Xu Cui

Below are some pictures of my own brain.

001

rawavg

orig

nu

T1

brainmask

norm

nu_noneck

aseg

brain

brain.finalsurfs

wm.seg

wm

filled

lh.orig.nofix

lh.smoothwm.nofix

lh.inflated.nofix

lh.qsphere.nofix

lh.orig

lh.white

lh.pial

lh.smoothwm

lh.inflated

lh.sphere

The images above were generated by freesurfer.

Check out recon-all procedures here

rawavg

orig

nu

T1

brainmask

norm

nu_noneck

aseg.auto

aseg.auto_noCCseg

aseg

brain

wm.seg

filled

lh.orig

lh.smoothwm

lh.inflated.nofix

lh.qsphere.nofix

lh.white

lh.pial

lh.inflated

lh.sphere

Assume CON14 is our subject ID and it is inside directory $SUBJECTS_DIR.

After the display window and the control panel pops up, try playing with the menus and buttons.

tkmedit for volume

tkmedit CON14 rawavg.mgz
tkmedit CON14 rawavg.mgz lh.pial -aux-surface rh.pial
File > Load Segmentation > (for "Load Volume") aparc+aseg.mgz (for "Load Color Table") FreeSurferColorLUT.txt

tksurfer for surface

tksurfer CON14 lh inflated
tksurfer CON14 lh pial
tksurfer CON14 rh pial
File > Label > Import Annotation > (browse to) lh.aparc.annot

tkregister2 for check registration

tkregister2 --s CON14 --fstal

recon-all is a batch program and runs >30 steps.  It easily takes 30 hours to finish one subject.

Use tkmedit CON14 T1.mgz and tksurfer CON14 lh inflated to visualize images. More about visualization with freesurfer.

Note: commands are in green. They are copied from the log files.

View images for every step here

Before you start, you need to put the structural images into certain directory hierarchy and set environment variable. Assume folder “structural” is where subjects’ structural images are. Under “structural”, you have folders “SUBJ1″, “SUBJ2″, “CON14″ etc for every subject. You will have to put your structural images to “structural/SUBJ1/mri/orig/001.mgz”. If you have multiple images for this subject, use “002.mgz” etc. (If you original file format is not mgz, check out how to convert image formats).

structural
    |--SUBJ1
        |--mri
            |--orig
                |--001.mgz
                |--002.mgz

