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Tufaro V, Jaffer FA, Serruys PW, Onuma Y, van der Steen AFW, Stone GW, Muller JE, Marcu L, Van Soest G, Courtney BK, Tearney GJ, Bourantas CV. Emerging Hybrid Intracoronary Imaging Technologies and Their Applications in Clinical Practice and Research. JACC Cardiovasc Interv 2024; 17:1963-1979. [PMID: 39260958 DOI: 10.1016/j.jcin.2024.07.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 06/24/2024] [Accepted: 07/02/2024] [Indexed: 09/13/2024]
Abstract
Intravascular ultrasound and optical coherence tomography are used with increasing frequency for the care of coronary patients and in research studies. These imaging tools can identify culprit lesions in acute coronary syndromes, assess coronary stenosis severity, guide percutaneous coronary intervention (PCI), and detect vulnerable plaques and patients. However, they have significant limitations that have stimulated the development of multimodality intracoronary imaging catheters, which provide improvements in assessing vessel wall pathology and guiding PCI. Prototypes combining 2 or even 3 imaging probes with complementary attributes have been developed, and several multimodality systems have already been used in patients, with near-infrared spectroscopy intravascular ultrasound-based studies showing promising results for the identification of high-risk plaques. Moreover, postmortem histology studies have documented that hybrid imaging catheters can enable more accurate characterization of plaque morphology than standalone imaging. This review describes the evolution in the field of hybrid intracoronary imaging; presents the available multimodality catheters; and discusses their potential role in PCI guidance, vulnerable plaque detection, and the assessment of endovascular devices and emerging pharmacotherapies targeting atherosclerosis.
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Affiliation(s)
- Vincenzo Tufaro
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, United Kingdom; Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom; Department of Biomedical Sciences, Humanitas University, Pieve Emanuele-Milan, Italy
| | - Farouc A Jaffer
- Division of Cardiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | | | - Yoshinobu Onuma
- Department of Cardiology, University of Galway, Galway, Ireland
| | | | - Gregg W Stone
- Department of Cardiology, The Zena and Michael A. Wiener Cardiovascular Institute, Mount Sinai, New York, New York, USA
| | - James E Muller
- Brigham and Women's Hospital, Division of Cardiovascular Medicine, Harvard Medical School, Boston, Massachusetts, USA
| | - Laura Marcu
- Department of Biomedical Engineering, University of California, Davis, California, USA
| | - Gijs Van Soest
- Department of Cardiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Brian K Courtney
- Sunnybrook Research Institute, University of Toronto, Toronto, Ontario, Canada; Conavi Medical Inc, Toronto, Ontario, Canada
| | - Guillermo J Tearney
- Wellman Center for Photomedicine, Massachusetts General Hospital, Boston, Massachusetts, USA; Harvard-MIT Health Sciences and Technology, Cambridge, Massachusetts, USA
| | - Christos V Bourantas
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, United Kingdom; Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom; Institute of Cardiovascular Sciences, University College London, London, United Kingdom.
