CoverBLIP: scalable iterative matched-filtering for MR Fingerprint recovery

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

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish
Title of host publicationJoint Annual Meeting ISMRM-ESMRMB 2018
Number of pages4
Publication statusAccepted/In press - 4 Feb 2018

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  • Cite this

    Golbabaee, M., Chen, Z., Wiaux, Y., & Davies, M. E. (Accepted/In press). CoverBLIP: scalable iterative matched-filtering for MR Fingerprint recovery. In Joint Annual Meeting ISMRM-ESMRMB 2018