Dictionaries as used in multi-parametric mapping are typically very large in size, take long to compute, and scale exponentially with the number of parameters. Here, we break the bond between dictionary size and representation accuracy by two modifications: First, we approximate the Bloch-response manifold by piece-wise linear functions, and second, we allow the sampling grid to be refined adaptively depending on the precision needed. Phantom and in vivo studies demonstrate efficient multi-parametric mapping with tiny dictionaries and subspace-constrained reconstruction. The presented method preserves accuracy and precision with dictionaries reduced in size by a factor of 10 and beyond.
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