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

4 Citations (Scopus)

Abstract

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)
PublisherIEEE
ISBN (Electronic)978-1-5090-6341-3
ISBN (Print)978-1-5090-6342-0
DOIs
Publication statusPublished - 7 Dec 2017

Publication series

NameMachine Learning for Signal Processing
PublisherIEEE
Volume2017
ISSN (Print)1551-2541
ISSN (Electronic)2378-928X

Keywords

  • stat.ML

Fingerprint

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

Cite this