree of polarization uniformity (DOPU), and optical axis orientation, are obtained to extract birefringent features, which are sensitive to subtle changes in structural arrangement and tissue composition. Experiments on ex vivo porcine skins burned at different temperatures were conducted for skin burn investigation. The burned depths estimated by PR and DOPU increase linearly with the burn temperature to a certain extent, which is helpful in classifying skin burn degrees. We also propose an algorithm of image fusion based on principal component analysis (PCA) to enhance tissue contrast for the multi-parameter data of PS-OCT imaging. The results show that the enhanced images generated by the PCA-based image fusion method have higher tissue contrast, compared to the en-face polarization images by traditional mean value projection. The proposed approaches in this study make it possible to assess skin burn severity and distinguish between burned and normal tissues.
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Affiliation(s)
- Jingjiang Xu
- Guangdong-Hong Kong-Macao Intelligent Micro-Nano Optoelectronic Technology Joint Laboratory, Foshan University
, Foshan, Guangdong 528000, China
- Innovation and Entrepreneurship Teams Project of Guangdong Pearl River Talents Program, Guangdong Weiren Meditech Co., Ltd., Foshan, Guangdong 528051, China
| | - Mingtao Zhu
- School of Mechatronic Engineering and Automation, Foshan University, Foshan, Guangdong 528000, China
| | - Peijun Tang
- College of Biophotonics, South China Normal University, Guangzhou 510006, China
| | - Junyun Li
- Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou 510060, China
| | - Kai Gao
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China
| | - Haixia Qiu
- Department of Laser Medicine, the First Medical Centre, Chinese PLA General Hospital, Beijing 100853, China
| | - Shiyong Zhao
- Tianjin Hengyu Medical Technology Co., Ltd., Tianjin 300000, China
| | - Gongpu Lan
- Guangdong-Hong Kong-Macao Intelligent Micro-Nano Optoelectronic Technology Joint Laboratory, Foshan University
, Foshan, Guangdong 528000, China
- Innovation and Entrepreneurship Teams Project of Guangdong Pearl River Talents Program, Guangdong Weiren Meditech Co., Ltd., Foshan, Guangdong 528051, China
| | - Haibo Jia
- Department of Cardiovascular Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150001, China
| | - Bo Yu
- Department of Cardiovascular Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150001, China
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Han T, Xia W, Tao K, Wang W, Gao J, Ding X, Zhong H, Liu R, Dou S, Liu Z, Kuang H, Hua J, Xu K. Automatic stent struts detection in optical coherence tomography based on a multiple attention convolutional model. Phys Med Biol 2023; 69:015008. [PMID: 38035376 DOI: 10.1088/1361-6560/ad111c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Accepted: 11/30/2023] [Indexed: 12/02/2023]
Abstract
Objective.Intravascular optical coherence tomography is a useful tool to assess stent adherence and dilation, thus guiding percutaneous coronary intervention and minimizing the risk of surgery. However, each pull-back OCT images may contain thousands of stent struts, which are tiny and dense, making manual stent labeling slow and costly for medical resources.Approach. This paper proposed a multiple attention convolutional model for automatic stent struts detection of OCT images. Multiple attention mechanisms were utilized to strengthen the feature extraction and feature fusion capabilities. In addition, to precisely detect tiny stent struts, the model integrated multiple anchor frames to predict targets in the output.Main results. The model was trained in 4625 frames OCT images of 37 patients and tested in 1156 frames OCT images of 9 patients, and achieved a precision of 0.9790 and a recall of 0.9541, which were significantly better than mainstream convolutional models. In terms of detection speed, the model achieved 25.2 ms per image. OCT images from different collection systems, collection times, and challenging scenarios were experimentally tested, and the model demonstrated stable robustness, achieving precision and recall higher than 0.9630. Meanwhile, clear 3D construction of the stent was achieved.Significance. In conclusion, the proposed model solves the problems of slow manual analysis and occupying a large amount of medical manpower resources. It enhances the detection efficiency of tiny and dense stent struts, thus facilitating the application of OCT quantitative analysis in real clinical scenarios.