Then set environment variable in linux shell:
setenv SUBJECTS_DIR $PWD

1. motion correction, and conform
   input: mri/orig/00?.mgz
   output: mri/orig.mgz (through mri/rawavg.mgz)
   If you have multiple structural images (001.mgz, 002.mgz, etc), then motion correction will be done and rawavg.mgz is created. If you have only one image (001.mgz), it simply copies the file and rename to rawavg.mgz.
   cp CON14/mri/orig/001.mgz CON14/mri/rawavg.mgz
   Then conform using mri_convert:
   mri_convert CON14/mri/rawavg.mgz CON14/mri/orig.mgz --conform
   Conform is to make voxel size 1×1x1mm and same number of voxels (256) along x,y,z directions (iso). Data is written to orig.mgz.
2. non-uniform intensity normalization
   input: mri/orig.mgz
   output: mri/nu.mgz
   mri_nu_correct.mni --i orig.mgz --o nu.mgz --n 2
   Non-parametric Non-uniform intensity Normalization (N3), corrects for intensity non-uniformity in MR data, making relatively few assumptions about the data. This runs the MINC tool ‘nu_correct’. By default, four iterations of nu_correct are run. The flag -nuiterations specification of some other number of iterations.
3. talairach transformation
   input: mri/nu.mgz
   output: transform/talairach.auto.xfm
   talairach_avi --i nu.mgz --xfm transforms/talairach.auto.xfm
   This computes the affine transform from the orig volume to the MNI305 atlas using the MINC program mritotal (see Collins, et al, 1994) through a FreeSurfer script called talairach. Several of the downstream programs use talairach coordinates as seed points. You can/should check how good the talairach registration is using tkregister2 --s subjid --fstal. tkregister2 allows you to compare the orig volume against the talairach volume resampled into the orig space. Run “tkregister2 –help” for more information. Creates the files mri/transform/talairach.auto.xfm and talairach.xfm.
   This step produces a long log.
4. intensity normalization
   input: mri/nu.mgz
   output: mri/T1.mgz
   mri_normalize -g 1 nu.mgz T1.mgz
   Performs intensity normalization of the orig volume and places the result in mri/T1.mgz. Attempts to correct for fluctuations in intensity that would otherwise make intensity-based segmentation much more difficult. Intensities for all voxels are scaled so that the mean intensity of the white matter is 110. If there are problems with the normalization, users can add control points. See also Normalization2.
5. skull strip
   input: mri/nu.mgz
   output: mri/brainmask.mgz, mri/brainmask.auto.mgz
   mri_em_register -skull nu.mgz /usr/local/freesurfer/average/RB_all_withskull_2008-03-26.gca transforms/talairach_with_skull.lta
   Removes the skull from mri/T1.mgz and stores the result in mri/brainmask.auto.mgz and mri/brainmask.mgz. Runs the mri_watershed program. If the strip fails, users can specify seed points (-wsseed) or change the threshold (-wsthresh, -wsmore, -wsless). The -autorecon1 stage ends here.
6. EM (GCA) Registration (-<no>gcareg)
   input: mri/nu.mgz, mri/brainmask.mgz
   output: mri/transforms/talairach.lta
   mri_em_register -mask brainmask.mgz nu.mgz /usr/local/freesurfer/average/RB_all_2008-03-26.gca transforms/talairach.lta
   Computes transform to align the mri/nu.mgz volume to the default GCA atlas found in FREESURFER_HOME/average (see -gca flag for more info). Creates the file mri/transforms/talairach.lta. The -autorecon2 stage starts here.
7. CA Normalize (-<no>canorm)
   input: mri/nu.mgz, mri/brainmask.mgz
   output: mri/norm.mgz

   mri_ca_normalize -mask brainmask.mgz nu.mgz /usr/local/freesurfer/average/RB_all_2008-03-26.gca transforms/talairach.lta norm.mgz
   Further normalization, based on GCA model. Creates mri/norm.mgz.