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Ji T, Zan C, Li L, Cao J, Su Y, Wang H, Wu Z, Yang MF, Dou K, Li S. Molecular Imaging of Fibroblast Activation in Rabbit Atherosclerotic Plaques: a Preclinical PET/CT Study. Mol Imaging Biol 2024; 26:680-692. [PMID: 38664355 DOI: 10.1007/s11307-024-01919-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 04/13/2024] [Accepted: 04/18/2024] [Indexed: 07/28/2024]
Abstract
AIM Atherosclerosis remains the pathological basis of myocardial infarction and ischemic stroke. Early and accurate identification of plauqes is crucial to improve clinical outcomes of atherosclerosis patients. Our study aims to evaluate the potential value of fibroblast activation protein inhibitor (FAPI)-04 PET/CT in identifying plaques via a preclinical rabbit model of atherosclerosis. METHODS New Zealand white rabbits were fed high-fat diet (HFD), and randomly divided into the model group injured by the balloon, and the sham group only with incisions. Ultrasound was performed to detect plaques, and FAPI-avid was determined through Al18F-NOTA-FAPI-04 PET/CT. Mean standardized uptake values (SUVmean) in lesions were compared, and biodistribution of Al18F-NOTA-FAPI-04 and target-to-background ratios (TBRs) were calculated. Histological staining was performed to display arterial plaques, and autoradiography (ARG) was employed to measure the in vitro intensity of Al18F-NOTA-FAPI-04. At last, the correlation among FAP levels, plaque area, SUVmean values and fibrous cap thickness was assessed. RESULTS The rabbit carotid and abdominal atherosclerosis model was established. Al18F-NOTA-FAPI-04 showed a higher uptake in carotid plaques (SUVmean 1.32 ± 0.11) and abdominal plaques (SUVmean 0.73 ± 0.13) compared to corresponding controls (SUVmean 1.07 ± 0.06; 0.46 ± 0.03) (P < 0.05). Biodistribution analysis of Al18F-NOTA-FAPI-04 revealed that the bigger plaques were delineated with higher TBRs. Pathological staining showed the formation of arterial plaques, and ARG staining exhibited a higher intensity of Al18F-NOTA-FAPI-04 in the bigger plaques. Lastly, plaque area was found to be positively correlated to FAP expression and SUVmean, while FAP expression was negatively correlated to fibrous cap thickness of plaques. CONCLUSIONS We successfully achieve molecular imaging of fibroblast activation in atherosclerotic lesions of rabbits, suggesting Al18F-NOTA-FAPI-04 PET/CT may be a potentially valuable tool to identify plaques.
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Affiliation(s)
- Tianxiong Ji
- Department of Nuclear Medicine, First Hospital of Shanxi Medical University, Shanxi Medical University, Taiyuan, 030001, China
- Shanxi Key Laboratory of Molecular Imaging, Shanxi Medical University, Taiyuan, 030001, China
| | - Chunfang Zan
- Department of Nuclear Medicine, First Hospital of Shanxi Medical University, Shanxi Medical University, Taiyuan, 030001, China
- Shanxi Key Laboratory of Molecular Imaging, Shanxi Medical University, Taiyuan, 030001, China
- Collaborative Innovation Center for Molecular Imaging of Precision Medicine, Shanxi Medical University, Taiyuan, 030001, China
- College of Medical Imaging, Shanxi Medical University, Taiyuan, 030001, China
| | - Lina Li
- Department of Nuclear Medicine, Beijing Chaoyang Hospital, Capital Medical University, Beijing, 100020, China
| | - Jianbo Cao
- Department of Nuclear Medicine, First Hospital of Shanxi Medical University, Shanxi Medical University, Taiyuan, 030001, China
- Shanxi Key Laboratory of Molecular Imaging, Shanxi Medical University, Taiyuan, 030001, China
- Collaborative Innovation Center for Molecular Imaging of Precision Medicine, Shanxi Medical University, Taiyuan, 030001, China
| | - Yao Su
- Department of Nuclear Medicine, Beijing Chaoyang Hospital, Capital Medical University, Beijing, 100020, China
| | - Hongliang Wang
- Department of Nuclear Medicine, First Hospital of Shanxi Medical University, Shanxi Medical University, Taiyuan, 030001, China
- Shanxi Key Laboratory of Molecular Imaging, Shanxi Medical University, Taiyuan, 030001, China
- Collaborative Innovation Center for Molecular Imaging of Precision Medicine, Shanxi Medical University, Taiyuan, 030001, China
| | - Zhifang Wu
- Department of Nuclear Medicine, First Hospital of Shanxi Medical University, Shanxi Medical University, Taiyuan, 030001, China.
- Shanxi Key Laboratory of Molecular Imaging, Shanxi Medical University, Taiyuan, 030001, China.
- Collaborative Innovation Center for Molecular Imaging of Precision Medicine, Shanxi Medical University, Taiyuan, 030001, China.
| | - Min-Fu Yang
- Collaborative Innovation Center for Molecular Imaging of Precision Medicine, Shanxi Medical University, Taiyuan, 030001, China.
