Diffusion Tensor Estimation with Transformer Neural Networks

@article{Karimi2022DiffusionTE,
  title={Diffusion Tensor Estimation with Transformer Neural Networks},
  author={Davood Karimi and Ali Gholipour},
  journal={ArXiv},
  year={2022},
  volume={abs/2201.05701}
}
View PDF on arXiv
View With Semantic Reader
1 Citations
Methods Citations
1

Figures and Tables from this paper

One Citation

Atlas-powered deep learning (ADL) - application to diffusion weighted MRI
TLDR
This study shows that the advantages of deep learning and atlases can be synergistically combined to achieve unprecedented accuracy in biomarker estimation from dMRI data.

References

SHOWING 1-10 OF 65 REFERENCES
DeepDTI: High-fidelity six-direction diffusion tensor imaging using deep learning
Fast and Robust Diffusion Kurtosis Parametric Mapping Using a Three-Dimensional Convolutional Neural Network
TLDR
This result suggests that it is possible to achieve kurtosis mapping in most clinical scanners within one minute, which could significantly extend the clinical utility of the DKI.
Condition number as a measure of noise performance of diffusion tensor data acquisition schemes with MRI.
TLDR
Numerical algorithms are applied to optimize a DTI scheme by minimizing the condition number, hence improving the robustness to experimental noise and avoiding rotational variances of eigenvalues and anisotropy.
Highly accelerated, model‐free diffusion tensor MRI reconstruction using neural networks
TLDR
DiffNet improved DTI reconstruction accuracy, precision, and tumor delineation performance in GBM while permitting reconstruction from only three diffusion-encoding directions.
Fast learning of fiber orientation distribution function for MR tractography using convolutional neural network.
TLDR
A method to reconstruct the fODF from downsampled diffusion-weighted images (DWIs) by leveraging the strong inference ability of the deep convolutional neural network (CNN) and exhibits promising potential in acquisition acceleration for the reconstruction of fODFs with good accuracy.
Direct Estimation of Fiber Orientations Using Deep Learning in Diffusion Imaging
TLDR
This work presents a novel approach for estimating the fiber orientation directly from raw data, by converting the model fitting process into a classification problem based on a convolutional Deep Neural Network, which is able to identify correlated diffusion information within a single voxel.
Introduction to diffusion tensor imaging mathematics: Part III. Tensor calculation, noise, simulations, and optimization
The mathematical aspects of diffusion tensor magnetic resonance imaging (DTMRI, or DTI), the measurement of the diffusion tensor by magnetic resonance imaging (MRI), are discussed in this three-part…
Diffusion tensor imaging of the brain
q-Space Deep Learning: Twelve-Fold Shorter and Model-Free Diffusion MRI Scans
TLDR
It is demonstrated how deep learning, a group of algorithms based on recent advances in the field of artificial neural networks, can be applied to reduce diffusion MRI data processing to a single optimized step and how classical data processing can be streamlined by means of deep learning.
Neural networks for parameter estimation in microstructural MRI: Application to a diffusion-relaxation model of white matter
...
...