8. CA Register (-<no>careg)
   input: mri/norm.mgz, mri/brainmask.mgz
   output: talairach.m3z
   mri_ca_register -align-after -nobigventricles -mask brainmask.mgz -T transforms/talairach.lta norm.mgz /usr/local/freesurfer/average/RB_all_2008-03-26.gca transforms/talairach.m3z
   Computes a nonlinear transform to align with GCA atlas. Creates the file mri/transform/talairach.m3z.
   This step is very loooooong (10 hours)
9. Remove neck (-<no>rmneck)
   input: mri/nu.mgz
   output: mri/nu_noneck.mgz
   mri_remove_neck -radius 25 nu.mgz transforms/talairach.m3z /usr/local/freesurfer/average/RB_all_2008-03-26.gca nu_noneck.mgz
   The neck region is removed from the NU-corrected volume mri/nu.mgz. Makes use of transform computed from prior CA Register stage. Creates the file mri/nu_noneck.mgz.
10. EM Registration, with Skull (-<no>skull-lta)
    input: mri/nu_noneck.mgz
    output: mri/transforms/talairach_with_skull.lta
    mri_em_register -skull -t transforms/talairach.lta nu_noneck.mgz /usr/local/freesurfer/average/RB_all_withskull_2008-03-26.gca transforms/talairach_with_skull.lta
    Computes transform to align volume mri/nu_noneck.mgz with GCA volume possessing the skull. Creates the file mri/transforms/talairach_with_skull.lta.
11. CA Label (-<no>calabel)
    input: mri/norm.mgz, mri/transforms/talairach.m3z
    output: mri/aseg.auto.mgz, mri/aseg.mgz, (through mri/aseg.auto_noCCseg.mgz)
    mri_ca_label -align -nobigventricles norm.mgz transforms/talairach.m3z /usr/local/freesurfer/average/RB_all_2008-03-26.gca aseg.auto_noCCseg.mgz
mri_cc -aseg aseg.auto_noCCseg.mgz -o aseg.auto.mgz CON14
    Labels subcortical structures, based in GCA model. Creates the files mri/aseg.auto.mgz and mri/aseg.mgz.
12. ASeg Stats (-<no>segstats)
    ?Computes statistics on the segmented subcortical structures found in mri/aseg.mgz. Writes output to file stats/aseg.stats.
13. Normalization2 (-<no>normalization)
    input: mri/norm.mgz, mri/aseg.mgz, mri/brainmask.mgz
    output: mri/brain.mgz
    mri_normalize -aseg aseg.mgz -mask brainmask.mgz norm.mgz brain.mgz
mri_mask -T 5 brain.mgz brainmask.mgz brain.finalsurfs.mgz
    Performs a second (major) intensity correction using only the brain volume as the input (so that it has to be done after the skull strip). Intensity normalization works better when the skull has been removed. Creates a new brain.mgz volume. The -autorecon2-cp stage begins here. If -noaseg flag is used, then aseg.mgz is not used by mri_normalize.
14. WM Segmentation (-<no>segmentation)
    input: brain.mgz
    output: wm.seg.mgz
    mri_segment brain.mgz wm.seg.mgz
mri_edit_wm_with_aseg -keep-in wm.seg.mgz brain.mgz aseg.mgz wm.asegedit.mgz
mri_pretess wm.asegedit.mgz wm norm.mgz wm.mgz
    Attempts to separate white matter from everything else. The input is mri/brain.mgz, and the output is mri/wm.mgz. Uses intensity, neighborhood, and smoothness constraints. This is the volume that is edited when manually fixing defects. Calls mri_segment, mri_edit_wm_with_aseg, and mri_pretess. To keep previous edits, run with -keepwmedits. If -noaseg is used, them mri_edit_wm_aseg is skipped.
15. Cut/Fill (-<no>fill)
    input: wm.mgz
    output: filled.mgz
    mri_fill -a ../scripts/ponscc.cut.log -xform transforms/talairach.lta -segmentation aseg.auto_noCCseg.mgz wm.mgz filled.mgz
    This creates the subcortical mass from which the orig surface is created. The mid brain is cut from the cerebrum, and the hemispheres are cut from each other. The left hemisphere is binarized to 255. The right hemisphere is binarized to 127. The input is mri/wm.mgz and the output is mri/filled.mgz. Calls mri_fill. If the cut fails, then seed points can be supplied (see -cc-crs, -pons-crs, -lh-crs, -rh-crs). The actual points used for the cutting planes in the corpus callosum and pons can be found in scripts/ponscc.cut.log. The stage -autorecon2-wm begins here. This is the last stage of volumetric processing. If -noaseg is used, then aseg.mgz is not used by mri_fill.
16. Tessellation (-<no>tessellate)
    input: filled.mgz, norm.mgz
    output: surf/lh.orig.nofix (through filled-pretess255.mgz)
    mri_pretess ../mri/filled.mgz 255 ../mri/norm.mgz ../mri/filled-pretess255.mgz
mri_tessellate ../mri/filled-pretess255.mgz 255 ../surf/lh.orig.nofix
mris_extract_main_component ../surf/lh.orig.nofix ../surf/lh.orig.nofix