- Department of Nuclear Medicine, Beijing Chaoyang Hospital, Capital Medical University, Beijing, 100020, China.
| | - Kefei Dou
- State Key Laboratory of Cardiovascular Disease, Beijing, 100037, China.
- Cardiometabolic Medicine Center, Fu Wai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100037, China.
| | - Sijin Li
- Department of Nuclear Medicine, First Hospital of Shanxi Medical University, Shanxi Medical University, Taiyuan, 030001, China.
- Shanxi Key Laboratory of Molecular Imaging, Shanxi Medical University, Taiyuan, 030001, China.
- Collaborative Innovation Center for Molecular Imaging of Precision Medicine, Shanxi Medical University, Taiyuan, 030001, China.
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Kawai K, Kawakami R, Finn AV, Virmani R. Differences in Stable and Unstable Atherosclerotic Plaque. Arterioscler Thromb Vasc Biol 2024; 44:1474-1484. [PMID: 38924440 DOI: 10.1161/atvbaha.124.319396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/28/2024]
Affiliation(s)
- Kenji Kawai
- Department of Pathology, CVPath Institute, Gaithersburg, MD (K.K., R.K., A.V.F., R.V.)
| | - Rika Kawakami
- Department of Pathology, CVPath Institute, Gaithersburg, MD (K.K., R.K., A.V.F., R.V.)
| | - Aloke V Finn
- Department of Pathology, CVPath Institute, Gaithersburg, MD (K.K., R.K., A.V.F., R.V.)
- University of Maryland School of Medicine, Baltimore (A.V.F.)
| | - Renu Virmani
- Department of Pathology, CVPath Institute, Gaithersburg, MD (K.K., R.K., A.V.F., R.V.)
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Huang X, Bajaj R, Cui W, Hendricks MJ, Wang Y, Yap NAL, Ramasamy A, Maung S, Cap M, Zhou H, Torii R, Dijkstra J, Bourantas CV, Zhang Q. CARDIAN: a novel computational approach for real-time end-diastolic frame detection in intravascular ultrasound using bidirectional attention networks. Front Cardiovasc Med 2023; 10:1250800. [PMID: 37868778 PMCID: PMC10588184 DOI: 10.3389/fcvm.2023.1250800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 09/14/2023] [Indexed: 10/24/2023] Open
Abstract
Introduction Changes in coronary artery luminal dimensions during the cardiac cycle can impact the accurate quantification of volumetric analyses in intravascular ultrasound (IVUS) image studies. Accurate ED-frame detection is pivotal for guiding interventional decisions, optimizing therapeutic interventions, and ensuring standardized volumetric analysis in research studies. Images acquired at different phases of the cardiac cycle may also lead to inaccurate quantification of atheroma volume due to the longitudinal motion of the catheter in relation to the vessel. As IVUS images are acquired throughout the cardiac cycle, end-diastolic frames are typically identified retrospectively by human analysts to minimize motion artefacts and enable more accurate and reproducible volumetric analysis. Methods In this paper, a novel neural network-based approach for accurate end-diastolic frame detection in IVUS sequences is proposed, trained using electrocardiogram (ECG) signals acquired synchronously during IVUS acquisition. The framework integrates dedicated motion encoders and a bidirectional attention recurrent network (BARNet) with a temporal difference encoder to extract frame-by-frame motion features corresponding to the phases of the cardiac cycle. In addition, a spatiotemporal rotation encoder is included to capture the IVUS catheter's rotational movement with respect to the coronary artery. Results With a prediction tolerance range of 66.7 ms, the proposed approach was able to find 71.9%, 67.8%, and 69.9% of end-diastolic frames in the left anterior descending, left circumflex and right coronary arteries, respectively, when tested against ECG estimations. When the result was compared with two expert analysts' estimation, the approach achieved a superior performance. Discussion These findings indicate that the developed methodology is accurate and fully reproducible and therefore it should be preferred over experts for end-diastolic frame detection in IVUS sequences.
