Automated reference-free detection of motion artifacts in magnetic resonance images

By Thomas Kuestner, Annika Liebgott, Lukas Mauch, P. Martirosian, F. Bamberg, Konstantin Nikolaou, Bin Yang, F. Schick, Sergios Gatidis. 2018

100 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, 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 Biomedical Engineering

1. Arvid Lundervold

In Computer Vision and Pattern Recognition

1. Arvid Lundervold
2. Chenming Yang
3. Marc Fischer
4. Konstantin Nikolaou
5. Thomas Küstner
6. Sergios Gatidis
7. Bin Yang
8. Karim Armanious

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, background): “poor quality spectra in MRSI [187]; detection and removal of ghosting artifacts in MR spectroscopy [188]; and automated reference-free detection of patient motion artifacts in MRI [189].”
• MedGAN: Medical Image Translation using GANs (2018, methodology): “many). A T1- weighted spin echo (SE) sequence was acquired one under JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015 9 resting conditions and once under motion of the head in all volunteers [66]. Similar to the PET-CT dataset, the MR data was scaled to a spacing of 1×1×1mm3 and 2D axial sl”