Bayesian modeling of Dynamic Contrast Enhanced MRI data in cerebral glioma patients improves the diagnostic quality of hemodynamic parameter maps.
Anna TietzeAnne NielsenIrene K MikkelsenMikkel Bo HansenAnnette ObelLeif ØstergaardKim MouridsenPublished in: PloS one (2018)
The Bayesian method has the potential to increase the diagnostic reliability of Dynamic Contrast Enhanced parameter maps in brain tumors. In our data, images based on the 2-compartment-exchange model were superior to those based on the extended Toft's model.
Keyphrases
- end stage renal disease
- electronic health record
- ejection fraction
- chronic kidney disease
- newly diagnosed
- big data
- magnetic resonance imaging
- peritoneal dialysis
- deep learning
- prognostic factors
- contrast enhanced
- subarachnoid hemorrhage
- computed tomography
- optical coherence tomography
- machine learning
- data analysis
- artificial intelligence
- patient reported
- quality improvement
- blood brain barrier