CoverBLIP: scalable iterative matched-filtering for MR Fingerprint recovery

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

CoverBLIP: scalable iterative matched-filtering for MR Fingerprint recovery

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

University of Edinburgh

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Current popular methods for Magnetic Resonance Fingerprint (MRF) recovery are bottlenecked by the heavy computations of a matched- filtering step due to the size and complexity of the fingerprints dictionary. In this abstract we investigate and evaluate the advantages of incorporating an accelerated and scalable Approximate Nearest Neighbour Search (ANNS) scheme based on the Cover trees structure to shortcut the computations of this step within an iterative recovery algorithm and to obtain a good compromise between the computational cost and reconstruction accuracy of the MRF problem.

Original language	English
Title of host publication	Joint Annual Meeting ISMRM-ESMRMB 2018
Number of pages	4
Publication status	Accepted/In press - 4 Feb 2018

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Cite this

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title = "CoverBLIP: scalable iterative matched-filtering for MR Fingerprint recovery",

abstract = "Current popular methods for Magnetic Resonance Fingerprint (MRF) recovery are bottlenecked by the heavy computations of a matched- filtering step due to the size and complexity of the fingerprints dictionary. In this abstract we investigate and evaluate the advantages of incorporating an accelerated and scalable Approximate Nearest Neighbour Search (ANNS) scheme based on the Cover trees structure to shortcut the computations of this step within an iterative recovery algorithm and to obtain a good compromise between the computational cost and reconstruction accuracy of the MRF problem.",

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AU - Golbabaee, Mohammad

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AU - Wiaux, Yves

AU - Davies, Mike E.

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Y1 - 2018/2/4

N2 - Current popular methods for Magnetic Resonance Fingerprint (MRF) recovery are bottlenecked by the heavy computations of a matched- filtering step due to the size and complexity of the fingerprints dictionary. In this abstract we investigate and evaluate the advantages of incorporating an accelerated and scalable Approximate Nearest Neighbour Search (ANNS) scheme based on the Cover trees structure to shortcut the computations of this step within an iterative recovery algorithm and to obtain a good compromise between the computational cost and reconstruction accuracy of the MRF problem.

AB - Current popular methods for Magnetic Resonance Fingerprint (MRF) recovery are bottlenecked by the heavy computations of a matched- filtering step due to the size and complexity of the fingerprints dictionary. In this abstract we investigate and evaluate the advantages of incorporating an accelerated and scalable Approximate Nearest Neighbour Search (ANNS) scheme based on the Cover trees structure to shortcut the computations of this step within an iterative recovery algorithm and to obtain a good compromise between the computational cost and reconstruction accuracy of the MRF problem.

M3 - Conference contribution

BT - Joint Annual Meeting ISMRM-ESMRMB 2018

ER -