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Serruys PW, Kotoku N, Nørgaard BL, Garg S, Nieman K, Dweck MR, Bax JJ, Knuuti J, Narula J, Perera D, Taylor CA, Leipsic JA, Nicol ED, Piazza N, Schultz CJ, Kitagawa K, Bruyne BD, Collet C, Tanaka K, Mushtaq S, Belmonte M, Dudek D, Zlahoda-Huzior A, Tu S, Wijns W, Sharif F, Budoff MJ, Mey JD, Andreini D, Onuma Y. Computed tomographic angiography in coronary artery disease. EUROINTERVENTION 2023; 18:e1307-e1327. [PMID: 37025086 PMCID: PMC10071125 DOI: 10.4244/eij-d-22-00776] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Accepted: 11/14/2022] [Indexed: 04/05/2023]
Abstract
Coronary computed tomographic angiography (CCTA) is becoming the first-line investigation for establishing the presence of coronary artery disease and, with fractional flow reserve (FFRCT), its haemodynamic significance. In patients without significant epicardial obstruction, its role is either to rule out atherosclerosis or to detect subclinical plaque that should be monitored for plaque progression/regression following prevention therapy and provide risk classification. Ischaemic non-obstructive coronary arteries are also expected to be assessed by non-invasive imaging, including CCTA. In patients with significant epicardial obstruction, CCTA can assist in planning revascularisation by determining the disease complexity, vessel size, lesion length and tissue composition of the atherosclerotic plaque, as well as the best fluoroscopic viewing angle; it may also help in selecting adjunctive percutaneous devices (e.g., rotational atherectomy) and in determining the best landing zone for stents or bypass grafts.
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Affiliation(s)
| | - Nozomi Kotoku
- Department of Cardiology, University of Galway, Galway, Ireland
| | - Bjarne L Nørgaard
- Department of Cardiology, Aarhus University Hospital, Aarhus, Denmark
| | - Scot Garg
- Department of Cardiology, Royal Blackburn Hospital, Blackburn, UK
| | - Koen Nieman
- Department of Radiology and Division of Cardiovascular Medicine, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Marc R Dweck
- Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
| | - Jeroen J Bax
- Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands
- Heart Center, Turku University Hospital and University of Turku, Turku, Finland
| | - Juhani Knuuti
- Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands
- Heart Center, Turku University Hospital and University of Turku, Turku, Finland
| | | | - Divaka Perera
- School of Cardiovascular Medicine and Sciences, British Heart Foundation Centre of Research Excellence, King's College London, London, UK
| | | | - Jonathon A Leipsic
- Department of Medicine and Radiology, University of British Columbia, Vancouver, British Columbia, Canada
| | - Edward D Nicol
- Royal Brompton Hospital, London, UK
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Nicolo Piazza
- Department of Medicine, Division of Cardiology, McGill University Health Center, Montreal, Quebec, Canada
| | - Carl J Schultz
- Division of Internal Medicine, Medical School, University of Western Australia, Perth, WA, Australia
- Department of Cardiology, Royal Perth Hospital, Perth, WA, Australia
| | - Kakuya Kitagawa
- Department of Advanced Diagnostic Imaging, Mie University Graduate School of Medicine, Mie, Japan
| | - Bernard De Bruyne
- Cardiovascular Center Aalst, OLV-Clinic, Aalst, Belgium
- Department of Cardiology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Carlos Collet
- Cardiovascular Center Aalst, OLV-Clinic, Aalst, Belgium
| | - Kaoru Tanaka
- Department of Radiology, Universitair Ziekenhuis Brussel, VUB, Brussels, Belgium
| | | | | | - Darius Dudek
- Szpital Uniwersytecki w Krakowie, Krakow, Poland
| | - Adriana Zlahoda-Huzior
- Digital Innovations & Robotics Hub, Krakow, Poland
- Department of Measurement and Electronics, AGH University of Science and Technology, Krakow, Poland
| | - Shengxian Tu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - William Wijns
- Department of Cardiology, University of Galway, Galway, Ireland
- The Lambe Institute for Translational Medicine, The Smart Sensors Laboratory and CURAM, Galway, University of Galway, Galway, Ireland
| | - Faisal Sharif
- Department of Cardiology, University of Galway, Galway, Ireland
| | - Matthew J Budoff
- Division of Cardiology, Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Johan de Mey
- Department of Radiology, Universitair Ziekenhuis Brussel, VUB, Brussels, Belgium
| | - Daniele Andreini
- Division of Cardiology and Cardiac Imaging, IRCCS Galeazzi Sant'Ambrogio, Milan, Italy
- Department of Biomedical and Clinical Sciences, University of Milan, Milan, Italy
| | - Yoshinobu Onuma
- Department of Cardiology, University of Galway, Galway, Ireland
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Pham J, Wyetzner S, Pfaller MR, Parker DW, James DL, Marsden AL. svMorph: Interactive Geometry-Editing Tools for Virtual Patient-Specific Vascular Anatomies. J Biomech Eng 2023; 145:031001. [PMID: 36282508 PMCID: PMC9791670 DOI: 10.1115/1.4056055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 08/07/2022] [Indexed: 12/30/2022]
Abstract
We propose svMorph, a framework for interactive virtual sculpting of patient-specific vascular anatomic models. Our framework includes three tools for the creation of tortuosity, aneurysms, and stenoses in tubular vascular geometries. These shape edits are performed via geometric operations on the surface mesh and vessel centerline curves of the input model. The tortuosity tool also uses the physics-based Oriented Particles method, coupled with linear blend skinning, to achieve smooth, elastic-like deformations. Our tools can be applied separately or in combination to produce simulation-suitable morphed models. They are also compatible with popular vascular modeling software, such as simvascular. To illustrate our tools, we morph several image-based, patient-specific models to create a range of shape changes and simulate the resulting hemodynamics via three-dimensional, computational fluid dynamics. We also demonstrate the ability to quickly estimate the hemodynamic effects of the shape changes via the automated generation of associated zero-dimensional lumped-parameter models.
