Search Results for author:
Found 9 papers, 2 papers with code
Deep Separable Spatiotemporal Learning for Fast Dynamic Cardiac MRI
This challenge leads to necessitate extensive training data in many deep learning reconstruction methods.
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%.
High-efficient Bloch simulation of magnetic resonance imaging sequences based on deep learning
Significance: As a proof-of-concept work, Simu-Net shows the potential to apply deep learning for rapidly approximating the forward physical process of MRI and may increase the efficiency of Bloch simulation for optimization of MRI pulse sequences and deep learning-based methods.
Physics-driven Synthetic Data Learning for Biomedical Magnetic Resonance
Deep learning has innovated the field of computational imaging.
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).
A Segmentation-aware Deep Fusion Network for Compressed Sensing MRI
In this paper, we proposed a segmentation-aware deep fusion network called SADFN for compressed sensing MRI.
A Divide-and-Conquer Approach to Compressed Sensing MRI
Compressed sensing (CS) theory assures us that we can accurately reconstruct magnetic resonance images using fewer k-space measurements than the Nyquist sampling rate requires.
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.
