Cover Tree Compressed Sensing for Fast MR Fingerprint Recovery

Mohammad Golbabaee, Zhouye Chen, Yves Wiaux, Mike E. Davies

Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

5 Citations (SciVal)


We adopt data structure in the form of cover trees and iteratively apply approximate nearest neighbour (ANN) searches for fast compressed sensing reconstruction of signals living on discrete smooth manifolds. Levering on the recent stability results for the inexact Iterative Projected Gradient (IPG) algorithm and by using the cover tree's ANN searches, we decrease the projection cost of the IPG algorithm to be logarithmically growing with data population for low dimensional smooth manifolds. We apply our results to quantitative MRI compressed sensing and in particular within the Magnetic Resonance Fingerprinting (MRF) framework. For a similar (or sometimes better) reconstruction accuracy, we report 2-3 orders of magnitude reduction in computations compared to the standard iterative method which uses brute-force searches.
Original languageEnglish
Title of host publication2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)
ISBN (Electronic)978-1-5090-6341-3
ISBN (Print)978-1-5090-6342-0
Publication statusPublished - 7 Dec 2017

Publication series

NameMachine Learning for Signal Processing
ISSN (Print)1551-2541
ISSN (Electronic)2378-928X


  • stat.ML


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