    This is the step where the orig surface (ie, surf/?h.orig.nofix) is created. The surface is created by covering the filled hemisphere with triangles. Runs mri_tessellate. The places where the points of the triangles meet are called vertices. Creates the file surf/?h.orig.nofix Note: the topology fixer will create the surface ?h.orig.
17. Orig Surface Smoothing (-<no>smooth1, -<no>smooth2)
    input: surf/lh.orig.nofix
    output: surf/lh.smoothwm.nofix
    mris_smooth -nw ../surf/lh.orig.nofix ../surf/lh.smoothwm.nofix
    After tesselation, the orig surface is very jagged because each triangle is on the edge of a voxel face and so are at right angles to each other. The vertex positions are adjusted slightly here to reduce the angle. This is only necessary for the inflation processes. Creates surf/?h.smoothwm(.nofix). Calls mris_smooth. Smooth1 is the step just after tessellation, and smooth2 is the step just after topology fixing.
18. Inflation (-<no>inflate1, -<no>inflate2)
    input: surf/lh.smoothwm.nofix
    output: surf/lh.inflated.nofix, lh.sulc, lh.curv, lh.area
    mris_inflate -no-save-sulc ../surf/lh.smoothwm.nofix ../surf/lh.inflated.nofix
    Inflation of the surf/?h.smoothwm(.nofix) surface to create surf/?h.inflated. The inflation attempts to minimize metric distortion so that distances and areas are perserved (ie, the surface is not stretched). In this sense, it is like inflating a paper bag and not a balloon. Inflate1 is the step just after tessellation, and inflate2 is the step just after topology fixing. Calls mris_inflate. Creates ?h.inflated, ?h.sulc, ?h.curv, and ?h.area.
19. QSphere (-<no>qsphere)
    input: lh.inflated.nofix
    output: lh.qsphere.nofix
    mris_sphere -q ../surf/lh.inflated.nofix ../surf/lh.qsphere.nofix
    This is the initial step of automatic topology fixing. It is a quasi-homeomorphic spherical transformation of the inflated surface designed to localize topological defects for the subsequent automatic topology fixer. Calls mris_sphere. Creates surf/?h.qsphere.nofix.
20. Automatic Topology Fixer (-<no>fix)
    input: qsphere.nofix
    output: lh.orig
    mris_fix_topology -mgz -sphere qsphere.nofix -ga CON14 lh
mris_euler_number ../surf/lh.orig
mris_remove_intersection ../surf/lh.orig ../surf/lh.orig
    Finds topological defects (ie, holes in a filled hemisphere) using surf/?h.qsphere.nofix, and changes the orig surface (surf/?h.orig.nofix) to remove the defects. Changes the number of vertices. All the defects will be removed, but the user should check the orig surface in the volume to make sure that it looks appropriate. Calls mris_fix_topology. Creates surf/?h.orig (by iteratively fixing surf/?h.orig.nofix).
21. Final Surfaces (-<no>finalsurfs)
    input: lh.orig
    output: lh.white, lh.pial, lh.thickness, lh.curv
    mris_make_surfaces -noaparc -mgz -T1 brain.finalsurfs CON14 lh
    Creates the ?h.white and ?h.pial surfaces as well as the thickness file (?h.thickness) and curvature file (?h.curv). The white surface is created by “nudging” the orig surface so that it closely follows the white-gray intensity gradient as found in the T1 volume. The pial surface is created by expanding the white surface so that it closely follows the gray-CSF intensity gradient as found in the T1 volume. Calls mris_make_surfaces.
22. [need to double check, b/c in the log this step is not here, also find command such as
    mris_calc -o lh.area.mid lh.area add lh.area.pial
mris_calc -o lh.area.mid lh.area.mid div 2
mris_calc -o lh.volume lh.area.mid mul lh.thickness] Cortical Ribbon Mask (-<no>cortribbon)
    Creates binary volume masks of the cortical ribbon, ie, each voxel is either a 1 or 0 depending upon whether it falls in the ribbon or not. Saved as ?h.ribbon.mgz. Uses mgz regardless of whether the -mgz option is used. The -autorecon2 stage ends here.
23. Repeat step 17
    output: lh.smoothwm
    mris_smooth -n 3 -nw ../surf/lh.white ../surf/lh.smoothwm
24. Repeat step 18
    input: lh.smoothwm
    output: lh.inflated
    mris_inflate ../surf/lh.smoothwm ../surf/lh.inflated
mris_curvature -thresh .999 -n -a 5 -w -distances 10 10 ../surf/lh.inflated
25. Spherical Inflation (-<no>sphere)
    input: lh.inflated
    output: lh.sphere
    mris_sphere ../surf/lh.inflated ../surf/lh.sphere
