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Liu Y, Nezami FR, Edelman ER. A transformer-based pyramid network for coronary calcified plaque segmentation in intravascular optical coherence tomography images. Comput Med Imaging Graph 2024; 113:102347. [PMID: 38341945 DOI: 10.1016/j.compmedimag.2024.102347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 01/31/2024] [Accepted: 01/31/2024] [Indexed: 02/13/2024]
Abstract
Characterizing coronary calcified plaque (CCP) provides essential insight into diagnosis and treatment of atherosclerosis. Intravascular optical coherence tomography (OCT) offers significant advantages for detecting CCP and even automated segmentation with recent advances in deep learning techniques. Most of current methods have achieved promising results by adopting existing convolution neural networks (CNNs) in computer vision domain. However, their performance can be detrimentally affected by unseen plaque patterns and artifacts due to inherent limitation of CNNs in contextual reasoning. To overcome this obstacle, we proposed a Transformer-based pyramid network called AFS-TPNet for robust, end-to-end segmentation of CCP from OCT images. Its encoder is built upon CSWin Transformer architecture, allowing for better perceptual understanding of calcified arteries at a higher semantic level. Specifically, an augmented feature split (AFS) module and residual convolutional position encoding (RCPE) mechanism are designed to effectively enhance the capability of Transformer in capturing both fine-grained features and global contexts. Extensive experiments showed that AFS-TPNet trained using Lovasz Loss achieved superior performance in segmentation CCP under various contexts, surpassing prior state-of-the-art CNN and Transformer architectures by more than 6.58% intersection over union (IoU) score. The application of this promising method to extract CCP features is expected to enhance clinical intervention and translational research using OCT.
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Affiliation(s)
- Yiqing Liu
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
| | - Farhad R Nezami
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Division of Cardiac Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Elazer R Edelman
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA
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2
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Salazar-Martín AG, Kalluri AS, Villanueva MA, Hughes TK, Wadsworth MH, Dao TT, Balcells M, Nezami FR, Shalek AK, Edelman ER. Single-Cell RNA Sequencing Reveals That Adaptation of Human Aortic Endothelial Cells to Antiproliferative Therapies Is Modulated by Flow-Induced Shear Stress. Arterioscler Thromb Vasc Biol 2023; 43:2265-2281. [PMID: 37732484 PMCID: PMC10659257 DOI: 10.1161/atvbaha.123.319283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 09/07/2023] [Indexed: 09/22/2023]
Abstract
BACKGROUND Endothelial cells (ECs) are capable of quickly responding in a coordinated manner to a wide array of stresses to maintain vascular homeostasis. Loss of EC cellular adaptation may be a potential marker for cardiovascular disease and a predictor of poor response to endovascular pharmacological interventions such as drug-eluting stents. Here, we report single-cell transcriptional profiling of ECs exposed to multiple stimulus classes to evaluate EC adaptation. METHODS Human aortic ECs were costimulated with both pathophysiological flows mimicking shear stress levels found in the human aorta (laminar and turbulent, ranging from 2.5 to 30 dynes/cm2) and clinically relevant antiproliferative drugs, namely paclitaxel and rapamycin. EC state in response to these stimuli was defined using single-cell RNA sequencing. RESULTS We identified differentially expressed genes and inferred the TF (transcription factor) landscape modulated by flow shear stress using single-cell RNA sequencing. These flow-sensitive markers differentiated previously identified spatially distinct subpopulations of ECs in the murine aorta. Moreover, distinct transcriptional modules defined flow- and drug-responsive EC adaptation singly and in combination. Flow shear stress was the dominant driver of EC state, altering their response to pharmacological therapies. CONCLUSIONS We showed that flow shear stress modulates the cellular capacity of ECs to respond to paclitaxel and rapamycin administration, suggesting that while responding to different flow patterns, ECs experience an impairment in their transcriptional adaptation to other stimuli.
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Affiliation(s)
- Antonio G. Salazar-Martín
- Institute for Medical Engineering and Science (A.G.S.-M., A.S.K., M.A.V., T.K.H., M.H.W., T.T.D., M.B., A.K.S., E.R.E.), Massachusetts Institute of Technology (MIT), Cambridge, MA
- Broad Institute of MIT and Harvard, Cambridge, MA (A.G.S.-M., M.A.V., T.T.D., A.K.S.)
| | - Aditya S. Kalluri
- Institute for Medical Engineering and Science (A.G.S.-M., A.S.K., M.A.V., T.K.H., M.H.W., T.T.D., M.B., A.K.S., E.R.E.), Massachusetts Institute of Technology (MIT), Cambridge, MA
| | - Martin A. Villanueva
- Institute for Medical Engineering and Science (A.G.S.-M., A.S.K., M.A.V., T.K.H., M.H.W., T.T.D., M.B., A.K.S., E.R.E.), Massachusetts Institute of Technology (MIT), Cambridge, MA
- Broad Institute of MIT and Harvard, Cambridge, MA (A.G.S.-M., M.A.V., T.T.D., A.K.S.)
