Optimal decay rate constant estimates from phased array data utilizing joint Bayesian analysis.
Since their initial description, phased array coils have become increasingly popular due to their ease of customization for various applications. Numerous methods for combining data from individual channels have been proposed that attempt to optimize the SNR of the resultant images. One issue that has received comparatively little attention is how to apply these combination techniques to a series of images obtained from phased array coils that are then analyzed to produce quantitative estimates of tissue parameters. Herein, instead of the typical goal of maximizing the SNR in a single image, we are interested in maximizing the accuracy and precision of parameter estimates that are obtained from a series of such images. Our results demonstrate that a joint Bayesian analysis offers a "worry free" method for obtaining optimal parameter estimates from data generated by multiple coils (channels) from a single object (source). We also compare the properties of common channel combination techniques under different conditions to the results obtained from the joint Bayesian analysis. If the noise variance is constant for all channels, a sensitivity weighted average provides parameter estimates equivalent to the joint analysis. If both the noise variance and signal intensity are similar in all channels, a simple channel average gives an adequate result. However, if the noise variance differs between channels, an "ideal weighted" approach should be applied, where data are combined after weighting by the channel amplitude divided by the noise variance. Only this "ideal weighting" provides results similar to the automatic-weighting inherent in the joint Bayesian approach.
Quirk
James D
JD
Mallinckrodt Institute of Radiology, Washington University School of Medicine, 4525 Scott Avenue, Campus Box 8227, St. Louis, MO 63110, USA. jdquirk@wustl.edu
Sukstanskii
Alexander L
AL
Bretthorst
G Larry
GL
Yablonskiy
Dmitriy A
DA
D000465:Algorithms; D001499:Bayes Theorem; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D008279:Magnetic Resonance Imaging / Q000295:instrumentation / Q000706:statistics & numerical data*; D009682:Magnetic Resonance Spectroscopy / Q000295:instrumentation / Q000706:statistics & numerical data*