17:00 - 17:12ID: 645
DATA-DRIVEN FSI SIMULATION OF VENTRICLE AND AORTA INTEGRATING IN VIVO AND IN SILICO DATA
M. A. Scarpolini1,2, G. Piumini3, F. Cademartiri4, S. Celi1, F. Viola5
1BioCardioLab, Fondazione Toscana Gabriele Monasterio, Italy; 2University of Rome “Tor Vergata”, Italy; 3University of Twente, The Netherlands; 4Clinical Imaging Department, Fondazione Toscana Gabriele Monasterio, Italy; 5GSSI (Gran Sasso Science Institute), L’Aquila, Italy
17:12 - 17:24ID: 465
A CT-BASED DEEP LEARNING SYSTEM FOR AUTOMATIC ASSESSMENT OF AORTIC ROOT MORPHOLOGY FOR TAVI PLANNING
S. Saitta1, F. Sturla2,1, R. Gorla2, O. Oliva2, E. Votta1,2, F. Bedogni2, A. Redaelli1
1Politecnico di Milano, Italy; 2IRCCS Policlinico San Donato, Italy
17:24 - 17:36ID: 716
DATA-DRIVEN METHODS FOR PATIENT-SPECIFIC REDUCED ORDER MODELING OF COMPLEX AORTIC FLOWS
C. Chatpattanasiri1,2, G. Franzetti1, V. Diaz-Zuccarini1,2, S. Balabani1,2
1Department of Mechanical Engineering, University College London, London, UK; 2Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), Department of Medical Physics and Biomedical Engineering, University College London, London, UK
17:36 - 17:48ID: 853
AORTIC SEGMENTATION VIA SYNTHETIC DATA AUGMENTATION STRATEGY FROM PC-MRI SMALL DATASET
S. Garzia1,2, M. A. Scarpolini1,3, M. Mazzoli1,2, A. Monteleone4, F. Cademartiri4, S. Celi1
1BioCardioLab - Fondazione Toscana Gabriele Monasterio, Italy; 2Department of Information Engineering, University of Pisa, Italy; 3Department of Industrial Engineering, University of Rome "Tor Vergata", Italy; 4Clinical Imaging Department, Fondazione Toscana Gabriele Monasterio, Italy
17:48 - 18:00ID: 898
DEEP RESIDUAL AMBIVALENT GRAPH CONVOLUTIONAL NETWORKS FOR BIOMARKER PREDICTION IN LARGE VESSEL NETWORKS
A. B. Drysdale1, H. Arora1, A. Davies2, R. van Loon1
1Swansea University, United Kingdom; 2United Kingdom Atom Energy Authority, Culham Centre for Fusion Energy
18:00 - 18:12ID: 653
IMPLEMENTING DIGITAL TWINS OF THE CARDIOVASCULAR SYSTEM IN CLINICAL SETTINGS: AN AUTOMATED DEEP LEARNING PIPELINE
M. A. Scarpolini1,2, M. Mazzoli1,3, S. Garzia1,3, A. Monteleone4, S. Celi1
1BioCardioLab, Fondazione Toscana Gabriele Monasterio, Italy; 2University of Rome “Tor Vergata”, Italy; 3Department of Information Engineering, University of Pisa, Italy; 4Clinical Imaging Department, Fondazione Toscana Gabriele Monasterio, Italy
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