  A complete first and second order statistical characterization of noise in
SENSE reconstructed data is proposed. SENSE acquisitions have usually been
modeled as Rician distributed, since the data reconstruction takes place into
the spatial domain, where Gaussian noise is assumed. However, this model just
holds for the first order statistics and obviates other effects induced by
coils correlations and the reconstruction interpolation. Those effects are
properly taken into account in this study, in order to fully justify a final
SENSE noise model. As a result, some interesting features of the reconstructed
image arise: (1) There is a strong correlation between adjacent lines. (2) The
resulting distribution is non-stationary and therefore the variance of noise
will vary from point to point across the image. Closed equations for the
calculation of the variance of noise and the correlation coefficient between
lines are proposed. The proposed model is totally compatible with g-factor
formulations.