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA (M.A.V., T.K.H., M.H.W., T.T.D., A.K.S.)
- Departments of Biology (M.A.V.), Massachusetts Institute of Technology, Cambridge
| | - Travis K. Hughes
- Institute for Medical Engineering and Science (A.G.S.-M., A.S.K., M.A.V., T.K.H., M.H.W., T.T.D., M.B., A.K.S., E.R.E.), Massachusetts Institute of Technology (MIT), Cambridge, MA
- Koch Institute for Integrative Cancer Research (T.K.H., M.H.W., A.K.S., E.R.E.), Massachusetts Institute of Technology (MIT), Cambridge, MA
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA (M.A.V., T.K.H., M.H.W., T.T.D., A.K.S.)
- Department of Immunology, Harvard Medical School, Boston, MA (T.K.H., M.H.W., A.K.S.)
| | - Marc H. Wadsworth
- Institute for Medical Engineering and Science (A.G.S.-M., A.S.K., M.A.V., T.K.H., M.H.W., T.T.D., M.B., A.K.S., E.R.E.), Massachusetts Institute of Technology (MIT), Cambridge, MA
- Koch Institute for Integrative Cancer Research (T.K.H., M.H.W., A.K.S., E.R.E.), Massachusetts Institute of Technology (MIT), Cambridge, MA
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA (M.A.V., T.K.H., M.H.W., T.T.D., A.K.S.)
- Department of Immunology, Harvard Medical School, Boston, MA (T.K.H., M.H.W., A.K.S.)
| | - Tyler T. Dao
- Institute for Medical Engineering and Science (A.G.S.-M., A.S.K., M.A.V., T.K.H., M.H.W., T.T.D., M.B., A.K.S., E.R.E.), Massachusetts Institute of Technology (MIT), Cambridge, MA
- Broad Institute of MIT and Harvard, Cambridge, MA (A.G.S.-M., M.A.V., T.T.D., A.K.S.)
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA (M.A.V., T.K.H., M.H.W., T.T.D., A.K.S.)
- Biological Engineering (T.T.D.), Massachusetts Institute of Technology, Cambridge
| | - Mercedes Balcells
- Institute for Medical Engineering and Science (A.G.S.-M., A.S.K., M.A.V., T.K.H., M.H.W., T.T.D., M.B., A.K.S., E.R.E.), Massachusetts Institute of Technology (MIT), Cambridge, MA
| | - Farhad R. Nezami
- Division of Cardiac Surgery (F.R.N.), Brigham and Women’s Hospital, Boston, MA
| | - Alex K. Shalek
- Institute for Medical Engineering and Science (A.G.S.-M., A.S.K., M.A.V., T.K.H., M.H.W., T.T.D., M.B., A.K.S., E.R.E.), Massachusetts Institute of Technology (MIT), Cambridge, MA
- Koch Institute for Integrative Cancer Research (T.K.H., M.H.W., A.K.S., E.R.E.), Massachusetts Institute of Technology (MIT), Cambridge, MA
- Broad Institute of MIT and Harvard, Cambridge, MA (A.G.S.-M., M.A.V., T.T.D., A.K.S.)
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA (M.A.V., T.K.H., M.H.W., T.T.D., A.K.S.)
- Chemistry (A.K.S.), Massachusetts Institute of Technology, Cambridge
- Department of Immunology, Harvard Medical School, Boston, MA (T.K.H., M.H.W., A.K.S.)
| | - Elazer R. Edelman
- Institute for Medical Engineering and Science (A.G.S.-M., A.S.K., M.A.V., T.K.H., M.H.W., T.T.D., M.B., A.K.S., E.R.E.), Massachusetts Institute of Technology (MIT), Cambridge, MA
- Koch Institute for Integrative Cancer Research (T.K.H., M.H.W., A.K.S., E.R.E.), Massachusetts Institute of Technology (MIT), Cambridge, MA
- Division of Cardiovascular Medicine, Department of Medicine (E.R.E.), Brigham and Women’s Hospital, Boston, MA
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Straughan R, Kadry K, Parikh SA, Edelman ER, Nezami FR. Fully automated construction of three-dimensional finite element simulations from Optical Coherence Tomography. Comput Biol Med 2023; 165:107341. [PMID: 37611423 PMCID: PMC10528179 DOI: 10.1016/j.compbiomed.2023.107341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 07/18/2023] [Accepted: 08/07/2023] [Indexed: 08/25/2023]
Abstract
Despite recent advances in diagnosis and treatment, atherosclerotic coronary artery diseases remain a leading cause of death worldwide. Various imaging modalities and metrics can detect lesions and predict patients at risk; however, identifying unstable lesions is still difficult. Current techniques cannot fully capture the complex morphology-modulated mechanical responses that affect plaque stability, leading to catastrophic failure and mute the benefit of device and drug interventions. Finite Element (FE) simulations utilizing intravascular imaging OCT (Optical Coherence Tomography) are effective in defining physiological stress distributions. However, creating 3D FE simulations of coronary arteries from OCT images is challenging to fully automate given OCT frame sparsity, limited material contrast, and restricted penetration depth. To address such limitations, we developed an algorithmic approach to automatically produce 3D FE-ready digital twins from labeled OCT images. The 3D models are anatomically faithful and recapitulate mechanically relevant tissue lesion components, automatically producing morphologies structurally similar to manually constructed models whilst including more minute details. A mesh convergence study highlighted the ability to reach stress and strain convergence with average errors of just 5.9% and 1.6% respectively in comparison to FE models with approximately twice the number of elements in areas of refinement. Such an automated procedure will enable analysis of large clinical cohorts at a previously unattainable scale and opens the possibility for in-silico methods for patient specific diagnoses and treatment planning for coronary artery disease.
