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Khelimskii D, Badoyan A, Krymcov O, Baranov A, Manukian S, Lazarev M. AI in interventional cardiology: Innovations and challenges. Heliyon 2024; 10:e36691. [PMID: 39281582 PMCID: PMC11402142 DOI: 10.1016/j.heliyon.2024.e36691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Revised: 08/08/2024] [Accepted: 08/20/2024] [Indexed: 09/18/2024] Open
Abstract
Artificial Intelligence (AI) permeates all areas of our lives. Even now, we all use AI algorithms in our daily activities, and medicine is no exception. The potential of AI technology is hard to overestimate; AI has already proven its effectiveness in many fields of science and technology. A vast number of methods have been proposed and are being implemented in various areas of medicine, including interventional cardiology. A hallmark of this discipline is the extensive use of visualization techniques not only for diagnosis but also for the treatment of patients with coronary heart disease. The implementation of instrumental AI will reduce costs, in a broad sense. In this article, we provide an overview of AI research in interventional cardiology, practical applications, as well as the problems hindering the widespread use of neural network technologies in interventional cardiology.
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Affiliation(s)
- Dmitrii Khelimskii
- Meshalkin National Medical Research Center, Ministry of Health of Russian Federation, Novosibirsk, Russian Federation
| | - Aram Badoyan
- Meshalkin National Medical Research Center, Ministry of Health of Russian Federation, Novosibirsk, Russian Federation
| | - Oleg Krymcov
- Meshalkin National Medical Research Center, Ministry of Health of Russian Federation, Novosibirsk, Russian Federation
| | - Aleksey Baranov
- Meshalkin National Medical Research Center, Ministry of Health of Russian Federation, Novosibirsk, Russian Federation
| | - Serezha Manukian
- Meshalkin National Medical Research Center, Ministry of Health of Russian Federation, Novosibirsk, Russian Federation
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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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Noise Reduction of OCT Images Based on the External Patch Prior Guided Internal Clustering and Morphological Analysis. PHOTONICS 2022. [DOI: 10.3390/photonics9080543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Optical coherence tomography (OCT) is widely used in biomedical imaging. However, noise severely affects diagnosing and identifying diseased tissues on OCT images. Here, a noise reduction method based on the external patch prior guided internal clustering and morphological analysis (E2PGICMA) is developed to remove the noise of OCT images. The external patch prior guided internal clustering algorithm is used to reduce speckle noise. The morphological analysis algorithm is employed to the background for contrast enhancement. OCT images of in vivo normal skin tissues were analyzed to remove noise using the proposed method. The estimated standard deviations of the noise were chosen as different values for evaluating the quantitative metrics. The visual quality improvement includes more textures and fine detail preservation. The denoising effects of different methods were compared. Then, quantitative and qualitative evaluations of this proposed method were conducted. The results demonstrated that the SNR, PSNR, and XCOR were higher than those of the other noise-reduction methods, reaching 15.05 dB, 27.48 dB, and 0.9959, respectively. Furthermore, the presented method’s noise reduction ratio (NRR) reached 0.8999. This proposed method can efficiently remove the background and speckle noise. Improving the proposed noise reduction method would outperform existing state-of-the-art OCT despeckling methods.
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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] [Key Words] [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;
| | - Mergen H. Ghayesh
- School of Mechanical Engineering, University of Adelaide, Adelaide, SA 5005, Australia;
| | - 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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Zhang R, Fan Y, Qi W, Wang A, Tang X, Gao T. Current research and future prospects of IVOCT imaging-based detection of the vascular lumen and vulnerable plaque. JOURNAL OF BIOPHOTONICS 2022; 15:e202100376. [PMID: 35139263 DOI: 10.1002/jbio.202100376] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 01/17/2022] [Accepted: 02/07/2022] [Indexed: 06/14/2023]
Abstract
Intravascular optical coherence tomography (IVOCT) is an imaging method that has developed rapidly in recent years and is useful in coronary atherosclerosis diagnosis. It is widely used in the assessment of vulnerable plaque. This review summarizes the main research methods used in recent years for blood vessel lumen boundary detection and segmentation and vulnerable plaque segmentation and classification. This article aims to comprehensively and systematically introduce the research progress on internal tissues of blood vessels based on IVOCT images. The characteristics and advantages of various methods have been summarized to provide theoretical ideas and methods for the reference of relevant researchers and scholars.
