30 citations · 65 across the 5 of their papers we have counts for
5 papers
Training deep learning based dynamic MR image reconstruction using synthetic fractals
Anirudh Raman, Olivier Jaubert, Mark Wrobel +8
Purpose: To investigate whether synthetically generated fractal data can be used to train deep learning (DL) models for dynamic MRI reconstruction, thereby avoiding the privacy, li…
High Resolution Isotropic 3D Cine imaging with Automated Segmentation using Concatenated 2D Real-time Imaging and Deep Learning
Mark Wrobel, Michele Pascale, Tina Yao +5
Background: Conventional cardiovascular magnetic resonance (CMR) in paediatric and congenital heart disease uses 2D, breath-hold, balanced steady state free precession (bSSFP) cine…
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
Tina Yao, Endrit Pajaziti, Michael Quail +3
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 ti…
Deep Learning Pipeline for Preprocessing and Segmenting Cardiac Magnetic Resonance of Single Ventricle Patients from an Image Registry
Tina Yao, Nicole St. Clair, Gabriel F. Miller +13
Purpose: To develop and evaluate an end-to-end deep learning pipeline for segmentation and analysis of cardiac magnetic resonance images to provide core-lab processing for a multi-…
Deep neural networks for fast acquisition of aortic 3D pressure and velocity flow fields
Endrit Pajaziti, Javier Montalt-Tordera, Claudio Capelli +4
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 outco…