> I am using DARTEL in SPM8b to do longitudinal analyses of T1 images. For
> the initial Segment, I am looking at the different options for bias
> correction ranging from "very light" (default) to "extremely heavy". I
> tested all 6 levels of bias correction on one image. I applied the bias
> correction parameters from *seg_sn.mat to an image of all ones, using
> cspm_bias_writecorrected.m. This way the bias field that is applied can be
> visualized. I have attached the results as viewed in Check Reg, with "very
> light" entered first and all of them in order progressing up to "extremely
> heavy". I have a few questions...
> 1) Is it a concern that the shape of the brain is discernable? Might this
> mean that intensity data related to brain is being mistaken for intensity
> variation due to field inhomogeneity?
This is possible. The model assumes that each tissue class has fairly uniform
intensities throughout, but this is not necessarily the case. For example,
subcortical GM tends to have intensities that are closer to WM, so bias
correction may try to bring such GM to a similar intensity to that in the
cortex. If a region is darker for some physiological reason, then the bias
correction may attempt to "correct" this.
> 3) In some locations the intensity is adjusted down using the "very light"
> setting and up using the "extremely heavy" setting. The results appear to
> be qualitatively different; how can one decide which is best? Is there a
> recommendation what level of bias correction to use for longitudinal data,
> where we are especially concerned with potential influence of field change
> over time?
With little or no regularisation, the bias correction introduces ringing
effects ( http://en.wikipedia.org/wiki/Gibbs_phenomenon ) - particularly in
those regions of the image that are not modelled by the tissue classification
(ie far from the brain, where no tissue probability map information is
available). Ideally, the amount of regularisation should depend on prior
knowledge about the magnitude of the inhomogeneity artifact. Alternatively
(in principle), it could be determined using a type-II maximum likelihood
(REML) procedure - but in practice, the segmentation model would probably
need to be quite a lot more complicated for this to work well (eg proper MRF
modelling, multiple tissue class images for different kinds of GM, etc).
I tried to choose default settings that work as well as possible for typical
data. It is possible that some adjustments may be needed in order to tune
them. This would need to be done empirically, using cross-validation to
determine the most accurate segmentation.