A simple noniterative principal component technique for rapid noise reduction in parallel MR images

Anand S. Patel, Qi Duan, Philip M. Robson, Charles A. McKenzie, Daniel K. Sodickson

    Research output: Contribution to journalArticlepeer-review

    Abstract

    The utilization of parallel imaging permits increased MR acquisition speed and efficiency; however, parallel MRI usually leads to a deterioration in the signal-to-noise ratio when compared with otherwise equivalent unaccelerated acquisitions. At high accelerations, the parallel image reconstruction matrix tends to become dominated by one principal component. This has been utilized to enable substantial reductions in g-factor-related noise. A previously published technique achieved noise reductions via a computationally intensive search for multiples of the dominant singular vector which, when subtracted from the image, minimized joint entropy between the accelerated image and a reference image. We describe a simple algorithm that can accomplish similar results without a time-consuming search. Significant reductions in g-factor-related noise were achieved using this new algorithm with in vivo acquisitions at 1.5T with an eight-element array.

    Original languageEnglish (US)
    Pages (from-to)84-92
    Number of pages9
    JournalNMR in Biomedicine
    Volume25
    Issue number1
    DOIs
    StatePublished - Jan 2012

    Keywords

    • Artifact correction
    • Joint entropy
    • Noise reduction
    • Parallel MRI
    • Principal component analysis
    • SENSE
    • Signal processing
    • g-factor

    ASJC Scopus subject areas

    • Molecular Medicine
    • Radiology Nuclear Medicine and imaging
    • Spectroscopy

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