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Affiliation(s)
- Tingting Han
- Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, Tianjin Normal University, Tianjin 300387, People's Republic of China
| | - Wei Xia
- Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, Tianjin Normal University, Tianjin 300387, People's Republic of China
| | - Kuiyuan Tao
- State Key Laboratory of Mechanics and Control of Mechanical Structures, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu 210016, People's Republic of China
| | - Wei Wang
- Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, Tianjin Normal University, Tianjin 300387, People's Republic of China
| | - Jing Gao
- Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, Tianjin Normal University, Tianjin 300387, People's Republic of China
| | - Xiaoming Ding
- Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, Tianjin Normal University, Tianjin 300387, People's Republic of China
| | - Hongmei Zhong
- Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, Tianjin Normal University, Tianjin 300387, People's Republic of China
| | - Ruqian Liu
- Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, Tianjin Normal University, Tianjin 300387, People's Republic of China
| | - Shuwei Dou
- Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, Tianjin Normal University, Tianjin 300387, People's Republic of China
| | - Zixu Liu
- Nanjing Forssmann Medical Technology Co., Nanjing, Jiangsu, 210093, People's Republic of China
| | - Hao Kuang
- Nanjing Forssmann Medical Technology Co., Nanjing, Jiangsu, 210093, People's Republic of China
| | - Jiarui Hua
- Nanjing Forssmann Medical Technology Co., Nanjing, Jiangsu, 210093, People's Republic of China
| | - Keyong Xu
- Nanjing Forssmann Medical Technology Co., Nanjing, Jiangsu, 210093, People's Republic of China
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Huang S, Wang R, Wu R, Zhong J, Ge X, Liu Y, Ni G. SNR-Net OCT: brighten and denoise low-light optical coherence tomography images via deep learning. OPTICS EXPRESS 2023; 31:20696-20714. [PMID: 37381187 DOI: 10.1364/oe.491391] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 05/23/2023] [Indexed: 06/30/2023]
Abstract
Low-light optical coherence tomography (OCT) images generated when using low input power, low-quantum-efficiency detection units, low exposure time, or facing high-reflective surfaces, have low bright and signal-to-noise rates (SNR), and restrict OCT technique and clinical applications. While low input power, low quantum efficiency, and low exposure time can help reduce the hardware requirements and accelerate imaging speed; high-reflective surfaces are unavoidable sometimes. Here we propose a deep-learning-based technique to brighten and denoise low-light OCT images, termed SNR-Net OCT. The proposed SNR-Net OCT deeply integrated a conventional OCT setup and a residual-dense-block U-Net generative adversarial network with channel-wise attention connections trained using a customized large speckle-free SNR-enhanced brighter OCT dataset. Results demonstrated that the proposed SNR-Net OCT can brighten low-light OCT images and remove the speckle noise effectively, with enhancing SNR and maintaining the tissue microstructures well. Moreover, compared to the hardware-based techniques, the proposed SNR-Net OCT can be of lower cost and better performance.
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Liu H, Li X, Bamba AL, Song X, Brott BC, Litovsky SH, Gan Y. Toward reliable calcification detection: calibration of uncertainty in object detection from coronary optical coherence tomography images. JOURNAL OF BIOMEDICAL OPTICS 2023; 28:036008. [PMID: 36992694 PMCID: PMC10042069 DOI: 10.1117/1.jbo.28.3.036008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 03/06/2023] [Indexed: 06/19/2023]
Abstract
SIGNIFICANCE Optical coherence tomography (OCT) has become increasingly essential in assisting the treatment of coronary artery disease (CAD). However, unidentified calcified regions within a narrowed artery could impair the outcome of the treatment. Fast and objective identification is paramount to automatically procuring accurate readings on calcifications within the artery. AIM We aim to rapidly identify calcification in coronary OCT images using a bounding box and reduce the prediction bias in automated prediction models. APPROACH We first adopt a deep learning-based object detection model to rapidly draw the calcified region from coronary OCT images using a bounding box. We measure the uncertainty of predictions based on the expected calibration errors, thus assessing the certainty level of detection results. To calibrate confidence scores of predictions, we implement dependent logistic calibration using each detection result's confidence and center coordinates. RESULTS We implemented an object detection module to draw the boundary of the calcified region at a rate of 140 frames per second. With the calibrated confidence score of each prediction, we lower the uncertainty of predictions in calcification detection and eliminate the estimation bias from various object detection methods. The calibrated confidence of prediction results in a confidence error of ∼ 0.13 , suggesting that the confidence calibration on calcification detection could provide a more trustworthy result. CONCLUSIONS Given the rapid detection and effective calibration of the proposed work, we expect that it can assist in clinical evaluation of treating the CAD during the imaging-guided procedure.