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Affiliation(s)
- Ross Straughan
- Cardiac Surgery Division, Brigham and Women's Hospital, Harvard Medical School, Boston, 02115, MA, USA; Department of Mechanical and Process Engineering, ETH Zurich, Leonhardstrasse 21, 8092 Zurich, Switzerland.
| | - Karim Kadry
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, 02139, MA, USA.
| | - Sahil A Parikh
- Division of Cardiology, Columbia University Irving Medical Center, New York, 10032, NY, USA.
| | - Elazer R Edelman
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, 02139, MA, USA; Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, 02115, MA, USA.
| | - Farhad R Nezami
- Cardiac Surgery Division, Brigham and Women's Hospital, Harvard Medical School, Boston, 02115, MA, USA.
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Rouhollahi A, Willi JN, Haltmeier S, Mehrtash A, Straughan R, Javadikasgari H, Brown J, Itoh A, de la Cruz KI, Aikawa E, Edelman ER, Nezami FR. CardioVision: A fully automated deep learning package for medical image segmentation and reconstruction generating digital twins for patients with aortic stenosis. Comput Med Imaging Graph 2023; 109:102289. [PMID: 37633032 PMCID: PMC10599298 DOI: 10.1016/j.compmedimag.2023.102289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 08/11/2023] [Accepted: 08/12/2023] [Indexed: 08/28/2023]
Abstract
Aortic stenosis (AS) is the most prevalent heart valve disease in western countries that poses a significant public health challenge due to the lack of a medical treatment to prevent valve calcification. Given the aging population demographic, the prevalence of AS is projected to rise, resulting in a progressively significant healthcare and economic burden. While surgical aortic valve replacement (SAVR) has been the gold standard approach, the less invasive transcatheter aortic valve replacement (TAVR) is poised to become the dominant method for high- and medium-risk interventions. Computational simulations using patient-specific models, have opened new research avenues for optimizing emerging devices and predicting clinical outcomes. The traditional techniques of generating digital replicas of patients' aortic root, native valve, and calcification are time-consuming and labor-intensive processes requiring specialized tools and expertise in anatomy. Alternatively, deep learning models, such as the U-Net architecture, have emerged as reliable and fully automated methods for medical image segmentation. Two-dimensional U-Nets have been shown to produce comparable or more accurate results than trained clinicians' manual segmentation while significantly reducing computational costs. In this study, we have developed a fully automatic AI tool capable of reconstructing the digital twin geometry and analyzing the calcification distribution on the aortic valve. The developed automatic segmentation package enables the modeling of patient-specific anatomies, which can then be used to simulate virtual interventional procedures, optimize emerging prosthetic devices, and predict clinical outcomes.
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Affiliation(s)
- Amir Rouhollahi
- Division of Cardiac Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - James Noel Willi
- Division of Cardiac Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Sandra Haltmeier
- Division of Cardiac Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Alireza Mehrtash
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Ross Straughan
- Division of Cardiac Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Mechanical and Process Engineering, ETH Zurich, Zurich, Switzerland
| | - Hoda Javadikasgari
- Division of Cardiac Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jonathan Brown
- Clinical and Translation Science Institute, Tufts University, Boston, MA, USA; Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Akinobu Itoh
- Division of Cardiac Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Kim I de la Cruz
- Division of Cardiac Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Elena Aikawa
- Center for Interdisciplinary Cardiovascular Sciences, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Center for Excellence in Vascular Biology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Elazer R Edelman
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA; Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Farhad R Nezami
- Division of Cardiac Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
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Moradi H, Al-Hourani A, Concilia G, Khoshmanesh F, Nezami FR, Needham S, Baratchi S, Khoshmanesh K. Recent developments in modeling, imaging, and monitoring of cardiovascular diseases using machine learning. Biophys Rev 2023; 15:19-33. [PMID: 36909958 PMCID: PMC9995635 DOI: 10.1007/s12551-022-01040-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 12/21/2022] [Indexed: 01/12/2023] Open
Abstract
Cardiovascular diseases are the leading cause of mortality, morbidity, and hospitalization around the world. Recent technological advances have facilitated analyzing, visualizing, and monitoring cardiovascular diseases using emerging computational fluid dynamics, blood flow imaging, and wearable sensing technologies. Yet, computational cost, limited spatiotemporal resolution, and obstacles for thorough data analysis have hindered the utility of such techniques to curb cardiovascular diseases. We herein discuss how leveraging machine learning techniques, and in particular deep learning methods, could overcome these limitations and offer promise for translation. We discuss the remarkable capacity of recently developed machine learning techniques to accelerate flow modeling, enhance the resolution while reduce the noise and scanning time of current blood flow imaging techniques, and accurate detection of cardiovascular diseases using a plethora of data collected by wearable sensors.