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Affiliation(s)
- Xingru Huang
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, United Kingdom
- School of Communication Engineering, Hangzhou Dianzi University, Hangzhou, China
| | - Retesh Bajaj
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, United Kingdom
- Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
| | - Weiwei Cui
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, United Kingdom
| | | | - Yaqi Wang
- College of Media Engineering, Zhejiang University of Media and Communications, Hangzhou, China
| | - Nathan A. L. Yap
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, United Kingdom
- Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
| | - Anantharaman Ramasamy
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, United Kingdom
- Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
| | - Soe Maung
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, United Kingdom
- Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
| | - Murat Cap
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, United Kingdom
- Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
| | - Huiyu Zhou
- School of Computing and Mathematical Sciences, University of Leicester, Leicester, United Kingdom
| | - Ryo Torii
- Department of Mechanical Engineering, University College London, London, United Kingdom
| | | | - Christos V. Bourantas
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, United Kingdom
- Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, London, United Kingdom
| | - Qianni Zhang
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, United Kingdom
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Huang X, Bajaj R, Li Y, Ye X, Lin J, Pugliese F, Ramasamy A, Gu Y, Wang Y, Torii R, Dijkstra J, Zhou H, Bourantas CV, Zhang Q. POST-IVUS: A perceptual organisation-aware selective transformer framework for intravascular ultrasound segmentation. Med Image Anal 2023; 89:102922. [PMID: 37598605 DOI: 10.1016/j.media.2023.102922] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 07/06/2023] [Accepted: 08/01/2023] [Indexed: 08/22/2023]
Abstract
Intravascular ultrasound (IVUS) is recommended in guiding coronary intervention. The segmentation of coronary lumen and external elastic membrane (EEM) borders in IVUS images is a key step, but the manual process is time-consuming and error-prone, and suffers from inter-observer variability. In this paper, we propose a novel perceptual organisation-aware selective transformer framework that can achieve accurate and robust segmentation of the vessel walls in IVUS images. In this framework, temporal context-based feature encoders extract efficient motion features of vessels. Then, a perceptual organisation-aware selective transformer module is proposed to extract accurate boundary information, supervised by a dedicated boundary loss. The obtained EEM and lumen segmentation results will be fused in a temporal constraining and fusion module, to determine the most likely correct boundaries with robustness to morphology. Our proposed methods are extensively evaluated in non-selected IVUS sequences, including normal, bifurcated, and calcified vessels with shadow artifacts. The results show that the proposed methods outperform the state-of-the-art, with a Jaccard measure of 0.92 for lumen and 0.94 for EEM on the IVUS 2011 open challenge dataset. This work has been integrated into a software QCU-CMS2 to automatically segment IVUS images in a user-friendly environment.
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Affiliation(s)
- Xingru Huang
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, E3 4BL, UK; School of Communication Engineering, Hangzhou Dianzi University, Xiasha Higher Education Zone, Hangzhou, Zhejiang, China
| | - Retesh Bajaj
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London, EC1A 7BE, UK; Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, London, UK
| | - Yilong Li
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, E3 4BL, UK
| | - Xin Ye
- Zhejiang Provincial People's Hospital, 270 West Xueyuan Road, Wenzhou, Zhejiang, China
| | - Ji Lin
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, E3 4BL, UK
| | - Francesca Pugliese
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London, EC1A 7BE, UK; Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, London, UK
| | - Anantharaman Ramasamy
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London, EC1A 7BE, UK; Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, London, UK
| | - Yue Gu
- Zhejiang Institute of Mechanical and Electrical Engineering, Hangzhou, China
| | - Yaqi Wang
- College of Media Engineering, Communication University of Zhejiang, Hangzhou, China
| | - Ryo Torii
- Department of Mechanical Engineering, University College London, London, UK
| | | | - Huiyu Zhou
- School of Informatics, University of Leicester, University Road, Leicester, LE1 7RH, United Kingdom
| | - Christos V Bourantas
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London, EC1A 7BE, UK; Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, London, UK
| | - Qianni Zhang
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, E3 4BL, UK.
