Differences between revisions 44 and 45

Longitudinal Stream (FS 4.5)

1. 4.5. will be available probably sometime next week Aug 3-7

2. please wait if you plan to run longitudinal studies

This page describes the most current version of the longitudinal stream in FreeSurfer.

See LongitudinalChangeLog for a change log of the last versions.

Compared with cross-sectional studies, a longitudinal design can significantly reduce the confounding effect of intra-individual morphological variability by using each subject as his or her own control. As a result, longitudinal imaging studies are getting increased interest and popularity in various aspects of neuroscience. The default FreeSurfer pipeline is designed for the processing of individual data sets (cross-sectionally), and thus not optimal for the processing of longitudinal data series. It is an active research area at the Martinos Center for Biomedical Imaging, how to obtain robust and more reliable cortical and subcortical morphological measurements by incorporating additional (temporal) information in a longitudinal data series.

The longitudinal scheme is designed to be unbiased wrt. any time point. Instead of initializing it with information from a specific time point, a base/template volume is created and run through FreeSurfer. This template can be seen as an initial guess for the segmentation and surface reconstruction. The FreeSurfer cortical and subcortical segmentation and parcellation procedure involves solving many complex nonlinear optimization problems, such as the deformable surface reconstruction, the nonlinear atlas-image registration, and the nonlinear spherical surface registration. These nonlinear optimization problems are usually solved using iterative methods, and the final results are known to be highly sensitive to the selection of a particular starting point (a.k.a. algorithm initialization). It's our belief that by initializing the processing of a new data set in a longitudinal series using the processed results from the unbiased template, we can reduce the random variation in the processing procedure and improve the robustness and sensitivity of the overall longitudinal analysis. Such an initialization scheme makes sense also because a longitudinal design is often targeted at detecting small or subtle changes. Additionally to the base template new probabilistic methods (temporal fusion) were introduced to further reduce the variability of results across time points. For these algorithms it becomes necessary to process all time points cross-sectionally first.

The longitudinal processing scheme is coded in the recon-all script via the "-base" and "-long" flags. Use the -help flag for help on these options.

3. Workflow Summary

1. cross-sectionally process all time points with the default workflow (tpN is one of the timepoints):

recon-all -all -s <tpNid> -i path_to_tpN_dcm

2. "-base" create the base template and process it cross sectionally:

  recon-all -base <baseid> -tp <tp1id> -tp <tp2id> ... -all

can be started once all norm.mgz files are available from the cross sectional processing of the individual timepoint (step 1).

3. "-long" longitudinally process all timepoints:

  recon-all -long <tpNid> <baseid> -all

The longitudinal processing scheme is coded in the recon-all script via the "-long" flag. This step produces output subject data containing <tpNid>.long.<baseid> in the name (to help distinguish from the default stream).

4. Compare results from step 3:

E.g. calculate differences between <tp1id>.long.<baseid> and <tp2id>.long.<baseid>

In the following, we describe the two processing streams. We assume that the longitudinal series has time-points tpN (tp1, tp2, ...). All of these have already been processed cross sectionally using the standard FreeSurfer recon-all (step 1). First we discuss the construction of the base template (step 2) and then the longitudinal processing of tpN (step 3).

4. Creation of Base "-base"

In step 2 the base template is created. It is possible to start the base construction once the norm.mgz of all time points are available from step 1. After the base is fully processed it will be used to initialize many steps in the longitudinal stream (step 3). Also, the eTIV of the base can probably be used as a robust measure for head size (instead of using the eTIV of the individual time points), as eTIV should not change over time. However, this needs to be investigated further.

4.1. Base Init (-base-init)

The base template is created in the -base-init block of recon-all. This is achieved by mri_robust_template, which constructs a mean or median (default) norm_template.mgz volume together with the transforms that align each tp's norm.mgz volume with the template (see also mri_robust_register which is used by mri_robust_template to construct the maps). The longitudinal scheme later requires aligning the image data of tpN to base, thus all time points are aligned to an unbiased common space with the command:

 mri_robust_template --mov <tpsvols> --lta <tpsltas> --template <baseid>/mri/norm_template.mgz

where the input --mov <tpsvols> is a list of the time point's norm.mgz files and the output --lta <tpsltas> a list of the LTA registrations files that take each tp to the base and --template ... norm_template.mgz the median image. The LTA maps are stored in <baseid>/mri/transforms/<tpNid>_to_<baseid>.lta. Also the inverse maps are needed and constructed by

mri_concatenate_lta -invert1 <tpNid>_to_<baseid>.lta identity.nofile <baseid>_to_<tpNid>.lta

