Deep Image Priors for Magnetic Resonance Fingerprinting with pretrained Bloch-consistent denoising autoencoders

Autor:	Mayo, Perla, Cencini, Matteo, Fatania, Ketan, Pirkl, Carolin M., Menzel, Marion I., Menze, Bjoern H., Tosetti, Michela, Golbabaee, Mohammad
Rok vydání:	2024
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: 4 pages, 3 figures 1 table, presented at ISBI 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2407.19866 Zobrazit plný text záznamu View this record from Arxiv