Suppressing Multi-Channel Ultra-Low-Field MRI Measurement Noise Using Data Consistency and Image Sparsity

Fa-Hsuan Lin, Panu T. Vesanen, Yi-Cheng Hsu, Jaakko O. Nieminen, Koos C.J. Zevenhoven, Juhani Dabek, Lauri T. Parkkonen, Juha Simola, Antti I. Ahonen, Risto J. Ilmoniemi

    Research output: Contribution to journalArticleScientificpeer-review

    5 Citations (Scopus)
    126 Downloads (Pure)

    Abstract

    Ultra-low-field (ULF) MRI (B0 = 10–100 µT) typically suffers from a low signal-to-noise ratio (SNR). While SNR can be improved by pre-polarization and signal detection using highly sensitive superconducting quantum interference device (SQUID) sensors, we propose to use the inter-dependency of the k-space data from highly parallel detection with up to tens of sensors readily available in the ULF MRI in order to suppress the noise. Furthermore, the prior information that an image can be sparsely represented can be integrated with this data consistency constraint to further improve the SNR. Simulations and experimental data using 47 SQUID sensors demonstrate the effectiveness of this data consistency constraint and sparsity prior in ULF-MRI reconstruction.
    Original languageEnglish
    Article numbere61652
    Pages (from-to)1-6
    JournalPloS one
    Volume8
    Issue number4
    DOIs
    Publication statusPublished - 2013
    MoE publication typeA1 Journal article-refereed

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