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Affiliation(s)
- Hamed Moradi
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Akram Al-Hourani
- School of Engineering, RMIT University, Melbourne, Victoria Australia
| | | | - Farnaz Khoshmanesh
- School of Allied Health, Human Services & Sport, La Trobe University, Melbourne, Victoria Australia
| | - Farhad R. Nezami
- Division of Thoracic and Cardiac Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA USA
| | - Scott Needham
- Leading Technology Group, Melbourne, Victoria Australia
| | - Sara Baratchi
- School of Health and Biomedical Sciences, RMIT University, Melbourne, Victoria Australia
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6
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Asheghan MM, Javadikasgari H, Attary T, Rouhollahi A, Straughan R, Willi JN, Awal R, Sabe A, de la Cruz KI, Nezami FR. Predicting one-year left ventricular mass index regression following transcatheter aortic valve replacement in patients with severe aortic stenosis: A new era is coming. Front Cardiovasc Med 2023; 10:1130152. [PMID: 37082454 PMCID: PMC10111021 DOI: 10.3389/fcvm.2023.1130152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 03/16/2023] [Indexed: 04/22/2023] Open
Abstract
Aortic stenosis (AS) is the most common valvular heart disease in the western world, particularly worrisome with an ever-aging population wherein postoperative outcome for aortic valve replacement is strongly related to the timing of surgery in the natural course of disease. Yet, guidelines for therapy planning overlook insightful, quantified measures from medical imaging to educate clinical decisions. Herein, we leverage statistical shape analysis (SSA) techniques combined with customized machine learning methods to extract latent information from segmented left ventricle (LV) shapes. This enabled us to predict left ventricular mass index (LVMI) regression a year after transcatheter aortic valve replacement (TAVR). LVMI regression is an expected phenomena in patients undergone aortic valve replacement reported to be tightly correlated with survival one and five year after the intervention. In brief, LV geometries were extracted from medical images of a cohort of AS patients using deep learning tools, and then analyzed to create a set of statistical shape models (SSMs). Then, the supervised shape features were extracted to feed a support vector regression (SVR) model to predict the LVMI regression. The average accuracy of the predictions was validated against clinical measurements calculating root mean square error and R 2 score which yielded the satisfactory values of 0.28 and 0.67, respectively, on test data. Our work reveals the promising capability of advanced mathematical and bioinformatics approaches such as SSA and machine learning to improve medical output prediction and treatment planning.
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Affiliation(s)
- Mohammad Mostafa Asheghan
- Division of Thoracic and Cardiac Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Hoda Javadikasgari
- Division of Thoracic and Cardiac Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Taraneh Attary
- Bio-Intelligence Unit, Sharif Brain Center, Electrical Engineering Department, Sharif University of Technology, Tehran, Iran
| | - Amir Rouhollahi
- Division of Thoracic and Cardiac Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Ross Straughan
- Division of Thoracic and Cardiac Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - James Noel Willi
- Division of Thoracic and Cardiac Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Rabina Awal
- Mechanical Engineering Department, University of Louisiana at Lafayette, Louisiana, LA, United States
| | - Ashraf Sabe
- Division of Thoracic and Cardiac Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Kim I. de la Cruz
- Division of Thoracic and Cardiac Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Farhad R. Nezami
- Division of Thoracic and Cardiac Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
- Correspondence: Farhad R. Nezami
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Williams JG, Marlevi D, Bruse JL, Nezami FR, Moradi H, Fortunato RN, Maiti S, Billaud M, Edelman ER, Gleason TG. Aortic Dissection is Determined by Specific Shape and Hemodynamic Interactions. Ann Biomed Eng 2022; 50:1771-1786. [PMID: 35943618 DOI: 10.1007/s10439-022-02979-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Accepted: 05/11/2022] [Indexed: 12/30/2022]
Abstract
The aim of this study was to determine whether specific three-dimensional aortic shape features, extracted via statistical shape analysis (SSA), correlate with the development of thoracic ascending aortic dissection (TAAD) risk and associated aortic hemodynamics. Thirty-one patients followed prospectively with ascending thoracic aortic aneurysm (ATAA), who either did (12 patients) or did not (19 patients) develop TAAD, were included in the study, with aortic arch geometries extracted from computed tomographic angiography (CTA) imaging. Arch geometries were analyzed with SSA, and unsupervised and supervised (linked to dissection outcome) shape features were extracted with principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), respectively. We determined PLS-DA to be effective at separating dissection and no-dissection patients ([Formula: see text]), with decreased tortuosity and more equal ascending and descending aortic diameters associated with higher dissection risk. In contrast, neither PCA nor traditional morphometric parameters (maximum diameter, tortuosity, or arch volume) were effective at separating dissection and no-dissection patients. The arch shapes associated with higher dissection probability were supported with hemodynamic insight. Computational fluid dynamics (CFD) simulations revealed a correlation between the PLS-DA shape features and wall shear stress (WSS), with higher maximum WSS in the ascending aorta associated with increased risk of dissection occurrence. Our work highlights the potential importance of incorporating higher dimensional geometric assessment of aortic arch anatomy in TAAD risk assessment, and in considering the interdependent influences of arch shape and hemodynamics as mechanistic contributors to TAAD occurrence.
