mincProbDeconvolve Man page

NAME

mincProbDeconvolve - uses probablistic deconvolution to estimate the orientation distribution function (ODF)

SYNOPSIS

 mincProbDeconvolve [options] -DWIs <DWIs.mnc> -mask <calc_mask.mnc> -o <out.mnc>

DESCRIPTION

mincDeconvolve uses the FORECAST (Anderson 2005) algorithm for deconvolution of the diffusion signal. This program allows the reconstruction of multiple fibre orientations per voxel and generates a cone of uncertainty describing the confidence in a particular direction. Your data should include at least 64 non-colinear diffusion directions to succesfully detect multiple fibers. Results of the probablisitic deconvolution can be viewed with minc3Dvis, using the -ODF option. Probablistic tractography using mincFibreTrack must be carried out with the output from this function.

OPTIONS

Usage: ProbDeconvolve [options] -DWIs <DWIs.mnc> -mask <calc_mask.mnc>  -o <out.mnc> 

DWIs: minc file must include a single nonzero bvalue series of DWIs in different directions and at least one b=0 image, which must be at the *beginning* of the series
mask: voxels in which deconvolution will be performed

options:

-response_voxel <MaskforFiberResponse.mnc>: a file in which the voxel or voxels that are considered the single fibre response function are nonzero.
-iterations <number of iterations> default: 100
-lambda1 <lambda1.mnc> -lambda2 <lambda2.mnc>: eigenvalues from a previous tensor fit: used for the FORECAST algorithm (The FORECAST algorithm is the only algorithm currently available - default.)
-clobber: overwrite existing <out.mnc>
-order <Order of deconvolution> (default (and maximum) is 8)


NOTE: all inputs must be sampled the same way


Output file format:

The minc file <out.mnc> is a 4D volume. The size of the 4th dimension is 36.  For each geometry, there is an occurence rate.  For each maximum direction, there is one confidence value, one sigma_theta value, and one 3D vector representing the mean maximum direction. Since 3 different cases are considered (1 single fiber, 2 crossing fibers(double), 3 crossing fibers(triple)), we will have (1+2+3)*6 values, which are saved in the order below.

0 global occurence of (single fibre) vector (Ov)
1 occurence of single fibre geometry
2 sigma1
3 x1_mean
4 y1_mean
5 z1_mean
6 global occurence of first vector: (crossing fibers(double))
7 occurence of double fibre geometry
8 sigma1
9 x1_mean
10 y1_mean
11 z1_mean
12 global occurence of second vector
13 occurence of double fibre geometry
14 sigma2
15 x2_mean
16 y2_mean
17 z2_mean
18 global occurence of first vector (crossing fibers(triple))
19 occurence of triple fibre geometry
20 sigma1
21 x1_mean
22 y1_mean
23 z1_mean
24 global occurence of second vector
25 occurence of triple fibre geometry
26 sigma2
27 x2_mean
28 y2_mean
29 z2_mean
30 global occurence of third vector
31 occurence of triple fibre geometry
32 sigma3
33 x3_mean
34 y3_mean
35 z3_mean

Other options:

-clobber:overwrite existing <out.mnc>

Generic options
       -help: Print summary of command-line options and abort.

EXAMPLES

Apply deconvolution to your diffusion input data (could be the raw images or the ones registered to the T1 antomical from diff_preprocess.)
NOTE: make sure input and mask are in the same space!

mincProbDeconvolve -DWIs infile-reg-with-t1.mnc -mask anat-n3-bet_mask-diffspace.mnc -o probdeconvolve.mnc 

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Ilana LEPPERT
created: March 24th 2011
last modified: March 24th 2011