28 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
4 papers
Bayesian Parameter Inference and Uncertainty Quantification for a Computational Pulmonary Hemodynamics Model Using Gaussian Processes
Amirreza Kachabi, Sofia Altieri Correa, Naomi C. Chesler +1
Subject-specific modeling is a powerful tool in cardiovascular research, providing insights beyond the reach of current clinical diagnostics. Limitations in available clinical data…
Efficient Uncertainty Quantification in a Multiscale Model of Pulmonary Arterial and Venous Hemodynamics
Mitchel J. Colebank, Naomi C. Chesler
Computational hemodynamics models are becoming increasingly useful in the management and prognosis of complex, multiscale pathologies, including those attributed to the development…
Parameter inference in a computational model of hemodynamics in pulmonary hypertension
Amanda L. Colunga, Mitchel J. Colebank, REU Program +1
Pulmonary hypertension (PH), defined by a mean pulmonary arterial pressure (mPAP) 20 mmHg, is characterized by increased pulmonary vascular resistance and decreased pulmonary a…
A Combined Deep-Learning and Deformable-Model Approach to Fully Automatic Segmentation of the Left Ventricle in Cardiac MRI
M. R. Avendi, A. Kheradvar, H. Jafarkhani
Segmentation of the left ventricle (LV) from cardiac magnetic resonance imaging (MRI) datasets is an essential step for calculation of clinical indices such as ventricular volume a…