Differences between revisions 26 and 27
Deletions are marked like this. Additions are marked like this.
Line 5: Line 5:
This page will describe the new version of the Longitudinal stream, while [[LongitudinalProcessingOld]] describes the old version. This page will describe the new version of the Longitudinal stream, while [[LongitudinalProcessingOld]] describes the old version (pre FS 4.4).
Line 47: 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. Thus, all timepoints are aligned to an unbiased common space with the command: 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:
Line 51: Line 51:
where <subjInVols> is a list of the time point's T1.mgz files and <ltaXforms> a list of the LTA registrations files that take each tp to the base. These maps are stored in <baseid>/mri/transforms/<tpNsubjid>_to_<baseid>.lta. As also the inverse maps are needed they are constructed by where <subjInVols> is a list of the time point's T1.mgz files and <ltaXforms> a list of the LTA registrations files that take each tp to the base. These maps are stored in <baseid>/mri/transforms/<tpNsubjid>_to_<baseid>.lta. As also the inverse maps are needed. They are constructed by
Line 55: Line 55:
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.
Line 56: Line 57:
This template T1 volume 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. Once 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. 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.
Line 59: Line 60:
The longitudinal scheme requires aligning the image data of tpN to base, but this map has already been constructed in a robust manner during the base creation (see also [[mri_robust_register]] which is used by [[mri_robust_template]] to construct the maps). 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 registration result (a coordinate transformation matrix) and its inverse have been saved during the template construction and will be used to transfer information between time points in later steps.

Longitudinal analysis (FS 4.4)

The longitudinal stream of FreeSurfer is currently overhauled. Please stay tuned ...

This page will describe the new version of the Longitudinal stream, while LongitudinalProcessingOld describes the old version (pre FS 4.4).

It is advised not to use the old version due to its bias with respect to the base time point (usually tp1).


Compared with cross-sectional studies, a longitudinal design can significantly reduce the confounding effect of inter-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. Aditionally to the base template new probabilistic methods (temporal fusion) were introduced to further reduce the variablility of results across timepoints. For these algorithms it becomes necessary to process all timepoints cross sectionally first.

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

1. Workflow Summary

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

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

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

  recon-all -base <baseid> -base-insubj <tp1subjid> -base-insubj <tp2subjid> ... -all

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

3. longitudinally process all timepoints:

  recon-all -long <tpNsubjid> <baseid> -all

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

4. Compare results from step 3: E.g. calculate differences between <tp1subjid>.long.<baseid> and <tp2subjid>.long.<baseid>

In the following, we list the major differences between the two processing streams. We assume that the longitudinal series has time-points tpN (tp1, tp2, ...). All 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).

2. Creation of Base

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 <subjInVols> --lta <ltaXforms> --template <baseid>/mri/orig/001.mgz 

where <subjInVols> is a list of the time point's T1.mgz files and <ltaXforms> a list of the LTA registrations files that take each tp to the base. These maps are stored in <baseid>/mri/transforms/<tpNsubjid>_to_<baseid>.lta. As also the inverse maps are needed. They are constructed by

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

3. Difference 1: Register tpN with Base

...

More later


MartinReuter

LongitudinalProcessing (last edited 2021-05-03 07:53:08 by DevaniCordero)