    Inflates the orig surface into a sphere while minimizing metric distortion. This step is necessary in order to register the surface to the spherical atlas. (also known as the spherical morph). Calls mris_sphere. Creates surf/?h.sphere. The -autorecon3 stage begins here.
26. Ipsilateral Surface Registation (Spherical Morph) (-<no>surfreg)
    input: lh.sphere
    output: lh.sphere.reg
    mris_register -curv ../surf/lh.sphere /usr/local/freesurfer/average/lh.average.curvature.filled.buckner40.tif ../surf/lh.sphere.reg
    Registers the orig surface to the spherical atlas through surf/?h.sphere. The surfaces are first coarsely registered by aligning the large scale folding patterns found in ?h.sulc and then fine tuned using the small-scale patterns as in ?h.curv. Calls mris_register. Creates surf/?h.sphere.reg.
27. [can't find this step, but find command mris_jacobian ../surf/lh.white ../surf/lh.sphere.reg ../surf/lh.jacobian_white ]Contralateral Surface Registation (Spherical Morph) (-<no>contasurfreg)
    Same as ipsilateral but registers to the contralateral atlas. Creates lh.rh.sphere.reg and rh.lh.sphere.reg.
28. Average Curvature (-<no>avgcurv)
    input: lh.sphere.reg
    output: lh.avg_curv
    mrisp_paint -a 5 /usr/local/freesurfer/average/lh.average.curvature.filled.buckner40.tif#6 ../surf/lh.sphere.reg ../surf/lh.avg_curv
    Resamples the average curvature from the atlas to that of the subject. Allows the user to display activity on the surface of an individual with the folding pattern (ie, anatomy) of a group. Calls mrisp_paint. Creates surf/?h.avg_curv.
29. Cortical Parcellation (-<no>cortparc, -<no>cortparc2)
    input: mri/aseg.mgz, surf/lh.sphere.reg
    output: label/lh.aparc.annot
    mris_ca_label -aseg ../mri/aseg.mgz CON14 lh ../surf/lh.sphere.reg /usr/local/freesurfer/average/lh.curvature.buckner40.filled.desikan_killiany.2007-06-20.gcs ../label/lh.aparc.annot
    Assigns a neuroanatomical label to each location on the cortical surface. Incorporates both geometric information derived from the cortical model (sulcus and curvature), and neuroanatomical convention. Calls mris_ca_label. -cortparc creates label/?h.aparc.annot, and -cortparc2 creates /label/?h.aparc.a2005s.annot.
30. Parcellation Statistics (-<no>parcstats)
    input: stats/lh.aparc.stats, label/lh.aparc.annot
    output: label/aparc.annot.ctab
    mris_anatomical_stats -mgz -f ../stats/lh.aparc.stats -b -a ../label/lh.aparc.annot -c ../label/aparc.annot.ctab CON14 lh
    Runs mris_anatomical_stats to create a summary table of cortical parcellation statistics for each structure, including 1. structure name 2. number of vertices 3. total surface area (mm^2) 4. total gray matter volume (mm^3) 5. average cortical thickness (mm) 6. standard error of cortical thicknessr (mm) 7. integrated rectified mean curvature 8. integrated rectified Gaussian curvature 9. folding index 10. intrinsic curvature index. For -parcstats, the file is saved in stats/?h.aparc.stats. For -parcstats2, the file is saved in stats/?h.aparc.a2005s.stats.
31. Repeat step 29 (Cortical Parcellation)
    mris_ca_label -aseg ../mri/aseg.mgz CON14 lh ../surf/lh.sphere.reg /usr/local/freesurfer/average/lh.atlas2005_simple.gcs ../label/lh.aparc.a2005s.annot
32. Repeat step 30 (Parcellation statistics)
    mris_anatomical_stats -mgz -f ../stats/lh.aparc.a2005s.stats -b -a ../label/lh.aparc.a2005s.annot -c ../label/aparc.annot.a2005s.ctab CON14 lh
33. Repeat step 16-32 for right hemisphere
34. ASeg Stats
    mri_segstats –seg mri/aseg.mgz –sum stats/aseg.stats –pv mri/norm.mgz –excludeid 0 –brain-vol-from-seg –brainmask mri/brainmask.mgz –in mri/norm.mgz –in-intensity-name norm –in-intensity-units MR –etiv –subject CON14 –surf-wm-vol –ctab /usr/local/freesurfer/ASegStatsLUT.txt
35. Cortical ribbon mask
    mris_volmask –label_left_white 2 –label_left_ribbon 3 –label_right_white 41 –label_right_ribbon 42 –save_ribbon –save_distance CON14
36. AParc-to-ASeg
    mri_aparc2aseg –s CON14 –volmask
    mri_aparc2aseg –s CON14 –volmask –a2005s
37. WMParc
    mri_aparc2aseg –s CON14 –labelwm –hypo-as-wm –rip-unknown –volmask –o mri/wmparc.mgz –ctxseg aparc+aseg.mgz
38. mri_segstats –seg mri/wmparc.mgz –sum stats/wmparc.stats –pv mri/norm.mgz –excludeid 0 –brain-vol-from-seg –brainmask mri/brainmask.mgz –in mri/norm.mgz –in-intensity-name norm –in-intensity-units MR –etiv –subject CON14 –surf-wm-vol –ctab /usr/local/freesurfer/

