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…
Automated Labeling of Intracranial Arteries with Uncertainty Quantification Using Deep Learning
Javier Bisbal, Patrick Winter, Sebastian Jofre +10
Accurate anatomical labeling of intracranial arteries is essential for cerebrovascular diagnosis and hemodynamic analysis but remains time-consuming and subject to interoperator va…
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…
Comprehensive Analysis of Relative Pressure Estimation Methods Utilizing 4D Flow MRI
Brandon Hardy, Judith Zimmermann, Vincent Lechner +6
Magnetic resonance imaging (MRI) can estimate three-dimensional (3D) time-resolved relative pressure fields using 4D-flow MRI, thereby providing rich pressure field information. Cl…
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…