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14. Revealing unforeseen diagnostic image features with deep learning by detecting cardiovascular diseases from apical four-chamber ultrasounds

Author

Cheng, Li-Hsin, Bosch, Pablo B. J., Hofman, Rutger F. H., Brakenhoff, Timo B., Bruggemans, Eline F., van der Geest, Rob J., and Holman, Eduard R.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Physics - Medical Physics
Abstract

Background. With the rise of highly portable, wireless, and low-cost ultrasound devices and automatic ultrasound acquisition techniques, an automated interpretation method requiring only a limited set of views as input could make preliminary cardiovascular disease diagnoses more accessible. In this study, we developed a deep learning (DL) method for automated detection of impaired left ventricular (LV) function and aortic valve (AV) regurgitation from apical four-chamber (A4C) ultrasound cineloops and investigated which anatomical structures or temporal frames provided the most relevant information for the DL model to enable disease classification. Methods and Results. A4C ultrasounds were extracted from 3,554 echocardiograms of patients with either impaired LV function (n=928), AV regurgitation (n=738), or no significant abnormalities (n=1,888). Two convolutional neural networks (CNNs) were trained separately to classify the respective disease cases against normal cases. The overall classification accuracy of the impaired LV function detection model was 86%, and that of the AV regurgitation detection model was 83%. Feature importance analyses demonstrated that the LV myocardium and mitral valve were important for detecting impaired LV function, while the tip of the mitral valve anterior leaflet, during opening, was considered important for detecting AV regurgitation. Conclusion. The proposed method demonstrated the feasibility of a 3D CNN approach in detection of impaired LV function and AV regurgitation using A4C ultrasound cineloops. The current research shows that DL methods can exploit large training data to detect diseases in a different way than conventionally agreed upon methods, and potentially reveal unforeseen diagnostic image features.

Published
2021

19. 4D Flow cardiovascular magnetic resonance consensus statement: 2023 update

Author

Bissell, Malenka M., Raimondi, Francesca, Ait Ali, Lamia, Allen, Bradley D., Barker, Alex J., Bolger, Ann, Burris, Nicholas, Carhäll, Carl-Johan, Collins, Jeremy D., Ebbers, Tino, Francois, Christopher J., Frydrychowicz, Alex, Garg, Pankaj, Geiger, Julia, Ha, Hojin, Hennemuth, Anja, Hope, Michael D., Hsiao, Albert, Johnson, Kevin, Kozerke, Sebastian, Ma, Liliana E., Markl, Michael, Martins, Duarte, Messina, Marci, Oechtering, Thekla H., van Ooij, Pim, Rigsby, Cynthia, Rodriguez-Palomares, Jose, Roest, Arno A. W., Roldán-Alzate, Alejandro, Schnell, Susanne, Sotelo, Julio, Stuber, Matthias, Syed, Ali B., Töger, Johannes, van der Geest, Rob, Westenberg, Jos, Zhong, Liang, Zhong, Yumin, Wieben, Oliver, and Dyverfeldt, Petter

Published
2023
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20. Automated 4D flow cardiac MRI pipeline to derive peak mitral inflow diastolic velocities using short-axis cine stack: two centre validation study against echocardiographic pulse-wave doppler

Author

Assadi, Hosamadin, Li, Rui, Grafton-Clarke, Ciaran, Uthayachandran, Bhalraam, Alabed, Samer, Maiter, Ahmed, Archer, Gareth, Swoboda, Peter P., Sawh, Chris, Ryding, Alisdair, Nelthorpe, Faye, Kasmai, Bahman, Ricci, Fabrizio, van der Geest, Rob J., Flather, Marcus, Vassiliou, Vassilios S., Swift, Andrew J., and Garg, Pankaj

Published
2023
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22. Signal intensity and volume of carotid intraplaque hemorrhage on magnetic resonance imaging and the risk of ipsilateral cerebrovascular events: The Plaque At RISK (PARISK) study

Author

Nies, Kelly P.H., Aizaz, Mueez, van Dam-Nolen, Dianne H.K., Goring, Timothy C.D., Schreuder, Tobien A.H.C.M.L., van Orshoven, Narender P., Postma, Alida A., Bos, Daniel, Hendrikse, Jeroen, Nederkoorn, Paul, van der Geest, Rob, van Oostenbrugge, Robert J., Mess, Werner H., and Kooi, M. Eline

