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Found 10 papers, 2 papers with code
Non-Uniform Sampling Reconstruction for Symmetrical NMR Spectroscopy by Exploiting Inherent Symmetry
Symmetrical NMR spectroscopy constitutes a vital branch of multidimensional NMR spectroscopy, providing a powerful tool for the structural elucidation of biological macromolecules.
FlexDTI: Flexible diffusion gradient encoding scheme-based highly efficient diffusion tensor imaging using deep learning
Significance: FlexDTI can well learn diffusion gradient direction information to achieve generalized DTI reconstruction with flexible diffusion gradient scheme.
One for Multiple: Physics-informed Synthetic Data Boosts Generalizable Deep Learning for Fast MRI Reconstruction
We demonstrate that training DL models on synthetic data, coupled with enhanced learning techniques, yields in vivo MRI reconstructions comparable to or surpassing those of models trained on matched realistic datasets, reducing the reliance on real-world MRI data by up to 96%.
Convex Dual Theory Analysis of Two-Layer Convolutional Neural Networks with Soft-Thresholding
Soft-thresholding has been widely used in neural networks.
Model-based Synthetic Data-driven Learning (MOST-DL): Application in Single-shot T2 Mapping with Severe Head Motion Using Overlapping-echo Acquisition
Use of synthetic data has provided a potential solution for addressing unavailable or insufficient training samples in deep learning-based magnetic resonance imaging (MRI).
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.
