Deep Image Priors for Magnetic Resonance Fingerprinting with Pretrained Bloch-Consistent Denoising Autoencoders
Published in 2024 IEEE International Symposium on Biomedical Imaging (ISBI), 2024
Recommended citation: Perla Mayo, Matteo Cencini, Ketan Fatania, Carolin M. Pirkl, Marion I. Menzel, Bjoern. H. Menze, Michela Tosetti, and Mohammad Golbabaee, "Deep Image Priors for Magnetic Resonance Fingerprinting with Pretrained Bloch-Consistent Denoising Autoencoders" in 2024 IEEE International Symposium on Biomedical Imaging (ISBI).
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.
Recommended citation: P. Mayo, M. Cencini, K. Fatania, C. M. Pirkl, M. I. Menzel, B. H. Menze, M. Tosetti, and M. Golbabaee, “Deep Image Priors for Magnetic Resonance Fingerprinting with Pretrained Bloch-Consistent Denoising Autoencoders,” 2024 IEEE International Symposium on Biomedical Imaging (ISBI), Athens, Greece, 2024, pp. 1-4, doi: 10.1109/ISBI56570.2024.10635677.