@inproceedings{81ecc847e8184b39a2bc4557eefd1fee,
title = "Deep Image Priors for Magnetic Resonance Fingerprinting with Pretrained Bloch-Consistent Denoising Autoencoders",
abstract = "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.",
keywords = "deep image priors, deep learning, magnetic resonance fingerprinting, quantitative magnetic resonance imaging",
author = "Perla Mayo and Matteo Cencini and Ketan Fatania and Pirkl, {Carolin M.} and Menzel, {Marion I.} and Menze, {Bjoern H.} and Michela Tosetti and Mohammad Golbabaee",
year = "2024",
month = may,
day = "30",
doi = "10.1109/ISBI56570.2024.10635677",
language = "English",
isbn = "9798350313345",
series = "Proceedings - International Symposium on Biomedical Imaging",
publisher = "IEEE",
booktitle = "IEEE International Symposium on Biomedical Imaging, ISBI 2024 - Conference Proceedings",
address = "USA United States",
note = "21st IEEE International Symposium on Biomedical Imaging, ISBI 2024 ; Conference date: 27-05-2024 Through 30-05-2024",
}