Check out recon-all manual in freesurfer wiki.

[this post is under frequent updating]

Retinotopy analysis consists two parts, one on high resolution structural images (segmentation, inflation, cut, etc), the other on functional images.

structural
Before you start, you need to put the structural images into certain directory hierarchy and set environment variable. Assume folder “structural” is where subjects’ structural images are. Under “structural”, you have folders “SUBJ1″, “SUBJ2″, “CON14″ etc for every subject. You will have to put your structural images to “structural/SUBJ1/mri/orig/001.mgz”. If you have multiple images for this subject, use “002.mgz” etc. (If you original file format is not mgz, check out how to convert image formats).

structural
    |--SUBJ1
        |--mri
            |--orig
                |--001.mgz
                |--002.mgz

Then set environment variable in linux shell:
setenv SUBJECTS_DIR $PWD

1. Run command recon-all
   recon-all -all -subjid CON14
   to process structural image. This will run for a long time (30 hours). To see what steps are being done, checkout recon-all manual.
2. Cut occiput surface
   Run command
   tksurfer CON14 lh inflated
   to display the inflated left hemisphere.
   Rotate the brain until the medial surface is facing you.
   Then select points along calcarine fissure and press button “Cut line”.
   
   Select 3 points to define the cutting plane: 2 on medial side and 1 on lateral side. Choose a 4th point to specify which portion of surface to keep and press button “Cut plane”.
   
   
   Save (File > Patch > Save As) as file lh.occip.patch.3d.
   (To know how to cut full brain, check out this PDF)
3. Flatten occiput surface
   cd to the subject’s “surf” directory and run
   mris_flatten -w 0 -distances 20 7 lh.occip.patch.3d  lh.occip.patch.flat
   To visualize the patch, you can first load the subject’s inflated surface, then File > Patch > Load Patch …
   Flattening takes 1-2 hours.
4. Repeat step 2 and 3 for the right hemisphere.

functional

1. Create directory hierarchy
   Create directory “retinotopy”, then under this folder, create directories SUBJ01, SUBJ02, SUBJ03, CON14 etc. Each of these directories is for a subject. Under each subject’s directory, create folder “bold”. In “bold”, create folder “001″, “002″ etc, they are folders for runs of a single subject.  Note, the name of run folders has to be 3-digit with padding 0s. Something like “01″ or “1″ won’t work. Under run folders, put the functional imaging data “f.nii” and paradigm file (rtopy.par) there. If your functional image is not a nii file, check out how to convert functional images to Nifti format.

   Under SUBJ01, create a file called “subjectname” with one line string “SUBJ1″ — assuming “SUBJ1″ is this subject’s name under “structural” folder. This file is to link this subject’s functional and structural data.

