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Fernández-Martínez D, González-Fernández MR, Nogales-Asensio JM, Ferrera C. Impact of minimal lumen segmentation uncertainty on patient-specific coronary simulations: A look at FFR CT. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2024; 40:e3822. [PMID: 38566253 DOI: 10.1002/cnm.3822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 02/20/2024] [Accepted: 03/24/2024] [Indexed: 04/04/2024]
Abstract
We examined the effect of minimal lumen segmentation uncertainty on Fractional Flow Reserve obtained from Coronary Computed Tomography AngiographyFFR CT . A total of 14 patient-specific coronary models with different stenosis locations and degrees of severity were enrolled in this study. The optimal segmented coronary lumens were disturbed using intra± 6 % and inter-operator± 15 % variations on the segmentation threshold.FFR CT was evaluated in each case by 3D-OD CFD simulations. The findings suggest that the sensitivity ofFFR CT to this type of uncertainty increases distally and with the stenosis severity. Cases with moderate or severe distal coronary lesions should undergo either exact and thorough segmentation operations or invasive FFR measurements, particularly if theFFR CT is close to the cutoff (0.80). Therefore, we conclude that it is crucial to consider the lesion's location and degree of severity when evaluatingFFR CT results.
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Affiliation(s)
- Daniel Fernández-Martínez
- Departamento de Ingeniería Mecánica, Energética y de los Materiales, Universidad de Extremadura, Badajoz, Spain
| | | | | | - Conrado Ferrera
- Departamento de Ingeniería Mecánica, Energética y de los Materiales, Universidad de Extremadura, Badajoz, Spain
- Instituto de Computación Científica Avanzada, Universidad de Extremadura, Badajoz, Spain
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2
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Park D, Park EA, Jeong B, Lee YS, Lee W. Quantitative analysis of blooming artifact caused by calcification based on X-ray energy difference using computed tomography. Sci Rep 2024; 14:11539. [PMID: 38773167 PMCID: PMC11109228 DOI: 10.1038/s41598-024-61187-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 05/02/2024] [Indexed: 05/23/2024] Open
Abstract
Blooming artifacts caused by calcifications appearing on computed tomography (CT) images lead to an underestimation of the coronary artery lumen size, and higher X-ray energy levels are suggested to reduce the blooming artifacts with subjective visual assessment. This study aimed to evaluate the effect of higher X-ray energy levels on the quantitative measurement of adjacent pixels affected by calcification using CT images. In this two-part study, CT images were acquired from dual-energy CT scanners by changing the X-ray energy levels such as kilovoltage peak (kVp) and kilo-electron volts (keV). Adjacent pixels affected by calcification were measured using the brightened length, excluding the actual calcified length, as determined by the full width at third maximum. In a separate clinical study, the adjacent affected pixels associated with 23 calcifications across 10 patients were measured using the same method as that used in the phantom study. Phantom and clinical studies showed that the change in kVp (field of view [FOV] 300 mm: p = 0.167, 0.494, and 0.861 for vendors 1, 2, and 3, respectively) and keV levels (p = 0.178 for vendor 2) failed to reduce the adjacent pixels affected by calcification, respectively. Moreover, the change in keV levels showed different aspects of adjacent pixels affected by calcification in the phantom study (FOV 300 mm: no significant difference [p = 0.191], increase [p < 0.001], and decrease [p < 0.001] for vendors 1, 2, and 3, respectively). Quantitative measurements revealed no significant relationship between higher X-ray energy levels and the adjacent pixels affected by calcification.
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Affiliation(s)
- Daebeom Park
- Department of Clinical Medical Sciences, Seoul National University College of Medicine, Seoul, Korea
| | - Eun-Ah Park
- Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
| | - Baren Jeong
- Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea
| | - Yoon Seong Lee
- Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea
| | - Whal Lee
- Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea.
- Department of Radiology, Seoul National University College of Medicine, Seoul, Korea.
- Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea.
- Department of Clinical Medical Sciences, Seoul National University College of Medicine, Seoul, Korea.
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3
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Herten VRLM, Hampe N, Takx RAP, Franssen KJ, Wang Y, Sucha D, Henriques JP, Leiner T, Planken RN, Isgum I. Automatic Coronary Artery Plaque Quantification and CAD-RADS Prediction Using Mesh Priors. IEEE TRANSACTIONS ON MEDICAL IMAGING 2024; 43:1272-1283. [PMID: 37862273 DOI: 10.1109/tmi.2023.3326243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/22/2023]
Abstract
Coronary artery disease (CAD) remains the leading cause of death worldwide. Patients with suspected CAD undergo coronary CT angiography (CCTA) to evaluate the risk of cardiovascular events and determine the treatment. Clinical analysis of coronary arteries in CCTA comprises the identification of atherosclerotic plaque, as well as the grading of any coronary artery stenosis typically obtained through the CAD-Reporting and Data System (CAD-RADS). This requires analysis of the coronary lumen and plaque. While voxel-wise segmentation is a commonly used approach in various segmentation tasks, it does not guarantee topologically plausible shapes. To address this, in this work, we propose to directly infer surface meshes for coronary artery lumen and plaque based on a centerline prior and use it in the downstream task of CAD-RADS scoring. The method is developed and evaluated using a total of 2407 CCTA scans. Our method achieved lesion-wise volume intraclass correlation coefficients of 0.98, 0.79, and 0.85 for calcified, non-calcified, and total plaque volume respectively. Patient-level CAD-RADS categorization was evaluated on a representative hold-out test set of 300 scans, for which the achieved linearly weighted kappa ( κ ) was 0.75. CAD-RADS categorization on the set of 658 scans from another hospital and scanner led to a κ of 0.71. The results demonstrate that direct inference of coronary artery meshes for lumen and plaque is feasible, and allows for the automated prediction of routinely performed CAD-RADS categorization.
