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Found 4 papers, 3 papers with code
Enhance the Image: Super Resolution using Artificial Intelligence in MRI
This chapter provides an overview of deep learning techniques for improving the spatial resolution of MRI, ranging from convolutional neural networks, generative adversarial networks, to more advanced models including transformers, diffusion models, and implicit neural representations.
Improving accuracy and uncertainty quantification of deep learning based quantitative MRI using Monte Carlo dropout
Dropout is conventionally used during the training phase as regularization method and for quantifying uncertainty in deep learning.
SDnDTI: Self-supervised deep learning-based denoising for diffusion tensor MRI
The noise in diffusion-weighted images (DWIs) decreases the accuracy and precision of diffusion tensor magnetic resonance imaging (DTI) derived microstructural parameters and leads to prolonged acquisition time for achieving improved signal-to-noise ratio (SNR).
SRDTI: Deep learning-based super-resolution for diffusion tensor MRI
High-resolution diffusion tensor imaging (DTI) is beneficial for probing tissue microstructure in fine neuroanatomical structures, but long scan times and limited signal-to-noise ratio pose significant barriers to acquiring DTI at sub-millimeter resolution.
