Biological Fluid Dynamics: Data-driven Hemodynamics
ORAL · H14 · ID: 22754
Presentations
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Fast surrogate of 3-D patient-specific computational fluid dynamics using statistical shape modeling and deep Learning
ORAL
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Presenters
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Pan Du
University of Notre Dame
Authors
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Pan Du
University of Notre Dame
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Xiaozhi Zhu
University of Notre Dame
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Jian-Xun Wang
University of Notre Dame
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Inferring the left atrial appendage (LAA) hemodynamics from 4D CT contrast dynamics: reduced order models (ROMs) and physics informed neural networks (PINNs).
ORAL
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Presenters
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Bahetihazi Maidu
UC San Diego, University of California, San Diego
Authors
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Bahetihazi Maidu
UC San Diego, University of California, San Diego
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Alejandro Gonzalo
UC San Diego & University of Washington, University of California San Diego
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Lorenzo Rossini
UC San Diego
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Davis Vigneault
UC San Diego, Department of Radiology, Stanford University, Stanford, CA, 94305, USA
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Pablo Martinez-Legazpi
Universidad Nacional de Educación a Distancia, Gregorio Marañon Hospital, Spain, Dpt. Física Matemática y Fluidos. UNED
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Javier Bermejo
Hospital G.U. Gregorio Marañón, Gregorio Marañon Hospital, Spain, Hospital General Universitario Gregorio Marañon, Hospital General Universitario Gregorio Marañón, Hospital Gregorio Maranon, Madrid, Spain
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Oscar Flores
Univ. Carlos III De Madrid, Univ Carlos III De Madrid, Univ. Carlos III de Madrid
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Manuel Garcia-Villalba
Univ Carlos III De Madrid, Univ. Carlos III de Madrid
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Elliot McVeigh
UC San Diego, University of California San Diego
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Andrew M Kahn
University of California San Diego, UC San Diego, University of California, San Diego
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Juan Carlos del Alamo
University of Washington; University of California San Diego, UC San Diego & University of Washington, University of Washington, University of Washington & University of California, San Diego
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A Machine Learning Methodology for estimation of vascular characteristics using a single carotid waveform
ORAL
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Presenters
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Soha Niroumandi
Department of Aerospace and Mechanical Engineering, University of Southern California, Los Angeles, CA, USA, University of Southern California
Authors
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Soha Niroumandi
Department of Aerospace and Mechanical Engineering, University of Southern California, Los Angeles, CA, USA, University of Southern California
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Rashid Alavi
Department of Aerospace and Mechanical Engineering, University of Southern California, Los Angeles, CA, USA, University of Southern California
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Niema M Pahlevan
University of Southern California, Univ of Southern California, Department of Aerospace and Mechanical Engineering, University of Southern California, Los Angeles, CA, USA
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Towards resolving hemodynamic velocities from time-resolved contrast-enhanced magnetic resonance angiography using physics-informed machine learning
ORAL
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Presenters
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Roshan M D'Souza
University of Wisconsin - Milwaukee
Authors
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Roshan M D'Souza
University of Wisconsin - Milwaukee
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Amirhossein Arzani
Department of Mechanical Engineering, Northern Arizona University, Northern Arizona University
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Isaac Perez-Raya
Rochester Institute of Technology
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Amin Pashaei
University of Wisconsin Milwaukee
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Data-driven near-wall blood flow and wall shear stress modeling with physics-informed neural networks
ORAL
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Presenters
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Amirhossein Arzani
Department of Mechanical Engineering, Northern Arizona University, Northern Arizona University
Authors
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Amirhossein Arzani
Department of Mechanical Engineering, Northern Arizona University, Northern Arizona University
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Jian-Xun Wang
University of Notre Dame
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Roshan M D'Souza
University of Wisconsin - Milwaukee
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Brain Hemodynamic Predictions from Noninvasive Transcranial Doppler Ultrasound and Angiography Data Using Physics-Informed Neural Networks
ORAL
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Publication: Sarabian, M., Babaee, B., & Laksari, K. (2020). Brain haemodynamic predictions from non‑invasive Transcranial Doppler Ultrasound data using physics‑informed neural networks. Manuscript submitted
Presenters
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Mohammad Sarabian
University of Arizona
Authors
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Mohammad Sarabian
University of Arizona
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Hessam Babaee
University of Pittsburgh
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Kaveh Laksari
University of Arizona
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Improving the Diagnostic Accuracy of Cardiac Auscultation using Supervised Learning: a Computational Hemoacoustic Study
ORAL
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Publication: Bailoor S, Seo JH, Schena S and Mittal R. "Detecting Aortic Valve Anomaly from Induced Murmurs: Insights from Computational Hemodynamic Models". Frontiers in Physiology (under review)
Presenters
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Shantanu Bailoor
Johns Hopkins University
Authors
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Shantanu Bailoor
Johns Hopkins University
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Jung-Hee Seo
Johns Hopkins University, Johns Hopkins Univ
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Stefano Schena
Johns Hopkins Medical Institute
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Rajat Mittal
Johns Hopkins University
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Analyzing Patient-Specific Coronary Arteries with and without Stents using Proper Orthogonal Decomposition
ORAL
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Presenters
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Daniela Caraeni
University of Massachusetts Amherst
Authors
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Daniela Caraeni
University of Massachusetts Amherst
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Amir Lotfi
Baystate Medical Center
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Yahya Modarres-Sadeghi
University of Massachusetts Amherst
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All-in-one, physics-informed dealiasing method to regularize cardiac 4D flow MRI data.
ORAL
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Presenters
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Christian Chazo Paz
Hospital G.U. Gregorio Marañón; Univ. Carlos III de Madrid
Authors
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Christian Chazo Paz
Hospital G.U. Gregorio Marañón; Univ. Carlos III de Madrid
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Oscar Flores
Univ. Carlos III De Madrid, Univ Carlos III De Madrid, Univ. Carlos III de Madrid
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Pablo Martinez-Legazpi
Universidad Nacional de Educación a Distancia, Gregorio Marañon Hospital, Spain, Dpt. Física Matemática y Fluidos. UNED
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Cathleen M Nguyen
University of Washington; University of California San Diego, University of Washington & University of California, San Diego
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Cristina Santa Marta
Universidad Nacional de Educación a Distancia
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Andrew M Kahn
University of California San Diego, UC San Diego, University of California, San Diego
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Javier Bermejo
Hospital G.U. Gregorio Marañón, Gregorio Marañon Hospital, Spain, Hospital General Universitario Gregorio Marañon, Hospital General Universitario Gregorio Marañón, Hospital Gregorio Maranon, Madrid, Spain
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Juan Carlos del Alamo
University of Washington; University of California San Diego, UC San Diego & University of Washington, University of Washington, University of Washington & University of California, San Diego
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Super-resolution study of aneurism based on AI PIV - preliminary report
ORAL
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Presenters
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Wojciech Majewski
Authors
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Wojciech Majewski
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Wojciech Kaspera
Medical University of Silesia, Sosnowiec, Poland
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Marek Ples
Silesian University of Technology, Gliwice, Poland
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Marta Sobkowiak-Pilorz
Silesian University of Technology, Gliwice, Poland
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Runjie Wei
Microvec Pte Ltd, Singapore
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Wojciech Wolański
Silesian University of Technology, Gliwice, Poland
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