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Affiliation(s)
- Jonathan Pham
- Department of Mechanical Engineering, Stanford University, Stanford, CA 94305
| | - Sofia Wyetzner
- Department of Computer Science, Stanford University, Stanford, CA 94305
| | - Martin R Pfaller
- Department of Pediatrics, Stanford University, Stanford, CA 94305
| | - David W Parker
- Stanford Research Computing Center, Stanford University, Stanford, CA 94305
| | - Doug L James
- Department of Computer Science, Stanford University, Stanford, CA 94305
| | - Alison L Marsden
- Department of Bioengineering, Department of Pediatrics, Stanford University, Stanford, CA 94305
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Gharleghi R, Chen N, Sowmya A, Beier S. Towards automated coronary artery segmentation: A systematic review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 225:107015. [PMID: 35914439 DOI: 10.1016/j.cmpb.2022.107015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 07/03/2022] [Accepted: 07/06/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVE Vessel segmentation is the first processing stage of 3D medical images for both clinical and research use. Current segmentation methods are tedious and time consuming, requiring significant manual correction and hence are infeasible to use in large data sets. METHODS Here, we review and analyse available coronary artery segmentation methods, focusing on fully automated methods capable of handling the rapidly growing medical images available. All manuscripts published since 2010 are systematically reviewed, categorised into different groups based on the approach taken, and characteristics of the different approaches as well as trends over the past decade are explored. RESULTS The manuscripts were divided intro three broad categories, consisting of region growing, voxelwise prediction and partitioning approaches. The most common approach overall was region growing, particularly using active contour models, however these have had a sharp fall in popularity in recent years with convolutional neural networks becoming significantly more popular. CONCLUSIONS The systematic review of current coronary artery segmentation methods shows interesting trends, with rising popularity of machine learning methods, a focus on efficient methods, and falling popularity of computationally expensive processing steps such as vesselness and multiplanar reformation.
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Affiliation(s)
- Ramtin Gharleghi
- School of Mechanical and Manufacturing Engineering, UNSW, Sydney NSW 2053, Australia.
| | - Nanway Chen
- School of Mechanical and Manufacturing Engineering, UNSW, Sydney NSW 2053, Australia
| | - Arcot Sowmya
- School of Computer Science and Engineering, UNSW, Sydney NSW 2053, Australia; Tyree Foundation Institute of Health Engineering (Tyree IHealthE), Sydney, Australia
| | - Susann Beier
- School of Mechanical and Manufacturing Engineering, UNSW, Sydney NSW 2053, Australia
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Horn JD, Starosolski Z, Johnson MJ, Meoded A, Hossain SS. A Novel Method for Improving the Accuracy of MR-derived Patient-specific Vascular Models using X-ray Angiography. ENGINEERING WITH COMPUTERS 2022; 38:3879-3891. [PMID: 39155891 PMCID: PMC11329233 DOI: 10.1007/s00366-022-01685-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Accepted: 05/27/2022] [Indexed: 08/20/2024]
Abstract
MR imaging, a noninvasive radiation-free imaging modality commonly used during clinical follow up, has been widely utilized to reconstruct realistic 3D vascular models for patient-specific analysis. In recent work, we used patient-specific hemodynamic analysis of the circle of Willis to noninvasively assess stroke risk in pediatric Moyamoya disease (MMD)-a progressive steno-occlusive cerebrovascular disorder that leads to recurrent stroke. The objective was to identify vascular regions with critically high wall shear rate (WSR) that signifies elevated stroke risk. However, sources of error such as insufficient resolution of MR images can negatively impact vascular model accuracy, especially in areas of severe pathological narrowing, and thus diminish clinical relevance of simulation results, as local hemodynamics are sensitive to vessel geometry. To improve the accuracy of MR-derived vascular models, we have developed a novel method for adjusting model vessel geometry utilizing 2D X-ray angiography (XA), which is considered the gold standard for clinically assessing vessel caliber. In this workflow, "virtual angiographies" (VAs) of 3D MR-derived vascular models are conducted, producing 2D projections that are compared with corresponding XA images to guide the local adjustment of modeled vessels. This VA-comparison-adjustment loop is iterated until the two agree, as confirmed by an expert neuroradiologist. Using this method, we generated models of the circle of Willis of two patients with a history of unilateral stroke. Blood flow simulations were performed using a Navier-Stokes solver within an isogeometric analysis framework, and WSR distributions were quantified. Results for one patient show as much as 45% underestimation of local WSR in the stenotic left anterior cerebral artery (LACA), and up to a 56% underestimation in the right anterior cerebral artery when using the initial MR-derived model compared to the XA-adjusted model. To evaluate whether XA-based adjustment improves model accuracy, vessel cross-sectional areas of the pre- and post-adjustment models were compared to those seen in 3D CTA images of the same patient. CTA has superior resolution and signal-to-noise ratio compared to MR imaging but is not commonly used in the clinic due to radiation exposure concerns, especially in pediatric patients. While the vessels in the initial model had normalized root mean squared deviations (NRMSDs) ranging from 26% to 182% and 31% to 69% in two patients with respect to CTA, the adjusted vessel NRMSDs were comparatively smaller (32% to 53% and 11% to 42%). In the mildly stenotic LACA of patient 1, the NRMSDs for the pre- and post-adjusted models were 49% and 32%, respectively. These findings suggest that our XA-based adjustment method can considerably improve the accuracy of vascular models, and thus, stroke-risk prediction. An accurate, individualized assessment of stroke risk would be of substantial help in guiding the timing of preventive surgical interventions in pediatric MMD patients.
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Affiliation(s)
- John D. Horn
- Molecular Cardiology Research Laboratory, Texas Heart Institute, Houston, TX, USA
| | - Zbigniew Starosolski
- Department of Radiology, Texas Children’s Hospital, Houston, TX, USA
- Department of Radiology, Baylor College of Medicine, Houston, TX, USA
| | - Michael J. Johnson
- Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, TX, USA
| | - Avner Meoded
- Department of Radiology, Texas Children’s Hospital, Houston, TX, USA
- Department of Radiology, Baylor College of Medicine, Houston, TX, USA
| | - Shaolie S. Hossain
- Molecular Cardiology Research Laboratory, Texas Heart Institute, Houston, TX, USA
- Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, TX, USA
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Chakshu NK, Carson JM, Sazonov I, Nithiarasu P. Automating fractional flow reserve (FFR) calculation from CT scans: A rapid workflow using unsupervised learning and computational fluid dynamics. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2022; 38:e3559. [PMID: 34865317 DOI: 10.1002/cnm.3559] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2021] [Revised: 10/18/2021] [Accepted: 11/30/2021] [Indexed: 06/13/2023]
Abstract
Fractional flow reserve (FFR) provides the functional relevance of coronary atheroma. The FFR-guided strategy has been shown to reduce unnecessary stenting, improve overall health outcome, and to be cost-saving. The non-invasive, coronary computerised tomography (CT) angiography-derived FFR (cFFR) is an emerging method in reducing invasive catheter based measurements. This computational fluid dynamics-based method is laborious as it requires expertise in multidisciplinary analysis of combining image analysis and computational mechanics. In this work, we present a rapid method, powered by unsupervised learning, to automatically calculate cFFR from CT scans without manual intervention.
