Longitudinal Stream Change Log

A detailed description of the current longitudinal stream can be found here: LongitudinalProcessing

FS Version 5.0

No significant changes.

Attention: a non-related 'bug-fix' in the transformation library resulted in a bug that breaks mri_robust_template (maybe more).

!!! Do not use FS 5.0 for longitudinal processing !!!

Improvements to the new longitudinal stream (modified base stream, fixes/improvements in the long stream).

Base Stream:

Only one template estimation on the norm.mgz files of all timepoints, to prevent difficulties with two possibly different template locations (T1 and norm as in 4.4).
A new block -base-init added to recon-all for the initial template creation, to be able to call later parts of the script individually without re-running the initialization everytime.
The -base-init block creates the maps from each TP to base and the norm_template (using the norm.mgz of all TPs).
Then the orig.mgz of all TPs are resampled and averaged as orig/001.mgz to initialize the base run.
The norm_template is used as brainmask (as it contains only brain).
In -gcareg and -canorm the norm_template is used instead of the nu.mgz.
This setup (with only one estimation) should lead to significant run time improvements especially with several time points.

Long Stream:

The talairach.lta is now created by concatenation (tpN -> base -> talairach)
The brainmask is copied/mapped from the base (which is basically an OR of all TPs)
mri_ca_normalize has a new -long algorithm using the base aseg as init, fixing the bias towards smaller volumes in subcortical structures, as seen in 4.4.
mri_ca_label now uses the correct intensity scaling factors of the base.

First version of a working longitudinal stream, without optimizing each step.

Main Differences:

We have one -base run and -long runs for each TP.
A base template (median) is created and used to initialize the longitudinal runs.
The base is unbiased and can be viewed as an initial guess where things are.
New tools: mri_robust_register (symmetric registration)
and mri_robust_template (unbiased robust template estimation).
Probabilistic fusion was added mri_fuse_segmentations to incorporate label information from other TPs at a specific location.
All TPs have to be processed cross sectionally (independently) first.

These old versions of the longitudinal stream should not be used!

The old stream is described here: LongitudinalProcessingPreV4.4.

Short Info:

One of the TPs was used to initialize the others (TP1 by default).
This lead to a bias wrt to TP1.
Selecting a different TP for the initialization completely changed the results.
A quick fix to init TP1 with itself improved things, but did not remove the bias.

Original Author: MartinReuter