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Affiliation(s)
- Jessica G Williams
- Thoracic and Cardiac Surgery Division, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA, 02139, USA
| | - David Marlevi
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA, 02139, USA
| | - Jan L Bruse
- Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), Mikeletegi 57, 20009, Donostia-San Sebastián, Spain
| | - Farhad R Nezami
- Thoracic and Cardiac Surgery Division, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Hamed Moradi
- School of Mechanical Engineering, Sharif University of Technology, Tehran, Iran
| | - Ronald N Fortunato
- Department of Mechanical Engineering and Materials Science, University of Pittsburgh, Pittsburgh, PA, USA
| | - Spandan Maiti
- Department of Mechanical Engineering and Materials Science, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Marie Billaud
- Thoracic and Cardiac Surgery Division, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Elazer R Edelman
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA, 02139, USA
| | - Thomas G Gleason
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA, 02139, USA.
- University of Maryland School of Medicine, 110 S, Paca Street, 7th Floor, Baltimore, MD, 21201, USA.
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8
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Karmakar A, Olender ML, Marlevi D, Shlofmitz E, Shlofmitz RA, Edelman ER, Nezami FR. Framework for lumen-based nonrigid tomographic coregistration of intravascular images. J Med Imaging (Bellingham) 2022; 9:044006. [PMID: 36043032 PMCID: PMC9402451 DOI: 10.1117/1.jmi.9.4.044006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 08/09/2022] [Indexed: 08/25/2023] Open
Abstract
Purpose: Modern medical imaging enables clinicians to effectively diagnose, monitor, and treat diseases. However, clinical decision-making often relies on combined evaluation of either longitudinal or disparate image sets, necessitating coregistration of multiple acquisitions. Promising coregistration techniques have been proposed; however, available methods predominantly rely on time-consuming manual alignments or nontrivial feature extraction with limited clinical applicability. Addressing these issues, we present a fully automated, robust, nonrigid registration method, allowing for coregistering of multimodal tomographic vascular image datasets using luminal annotation as the sole alignment feature. Approach: Registration is carried out by the use of the registration metrics defined exclusively for lumens shapes. The framework is primarily broken down into two sequential parts: longitudinal and rotational registration. Both techniques are inherently nonrigid in nature to compensate for motion and acquisition artifacts in tomographic images. Results: Performance was evaluated across multimodal intravascular datasets, as well as in longitudinal cases assessing pre-/postinterventional coronary images. Low registration error in both datasets highlights method utility, with longitudinal registration errors-evaluated throughout the paired tomographic sequences-of 0.29 ± 0.14 mm ( < 2 longitudinal image frames) and 0.18 ± 0.16 mm ( < 1 frame) for multimodal and interventional datasets, respectively. Angular registration for the interventional dataset rendered errors of 7.7 ° ± 6.7 ° , and 29.1 ° ± 23.2 ° for the multimodal set. Conclusions: Satisfactory results across datasets, along with additional attributes such as the ability to avoid longitudinal over-fitting and correct nonlinear catheter rotation during nonrigid rotational registration, highlight the potential wide-ranging applicability of our presented coregistration method.