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Hamimi AH, Ghanem AM, Hannah-Shmouni F, Elgarf RM, Matta JR, Gharib AM, Abd-Elmoniem KZ. Ascending Aorta 4D Time to Peak Distention Sexual Dimorphism and Association with Coronary Plaque Burden Severity in Women. J Cardiovasc Transl Res 2024; 17:298-307. [PMID: 37556037 DOI: 10.1007/s12265-023-10422-5] [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: 01/01/2023] [Accepted: 07/31/2023] [Indexed: 08/10/2023]
Abstract
Coronary artery disease (CAD) risk and plaque scores are often subjective and biased, particularly in mid-age asymptomatic women, whose CAD risk assessment has been historically underestimated. In this study, a new automatic ascending aorta time-to-peak-distention (TPD) analysis was developed for fast screening and as an independent surrogate for subclinical atherosclerosis in asymptomatic women. CCTA was obtained in 50 asymptomatic adults. Plaque burden segment involvement score (SIS) and automatic TPD were obtained from all subjects. Logistic regression analyses were performed to investigate the association between CAD risk scores and TPD with severe coronary plaque burden (SIS>5). TPD, individually, was found to be a significant predictor of SIS>5. Additionally, sex was a significant effect modifier of TPD, with a stronger statistically significant association with women. Four-dimensional aortic time-to-peak distention could supplement conventional CCTA analysis and offer a quick objective screening tool for plaque burden severity and CAD risk stratification, especially in women.
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Affiliation(s)
- Ahmed H Hamimi
- Biomedical and Metabolic Imaging Branch (BMIB), National Institute of Diabetes, Digestive and Kidney Diseases (NIDDK), National Institutes of Health (NIH), 10 Center Drive, 1C334, Bethesda, MD, 20892, USA
| | - Ahmed M Ghanem
- Biomedical and Metabolic Imaging Branch (BMIB), National Institute of Diabetes, Digestive and Kidney Diseases (NIDDK), National Institutes of Health (NIH), 10 Center Drive, 1C334, Bethesda, MD, 20892, USA
| | - Fady Hannah-Shmouni
- Internal Medicine, Endocrinology, and Genetics, Division of Endocrinology, University of British Columbia, Vancouver, BC, Canada
| | - Reham M Elgarf
- Biomedical and Metabolic Imaging Branch (BMIB), National Institute of Diabetes, Digestive and Kidney Diseases (NIDDK), National Institutes of Health (NIH), 10 Center Drive, 1C334, Bethesda, MD, 20892, USA
| | - Jatin R Matta
- Biomedical and Metabolic Imaging Branch (BMIB), National Institute of Diabetes, Digestive and Kidney Diseases (NIDDK), National Institutes of Health (NIH), 10 Center Drive, 1C334, Bethesda, MD, 20892, USA
| | - Ahmed M Gharib
- Biomedical and Metabolic Imaging Branch (BMIB), National Institute of Diabetes, Digestive and Kidney Diseases (NIDDK), National Institutes of Health (NIH), 10 Center Drive, 1C334, Bethesda, MD, 20892, USA.
| | - Khaled Z Abd-Elmoniem
- Biomedical and Metabolic Imaging Branch (BMIB), National Institute of Diabetes, Digestive and Kidney Diseases (NIDDK), National Institutes of Health (NIH), 10 Center Drive, 1C334, Bethesda, MD, 20892, USA.
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5
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van Driest FY, Broersen A, van der Geest RJ, Wouter Jukema J, Scholte AJHA, Dijkstra J. Automatic Quantification of Local Plaque Thickness Differences as Assessed by Serial Coronary Computed Tomography Angiography Using Scan-Quality-Based Vessel-Specific Thresholds. Cardiol Ther 2024; 13:103-116. [PMID: 38062285 PMCID: PMC10899547 DOI: 10.1007/s40119-023-00341-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 11/03/2023] [Indexed: 02/29/2024] Open
Abstract
INTRODUCTION The use of serial coronary computed tomography angiography (CCTA) allows for the early assessment of coronary plaque progression, a crucial factor in averting major adverse cardiac events (MACEs). Traditionally, serial CCTA is assessed using anatomical landmarks to match baseline and follow-up scans. Recently, a tool has been developed that allows for the automatic quantification of local plaque thickness differences in serial CCTA utilizing plaque contour delineation. The aim of this study was to determine thresholds of plaque thickness differences that define whether there is plaque progression and/or regression. These thresholds depend on the contrast-to-noise ratio (CNR). METHODS Plaque thickness differences between two scans acquired at the same moment in time should always be zero. The negative and positive differences in plaque contour delineation in these scans were used along with the CNR in order to create calibration graphs on which a linear regression analysis was performed. This analysis was conducted on a cohort of 50 patients referred for a CCTA due to chest complaints. A total of 300 coronary vessels were analyzed. First, plaque contours were semi-automatically determined for all major epicardial coronary vessels. Second, manual drawings of seven regions of interest (ROIs) per scan were used to quantify the scan quality based on the CNR for each vessel. RESULTS A linear regression analysis was performed on the CNR and negative and positive plaque contour delineation differences. Accounting for the standard error of the estimate, the linear regression analysis revealed that above 1.009 - 0.002 × CNR there is an increase in plaque thickness (progression), and below - 1.638 + 0.012 × CNR there is a decrease in plaque thickness (regression). CONCLUSION This study demonstrates the feasibility of developing vessel-specific, quality-based thresholds for visualizing local plaque thickness differences evaluated by serial CCTA. These thresholds have the potential to facilitate the early detection of atherosclerosis progression.
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Affiliation(s)
- Finn Y van Driest
- Department of Cardiology, Leiden Heart-Lung Center, Leiden University Medical Center, Leiden, The Netherlands
| | - Alexander Broersen
- Department of Radiology, Division of Image Processing, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands
| | - Rob J van der Geest
- Department of Radiology, Division of Image Processing, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands
| | - J Wouter Jukema
- Department of Cardiology, Leiden Heart-Lung Center, Leiden University Medical Center, Leiden, The Netherlands
| | - Arthur J H A Scholte
- Department of Cardiology, Leiden Heart-Lung Center, Leiden University Medical Center, Leiden, The Netherlands
| | - Jouke Dijkstra
- Department of Radiology, Division of Image Processing, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands.
