Compressive MRI quantification using convex spatiotemporal priors and deep auto-encoders.
Mohammad GolbabaeeGuido BounincontriCarolin M. PirklMarion I. MenzelBjoern H. MenzeMike E. DaviesPedro A. GómezPublished in: CoRR (2020)
Keyphrases
- magnetic resonance imaging
- belief nets
- medical images
- convex optimization
- magnetic resonance images
- mr images
- labeling problems
- high resolution
- compressive sensing
- spatial and temporal
- space time
- mri images
- video compression
- prior information
- mri data
- moving objects
- brain tissue
- convex functions
- quantitative evaluation
- piecewise linear
- prior knowledge
- spatio temporal
- globally optimal
- prior probabilities
- error propagation
- computational complexity
- bayesian framework
- deep learning
- learned from training data
- brain tumor segmentation