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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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Zheng S, Jiejie D, Yue Y, Qi M, Huifeng S. A Deep Learning Method for Motion Artifact Correction in Intravascular Photoacoustic Image Sequence. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:66-78. [PMID: 36037455 DOI: 10.1109/tmi.2022.3202910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
In vivo application of intravascular photoacoustic (IVPA) imaging for coronary arteries is hampered by motion artifacts associated with the cardiac cycle. Gating is a common strategy to mitigate motion artifacts. However, a large amount of diagnostically valuable information might be lost due to one frame per cycle. In this work, we present a deep learning-based method for directly correcting motion artifacts in non-gated IVPA pullback sequences. The raw signal frames are classified into dynamic and static frames by clustering. Then, a neural network named Motion Artifact Correction (MAC)-Net is designed to correct motion in dynamic frames. Given the lack of the ground truth information on the underlying dynamics of coronary arteries, we trained and tested the network using a computer-generated dataset. Based on the results, it has been observed that the trained network can directly correct motion in successive frames while preserving the original structures without discarding any frames. The improvement in the visual effect of the longitudinal view has been demonstrated based on quantitative evaluation of the inter-frame dissimilarity. The comparison results validated the motion-suppression ability of our method comparable to gating and image registration-based non-learning methods, while maintaining the integrity of the pullbacks without image preprocessing. Experimental results from in vivo intravascular ultrasound and optical coherence tomography pullbacks validated the feasibility of our method in the in vivo intracoronary imaging scenario.
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Collins GC, Jing B, Lindsey BD. High contrast power Doppler imaging in side-viewing intravascular ultrasound imaging via angular compounding. ULTRASONICS 2020; 108:106200. [PMID: 32521337 PMCID: PMC7502537 DOI: 10.1016/j.ultras.2020.106200] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2019] [Revised: 05/26/2020] [Accepted: 05/28/2020] [Indexed: 05/11/2023]
Abstract
The ability to assess likelihood of plaque rupture can determine the course of treatment in coronary artery disease. One indicator of plaque vulnerability is the development of blood vessels within the plaque, or intraplaque neovascularization. In order to visualize these vessels with increased sensitivity in the cardiac catheterization lab, a new approach for imaging blood flow in small vessels using side-viewing intravascular ultrasound (IVUS) is proposed. This approach based on compounding adjacent angular acquisitions was evaluated in tissue mimicking phantoms and ex vivo vessels. In phantom studies, the Doppler CNR increased from 3.3 ± 1.0 to 13 ± 2.6 (conventional clutter filtering) and from 1.9 ± 0.15 to 7.5 ± 1.1 (SVD filtering) as a result of applying angular compounding. When imaging flow at a rate of 5.6 mm/s in 200 µm tubes adjacent to the lumen of ex vivo porcine arteries, the Doppler CNR increased from 5.3 ± 0.95 to 7.2 ± 1.3 (conventional filtering) and from 23 ± 3.3 to 32 ± 6.7 (SVD filtering). Applying these strategies could allow increased sensitivity to slow flow in side-viewing intravascular ultrasound imaging.
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Affiliation(s)
- Graham C Collins
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA 30332, United States.
| | - Bowen Jing
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA 30332, United States
| | - Brooks D Lindsey
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA 30332, United States
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Dolic K, Siddiqui AH, Karmon Y, Marr K, Zivadinov R. The role of noninvasive and invasive diagnostic imaging techniques for detection of extra-cranial venous system anomalies and developmental variants. BMC Med 2013; 11:155. [PMID: 23806142 PMCID: PMC3699429 DOI: 10.1186/1741-7015-11-155] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2013] [Accepted: 05/30/2013] [Indexed: 02/08/2023] Open
Abstract
The extra-cranial venous system is complex and not well studied in comparison to the peripheral venous system. A newly proposed vascular condition, named chronic cerebrospinal venous insufficiency (CCSVI), described initially in patients with multiple sclerosis (MS) has triggered intense interest in better understanding of the role of extra-cranial venous anomalies and developmental variants. So far, there is no established diagnostic imaging modality, non-invasive or invasive, that can serve as the "gold standard" for detection of these venous anomalies. However, consensus guidelines and standardized imaging protocols are emerging. Most likely, a multimodal imaging approach will ultimately be the most comprehensive means for screening, diagnostic and monitoring purposes. Further research is needed to determine the spectrum of extra-cranial venous pathology and to compare the imaging findings with pathological examinations. The ability to define and reliably detect noninvasively these anomalies is an essential step toward establishing their incidence and prevalence. The role for these anomalies in causing significant hemodynamic consequences for the intra-cranial venous drainage in MS patients and other neurologic disorders, and in aging, remains unproven.
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Affiliation(s)
- Kresimir Dolic
- Buffalo Neuroimaging Analysis Center, Department of Neurology, School of Medicine and Biomedical Sciences, University at Buffalo, 100 High St, Buffalo, NY 14203, USA
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Kang W, Wang H, Wang Z, Jenkins MW, Isenberg GA, Chak A, Rollins AM. Motion artifacts associated with in vivo endoscopic OCT images of the esophagus. OPTICS EXPRESS 2011; 19:20722-35. [PMID: 21997082 PMCID: PMC3495872 DOI: 10.1364/oe.19.020722] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
3-D optical coherence tomography (OCT) has been extensively investigated as a potential screening and/or surveillance tool for Barrett's esophagus (BE). Understanding and correcting motion artifact may improve image interpretation. In this work, the motion trace was analyzed to show the physiological origin (respiration and heart beat) of the artifacts. Results showed that increasing balloon pressure did not sufficiently suppress the physiological motion artifact. An automated registration algorithm was designed to correct such artifacts. The performance of the algorithm was evaluated in images of normal porcine esophagus and demonstrated in images of BE in human patients.
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Affiliation(s)
- Wei Kang
- Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, Ohio 44106,
USA
- These authors contributed equally to this work
| | - Hui Wang
- Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, Ohio 44106,
USA
- These authors contributed equally to this work
| | - Zhao Wang
- Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, Ohio 44106,
USA
| | - Michael W. Jenkins
- Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, Ohio 44106,
USA
| | - Gerard A. Isenberg
- Department of Medicine, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, Ohio 44106,
USA
| | - Amitabh Chak
- Department of Medicine, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, Ohio 44106,
USA
| | - Andrew M. Rollins
- Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, Ohio 44106,
USA
- Department of Medicine, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, Ohio 44106,
USA
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Katouzian A, Selver M, Angelini ED, Sturm B, Laine AF. Classification of blood regions in IVUS images using three dimensional brushlet expansions. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2009; 2009:471-474. [PMID: 19964741 DOI: 10.1109/iembs.2009.5334419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
The presence of non-coherent blood speckle patterns makes the assessment of lumen size in intravascular ultrasound (IVUS) images a challenging problem, especially for images acquired with recent high frequency transducers. In this paper, we present a robust three-dimensional (3D) feature extraction algorithm based on the expansion of IVUS cross-sectional images and pullback directions onto an orthonormal complex brushlet basis. Several features are selected from the projections of low-frequency 3D brushlet coefficients. These representations are used as inputs to a neural network that is trained to classify blood maps on IVUS images. We evaluated the algorithm performance using repeated randomized experiments on sub-samples to validate the quantification of the blood maps when compared to expert manual tracings of 258 frames collected from three patients. Our results demonstrate that the proposed features extracted in the brushlet domain capture well the non-coherent structures of blood speckle, enabling identification of blood pools and enhancement of the lumen area.
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Affiliation(s)
- Amin Katouzian
- Department of Biomedical Engineering of Columbia University, New York, NY USA.
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