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Castro R, Gullette S, Whalen C, Mattie FJ, Ge X, Ross AC, Neuberger T. High-field magnetic resonance microscopy of aortic plaques in a mouse model of atherosclerosis. MAGMA (NEW YORK, N.Y.) 2023; 36:887-896. [PMID: 37421501 PMCID: PMC10667155 DOI: 10.1007/s10334-023-01102-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 04/26/2023] [Accepted: 05/15/2023] [Indexed: 07/10/2023]
Abstract
OBJECTIVES Pre-clinical models of human atherosclerosis are extensively used; however, traditional histological methods do not allow for a holistic view of vascular lesions. We describe an ex-vivo, high-resolution MRI method that allows the 3 dimensional imaging of the vessel for aortic plaque visualization and quantification. MATERIALS AND METHODS Aortas from apolipoprotein-E-deficient (apoE-/-) mice fed an atherogenic diet (group 1) or a control diet (group 2) were subjected to 14 T MR imaging using a 3D gradient echo sequence. The obtained data sets were reconstructed (Matlab), segmented, and analyzed (Avizo). The aortas were further sectioned and subjected to traditional histological analysis (Oil-Red O and hematoxylin staining) for comparison. RESULTS A resolution up to 15 × 10x10 μm3 revealed that plaque burden (mm3) was significantly (p < 0.05) higher in group 1 (0.41 ± 0.25, n = 4) than in group 2 (0.01 ± 0.01, n = 3). The achieved resolution provided similar detail on the plaque and the vessel wall morphology compared with histology. Digital image segmentation of the aorta's lumen, plaque, and wall offered three-dimensional visualizations of the entire, intact aortas. DISCUSSION 14 T MR microscopy provided histology-like details of pathologically relevant vascular lesions. This work may provide the path research needs to take to enable plaque characterization in clinical applications.
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Affiliation(s)
- Rita Castro
- Department of Nutritional Sciences, Penn State University, PA, 16802, University Park, USA
- Faculty of Pharmacy, Universidade de Lisboa, Lisbon, Portugal
| | - Sean Gullette
- Huck Institutes of The Life Sciences, Penn State University, PA, 16802, University Park, USA
| | - Courtney Whalen
- Department of Nutritional Sciences, Penn State University, PA, 16802, University Park, USA
| | - Floyd J Mattie
- Department of Nutritional Sciences, Penn State University, PA, 16802, University Park, USA
| | - Ximing Ge
- Department of Nutritional Sciences, Penn State University, PA, 16802, University Park, USA
| | - A Catharine Ross
- Department of Nutritional Sciences, Penn State University, PA, 16802, University Park, USA
| | - Thomas Neuberger
- Huck Institutes of The Life Sciences, Penn State University, PA, 16802, University Park, USA.
- Department of Biomedical Engineering, Penn State University, PA, 16802, University Park, USA.
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7
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Bienstock S, Lin F, Blankstein R, Leipsic J, Cardoso R, Ahmadi A, Gelijns A, Patel K, Baldassarre LA, Hadley M, LaRocca G, Sanz J, Narula J, Chandrashekhar YS, Shaw LJ, Fuster V. Advances in Coronary Computed Tomographic Angiographic Imaging of Atherosclerosis for Risk Stratification and Preventive Care. JACC Cardiovasc Imaging 2023; 16:1099-1115. [PMID: 37178070 DOI: 10.1016/j.jcmg.2023.02.002] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 01/04/2023] [Accepted: 02/01/2023] [Indexed: 05/15/2023]
Abstract
The diagnostic evaluation of coronary artery disease is undergoing a dramatic transformation with a new focus on atherosclerotic plaque. This review details the evidence needed for effective risk stratification and targeted preventive care based on recent advances in automated measurement of atherosclerosis from coronary computed tomography angiography (CTA). To date, research findings support that automated stenosis measurement is reasonably accurate, but evidence on variability by location, artery size, or image quality is unknown. The evidence for quantification of atherosclerotic plaque is unfolding, with strong concordance reported between coronary CTA and intravascular ultrasound measurement of total plaque volume (r >0.90). Statistical variance is higher for smaller plaque volumes. Limited data are available on how technical or patient-specific factors result in measurement variability by compositional subgroups. Coronary artery dimensions vary by age, sex, heart size, coronary dominance, and race and ethnicity. Accordingly, quantification programs excluding smaller arteries affect accuracy for women, patients with diabetes, and other patient subsets. Evidence is unfolding that quantification of atherosclerotic plaque is useful to enhance risk prediction, yet more evidence is required to define high-risk patients across varied populations and to determine whether such information is incremental to risk factors or currently used coronary computed tomography techniques (eg, coronary artery calcium scoring or visual assessment of plaque burden or stenosis). In summary, there is promise for the utility of coronary CTA quantification of atherosclerosis, especially if it can lead to targeted and more intensive cardiovascular prevention, notably for those patients with nonobstructive coronary artery disease and high-risk plaque features. The new quantification techniques available to imagers must not only provide sufficient added value to improve patient care, but also add minimal and reasonable cost to alleviate the financial burden on our patients and the health care system.
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Affiliation(s)
- Solomon Bienstock
- Division of Cardiology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Fay Lin
- Division of Cardiology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Ron Blankstein
- Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Jonathon Leipsic
- University of British Columbia, Vancouver, British Columbia, Canada
| | - Rhanderson Cardoso
- Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Amir Ahmadi
- Division of Cardiology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Annetine Gelijns
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Krishna Patel
- Division of Cardiology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Lauren A Baldassarre
- Department of Cardiovascular Medicine and Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Michael Hadley
- Division of Cardiology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Gina LaRocca
- Division of Cardiology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Javier Sanz
- Division of Cardiology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Jagat Narula
- Division of Cardiology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | | | - Leslee J Shaw
- Division of Cardiology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA; Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
| | - Valentin Fuster
- Division of Cardiology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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Amini A, Jafari E, Pourbehi MR, Iranpour D, Nemati R, Ahmadzadehfar H, Assadi M. Potential Role of Somatostatin Receptor Scintigraphy for In Vivo Imaging of Vulnerable Atherosclerotic Plaques and Its Association with Myocardial Perfusion Imaging Finding: A Preliminary Study. Mol Imaging Radionucl Ther 2023; 32:123-130. [PMID: 37337773 PMCID: PMC10284178 DOI: 10.4274/mirt.galenos.2022.08860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 08/15/2022] [Indexed: 06/21/2023] Open
Abstract
Objectives This study was conducted to detect atherosclerotic plaques with somatostatin receptor scintigraphy (SRS) using Tc-99m-octreotide that binds to somatostatin receptor-2. Methods Of the 783 patients referred for myocardial perfusion imaging (MPI), 52 underwent additional chest single-photon emission computed tomography (SPECT) with Tc-99m-octreotide and participated in this study. In addition, 43 patients who underwent Tc-99m-octreotide scan for neuroendocrine tumor (NET) also received cardiac SPECT. Angiography was performed within 1 month after SRS for 19 patients who showed intensive uptake in SRS and had cardiac risk factors. Results Of 52 patients who underwent MPI and SRS, 15 showed intensive cardiac uptake in SRS. Moreover, of 43 patients who were referred for NET, 4 patients had marked cardiac uptake in SRS in the heart. Nineteen patients including 12 women and 7 men aged 28 to 84 (58±8.04) years underwent coronary angiography. SRS and angiography in the left anterior descending territory were concordant in 15/19 (79%) patients, whereas only 7/15 (46%) cases had concordant MPI and angiography results. In the right coronary artery territory, SRS and angiography were concordant in 16/19 (84%) cases, while MPI and angiography were concordant in 11/15 (73%) cases. In the left circumflex artery territory, SRS and angiography were concordant in 15/19 (79%) cases, whereas MPI and angiography were concordant in 6/15 (40%) cases. In the remaining 76 patients who did not undergo coronary angiography based on cardiovascular profile and SRS, no cardiac events occurred in a follow-up of 2-11 months (7.52±2.71). Conclusion Tc-99m-octreotide uptake was more concordant with coronary plaques relative to MPI findings, suggesting a potential role for Tc-99m-octreotide in the evaluation of atherosclerosis.
