Automated artifact detection in abbreviated dynamic contrast-enhanced (DCE) MRI-derived maximum intensity projections (MIPs) of the breast.

Lorenz A KapsnerSabine OhlmeyerLukas FolleFrederik B LaunArmin M NagelAndrzej LiebertHannes SchreiterMatthias W BeckmannMichael UderEvelyn WenkelSebastian Bickelhaupt

Published in: European radiology (2022)

• Deep learning classifiers are able to reliably detect MRI artifacts in dynamic contrast-enhanced protocol-derived maximum intensity projections of the breast. • Automated quality assurance of maximum intensity projections of the breast may be of special relevance for abbreviated breast MRI, e.g., in high-throughput settings, such as cancer screening programs.

Keyphrases

high throughput
deep learning
contrast enhanced
magnetic resonance imaging
machine learning
high intensity
diffusion weighted imaging
randomized controlled trial
computed tomography
magnetic resonance
papillary thyroid
artificial intelligence
image quality
convolutional neural network
real time pcr