The CoverBLIP algorithm uses fast Approximate Nearest Neighbour (ANN) searches based on cover trees data structure in order to accelerate iterative matched-filtering for solving Magnetic Resonance Fingerprinting (MRF) inverse problem. The idea of using cover tree searches for the general class of data-driven compressed sensing problems was originally presented in:
M Golbabaee, ME Davies, “Inexact Gradient Projection and Fast Data Driven Compressed Sensing”, IEEE Transactions on Information Theory, 2018 (doi: 10.1109/TIT.2018.2841379)
The customized application of this framework to the MRF reconstruction problem (i.e. the CoverBLIP algorithm) appeared in the following articles:
M Golbabaee, Z Chen, Y Wiaux, M Davies, “CoverBLIP: accelerated and scalable iterative matched-filtering for Magnetic Resonance Fingerprint reconstruction”, arxiv, 2018. (Extended version)
M Golbabaee, Z Chen, Y Wiaux, M Davies, “Cover tree compressed sensing for fast MR fingerprint recovery”, IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP), 2017, (doi: 10.1109/MLSP.2017.8168167).
More details about the package can be found in Readme.docx file.
© M Golbabaee (2018)