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Affiliation(s)
- Ruolin Zhang
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Yingwei Fan
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Wenliu Qi
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Ancong Wang
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Xiaoying Tang
- School of Life Science, Beijing Institute of Technology, Beijing, China
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Tianxin Gao
- School of Life Science, Beijing Institute of Technology, Beijing, China
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Automatic Classification of A-Lines in Intravascular OCT Images Using Deep Learning and Estimation of Attenuation Coefficients. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11167412] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Intravascular Optical Coherence Tomography (IVOCT) images provide important insight into every aspect of atherosclerosis. Specifically, the extent of plaque and its type, which are indicative of the patient’s condition, are better assessed by OCT images in comparison to other in vivo modalities. A large amount of imaging data per patient require automatic methods for rapid results. An effective step towards automatic plaque detection and plaque characterization is axial lines (A-lines) based classification into normal and various plaque types. In this work, a novel automatic method for A-line classification is proposed. The method employed convolutional neural networks (CNNs) for classification in its core and comprised the following pre-processing steps: arterial wall segmentation and an OCT-specific (depth-resolved) transformation and a post-processing step based on the majority of classifications. The important step was the OCT-specific transformation, which was based on the estimation of the attenuation coefficient in every pixel of the OCT image. The dataset used for training and testing consisted of 183 images from 33 patients. In these images, four different plaque types were delineated. The method was evaluated by cross-validation. The mean values of accuracy, sensitivity and specificity were 74.73%, 87.78%, and 61.45%, respectively, when classifying into plaque and normal A-lines. When plaque A-lines were classified into fibrolipidic and fibrocalcific, the overall accuracy was 83.47% for A-lines of OCT-specific transformed images and 74.94% for A-lines of original images. This large improvement in accuracy indicates the advantage of using attenuation coefficients when characterizing plaque types. The proposed automatic deep-learning pipeline constitutes a positive contribution to the accurate classification of A-lines in intravascular OCT images.
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Wu W, Samant S, de Zwart G, Zhao S, Khan B, Ahmad M, Bologna M, Watanabe Y, Murasato Y, Burzotta F, Brilakis ES, Dangas G, Louvard Y, Stankovic G, Kassab GS, Migliavacca F, Chiastra C, Chatzizisis YS. 3D reconstruction of coronary artery bifurcations from coronary angiography and optical coherence tomography: feasibility, validation, and reproducibility. Sci Rep 2020; 10:18049. [PMID: 33093499 PMCID: PMC7582159 DOI: 10.1038/s41598-020-74264-w] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Accepted: 09/10/2020] [Indexed: 11/09/2022] Open
Abstract
The three-dimensional (3D) representation of the bifurcation anatomy and disease burden is essential for better understanding of the anatomical complexity of bifurcation disease and planning of stenting strategies. We propose a novel methodology for 3D reconstruction of coronary artery bifurcations based on the integration of angiography, which provides the backbone of the bifurcation, with optical coherence tomography (OCT), which provides the vessel shape. Our methodology introduces several technical novelties to tackle the OCT frame misalignment, correct positioning of the OCT frames at the carina, lumen surface reconstruction, and merging of bifurcation lumens. The accuracy and reproducibility of the methodology were tested in n = 5 patient-specific silicone bifurcations compared to contrast-enhanced micro-computed tomography (µCT), which was used as reference. The feasibility and time-efficiency of the method were explored in n = 7 diseased patient bifurcations of varying anatomical complexity. The OCT-based reconstructed bifurcation models were found to have remarkably high agreement compared to the µCT reference models, yielding r2 values between 0.91 and 0.98 for the normalized lumen areas, and mean differences of 0.005 for lumen shape and 0.004 degrees for bifurcation angles. Likewise, the reproducibility of our methodology was remarkably high. Our methodology successfully reconstructed all the patient bifurcations yielding favorable processing times (average lumen reconstruction time < 60 min). Overall, our method is an easily applicable, time-efficient, and user-friendly tool that allows accurate and reproducible 3D reconstruction of coronary bifurcations. Our technique can be used in the clinical setting to provide information about the bifurcation anatomy and plaque burden, thereby enabling planning, education, and decision making on bifurcation stenting.
