'''dt_recon'''

'''Index''' <<TableOfContents>>

= dt_recon =
Performs processing of native diffusion tensor imaging (DTI/DWI) data. It takes original dicom images as input, and can automatically detect the bvalue and direction information from certain Siemens sequences. Other users may have to input the bvalue and direction information using bvec and bval text files with the same format as the files used in FSL diffusion processing. The subjectid refers to the subject's cortical reconstruction directory (from Freesurfer/T1 processing). This is used for registration of the diffusion data to the structural images. dt_recon outputs a variety of maps of interest in the study of neural anatomy and integrity as described in more detail below. Output maps can be entered into a variety of region of interest and voxel based analysis procedures (including TBSS from FSL).

If bvalues and bvectors are not specified with --b, it is assumed that the input is a Siemens dicom file, and gets gradient directions and bvalues based on values found in the dicom file. See $FREESURFER_HOME/diffusion/mgh-dti-seqpack/README. If the bvalues and bvectors are specified, then the input volume can be anything.

The bvalues are in a simple text file, one for each direction (including b=0). The bvectors (gradient directions) are also in a simple text file with three components on each row. These also include the b=0 values. There must be as many rows in the bvals/bvecs as there are frames in the input.

= Synopsis =
||dt_recon ||--i dti_dicom --s subjectid [Directive] ||


= Arguments =
== Required Aruments ==
||--i invol || input volume ||
||--b bvals bvecs || b-values and b-vectors ||
||--s subjectid || subject ||
||--o outputdir || output directory ||


== Other Arguments (Optional) ==
||--info-dump infodump.dat || use info dup created by unpacksdcmdir or dcmunpack ||
||--ecref TP || Use TP as 0-based reference time points for EC ||
||--no-ec || turn off eddy/motion correction ||
||--no-reg || do not register to subject or resample to talairach ||
||--no-tal || do not resample FA to talairch space ||
||--sd subjectsdir || specify subjects dir (default env SUBJECTS_DIR) ||
||--eres-save || save residual error (dwires and eres) ||
||--pca || run PCA/SVD analysis on eres (saves in pca-eres dir) ||
||--prune_thr thr || set threshold for masking (default is FLT_MIN) ||
||--init-spm || init BBR with SPM instead of FSL (requires matlab) ||
||--debug || print out lots of info ||
||--version || print version of this script and exit ||
||--help || voluminous bits of wisdom ||


= Stages and output =
1. Convert input to nifti (creates dwi.nii)

2. Eddy current and motion correction using FSL's eddy_correct, creates dwi-ec.nii. Can take 1-2 hours.

3. DTI GLM Fit and tensor construction. Includes creation of:
{{{
tensor.nii -- maps of the tensor (9 frames)
eigvals.nii -- maps of the eigenvalues
eigvec?.nii -- maps of the eigenvectors
adc.nii -- apparent diffusion coefficient
fa.nii -- fractional anisotropy
ra.nii -- relative anisotropy
vr.nii -- volume ratio
ivc.nii -- intervoxel correlation
lowb.nii -- Low B
bvals.dat -- bvalues
bvecs.dat -- directions
}}}
{{{
Also creates glm-related images:
beta.nii - regression coefficients
eres.nii - residual error (log of dwi intensity)
rvar.nii - residual variance (log)
rstd.nii - residual stddev (log)
dwires.nii - residual error (dwi intensity)
dwirvar.nii - residual variance (dwi intensity)
}}}
4.Registration of lowb to same-subject anatomical using:bbregister (creates mask.nii and register.lta)

5.Map FA to talairach space (creates fa-tal.nii)


= Example usage: =
{{{
dt_recon --i 6-1025.dcm --s M87102113 --o dti 
dt_recon --i f.nii --b f.bvals f.bvecs --s M87102113 --o dti
}}}
== Check registration ==
{{{
tkregister2 --mov dti/lowb.nii --reg dti/register.lta \
 --surf orig --tag
}}}
== View FA on the subject's anat: ==
{{{
tkmedit  M87102113 orig.mgz -overlay dti/fa.nii \
 -overlay-reg dti/register.dat
}}}
Note: this only works with .dat
== View FA on fsaverage ==
{{{
tkmedit fsaverage orig.mgz -overlay dti/fa-tal.nii
}}}
== Group/Higher level GLM analysis: ==
Concatenate FA from individuals into one file

Make sure the order agrees with the fsgd below
{{{
mri_concat */fa-tal.nii --o group-fa-tal.nii
}}}
== Create a mask: ==
{{{
mri_concat */mask-tal.nii --o group-masksum-tal.nii --mean 
mri_binarize --i group-masksum-tal.nii --min .999 --o group-mask-tal.nii
}}}
== GLM Fit ==
{{{
mri_glmfit --y group-fa-tal.nii --mask group-mask-tal.nii\
 --fsgd your.fsgd --C contrast --glmdir groupanadir
}}}