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Affiliation(s)
- Abdullatif Amini
- Bushehr University of Medical Sciences Faculty of Medicine, Bushehr Medical Heart Center, Bushehr, Iran
| | - Esmail Jafari
- The Persian Gulf Nuclear Medicine Research Center, Bushehr Medical University Hospital, School of Medicine, Bushehr University of Medical Sciences, Department of Molecular Imaging and Theranostics, Bushehr, Iran
| | - Mohammad Reza Pourbehi
- Bushehr University of Medical Sciences Faculty of Medicine, Bushehr Medical Heart Center, Bushehr, Iran
| | - Dariush Iranpour
- Bushehr University of Medical Sciences Faculty of Medicine, Bushehr Medical Heart Center, Bushehr, Iran
| | - Reza Nemati
- Bushehr University of Medical Sciences, Bushehr Medical University Hospital, Department of Neurology, Bushehr, Iran
| | | | - Majid Assadi
- The Persian Gulf Nuclear Medicine Research Center, Bushehr Medical University Hospital, School of Medicine, Bushehr University of Medical Sciences, Department of Molecular Imaging and Theranostics, Bushehr, Iran
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Hampe N, van Velzen SGM, Planken RN, Henriques JPS, Collet C, Aben JP, Voskuil M, Leiner T, Išgum I. Deep learning-based detection of functionally significant stenosis in coronary CT angiography. Front Cardiovasc Med 2022; 9:964355. [PMID: 36457806 PMCID: PMC9705580 DOI: 10.3389/fcvm.2022.964355] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 10/17/2022] [Indexed: 07/20/2023] Open
Abstract
Patients with intermediate anatomical degree of coronary artery stenosis require determination of its functional significance. Currently, the reference standard for determining the functional significance of a stenosis is invasive measurement of the fractional flow reserve (FFR), which is associated with high cost and patient burden. To address these drawbacks, FFR can be predicted non-invasively from a coronary CT angiography (CCTA) scan. Hence, we propose a deep learning method for predicting the invasively measured FFR of an artery using a CCTA scan. The study includes CCTA scans of 569 patients from three hospitals. As reference for the functional significance of stenosis, FFR was measured in 514 arteries in 369 patients, and in the remaining 200 patients, obstructive coronary artery disease was ruled out by Coronary Artery Disease-Reporting and Data System (CAD-RADS) category 0 or 1. For prediction, the coronary tree is first extracted and used to reconstruct an MPR for the artery at hand. Thereafter, the coronary artery is characterized by its lumen, its attenuation and the area of the coronary artery calcium in each artery cross-section extracted from the MPR using a CNN. Additionally, characteristics indicating the presence of bifurcations and information indicating whether the artery is a main branch or a side-branch of a main artery are derived from the coronary artery tree. All characteristics are fed to a second network that predicts the FFR value and classifies the presence of functionally significant stenosis. The final result is obtained by merging the two predictions. Performance of our method is evaluated on held out test sets from multiple centers and vendors. The method achieves an area under the receiver operating characteristics curve (AUC) of 0.78, outperforming other works that do not require manual correction of the segmentation of the artery. This demonstrates that our method may reduce the number of patients that unnecessarily undergo invasive measurements.
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Affiliation(s)
- Nils Hampe
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands
- Amsterdam Cardiovascular Sciences, Heart Failure and Arrhythmias, Amsterdam, Netherlands
- Informatics Institute, University of Amsterdam, Amsterdam, Netherlands
| | - Sanne G. M. van Velzen
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands
- Amsterdam Cardiovascular Sciences, Heart Failure and Arrhythmias, Amsterdam, Netherlands
- Informatics Institute, University of Amsterdam, Amsterdam, Netherlands
| | - R. Nils Planken
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands
| | - José P. S. Henriques
- AMC Heart Center, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands
| | - Carlos Collet
- Onze Lieve Vrouwziekenhuis, Cardiovascular Center Aalst, Aalst, Belgium
| | | | - Michiel Voskuil
- Department of Cardiology, University Medical Centre Utrecht, Utrecht, Netherlands
| | - Tim Leiner
- Department of Radiology, University Medical Center Utrecht, Utrecht, Netherlands
- Department of Radiology, Mayo Clinic, Rochester, MN, United States
| | - Ivana Išgum
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands
- Amsterdam Cardiovascular Sciences, Heart Failure and Arrhythmias, Amsterdam, Netherlands
- Informatics Institute, University of Amsterdam, Amsterdam, Netherlands
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands
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10
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Automated Classification of Atherosclerotic Radiomics Features in Coronary Computed Tomography Angiography (CCTA). Diagnostics (Basel) 2022; 12:diagnostics12071660. [PMID: 35885564 PMCID: PMC9318450 DOI: 10.3390/diagnostics12071660] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 06/23/2022] [Accepted: 07/01/2022] [Indexed: 12/24/2022] Open
Abstract
Radiomics is the process of extracting useful quantitative features of high-dimensional data that allows for automated disease classification, including atherosclerotic disease. Hence, this study aimed to quantify and extract the radiomic features from Coronary Computed Tomography Angiography (CCTA) images and to evaluate the performance of automated machine learning (AutoML) model in classifying the atherosclerotic plaques. In total, 202 patients who underwent CCTA examination at Institut Jantung Negara (IJN) between September 2020 and May 2021 were selected as they met the inclusion criteria. Three primary coronary arteries were segmented on axial sectional images, yielding a total of 606 volume of interest (VOI). Subsequently, the first order, second order, and shape order of radiomic characteristics were extracted for each VOI. Model 1, Model 2, Model 3, and Model 4 were constructed using AutoML-based Tree-Pipeline Optimization Tools (TPOT). The heatmap confusion matrix, recall (sensitivity), precision (PPV), F1 score, accuracy, receiver operating characteristic (ROC), and area under the curve (AUC) were analysed. Notably, Model 1 with the first-order features showed superior performance in classifying the normal coronary arteries (F1 score: 0.88; Inverse F1 score: 0.94), as well as in classifying the calcified (F1 score: 0.78; Inverse F1 score: 0.91) and mixed plaques (F1 score: 0.76; Inverse F1 score: 0.86). Moreover, Model 2 consisting of second-order features was proved useful, specifically in classifying the non-calcified plaques (F1 score: 0.63; Inverse F1 score: 0.92) which are a key point for prediction of cardiac events. Nevertheless, Model 3 comprising the shape-based features did not contribute to the classification of atherosclerotic plaques. Overall, TPOT shown promising capabilities in terms of finding the best pipeline and tailoring the model using CCTA-based radiomic datasets.
