ketanfatania/QMRI-PnP-Recon-POC
Plug-and-Play Magnetic Resonance Fingerprinting based Quantitative MRI Reconstruction using Deep Denoisers (Proof of Concept) (IEEE ISBI 2022)
This project helps medical imaging researchers and practitioners improve the quality of Quantitative Magnetic Resonance Imaging (QMRI) scans. It takes raw, undersampled k-space data (from either spiral or EPI acquisition patterns) and reconstructs clearer Time-Series Magnetisation Images (TSMIs). From these improved TSMIs, it then accurately infers critical tissue maps (T1, T2, and Proton Density), which are vital for diagnostics and research.
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Use this if you need to reconstruct high-quality QMRI images and tissue maps from undersampled k-space data, particularly when using Magnetic Resonance Fingerprinting (MRF) techniques.
Not ideal if you require a production-ready solution for real-time clinical use or if your QMRI acquisitions involve complex multi-coil setups or non-gridded subsampling patterns.
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MATLAB
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May 26, 2022
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