ta7029.zip
​Hi Bruce,

Thank you for your prompt response. I added an expert.opts file with the line "mris_make_surfaces -max_gray_at_csf_border 60 -max_csf 35" and ran recon-all -s ta7029 -autorecon2 -autorecon3 -expert ta7029/scripts/expert.opts -3T -qcache  for one of the affected subjects, but it doesn't seem to have corrected the pial surface. I have attached the subject folder here. If you have time to take a look and try a couple of things, I would really appreciate it!

Thanks,
Meaghan

On Wed, Mar 22, 2017 at 11:21 AM, Bruce Fischl <fischl@nmr.mgh.harvard.edu> wrote:
Hi Meaghan

it looks like the white surface is pretty accurate but the gm doesn't get out far enough. I would play with some of the expert options to mris_make_surfaces like max_gray_at_csf_border. If you upload a subject I can try it out and see if I can improve things and get back to you.

cheers
Bruce



On Wed, 22 Mar 2017, Meaghan Perdue wrote:


      Hello,

      I attended the September 2016 Freesurfer training course, and I am seeking help to resolve a consistent issue with white and pial surface extraction
      that seems to be rooted in poor intensity normalization. Our lab is working with a pediatric dataset acquired on a 3T scanner and recon-all was run in
      Freesurfer v5.3. Throughout the dataset, we have found that the surfaces are cutting off white matter and excluding a significant amount of gray
      matter, particularly in the lateral temporal lobes.  I have attached several images to illustrate the problem. As you can see in the images, the pial
      surface in the lateral temporal lobes hugs close to the white/gray boundary and excludes gray matter.

      We have used the -3T flag when running recon-all for all subjects, and we have manually changed the smoothing distance in mi_nu_correct to 30 for
      several subjects in an attempt to improve this, but we did not get a better result. We are hesitant to address this problem using control points
      because it would require adding many control points across many slices in nearly all the subjects, and we would like to avoid the
      reliability/replicability issues of so much manual editing. (Not to mention the time required to do such heavy editing). Is there any way to address
      this automatically through the recon pipeline? 


      Many thanks,

      Meaghan Perdue




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