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Current popular methods for Magnetic Resonance Fingerprint (MRF) recovery arebottlenecked by the heavy computations of a matched-filtering step due to thegrowing size and complexity of the fingerprint dictionaries in multi-parametricquantitative MRI applications. We address this shortcoming by arrangingdictionary atoms in the form of cover tree structures and adopt thecorresponding fast approximate nearest neighbour searches to acceleratematched-filtering. For datasets belonging to smooth low-dimensional manifoldscover trees offer search complexities logarithmic in terms of data population.With this motivation we propose an iterative reconstruction algorithm, namedCoverBLIP, to address large-size MRF problems where the fingerprint dictionaryi.e. discrete manifold of Bloch responses, encodes several intrinsic NMRparameters. We study different forms of convergence for this algorithm and weshow that provided with a notion of embedding, the inexact and non-convexiterations of CoverBLIP linearly convergence toward a near-global solution withthe same order of accuracy as using exact brute-force searches. Our furtherexaminations on both synthetic and real-world datasets and using differentsampling strategies, indicates between 2 to 3 orders of magnitude reduction intotal search computations. Cover trees are robust against thecurse-of-dimensionality and therefore CoverBLIP provides a notion ofscalability -- a consistent gain in time-accuracy performance-- for searchinghigh-dimensional atoms which may not be easily preprocessed (i.e. fordimensionality reduction) due to the increasing degrees of non-linearitiesappearing in the emerging multi-parametric MRF dictionaries.

CoverBLIP: accelerated and scalable iterative matched-filtering for Magnetic Resonance Fingerprint reconstruction