Ah, I see I skipped Greg's munger explanation. So based on what I was saying earlier, you'd want to increase your -X and -G parameters by about the same factor; see what happens.

Another thing I realized looking through Greg's list: you may also want to change the image similarity measure, if you haven't already. The default, NMI, tends to be rather insensitive to magnitude of intensity differences, which makes it well-behaved and that is usually a good thing.

But if you have essentially single-modality data, i.e., you expect the same structures to be bright in both images (and others dark in both, also), then you may be able to use a more aggressive metric, such as NCC (normalized cross correlation; assumes a linear relationship between intensities in both images), or even MSD (mean-squared difference; assumes that identical structures in both images will have identical intensities).

If these work better for you but go overboard in some areas and create excessive warpings, then you might be able to counteract that by increasing the -E parameter.

All this is, unfortunately, a little bit like a dark art, but it's the best I can offer.

Torsten