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11
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Mostafa A, Ghanem AM, El-Shatoury M, Basha T. Improved Centerline Extraction in Fully Automated Coronary Ostium Localization and Centerline Extraction Framework using Deep Learning. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:3846-3849. [PMID: 34892073 DOI: 10.1109/embc46164.2021.9629655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Coronary artery extraction in cardiac CT angiography (CCTA) image volume is a necessary step for any quantitative assessment of stenoses and atherosclerotic plaque. In this work, we propose a fully automated workflow that depends on convolutional networks to extract the centerlines of the coronary arteries from CCTA image volumes, starting from identifying the ostium points and then tracking the vessel till its end based on its radius and direction. First, a regression U-Net is employed to identify the ostium points in the image volume, then these points are fed to an orientation and radius predictor CNN model to track and extract each artery till its end point. Our results show that an average of 96% of the ostium points were identified and located within less than 5mm from their true location. The coronary arteries centerlines extraction was performed with high accuracy and lower number of training parameters making it suitable for real clinical applications and continuous learning.
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12
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Liu H, Wingert A, Wang J, Zhang J, Wang X, Sun J, Chen F, Khalid SG, Jiang J, Zheng D. Extraction of Coronary Atherosclerotic Plaques From Computed Tomography Imaging: A Review of Recent Methods. Front Cardiovasc Med 2021; 8:597568. [PMID: 33644127 PMCID: PMC7903898 DOI: 10.3389/fcvm.2021.597568] [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] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 01/18/2021] [Indexed: 12/21/2022] Open
Abstract
Background: Atherosclerotic plaques are the major cause of coronary artery disease (CAD). Currently, computed tomography (CT) is the most commonly applied imaging technique in the diagnosis of CAD. However, the accurate extraction of coronary plaque geometry from CT images is still challenging. Summary of Review: In this review, we focused on the methods in recent studies on the CT-based coronary plaque extraction. According to the dimension of plaque extraction method, the studies were categorized into two-dimensional (2D) and three-dimensional (3D) ones. In each category, the studies were analyzed in terms of data, methods, and evaluation. We summarized the merits and limitations of current methods, as well as the future directions for efficient and accurate extraction of coronary plaques using CT imaging. Conclusion: The methodological innovations are important for more accurate CT-based assessment of coronary plaques in clinical applications. The large-scale studies, de-blooming algorithms, more standardized datasets, and more detailed classification of non-calcified plaques could improve the accuracy of coronary plaque extraction from CT images. More multidimensional geometric parameters can be derived from the 3D geometry of coronary plaques. Additionally, machine learning and automatic 3D reconstruction could improve the efficiency of coronary plaque extraction in future studies.
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Affiliation(s)
- Haipeng Liu
- Research Centre for Intelligent Healthcare, Coventry University, Coventry, United Kingdom.,Faculty of Health, Education, Medicine, and Social Care, Anglia Ruskin University, Chelmsford, United Kingdom
| | - Aleksandra Wingert
- Faculty of Health, Education, Medicine, and Social Care, Anglia Ruskin University, Chelmsford, United Kingdom
| | - Jian'an Wang
- Department of Cardiology, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Jucheng Zhang
- Department of Clinical Engineering, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Xinhong Wang
- Department of Radiology, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Jianzhong Sun
- Department of Radiology, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Fei Chen
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Syed Ghufran Khalid
- Research Centre for Intelligent Healthcare, Coventry University, Coventry, United Kingdom
| | - Jun Jiang
- Department of Cardiology, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, China
| | - Dingchang Zheng
- Research Centre for Intelligent Healthcare, Coventry University, Coventry, United Kingdom
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13
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Weikert T, Francone M, Abbara S, Baessler B, Choi BW, Gutberlet M, Hecht EM, Loewe C, Mousseaux E, Natale L, Nikolaou K, Ordovas KG, Peebles C, Prieto C, Salgado R, Velthuis B, Vliegenthart R, Bremerich J, Leiner T. Machine learning in cardiovascular radiology: ESCR position statement on design requirements, quality assessment, current applications, opportunities, and challenges. Eur Radiol 2020; 31:3909-3922. [PMID: 33211147 PMCID: PMC8128798 DOI: 10.1007/s00330-020-07417-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Revised: 09/01/2020] [Accepted: 10/13/2020] [Indexed: 12/31/2022]
Abstract
Abstract Machine learning offers great opportunities to streamline and improve clinical care from the perspective of cardiac imagers, patients, and the industry and is a very active scientific research field. In light of these advances, the European Society of Cardiovascular Radiology (ESCR), a non-profit medical society dedicated to advancing cardiovascular radiology, has assembled a position statement regarding the use of machine learning (ML) in cardiovascular imaging. The purpose of this statement is to provide guidance on requirements for successful development and implementation of ML applications in cardiovascular imaging. In particular, recommendations on how to adequately design ML studies and how to report and interpret their results are provided. Finally, we identify opportunities and challenges ahead. While the focus of this position statement is ML development in cardiovascular imaging, most considerations are relevant to ML in radiology in general. Key Points • Development and clinical implementation of machine learning in cardiovascular imaging is a multidisciplinary pursuit. • Based on existing study quality standard frameworks such as SPIRIT and STARD, we propose a list of quality criteria for ML studies in radiology. • The cardiovascular imaging research community should strive for the compilation of multicenter datasets for the development, evaluation, and benchmarking of ML algorithms. Supplementary Information The online version contains supplementary material available at (10.1007/s00330-020-07417-0).
