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Haft-Javaherian M, Villiger M, Otsuka K, Daemen J, Libby P, Golland P, Bouma BE. Segmentation of anatomical layers and imaging artifacts in intravascular polarization sensitive optical coherence tomography using attending physician and boundary cardinality losses. BIOMEDICAL OPTICS EXPRESS 2024; 15:1719-1738. [PMID: 38495711 PMCID: PMC10942710 DOI: 10.1364/boe.514673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 02/03/2024] [Accepted: 02/04/2024] [Indexed: 03/19/2024]
Abstract
Intravascular ultrasound and optical coherence tomography are widely available for assessing coronary stenoses and provide critical information to optimize percutaneous coronary intervention. Intravascular polarization-sensitive optical coherence tomography (PS-OCT) measures the polarization state of the light scattered by the vessel wall in addition to conventional cross-sectional images of subsurface microstructure. This affords reconstruction of tissue polarization properties and reveals improved contrast between the layers of the vessel wall along with insight into collagen and smooth muscle content. Here, we propose a convolutional neural network model, optimized using two new loss terms (Boundary Cardinality and Attending Physician), that takes advantage of the additional polarization contrast and classifies the lumen, intima, and media layers in addition to guidewire and plaque shadows. Our model segments the media boundaries through fibrotic plaques and continues to estimate the outer media boundary behind shadows of lipid-rich plaques. We demonstrate that our multi-class classification model outperforms existing methods that exclusively use conventional OCT data, predominantly segment the lumen, and consider subsurface layers at most in regions of minimal disease. Segmentation of all anatomical layers throughout diseased vessels may facilitate stent sizing and will enable automated characterization of plaque polarization properties for investigation of the natural history and significance of coronary atheromas.
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Affiliation(s)
- Mohammad Haft-Javaherian
- Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Wellman Center for Photomedicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Martin Villiger
- Wellman Center for Photomedicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Kenichiro Otsuka
- Wellman Center for Photomedicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Joost Daemen
- Department of Cardiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Peter Libby
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Polina Golland
- Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Brett E. Bouma
- Wellman Center for Photomedicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
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Wu W, Roby M, Banga A, Oguz UM, Gadamidi VK, Hasini Vasa C, Zhao S, Dasari VS, Thota AK, Tanweer S, Lee C, Kassab GS, Chatzizisis YS. Rapid automated lumen segmentation of coronary optical coherence tomography images followed by 3D reconstruction of coronary arteries. J Med Imaging (Bellingham) 2024; 11:014004. [PMID: 38173655 PMCID: PMC10760146 DOI: 10.1117/1.jmi.11.1.014004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 11/08/2023] [Accepted: 12/11/2023] [Indexed: 01/05/2024] Open
Abstract
Purpose Optical coherence tomography has emerged as an important intracoronary imaging technique for coronary artery disease diagnosis as it produces high-resolution cross-sectional images of luminal and plaque morphology. Precise and fast lumen segmentation is essential for efficient OCT morphometric analysis. However, due to the presence of various image artifacts, including side branches, luminal blood artifacts, and complicated lesions, this remains a challenging task. Approach Our research study proposes a rapid automatic segmentation method that utilizes nonuniform rational B-spline to connect limited pixel points and identify the edges of the OCT lumen. The proposed method suppresses image noise and accurately extracts the lumen border with a high correlation to ground truth images based on the area, minimal diameter, and maximal diameter. Results We evaluated the method using 3300 OCT frames from 10 patients and found that it achieved favorable results. The average time taken for automatic segmentation by the proposed method is 0.17 s per frame. Additionally, the proposed method includes seamless vessel reconstruction following the lumen segmentation. Conclusions The developed automated system provides an accurate, efficient, robust, and user-friendly platform for coronary lumen segmentation and reconstruction, which can pave the way for improved assessment of the coronary artery lumen morphology.
