title: A simple noniterative principal component technique for rapid noise reduction in parallel MR images.
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 1.5 T with an eight-element array.
authors.affiliation: Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94143‐0628, USA. anand.patel@ucsf.edu
authors.forename: Anand S
authors.initials: AS
authors.lastname: Patel
authors.forename: Qi
authors.initials: Q
authors.lastname: Duan
authors.forename: Philip M
authors.initials: PM
authors.lastname: Robson
authors.forename: Charles A
authors.initials: CA
authors.lastname: McKenzie
authors.forename: Daniel K
authors.initials: DK
authors.lastname: Sodickson
mesh_terms: D016477:Artifacts; D001921:Brain / Q000033:anatomy & histology; D006801:Humans; D007091:Image Processing, Computer-Assisted; D016018:Least-Squares Analysis; D008279:Magnetic Resonance Imaging / Q000379:methods*; D025341:Principal Component Analysis / Q000379:methods*; D059629:Signal-To-Noise Ratio