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Affiliation(s)
- Neeraj Kavan Chakshu
- Biomedical Engineering Group, Zienkiewicz Centre for Computational Engineering, Faculty of Science and Engineering, Swansea University, Swansea, UK
| | - Jason M Carson
- Biomedical Engineering Group, Zienkiewicz Centre for Computational Engineering, Faculty of Science and Engineering, Swansea University, Swansea, UK
| | - Igor Sazonov
- Biomedical Engineering Group, Zienkiewicz Centre for Computational Engineering, Faculty of Science and Engineering, Swansea University, Swansea, UK
| | - Perumal Nithiarasu
- Biomedical Engineering Group, Zienkiewicz Centre for Computational Engineering, Faculty of Science and Engineering, Swansea University, Swansea, UK
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Fournier S, Keulards DC, van 't Veer M, Colaiori I, Di Gioia G, Zimmermann F, Mizukami T, Nagumo S, Kodeboina M, El Farissi M, Zelis J, Sonck J, Collet C, Pijls NH, De Bruyne B. Normal values of thermodilution-derived absolute coronary blood flow and microvascular resistance in humans. EUROINTERVENTION 2021; 17:e309-e316. [PMID: 33016881 PMCID: PMC9724861 DOI: 10.4244/eij-d-20-00684] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
BACKGROUND Absolute hyperaemic coronary blood flow (Q, in mL/min) and resistance (R, in Wood units [WU]) can be measured invasively by continuous thermodilution. AIMS The aim of this study was to assess normal reference values of Q and R. METHODS In 177 arteries (69 patients: 25 controls, i.e., without identifiable coronary atherosclerosis; 44 patients with mild, non-obstructive atherosclerosis), thermodilution-derived hyperaemic Q and total, epicardial, and microvascular absolute resistances (Rtot, Repi, and Rmicro) were measured. In 20 controls and 29 patients, measurements were obtained in all three major coronary arteries, thus allowing calculations of Q and R for the whole heart. In 15 controls (41 vessels) and 25 patients (71 vessels), vessel-specific myocardial mass was derived from coronary computed tomography angiography. RESULTS Whole heart hyperaemic Q tended to be higher in controls compared to patients (668±185 vs 582±138 mL/min, p=0.068). In the left anterior descending coronary artery (LAD), hyperaemic Q was significantly higher (293±102 mL/min versus 228±71 mL/min, p=0.004) in controls than in patients. This was driven mainly by a difference in Repi (43±23 vs 83±41 WU, p=0.048), without significant differences in Rmicro. After adjustment for vessel-specific myocardial mass, hyperaemic Q was similar in the three vascular territories (5.9±1.9, 4.9±1.7, and 5.3±2.1 mL/min/g, p=0.44, in the LAD, left circumflex and right coronary artery, respectively). CONCLUSIONS The present report provides reference values of absolute coronary hyperaemic Q and R. Q was homogeneously distributed in the three major myocardial territories but the large ranges of observed hyperaemic values of flow and of microvascular resistance preclude their clinical use for inter-patient comparison.
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Affiliation(s)
- Stephane Fournier
- Cardiovascular Centre Aalst, OLV Clinic, Aalst, Belgium,Lausanne University Centre Hospital, Lausanne, Switzerland,Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | | | | | | | - Giuseppe Di Gioia
- Cardiovascular Centre Aalst, OLV Clinic, Aalst, Belgium,Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | | | | | - Sakura Nagumo
- Cardiovascular Centre Aalst, OLV Clinic, Aalst, Belgium
| | | | | | - Jo Zelis
- Department of Cardiology, Catharina Hospital Eindhoven, the Netherlands
| | - Jeroen Sonck
- Cardiovascular Centre Aalst, OLV Clinic, Aalst, Belgium,Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Carlos Collet
- Cardiovascular Centre Aalst, OLV Clinic, Aalst, Belgium
| | - Nico H.J. Pijls
- Department of Cardiology, Catharina Hospital Eindhoven, the Netherlands,Department of Biomedical Engineering Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Bernard De Bruyne
- Cardiovascular Centre Aalst, OLV-Clinic Aalst, Moorselbaan 164, B-9300 Aalst, Belgium
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Jia D, Zhuang X. Learning-based algorithms for vessel tracking: A review. Comput Med Imaging Graph 2021; 89:101840. [PMID: 33548822 DOI: 10.1016/j.compmedimag.2020.101840] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Revised: 10/07/2020] [Accepted: 12/03/2020] [Indexed: 11/24/2022]
Abstract
Developing efficient vessel-tracking algorithms is crucial for imaging-based diagnosis and treatment of vascular diseases. Vessel tracking aims to solve recognition problems such as key (seed) point detection, centerline extraction, and vascular segmentation. Extensive image-processing techniques have been developed to overcome the problems of vessel tracking that are mainly attributed to the complex morphologies of vessels and image characteristics of angiography. This paper presents a literature review on vessel-tracking methods, focusing on machine-learning-based methods. First, the conventional machine-learning-based algorithms are reviewed, and then, a general survey of deep-learning-based frameworks is provided. On the basis of the reviewed methods, the evaluation issues are introduced. The paper is concluded with discussions about the remaining exigencies and future research.
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Affiliation(s)
- Dengqiang Jia
- School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Xiahai Zhuang
- School of Data Science, Fudan University, Shanghai, China.
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8
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Zhao FJ, Fan SQ, Ren JF, von Deneen KM, He XW, Chen XL. Machine learning for diagnosis of coronary artery disease in computed tomography angiography: A survey. Artif Intell Med Imaging 2020; 1:31-39. [DOI: 10.35711/aimi.v1.i1.31] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Revised: 06/12/2020] [Accepted: 06/16/2020] [Indexed: 02/06/2023] Open
Abstract
Coronary artery disease (CAD) has become a major illness endangering human health. It mainly manifests as atherosclerotic plaques, especially vulnerable plaques without obvious symptoms in the early stage. Once a rupture occurs, it will lead to severe coronary stenosis, which in turn may trigger a major adverse cardiovascular event. Computed tomography angiography (CTA) has become a standard diagnostic tool for early screening of coronary plaque and stenosis due to its advantages in high resolution, noninvasiveness, and three-dimensional imaging. However, manual examination of CTA images by radiologists has been proven to be tedious and time-consuming, which might also lead to intra- and interobserver errors. Nowadays, many machine learning algorithms have enabled the (semi-)automatic diagnosis of CAD by extracting quantitative features from CTA images. This paper provides a survey of these machine learning algorithms for the diagnosis of CAD in CTA images, including coronary artery extraction, coronary plaque detection, vulnerable plaque identification, and coronary stenosis assessment. Most included articles were published within this decade and are found in the Web of Science. We wish to give readers a glimpse of the current status, challenges, and perspectives of these machine learning-based analysis methods for automatic CAD diagnosis.