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Affiliation(s)
- Thomas Weikert
- Department of Radiology, University Hospital Basel, University of Basel, Petersgraben 4, 4031, Basel, Switzerland.
| | - Marco Francone
- Department of Radiological, Oncological and Pathological Sciences, Sapienza University of Rome, Policlinico Umberto I, V.le Regina Elena 324, 00161, Rome, Italy
| | - Suhny Abbara
- Department of Radiology, UT Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX, 75390-9316, USA
| | - Bettina Baessler
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Raemistrasse 100, 8091, Zurich, Switzerland
| | - Byoung Wook Choi
- Radiology Department, Research Institute of Radiological Science, Center for Clinical Imaging Data Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea
| | - Matthias Gutberlet
- Department of Diagnostic and Interventional Radiology, Heart Center Leipzig - University Leipzig, Strümpellstrasse 39, 04289, Leipzig, Germany
| | - Elizabeth M Hecht
- Department of Radiology, Weill Cornell Medicine, 520 East 70th Street, New York, NY, 10021, USA
| | - Christian Loewe
- Division of Cardiovascular and Interventional Radiology, Department of Bioimaging and Image-Guided Therapy, Medical University Vienna, Waehringer Guertel 18-20, A-1090, Vienna, Austria
| | - Elie Mousseaux
- Department of Radiology, Hôpital Européen Georges Pompidou, APHP, University of Paris & INSERM, U970 29 rue Leblanc, 75015, Paris, France
| | - Luigi Natale
- Radiological and Haematological Sciences Department, Fondazione Policlinico Universitario A. Gemelli- IRCCS, Università Cattolica S. Cuore, Largo Agostino Gemelli 8, 00168, Rome, Italy
| | - Konstantin Nikolaou
- Department of Diagnostic and Interventional Radiology, University Hospital Tuebingen, Hoppe-Seyler-Strasse 3, 72076, Tübingen, Germany
| | - Karen G Ordovas
- Department of Radiology and Biomedical Imaging, University of California- San Francisco, 505 Parnassus Ave, M396 Box 0628, San Francisco, CA, 94143-0628, USA
| | - Charles Peebles
- Department of Radiology, University Hospital Southampton, Tremona Road, Southampton, SO16 6YD, UK
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, SE1 7EH, UK
| | - Rodrigo Salgado
- Department of Radiology, Antwerp University Hospital & Holy Heart Hospital Lier, Wilrijkstraat 10, 2650, Edegem, Belgium
| | - Birgitta Velthuis
- Department of Radiology, Utrecht University Medical Center, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
| | - Rozemarijn Vliegenthart
- Department of Radiology, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands
| | - Jens Bremerich
- Department of Radiology, University Hospital Basel, University of Basel, Petersgraben 4, 4031, Basel, Switzerland
| | - Tim Leiner
- Department of Radiology, Utrecht University Medical Center, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
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14
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Lee SE, Sung JM, Andreini D, Al-Mallah MH, Budoff MJ, Cademartiri F, Chinnaiyan K, Choi JH, Chun EJ, Conte E, Gottlieb I, Hadamitzky M, Kim YJ, Lee BK, Leipsic JA, Maffei E, Marques H, de Araújo Gonçalves P, Pontone G, Shin S, Stone PH, Samady H, Virmani R, Narula J, Berman DS, Shaw LJ, Bax JJ, Lin FY, Min JK, Chang HJ. Per-lesion versus per-patient analysis of coronary artery disease in predicting the development of obstructive lesions: the Progression of AtheRosclerotic PlAque DetermIned by Computed TmoGraphic Angiography Imaging (PARADIGM) study. Int J Cardiovasc Imaging 2020; 36:2357-2364. [PMID: 32779077 DOI: 10.1007/s10554-020-01960-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Accepted: 07/30/2020] [Indexed: 10/23/2022]
Abstract
To determine whether the assessment of individual plaques is superior in predicting the progression to obstructive coronary artery disease (CAD) on serial coronary computed tomography angiography (CCTA) than per-patient assessment. From a multinational registry of 2252 patients who underwent serial CCTA at a ≥ 2-year inter-scan interval, patients with only non-obstructive lesions at baseline were enrolled. CCTA was quantitatively analyzed at both the per-patient and per-lesion level. Models predicting the development of an obstructive lesion at follow up using either the per-patient or per-lesion level CCTA measures were constructed and compared. From 1297 patients (mean age 60 ± 9 years, 43% men) enrolled, a total of 3218 non-obstructive lesions were identified at baseline. At follow-up (inter-scan interval: 3.8 ± 1.6 years), 76 lesions (2.4%, 60 patients) became obstructive, defined as > 50% diameter stenosis. The C-statistics of Model 1, adjusted only by clinical risk factors, was 0.684. The addition of per-patient level total plaque volume (PV) and the presence of high-risk plaque (HRP) features to Model 1 improved the C-statistics to 0.825 [95% confidence interval (CI) 0.823-0.827]. When per-lesion level PV and the presence of HRP were added to Model 1, the predictive value of the model improved the C-statistics to 0.895 [95% CI 0.893-0.897]. The model utilizing per-lesion level CCTA measures was superior to the model utilizing per-patient level CCTA measures in predicting the development of an obstructive lesion (p < 0.001). Lesion-level analysis of coronary atherosclerotic plaques with CCTA yielded better predictive power for the development of obstructive CAD than the simple quantification of total coronary atherosclerotic burden at a per-patient level.Clinical Trial Registration: ClinicalTrials.gov NCT0280341.