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Affiliation(s)
- Abhishek Karmakar
- Cornell University, Department of Biomedical Engineering, Ithaca, New York, United States
| | - Max L. Olender
- Massachusetts Institute of Technology, Institute for Medical Engineering and Science, Cambridge, Massachusetts, United States
| | - David Marlevi
- Massachusetts Institute of Technology, Institute for Medical Engineering and Science, Cambridge, Massachusetts, United States
| | - Evan Shlofmitz
- St. Francis Hospital, Department of Cardiology, Roslyn, New York, United States
| | | | - Elazer R. Edelman
- Massachusetts Institute of Technology, Institute for Medical Engineering and Science, Cambridge, Massachusetts, United States
| | - Farhad R. Nezami
- Brigham and Women’s Hospital, Harvard Medical School, Division of Thoracic and Cardiac Surgery, Boston, Massachusetts, United States
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Nezami FR, Ramezanpour M, Khodaee F, Goffer E, Edelman ER, Keller SP. Simulation of Fluid-Structure Interaction in Extracorporeal Membrane Oxygenation Circulatory Support Systems. J Cardiovasc Transl Res 2022; 15:249-257. [PMID: 34128180 DOI: 10.1007/s12265-021-10143-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Accepted: 05/31/2021] [Indexed: 11/25/2022]
Abstract
Extracorporeal membrane oxygenation (ECMO) is a vital mechanical circulatory support modality capable of restoring perfusion for the patient in circulatory failure. Despite increasing adoption of ECMO, there is incomplete understanding of its effects on systemic hemodynamics and how the vasculature responds to varying levels of continuous retrograde perfusion. To gain further insight into the complex ECMO:failing heart circulation, computational fluid dynamics simulations focused on perfusion distribution and hemodynamic flow patterns were conducted using a patient-derived aorta geometry. Three case scenarios were simulated: (1) healthy control; (2) 90% ECMO-derived perfusion to model profound heart failure; and, (3) 50% ECMO-derived perfusion to model the recovering heart. Fluid-structure interface simulations were performed to quantify systemic pressure and vascular deformation throughout the aorta over the cardiac cycle. ECMO support alters pressure distribution while decreasing shear stress. Insights derived from computational modeling may lead to better understanding of ECMO support and improved patient outcomes.
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Affiliation(s)
- Farhad R Nezami
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Surgery (Thoracic and Cardiac Surgery), Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Mehdi Ramezanpour
- Department of Mechanical Engineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Farhan Khodaee
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Efrat Goffer
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Elazer R Edelman
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Medicine (Cardiovascular Medicine), Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Steven P Keller
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Department of Medicine (Pulmonary and Critical Care Medicine), Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
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10
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Olender ML, Niu Y, Marlevi D, Edelman ER, Nezami FR. Impact and Implications of Mixed Plaque Class in Automated Characterization of Complex Atherosclerotic Lesions. Comput Med Imaging Graph 2022; 97:102051. [DOI: 10.1016/j.compmedimag.2022.102051] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2021] [Revised: 12/19/2021] [Accepted: 02/17/2022] [Indexed: 01/16/2023]
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11
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Khodaee F, Nezami FR, Zampell BA, Galper E, Edelman ER, Keller SP. Effect of anatomical variation on extracorporeal membrane oxygenation circulatory support: A computational study. Comput Biol Med 2022; 141:105178. [PMID: 34995875 PMCID: PMC10600951 DOI: 10.1016/j.compbiomed.2021.105178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 12/23/2021] [Accepted: 12/24/2021] [Indexed: 11/23/2022]
Abstract
BACKGROUND Extracorporeal membrane oxygenation (ECMO) via femoral cannulation is a vital intervention capable of rapidly restoring perfusion for patients in shock. Despite increasing use to provide circulatory support, its hemodynamic effects are poorly understood and the impact of patient-specific anatomical variation on perfusion is unknown. This study investigates the complex failing heart-mechanical circulatory support circulation and analyzes the effect of patient-specific vascular anatomical variations on hemodynamics and end-organ perfusion. METHODS Patient-specific vascular geometries were constructed from segmenting clinical computerized tomography angiography images and quantitatively compared using tortuosity, curvature, torsion, and lumen diameter. Computational fluid dynamic simulations were performed on a subset of geometries selected to represent a range of anatomical variation. Heart failure severity was modeled by varying the relative fraction of total flow provided by the heart and the extracorporeal circuit. A 3-element lumped parameter model was applied to accurately and dynamically model distal perfusion boundary conditions. Hemodynamic parameters and end-organ perfusion were analyzed and compared to assess the effect of anatomical variation. RESULTS Pulsatile antegrade cardiac perfusion and ECMO retrograde perfusion collide in the aorta to form a dynamic watershed region. The size, position, and variation of this region over the cardiac cycle is substantially altered by patient anatomical region. Increased vascular tortuosity reduces the proximal extent of flow from circulatory support and decreases the size of the watershed region. CONCLUSIONS Patient vascular anatomy is a key determinant of the ECMO-failing heart circulation that alters the location and extent of the watershed region and affects the tissues at risk for differential hypoxia and circuit-derived thromboemboli for a given level of support.
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Affiliation(s)
- Farhan Khodaee
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Farhad R Nezami
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Surgery (Thoracic and Cardiac Surgery), Brigham and Women's Hospital, Boston, MA, USA
| | - Brooke A Zampell
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA; École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Eitan Galper
- Department of Mechanical Engineering, University of Maryland, College Park, MD, USA
| | - Elazer R Edelman
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Medicine (Cardiovascular Medicine), Brigham and Women's Hospital, Boston, MA, USA
| | - Steven P Keller
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Medicine (Pulmonary and Critical Care Medicine), Johns Hopkins Hospital, Baltimore, MD, USA.
