1 citations · 2 across the 5 of their papers we have counts for
5 papers
Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI
Oliver Welin Odeback, Arivazhagan Geetha Balasubramanian, Jonas Schollenberger +9
4D Flow Magnetic Resonance Imaging (4D Flow MRI) enables non-invasive quantification of blood flow and hemodynamic parameters. However, its clinical application is limited by low s…
A Computational Pipeline for Advanced Analysis of 4D Flow MRI in the Left Atrium
Xabier Morales, Ayah Elsayed, Debbie Zhao +13
The left atrium (LA) plays a pivotal role in modulating left ventricular filling, but our comprehension of its hemodynamics is significantly limited by the constraints of conventio…
Deep learning for temporal super-resolution 4D Flow MRI
Pia Callmer, Mia Bonini, Edward Ferdian +5
4D Flow Magnetic Resonance Imaging (4D Flow MRI) is a non-invasive technique for volumetric, time-resolved blood flow quantification. However, apparent trade-offs between acquisiti…
Generalized super-resolution 4D Flow MRI $\unicode{x2013}$ using ensemble learning to extend across the cardiovascular system
Leon Ericsson, Adam Hjalmarsson, Muhammad Usman Akbar +14
4D Flow Magnetic Resonance Imaging (4D Flow MRI) is a non-invasive measurement technique capable of quantifying blood flow across the cardiovascular system. While practical use is…
Non-invasive hemodynamic analysis for aortic regurgitation using computational fluid dynamics and deep learning
Derek Long, Cameron McMurdo, Edward Ferdian +4
Changes in cardiovascular hemodynamics are closely related to the development of aortic regurgitation, a type of valvular heart disease. Metrics derived from blood flows are used t…