Phase ordering with automatic window selection (PAWS): a novel motion-resistant technique for 3D coronary imaging.

Abstract

Navigator acceptance imaging methods are hindered by the loss in scan efficiency which results from the changes in the breathing pattern of a subject over time. The diminishing variance algorithm (DVA), which does not use a predefined acceptance window, is less influenced by such changes. The use of phase ordering and weighting techniques has been shown to significantly improve image quality over nonordered window methods. However, the use of an acceptance window is inherent in all these techniques as a decision to accept or reject data must still be made. A technique is presented which is resistant to changes in breathing while allowing the use of phase ordering to provide effective motion artifact reduction in optimal time. The basic principle is described and illustrated for this automatic window-selection technique with in vitro results to demonstrate the feasibility of this method. Results of an in vivo study are also presented which demonstrate significant improvement in image quality over the DVA (p < 0.01) and hybrid-ordered phase encoding methods (p < 0.05).