MR fingerprinting Deep RecOnstruction NEtwork (DRONE)

By Ouri Cohen, Bo Zhu, Matthew S. Rosen. 2017

297 citations recorded by the source corpus.

Demonstrate a novel fast method for reconstruction of multi‐dimensional MR fingerprinting (MRF) data using deep learning methods.

Subfields: Materials Chemistry, Radiology, Nuclear Medicine and Imaging

How researchers see this paper

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

In Materials Chemistry

1. Arvid Lundervold

In Radiology, Nuclear Medicine and Imaging

1. Arvid Lundervold
2. Florian Tramèr
3. Dan Boneh

Citations in the corpus

• An overview of deep learning in medical imaging focusing on MRI (2018, methodology): “In contrast to [164], their deep neural network architecture was inspired from the domain of speech recognition due to the similarity of the two tasks.”
• Deep Magnetic Resonance Image Reconstruction: Inverse Problems Meet Neural Networks (2020, unknown): “Inspired by this, researchers have applied deep learning to computational MRI, where it has shown the potential to significantly accelerate MR reconstruction [5]–[28].”