Keywords: MR Fingerprinting, Visualization
Motivation: Address the challenge of integrating MRF data into existing MRI analysis via synthetic images without introducing spatial artifacts or hallucinations possible with CNNs.
Goal(s): Generate static lookup tables (LUTs) mapping from T1/T2 value space directly to grayscale visualizations matching clinical contrasts.
Approach: A simple pixel-wise regression network was trained on a public dataset of MRF data and weighted images. Static LUTs were generated from dictionaries of T1/T2 combinations, then applied to MRF-derived maps for visualization and processing via FSL.
Results: Successful generation of synthetic contrast LUTs ensures reproducibility and allows instantaneous visualization or registration of MRF maps in a more conventional grayscale format.
Impact: Integration of MRF into traditional analysis pipelines suffers because quantitative maps have inherently different contrasts from weighted images. LUTs for instant, deterministic generation of weighted contrasts from T1/T2 maps allow for direct use of tools like FSL with MRF data.
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