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12
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Conway C, Nezami FR, Rogers C, Groothuis A, Squire JC, Edelman ER. Acute Stent-Induced Endothelial Denudation: Biomechanical Predictors of Vascular Injury. Front Cardiovasc Med 2021; 8:733605. [PMID: 34722666 PMCID: PMC8553954 DOI: 10.3389/fcvm.2021.733605] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 09/15/2021] [Indexed: 01/03/2023] Open
Abstract
Recent concern for local drug delivery and withdrawal of the first Food and Drug Administration-approved bioresorbable scaffold emphasizes the need to optimize the relationships between stent design and drug release with imposed arterial injury and observed pharmacodynamics. In this study, we examine the hypothesis that vascular injury is predictable from stent design and that the expanding force of stent deployment results in increased circumferential stress in the arterial tissue, which may explain acute injury poststent deployment. Using both numerical simulations and ex vivo experiments on three different stent designs (slotted tube, corrugated ring, and delta wing), arterial injury due to device deployment was examined. Furthermore, using numerical simulations, the consequence of changing stent strut radial thickness on arterial wall shear stress and arterial circumferential stress distributions was examined. Regions with predicted arterial circumferential stress exceeding a threshold of 49.5 kPa compared favorably with observed ex vivo endothelial denudation for the three considered stent designs. In addition, increasing strut thickness was predicted to result in more areas of denudation and larger areas exposed to low wall shear stress. We conclude that the acute arterial injury, observed immediately following stent expansion, is caused by high circumferential hoop stresses in the interstrut region, and denuded area profiles are dependent on unit cell geometric features. Such findings when coupled with where drugs move might explain the drug–device interactions.
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Affiliation(s)
- Claire Conway
- Department of Anatomy and Regenerative Medicine, Royal College of Surgeons in Ireland, Dublin, Ireland.,Institute for Medical Engineering and Science, Massachusetts Institute of Technology (MIT), Cambridge, MA, United States.,Trinity Centre for Biomedical Engineering, Trinity College Dublin and Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Farhad R Nezami
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology (MIT), Cambridge, MA, United States.,Thoracic and Cardiac Surgery Division, Department of Surgery, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
| | - Campbell Rogers
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology (MIT), Cambridge, MA, United States.,Cardiovascular Division, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States.,HeartFlow Inc., Redwood City, CA, United States
| | - Adam Groothuis
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology (MIT), Cambridge, MA, United States
| | - James C Squire
- Department of Electrical and Computer Engineering, Virginia Military Institute, Lexington City, KY, United States
| | - Elazer R Edelman
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology (MIT), Cambridge, MA, United States.,Cardiovascular Division, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
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Kadry K, Olender ML, Marlevi D, Edelman ER, Nezami FR. A platform for high-fidelity patient-specific structural modelling of atherosclerotic arteries: from intravascular imaging to three-dimensional stress distributions. J R Soc Interface 2021; 18:20210436. [PMID: 34583562 DOI: 10.1098/rsif.2021.0436] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
The pathophysiology of atherosclerotic lesions, including plaque rupture triggered by mechanical failure of the vessel wall, depends directly on the plaque morphology-modulated mechanical response. The complex interplay between lesion morphology and structural behaviour can be studied with high-fidelity computational modelling. However, construction of three-dimensional (3D) and heterogeneous models is challenging, with most previous work focusing on two-dimensional geometries or on single-material lesion compositions. Addressing these limitations, we here present a semi-automatic computational platform, leveraging clinical optical coherence tomography images to effectively reconstruct a 3D patient-specific multi-material model of atherosclerotic plaques, for which the mechanical response is obtained by structural finite-element simulations. To demonstrate the importance of including multi-material plaque components when recovering the mechanical response, a computational case study was conducted in which systematic variation of the intraplaque lipid and calcium was performed. The study demonstrated that the inclusion of various tissue components greatly affected the lesion mechanical response, illustrating the importance of multi-material formulations. This platform accordingly provides a viable foundation for studying how plaque micro-morphology affects plaque mechanical response, allowing for patient-specific assessments and extension into clinically relevant patient cohorts.
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Affiliation(s)
- Karim Kadry
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA.,Laboratory of Hemodynamics and Cardiovascular Technology, Swiss Federal Institute of Technology, MED 3.2922, 1015 Lausanne, Switzerland
| | - Max L Olender
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - David Marlevi
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Elazer R Edelman
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA.,Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Farhad R Nezami
- Thoracic and Cardiac Surgery Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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Olender ML, de la Torre Hernández JM, Athanasiou LS, Nezami FR, Edelman ER. Artificial intelligence to generate medical images: augmenting the cardiologist's visual clinical workflow. Eur Heart J Digit Health 2021; 2:539-544. [PMID: 36713593 PMCID: PMC9707980 DOI: 10.1093/ehjdh/ztab052] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 04/21/2021] [Accepted: 06/04/2021] [Indexed: 06/18/2023]
Abstract
Artificial intelligence (AI) offers great promise in cardiology, and medicine broadly, for its ability to tirelessly integrate vast amounts of data. Applications in medical imaging are particularly attractive, as images are a powerful means to convey rich information and are extensively utilized in cardiology practice. Departing from other AI approaches in cardiology focused on task automation and pattern recognition, we describe a digital health platform to synthesize enhanced, yet familiar, clinical images to augment the cardiologist's visual clinical workflow. In this article, we present the framework, technical fundamentals, and functional applications of the methodology, especially as it pertains to intravascular imaging. A conditional generative adversarial network was trained with annotated images of atherosclerotic diseased arteries to generate synthetic optical coherence tomography and intravascular ultrasound images on the basis of specified plaque morphology. Systems leveraging this unique and flexible construct, whereby a pair of neural networks is competitively trained in tandem, can rapidly generate useful images. These synthetic images replicate the style, and in several ways exceed the content and function, of normally acquired images. By using this technique and employing AI in such applications, one can ameliorate challenges in image quality, interpretability, coherence, completeness, and granularity, thereby enhancing medical education and clinical decision-making.
