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Computing Fractional Flow Reserve During Coronary Angiography

JACC. Cardiovascular interventions, 2016-10, Vol.9 (19), p.2036-2038

Giddens, Don P., PhD

2016

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Machine Learning Model Comparison for Automatic Segmentation of Intracoronary Optical Coherence Tomography and Plaque Cap Thickness Quantification

Computer modeling in engineering & sciences, 2020-01, Vol.123 (2), p.631-646

2020

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Using Optical Coherence Tomography and Intravascular Ultrasound Imaging to Quantify Coronary Plaque Cap Stress/Strain and Progression: A Follow-Up Study Using 3D Thin-Layer Models

Frontiers in bioengineering and biotechnology, 2021-08, Vol.9, p.713525-713525

2021

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Predicting plaque vulnerability change using intravascular ultrasound + optical coherence tomography image-based fluid-structure interaction models and machine learning methods with patient follow-up data: a feasibility study

Biomedical engineering online, 2021-04, Vol.20 (1), p.34-18, Article 34

2021

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Using optical coherence tomography and intravascular ultrasound imaging to quantify coronary plaque cap thickness and vulnerability: a pilot study

Biomedical engineering online, 2020-11, Vol.19 (1), p.1-90, Article 90

2020

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Predicting Coronary Stenosis Progression Using Plaque Fatigue From IVUS-Based Thin-Slice Models: A Machine Learning Random Forest Approach

Frontiers in physiology, 2022-05, Vol.13, p.912447-912447

2022

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Combining IVUS + OCT Data, Biomechanical Models and Machine Learning Method for Accurate Coronary Plaque Morphology Quantification and Cap Thickness and Stress/Strain Index Predictions

Journal of functional biomaterials, 2023-01, Vol.14 (1), p.41

2023

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A Machine Learning-Based Method for Intracoronary OCT Segmentation and Vulnerable Coronary Plaque Cap Thickness Quantification

International journal of computational methods, 2019-05, Vol.16 (3), p.1842008

2019

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Convolution Neural Networks and Support Vector Machines for Automatic Segmentation of Intracoronary Optical Coherence Tomography

Molecular & cellular biomechanics, 2019, Vol.16 (2), p.153-161

2019

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Effects of wall motion and compliance on flow patterns in the ascending aorta

Journal of biomechanical engineering, 2003-06, Vol.125 (3), p.347-354

2003

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High Coronary Shear Stress in Patients With Coronary Artery Disease Predicts Myocardial Infarction

Journal of the American College of Cardiology, 2018-10, Vol.72 (16), p.1926-1935

2018

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Computing Fractional Flow Reserve During Coronary Angiography: How Good Is Good Enough?

JACC. Cardiovascular interventions, 2016-10, Vol.9 (19), p.2036-2038

Giddens, Don P.

2016

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A model of disturbed flow-induced atherosclerosis in mouse carotid artery by partial ligation and a simple method of RNA isolation from carotid endothelium

Journal of visualized experiments, 2010-06 (40)

2010

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Optimization of pulsatile flow for mechanical circulatory support

The Journal of heart and lung transplantation, 2013-06, Vol.32 (6), p.577-578

2013

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Oscillatory wall shear stress is a dominant flow characteristic affecting lesion progression patterns and plaque vulnerability in patients with coronary artery disease

Journal of the Royal Society interface, 2017-02, Vol.14 (127), p.20160972

2017

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Choice of in vivo versus idealized velocity boundary conditions influences physiologically relevant flow patterns in a subject-specific simulation of flow in the human carotid bifurcation

Journal of biomechanical engineering, 2009-02, Vol.131 (2), p.021013

2009

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Magnetic Targeting of Human Mesenchymal Stem Cells with Internalized Superparamagnetic Iron Oxide Nanoparticles

Small (Weinheim an der Bergstrasse, Germany), 2013-12, Vol.9 (23), p.4017-4026

2013

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Biomechanical Assessment of Fully Bioresorbable Devices

JACC. Cardiovascular interventions, 2013-07, Vol.6 (7), p.760-761

2013

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Do-it-yourself in vitro vasculature that recapitulates in vivo geometries for investigating endothelial-blood cell interactions