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Affiliation(s)
- Wei Wu
- Cardiovasclar Biology and Biomechanics Laboratory, Cardiovascular Division, University of Nebraska Medical Center, Omaha, 68105, USA
| | - Saurabhi Samant
- Cardiovasclar Biology and Biomechanics Laboratory, Cardiovascular Division, University of Nebraska Medical Center, Omaha, 68105, USA
| | - Gijs de Zwart
- StudioGijs, Daendelsstraat 40, 5018 ES, Tilburg, The Netherlands
| | - Shijia Zhao
- Cardiovasclar Biology and Biomechanics Laboratory, Cardiovascular Division, University of Nebraska Medical Center, Omaha, 68105, USA
| | - Behram Khan
- Cardiovasclar Biology and Biomechanics Laboratory, Cardiovascular Division, University of Nebraska Medical Center, Omaha, 68105, USA
| | - Mansoor Ahmad
- Cardiovasclar Biology and Biomechanics Laboratory, Cardiovascular Division, University of Nebraska Medical Center, Omaha, 68105, USA
| | - Marco Bologna
- Biosignals, Bioimaging and Bioinformatics Laboratory (B3-Lab), Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133, Milan, Italy
| | - Yusuke Watanabe
- Department of Cardiology, Teikyo University Hospital, Tokyo, 173-0003, Japan
| | - Yoshinobu Murasato
- Department of Cardiology, National Hospital Organization Kyushu Medical Center, Fukuoka, 810-0065, Japan
| | - Francesco Burzotta
- Department of Cardiovascular Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS Università Cattolica del Sacro Cuore, 00168, Rome, Italy
| | | | - George Dangas
- Department of Cardiovascular Medicine, Mount Sinai Hospital, New York City, 10029, USA
| | - Yves Louvard
- Institut Cardiovasculaire Paris Sud, 91300, Massy, France
| | - Goran Stankovic
- Department of Cardiology, Clinical Center of Serbia, 11000, Belgrade, Serbia
| | - Ghassan S Kassab
- California Medical Innovation Institute, San Diego, CA, 92121, USA
| | - Francesco Migliavacca
- Laboratory of Biological Structure Mechanics (LaBS), Department of Chemistry, Materials and Chemical Engineering "Giulio Natta, Politecnico di Milano, 20133, Milan, Italy
| | - Claudio Chiastra
- PoliToBIOMed Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129, Turin, Italy
| | - Yiannis S Chatzizisis
- Cardiovasclar Biology and Biomechanics Laboratory, Cardiovascular Division, University of Nebraska Medical Center, Omaha, 68105, USA.
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Muller J, Madder R. OCT-NIRS Imaging for Detection of Coronary Plaque Structure and Vulnerability. Front Cardiovasc Med 2020; 7:90. [PMID: 32582767 PMCID: PMC7287010 DOI: 10.3389/fcvm.2020.00090] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2019] [Accepted: 04/27/2020] [Indexed: 12/25/2022] Open
Abstract
A combination optical coherence tomography and near-infrared spectroscopy (OCT-NIRS) coronary imaging system is being developed to improve the care of coronary patients. While stenting has improved, complications continue to occur at the stented site and new events are caused by unrecognized vulnerable plaques. An OCT-NIRS device has potential to improve secondary prevention by optimizing stenting and by identifying vulnerable patients and vulnerable plaques. OCT is already in widespread use world-wide to optimize coronary artery stenting. It provides automated lumen detection and can identify features of coronary plaques not accurately identified by angiography or intravascular ultrasound. The ILUMIEN IV study, to be completed in 2022, will determine if OCT-guided stenting will yield better clinical outcomes than angiographic guidance alone. While the superb spatial resolution of OCT enables the identification of many plaque structural features, the detection by OCT of lipids, an important component of vulnerable plaques, is limited by suboptimal specificity and interobserver agreement. In contrast, NIRS has been extensively validated for lipid-rich plaque detection against the gold-standard of histology and is the only FDA-approved method to identify coronary lipids. Studies in patients have demonstrated that NIRS detects lipid in culprit lesions causing coronary events. In 2019, the positive results of the prospective Lipid-Rich Plaque Study led to FDA approval of NIRS for detection of high-risk plaques and patients. The complementarity of OCT for plaque structure and NIRS for plaque composition led to the sequential performance of NIRS and OCT imaging in patients. NIRS identified lipid while OCT determined the thickness of the cap over the lipid pool. The positive results obtained with OCT and NIRS imaging led to development of a prototype combined OCT-NIRS catheter that can provide co-registered OCT and NIRS data in a single pullback. The data will provide structural and chemical information likely to improve stenting and deliver more accurate identification of vulnerable plaques and vulnerable patients. More precise diagnosis will then lead to OCT-NIRS guided treatment trials to improve secondary prevention. Success in secondary prevention will then facilitate development of improved primary prevention with invasive imaging and effective treatment of patients identified by non-invasive methods.