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Affiliation(s)
- Sang-Eun Lee
- Division of Cardiology, Department of Internal Medicine, Ewha Womans University Seoul Hospital, Ewha Womans University College of Medicine, Seoul, South Korea.,Yonsei-Cedars-Sinai Integrative Cardiovascular Imaging Research Center, Yonsei University College of Medicine, Yonsei University Health System, Seoul, South Korea
| | - Ji Min Sung
- Yonsei-Cedars-Sinai Integrative Cardiovascular Imaging Research Center, Yonsei University College of Medicine, Yonsei University Health System, Seoul, South Korea.,Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Yonsei University Health System, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea
| | | | - Mouaz H Al-Mallah
- Houston Methodist DeBakey Heart & Vascular Center, Houston Methodist Hospital, Houston, TX, USA
| | - Matthew J Budoff
- Department of Medicine, Los Angeles Biomedical Research Institute, Torrance, CA, USA
| | | | | | | | - Eun Ju Chun
- Seoul National University Bundang Hospital, Seongnam, South Korea
| | | | - Ilan Gottlieb
- Department of Radiology, Casa de Saude São Jose, Rio de Janeiro, Brazil
| | - Martin Hadamitzky
- Department of Radiology and Nuclear Medicine, German Heart Center Munich, Munich, Germany
| | - Yong Jin Kim
- Department of Internal Medicine, Seoul National University College of Medicine, Cardiovascular Center, Seoul National University Hospital, Seoul, South Korea
| | - Byoung Kwon Lee
- Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Jonathon A Leipsic
- Department of Medicine and Radiology, University of British Columbia, Vancouver, BC, Canada
| | - Erica Maffei
- Department of Radiology, Area Vasta 1/ASUR Marche, Urbino, Italy
| | - Hugo Marques
- UNICA, Unit of Cardiovascular Imaging, Hospital da Luz, Lisbon, Portugal
| | | | | | - Sanghoon Shin
- Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Yonsei University Health System, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea
| | - Peter H Stone
- Division of Cardiovascular Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Habib Samady
- Division of Cardiology, Emory University School of Medicine, Atlanta, GA, USA
| | - Renu Virmani
- Department of Pathology, CVPath Institute, Gaithersburg, MD, USA
| | - Jagat Narula
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Daniel S Berman
- Department of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Leslee J Shaw
- Department of Radiology, New York-Presbyterian Hospital and Weill Cornell Medicine, New York, NY, USA
| | - Jeroen J Bax
- Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Fay Y Lin
- Department of Radiology, New York-Presbyterian Hospital and Weill Cornell Medicine, New York, NY, USA
| | - James K Min
- Department of Radiology, New York-Presbyterian Hospital and Weill Cornell Medicine, New York, NY, USA
| | - Hyuk-Jae Chang
- Yonsei-Cedars-Sinai Integrative Cardiovascular Imaging Research Center, Yonsei University College of Medicine, Yonsei University Health System, Seoul, South Korea. .,Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Yonsei University Health System, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea.
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15
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Infante T, Del Viscovo L, De Rimini ML, Padula S, Caso P, Napoli C. Network Medicine: A Clinical Approach for Precision Medicine and Personalized Therapy in Coronary Heart Disease. J Atheroscler Thromb 2020; 27:279-302. [PMID: 31723086 PMCID: PMC7192819 DOI: 10.5551/jat.52407] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Accepted: 09/24/2019] [Indexed: 12/13/2022] Open
Abstract
Early identification of coronary atherosclerotic pathogenic mechanisms is useful for predicting the risk of coronary heart disease (CHD) and future cardiac events. Epigenome changes may clarify a significant fraction of this "missing hereditability", thus offering novel potential biomarkers for prevention and care of CHD. The rapidly growing disciplines of systems biology and network science are now poised to meet the fields of precision medicine and personalized therapy. Network medicine integrates standard clinical recording and non-invasive, advanced cardiac imaging tools with epigenetics into deep learning for in-depth CHD molecular phenotyping. This approach could potentially explore developing novel drugs from natural compounds (i.e. polyphenols, folic acid) and repurposing current drugs, such as statins and metformin. Several clinical trials have exploited epigenetic tags and epigenetic sensitive drugs both in primary and secondary prevention. Due to their stability in plasma and easiness of detection, many ongoing clinical trials are focused on the evaluation of circulating miRNAs (e.g. miR-8059 and miR-320a) in blood, in association with imaging parameters such as coronary calcifications and stenosis degree detected by coronary computed tomography angiography (CCTA), or functional parameters provided by FFR/CT and PET/CT. Although epigenetic modifications have also been prioritized through network based approaches, the whole set of molecular interactions (interactome) in CHD is still under investigation for primary prevention strategies.
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Affiliation(s)
- Teresa Infante
- Department of Advanced Clinical and Surgical Sciences, University of Campania “Luigi Vanvitelli”, Naples, Italy
| | - Luca Del Viscovo
- Department of Precision Medicine, Section of Diagnostic Imaging, University of Campania “Luigi Vanvitelli”, Naples, Italy
| | | | - Sergio Padula
- Department of Cardiology, A.O.R.N. Dei Colli, Monaldi Hospital, Naples, Italy
| | - Pio Caso
- Department of Cardiology, A.O.R.N. Dei Colli, Monaldi Hospital, Naples, Italy
| | - Claudio Napoli
- Clinical Department of Internal Medicine and Specialistics, Department of Advanced Clinical and Surgical Sciences, University of Campania “Luigi Vanvitelli”, Naples, Italy
- IRCCS SDN, Naples, Italy
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16
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Machine Learning and Deep Neural Networks Applications in Computed Tomography for Coronary Artery Disease and Myocardial Perfusion. J Thorac Imaging 2020; 35 Suppl 1:S58-S65. [DOI: 10.1097/rti.0000000000000490] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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17
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Ghanem AM, Hamimi AH, Gharib AM, Abd-Elmoniem KZ. Automatic Assessment of 3D Coronary Artery Distensibility from Time-Resolved Coronary CT Angiography .. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:836-840. [PMID: 31946025 DOI: 10.1109/embc.2019.8856732] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Measuring coronary artery distensibility can determine the arterial remodeling type, arterial wall inflammation, and atherosclerotic plaques in early stage even before any observed narrowing in the lumen. This is crucial to promote an appropriate, preventive, and effective treatment. This study introduces a framework for calculating the 3D distensibility of the left coronary artery (LCA) from time-resolved coronary computerized tomography angiography (CCTA) images. Vesselness, region growing, and level sets are utilized for segmenting the LCA lumen in the systole and diastole CCTA time frames. The segmented arteries are then analyzed and registered using computational geometry to calculate the changes in the lumen cross-section areas between both time frames. In-vivo validation of the framework performance was accomplished against that of two radiologists and their consensus. Results demonstrate that the framework was accurate and reliable tool for measuring the coronary arteries distensibility.
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18
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Nakanishi R, Hashimoto H, Ikeda T. Improving Quality of Clinical Diagnosis Report with Single-Photon Emission Computed Tomography Myocardial Perfusion Imaging and Coronary Computed Tomography Angiography. ANNALS OF NUCLEAR CARDIOLOGY 2020; 6:86-90. [PMID: 37123487 PMCID: PMC10133933 DOI: 10.17996/anc.20-00111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Revised: 02/14/2020] [Accepted: 02/24/2020] [Indexed: 05/02/2023]
Abstract
Non-invasive cardiac imaging modalities including single-photon emission computed tomography myocardial perfusion image (SPECT-MPI) and coronary computed tomography angiography (CTA) have been widely used for diagnosis of coronary artery disease (CAD). The American Society of Nuclear Cardiology and Society of Cardiovascular Computed Tomography have recently published the guidelines for the instrumentation, acquisition, processing, interpretation, as well as reporting of SPECT and coronary CTA. These guidelines have highlighted and well documented how the imaging reporting influences medical practice for physician and treatment care for patients, suggesting that cardiac imaging reports for interpretation for patient management. This review article here summarizes improving quality of cardiac imaging reports by SPECT-MPI and coronary CTA.
