Výsledky vyhledávání - "Pajaziti, Endrit"

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Image2Flow: A hybrid image and graph convolutional neural network for rapid patient-specific pulmonary artery segmentation and CFD flow field calculation from 3D cardiac MRI data

Autor: Yao, Tina, Pajaziti, Endrit, Quail, Michael, Schievano, Silvia, Steeden, Jennifer A, Muthurangu, Vivek

Computational fluid dynamics (CFD) can be used for evaluation of hemodynamics. However, its routine use is limited by labor-intensive manual segmentation, CFD mesh creation, and time-consuming simulation. This study aims to train a deep learning mode

Externí odkaz: http://arxiv.org/abs/2402.18236

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Automatic Segmentation of the Great Arteries for Computational Hemodynamic Assessment

Autor: Montalt-Tordera, Javier, Pajaziti, Endrit, Jones, Rod, Sauvage, Emilie, Puranik, Rajesh, Singh, Aakansha Ajay Vir, Capelli, Claudio, Steeden, Jennifer, Schievano, Silvia, Muthurangu, Vivek

Background: Computational fluid dynamics (CFD) is increasingly used to assess blood flow conditions in patients with congenital heart disease (CHD). This requires patient-specific anatomy, usually obtained from segmented 3D cardiovascular magnetic re

Externí odkaz: http://arxiv.org/abs/2209.06117

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Deep neural networks for fast acquisition of aortic 3D pressure and velocity flow fields

Autor: Pajaziti, Endrit, Montalt-Tordera, Javier, Capelli, Claudio, Sivera, Raphael, Sauvage, Emilie, Schievano, Silvia, Muthurangu, Vivek

Computational fluid dynamics (CFD) can be used to simulate vascular haemodynamics and analyse potential treatment options. CFD has shown to be beneficial in improving patient outcomes. However, the implementation of CFD for routine clinical use is ye

Externí odkaz: http://arxiv.org/abs/2208.12156

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Akademický článek

Image2Flow: A proof-of-concept hybrid image and graph convolutional neural network for rapid patient-specific pulmonary artery segmentation and CFD flow field calculation from 3D cardiac MRI data.

Autor: Yao, Tina¹ (AUTHOR), Pajaziti, Endrit¹ (AUTHOR), Quail, Michael¹ (AUTHOR), Schievano, Silvia¹ (AUTHOR), Steeden, Jennifer¹ (AUTHOR), Muthurangu, Vivek¹ (AUTHOR) v.muthurangu@ucl.ac.uk

Publikováno v: PLoS Computational Biology. 6/20/2024, Vol. 20 Issue 6, p1-21. 21p.

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Akademický článek

Shape-driven deep neural networks for fast acquisition of aortic 3D pressure and velocity flow fields.

Autor: Pajaziti, Endrit¹ (AUTHOR) endrit.pajaziti.13@ucl.ac.uk, Montalt-Tordera, Javier¹ (AUTHOR), Capelli, Claudio¹ (AUTHOR), Sivera, Raphaël¹ (AUTHOR), Sauvage, Emilie¹ (AUTHOR), Quail, Michael² (AUTHOR), Schievano, Silvia¹ (AUTHOR), Muthurangu, Vivek¹ (AUTHOR)

Publikováno v: PLoS Computational Biology. 4/24/2023, Vol. 19 Issue 4, p1-20. 20p. 1 Color Photograph, 2 Black and White Photographs, 5 Diagrams, 1 Chart, 2 Graphs.

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