Since all data sets come from the same subject, these rigid registrations with 6 dof (translation,rotation) are sufficient to get a good alignment between the (intensity normalized) images (i.e. norm.mgz). The registrations and its inverse will be used to transfer information between time points mutually and between time points and the template in the longitudinal stream. The routine mri_robust_template uses robust statistics to automatically detect outliers and align the rest of the image in an optimal manner. The median template is unbiased with respect to any timepoint and therefore perfectly suited as a base to produce initializations for several steps in the longitudinal runs (see below).

4.8. CA Normalize (-canorm)

4.9. CA Nonlinear Registration (-careg)

4.10. CA Nonlinear Registration Inverse (-careginv)

No change.

4.11. Remove Neck (-rmneck)

No change.

4.12. EM Registration (with skull but no neck) (-skull-lta)

No change.

4.13. CA Label (-calabel)

4.14. Rest of the stream

No change.

5. Longitudinal Stream "-long"

In the -long stream we set NoRandomness = 1 (always use same seed for RNG). Affects mris_smooth, mris_fix_topology, mris_topo_fixer, mris_sphere, and mris_ca_label)

5.1. Input (-i)

Copy the orig/00?.mgz from the cross sectionals.

5.2. Motion Corrections (-motioncor)

Copy the rawavg.mgz and orig.mgz from the cross sectionals.

5.3. NU Intensity Correction (-nuintensitycor)

Copy the nu.mgz from the cross sectionals.

5.4. Talairach (-talairach)

Copy the talairach.xfm from the cross sectionals.

Note: the talairach.xfm is used for the eTIV estimation. It might turn out to be better to use the eTIV from the base template instead of the slighly varying eTIV from the cross sectionals (eTIV should not change over time). In that case the talairach.xfm will be copied from the base to each TP in the future.

5.5. Normalization (-normalization)

Copy the T1.mgz from the cross sectionals.

5.6. Skull Strip (-skullstrip)

Map the brainmask.mgz (i.e. the norm_template.mgz) from the base to the current TP. Use it to mask the T1.mgz to obtain the brainmask. Note, the brainmask in the base can be seen as an OR of all brainmasks of the idividual TPs, as it is the norm_template image containing all stripped brains.

5.7. EM (GCA) Registration (-gcareg)

The talairach.lta is constructed from the base by concatenation with the map from the current TP to the base.

(internal note: in the future maybe use the nu.mgz's from all TPS to construct the registration simultaneously (in the -base))

5.8. CA Normalize (-canorm)

The normalization is initialized with the aseg.mgz of the base mapped to the current TP. Thus all TP's use similar control points for the normalization.

(internal note: in the future maybe find the control points by looking at all nu.mgz's simultaneously (in the -base))

5.9. CA Nonlinear Registration (-careg)

Uses the talairach.m3z from the base as initialization (after concatenation). Also different flags:

mri_ca_register -levels 2 -A 1 -l $longbasedir/mri/transforms/talairach.m3z  $subjdir/mri/transforms/$longbasetotpN_regfile

5.10. CA Nonlinear Registration Inverse (-careginv)

No change.

5.11. Remove Neck (-rmneck)

No change.

5.12. EM Registration (with skull but no neck) (-skull-lta)

No change.

5.13. CA Label (-calabel)

5.28. Spherical Inflation (-sphere)

?h.sphere copied from base.

5.29. Ipsilateral Surface Registation (Spherical Morph) (-surfreg)

Extra flags to mris_register:

-nosulc -norot $longbasedir/surf/$hemi.sphere.reg

5.30. Jacobian (-jacobian_white)

5.37. Add Parcellation to ASeg (-aparc2aseg)

No change.

5.38. Update WMparc(-wmparc)

No change.

