Cover Tree Compressed Sensing for Fast MR Fingerprint Recovery

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

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 publicationProceedings of the 2017 IEEE Workshop on Machine Learning for Signal Processing (MLSP)
Publication statusPublished - 1 Jun 2017

Keywords / Materials (for Non-textual outputs)

  • Statistics - Machine Learning

Fingerprint

Dive into the research topics of 'Cover Tree Compressed Sensing for Fast MR Fingerprint Recovery'. Together they form a unique fingerprint.

Cite this