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.

翻译：磁共振指纹成像（MRF）压缩采样采集的多参数定量图谱估计虽已取得成功，但由于高欠采样率及图像重建过程中固有的伪影，该任务仍具挑战性。尽管当前最先进的深度学习方法能成功处理此任务，为充分发挥其性能，通常需要在配对数据集上进行训练，而该领域常缺乏真实数据。本研究提出一种结合深度图像先验模块的方法，该模块无需真实数据，通过与强制布洛赫一致性的自编码器协同工作，能够有效解决该问题，最终形成一种比DIP-MRF方法速度更快、精度相当或更优的技术方案。