@inproceedings{f841eab44f134ef084aef2e6aeba0019,
title = "Learning patient-specific lumped models for interactive coronary blood flow simulations",
abstract = "We propose a parametric lumped model (LM) for fast patientspecific computational fluid dynamic simulations of blood flowin elongated vessel networks to alleviate the computational burden of 3D finite element (FE) simulations. We learn the coefficients balancing the local nonlinear hydraulic effects from a training set of precomputed FE simulations. Our LM yields pressure predictions accurate up to 2.76mmHg on 35 coronary trees obtained from 32 coronary computed tomography angiograms. We also observe a very good predictive performance on a validation set of 59 physiologicalmeasurements suggesting thatFEsimulations can be replaced by our LM. As LM predictions can be computed extremely fast, our approach paves the way to use a personalised interactive biophysical model with realtime feedback in clinical practice.",
keywords = "CCTA, Coronary blood flow, Lumped parameter biophysical simulation, Patient specific model",
author = "Hannes Nickisch and Yechiel Lamash and Sven Prevrhal and Moti Freiman and Mani Vembar and Liran Goshen and Holger Schmitt",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015 ; Conference date: 05-10-2015 Through 09-10-2015",
year = "2015",
doi = "https://doi.org/10.1007/978-3-319-24571-3_52",
language = "الإنجليزيّة",
isbn = "9783319245706",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "433--441",
editor = "Joachim Hornegger and Frangi, {Alejandro F.} and Wells, {William M.} and Nassir Navab",
booktitle = "Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015 - 18th International Conference, Proceedings",
}