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Revaiah PC, Onuma Y, Serruys PW. Editorial: Manual Versus Automated Methods of IVUS Analysis - The Future of Core Laboratory Appears Gloomy! CARDIOVASCULAR REVASCULARIZATION MEDICINE 2023; 54:39-40. [PMID: 37302953 DOI: 10.1016/j.carrev.2023.05.433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 05/31/2023] [Indexed: 06/13/2023]
Affiliation(s)
- Pruthvi C Revaiah
- CORRIB Research Centre for Advanced Imaging and Core Laboratory, University of Galway, Ireland
| | - Yoshinobu Onuma
- CORRIB Research Centre for Advanced Imaging and Core Laboratory, University of Galway, Ireland
| | - Patrick W Serruys
- CORRIB Research Centre for Advanced Imaging and Core Laboratory, University of Galway, Ireland.
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Ramasamy A, Sokooti H, Zhang X, Tzorovili E, Bajaj R, Kitslaar P, Broersen A, Amersey R, Jain A, Ozkor M, Reiber JHC, Dijkstra J, Serruys PW, Moon JC, Mathur A, Baumbach A, Torii R, Pugliese F, Bourantas CV. Novel near-infrared spectroscopy-intravascular ultrasound-based deep-learning methodology for accurate coronary computed tomography plaque quantification and characterization. EUROPEAN HEART JOURNAL OPEN 2023; 3:oead090. [PMID: 37908441 PMCID: PMC10615127 DOI: 10.1093/ehjopen/oead090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 07/16/2023] [Accepted: 08/17/2023] [Indexed: 11/02/2023]
Abstract
Aims Coronary computed tomography angiography (CCTA) is inferior to intravascular imaging in detecting plaque morphology and quantifying plaque burden. We aim to, for the first time, train a deep-learning (DL) methodology for accurate plaque quantification and characterization in CCTA using near-infrared spectroscopy-intravascular ultrasound (NIRS-IVUS). Methods and results Seventy patients were prospectively recruited who underwent CCTA and NIRS-IVUS imaging. Corresponding cross sections were matched using an in-house developed software, and the estimations of NIRS-IVUS for the lumen, vessel wall borders, and plaque composition were used to train a convolutional neural network in 138 vessels. The performance was evaluated in 48 vessels and compared against the estimations of NIRS-IVUS and the conventional CCTA expert analysis. Sixty-four patients (186 vessels, 22 012 matched cross sections) were included. Deep-learning methodology provided estimations that were closer to NIRS-IVUS compared with the conventional approach for the total atheroma volume (ΔDL-NIRS-IVUS: -37.8 ± 89.0 vs. ΔConv-NIRS-IVUS: 243.3 ± 183.7 mm3, variance ratio: 4.262, P < 0.001) and percentage atheroma volume (-3.34 ± 5.77 vs. 17.20 ± 7.20%, variance ratio: 1.578, P < 0.001). The DL methodology detected lesions more accurately than the conventional approach (Area under the curve (AUC): 0.77 vs. 0.67, P < 0.001) and quantified minimum lumen area (ΔDL-NIRS-IVUS: -0.35 ± 1.81 vs. ΔConv-NIRS-IVUS: 1.37 ± 2.32 mm2, variance ratio: 1.634, P < 0.001), maximum plaque burden (4.33 ± 11.83% vs. 5.77 ± 16.58%, variance ratio: 2.071, P = 0.004), and calcific burden (-51.2 ± 115.1 vs. -54.3 ± 144.4, variance ratio: 2.308, P < 0.001) more accurately than conventional approach. The DL methodology was able to segment a vessel on CCTA in 0.3 s. Conclusions The DL methodology developed for CCTA analysis from co-registered NIRS-IVUS and CCTA data enables rapid and accurate assessment of lesion morphology and is superior to expert analysts (Clinicaltrials.gov: NCT03556644).