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Affiliation(s)
- Feng-Jun Zhao
- School of Information Science and Technology, Northwest University, Xi’an 710069, Shaanxi Province, China
- Xi’an Key Lab of Radiomics and Intelligent Perception, Northwest University, Xi’an 710069, Shaanxi Province, China
| | - Si-Qi Fan
- School of Information Science and Technology, Northwest University, Xi’an 710069, Shaanxi Province, China
- Xi’an Key Lab of Radiomics and Intelligent Perception, Northwest University, Xi’an 710069, Shaanxi Province, China
| | - Jing-Fang Ren
- School of Information Science and Technology, Northwest University, Xi’an 710069, Shaanxi Province, China
- Xi’an Key Lab of Radiomics and Intelligent Perception, Northwest University, Xi’an 710069, Shaanxi Province, China
| | - Karen M von Deneen
- Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, School of Life Science and Technology, Xidian University, Xi’an 710126, Shaanxi Province, China
| | - Xiao-Wei He
- School of Information Science and Technology, Northwest University, Xi’an 710069, Shaanxi Province, China
- Xi’an Key Lab of Radiomics and Intelligent Perception, Northwest University, Xi’an 710069, Shaanxi Province, China
| | - Xue-Li Chen
- Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, School of Life Science and Technology, Xidian University, Xi’an 710126, Shaanxi Province, China
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9
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Keulards DCJ, Fournier S, van 't Veer M, Colaiori I, Zelis JM, El Farissi M, Zimmermann FM, Collet C, De Bruyne B, Pijls NHJ. Computed tomographic myocardial mass compared with invasive myocardial perfusion measurement. Heart 2020; 106:1489-1494. [PMID: 32471907 PMCID: PMC7509389 DOI: 10.1136/heartjnl-2020-316689] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Revised: 04/23/2020] [Accepted: 04/24/2020] [Indexed: 12/21/2022] Open
Abstract
Objective The prognostic importance of a coronary stenosis depends on its functional severity and its depending myocardial mass. Functional severity can be assessed by fractional flow reserve (FFR), estimated non-invasively by a specific validated CT algorithm (FFRCT). Calculation of myocardial mass at risk by that same set of CT data (CTmass), however, has not been prospectively validated so far. The aim of the present study was to compare relative territorial-based CTmass assessment with relative flow distribution, which is closely linked to true myocardial mass. Methods In this exploratory study, 35 patients with (near) normal coronary arteries underwent CT scanning for computed flow-based CTmass assessment and underwent invasive myocardial perfusion measurement in all 3 major coronary arteries by continuous thermodilution. Next, the mass and flows were calculated as relative percentages of total mass and perfusion. Results The mean difference between CTmass per territory and invasively measured myocardial perfusion, both expressed as percentage of total mass and perfusion, was 5.3±6.2% for the left anterior descending territory, −2.0±7.4% for the left circumflex territory and −3.2±3.4% for the right coronary artery territory. The intraclass correlation between the two techniques was 0.90. Conclusions Our study shows a close relationship between the relative mass of the perfusion territory calculated by the specific CT algorithm and invasively measured myocardial perfusion. As such, these data support the use of CTmass to estimate territorial myocardium-at-risk in proximal coronary arteries.
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Affiliation(s)
- Daniëlle C J Keulards
- Department of Cardiology, Catharina Hospital, Eindhoven, North Brabant, The Netherlands
| | - Stephane Fournier
- Department of Cardiology, University Hospital of Lausanne, Lausanne, Switzerland.,Department of Cardiology, Cardiovascular Center Aalst, OLV Clinic Aalst, Leopoldlaan, Belgium
| | - Marcel van 't Veer
- Department of Cardiology, Catharina Hospital, Eindhoven, North Brabant, The Netherlands.,Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Iginio Colaiori
- Department of Cardiology, Cardiovascular Center Aalst, OLV Clinic Aalst, Leopoldlaan, Belgium
| | - Jo M Zelis
- Department of Cardiology, Catharina Hospital, Eindhoven, North Brabant, The Netherlands
| | - Mohamed El Farissi
- Department of Cardiology, Catharina Hospital, Eindhoven, North Brabant, The Netherlands
| | - Frederik M Zimmermann
- Department of Cardiology, Catharina Hospital, Eindhoven, North Brabant, The Netherlands
| | - Carlos Collet
- Department of Cardiology, Cardiovascular Center Aalst, OLV Clinic Aalst, Leopoldlaan, Belgium
| | - Bernard De Bruyne
- Department of Cardiology, University Hospital of Lausanne, Lausanne, Switzerland.,Department of Cardiology, Cardiovascular Center Aalst, OLV Clinic Aalst, Leopoldlaan, Belgium
| | - Nico H J Pijls
- Department of Cardiology, Catharina Hospital, Eindhoven, North Brabant, The Netherlands.,Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
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10
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Axis-guided patch based accurate segmentation for pathological vessels using adaptive weight sparse representation. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2019.101817] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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11
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Zhao J, Ai D, Yang Y, Song H, Huang Y, Wang Y, Yang J. Deep feature regression (DFR) for 3D vessel segmentation. ACTA ACUST UNITED AC 2019; 64:115006. [DOI: 10.1088/1361-6560/ab0eee] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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12
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Vigneshwaran V, Sands GB, LeGrice IJ, Smaill BH, Smith NP. Reconstruction of coronary circulation networks: A review of methods. Microcirculation 2019; 26:e12542. [DOI: 10.1111/micc.12542] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2018] [Revised: 01/25/2019] [Accepted: 02/27/2019] [Indexed: 12/12/2022]
Affiliation(s)
- Vibujithan Vigneshwaran
- Auckland Bioengineering Institute University of Auckland Auckland New Zealand
- Faculty of Engineering University of Auckland Auckland New Zealand
| | - Gregory B. Sands
- Auckland Bioengineering Institute University of Auckland Auckland New Zealand
| | - Ian J. LeGrice
- Department of Physiology University of Auckland Auckland New Zealand
| | - Bruce H. Smaill
- Auckland Bioengineering Institute University of Auckland Auckland New Zealand
| | - Nicolas P. Smith
- Auckland Bioengineering Institute University of Auckland Auckland New Zealand
- Faculty of Engineering University of Auckland Auckland New Zealand
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13
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Wolterink JM, van Hamersvelt RW, Viergever MA, Leiner T, Išgum I. Coronary artery centerline extraction in cardiac CT angiography using a CNN-based orientation classifier. Med Image Anal 2019; 51:46-60. [DOI: 10.1016/j.media.2018.10.005] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2018] [Revised: 10/05/2018] [Accepted: 10/18/2018] [Indexed: 01/16/2023]
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Hu X, Ding D, Chu D. Multiple Hidden Markov Model for Pathological Vessel Segmentation. BIOMED RESEARCH INTERNATIONAL 2018; 2018:9868215. [PMID: 30643827 PMCID: PMC6311274 DOI: 10.1155/2018/9868215] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/27/2018] [Revised: 09/12/2018] [Accepted: 11/28/2018] [Indexed: 11/27/2022]
Abstract
One of the obstacles that prevent the accurate delineation of vessel boundaries is the presence of pathologies, which results in obscure boundaries and vessel-like structures. Targeting this limitation, we present a novel segmentation method based on multiple Hidden Markov Models. This method works with a vessel axis + cross-section model, which constrains the classifier around the vessel. The vessel axis constraint gives our method the potential to be both physiologically accurate and computationally effective. Focusing on pathological vessels, we reap the benefits of the redundant information embedded in multiple vessel-specific features and the good statistical properties coming with Hidden Markov Model, to cover the widest possible spectrum of complex situations. The performance of our method is evaluated on synthetic complex-structured datasets, where we achieve a 91% high overlap ratio. We also validate the proposed method on a real challenging case, segmentation of pathological abdominal arteries. The performance of our method is promising, since our method yields better results than two state-of-the-art methods on both synthetic datasets and real clinical datasets.
