Keywords: MR Fingerprinting, Quantitative Imaging, MR Fingerprinting, Compressed sensing, Image reconstruction, AI/ML Image Reconstruction
Motivation: Deep learning excels at compressed-sensing image reconstruction given large training datasets. Applying this paradigm to accelerated quantitative MRI, including magnetic resonance fingerprinting (MRF), is challenging because quantitative imaging datasets for training are scarce.
Goal(s): Can we overcome this limitation using new sources of training data from routine, largely available weighted-MRI images?
Approach: We introduce MRI2Qmap, a plug-and-play quantitative image reconstruction algorithm based on deep image denoising models pretrained on large multimodal weighted-MRI datasets.
Results: We showed, for the first time, that spatial/structural priors learned from independently-acquired datasets of routine weighted-MRI images can be effectively used for quantitative MRI image reconstruction.
Impact: Thanks to the widespread use of MRIs, our approach could enable much larger datasets to be used for training potentially enhanced AI models for fast quantitative MRI/MRF image reconstruction.
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