Hello freesurfers,
I currently have a series of images with various TR,TE,TI,FA values. I would like to use mri_ca_train to create a .gca atlas based on these segmented volumes. Unfortunately, they all have slightly different control parameters (TR,TE,TI,FA). Is there any way around this problem, or would it be best to break the subjects into multiple groups with the same control parameters?
Here are the various parameters: [each row is a subject]
*TR* *TE* *TI* *flip angle* 9.38 3.86 450 12 9.34 3.86 450 12 9.38 3.86 450 12 9.38 3.86 450 12 9.34 3.86 450 12 9.44 3.87 450 12 9.39 3.86 450 12 2065.98 7.61 886.92 90 9.45 3.89 450 12 9.41 3.87 450 12 9.41 3.87 450 12 9.42 3.91 450 12 9.38 3.86 450 12 9.38 3.86 450 12 9.44 3.86 450 12 9.44 3.86 450 12 9.38 3.86 450 12 9.38 3.86 450 12 9.38 3.86 450 12 9.34 3.86 450 12 9.34 3.86 450 12 9.39 3.86 450 12 1900 2.6 900 9 1900 2.6 900 9 1900 2.6 900 9 9.41 3.87 450 12 9.41 3.87 450 12 9.41 3.87 450 12 1900 2.6 900 9 9.41 3.87 450 12 1900 2.6 900 9 1900 2.6 900 9
Thank you in advance.
-Mark
Hi Mark
the formatting got messed up so I can't parse your table. You can get around this using mri_modify, and just setting everything to 0. As for the effects, look at the images and see what you think.
cheers Bruce
On Thu, 1 Aug 2013, Mark Plantz wrote:
Hello freesurfers, I currently have a series of images with various TR,TE,TI,FA values. I would like to use mri_ca_train to create a .gca atlas based on these segmented volumes. Unfortunately, they all have slightly different control parameters (TR,TE,TI,FA). Is there any way around this problem, or would it be best to break the subjects into multiple groups with the same control parameters?
Here are the various parameters: [each row is a subject]
TR TE TI flip angle 9.38 3.86 450 12 9.34 3.86 450 12 9.38 3.86 450 12 9.38 3.86 450 12 9.34 3.86 450 12 9.44 3.87 450 12 9.39 3.86 450 12 2065.98 7.61 886.92 90 9.45 3.89 450 12 9.41 3.87 450 12 9.41 3.87 450 12 9.42 3.91 450 12 9.38 3.86 450 12 9.38 3.86 450 12 9.44 3.86 450 12 9.44 3.86 450 12 9.38 3.86 450 12 9.38 3.86 450 12 9.38 3.86 450 12 9.34 3.86 450 12 9.34 3.86 450 12 9.39 3.86 450 12 1900 2.6 900 9 1900 2.6 900 9 1900 2.6 900 9 9.41 3.87 450 12 9.41 3.87 450 12 9.41 3.87 450 12 1900 2.6 900 9 9.41 3.87 450 12 1900 2.6 900 9 1900 2.6 900 9
Thank you in advance.
-Mark
freesurfer@nmr.mgh.harvard.edu