Deep Learning Archives

April 17, 2024

Reduction of ADC bias in diffusion MRI with deep learning-based acceleration: A phantom validation study at 3.0 T

https://www.sciencedirect.com/science/article/pii/S0730725X24001310 Authors [/author] Published Date: 2024-04 CaliberMRI Reference Library content, paper, article, journal, etc. published 2024-04

October 19, 2023

Clinical feasibility of deep learning reconstruction in liver diffusion-weighted imaging: Improvement of image quality and impact on apparent diffusion coefficient value

https://www.ejradiology.com/article/S0720-048X(23)00463-1/fulltext Published Date: 2023-10

July 21, 2023

Deep learning-based quantitative susceptibility mapping (QSM) in the presence of fat using synthetically generated multi-echo phase training data

https://onlinelibrary.wiley.com/doi/epdf/10.1002/mrm.29265 Published Date: 2022-Mar

July 11, 2023

Deep Learning for Magnetic Resonance Fingerprinting: A New Approach for Predicting Quantitative Parameter Values from Time Series

https://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2017/Hoppe17-DLF.pdf Published Date: 2017-Jan

Reduction of ADC bias in diffusion MRI with deep learning-based acceleration: A phantom validation study at 3.0 T

Clinical feasibility of deep learning reconstruction in liver diffusion-weighted imaging: Improvement of image quality and impact on apparent diffusion coefficient value

Deep learning-based quantitative susceptibility mapping (QSM) in the presence of fat using synthetically generated multi-echo phase training data

Deep Learning for Magnetic Resonance Fingerprinting: A New Approach for Predicting Quantitative Parameter Values from Time Series

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Reduction of ADC bias in diffusion MRI with deep learning-based acceleration: A phantom validation study at 3.0 T

Clinical feasibility of deep learning reconstruction in liver diffusion-weighted imaging: Improvement of image quality and impact on apparent diffusion coefficient value

Deep learning-based quantitative susceptibility mapping (QSM) in the presence of fat using synthetically generated multi-echo phase training data

Deep Learning for Magnetic Resonance Fingerprinting: A New Approach for Predicting Quantitative Parameter Values from Time Series

Reduction of ADC bias in diffusion MRI with deep learning-based acceleration: A phantom validation study at 3.0 T