Cerebrospinal fluid flow artifact reduction with deep learning to optimize the evaluation of spinal canal stenosis on spine MRI.

Ue-Hwan KimHyo Jin KimJiwoon SeoJee Won ChaiJiseon OhYoon-Hee ChoiDong Hyun Kim

Published in: Skeletal radiology (2023)

CycleGAN demonstrated the capability to produce images with diminished CSF flow artifacts. When paired with T2 TSE images, the CycleGAN-generated images allowed for more consistent assessment of spinal canal stenosis and exhibited agreement levels that were comparable to the combination of T2 TSE and FFE images.

Keyphrases

deep learning
convolutional neural network
cerebrospinal fluid
artificial intelligence
optical coherence tomography
spinal cord
machine learning
magnetic resonance imaging
magnetic resonance
image quality
spinal cord injury