Confidence images for MR spectroscopic imaging

Automated spectral analysis and estimation of signal amplitudes from magnet ic resonance data generally constitutes a difficult nonlinear optimization problem. Obtaining a measure of the degree of confidence that one has in th e estimated parameters is as important as the estimates themselves. This is particularly important if clinical diagnoses are to be based on estimated metabolite levels, as in applications of MR Spectroscopic Imaging for human studies. In this report, a standard method of obtaining confidence interva ls for nonlinear estimation is applied to simulated data and short-TE clini cal proton spectroscopic imaging data sets of human brain. So-called "confi dence images" are generated to serve as visual indicators of how much trust should be placed in interpretation of spatial variations seen in images de rived from fitted metabolite parameter estimates. This method is introduced in a Bayesian framework to enable comparison with similar techniques using Cramer-Rao bounds and the residuals of fitted results. (C) 2000 Wiley-Liss , Inc.