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Found 6 papers, 0 papers with code
Model-based Synthetic Data-driven Learning (MOST-DL): Application in Single-shot T2 Mapping with Severe Head Motion Using Overlapping-echo Acquisition
In this study, a general framework, MOdel-based SyntheTic Data-driven Learning (MOST-DL), was proposed to generate pairing data from the first principle to address the lack of labeling in supervised learning scenarios.
Accelerated Nuclear Magnetic Resonance Spectroscopy with Deep Learning
Nuclear magnetic resonance (NMR) spectroscopy serves as an indispensable tool in chemistry and biology but often suffers from long experimental time.
High Efficient Reconstruction of Single-shot T2 Mapping from OverLapping-Echo Detachment Planar Imaging Based on Deep Residual Network
After the ResNet was trained, it was applied to reconstruct the T2 mapping from simulation and in vivo human brain data.
Hankel Matrix Nuclear Norm Regularized Tensor Completion for $N$-dimensional Exponential Signals
Signals are generally modeled as a superposition of exponential functions in spectroscopy of chemistry, biology and medical imaging.
Projected Iterative Soft-thresholding Algorithm for Tight Frames in Compressed Sensing Magnetic Resonance Imaging
It has been shown that, redundant image representations, e. g. tight frames, can significantly improve the image quality.
Fast Multi-class Dictionaries Learning with Geometrical Directions in MRI Reconstruction
The proposed method is compared with state-of-the-art magnetic resonance image reconstruction methods.