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Affiliation(s)
- Wei Wu
- University of Miami, Miller School of Medicine, Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, Miami, Florida, United States
| | - Merjulah Roby
- The University of Texas San Antonio, Department of Mechanical Engineering, Vascular Biomechanics and Biofluids, San Antonio, Texas, United States
| | - Akshat Banga
- University of Miami, Miller School of Medicine, Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, Miami, Florida, United States
| | - Usama M. Oguz
- University of Miami, Miller School of Medicine, Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, Miami, Florida, United States
| | - Vinay Kumar Gadamidi
- University of Miami, Miller School of Medicine, Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, Miami, Florida, United States
| | - Charu Hasini Vasa
- University of Miami, Miller School of Medicine, Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, Miami, Florida, United States
| | - Shijia Zhao
- University of Miami, Miller School of Medicine, Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, Miami, Florida, United States
| | - Vineeth S. Dasari
- University of Miami, Miller School of Medicine, Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, Miami, Florida, United States
| | - Anjani Kumar Thota
- University of Miami, Miller School of Medicine, Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, Miami, Florida, United States
| | - Sartaj Tanweer
- University of Miami, Miller School of Medicine, Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, Miami, Florida, United States
| | - Changkye Lee
- University of Miami, Miller School of Medicine, Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, Miami, Florida, United States
| | - Ghassan S. Kassab
- California Medical Innovation Institute, San Diego, California, United States
| | - Yiannis S. Chatzizisis
- University of Miami, Miller School of Medicine, Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, Miami, Florida, United States
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Lee YJ, Kim YW, Ha J, Kim M, Guagliumi G, Granada JF, Lee SG, Lee JJ, Cho YK, Yoon HJ, Lee JH, Kim U, Jang JY, Oh SJ, Lee SJ, Hong SJ, Ahn CM, Kim BK, Chang HJ, Ko YG, Choi D, Hong MK, Jang Y, Lee JS, Kim JS. Computational Fractional Flow Reserve From Coronary Computed Tomography Angiography—Optical Coherence Tomography Fusion Images in Assessing Functionally Significant Coronary Stenosis. Front Cardiovasc Med 2022; 9:925414. [PMID: 35770218 PMCID: PMC9234158 DOI: 10.3389/fcvm.2022.925414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 05/25/2022] [Indexed: 11/13/2022] Open
Abstract
Background Coronary computed tomography angiography (CTA) and optical coherence tomography (OCT) provide additional functional information beyond the anatomy by applying computational fluid dynamics (CFD). This study sought to evaluate a novel approach for estimating computational fractional flow reserve (FFR) from coronary CTA-OCT fusion images. Methods Among patients who underwent coronary CTA, 148 patients who underwent both pressure wire-based FFR measurement and OCT during angiography to evaluate intermediate stenosis in the left anterior descending artery were included from the prospective registry. Coronary CTA-OCT fusion images were created, and CFD was applied to estimate computational FFR. Based on pressure wire-based FFR as a reference, the diagnostic performance of Fusion-FFR was compared with that of CT-FFR and OCT-FFR. Results Fusion-FFR was strongly correlated with FFR (r = 0.836, P < 0.001). Correlation between FFR and Fusion-FFR was stronger than that between FFR and CT-FFR (r = 0.682, P < 0.001; z statistic, 5.42, P < 0.001) and between FFR and OCT-FFR (r = 0.705, P < 0.001; z statistic, 4.38, P < 0.001). Area under the receiver operating characteristics curve to assess functionally significant stenosis was higher for Fusion-FFR than for CT-FFR (0.90 vs. 0.83, P = 0.024) and OCT-FFR (0.90 vs. 0.83, P = 0.043). Fusion-FFR exhibited 84.5% accuracy, 84.6% sensitivity, 84.3% specificity, 80.9% positive predictive value, and 87.5% negative predictive value. Especially accuracy, specificity, and positive predictive value were superior for Fusion-FFR than for CT-FFR (73.0%, P = 0.007; 61.4%, P < 0.001; 64.0%, P < 0.001) and OCT-FFR (75.7%, P = 0.021; 73.5%, P = 0.020; 69.9%, P = 0.012). Conclusion CFD-based computational FFR from coronary CTA-OCT fusion images provided more accurate functional information than coronary CTA or OCT alone. Clinical Trial Registration [www.ClinicalTrials.gov], identifier [NCT03298282].
