Keywords: Quantitative Imaging, Image Reconstruction, Accelerated parameter mapping, Self-supervised; Zero-shot self-supervised learning
Motivation: Ground-truth-free (GT-free) deep learning (DL) approaches are expected to lower the cost of training DL models in accelerated quantitative MRI, but their performance has not been well compared to supervised approaches, and their application to quantitative MRI is still limited.
Goal(s): Evaluation of the effectiveness of GT-free approaches in quantitative MRI.
Approach: Three quantitative MRI methods (variable flip angle, multi-slice multi-echo, double echo steady state) were used to compare model-based DL architectures with three learning schemes: supervised learning, self-supervised learning, and zero-shot self-supervised learning in multiple acceleration factors.
Results: GT-free deep Learning approaches had high performance comparable to SL in many cases.
Impact: In this study, we compared GT-free approaches with SL and showed that they had high performance comparable to SL in many cases. These results indicate that GT-free approaches are applicable to a variety of sequences in accelerated quantitative MRI.
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