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Affiliation(s)
- Anantharaman Ramasamy
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London EC1A 7BE, UK
- Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, Mile End Road, London E1 4NS, UK
| | | | - Xiaotong Zhang
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Evangelia Tzorovili
- Pragmatic Clinical Trials Unit, Centre for Evaluation and Methods, Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | - Retesh Bajaj
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London EC1A 7BE, UK
- Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, Mile End Road, London E1 4NS, UK
| | - Pieter Kitslaar
- Medis Medical Imaging Systems, Leiden, The Netherlands
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Alexander Broersen
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Rajiv Amersey
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London EC1A 7BE, UK
| | - Ajay Jain
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London EC1A 7BE, UK
| | - Mick Ozkor
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London EC1A 7BE, UK
| | - Johan H C Reiber
- Medis Medical Imaging Systems, Leiden, The Netherlands
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Jouke Dijkstra
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Patrick W Serruys
- Faculty of Medicine, National Heart and Lung Institute, Imperial College London, Cale Street, London SW3 6LY, UK
- Department of Cardiology, National University of Ireland, Galway, Ireland
| | - James C Moon
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London EC1A 7BE, UK
- Institute of Cardiovascular Sciences, University College London, Gower Street, London WC1E 6BT, UK
| | - Anthony Mathur
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London EC1A 7BE, UK
- Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, Mile End Road, London E1 4NS, UK
| | - Andreas Baumbach
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London EC1A 7BE, UK
- Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, Mile End Road, London E1 4NS, UK
| | - Ryo Torii
- Department of Mechanical Engineering, University College London, Torrington Place, London WC1E 7JE, UK
| | - Francesca Pugliese
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London EC1A 7BE, UK
- Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, Mile End Road, London E1 4NS, UK
| | - Christos V Bourantas
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, West Smithfield, London EC1A 7BE, UK
- Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University of London, Mile End Road, London E1 4NS, UK
- Institute of Cardiovascular Sciences, University College London, Gower Street, London WC1E 6BT, UK
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Gu SZ, Huang Y, Costopoulos C, Jessney B, Bourantas C, Teng Z, Losdat S, Maehara A, Räber L, Stone GW, Bennett MR. Heterogeneous plaque-lumen geometry is associated with major adverse cardiovascular events. EUROPEAN HEART JOURNAL OPEN 2023; 3:oead038. [PMID: 37143612 PMCID: PMC10152392 DOI: 10.1093/ehjopen/oead038] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 03/14/2023] [Accepted: 04/12/2023] [Indexed: 05/06/2023]
Abstract
Aims Prospective studies show that only a minority of plaques with higher risk features develop future major adverse cardiovascular events (MACE), indicating the need for more predictive markers. Biomechanical estimates such as plaque structural stress (PSS) improve risk prediction but require expert analysis. In contrast, complex and asymmetric coronary geometry is associated with both unstable presentation and high PSS, and can be estimated quickly from imaging. We examined whether plaque-lumen geometric heterogeneity evaluated from intravascular ultrasound affects MACE and incorporating geometric parameters enhances plaque risk stratification. Methods and results We examined plaque-lumen curvature, irregularity, lumen aspect ratio (LAR), roughness, PSS, and their heterogeneity indices (HIs) in 44 non-culprit lesions (NCLs) associated with MACE and 84 propensity-matched no-MACE-NCLs from the PROSPECT study. Plaque geometry HI were increased in MACE-NCLs vs. no-MACE-NCLs across whole plaque and peri-minimal luminal area (MLA) segments (HI curvature: adjusted P = 0.024; HI irregularity: adjusted P = 0.002; HI LAR: adjusted P = 0.002; HI roughness: adjusted P = 0.004). Peri-MLA HI roughness was an independent predictor of MACE (hazard ratio: 3.21, P < 0.001). Inclusion of HI roughness significantly improved the identification of MACE-NCLs in thin-cap fibroatheromas (TCFA, P < 0.001), or with MLA ≤ 4 mm2 (P < 0.001), or plaque burden (PB) ≥ 70% (P < 0.001), and further improved the ability of PSS to identify MACE-NCLs in TCFA (P = 0.008), or with MLA ≤ 4 mm2 (P = 0.047), and PB ≥ 70% (P = 0.003) lesions. Conclusion Plaque-lumen geometric heterogeneity is increased in MACE vs. no-MACE-NCLs, and inclusion of geometric heterogeneity improves the ability of imaging to predict MACE. Assessment of geometric parameters may provide a simple method of plaque risk stratification.
