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attachment:det_vs_tiv.tif | |

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The red line in the plot is the best fit to the points, and validates the assumption that there is a simple linear correlation between TIV and a single parameter (the determinant of the transform matrix aligning the subject to an atlas). | |

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The value of the determinant is not itself the scale factor. Rather, this is determined iteratively via the eTIV vs. manual TIV plot, which is shown here for a scale factor of 2150: | |

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The eTIV vs manual TIV plot has a unity slope line which is the ideal (the blue line). The best fit line, in red, shows how close to ideal the eTIV_scale_factor is doing to match this fit. eTIV_scale_factor is adjusted iteratively to find the best match. |
attachment:etiv_vs_tiv_2150.tif The eTIV vs manual TIV plot has a unity slope line which is the ideal (the blue line). The best fit line, in red, shows how close to ideal the eTIV_scale_factor is doing to match this fit. eTIV_scale_factor is adjusted iteratively to find the best match. |

# eTIV - Estimated Total Intracranial Volume

## Intro

The estimated total intracranial volume calculation is based on this work:

[attachment:buckner2004.pdf A unified approach for morphometric and functional data analysis in young, old, and demented adults using automated atlas-based head size normalization: reliability and validation against manual measurement of total intracranial volume] Buckner et al. (2004) NeuroImage 23:724-738.

Basically, total intracranial volume is found to correlate with the determinant of the transform matrix used to align an image with an atlas. A one-parameter scaling factor results from this.

## Usage

The Freesurfer binary that calculates eTIV is called `mri_label_volume`. It requires as input the subjects:

- transform matrix (talairach_with_skull.lta) which aligns with the atlas RB_all_withskull_2006-02-15.gca
- the Aseg volume (aseg.mgz)

Both of these are created by the standard recon-all stream (during -autorecon1 and -autorecon2).

Once those files are available, the command-line to issue is:

mri_label_volume -eTIV \ $sdir/transforms/talairach_with_skull.lta \ $sdir/aseg.mgz 17 53

where $sdir is the path to the subject's mri directory (ie, $SUBJECTS_DIR/<subjid>/mri). The '17' and '53' are label ID's, in this case left and right hippocampus (see the subjects /stats/aseg.stats file for IDs). The output will look like:

using eTIV from atlas transform of 1528 cm^3 processing label 17... 3822 voxels (3822.0 mm^3) in label 17, %0.250119 of eTIV volume (1528075) processing label 53... 4410 voxels (4410.0 mm^3) in label 53, %0.288598 of eTIV volume (1528075)

Here, the eTIV is 1528075mm^3.

## Checking validity

In the file mri_label_volume/mri_label_volume.c, the static variable 'eTIV_scale_factor' is the multiplied by the atlas TIV to arrive at the subjects eTIV. The value for eTIV_scale_factor is determinedt by finding the best fit of the eTIV of 22 reference subjects against their known TIV, which was determined manually via their T2 scans (which clearly show skull). These subjects are found in `/autofs/space/jc_001/users/nicks/subjects/SASHA`.

In the scripts directory, the script `run_rb.csh` will run the `mri_label_volume` binary and generate a matlab data file called `det_eTIV_matdat.m` which contains the determinant and eTIV data. Then, the matlab script `plot_det.m` plots the determinant against the manual TIV, and also the eTIV against manual TIV, for each subject.

The det vs. manual TIV plot is shown here:

attachment:det_vs_tiv.tif

The red line in the plot is the best fit to the points, and validates the assumption that there is a simple linear correlation between TIV and a single parameter (the determinant of the transform matrix aligning the subject to an atlas).

The value of the determinant is not itself the scale factor. Rather, this is determined iteratively via the eTIV vs. manual TIV plot, which is shown here for a scale factor of 2150:

attachment:etiv_vs_tiv_2150.tif

The eTIV vs manual TIV plot has a unity slope line which is the ideal (the blue line). The best fit line, in red, shows how close to ideal the eTIV_scale_factor is doing to match this fit. eTIV_scale_factor is adjusted iteratively to find the best match.