I'm working on the latest developmental release for Rh. 9. and I'm having problems with the normalization procedures on my datasets. My datasets have large intensity inhomogeneities due to their acquisition with a surface coil (8-ch. GE). To fix this problem I have resorted to the use of control points. This works if I add enough points across the brain ( over 200), and I get a better normalization result after running "recon-all -normalization -usecontrolpoints" and another skull-stripping on the new T1-volume. To test whether or not this produces a good segmentaition of white matter, I run mri_segment on my brain.mgz -volume. If I'm satisfied with the segmentation result, I move on to the surface processing stage, and run the -autorecon2 -script. The problem is that after running the -autorecon2, the normalization is completely wrong again, looking more like the first normalization done without control points, and the wm.mgz -volume is also bad. So are the surfaces. How do I make freesurfer produce the same good results as I got during the first normalization? Do I need to specify the use of control points again, even though the brain.mgz volume looks ok?
Thanks,
Martin Ystad Medical Student University of Bergen Institute of Biomedicine Jonas Lies vei 91, 5009 Bergen, Norway.