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Affiliation(s)
- Sophie Z Gu
- Section of CardioRespiratory Medicine, University of Cambridge, Heart & Lung Research Institute, Papworth Road, Cambridge Biomedical Campus, Cambridge CB2 0BB, UK
| | - Yuan Huang
- Centre for Mathematical and Statistical Analysis of Multimodal Imaging, University of Cambridge, 20 Clarkson Road, Cambridge CB3 0EH, UK
- Department of Radiology, University of Cambridge, Addenbrooke’s Hospital, Hills Road, Cambridge CB2 0QQ, UK
| | - Charis Costopoulos
- Department of Cardiology, Royal Papworth Hospital, Papworth Road, Cambridge CB2 0AY, UK
| | - Benn Jessney
- Section of CardioRespiratory Medicine, University of Cambridge, Heart & Lung Research Institute, Papworth Road, Cambridge Biomedical Campus, Cambridge CB2 0BB, UK
| | - Christos Bourantas
- Institute of Cardiovascular Sciences, University College London, 62 Huntley Street, London WC1E 6DD, UK
| | - Zhongzhao Teng
- Tenoke Ltd., Cambridge Biomedical Campus, Hills Road, Cambridge CB2 0AH, UK
- Nanjing Jingsan Medical Science and Technology Ltd., 6 Shui You Gang, Nanjing, Jiangsu 210013, China
| | - Sylvain Losdat
- Institute of Social and Preventive Medicine and Clinical Trials Unit, University of Bern, Hochschulstrasse 6, 3012 Bern, Switzerland
| | - Akiko Maehara
- Cardiovascular Research Foundation, 1700 Broadway, New York, NY 10019, USA
| | - Lorenz Räber
- Department of Cardiology, Bern University Hospital, Freiburgstrasse 18, 3010 Bern, Switzerland
| | - Gregg W Stone
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, 1190 Fifth Avenue, New York, NY 10029, USA
| | - Martin R Bennett
- Section of CardioRespiratory Medicine, University of Cambridge, Heart & Lung Research Institute, Papworth Road, Cambridge Biomedical Campus, Cambridge CB2 0BB, UK
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Intravascular Imaging During Percutaneous Coronary Intervention: JACC State-of-the-Art Review. J Am Coll Cardiol 2023; 81:590-605. [PMID: 36754518 DOI: 10.1016/j.jacc.2022.11.045] [Citation(s) in RCA: 50] [Impact Index Per Article: 50.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 11/14/2022] [Accepted: 11/15/2022] [Indexed: 02/09/2023]
Abstract
Coronary angiography has historically served as the gold standard for diagnosis of coronary artery disease and guidance of percutaneous coronary intervention (PCI). Adjunctive use of contemporary intravascular imaging (IVI) technologies has emerged as a complement to conventional angiography-to further characterize plaque morphology and optimize the performance of PCI. IVI has utility for preintervention lesion and vessel assessment, periprocedural guidance of lesion preparation and stent deployment, and postintervention assessment of optimal endpoints and exclusion of complications. The role of IVI in reducing major adverse cardiac events in complex lesion subsets is emerging, and further studies evaluating broader use are underway or in development. This paper provides an overview of currently available IVI technologies, reviews data supporting their utilization for PCI guidance and optimization across a variety of lesion subsets, proposes best practices, and advocates for broader use of these technologies as a part of contemporary practice.
