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

Perla Mayo; Matteo Cencini; Ketan Fatania; Carolin M. Pirkl; Marion I. Menzel; Bjoern H. Menze; Michela Tosetti; Mohammad Golbabaee

arXiv · 2407.19866

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

Perla Mayo·Matteo Cencini·Ketan Fatania·Carolin M. Pirkl·Marion I. Menzel·Bjoern H. Menze·Michela Tosetti·Mohammad Golbabaee

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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.

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Perla Mayo, Matteo Cencini, Ketan Fatania, Carolin M. Pirkl, Marion I. Menzel, Bjoern H. Menze, Michela Tosetti, Mohammad Golbabaee. 2024-07-29. Deep Image Priors for Magnetic Resonance Fingerprinting with pretrained Bloch-consistent denoising autoencoders. https://arxiv.org/abs/2407.19866

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