Published
2024
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26. Age- and sex-specific reference values of biventricular flow components and kinetic energy by 4D flow cardiovascular magnetic resonance in healthy subjects

Author

Zhao, Xiaodan, Tan, Ru-San, Garg, Pankaj, Chai, Ping, Leng, Shuang, Bryant, Jennifer Ann, Teo, Lynette L.S., Yeo, Tee Joo, Fortier, Marielle V., Low, Ting Ting, Ong, Ching Ching, Zhang, Shuo, Van der Geest, Rob J., Allen, John C., Tan, Teng Hong, Yip, James W., Tan, Ju Le, Hughes, Marina, Plein, Sven, Westenberg, Jos J.M., and Zhong, Liang

Published
2023
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27. Combination Special Data Augmentation and Sampling Inspection Network for Cardiac Magnetic Resonance Imaging Quality Classification

Author

Sun, Xiaowu, Cheng, Li-Hsin, van der Geest, Rob J., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Camara, Oscar, editor, Puyol-Antón, Esther, editor, Qin, Chen, editor, Sermesant, Maxime, editor, Suinesiaputra, Avan, editor, Wang, Shuo, editor, and Young, Alistair, editor

Published
2022
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28. Transformer Based Feature Fusion for Left Ventricle Segmentation in 4D Flow MRI

Author

Sun, Xiaowu, Cheng, Li-Hsin, Plein, Sven, Garg, Pankaj, van der Geest, Rob J., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Wang, Linwei, editor, Dou, Qi, editor, Fletcher, P. Thomas, editor, Speidel, Stefanie, editor, and Li, Shuo, editor

Published
2022
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29. Contrastive Learning for Echocardiographic View Integration

Author

Cheng, Li-Hsin, Sun, Xiaowu, van der Geest, Rob J., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Wang, Linwei, editor, Dou, Qi, editor, Fletcher, P. Thomas, editor, Speidel, Stefanie, editor, and Li, Shuo, editor

Published
2022
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31. Right Ventricle Segmentation via Registration and Multi-input Modalities in Cardiac Magnetic Resonance Imaging from Multi-disease, Multi-view and Multi-center

Author

Sun, Xiaowu, Cheng, Li-Hsin, van der Geest, Rob J., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Puyol Antón, Esther, editor, Pop, Mihaela, editor, Martín-Isla, Carlos, editor, Sermesant, Maxime, editor, Suinesiaputra, Avan, editor, Camara, Oscar, editor, Lekadir, Karim, editor, and Young, Alistair, editor

Published
2022
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32. Left Ventricle Segmentation via Optical-Flow-Net from Short-axis Cine MRI: Preserving the Temporal Coherence of Cardiac Motion

Author

Yan, Wenjun, Wang, Yuanyuan, Li, Zeju, van der Geest, Rob J., and Tao, Qian

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Quantitative assessment of left ventricle (LV) function from cine MRI has significant diagnostic and prognostic value for cardiovascular disease patients. The temporal movement of LV provides essential information on the contracting/relaxing pattern of heart, which is keenly evaluated by clinical experts in clinical practice. Inspired by the expert way of viewing Cine MRI, we propose a new CNN module that is able to incorporate the temporal information into LV segmentation from cine MRI. In the proposed CNN, the optical flow (OF) between neighboring frames is integrated and aggregated at feature level, such that temporal coherence in cardiac motion can be taken into account during segmentation. The proposed module is integrated into the U-net architecture without need of additional training. Furthermore, dilated convolution is introduced to improve the spatial accuracy of segmentation. Trained and tested on the Cardiac Atlas database, the proposed network resulted in a Dice index of 95% and an average perpendicular distance of 0.9 pixels for the middle LV contour, significantly outperforming the original U-net that processes each frame individually. Notably, the proposed method improved the temporal coherence of LV segmentation results, especially at the LV apex and base where the cardiac motion is difficult to follow., Comment: MICCAI 2018

Published
2018

33. Dynamic vortical flow profile quantitatively characterizes mitral valvular-left ventricular hemodynamic coupling: In vivo analysis by dynamic 3D enstrophy mapping from 4D Flow MRI

Author

Elbaz, Mohammed S. M., Le, Trung Bao, Garg, Pankaj, Roest, Arno A. W., Lelieveldt, Boudewijn P. F., Plein, Sven, Westenberg, Jos J. M., Sotiropoulos, Fotis, and van der Geest, Rob J.

