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 * mri_ca_label now uses the correct intensity scaling factors of the base
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 * mri_ca_normalize has a new -long algorithm using the base aseg as init
 * mri_ca_label now uses the correct intensity scaling factors of the base

Longitudinal Stream Change Log

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

FS Version 4.5

Improvements to the new longitudinal stream (only on volume related issues, surface improvements to come).

Base Stream:

  • only one template estimation on the norm.mgz files of all timepoints

  • a new block -base-init added to recon-all for the initial template creation

  • this creates the maps from each TP to base and the norm_template

  • then create an orig_template as orig/001.mgz to initialize the base run
  • the norm_template is used as brainmask
  • in gcareg and canorm the norm_template is used instead of the nu
  • this setup reduced the difficulties of dealing with two spaces (two template estimations)
  • 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
  • mri_ca_label now uses the correct intensity scaling factors of the base

FS Version 4.4

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 the other TPs at a specific location

  • All TPs have to be processed cross sectionally (independently) first

FS pre Version 4.4

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

The 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 fix to init TP1 with itself improved things, but did not remove the bias


Original Author: MartinReuter

LongitudinalChangeLog (last edited 2018-06-21 15:06:52 by AndrewHoopes)