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Affiliation(s)
- James Muller
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Ryan Madder
- Spectrum Health, Grand Rapids, MI, United States
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Wang X, Zhang Z, Luo Y, Yu Q. Hierarchical interpolation point anonymity for trajectory privacy protection. INTELL DATA ANAL 2019. [DOI: 10.3233/ida-184334] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Xiaohan Wang
- College of Geography and Tourism, Anhui Normal University, Wuhu, Anhui 241003, China
- College of Computer and Information, Anhui Normal University, Wuhu, Anhui 241003, China
- Anhui Provincial Key Laboratory of Network and Information Security, Anhui Normal University, Wuhu, Anhui 241003, China
| | - Zepei Zhang
- College of Computer and Information, Anhui Normal University, Wuhu, Anhui 241003, China
- Anhui Provincial Key Laboratory of Network and Information Security, Anhui Normal University, Wuhu, Anhui 241003, China
| | - Yonglong Luo
- College of Geography and Tourism, Anhui Normal University, Wuhu, Anhui 241003, China
- College of Computer and Information, Anhui Normal University, Wuhu, Anhui 241003, China
- Anhui Provincial Key Laboratory of Network and Information Security, Anhui Normal University, Wuhu, Anhui 241003, China
| | - Qingying Yu
- College of Geography and Tourism, Anhui Normal University, Wuhu, Anhui 241003, China
- College of Computer and Information, Anhui Normal University, Wuhu, Anhui 241003, China
- Anhui Provincial Key Laboratory of Network and Information Security, Anhui Normal University, Wuhu, Anhui 241003, China
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Rico-Jimenez JJ, Campos-Delgado DU, Buja LM, Vela D, Jo JA. Intravascular optical coherence tomography method for automated detection of macrophage infiltration within atherosclerotic coronary plaques. Atherosclerosis 2019; 290:94-102. [PMID: 31604172 DOI: 10.1016/j.atherosclerosis.2019.09.023] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Revised: 08/22/2019] [Accepted: 09/27/2019] [Indexed: 02/05/2023]
Abstract
BACKGROUND AND AIMS Significant macrophages infiltration in advanced atherosclerotic plaques promotes acute coronary events. Hence, the clinical imaging of macrophage content in coronary atherosclerotic plaques could potentially aid in identifying patients most at risk of future acute coronary events. The aim of this study was to introduce and validate a simple intravascular optical coherence tomography (IV-OCT) image processing method for automated, accurate and fast detection of macrophage infiltration within coronary atherosclerotic plaques. METHODS This method calculates the ratio of the normalized-intensity standard deviation (NSD) values estimated over two axially-adjacent regions of interest in an IV-OCT cross-sectional image (B-scan). When applied to entire IV-OCT B-scans, this method highlights plaque areas with high NSD ratio values (NSDRatio), which was demonstrated to be correlated with the degree of coronary plaque macrophage infiltration. RESULTS Using an optimized NSDRatio threshold value, coronary plaque macrophage infiltration could be detected with ~88% sensitivity and specificity in a database of 28 IV-OCT scans from postmortem coronary segments. For comparison, using an optimized NSD threshold value, considered the standard IV-OCT signature for macrophages, coronary plaque macrophage infiltration could be detected with only ~55% sensitivity and specificity. CONCLUSIONS The proposed NSDRatio method significantly increases the sensitivity and specificity for the detection of coronary plaque macrophage infiltration compared to the standard NSD method. This computationally efficient method can be seamlessly implemented within standard IV-OCT imaging systems for in-vivo real-time imaging of macrophage content in coronary plaques, which could potentially aid in identifying patients most at risk of future acute coronary events.