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Affiliation(s)
- Yong-Joon Lee
- Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Young Woo Kim
- Department of Mechanical Engineering, Yonsei University, Seoul, South Korea
| | - Jinyong Ha
- Department of Electrical Engineering, Sejong University, Seoul, South Korea
| | - Minug Kim
- Department of Electrical Engineering, Sejong University, Seoul, South Korea
| | - Giulio Guagliumi
- Department of Cardiovascular, Ospedale Papa Giovanni XXIII, Bergamo, Italy
| | - Juan F. Granada
- Cardiovascular Research Foundation, Columbia University Medical Center, New York, NY, United States
| | - Seul-Gee Lee
- Yonsei Cardiovascular Research Institute, Yonsei University College of Medicine, Seoul, South Korea
| | - Jung-Jae Lee
- Yonsei Cardiovascular Research Institute, Yonsei University College of Medicine, Seoul, South Korea
| | - Yun-Kyeong Cho
- Department of Cardiology, Keimyung University Dongsan Hospital, Daegu, South Korea
| | - Hyuck Jun Yoon
- Department of Cardiology, Keimyung University Dongsan Hospital, Daegu, South Korea
| | - Jung Hee Lee
- Division of Cardiology, Yeungnam University Medical Center, Yeungnam University College of Medicine, Daegu, South Korea
| | - Ung Kim
- Division of Cardiology, Yeungnam University Medical Center, Yeungnam University College of Medicine, Daegu, South Korea
| | - Ji-Yong Jang
- National Health Insurance Service Ilsan Hospital, Goyang, South Korea
| | - Seung-Jin Oh
- National Health Insurance Service Ilsan Hospital, Goyang, South Korea
| | - Seung-Jun Lee
- Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Sung-Jin Hong
- Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Chul-Min Ahn
- Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Byeong-Keuk Kim
- Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Hyuk-Jae Chang
- Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Young-Guk Ko
- Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Donghoon Choi
- Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Myeong-Ki Hong
- Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Yangsoo Jang
- Division of Cardiology, CHA Bundang Medical Center, CHA University College of Medicine, Seongnam, South Korea
| | - Joon Sang Lee
- Department of Mechanical Engineering, Yonsei University, Seoul, South Korea
- *Correspondence: Joon Sang Lee,
| | - Jung-Sun Kim
- Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul, South Korea
- Jung-Sun Kim,
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Carpenter HJ, Ghayesh MH, Zander AC, Li J, Di Giovanni G, Psaltis PJ. Automated Coronary Optical Coherence Tomography Feature Extraction with Application to Three-Dimensional Reconstruction. Tomography 2022; 8:1307-1349. [PMID: 35645394 PMCID: PMC9149962 DOI: 10.3390/tomography8030108] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 05/03/2022] [Accepted: 05/10/2022] [Indexed: 11/16/2022] Open
Abstract
Coronary optical coherence tomography (OCT) is an intravascular, near-infrared light-based imaging modality capable of reaching axial resolutions of 10–20 µm. This resolution allows for accurate determination of high-risk plaque features, such as thin cap fibroatheroma; however, visualization of morphological features alone still provides unreliable positive predictive capability for plaque progression or future major adverse cardiovascular events (MACE). Biomechanical simulation could assist in this prediction, but this requires extracting morphological features from intravascular imaging to construct accurate three-dimensional (3D) simulations of patients’ arteries. Extracting these features is a laborious process, often carried out manually by trained experts. To address this challenge, numerous techniques have emerged to automate these processes while simultaneously overcoming difficulties associated with OCT imaging, such as its limited penetration depth. This systematic review summarizes advances in automated segmentation techniques from the past five years (2016–2021) with a focus on their application to the 3D reconstruction of vessels and their subsequent simulation. We discuss four categories based on the feature being processed, namely: coronary lumen; artery layers; plaque characteristics and subtypes; and stents. Areas for future innovation are also discussed as well as their potential for future translation.