MartinReuter

-Deletions are marked like this.
+Additions are marked like this.
 Line 1:
-= Longitudinal Stream (FS 4.4) =
+= Longitudinal Stream (FS 4.5) =

== 4.5. will be available probably sometime next week Aug 3-7 ==

== please wait if you plan to run longitudinal studies ==
-Line 33:
+Line 35:
-The longitudinal processing scheme is coded in the recon-all script via the "-long" flag. '''Use the -help flag for help on its options.'''
+The longitudinal processing scheme is coded in the recon-all script via the "-long" flag.
-Line 36:
+Line 38:
-'''4. Compare results from step 3:''' E.g. calculate differences between <tp1id>.long.<baseid> and <tp2id>.long.<baseid>
+'''4. Compare results from step 3:''' <<BR>>

 E.g. calculate differences between <tp1id>.long.<baseid> and <tp2id>.long.<baseid>



----
-Line 44:
+Line 47:
-In step 2 the base template is created. This is done by [[mri_robust_template]], which constructs a mean/median T1.mgz volume together with the transforms that align each tp's T1.mgz volume with the template (see also [[mri_robust_register]] which is used by [[mri_robust_template]] to construct the maps). The longitudinal scheme later requires aligning the image data of tpN to base, thus all time points are aligned to an unbiased common space with the command:

{{{

 mri_robust_template --mov <tpsvols> --lta <tpsltas> --template <baseid>/mri/orig/001.mgz 

}}}

where <tpsvols> is a list of the time point's T1.mgz files and <tpsltas> a list of the LTA registrations files that take each tp to the base. These maps are stored in <baseid>/mri/transforms/<tpNid>_to_<baseid>.lta. As also the inverse maps are needed. They are constructed by
+In step 2 the base template is created. It is possible to start the base construction once the norm.mgz of all time points are available from step 1. After the base is fully processed it will be used to initialize many steps in the longitudinal stream (step 3). Also, the [[eTIV]] of the base can probably be used as a robust measure for head size (instead of using the [[eTIV]] of the individual time points), as eTIV should not change over time. However, this needs to be investigated further.



=== Base Init (-base-init) ===



The base template is created in the -base-init block of recon-all. This is achieved by [[mri_robust_template]], which constructs a mean or median (default) norm_template.mgz volume together with the transforms that align each tp's norm.mgz volume with the template (see also [[mri_robust_register]] which is used by [[mri_robust_template]] to construct the maps). The longitudinal scheme later requires aligning the image data of tpN to base, thus all time points are aligned to an unbiased common space with the command:

{{{

 mri_robust_template --mov <tpsvols> --lta <tpsltas> --template <baseid>/mri/norm_template.mgz

}}}

where the input --mov <tpsvols> is a list of the time point's norm.mgz files and the output  --lta <tpsltas> a list of the LTA registrations files that take each tp to the base and --template ... norm_template.mgz the median image. The LTA maps are stored in <baseid>/mri/transforms/<tpNid>_to_<baseid>.lta. Also the inverse maps are needed and constructed by
-Line 52:
+Line 59:
-Since all data sets come from the same subject, these rigid registrations with 6 dof (translation,rotation) are sufficient to get a good alignment between the (intensity normalized) images (i.e. the T1 and norm). The registrations and its inverse will be used to transfer information between time points mutually and between time points and the template in the longitudinal stream. 



After the registrations and the template T1 volume are created, the T1 template is then processed cross-sectionally with the standard FreeSurfer stream. The only difference is that the norm.mgz is not created in the usual way, but again computed as the mean/median of the norm.mgz of all time points (again with mri_robust_template). The corresponding transforms are more accurate than the transforms obtained from the T1 image. It is possible to start the base construction once the norm.mgz of all time points are available from step 1. After the base is fully processed it will be used to initialize many steps in the longitudinal stream (step 3). Also, the [[eTIV]] of the base can be used as a robust measure for head size (instead of using the [[eTIV]] of the individual time points), as eTIV should not change over time.
+Since all data sets come from the same subject, these rigid registrations with 6 dof (translation,rotation) are sufficient to get a good alignment between the (intensity normalized) images (i.e. norm.mgz). The registrations and its inverse will be used to transfer information between time points mutually and between time points and the template in the longitudinal stream. The routine [[mri_robust_template]] uses robust statistics to automatically detect outliers and align the rest of the image in an optimal manner. The median template is unbiased with respect to any timepoint and therefore perfectly suited as a base to produce initializations for several steps in the longitudinal runs (see below).



