Deep learning enables reduced gadolinium dose for contrast‐enhanced brain MRI

By Enhao Gong, John M. Pauly, M. Wintermark, Greg Zaharchuk. 2018

305 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Materials Chemistry, Radiation

How researchers see this paper

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

In Biomedical Engineering

1. Arvid Lundervold
2. Dominik Fleischmann
3. Ronald M. Summers
4. Martin J. Willemink

In Materials Chemistry

1. Arvid Lundervold

In Radiation

1. Arvid Lundervold
2. Ronald M. Summers

Citations in the corpus

• An overview of deep learning in medical imaging focusing on MRI (2018, background): “They can also be used to reduce the gadolinium dose in contrast-enhanced brain MRI by an order of magnitude [87] without significant reduction in image quality.”
• Preparing Medical Imaging Data for Machine Learning (2020, background): “For example, a head CT might have findings supporting a diagnosis of stroke, but an MRI could definitively confirm.”
• Survey on Deep Neural Networks in Speech and Vision Systems (2019, background): “Advances in deep neural networks have shown tremendous potential in almost all areas of medical imaging such as ophthalmology [215], dental radiography [216], skin cancer imaging [217], brain imaging [218], cardiac imaging [219, 220], urology [221], lung imaging [222], stroke imaging [223], and so o”