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Affiliation(s)
- Max L Olender
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139 USA
- Department of Mechanical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139 USA
| | | | - Lambros S Athanasiou
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139 USA
| | - Farhad R Nezami
- Thoracic and Cardiac Surgery Division, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Elazer R Edelman
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139 USA
- Cardiovascular Division, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
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Olender ML, Nezami FR, Athanasiou LS, de la Torre Hernández JM, Edelman ER. Translational challenges for synthetic imaging in cardiology. Eur Heart J Digit Health 2021; 2:559-560. [PMID: 36713104 PMCID: PMC9707872 DOI: 10.1093/ehjdh/ztab079] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Affiliation(s)
- Max L Olender
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA,Corresponding author. Tel: +1 617-253-1416, Fax: +1 617-253-2514,
| | - Farhad R Nezami
- Thoracic and Cardiac Surgery Division, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Lambros S Athanasiou
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
| | | | - Elazer R Edelman
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA,Cardiovascular Division, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
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Kusner J, Luraghi G, Khodaee F, Rodriguez Matas JF, Migliavacca F, Edelman ER, Nezami FR. Understanding TAVR device expansion as it relates to morphology of the bicuspid aortic valve: A simulation study. PLoS One 2021; 16:e0251579. [PMID: 33999969 PMCID: PMC8128244 DOI: 10.1371/journal.pone.0251579] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 04/29/2021] [Indexed: 12/23/2022] Open
Abstract
The bicuspid aortic valve (BAV) is a common and heterogeneous congenital heart abnormality that is often complicated by aortic stenosis. Although initially developed for tricuspid aortic valves (TAV), transcatheter aortic valve replacement (TAVR) devices are increasingly applied to the treatment of BAV stenosis. It is known that patient-device relationship between TAVR and BAV are not equivalent to those observed in TAV but the nature of these differences are not well understood. We sought to better understand the patient-device relationships between TAVR devices and the two most common morphologies of BAV. We performed finite element simulation of TAVR deployment into three cases of idealized aortic anatomies (TAV, Sievers 0 BAV, Sievers 1 BAV), derived from patient-specific measurements. Valve leaflet von Mises stress at the aortic commissures differed by valve configuration over a ten-fold range (TAV: 0.55 MPa, Sievers 0: 6.64 MPa, and Sievers 1: 4.19 MPa). First principle stress on the aortic wall was greater in Sievers 1 (0.316 MPa) and Sievers 0 BAV (0.137 MPa) compared to TAV (0.056 MPa). TAVR placement in Sievers 1 BAV demonstrated significant device asymmetric alignment, with 1.09 mm of displacement between the center of the device measured at the annulus and at the leaflet free edge. This orifice displacement was marginal in TAV (0.33 mm) and even lower in Sievers 0 BAV (0.23 mm). BAV TAVR, depending on the subtype involved, may encounter disparate combinations of device under expansion and asymmetry compared to TAV deployment. Understanding the impacts of BAV morphology on patient-device relationships can help improve device selection, patient eligibility, and the overall safety of TAVR in BAV.
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Affiliation(s)
- Jonathan Kusner
- Harvard Medical School, Boston, MA, United States of America
| | - Giulia Luraghi
- Laboratory of Biological Structure Mechanics (LaBS), Department of Chemistry, Materials and Chemical Engineering ‘Giulio Natta’, Politecnico di Milano, Milan, Italy
| | - Farhan Khodaee
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, United States of America
| | - José Félix Rodriguez Matas
- Laboratory of Biological Structure Mechanics (LaBS), Department of Chemistry, Materials and Chemical Engineering ‘Giulio Natta’, Politecnico di Milano, Milan, Italy
| | - Francesco Migliavacca
- Laboratory of Biological Structure Mechanics (LaBS), Department of Chemistry, Materials and Chemical Engineering ‘Giulio Natta’, Politecnico di Milano, Milan, Italy
| | - Elazer R. Edelman
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, United States of America
- Cardiovascular Division, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States of America
| | - Farhad R. Nezami
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, United States of America
- Thoracic and Cardiac Surgery Division, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States of America
- * E-mail: ,
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