Scan-specific Robust Artificial-neural-networks for k-space Interpolation (RAKI) Reconstruction: Database-free Deep Learning for Fast Imaging
By Mehmet Akçakaya, Steen Moeller, Sebastian Weingärtner, Kamil Ugurbil. Magnetic Resonance in Medicine, 2018
403 citations recorded by the source corpus.
To develop an improved k‐space reconstruction method using scan‐specific deep learning that is trained on autocalibration signal (ACS) data.
Subfields: Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/02c802a67507f105a79fef2860ffb84d2abb82e2
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
- Steen Moeller
- Mehmet Akçakaya
- Daniel K. Sodickson
- Kerstin Hammernik
- Florian Knoll
- Thomas Pock
- Chi Zhang
- Jong Chul Ye
Citations in the corpus
- Deep Magnetic Resonance Image Reconstruction: Inverse Problems Meet Neural Networks (2020, neutral): “Robust ANNs for k-space interpolation (called RAKI) [20] uses a three-layer CNN to learn the k-space interpolation for parallel imaging and then obtains the reconstructed image from the interpolated full k-space data.”
- Deep-Learning Methods for Parallel Magnetic Resonance Imaging Reconstruction: A Survey of the Current Approaches, Trends, and Issues (2019, background): “A robust artificial neural network for k-space interpolation (RAKI) constitutes a scan-specific machine-learning approach for improved results [28].”