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 metho...

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Bibliographic Details
Published in2024 IEEE International Symposium on Biomedical Imaging (ISBI) pp. 1 - 4
Main Authors Mayo, Perla, Cencini, Matteo, Fatania, Ketan, Pirkl, Carolin M., Menzel, Marion I., Menze, Bjoern H., Tosetti, Michela, Golbabaee, Mohammad
Format Conference Proceeding
LanguageEnglish
Published IEEE 27.05.2024
Subjects
Accuracy
deep image priors
deep learning
Estimation
Image coding
Magnetic resonance
magnetic resonance fingerprinting
Noise reduction
quantitative magnetic resonance imaging
Reconstruction algorithms
Training
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Summary: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.
ISSN:1945-8452
DOI:10.1109/ISBI56570.2024.10635677