Subjects
Physics - Medical Physics, Physics - Biological Physics, Physics - Fluid Dynamics, Quantitative Biology - Quantitative Methods
Abstract

Vortical blood flow in the human left ventricular (LV) inflow initiates from the mitral valve (MV) and evolves within the LV during diastolic E-filling. Hence, vortical flow links MV and LV hemodynamics. This study sought to elucidate and quantitatively characterize the in vivo 3D dynamics of LV vortical flow over E-filling and relation to MV-LV hemodynamic coupling using 4D Flow MRI flow field. 34 healthy volunteers and 5 example patients underwent 4D Flow MRI. Vortical blood flow evolution was mapped in the LV over E-wave using enstrophy density. A new dimensionless profile PMV-LV was derived as a function of both MV vortex formation time (VFT) and LV volumetric enstrophy density. Results reveal that 3D vortical flow evolution in the healthy LV follows a bi-phasic behavior with a vortical growth phase followed by a vortical decay phase. In healthy LVs studied, the PMV-LV profile showed that the vortical growth and decay phases are characterized by a vortical growth time T_growth= 1.23+/-0.25, growth rate {\alpha}=0.80+/-0.17, decay time T_decay= 0.96+/-0.39 and decay rate \b{eta}=-1.02+/-0.49. Distinctly altered parameters were found in the pilot patients studied. The derived PMV-LV profile quantitatively characterizes MV-LV hemodynamic coupling by vortical flow dynamics. Results herein unravel new insights into cardiac physiology and could enable a novel standardized methodology to study MV-LV hemodynamic coupling and association to cardiac function in health and disease., Comment: 24 Pages, 3 Figures, 2 Tables

Published
2018

34. Fully Automated Left Atrium Cavity Segmentation from 3D GE-MRI by Multi-Atlas Selection and Registration

Author

Qiao, Mengyun, Wang, Yuanyuan, van der Geest, Rob J., and Tao, Qian

Subjects
Electrical Engineering and Systems Science - Image and Video Processing
Abstract

This paper presents a fully automated method to segment the complex left atrial (LA) cavity, from 3D Gadolinium-enhanced magnetic resonance imaging (GE-MRI) scans. The proposed method consists of four steps: (1) preprocessing to convert the original GE-MRI to a probability map, (2) atlas selection to match the atlases to the target image, (3) multi-atlas registration and fusion, and (4) level-set refinement. The method was evaluated on the datasets provided by the MICCAI 2018 STACOM Challenge with 100 dataset for training. Compared to manual annotation, the proposed method achieved an average Dice overlap index of 0.88.

Published
2018

39. Sudden cardiac death after myocardial infarction: individual participant data from pooled cohorts.

Author

Peek, Niels, Hindricks, Gerhard, Akbarov, Artur, Tijssen, Jan G P, Jenkins, David A, Kapacee, Zoher, Parkes, Le Mai, Geest, Rob J van der, Longato, Enrico, Sprague, Daniel, Taleb, Youssef, Ong, Marcus, Miller, Christopher A, Shamloo, Alireza Sepehri, Albert, Christine, Barthel, Petra, Boveda, Serge, Braunschweig, Frieder, Johansen, Jens Brock, and Cook, Nancy

Subjects
CARDIAC magnetic resonance imaging, CARDIAC arrest, IMPLANTABLE cardioverter-defibrillators, SUDDEN death prevention, MYOCARDIAL infarction
Abstract

