Longitudinal Stream Change Log

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

FS Dev

New dedicated longitudinal pipeline for subfield segmentation - See LongitudinalHippocampalSubfields
Fix for appending new time point (see bug in 5.3)
Matlab Linear Mixed Effects Tools:
- Updated Matlab LME tools to newer Matlab versions
- Allow missing Parallel Toolbox (process sequentially)
- Minor improvements to F-test

Uses cubic B-spline interpolation to create base and map longitudinals
Allows processing of subjects with single time point through the longitudinal stream to include them without bias into statistical analysis (LME)
Improvements on tools for statistical post-processing (see LongitudinalTwoStageModel ).
Minor fixes in post processing (e.g. mris_calc file name bug)

Due to problems in the cross-sectional surface creation (wich also affects the base), this version should not be used. Use 5.3 instead.

Fixed known bugs
Now resample all time points to the base space (in the motion correction / conformalize step to avoid additional resampling)
Includes revised/improved editing (see LongitudinalEdits and FsTutorial/LongitudinalTutorial).
More sanity checks in recon-all
Additional tools to post-process longitudinal data: long_mris_slopes, long_stats_slopes, long_qdec_table

Initial studies show improved results due to the common voxel space. Furthermore, manual inspection is significantly simplified.

!!! Do not use FS 5.0 for longitudinal processing, before updating some binaries !!!

A non-related, last minute, incomplete 'bug-fix' in the transformation library introduced a new bug that breaks mri_robust_template, mri_robust_register (half way space).
You can find fixed versions of these binaries for your system here.
Also grab a modified recon-all from here that fixes the partial matching in the sanity check.

Improvements/Fixes:

More robust base space estimation: keeps the base image centered
Added sanity checks (if longitudinal TP is in base and if baseid is different from the time points id's)

No modifications to the data flow in the base or long stream.

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 time points, 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