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Gianluca Pontone
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2020 – today
- 2025
- [j10]Luca Crugnola
, Christian Vergara
Computational hemodynamic indices to identify Transcatheter Aortic Valve Implantation degeneration. Comput. Methods Programs Biomed. 259: 108517 (2025) - 2024
- [j9]Emily Louise Manchester
Aortic valve neocuspidization and bioprosthetic valves: Evaluating turbulence haemodynamics. Comput. Biol. Medicine 171: 108123 (2024) - [j8]Alessia Gerbasi
CAD-RADS scoring of coronary CT angiography with Multi-Axis Vision Transformer: A clinically-inspired deep learning pipeline. Comput. Methods Programs Biomed. 244: 107989 (2024) - [j7]Guido Nannini
An automated and time-efficient framework for simulation of coronary blood flow under steady and pulsatile conditions. Comput. Methods Programs Biomed. 257: 108415 (2024) - [i5]Andrea Tonini, Francesco Regazzoni, Matteo Salvador, Luca Dede', Roberto Scrofani, Laura Fusini, Chiara Cogliati, Gianluca Pontone, Christian Vergara, Alfio Quarteroni:
Two new calibration techniques of lumped-parameter mathematical models for the cardiovascular system. CoRR abs/2405.11915 (2024) - [i4]Guido Nannini, Simone Saitta, Luca Mariani, Riccardo Maragna, Andrea Baggiano, Saima Mushtaq, Gianluca Pontone, Alberto Redaelli:
An automated and time efficient framework for simulation of coronary blood flow under steady and pulsatile conditions. CoRR abs/2408.16496 (2024) - [i3]Julian Suk, Guido Nannini, Patryk Rygiel, Christoph Brune, Gianluca Pontone, Alberto Redaelli, Jelmer M. Wolterink:
Deep vectorised operators for pulsatile hemodynamics estimation in coronary arteries from a steady-state prior. CoRR abs/2410.11920 (2024) - 2023
- [j6]Marco Penso
A token-mixer architecture for CAD-RADS classification of coronary stenosis on multiplanar reconstruction CT images. Comput. Biol. Medicine 153: 106484 (2023) - [i2]Alessia Gerbasi, Arianna Dagliati, Giuseppe Albi, Mattia Chiesa, Daniele Andreini, Andrea Baggiano, Saima Mushtaq, Gianluca Pontone, Riccardo Bellazzi, Gualtiero Colombo
CAD-RADS scoring of coronary CT angiography with Multi-Axis Vision Transformer: a clinically-inspired deep learning pipeline. CoRR abs/2304.07277 (2023) - 2022
- [j5]Giuseppe Muscogiuri, Marco Guglielmo
Multimodality Imaging in Ischemic Chronic Cardiomyopathy. J. Imaging 8(2): 35 (2022) - [c4]Aditya Killekar, Kajetan Grodecki, Andrew Lin
COVID-19 lesion segmentation using convolutional LSTM for self-attention. Image Processing 2022 - 2021
- [j4]Marco Penso
Automated left and right ventricular chamber segmentation in cardiac magnetic resonance images using dense fully convolutional neural network. Comput. Methods Programs Biomed. 204: 106059 (2021) - [j3]Simone Di Gregorio
A computational model applied to myocardial perfusion in the human heart: From large coronaries to microvasculature. J. Comput. Phys. 424: 109836 (2021) - [i1]Kajetan Grodecki, Aditya Killekar, Andrew Lin
Rapid quantification of COVID-19 pneumonia burden from computed tomography with convolutional LSTM networks. CoRR abs/2104.00138 (2021) - 2020
- [j2]Concetta Piazzese
3D right ventricular endocardium segmentation in cardiac magnetic resonance images by using a new inter-modality statistical shape modelling method. Biomed. Signal Process. Control. 58: 101866 (2020) - [c3]Sara Moccia, Alessandro Cagnoli, Chiara Martini
A Novel Approach Based on Spatio-temporal Features and Random Forest for Scar Detection Using Cine Cardiac Magnetic Resonance Images. CinC 2020: 1-4 - [c2]Marco Penso, Sara Moccia, Stefano Scafuri, Giuseppe Muscogiuri
Automated Left and Right Chamber Segmentation in Cardiac MRI Using Dense Fully Convolutional Neural Network. CinC 2020: 1-4
2010 – 2019
- 2018
- [j1]Maria Chiara Carminati
A statistical shape model of the left ventricle from real-time 3D echocardiography and its application to myocardial segmentation of cardiac magnetic resonance images. Comput. Biol. Medicine 96: 241-251 (2018) - [c1]Sara Moccia
Automated Scar Segmentation From Cardiac Magnetic Resonance-Late Gadolinium Enhancement Images Using a Deep-Learning Approach. CinC 2018: 1-4
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last updated on 2025-01-09 13:16 CET by the dblp team
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