Simulation of acquisition shifts in T2 weighted fluid-attenuated inversion recovery magnetic resonance images to stress test artificial intelligence segmentation networks.
Christiane PosseltMehmet Yigit AvciMehmet YiğitsoyPatrick SchuenkeChristoph KolbitschTobias SchaeffterStefanie RemmelePublished in: Journal of medical imaging (Bellingham, Wash.) (2024)
We show that these deviations are in the range of values as may be caused by erroneous or individual differences in relaxation times as described by literature. The coefficients of the F1 model function allow for a quantitative comparison of the influences of TE and TI. Limitations arise mainly from tissues with a low baseline signal (like cerebrospinal fluid) and when the protocol contains contrast-affecting measures that cannot be modeled due to missing information in the DICOM header.
Keyphrases
- artificial intelligence
- deep learning
- magnetic resonance
- contrast enhanced
- cerebrospinal fluid
- convolutional neural network
- big data
- machine learning
- systematic review
- magnetic resonance imaging
- randomized controlled trial
- gene expression
- computed tomography
- high resolution
- single molecule
- health information
- virtual reality
- healthcare
- heat stress
- optical coherence tomography
- mass spectrometry