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Affiliation(s)
- Harry J. Carpenter
- School of Mechanical Engineering, University of Adelaide, Adelaide, SA 5005, Australia;
- Correspondence: (H.J.C.); (M.H.G.)
| | - Mergen H. Ghayesh
- School of Mechanical Engineering, University of Adelaide, Adelaide, SA 5005, Australia;
- Correspondence: (H.J.C.); (M.H.G.)
| | - Anthony C. Zander
- School of Mechanical Engineering, University of Adelaide, Adelaide, SA 5005, Australia;
| | - Jiawen Li
- School of Electrical Electronic Engineering, University of Adelaide, Adelaide, SA 5005, Australia;
- Australian Research Council Centre of Excellence for Nanoscale BioPhotonics, The University of Adelaide, Adelaide, SA 5005, Australia
- Institute for Photonics and Advanced Sensing, University of Adelaide, Adelaide, SA 5005, Australia
| | - Giuseppe Di Giovanni
- Vascular Research Centre, Lifelong Health Theme, South Australian Health and Medical Research Institute (SAHMRI), Adelaide, SA 5000, Australia; (G.D.G.); (P.J.P.)
| | - Peter J. Psaltis
- Vascular Research Centre, Lifelong Health Theme, South Australian Health and Medical Research Institute (SAHMRI), Adelaide, SA 5000, Australia; (G.D.G.); (P.J.P.)
- Adelaide Medical School, University of Adelaide, Adelaide, SA 5005, Australia
- Department of Cardiology, Central Adelaide Local Health Network, Adelaide, SA 5000, Australia
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5
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Zhu F, Ding Z, Tao K, Li Q, Kuang H, Tian F, Zhou S, Hua P, Hu J, Shang M, Yu Y, Liu T. Automatic lumen segmentation using uniqueness of vascular connected region for intravascular optical coherence tomography. JOURNAL OF BIOPHOTONICS 2021; 14:e202100124. [PMID: 34185435 DOI: 10.1002/jbio.202100124] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 05/25/2021] [Accepted: 06/14/2021] [Indexed: 06/13/2023]
Abstract
We present an automatic lumen segmentation method using uniqueness of connected region for intravascular optical coherence tomography (IVOCT), which can effectively remove the effect on lumen segmentation caused by blood artifacts. Utilizing the uniqueness of vascular wall on A-lines, we detect the A-lines shared by multiple connected regions, identify connected regions generated by blood artifacts using traversal comparison of connected regions' location, shared ratio and area ratio and then remove all artifacts. We compare these three methods by 216 challenging images with severe blood artifacts selected from clinical 1076 IVOCT images. The metrics of the proposed method are evaluated including Dice index, Jaccard index and accuracy of 94.57%, 90.12%, 98.02%. Compared with automatic lumen segmentation based on the previous morphological feature method and widely used dynamic programming method, the metrics of the proposed method are significantly enhanced, especially in challenging images with severe blood artifacts.
