MRI2Qmap: compressed-sampled multiparametric quantitative MRI reconstruction using learned spatial priors from multimodal MRI datasets

Magnetic Resonance Fingerprinting1 (MRF) and other highly-accelerated transient-state parameter mapping techniques4 excel in quantifying multiple tissue properties, but can suffer from aliasing artifacts due to compressed-sampled scans. Incorporating spatial image priors can mitigate these issues, with deep learning showing promise given large training datasets. However, applying this paradigm to MRF-type sequences is challenging due to the scarcity of quantitative imaging datasets for training. We introduce MRI2Qmap, a quantitative image reconstruction approach that can take advantage of learned spatial-domain image priors from independently-acquired, large datasets of routine weighted-MRI images. We validate our findings using simulated and in-vivo acquisitions.