Super‐resolution methods in MRI: Can they improve the trade‐off between resolution, signal‐to‐noise ratio, and acquisition time?

By E. Plenge, D. Poot, M. Bernsen, G. Kotek, G. Houston, P. Wielopolski, L. van der Weerd, Wiro J. Niessen, Erik H. W. Meijering. 2012

282 citations recorded by the source corpus.

Subfields: 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 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): “31 Super-resolution for MRI have been around for almost 10 years [191,192] and can be used to improve the trade-off between resolution, SNR, and acquisition time [193], generate 7T-like MR images on 3T MRI scanners [194], or obtain super-resolution T1 maps from a set of low resolution T1”
• Super-Resolution Musculoskeletal MRI Using Deep Learning (2018, background): “13,18,19 22 Although promising, this super-resolution method has not been pervasive in medical imaging because of limited resolution improvements and slow execution speeds for 3D data.”