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Affiliation(s)
- Fengyu Zhu
- School of Precision Instruments and Opto-Electronics Engineering, Tianjin University, Tianjin, China
- Tianjin Optical Fiber Sensing Engineering Center, Institute of Optical Fiber Sensing of Tianjin University, Tianjin, China
- Key Laboratory of Opto-Electronics Information Technology, Tianjin University, Ministry of Education, Tianjin, China
| | - Zhenyang Ding
- School of Precision Instruments and Opto-Electronics Engineering, Tianjin University, Tianjin, China
- Tianjin Optical Fiber Sensing Engineering Center, Institute of Optical Fiber Sensing of Tianjin University, Tianjin, China
- Key Laboratory of Opto-Electronics Information Technology, Tianjin University, Ministry of Education, Tianjin, China
| | - Kuiyuan Tao
- School of Precision Instruments and Opto-Electronics Engineering, Tianjin University, Tianjin, China
- Tianjin Optical Fiber Sensing Engineering Center, Institute of Optical Fiber Sensing of Tianjin University, Tianjin, China
- Key Laboratory of Opto-Electronics Information Technology, Tianjin University, Ministry of Education, Tianjin, China
| | - Qingrui Li
- School of Precision Instruments and Opto-Electronics Engineering, Tianjin University, Tianjin, China
- Tianjin Optical Fiber Sensing Engineering Center, Institute of Optical Fiber Sensing of Tianjin University, Tianjin, China
- Key Laboratory of Opto-Electronics Information Technology, Tianjin University, Ministry of Education, Tianjin, China
| | - Hao Kuang
- Nanjing Forssmann Medical Technology Co., Nanjing, China
| | - Feng Tian
- Department of Cardiology, Chinese PLA General Hospital, Beijing, China
| | - Shanshan Zhou
- Department of Cardiology, Chinese PLA General Hospital, Beijing, China
| | - Peidong Hua
- School of Precision Instruments and Opto-Electronics Engineering, Tianjin University, Tianjin, China
- Tianjin Optical Fiber Sensing Engineering Center, Institute of Optical Fiber Sensing of Tianjin University, Tianjin, China
- Key Laboratory of Opto-Electronics Information Technology, Tianjin University, Ministry of Education, Tianjin, China
| | - Jingqi Hu
- School of Precision Instruments and Opto-Electronics Engineering, Tianjin University, Tianjin, China
- Tianjin Optical Fiber Sensing Engineering Center, Institute of Optical Fiber Sensing of Tianjin University, Tianjin, China
- Key Laboratory of Opto-Electronics Information Technology, Tianjin University, Ministry of Education, Tianjin, China
| | - Mingjian Shang
- School of Precision Instruments and Opto-Electronics Engineering, Tianjin University, Tianjin, China
- Tianjin Optical Fiber Sensing Engineering Center, Institute of Optical Fiber Sensing of Tianjin University, Tianjin, China
- Key Laboratory of Opto-Electronics Information Technology, Tianjin University, Ministry of Education, Tianjin, China
| | - Yin Yu
- School of Precision Instruments and Opto-Electronics Engineering, Tianjin University, Tianjin, China
- Tianjin Optical Fiber Sensing Engineering Center, Institute of Optical Fiber Sensing of Tianjin University, Tianjin, China
- Key Laboratory of Opto-Electronics Information Technology, Tianjin University, Ministry of Education, Tianjin, China
| | - Tiegen Liu
- School of Precision Instruments and Opto-Electronics Engineering, Tianjin University, Tianjin, China
- Tianjin Optical Fiber Sensing Engineering Center, Institute of Optical Fiber Sensing of Tianjin University, Tianjin, China
- Key Laboratory of Opto-Electronics Information Technology, Tianjin University, Ministry of Education, Tianjin, China
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Zhu Y, Zhu F, Ding Z, Tao K, Lai T, Kuang H, Hua P, Shang M, Hu J, Yu Y, Liu T. Three-dimensional spatial reconstruction of coronary arteries based on fusion of intravascular optical coherence tomography and coronary angiography. JOURNAL OF BIOPHOTONICS 2021; 14:e202000370. [PMID: 33247508 DOI: 10.1002/jbio.202000370] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 11/04/2020] [Accepted: 11/20/2020] [Indexed: 06/12/2023]
Abstract
We present a three-dimensional (3D) spatial reconstruction of coronary arteries based on fusion of intravascular optical coherence tomography (IVOCT) and digital subtraction angiography (DSA). Centerline of vessel in DSA images is exacted by multi-scale filtering, adaptive segmentation, morphology thinning and Dijkstra's shortest path algorithm. We apply the cross-correction between lumen shapes of IVOCT and DSA images and match their stenosis positions to realize co-registration. By matching the location and tangent direction of the vessel centerline of DSA images and segmented lumen coordinates of IVOCT along pullback path, 3D spatial models of vessel lumen are reconstructed. Using 1121 distinct positions selected from eight vessels, the correlation coefficient between 3D IVOCT model and DSA image in measuring lumen radius is 0.94% and 97.7% of the positions fall within the limit of agreement by Bland-Altman analysis, which means that the 3D spatial reconstruction IVOCT models and DSA images have high matching level.
