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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