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Affiliation(s)
- Xin Hu
- School of Computer Science and Technology, Harbin Institute of Technology at Weihai, Weihai 264209, China
| | - Deqiong Ding
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Dianhui Chu
- School of Computer Science and Technology, Harbin Institute of Technology at Weihai, Weihai 264209, China
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Moccia S, De Momi E, El Hadji S, Mattos LS. Blood vessel segmentation algorithms - Review of methods, datasets and evaluation metrics. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 158:71-91. [PMID: 29544791 DOI: 10.1016/j.cmpb.2018.02.001] [Citation(s) in RCA: 219] [Impact Index Per Article: 36.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2017] [Revised: 12/23/2017] [Accepted: 02/02/2018] [Indexed: 05/09/2023]
Abstract
BACKGROUND Blood vessel segmentation is a topic of high interest in medical image analysis since the analysis of vessels is crucial for diagnosis, treatment planning and execution, and evaluation of clinical outcomes in different fields, including laryngology, neurosurgery and ophthalmology. Automatic or semi-automatic vessel segmentation can support clinicians in performing these tasks. Different medical imaging techniques are currently used in clinical practice and an appropriate choice of the segmentation algorithm is mandatory to deal with the adopted imaging technique characteristics (e.g. resolution, noise and vessel contrast). OBJECTIVE This paper aims at reviewing the most recent and innovative blood vessel segmentation algorithms. Among the algorithms and approaches considered, we deeply investigated the most novel blood vessel segmentation including machine learning, deformable model, and tracking-based approaches. METHODS This paper analyzes more than 100 articles focused on blood vessel segmentation methods. For each analyzed approach, summary tables are presented reporting imaging technique used, anatomical region and performance measures employed. Benefits and disadvantages of each method are highlighted. DISCUSSION Despite the constant progress and efforts addressed in the field, several issues still need to be overcome. A relevant limitation consists in the segmentation of pathological vessels. Unfortunately, not consistent research effort has been addressed to this issue yet. Research is needed since some of the main assumptions made for healthy vessels (such as linearity and circular cross-section) do not hold in pathological tissues, which on the other hand require new vessel model formulations. Moreover, image intensity drops, noise and low contrast still represent an important obstacle for the achievement of a high-quality enhancement. This is particularly true for optical imaging, where the image quality is usually lower in terms of noise and contrast with respect to magnetic resonance and computer tomography angiography. CONCLUSION No single segmentation approach is suitable for all the different anatomical region or imaging modalities, thus the primary goal of this review was to provide an up to date source of information about the state of the art of the vessel segmentation algorithms so that the most suitable methods can be chosen according to the specific task.
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Affiliation(s)
- Sara Moccia
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy; Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genoa, Italy.
| | - Elena De Momi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Sara El Hadji
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.
| | - Leonardo S Mattos
- Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genoa, Italy
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Hu X, Cheng Y, Ding D, Chu D. Axis-Guided Vessel Segmentation Using a Self-Constructing Cascade-AdaBoost-SVM Classifier. BIOMED RESEARCH INTERNATIONAL 2018; 2018:3636180. [PMID: 29750151 PMCID: PMC5884412 DOI: 10.1155/2018/3636180] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/24/2017] [Revised: 02/04/2018] [Accepted: 02/13/2018] [Indexed: 11/23/2022]
Abstract
One major limiting factor that prevents the accurate delineation of vessel boundaries has been the presence of blurred boundaries and vessel-like structures. Overcoming this limitation is exactly what we are concerned about in this paper. We describe a very different segmentation method based on a cascade-AdaBoost-SVM classifier. This classifier works with a vessel axis + cross-section model, which constrains the classifier around the vessel. This has the potential to be both physiologically accurate and computationally effective. To further increase the segmentation accuracy, we organize the AdaBoost classifiers and the Support Vector Machine (SVM) classifiers in a cascade way. And we substitute the AdaBoost classifier with the SVM classifier under special circumstances to overcome the overfitting issue of the AdaBoost classifier. The performance of our method is evaluated on synthetic complex-structured datasets, where we obtain high overlap ratios, around 91%. We also validate the proposed method on one challenging case, segmentation of carotid arteries over real clinical datasets. The performance of our method is promising, since our method yields better results than two state-of-the-art methods on both synthetic datasets and real clinical datasets.