Scientific reports, 2015-07, Vol.5 (1), p.12401-12401, Article 12401

2015

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Localization of culprit lesions in coronary arteries of patients with ST-segment elevation myocardial infarctions: Relation to bifurcations and curvatures

The American heart journal, 2011-03, Vol.161 (3), p.508-515

2011

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Force and torque on spherical particles in micro-channel flows using computational fluid dynamics

Royal Society open science, 2016-07, Vol.3 (7), p.160298-160298

2016

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Image-based biomechanical modeling for coronary atherosclerotic plaque progression and vulnerability prediction

International journal of cardiology, 2022-04, Vol.352, p.1-8

2022

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Association of Wall Shear Stress with Coronary Plaque Progression and Transformation

Interventional cardiology clinics, 2015-10, Vol.4 (4), p.491-502

2015

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A Multimodality Image-Based Fluid–Structure Interaction Modeling Approach for Prediction of Coronary Plaque Progression Using IVUS and Optical Coherence Tomography Data With Follow-Up

Journal of biomechanical engineering, 2019-09, Vol.141 (9)

2019

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Evaluation of a framework for the co-registration of intravascular ultrasound and optical coherence tomography coronary artery pullbacks

Journal of biomechanics, 2016-12, Vol.49 (16), p.4048-4056

2016

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Computing Fractional Flow Reserve During Coronary Angiography

Machine Learning Model Comparison for Automatic Segmentation of Intracoronary Optical Coherence Tomography and Plaque Cap Thickness Quantification

Using Optical Coherence Tomography and Intravascular Ultrasound Imaging to Quantify Coronary Plaque Cap Stress/Strain and Progression: A Follow-Up Study Using 3D Thin-Layer Models

Predicting plaque vulnerability change using intravascular ultrasound + optical coherence tomography image-based fluid-structure interaction models and machine learning methods with patient follow-up data: a feasibility study

Using optical coherence tomography and intravascular ultrasound imaging to quantify coronary plaque cap thickness and vulnerability: a pilot study

Predicting Coronary Stenosis Progression Using Plaque Fatigue From IVUS-Based Thin-Slice Models: A Machine Learning Random Forest Approach

Combining IVUS + OCT Data, Biomechanical Models and Machine Learning Method for Accurate Coronary Plaque Morphology Quantification and Cap Thickness and Stress/Strain Index Predictions

A Machine Learning-Based Method for Intracoronary OCT Segmentation and Vulnerable Coronary Plaque Cap Thickness Quantification

Convolution Neural Networks and Support Vector Machines for Automatic Segmentation of Intracoronary Optical Coherence Tomography

Effects of wall motion and compliance on flow patterns in the ascending aorta

High Coronary Shear Stress in Patients With Coronary Artery Disease Predicts Myocardial Infarction

Computing Fractional Flow Reserve During Coronary Angiography: How Good Is Good Enough?

A model of disturbed flow-induced atherosclerosis in mouse carotid artery by partial ligation and a simple method of RNA isolation from carotid endothelium

Optimization of pulsatile flow for mechanical circulatory support

Oscillatory wall shear stress is a dominant flow characteristic affecting lesion progression patterns and plaque vulnerability in patients with coronary artery disease

Choice of in vivo versus idealized velocity boundary conditions influences physiologically relevant flow patterns in a subject-specific simulation of flow in the human carotid bifurcation

Magnetic Targeting of Human Mesenchymal Stem Cells with Internalized Superparamagnetic Iron Oxide Nanoparticles

Biomechanical Assessment of Fully Bioresorbable Devices

Do-it-yourself in vitro vasculature that recapitulates in vivo geometries for investigating endothelial-blood cell interactions

Localization of culprit lesions in coronary arteries of patients with ST-segment elevation myocardial infarctions: Relation to bifurcations and curvatures

Force and torque on spherical particles in micro-channel flows using computational fluid dynamics

Image-based biomechanical modeling for coronary atherosclerotic plaque progression and vulnerability prediction

Association of Wall Shear Stress with Coronary Plaque Progression and Transformation

A Multimodality Image-Based Fluid–Structure Interaction Modeling Approach for Prediction of Coronary Plaque Progression Using IVUS and Optical Coherence Tomography Data With Follow-Up

Evaluation of a framework for the co-registration of intravascular ultrasound and optical coherence tomography coronary artery pullbacks

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