   Create file sessid under retinotopy. In this file each line is the folder’s name for each subject.

   retinotopy
       |--sessid
       |--SUBJ01
           |--subjectname
   
           |--bold
               |--001
                   |--f.nii
                   |--rtopy.par
               |--002
                   |--f.nii
                   |--rtopy.par
   

   Please refer to freesurfer’s wiki for detailed explanation. And the following diagram is very helpful:
   

   In rtopy.par file, write two lines
   stimtype eccen
direction pos
   or
   stimtype polar
direction neg

2. Stay in folder “retinotopy”
3. Create analysis
   Run the following command
   mkanalysis-sess.new -a rtopy -TR 2 -designtype retinotopy -paradigm rtopy.par -funcstem fmcsm5 -ncycles 8
Note: ncycles is the number of periods in each run of either ecc or mm. For example, if you have 10 steps going from a small circle to a big circle in ecc, and repeat this for 4 times, then ncycles=4.
   After running this program, you will find a directory “rtopy” newly created. There you find two files, analysis.cfg and analysis.info. You may want to change some values there for your own data (e.g. -nskip). If you already removed the extra images, you don’t need to specify -nskip.
4. Preprocess functional data
   Run command
   preproc-sess -sf sessid -fwhm 5
   or
   preproc-sess -s SUBJ01 -fwhm 5
   It takes about 40s for one run. (For xc only: you should run this command on scuttlebutt, not rumor)
5. Register against structural images
   Before and after registration, you may use tkregister-sess to view how well the functional images are aligned with structural images.
   tkregister-sess -sf sessid -regheader
   Green lines are the cortical surface. Hit button “compare” to see the alignment.
   To run registration, run command
   fslregister-sess -sf sessid
6. Run the analysis
   sfa-sess -a rtopy -sf sessid
   (for xc: use scuttlebutt. It takes ~2min)
7. View intermediate results
   sliceview-sess -sf sessid -a rtopy -c eccen -map h -slice mos
sliceview-sess -sf sessid -a rtopy -c polar -map h -slice mos
8. Run paint
   paint-sess -a rtopy -sf sessid
   It takes ~1min.
9. View final result
   surf-sess -sf sessid -a rtopy -retinotopy fieldsign -flat
surf-sess -sf sessid -a rtopy -retinotopy eccen -flat
surf-sess -sf sessid -a rtopy -retinotopy polar -flat
surf-sess -sf sessid -a rtopy -c polar -flat

For single file to single file conversion, you usually use mri_convert of freesurfer. For example
mri_convert x.img y.nii

Other options would be LONI Debabeler or MRICro.

Here are some special cases:
multiple 3D ANALYZE to a single 4D Nifti: mri_concat
mri_concat is a program of freesurfer. Assume the original image files are f001.img, f002.img … etc. The output file is f.nii.

mri_concat f???.img --o f.nii

You can also use FSL’s fslmerge to achieve the same thing (but pay attention to your fsl environment setting for default output format).
multiple DICOM to other format
To convert DICOM files to other format, use mri_convert

mri_convert I0001.dcm  T1.mgz

If you have a bunch of DICOM files (slices) for a single brain, you only need to input the 1st one as the argument of mri_convert.

Another way is to use SPM’s DICOM import. In the end you get ANALYZE files.
GE 7 file to ANALYZE or Nifti: makevols and makenifti
.7 files are functional images produced by GE scanners in Stanford. You use makevols to convert.

makevols E*P*.7 I
rename I.V I_ I.V*

You will get a bunch of .img/.hdr files.
To make a Nifti file, you run

makenifti Efilename outfilename

The output is a single nii file (outfilename.nii).

Note: If you rename the .7 files, you might not get makevols to work. For example, if you rename P06144.7 to P06144_mer1.7, makevols doesn’t work (only produces a lot .hdr files but not .img files).

makevols and makenifti are written by Dr. Gary Glover at Stanford University. Here you can download the two programs.

Check out here to see how to convert images with different number of bytes per voxel.