Official code of the paper "Fine-tuning deep learning model parameters for improved super-resolution of dynamic MRI with prior-knowledge" (https://doi.org/10.1016/j.artmed.2021.102196 or https://arxiv.org/abs/2102.02711)

You can access this paper for free directly from Elsevier (till 15th Dec): https://lnkd.in/d4Dti_Pw

The pre-print of this work is available on ArXiv: https://arxiv.org/abs/2102.02711

Abstract of this work was presented at ISMRM 2021 (Abstract available on RG: https://www.researchgate.net/publication/349588965_Fine-tuning_deep_learning_model_parameters_for_improved_super-resolution_of_dynamic_MRI_with_prior-knowledge)

Furher extension of this work, DDoS model, also incorporates the temporal information to improve the reconstruction further, was presented as an abstract at ESMRMB 2021 (Abstract availalble on RG: https://www.researchgate.net/publication/354888919_DDoS_Dynamic_Dual-Channel_U-Net_for_Improving_Deep_Learning_Based_Super-Resolution_of_Abdominal_Dynamic_MRI)

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If you use this approach in your research or use codes from this repository, please cite the following in your publications:

Chompunuch Sarasaen, Soumick Chatterjee, Mario Breitkopf, Georg Rose, Andreas Nürnberger, Oliver Speck: Fine-tuning deep learning model parameters for improved super-resolution of dynamic MRI with prior-knowledge (Artificial Intelligence in Medicine, Nov 2021)

BibTeX entry:

@article{sarasaen2021fine,
title = {Fine-tuning deep learning model parameters for improved super-resolution of dynamic MRI with prior-knowledge},
journal = {Artificial Intelligence in Medicine},
pages = {102196},
year = {2021},
issn = {0933-3657},
doi = {https://doi.org/10.1016/j.artmed.2021.102196},
author = {Sarasaen, Chompunuch and Chatterjee, Soumick and Breitkopf, Mario and Rose, Georg and N{\"u}rnberger, Andreas and Speck, Oliver},
}

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