Cover Tree Compressed Sensing for Fast MR Fingerprint Recovery

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

Cover Tree Compressed Sensing for Fast MR Fingerprint Recovery

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

School of Engineering

Research output: Contribution to conference › Paper › peer-review

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 language	Undefined/Unknown
Publication status	Published - 23 Jun 2017
Event	IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP): MLSP 2017 - Tokyo, Tokyo, Japan Duration: 25 Sept 2017 → 28 Jan 2018 https://signalprocessingsociety.org/blog/mlsp-2017-2017-ieee-international-workshop-machine-learning-signal-processing

Conference

Conference	IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)
Country/Territory	Japan
City	Tokyo
Period	25/09/17 → 28/01/18
Internet address	https://signalprocessingsociety.org/blog/mlsp-2017-2017-ieee-international-workshop-machine-learning-signal-processing

Keywords / Materials (for Non-textual outputs)

stat.ML

Access to Document

https://arxiv.org/pdf/1706.07834.pdf

C-SENSE: Exploiting low dimensional models in sensing, computation and signal processing
Davies, M. (Principal Investigator)
EU government bodies
1/09/16 → 31/08/22
Project: Research
CQ-MRI: Compressed Quantitative MRI
Marshall, I. (Principal Investigator) & Davies, M. (Co-investigator)
EPSRC
1/07/15 → 31/12/18
Project: Research

Cite this

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title = "Cover Tree Compressed Sensing for Fast MR Fingerprint Recovery",

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.",

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author = "Mohammad Golbabaee and Zhouye Chen and Yves Wiaux and Davies, {Mike E.}",

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note = "IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP) ; Conference date: 25-09-2017 Through 28-01-2018",

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T1 - Cover Tree Compressed Sensing for Fast MR Fingerprint Recovery

AU - Golbabaee, Mohammad

AU - Chen, Zhouye

AU - Wiaux, Yves

AU - Davies, Mike E.

PY - 2017/6/23

Y1 - 2017/6/23

N2 - 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.

AB - 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.

KW - stat.ML

M3 - Paper

T2 - IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)

Y2 - 25 September 2017 through 28 January 2018

ER -

Cover Tree Compressed Sensing for Fast MR Fingerprint Recovery

Abstract

Conference

Keywords / Materials (for Non-textual outputs)

Access to Document

Projects

C-SENSE: Exploiting low dimensional models in sensing, computation and signal processing

CQ-MRI: Compressed Quantitative MRI

Cite this