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Affiliation(s)
- Yanan Zhu
- School of Precision Instruments and Opto-Electronics Engineering, Tianjin University, Tianjin, China
- Tianjin Optical Fiber Sensing Engineering Center, Institute of Optical Fiber Sensing of Tianjin University, Tianjin, China
- Key Laboratory of Opto-electronics Information Technology (Tianjin University), Ministry of Education, Tianjin, China
| | - Fengyu Zhu
- School of Precision Instruments and Opto-Electronics Engineering, Tianjin University, Tianjin, China
- Tianjin Optical Fiber Sensing Engineering Center, Institute of Optical Fiber Sensing of Tianjin University, Tianjin, China
- Key Laboratory of Opto-electronics Information Technology (Tianjin University), Ministry of Education, Tianjin, China
| | - Zhenyang Ding
- School of Precision Instruments and Opto-Electronics Engineering, Tianjin University, Tianjin, China
- Tianjin Optical Fiber Sensing Engineering Center, Institute of Optical Fiber Sensing of Tianjin University, Tianjin, China
- Key Laboratory of Opto-electronics Information Technology (Tianjin University), Ministry of Education, Tianjin, China
| | - Kuiyuan Tao
- School of Precision Instruments and Opto-Electronics Engineering, Tianjin University, Tianjin, China
- Tianjin Optical Fiber Sensing Engineering Center, Institute of Optical Fiber Sensing of Tianjin University, Tianjin, China
- Key Laboratory of Opto-electronics Information Technology (Tianjin University), Ministry of Education, Tianjin, China
| | - Tianduo Lai
- School of Precision Instruments and Opto-Electronics Engineering, Tianjin University, Tianjin, China
- Tianjin Optical Fiber Sensing Engineering Center, Institute of Optical Fiber Sensing of Tianjin University, Tianjin, China
- Key Laboratory of Opto-electronics Information Technology (Tianjin University), Ministry of Education, Tianjin, China
| | - Hao Kuang
- Nanjing Forssmann Medical Technology Co., Nanjing, Jiangsu, China
| | - Peidong Hua
- School of Precision Instruments and Opto-Electronics Engineering, Tianjin University, Tianjin, China
- Tianjin Optical Fiber Sensing Engineering Center, Institute of Optical Fiber Sensing of Tianjin University, Tianjin, China
- Key Laboratory of Opto-electronics Information Technology (Tianjin University), Ministry of Education, Tianjin, China
| | - Mingjian Shang
- School of Precision Instruments and Opto-Electronics Engineering, Tianjin University, Tianjin, China
- Tianjin Optical Fiber Sensing Engineering Center, Institute of Optical Fiber Sensing of Tianjin University, Tianjin, China
- Key Laboratory of Opto-electronics Information Technology (Tianjin University), Ministry of Education, Tianjin, China
| | - Jingqi Hu
- School of Precision Instruments and Opto-Electronics Engineering, Tianjin University, Tianjin, China
- Tianjin Optical Fiber Sensing Engineering Center, Institute of Optical Fiber Sensing of Tianjin University, Tianjin, China
- Key Laboratory of Opto-electronics Information Technology (Tianjin University), Ministry of Education, Tianjin, China
| | - Yin Yu
- School of Precision Instruments and Opto-Electronics Engineering, Tianjin University, Tianjin, China
- Tianjin Optical Fiber Sensing Engineering Center, Institute of Optical Fiber Sensing of Tianjin University, Tianjin, China
- Key Laboratory of Opto-electronics Information Technology (Tianjin University), Ministry of Education, Tianjin, China
| | - Tiegen Liu
- School of Precision Instruments and Opto-Electronics Engineering, Tianjin University, Tianjin, China
- Tianjin Optical Fiber Sensing Engineering Center, Institute of Optical Fiber Sensing of Tianjin University, Tianjin, China
- Key Laboratory of Opto-electronics Information Technology (Tianjin University), Ministry of Education, Tianjin, China
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