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Affiliation(s)
- Xin Hu
- School of Computer Science and Technology, Harbin Institute of Technology at Weihai, Weihai 264209, China
| | - Yuanzhi Cheng
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Deqiong Ding
- Department of Mathematics, Harbin Institute of Technology at Weihai, Weihai 264209, China
| | - Dianhui Chu
- School of Computer Science and Technology, Harbin Institute of Technology at Weihai, Weihai 264209, China
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Amir-Khalili A, Hamarneh G, Abugharbieh R. Modelling and extraction of pulsatile radial distension and compression motion for automatic vessel segmentation from video. Med Image Anal 2017; 40:184-198. [PMID: 28692857 DOI: 10.1016/j.media.2017.06.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2016] [Revised: 04/13/2017] [Accepted: 06/19/2017] [Indexed: 11/24/2022]
Abstract
Identification of vascular structures from medical images is integral to many clinical procedures. Most vessel segmentation techniques ignore the characteristic pulsatile motion of vessels in their formulation. In a recent effort to automatically segment vessels that are hidden under fat, we motivated the use of the magnitude of local pulsatile motion extracted from surgical endoscopic video. In this article we propose a new approach that leverages the local orientation, in addition to magnitude of motion, and demonstrate that the extended computation and utilization of motion vectors can improve the segmentation of vascular structures. We implement our approach using four alternatives to magnitude-only motion estimation by using traditional optical flow and by exploiting the monogenic signal for fast flow estimation. Our evaluations are conducted on both synthetic phantoms as well as two real ultrasound datasets showing improved segmentation results with negligible change in computational performance compared to the previous magnitude only approach.
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Affiliation(s)
- Alborz Amir-Khalili
- Biomedical Signal and Image Computing Lab, University of British Columbia, Vancouver, BC, Canada.
| | - Ghassan Hamarneh
- Medical Image Analysis Lab, Simon Fraser University, Burnaby, BC, Canada
| | - Rafeef Abugharbieh
- Biomedical Signal and Image Computing Lab, University of British Columbia, Vancouver, BC, Canada
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Sankaran S, Grady L, Taylor CA. Fast Computation of Hemodynamic Sensitivity to Lumen Segmentation Uncertainty. IEEE TRANSACTIONS ON MEDICAL IMAGING 2015; 34:2562-2571. [PMID: 26087484 DOI: 10.1109/tmi.2015.2445777] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Patient-specific blood flow modeling combining imaging data and computational fluid dynamics can aid in the assessment of coronary artery disease. Accurate coronary segmentation and realistic physiologic modeling of boundary conditions are important steps to ensure a high diagnostic performance. Segmentation of the coronary arteries can be constructed by a combination of automated algorithms with human review and editing. However, blood pressure and flow are not impacted equally by different local sections of the coronary artery tree. Focusing human review and editing towards regions that will most affect the subsequent simulations can significantly accelerate the review process. We define geometric sensitivity as the standard deviation in hemodynamics-derived metrics due to uncertainty in lumen segmentation. We develop a machine learning framework for estimating the geometric sensitivity in real time. Features used include geometric and clinical variables, and reduced-order models. We develop an anisotropic kernel regression method for assessment of lumen narrowing score, which is used as a feature in the machine learning algorithm. A multi-resolution sensitivity algorithm is introduced to hierarchically refine regions of high sensitivity so that we can quantify sensitivities to a desired spatial resolution. We show that the mean absolute error of the machine learning algorithm compared to 3D simulations is less than 0.01. We further demonstrate that sensitivity is not predicted simply by anatomic reduction but also encodes information about hemodynamics which in turn depends on downstream boundary conditions. This sensitivity approach can be extended to other systems such as cerebral flow, electro-mechanical simulations, etc.
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Min JK, Taylor CA, Achenbach S, Koo BK, Leipsic J, Nørgaard BL, Pijls NJ, De Bruyne B. Noninvasive Fractional Flow Reserve Derived From Coronary CT Angiography. JACC Cardiovasc Imaging 2015; 8:1209-1222. [DOI: 10.1016/j.jcmg.2015.08.006] [Citation(s) in RCA: 160] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/05/2015] [Revised: 08/24/2015] [Accepted: 08/27/2015] [Indexed: 11/25/2022]
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Sequential Monte Carlo tracking of the marginal artery by multiple cue fusion and random forest regression. Med Image Anal 2014; 19:164-75. [PMID: 25461335 DOI: 10.1016/j.media.2014.09.006] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2014] [Revised: 09/12/2014] [Accepted: 09/23/2014] [Indexed: 01/02/2023]
Abstract
Given the potential importance of marginal artery localization in automated registration in computed tomography colonography (CTC), we have devised a semi-automated method of marginal vessel detection employing sequential Monte Carlo tracking (also known as particle filtering tracking) by multiple cue fusion based on intensity, vesselness, organ detection, and minimum spanning tree information for poorly enhanced vessel segments. We then employed a random forest algorithm for intelligent cue fusion and decision making which achieved high sensitivity and robustness. After applying a vessel pruning procedure to the tracking results, we achieved statistically significantly improved precision compared to a baseline Hessian detection method (2.7% versus 75.2%, p<0.001). This method also showed statistically significantly improved recall rate compared to a 2-cue baseline method using fewer vessel cues (30.7% versus 67.7%, p<0.001). These results demonstrate that marginal artery localization on CTC is feasible by combining a discriminative classifier (i.e., random forest) with a sequential Monte Carlo tracking mechanism. In so doing, we present the effective application of an anatomical probability map to vessel pruning as well as a supplementary spatial coordinate system for colonic segmentation and registration when this task has been confounded by colon lumen collapse.
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21
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Development and validation of intracranial thrombus segmentation on CT angiography in patients with acute ischemic stroke. PLoS One 2014; 9:e101985. [PMID: 25032691 PMCID: PMC4102487 DOI: 10.1371/journal.pone.0101985] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2014] [Accepted: 06/13/2014] [Indexed: 11/25/2022] Open
Abstract
Background and Purpose Thrombus characterization is increasingly considered important in predicting treatment success for patients with acute ischemic stroke. The lack of intensity contrast between thrombus and surrounding tissue in CT images makes manual delineation a difficult and time consuming task. Our aim was to develop an automated method for thrombus measurement on CT angiography and validate it against manual delineation. Materials and Methods Automated thrombus segmentation was achieved using image intensity and a vascular shape prior derived from the segmentation of the contralateral artery. In 53 patients with acute ischemic stroke due to proximal intracranial arterial occlusion, automated length and volume measurements were performed. Accuracy was assessed by comparison with inter-observer variation of manual delineations using intraclass correlation coefficients and Bland–Altman analyses. Results The automated method successfully segmented the thrombus for all 53 patients. The intraclass correlation of automated and manual length and volume measurements were 0.89 and 0.84. Bland-Altman analyses yielded a bias (limits of agreement) of −0.4 (−8.8, 7.7) mm and 8 (−126, 141) mm3 for length and volume, respectively. This was comparable to the best interobserver agreement, with an intraclass correlation coefficients of 0.90 and 0.85 and a bias (limits of agreement) of −0.1 (−11.2, 10.9) mm and −17 (−216, 185) mm3. Conclusions The method facilitates automated thrombus segmentation for accurate length and volume measurements, is relatively fast and requires minimal user input, while being insensitive to high hematocrit levels and vascular calcifications. Furthermore, it has the potential to assess thrombus characteristics of low-density thrombi.
