AbstractID: 7867 Title: Estimation of Exponential Decay Constants
R2 estimation is simple when the noise is small. However, the Rician nature of the noise in MRI is problematic if the noise is
significant. Rician noise adds an offset to the data so it asymptotically approaches the noise floor rather than zero. This noise floor is
difficult to model effectively. We evaluate three new methods of reducing the bias resulting from Rician noise and limiting the
resulting increase in variance: 1) replacing the variance with the cumulative variance from all shorter TE’s, 2) adjusting the
magnitudes for the probabilities it is noise and 3) forcing the data to decrease with increasing TE which is equivalent to non-negative
least squares. The three methods were tested using simulations of exponentially decaying MR signals assumed a R2 of 0.02 ms-1 and
TE’s ranging from 2 to 360 ms, however, the results are equivalent to any scaled version. Ten thousand pseudorandom noise vectors
were used for each simulation. Using the cumulative variance and monotonic fit produced the best results. These weighted least
squares methods have equivalent analoges in time domain fitting as well.
Supported by NIH/NCI PO1 #CA80139-01A1 and NIH/NDS RO1 NS38471