  Current proposed solutions for the high dimensionality of the MRF
reconstruction problem rely on a linear compression step to reduce the matching
computations and boost the efficiency of fast but non-scalable searching
schemes such as the KD-trees. However such methodologies often introduce an
unfavourable compromise in the estimation accuracy when applied to nonlinear
data structures such as the manifold of Bloch responses with possible increased
dynamic complexity and growth in data population. To address this shortcoming
we propose an inexact iterative reconstruction method, dubbed as the Cover
BLoch response Iterative Projection (CoverBLIP). Iterative methods improve the
accuracy of their non-iterative counterparts and are additionally robust
against certain accelerated approximate updates, without compromising their
final accuracy. Leveraging on these results, we accelerate matched-filtering
using an ANNS algorithm based on Cover trees with a robustness feature against
the curse of dimensionality.