Background and Aims Risk stratification of sudden cardiac death after myocardial infarction and prevention by defibrillator rely on left ventricular ejection fraction (LVEF). Improved risk stratification across the whole LVEF range is required for decision-making on defibrillator implantation. Methods The analysis pooled 20 data sets with 140 204 post-myocardial infarction patients containing information on demographics, medical history, clinical characteristics, biomarkers, electrocardiography, echocardiography, and cardiac magnetic resonance imaging. Separate analyses were performed in patients (i) carrying a primary prevention cardioverter-defibrillator with LVEF ≤ 35% [implantable cardioverter-defibrillator (ICD) patients], (ii) without cardioverter-defibrillator with LVEF ≤ 35% (non-ICD patients ≤ 35%), and (iii) without cardioverter-defibrillator with LVEF > 35% (non-ICD patients >35%). Primary outcome was sudden cardiac death or, in defibrillator carriers, appropriate defibrillator therapy. Using a competing risk framework and systematic internal–external cross-validation, a model using LVEF only, a multivariable flexible parametric survival model, and a multivariable random forest survival model were developed and externally validated. Predictive performance was assessed by random effect meta-analysis. Results There were 1326 primary outcomes in 7543 ICD patients, 1193 in 25 058 non-ICD patients ≤35%, and 1567 in 107 603 non-ICD patients >35% during mean follow-up of 30.0, 46.5, and 57.6 months, respectively. In these three subgroups, LVEF poorly predicted sudden cardiac death (c -statistics between 0.50 and 0.56). Considering additional parameters did not improve calibration and discrimination, and model generalizability was poor. Conclusions More accurate risk stratification for sudden cardiac death and identification of low-risk individuals with severely reduced LVEF or of high-risk individuals with preserved LVEF was not feasible, neither using LVEF nor using other predictors. [ABSTRACT FROM AUTHOR]

Published
2024
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44. Training and clinical testing of artificial intelligence derived right atrial cardiovascular magnetic resonance measurements

Author

Alandejani, Faisal, Alabed, Samer, Garg, Pankaj, Goh, Ze Ming, Karunasaagarar, Kavita, Sharkey, Michael, Salehi, Mahan, Aldabbagh, Ziad, Dwivedi, Krit, Mamalakis, Michail, Metherall, Pete, Uthoff, Johanna, Johns, Chris, Rothman, Alexander, Condliffe, Robin, Hameed, Abdul, Charalampoplous, Athanasios, Lu, Haiping, Plein, Sven, Greenwood, John P., Lawrie, Allan, Wild, Jim M., de Koning, Patrick J. H., Kiely, David G., Van Der Geest, Rob, and Swift, Andrew J.

Published
2022
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45. Right ventricular energetic biomarkers from 4D Flow CMR are associated with exertional capacity in pulmonary arterial hypertension

Author

Zhao, Xiaodan, Leng, Shuang, Tan, Ru-San, Chai, Ping, Yeo, Tee Joo, Bryant, Jennifer Ann, Teo, Lynette L. S., Fortier, Marielle V., Ruan, Wen, Low, Ting Ting, Ong, Ching Ching, Zhang, Shuo, van der Geest, Rob J., Allen, John C., Hughes, Marina, Garg, Pankaj, Tan, Teng Hong, Yip, James W., Tan, Ju Le, and Zhong, Liang

Published
2022
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46. Ventricular flow analysis and its association with exertional capacity in repaired tetralogy of Fallot: 4D flow cardiovascular magnetic resonance study

Author

Zhao, Xiaodan, Hu, Liwei, Leng, Shuang, Tan, Ru-San, Chai, Ping, Bryant, Jennifer Ann, Teo, Lynette L. S., Fortier, Marielle V., Yeo, Tee Joo, Ouyang, Rong Zhen, Allen, John C., Hughes, Marina, Garg, Pankaj, Zhang, Shuo, van der Geest, Rob J., Yip, James W., Tan, Teng Hong, Tan, Ju Le, Zhong, Yumin, and Zhong, Liang

Published
2022
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47. 179 Deriving mean right atrial pressure from CMR: development of a model from the aspire registry

Author

Newman, Tom, primary, Matthews, Gareth, additional, Assadi, Hosamadin, additional, Li, Rui, additional, Grafton-Clarke, Ciaran, additional, Mehmood, Zia, additional, Kasmai, Bahman, additional, Sawh, Chris, additional, Zhong, Liang, additional, Cavalcante, João, additional, Thomson, Ross, additional, Aung, Nay, additional, van der Geest, Rob, additional, Swift, Andrew, additional, and Garg, Pankaj, additional

Published
2024
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50. Standard and emerging CMR methods for mitral regurgitation quantification

Author

Fidock, Benjamin, Archer, Gareth, Barker, Natasha, Elhawaz, Alaa, Al-Mohammad, Abdallah, Rothman, Alexander, Hose, Rod, Hall, Ian R., Grech, Ever, Briffa, Norman, Lewis, Nigel, van der Geest, Rob J., Zhang, Jun-Mei, Zhong, Liang, Swift, Andrew J., Wild, James M., De Gárate, Estefania, Bucciarelli-Ducci, Chiara, Bax, Jeroen J., Plein, Sven, Myerson, Saul, and Garg, Pankaj

Published
2021
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