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Baka N, Metz CT, Schultz CJ, van Geuns RJ, Niessen WJ, van Walsum T. Oriented Gaussian mixture models for nonrigid 2D/3D coronary artery registration. IEEE TRANSACTIONS ON MEDICAL IMAGING 2014; 33:1023-1034. [PMID: 24770908 DOI: 10.1109/tmi.2014.2300117] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
2D/3D registration of patient vasculature from preinterventional computed tomography angiography (CTA) to interventional X-ray angiography is of interest to improve guidance in percutaneous coronary interventions. In this paper we present a novel feature based 2D/3D registration framework, that is based on probabilistic point correspondences, and show its usefulness on aligning 3D coronary artery centerlines derived from CTA images with their 2D projection derived from interventional X-ray angiography. The registration framework is an extension of the Gaussian mixture model (GMM) based point-set registration to the 2D/3D setting, with a modified distance metric. We also propose a way to incorporate orientation in the registration, and show its added value for artery registration on patient datasets as well as in simulation experiments. The oriented GMM registration achieved a median accuracy of 1.06 mm, with a convergence rate of 81% for nonrigid vessel centerline registration on 12 patient datasets, using a statistical shape model. The method thereby outperformed the iterative closest point algorithm, the GMM registration without orientation, and two recently published methods on 2D/3D coronary artery registration.
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Segmenting patients and physicians using preferences from discrete choice experiments. THE PATIENT 2013. [PMID: 24327338 DOI: 10.1007/s40271‐013‐0037‐9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 09/29/2022]
Abstract
People often form groups or segments that have similar interests and needs and seek similar benefits from health providers. Health organizations need to understand whether the same health treatments, prevention programs, services, and products should be applied to everyone in the relevant population or whether different treatments need to be provided to each of several segments that are relatively homogeneous internally but heterogeneous among segments. Our objective was to explain the purposes, benefits, and methods of segmentation for health organizations, and to illustrate the process of segmenting health populations based on preference coefficients from a discrete choice conjoint experiment (DCE) using an example study of prevention of cyberbullying among university students. We followed a two-level procedure for investigating segmentation incorporating several methods for forming segments in Level 1 using DCE preference coefficients and testing their quality, reproducibility, and usability by health decision makers. Covariates (demographic, behavioral, lifestyle, and health state variables) were included in Level 2 to further evaluate quality and to support the scoring of large databases and developing typing tools for assigning those in the relevant population, but not in the sample, to the segments. Several segmentation solution candidates were found during the Level 1 analysis, and the relationship of the preference coefficients to the segments was investigated using predictive methods. Those segmentations were tested for their quality and reproducibility and three were found to be very close in quality. While one seemed better than others in the Level 1 analysis, another was very similar in quality and proved ultimately better in predicting segment membership using covariates in Level 2. The two segments in the final solution were profiled for attributes that would support the development and acceptance of cyberbullying prevention programs among university students. Those segments were very different-where one wanted substantial penalties against cyberbullies and were willing to devote time to a prevention program, while the other felt no need to be involved in prevention and wanted only minor penalties. Segmentation recognizes key differences in why patients and physicians prefer different health programs and treatments. A viable segmentation solution may lead to adapting prevention programs and treatments for each targeted segment and/or to educating and communicating to better inform those in each segment of the program/treatment benefits. Segment members' revealed preferences showing behavioral changes provide the ultimate basis for evaluating the segmentation benefits to the health organization.
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Deal K. Segmenting Patients and Physicians Using Preferences from Discrete Choice Experiments. PATIENT-PATIENT CENTERED OUTCOMES RESEARCH 2013; 7:5-21. [DOI: 10.1007/s40271-013-0037-9] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Tang H, van Walsum T, Hameeteman R, Shahzad R, van Vliet LJ, Niessen WJ. Lumen segmentation and stenosis quantification of atherosclerotic carotid arteries in CTA utilizing a centerline intensity prior. Med Phys 2013; 40:051721. [PMID: 23635269 DOI: 10.1118/1.4802751] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE The degree of stenosis is an important biomarker in assessing the severity of cardiovascular disease. The purpose of our work is to develop and evaluate a semiautomatic method for carotid lumen segmentation and subsequent carotid artery stenosis quantification in CTA images. METHODS The authors present a semiautomatic stenosis detection and quantification method following lumen segmentation. The lumen of the carotid arteries is segmented in three steps. First, centerlines of the internal and external carotid arteries are extracted with an iterative minimum cost path approach in which the costs are based on a measure of medialness and intensity similarity to lumen. Second, the lumen boundary is delineated using a level set procedure which is steered by gradient information, regional intensity information, and spatial information. Special effort is made in adding terms based on local centerline intensity prior so as to exclude all possible plaque tissues from the segmentation. Third, side branches in the segmented lumen are removed by applying a shape constraint to the envelope of the maximum inscribed spheres of the segmentation. From the segmented lumen, the authors detect and quantify the cross-sectional area-based and cross-sectional diameter-based stenosis degrees according to the North American Symptomatic Carotid En-darterectomy Trial criterion. RESULTS The method is trained and tested on a publicly available database from the cls2009 challenge. For the segmentation, the authors obtain a Dice similarity coefficient of 90.2% and a mean absolute surface distance of 0.34 mm. For the stenosis quantification, the authors obtain an average error of 15.7% for cross-sectional diameter-based stenosis and 19.2% for cross-sectional area-based stenosis quantification. CONCLUSIONS With these results, the method ranks second in terms of carotid lumen segmentation accuracy, and first in terms of carotid artery stenosis quantification.
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Affiliation(s)
- Hui Tang
- Biomedical Imaging Group Rotterdam, Departments of Radiology and Medical Informatics, Erasmus MC, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands.
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Metz CT, Schaap M, Klein S, Baka N, Neefjes LA, Schultz CJ, Niessen WJ, van Walsum T. Registration of 3D+t coronary CTA and monoplane 2D+t X-ray angiography. IEEE TRANSACTIONS ON MEDICAL IMAGING 2013; 32:919-931. [PMID: 23392343 DOI: 10.1109/tmi.2013.2245421] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
A method for registering preoperative 3D+t coronary CTA with intraoperative monoplane 2D+t X-ray angiography images is proposed to improve image guidance during minimally invasive coronary interventions. The method uses a patient-specific dynamic coronary model, which is derived from the CTA scan by centerline extraction and motion estimation. The dynamic coronary model is registered with the 2D+t X-ray sequence, considering multiple X-ray time points concurrently, while taking breathing induced motion into account. Evaluation was performed on 26 datasets of 17 patients by comparing projected model centerlines with manually annotated centerlines in the X-ray images. The proposed 3D+t/2D+t registration method performed better than a 3D/2D registration method with respect to the accuracy and especially the robustness of the registration. Registration with a median error of 1.47 mm was achieved.