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Affiliation(s)
- Rine Nakanishi
- Department of Cardiovascular Medicine, Toho University Graduate School of Medicine, Tokyo, Japan
| | - Hidenobu Hashimoto
- Department of Cardiovascular Medicine, Toho University Graduate School of Medicine, Tokyo, Japan
| | - Takanori Ikeda
- Department of Cardiovascular Medicine, Toho University Graduate School of Medicine, Tokyo, Japan
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19
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Vidal-Perez R, Abou Jokh Casas C, Agra-Bermejo RM, Alvarez-Alvarez B, Grapsa J, Fontes-Carvalho R, Rigueiro Veloso P, Garcia Acuña JM, Gonzalez-Juanatey JR. Myocardial infarction with non-obstructive coronary arteries: A comprehensive review and future research directions. World J Cardiol 2019; 11:305-315. [PMID: 31908730 PMCID: PMC6937414 DOI: 10.4330/wjc.v11.i12.305] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2019] [Revised: 09/17/2019] [Accepted: 10/29/2019] [Indexed: 02/06/2023] Open
Abstract
Acute coronary syndromes constitute a variety of myocardial injury presentations that include a subset of patients presenting with myocardial infarction with non-obstructive coronary arteries (MINOCA). This acute coronary syndrome differs from type 1 myocardial infarction (MI) regarding patient characteristics, presentation, physiopathology, management, treatment, and prognosis. Two-thirds of MINOCA subjects present ST-segment elevation; MINOCA patients are younger, are more often female and tend to have fewer cardiovascular risk factors. Moreover, MINOCA is a working diagnosis, and defining the aetiologic mechanism is relevant because it affects patient care and prognosis. In the absence of relevant coronary artery disease, myocardial ischaemia might be triggered by an acute event in epicardial coronary arteries, coronary microcirculation, or both. Epicardial causes of MINOCA include coronary plaque disruption, coronary dissection, and coronary spasm. Microvascular MINOCA mechanisms involve microvascular coronary spasm, takotsubo syndrome (TTS), myocarditis, and coronary thromboembolism. Coronary angiography with non-significant coronary stenosis and left ventriculography are first-line tests in the differential study of MINOCA patients. The diagnostic arsenal includes invasive and non-invasive techniques. Medical history and echocardiography can help indicate vasospasm or thrombosis, if one finite coronary territory is affected, or specify TTS if apical ballooning is present. Intravascular ultrasound, optical coherence tomography, and provocative testing are encouraged. Cardiac magnetic resonance is a cornerstone in myocarditis diagnosis. MINOCA is not a benign diagnosis, and its polymorphic forms differ in prognosis. MINOCA care varies across centres, and future multi-centre clinical trials with standardized criteria may have a positive impact on defining optimal cardiovascular care for MINOCA patients.
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Affiliation(s)
- Rafael Vidal-Perez
- Cardiology Department, Hospital Clinico Universitario de Santiago, Santiago de Compostela 15706, Spain
| | - Charigan Abou Jokh Casas
- Cardiology Department, Hospital Clinico Universitario de Santiago, Santiago de Compostela 15706, Spain
| | - Rosa Maria Agra-Bermejo
- Cardiology Department, Hospital Clinico Universitario de Santiago, Santiago de Compostela 15706, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Santiago de Compostela 15706, Spain
| | - Belén Alvarez-Alvarez
- Cardiology Department, Hospital Clinico Universitario de Santiago, Santiago de Compostela 15706, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Santiago de Compostela 15706, Spain
| | - Julia Grapsa
- Cardiology Department, St Bartholomew Hospital, Barts Health Trust, London EC1A 7BE, United Kingdom
| | - Ricardo Fontes-Carvalho
- Cardiology Department, Centro Hospitalar Gaia, Vila Nova Gaia 4434-502, Portugal
- Faculty of Medicine University of Porto, Porto 4200-319, Portugal
| | - Pedro Rigueiro Veloso
- Cardiology Department, Hospital Clinico Universitario de Santiago, Santiago de Compostela 15706, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Santiago de Compostela 15706, Spain
| | - Jose Maria Garcia Acuña
- Cardiology Department, Hospital Clinico Universitario de Santiago, Santiago de Compostela 15706, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Santiago de Compostela 15706, Spain
| | - Jose Ramon Gonzalez-Juanatey
- Cardiology Department, Hospital Clinico Universitario de Santiago, Santiago de Compostela 15706, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Santiago de Compostela 15706, Spain
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20
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Automated Ventricular System Segmentation in Paediatric Patients Treated for Hydrocephalus Using Deep Learning Methods. BIOMED RESEARCH INTERNATIONAL 2019; 2019:3059170. [PMID: 31360710 PMCID: PMC6642766 DOI: 10.1155/2019/3059170] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/10/2019] [Revised: 05/31/2019] [Accepted: 06/23/2019] [Indexed: 11/24/2022]
Abstract
Hydrocephalus is a common neurological condition that can have traumatic ramifications and can be lethal without treatment. Nowadays, during therapy radiologists have to spend a vast amount of time assessing the volume of cerebrospinal fluid (CSF) by manual segmentation on Computed Tomography (CT) images. Further, some of the segmentations are prone to radiologist bias and high intraobserver variability. To improve this, researchers are exploring methods to automate the process, which would enable faster and more unbiased results. In this study, we propose the application of U-Net convolutional neural network in order to automatically segment CT brain scans for location of CSF. U-Net is a neural network that has proven to be successful for various interdisciplinary segmentation tasks. We optimised training using state of the art methods, including “1cycle” learning rate policy, transfer learning, generalized dice loss function, mixed float precision, self-attention, and data augmentation. Even though the study was performed using a limited amount of data (80 CT images), our experiment has shown near human-level performance. We managed to achieve a 0.917 mean dice score with 0.0352 standard deviation on cross validation across the training data and a 0.9506 mean dice score on a separate test set. To our knowledge, these results are better than any known method for CSF segmentation in hydrocephalic patients, and thus, it is promising for potential practical applications.
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