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Affiliation(s)
- Jose J Rico-Jimenez
- Department of Biomedical Engineering, Texas A&M University, College Station, TX, USA
| | | | | | | | - Javier A Jo
- Department of Biomedical Engineering, Texas A&M University, College Station, TX, USA; School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, USA.
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Bologna M, Migliori S, Montin E, Rampat R, Dubini G, Migliavacca F, Mainardi L, Chiastra C. Automatic segmentation of optical coherence tomography pullbacks of coronary arteries treated with bioresorbable vascular scaffolds: Application to hemodynamics modeling. PLoS One 2019; 14:e0213603. [PMID: 30870477 PMCID: PMC6417773 DOI: 10.1371/journal.pone.0213603] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2018] [Accepted: 02/25/2019] [Indexed: 01/13/2023] Open
Abstract
Background / Objectives Automatic algorithms for stent struts segmentation in optical coherence tomography (OCT) images of coronary arteries have been developed over the years, particularly with application on metallic stents. The aim of this study is three-fold: (1) to develop and to validate a segmentation algorithm for the detection of both lumen contours and polymeric bioresorbable scaffold struts from 8-bit OCT images, (2) to develop a method for automatic OCT pullback quality assessment, and (3) to demonstrate the applicability of the segmentation algorithm for the creation of patient-specific stented coronary artery for local hemodynamics analysis. Methods The proposed OCT segmentation algorithm comprises four steps: (1) image pre-processing, (2) lumen segmentation, (3) stent struts segmentation, (4) strut-based lumen correction. This segmentation process is then followed by an automatic OCT pullback image quality assessment. This method classifies the OCT pullback image quality as ‘good’ or ‘poor’ based on the number of regions detected by the stent segmentation. The segmentation algorithm was validated against manual segmentation of 1150 images obtained from 23 in vivo OCT pullbacks. Results When considering the entire set of OCT pullbacks, lumen segmentation showed results comparable with manual segmentation and with previous studies (sensitivity ~97%, specificity ~99%), while stent segmentation showed poorer results compared to manual segmentation (sensitivity ~63%, precision ~83%). The OCT pullback quality assessment algorithm classified 7 pullbacks as ‘poor’ quality cases. When considering only the ‘good’ classified cases, the performance indexes of the scaffold segmentation were higher (sensitivity >76%, precision >86%). Conclusions This study proposes a segmentation algorithm for the detection of lumen contours and stent struts in low quality OCT images of patients treated with polymeric bioresorbable scaffolds. The segmentation results were successfully used for the reconstruction of one coronary artery model that included a bioresorbable scaffold geometry for computational fluid dynamics analysis.