After the registrations and the norm_template.mgz volume are created, the orig.mgz images from all TPs are mapped to the base location and averaged (again the default is the median image) to produce the <baseid>/mri/orig/001.mgz image. This image is then processed cross-sectionally with the standard FreeSurfer stream, however very early we switch over to the norm_template.mgz.



=== Motion Corrections (-motioncor) ===



No change.



=== NU Intensity Correction (-nuintensitycor) ===



No change.



=== Talairach (-talairach) ===



No change.



=== Normalization (-normalization) ===



No change.



=== Skull Strip (-skullstrip) ===



Use the norm_template.mgz as brainmask.mgz.



=== EM (GCA) Registration (-gcareg) ===





=== CA Normalize (-canorm) ===



=== CA Nonlinear Registration (-careg) ===





=== CA Nonlinear Registration Inverse (-careginv) ===



No change.



=== Remove Neck (-rmneck) ===



No change.



=== EM Registration (with skull but no neck) (-skull-lta) ===



No change.



=== CA Label (-calabel) ===



=== Rest of the stream ===



No change.



----
-Line 84:
+Line 139:
-Use the OR of all brain masks from the cross sectionals to make sure that no brain is clipped.



''(internal note: this can be done in the -base run in the future)''
+Map the brainmask.mgz (i.e. the norm_template.mgz) from the base to the current TP. Use it to mask the T1.mgz to obtain the brainmask.

Note, the brainmask in the base can be seen as an OR of all brainmasks of the idividual TPs, as it is the norm_template image containing all stripped brains.
-Line 96:
+Line 150:
-The normalization is initialized with the {{{aseg.mgz}}} of the base mapped to the current TP. Thus all TP's use basically the same control points for the normalization.
+The normalization is initialized with the {{{aseg.mgz}}} of the base mapped to the current TP. Thus all TP's use similar control points for the normalization.
-Line 121:
+Line 175:
-Registers this TP to all others. Then creates {{{aseg.fused.mgz}}} by incorporating information of all TPS. Finally uses the fused aseg as initialization to  mri_ca_label to construct the final labels.
+Registers this TP to all others. Then creates {{{aseg.fused.mgz}}} by incorporating information of all TPS. Finally uses the fused aseg as initialization to  mri_ca_label to construct the final labels. Furthermore, the intensity scaling factors are passed from the base.
-Line 181:
+Line 235:
+=== Spherical Inflation (-sphere) ===



{{{?h.sphere}}} copied from base.





=== Ipsilateral Surface Registation (Spherical Morph) (-surfreg) ===



Extra flags to [[mris_register]]:

{{{

-nosulc -norot $longbasedir/surf/$hemi.sphere.reg

}}}



=== Jacobian (-jacobian_white) ===



No change.



=== Average Curvature (-avgcurv) ===



No change.



=== Cortical Parcellation (-cortparc) ===



Extra flags with [[mris_ca_label]]:

{{{

-long -R $longbasedir/surf/$hemi.aparc.annot

}}}



=== Parcellation Statistics (-parcstats) ===



No change.



=== Cortical Parcellation 2 (-cortparc) ===



Extra flags with [[mris_ca_label]]:

{{{

-long -R $longbasedir/surf/$hemi.aparc.a2005s.annot

}}}



=== Parcellation Statistics (-parcstats) ===



No change.
-Line 185:
+Line 281:
-=== Spherical Inflation (-sphere) ===



{{{?h.sphere}}} copied from base.





=== Ipsilateral Surface Registation (Spherical Morph) (-surfreg) ===



Extra flags to [[mris_register]]:

{{{

-nosulc -norot $longbasedir/surf/$hemi.sphere.reg

}}}



=== Jacobian (-jacobian_white) ===



No change.



=== Average Curvature (-avgcurv) ===



No change.



=== Cortical Parcellation (-cortparc) ===



Extra flags with [[mris_ca_label]]:

{{{

-long -R $longbasedir/surf/$hemi.aparc.annot

}}}



=== Parcellation Statistics (-parcstats) ===



No change.



=== Cortical Parcellation 2 (-cortparc) ===



Extra flags with [[mris_ca_label]]:

{{{

-long -R $longbasedir/surf/$hemi.aparc.a2005s.annot

}}}



=== Parcellation Statistics (-parcstats) ===



No change.