This paper demonstrates the feasibility of learning T2 -weighted image priors for multiple TEs using tissue-based deep learning and generalized series-based learning. A new method was proposed to effectively integrate these image priors with low-rank and sparse modeling to reconstruct high-quality images from highly undersampled data. The proposed method will supplement other acquisition-based methods to achieve high-speed T2 mapping.
Keyphrases
- deep learning
- high speed
- high resolution
- convolutional neural network
- artificial intelligence
- atomic force microscopy
- machine learning
- big data
- high density
- magnetic resonance
- electronic health record
- resting state
- white matter
- neural network
- functional connectivity
- mass spectrometry
- network analysis
- multiple sclerosis
- computed tomography
- blood brain barrier
- cerebral ischemia
- single molecule