  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.