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Affiliation(s)
- Coert T Metz
- Department of Radiology and Department of Medical Informatics, ErasmusMC, 3015 GE Rotterdam, The Netherlands
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WANG YI, FANG BIN, PI JINGRUI, WU LEI, WANG PATRICKSP, WANG HONGGUANG. AUTOMATIC MULTI-SCALE SEGMENTATION OF INTRAHEPATIC VESSEL IN CT IMAGES FOR LIVER SURGERY PLANNING. INT J PATTERN RECOGN 2013. [DOI: 10.1142/s0218001413570012] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The processing of blood vessels is an indispensable part in complicated surgeries of livers and hearts as the development of medical image technologies, which requires an automatic segmentation system over CT images of organs. However, the vascular pattern of livers in CT images suffers from low contrast to background so that the existing segmentation technologies are not able to extract the blood vessels completely. In the paper, we propose a new algorithm to extract the blood vessels of livers based on the adaptive multi-scale segmentation. First, we prove that the background histogram of normal scale blood vessels obeys the Gaussian distribution in CT images, and obtain the vascular distribution function from the vascular signal segmented from the background with a local optimal threshold. Second, Hessian matrix is employed to enhance the thin blood vessels before the extraction, and a complete and clear segmentation system for blood vessels is constructed by combining the major and thin blood vessels via filtering. Experimental results show the effectiveness of the proposed method, which is able to extract more complete blood vessels for 3D system, and assist the clinical liver surgeries efficiently.
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Affiliation(s)
- YI WANG
- College of Computer Science, Chongqing University, Chongqing, 400030, P. R. China
| | - BIN FANG
- College of Computer Science, Chongqing University, Chongqing, 400030, P. R. China
| | - JINGRUI PI
- College of Computer Science, Chongqing University, Chongqing, 400030, P. R. China
| | - LEI WU
- College of Computer Science, Chongqing University, Chongqing, 400030, P. R. China
| | - PATRICK S. P. WANG
- College of Computer and Information Science, Northeastern University Boston, USA
| | - HONGGUANG WANG
- Hospital & Institute of Hepatobiliary Surgery, Chinese PLA General Hospital, Beijing 100853, P. R. China
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Statistical coronary motion models for 2D+t/3D registration of X-ray coronary angiography and CTA. Med Image Anal 2013; 17:698-709. [PMID: 23628692 DOI: 10.1016/j.media.2013.03.003] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2012] [Revised: 01/06/2013] [Accepted: 03/06/2013] [Indexed: 11/21/2022]
Abstract
Accurate alignment of intra-operative X-ray coronary angiography (XA) and pre-operative cardiac CT angiography (CTA) may improve procedural success rates of minimally invasive coronary interventions for patients with chronic total occlusions. It was previously shown that incorporating patient specific coronary motion extracted from 4D CTA increases the robustness of the alignment. However, pre-operative CTA is often acquired with gating at end-diastole, in which case patient specific motion is not available. For such cases, we investigate the possibility of using population based coronary motion models to provide constraints for the 2D+t/3D registration. We propose a methodology for building statistical motion models of the coronary arteries from a training population of 4D CTA datasets. We compare the 2D+t/3D registration performance of the proposed statistical models with other motion estimates, including the patient specific motion extracted from 4D CTA, the mean motion of a population, the predicted motion based on the cardiac shape. The coronary motion models, constructed on a training set of 150 patients, had a generalization accuracy of 1mm root mean square point-to-point distance. Their 2D+t/3D registration accuracy on one cardiac cycle of 12 monoplane XA sequences was similar to, if not better than, the 4D CTA based motion, irrespective of which respiratory model and which feature based 2D/3D distance metric was used. The resulting model based coronary motion estimate showed good applicability for registration of a subsequent cardiac cycle.
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Kirişli HA, Gupta V, Kirschbaum SW, Rossi A, Metz CT, Schaap M, van Geuns RJ, Mollet N, Lelieveldt BPF, Reiber JHC, van Walsum T, Niessen WJ. Comprehensive visualization of multimodal cardiac imaging data for assessment of coronary artery disease: first clinical results of the SMARTVis tool. Int J Comput Assist Radiol Surg 2011; 7:557-71. [PMID: 21948075 DOI: 10.1007/s11548-011-0657-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2011] [Accepted: 08/25/2011] [Indexed: 12/29/2022]
Abstract
PURPOSE In clinical practice, both coronary anatomy and myocardial perfusion information are needed to assess coronary artery disease (CAD). The extent and severity of coronary stenoses can be determined using computed tomography coronary angiography (CTCA); the presence and amount of ischemia can be identified using myocardial perfusion imaging, such as perfusion magnetic resonance imaging (PMR). To determine which specific stenosis is associated with which ischemic region, experts use assumptions on coronary perfusion territories. Due to the high variability between patient's coronary artery anatomies, as well as the uncertain relation between perfusion territories and supplying coronary arteries, patient-specific systems are needed. MATERIAL AND METHODS We present a patient-specific visualization system, called Synchronized Multimodal heART Visualization (SMARTVis), for relating coronary stenoses and perfusion deficits derived from CTCA and PMR, respectively. The system consists of the following comprehensive components: (1) two or three-dimensional fusion of anatomical and functional information, (2) automatic detection and ranking of coronary stenoses, (3) estimation of patient-specific coronary perfusion territories. RESULTS The potential benefits of the SMARTVis tool in assessing CAD were investigated through a case-study evaluation (conventional vs. SMARTVis tool): two experts analyzed four cases of patients with suspected multivessel coronary artery disease. When using the SMARTVis tool, a more reliable estimation of the relation between perfusion deficits and stenoses led to a more accurate diagnosis, as well as a better interobserver diagnosis agreement. CONCLUSION The SMARTVis comprehensive visualization system can be effectively used to assess disease status in multivessel CAD patients, offering valuable new options for the diagnosis and management of these patients.
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Affiliation(s)
- Hortense A Kirişli
- Biomedical Imaging Group Rotterdam (BIGR), Erasmus Medical Center Rotterdam, Rotterdam, The Netherlands.
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