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Affiliation(s)
- Hongshan Liu
- Stevens Institute of Technology, Biomedical Engineering Department, Hoboken, New Jersey, United States
| | - Xueshen Li
- Stevens Institute of Technology, Biomedical Engineering Department, Hoboken, New Jersey, United States
| | - Abdul Latif Bamba
- Columbia University, Department of Electrical Engineering, New York, United States
| | - Xiaoyu Song
- Icahn School of Medicine at Mount Sinai, New York, United States
| | - Brigitta C. Brott
- University of Alabama at Birmingham, School of Medicine, Birmingham, Alabama, United States
| | - Silvio H. Litovsky
- University of Alabama at Birmingham, School of Medicine, Birmingham, Alabama, United States
| | - Yu Gan
- Stevens Institute of Technology, Biomedical Engineering Department, Hoboken, New Jersey, United States
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Shi Y, Lu J, Le N, Wang RK. Integrating a pressure sensor with an OCT handheld probe to facilitate imaging of microvascular information in skin tissue beds. BIOMEDICAL OPTICS EXPRESS 2022; 13:6153-6166. [PMID: 36733756 PMCID: PMC9872897 DOI: 10.1364/boe.473013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 10/22/2022] [Accepted: 10/23/2022] [Indexed: 05/05/2023]
Abstract
Optical coherence tomography (OCT) and OCT angiography (OCTA) have been increasingly applied in skin imaging applications in dermatology, where the imaging is often performed with the OCT probe in contact with the skin surface. However, this contact mode imaging can introduce uncontrollable mechanical stress applied to the skin, inevitably complicating the interpretation of OCT/OCTA imaging results. There remains a need for a strategy for assessing local pressure applied on the skin during imaging acquisition. This study reports a handheld scanning probe integrated with built-in pressure sensors, allowing the operator to control the mechanical stress applied to the skin in real-time. With real time feedback information, the operator can easily determine whether the pressure applied to the skin would affect the imaging quality so as to obtain repeatable and reliable OCTA images for a more accurate investigation of skin conditions. Using this probe, imaging of palm skin was used in this study to demonstrate how the OCTA imaging would have been affected by different mechanical pressures ranging from 0 to 69 kPa. The results showed that OCTA imaging is relatively stable when the pressure is less than 11 kPa, and within this range, the change of vascular area density calculated from the OCTA imaging is below 0.13%. In addition, the probe was used to augment the OCT monitoring of blood flow changes during a reactive hyperemia experiment, in which the operator could properly control the amount of pressure applied to the skin surface and achieve full release after compression stimulation.
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Affiliation(s)
- Yaping Shi
- Department of Bioengineering, University of Washington, Seattle, WA 98105, USA
- These authors contributed equally to this study
| | - Jie Lu
- Department of Bioengineering, University of Washington, Seattle, WA 98105, USA
- These authors contributed equally to this study
| | - Nhan Le
- Department of Bioengineering, University of Washington, Seattle, WA 98105, USA
| | - Ruikang K. Wang
- Department of Bioengineering, University of Washington, Seattle, WA 98105, USA
- Department of Ophthalmology, University of Washington, Seattle, WA 98105, USA
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