MRI2Qmap: multi-parametric quantitative mapping with MRI-driven denoising priors
Magnetic Resonance Fingerprinting (MRF) and other highly accelerated transient-state parameter mapping techniques enable simultaneous quantification of multiple tissue properties, but often suffer from aliasing artifacts due to compressed sampling. Incorporating spatial image priors can mitigate these artifacts, and deep learning has shown strong potential when large training datasets are available. However, extending this paradigm to MRF-type sequences remains challenging due to the scarcity of quantitative imaging data for training. Can this limitation be overcome by leveraging sources of training data from clinically-routine weighted MRI images? To this end, we introduce MRI2Qmap, a pl...
| Authors | Mohammad Golbabaee, Matteo Cencini, Carolin Pirkl et al. |
|---|---|
| Primary category | physics.med-ph |
| Published | 11 March 2026 |
| Last updated at source | 11 March 2026 |
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