4D flow MRI

By Michael Markl, Alex Frydrychowicz, S. Kozerke, Mike Hope, Oliver Wieben. Journal of Magnetic Resonance Imaging, 2012

524 citations recorded by the source corpus.

Subfields: Cardiology and Cardiovascular Medicine, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/fc0fde63f0c1d1a7d92e8dfc93f2a5ffa9e74dda

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cardiology and Cardiovascular Medicine

1. Paris Perdikaris
2. Yibo Yang
3. Yang Liu

In Radiology, Nuclear Medicine and Imaging

1. John A. Detre
2. Paris Perdikaris
3. Yang Liu

Citations in the corpus

• AI in Medical Imaging Informatics: Current Challenges and Future Directions (2020, unknown): “Moreover, Magnetic Resonance Elastography (MRE) allows virtual palpation with significant applications in liver fibrosis [20], while 4D flow methods permit exquisite visualization of flow in 3D + t [17], [21].”
• Machine learning in cardiovascular flows modeling: Predicting pulse wave propagation from non-invasive clinical measurements using physics-informed deep learning (2019, background): “Rather than Doppler ultrasound, more recent studies leverage advances in 4D flow Magnetic Resonance Imaging (MRI) [31] to recover the full three-dimensional velocity flow field.”