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| = 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 == 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.''' == 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:''' <<BR>> 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). == 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. === 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). 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. ---- == 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]]) === Input (-i) === Copy the {{{orig/00?.mgz}}} from the cross sectionals. === Motion Corrections (-motioncor) === Copy the {{{rawavg.mgz}}} and {{{orig.mgz}}} from the cross sectionals. === NU Intensity Correction (-nuintensitycor) === Copy the {{{nu.mgz}}} from the cross sectionals. === 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. === Normalization (-normalization) === Copy the {{{T1.mgz}}} from the cross sectionals. === 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. === 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))'' === 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))'' === 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 }}} === 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) === 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. ''(internal notes: 1. Registrations can be done through the base, probably not much less accurate, but faster. 2. In the future maybe labeling all TPS simultaneously?)'' === Normalization 2 (-normalization2) === No change. === Mask Brain Final Surface (-maskbfs) === No change. === WM Segmentation (-segmentation) === Transfer WM edits and deletions from base to TP. === Cut/Fill (-fill) === No change. === Tessellate (-tesselate) === Skipped. === Orig Surface Smoothing 1 (-smooth1) === Skipped. === Inflation 1 (-inflate1) === Skipped. === QSphere (-qsphere) === Skipped. === Automatic Topology Fixer (-fix) === Skipped. === Final Surfaces (-finalsurfs) === Map and use {{{?h.white}}} and {{{?h.pial}}} to initialize white, pial and orig in TP. === Surface Volume (-surfvolume) === No change. === Orig Surface Smoothing 2 (-smooth2) === No change. === Inflate 2 (-inflate2) === No change. === ASeg Stats (-segstats) === No change. === 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. === Cortical Ribbon Mask (-cortribbon) === No change. === Add Parcellation to ASeg (-aparc2aseg) === No change. === Update WMparc(-wmparc) === No change. ---- MartinReuter |
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- Diff for "LongitudinalProcessing"