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10
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Ramasamy A, Hamid A Khan A, Cooper J, Simon J, Maurovich-Horvat P, Bajaj R, Kitslaar P, Amersey R, Jain A, Deaner A, Reiber JH, Moon JC, Dijkstra J, Serruys PW, Mathur A, Baumbach A, Torii R, Pugliese F, Bourantas CV. Implications of computed tomography reconstruction algorithms on coronary atheroma quantification: Comparison with intravascular ultrasound. J Cardiovasc Comput Tomogr 2023; 17:43-51. [PMID: 36270952 DOI: 10.1016/j.jcct.2022.09.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 09/03/2022] [Accepted: 09/17/2022] [Indexed: 10/14/2022]
Abstract
BACKGROUND Advances in coronary computed tomography angiography (CCTA) reconstruction algorithms are expected to enhance the accuracy of CCTA plaque quantification. We aim to evaluate different CCTA reconstruction approaches in assessing vessel characteristics in coronary atheroma using intravascular ultrasound (IVUS) as the reference standard. METHODS Matched cross-sections (n = 7241) from 50 vessels in 15 participants with chronic coronary syndrome who prospectively underwent CCTA and 3-vessel near-infrared spectroscopy-IVUS were included. Twelve CCTA datasets per patient were reconstructed using two different kernels, two slice thicknesses (0.75 mm and 0.50 mm) and three different strengths of advanced model-based iterative reconstruction (IR) algorithms. Lumen and vessel wall borders were manually annotated in every IVUS and CCTA cross-section which were co-registered using dedicated software. Image quality was sub-optimal in the reconstructions with a sharper kernel, so these were excluded. Intraclass correlation coefficient (ICC) and repeatability coefficient (RC) were used to compare the estimations of the 6 CT reconstruction approaches with those derived by IVUS. RESULTS Segment-level analysis showed good agreement between CCTA and IVUS for assessing atheroma volume with approach 0.50/5 (slice thickness 0.50 mm and highest strength 5 ADMIRE IR) being the best (total atheroma volume ICC: 0.91, RC: 0.67, p < 0.001 and percentage atheroma volume ICC: 0.64, RC: 14.06, p < 0.001). At lesion-level, there was no difference between the CCTA reconstructions for detecting plaques (accuracy range: 0.64-0.67; p = 0.23); however, approach 0.50/5 was superior in assessing IVUS-derived lesion characteristics associated with plaque vulnerability (minimum lumen area ICC: 0.64, RC: 1.31, p < 0.001 and plaque burden ICC: 0.45, RC: 32.0, p < 0.001). CONCLUSION CCTA reconstruction with thinner slice thickness, smooth kernel and highest strength advanced IR enabled more accurate quantification of the lumen and plaque at a segment-, and lesion-level analysis in coronary atheroma when validated against intravascular ultrasound. CLINICALTRIALS gov (NCT03556644).
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Affiliation(s)
- Anantharaman Ramasamy
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK; Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University London, UK
| | - Ameer Hamid A Khan
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK
| | - Jackie Cooper
- Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University London, UK
| | - Judit Simon
- MTA-SE Cardiovascular Imaging Research Group, Medical Imaging Centre, Semmelweis University, Budapest, Hungary
| | - Pal Maurovich-Horvat
- MTA-SE Cardiovascular Imaging Research Group, Medical Imaging Centre, Semmelweis University, Budapest, Hungary
| | - Retesh Bajaj
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK; Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University London, UK
| | - Pieter Kitslaar
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands; Medis Medical Imaging, Leiden, the Netherlands
| | - Rajiv Amersey
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK
| | - Ajay Jain
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK
| | - Andrew Deaner
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK
| | - Johan Hc Reiber
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands; Medis Medical Imaging, Leiden, the Netherlands
| | - James C Moon
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK; Institute of Cardiovascular Sciences, University College London, London, UK
| | - Jouke Dijkstra
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Patrick W Serruys
- Faculty of Medicine, National Heart & Lung Institute, Imperial College London, UK; Department of Cardiology, National University of Ireland, Galway, Ireland
| | - Anthony Mathur
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK; Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University London, UK
| | - Andreas Baumbach
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK; Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University London, UK
| | - Ryo Torii
- Department of Mechanical Engineering, University College London, London, UK
| | - Francesca Pugliese
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK; Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University London, UK
| | - Christos V Bourantas
- Department of Cardiology, Barts Heart Centre, Barts Health NHS Trust, London, UK; Centre for Cardiovascular Medicine and Devices, William Harvey Research Institute, Queen Mary University London, UK; Institute of Cardiovascular Sciences, University College London, London, UK.
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