Deep Image Priors for Magnetic Resonance Fingerprinting with pretrained Bloch-consistent denoising autoencoders
<|reference_start|>Deep Image Priors for Magnetic Resonance Fingerprinting with pretrained Bloch-consistent denoising autoencoders: The estimation of multi-parametric quantitative maps from Magnetic Resonance Fingerprinting (MRF) compressed sampled acquisitions, albeit successful, remains a challenge due to the high underspampling rate and artifacts naturally occuring during image reconstruction. Whilst state-of-the-art DL methods can successfully address the task, to fully exploit their capabilities they often require training on a paired dataset, in an area where ground truth is seldom available. In this work, we propose a method that combines a deep image prior (DIP) module that, without ground truth and in conjunction with a Bloch consistency enforcing autoencoder, can tackle the problem, resulting in a method faster and of equivalent or better accuracy than DIP-MRF.<|reference_end|>
@article{mayo2024deep,
    title={Deep Image Priors for Magnetic Resonance Fingerprinting with pretrained
  Bloch-consistent denoising autoencoders},
    author={Perla Mayo, Matteo Cencini, Ketan Fatania, Carolin M. Pirkl, Marion I.
  Menzel, Bjoern H. Menze, Michela Tosetti and Mohammad Golbabaee},
    journal={arXiv preprint arXiv:2407.19866},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.19866},
    primaryClass={eess.IV cs.LG}
}