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

Authors

Anand S. Patel, University of California, San Francisco
Qi Duan, NYU Langone Health
Philip M. Robson, Harvard Medical School
Charles A. McKenzie, Western UniversityFollow
Daniel K. Sodickson, NYU Langone Health

Document Type

Article

Publication Date

1-1-2012

Journal

NMR in Biomedicine

Volume

25

Issue

1

First Page

84

Last Page

92

URL with Digital Object Identifier

10.1002/nbm.1716

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. © 2011 John Wiley & Sons, Ltd.