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Affiliation(s)
- Marco Bologna
- Laboratory of Biological Structure Mechanics (LaBS), Department of Chemistry, Materials and Chemical Engineering “Giulio Natta”, Politecnico di Milano, Milan, Italy
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Susanna Migliori
- Laboratory of Biological Structure Mechanics (LaBS), Department of Chemistry, Materials and Chemical Engineering “Giulio Natta”, Politecnico di Milano, Milan, Italy
| | - Eros Montin
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
- Center for Advanced Imaging Innovation and Research (CAI2R), and the Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, United States of America
| | - Rajiv Rampat
- Sussex Cardiac Centre, Brighton and Sussex University Hospitals, Brighton, United Kingdom
| | - Gabriele Dubini
- Laboratory of Biological Structure Mechanics (LaBS), Department of Chemistry, Materials and Chemical Engineering “Giulio Natta”, Politecnico di Milano, Milan, Italy
| | - Francesco Migliavacca
- Laboratory of Biological Structure Mechanics (LaBS), Department of Chemistry, Materials and Chemical Engineering “Giulio Natta”, Politecnico di Milano, Milan, Italy
| | - Luca Mainardi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Claudio Chiastra
- Laboratory of Biological Structure Mechanics (LaBS), Department of Chemistry, Materials and Chemical Engineering “Giulio Natta”, Politecnico di Milano, Milan, Italy
- PoliToMed Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Turin, Italy
- * E-mail:
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14
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Fully Automated Lumen Segmentation Method for Intracoronary Optical Coherence Tomography. JOURNAL OF HEALTHCARE ENGINEERING 2018; 2018:1414076. [PMID: 30792831 PMCID: PMC6327279 DOI: 10.1155/2018/1414076] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/26/2018] [Revised: 10/05/2018] [Accepted: 12/10/2018] [Indexed: 11/17/2022]
Abstract
Background Optical coherence tomography (OCT) is an innovative imaging technique that generates high-resolution intracoronary images. In the last few years, the need for more precise analysis regarding coronary artery disease to achieve optimal treatment has made intravascular imaging an area of primary importance in interventional cardiology. One of the main challenges in OCT image analysis is the accurate detection of lumen which is significant for the further prognosis. Method In this research, we present a new approach to the segmentation of lumen in OCT images. The proposed work is focused on designing an efficient automatic algorithm containing the following steps: preprocessing (artifacts removal: speckle noise, circular rings, and guide wire), conversion between polar and Cartesian coordinates, and segmentation algorithm. Results The implemented method was tasted on 667 OCT frames. The lumen border was extracted with a high correlation compared to the ground truth: 0.97 ICC (0.97-0.98). Conclusions Proposed algorithm allows for fully automated lumen segmentation on optical coherence tomography images. This tool may be applied to automated quantitative lumen analysis.
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15
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Chiastra C, Migliori S, Burzotta F, Dubini G, Migliavacca F. Patient-Specific Modeling of Stented Coronary Arteries Reconstructed from Optical Coherence Tomography: Towards a Widespread Clinical Use of Fluid Dynamics Analyses. J Cardiovasc Transl Res 2017; 11:156-172. [PMID: 29282628 PMCID: PMC5908818 DOI: 10.1007/s12265-017-9777-6] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2017] [Accepted: 12/18/2017] [Indexed: 11/30/2022]
Abstract
The recent widespread application of optical coherence tomography (OCT) in interventional cardiology has improved patient-specific modeling of stented coronary arteries for the investigation of local hemodynamics. In this review, the workflow for the creation of fluid dynamics models of stented coronary arteries from OCT images is presented. The algorithms for lumen contours and stent strut detection from OCT as well as the reconstruction methods of stented geometries are discussed. Furthermore, the state of the art of studies that investigate the hemodynamics of OCT-based stented coronary artery geometries is reported. Although those studies analyzed few patient-specific cases, the application of the current reconstruction methods of stented geometries to large populations is possible. However, the improvement of these methods and the reduction of the time needed for the entire modeling process are crucial for a widespread clinical use of the OCT-based models and future in silico clinical trials.
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Affiliation(s)
- Claudio Chiastra
- Laboratory of Biological Structure Mechanics (LaBS), Department of Chemistry, Materials and Chemical Engineering "Giulio Natta", Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milan, Italy.
| | - Susanna Migliori
- Laboratory of Biological Structure Mechanics (LaBS), Department of Chemistry, Materials and Chemical Engineering "Giulio Natta", Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milan, Italy
| | - Francesco Burzotta
- Institute of Cardiology, Catholic University of the Sacred Heart, Rome, Italy
| | - Gabriele Dubini
- Laboratory of Biological Structure Mechanics (LaBS), Department of Chemistry, Materials and Chemical Engineering "Giulio Natta", Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milan, Italy
| | - Francesco Migliavacca
- Laboratory of Biological Structure Mechanics (LaBS), Department of Chemistry, Materials and Chemical Engineering "Giulio Natta", Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milan, Italy
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