TY - GEN
T1 - Deep Image Priors for Magnetic Resonance Fingerprinting with Pretrained Bloch-Consistent Denoising Autoencoders
AU - Mayo, Perla
AU - Cencini, Matteo
AU - Fatania, Ketan
AU - Pirkl, Carolin M.
AU - Menzel, Marion I.
AU - Menze, Bjoern H.
AU - Tosetti, Michela
AU - Golbabaee, Mohammad
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - deep image priors
KW - deep learning
KW - magnetic resonance fingerprinting
KW - quantitative magnetic resonance imaging
UR - http://www.scopus.com/inward/record.url?scp=85203370296&partnerID=8YFLogxK
U2 - 10.1109/ISBI56570.2024.10635677
DO - 10.1109/ISBI56570.2024.10635677
M3 - Conference contribution
AN - SCOPUS:85203370296
T3 - Proceedings - International Symposium on Biomedical Imaging
BT - IEEE International Symposium on Biomedical Imaging, ISBI 2024 - Conference Proceedings
PB - IEEE Computer Society
T2 - 21st IEEE International Symposium on Biomedical Imaging, ISBI 2024
Y2 - 27 May 2024 through 30 May 2024
ER -