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tbx_scfg_hmri_proc_pipeline.m
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tbx_scfg_hmri_proc_pipeline.m
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function proc_pipel = tbx_scfg_hmri_proc_pipeline
% Configuration file for the pipeline part of the processing modules of
% the "histological MRI" (hMRI) toolbox.
% -> Provides standard processign pipelines.
%
% For simplicity, 2 standard pipelines are also set up:
% - US+Smooth -> applies US, warps into MNI, then smoothes
% (weighted-average)
% - US+Dartel+Smooth -> applies US, builds Dartel template and warps into
% MNI, then smoothes (weighted-average)
% Most of the parameters are therefore pre-defined and hardcoded!
% For more flexibility, you ought to use the individual modules and build
% your own pipeline.
%_______________________________________________________________________
% Copyright (C) 2017 Cyclotron Research Centre
% Written by Christophe Phillips
% ---------------------------------------------------------------------
% indir Input directory as output directory
% ---------------------------------------------------------------------
indir = cfg_menu;
indir.tag = 'indir';
indir.name = 'Input directory';
indir.help = {['Output files will be written to the same folder as ',...
'each corresponding input file.']};
indir.labels = {'Yes'};
indir.values = {1};
indir.val = {1};
% ---------------------------------------------------------------------
% outdir Output directory for all data
% ---------------------------------------------------------------------
outdir = cfg_files;
outdir.tag = 'outdir';
outdir.name = 'Output directory, all together';
outdir.help = {['Select a directory where all output files from all '...
'subjects put together will be written to.']};
outdir.filter = 'dir';
outdir.ufilter = '.*';
outdir.num = [1 1];
% ---------------------------------------------------------------------
% outdir_ps Output directory for per-subject organisation
% ---------------------------------------------------------------------
outdir_ps = cfg_files;
outdir_ps.tag = 'outdir_ps';
outdir_ps.name = 'Output directory, with per-subject sub-directory';
outdir_ps.help = {['Select a directory where output files will be '...
'written to, in each subject''s sub-directory.']};
outdir_ps.filter = 'dir';
outdir_ps.ufilter = '.*';
outdir_ps.num = [1 1];
% ---------------------------------------------------------------------
% output Output choice
% ---------------------------------------------------------------------
output = cfg_choice;
output.tag = 'output';
output.name = 'Output choice';
output.help = {['Output directory can be the same as the input ',...
'directory for each input file or user selected (one for everything ',...
'or preserve a per-subject organisation).']};
output.values = {indir outdir outdir_ps };
output.val = {indir};
% -------------------------------------------------------------------------
% vols Volumes
% ---------------------------------------------------------------------
vols = cfg_files;
vols.tag = 's_vols';
vols.name = 'Structural images (T1w or MT) for segmentation';
vols.help = {['Select structural images, i.e. T1w or MT, for ',...
'"unified segmentation". They are used to create the individuam ',...
'tissue class maps, e.g. GM and WM posterior probability maps']};
vols.filter = 'image';
vols.ufilter = '.*';
vols.num = [1 Inf];
% ---------------------------------------------------------------------
% vols_pm Parametric maps
% ---------------------------------------------------------------------
vols_pm = cfg_files;
vols_pm.tag = 'vols_pm';
vols_pm.name = 'Maps (single type)';
vols_pm.help = {['Select whole brain parameter maps (e.g. MT, R2*, ',...
'FA, etc.) from all subjects for processing.']};
vols_pm.filter = 'image';
vols_pm.ufilter = '.*';
vols_pm.num = [1 Inf];
% ---------------------------------------------------------------------
% many_pams Parameter maps
% ---------------------------------------------------------------------
% used for 'many subjects', i.e. list the data per map type across subjects
many_pams = cfg_repeat;
many_pams.tag = 'maps';
many_pams.name = 'Parametric maps';
many_pams.values = {vols_pm };
many_pams.val = {vols_pm };
many_pams.num = [1 Inf];
many_pams.help = {['Select whole brain parameter maps (e.g. MT, ',...
'R2*, FA, etc.) from all subjects for processing, one type at a time.']};
% ---------------------------------------------------------------------
% pipe_c Pipeline choice
% ---------------------------------------------------------------------
pipe_c = cfg_menu;
pipe_c.tag = 'pipe_c';
pipe_c.name = 'Pipeline';
pipe_c.help = {
'Chose the predefined pipeline that you prefer:'
'- US+Smooth -> applies US, warps into MNI, then smoothes (weighted-average)'
['- US+Dartel+Smooth -> applies US, builds Dartel template and warps into' ...
'MNI, then smoothes (weighted-average)']
}';
pipe_c.labels = {
'US+smooth'
'US+Dartel+smooth'}';
pipe_c.values = {1 2};
pipe_c.val = {1};
% ---------------------------------------------------------------------
% Gaussian FWHM
% ---------------------------------------------------------------------
fwhm = cfg_entry;
fwhm.tag = 'fwhm';
fwhm.name = 'Gaussian FWHM';
fwhm.val = {[6 6 6]};
fwhm.strtype = 'e';
fwhm.num = [1 3];
fwhm.help = {['Specify the full-width at half maximum (FWHM) of the ',...
'Gaussian blurring kernel in mm. Three values should be entered',...
'denoting the FWHM in the x, y and z directions.']};
% ---------------------------------------------------------------------
% vox Voxel sizes
% ---------------------------------------------------------------------
vox = cfg_entry;
vox.tag = 'vox';
vox.name = 'Voxel sizes';
vox.num = [1 3];
vox.strtype = 'e';
vox.val = {[1 1 1]};
vox.help = {[...
'Specify the voxel sizes of the deformation field and tissue classes ',...
'to be produced. Non-finite values will default to the voxel sizes of ',...
'the template image that was originally used to estimate the deformation.']};
%--------------------------------------------------------------------------
% bb Bounding box
%--------------------------------------------------------------------------
bb = cfg_entry;
bb.tag = 'bb';
bb.name = 'Bounding box';
bb.help = {'The bounding box (in mm) of the volume which is to be written (relative to the anterior commissure).'};
bb.strtype = 'r';
bb.num = [2 3];
bb.def = @(val)spm_get_defaults('normalise.write.bb', val{:});
% ---------------------------------------------------------------------
% proc_pipel Preprocess maps -> pipelines
% ---------------------------------------------------------------------
proc_pipel = cfg_exbranch;
proc_pipel.tag = 'proc_pipel';
proc_pipel.name = 'Proc. hMRI -> Pipelines';
proc_pipel.help = {
['Parameter maps are spatially processed and brought into standard space',...
'for furhter statistical analysis. Only 2 tissue classes, GM & WM, '...
'are considered.']
[' ']
['For simplicity, 2 standard pipelines are also set up:']
['- US+Smooth -> applies US, warps into MNI, then smoothes (weighted-average)']
['- US+Dartel+Smooth -> applies US, builds Dartel template and warps' ...
'into MNI, then smoothes (weighted-average)']
}'; %#ok<*NBRAK>
proc_pipel.val = {output vols many_pams vox bb fwhm pipe_c};
proc_pipel.prog = @hmri_run_proc_pipeline;
proc_pipel.vout = @vout_proc_pipeline;
proc_pipel.check = @check_data;
end
%----------------------------------------------------------------------
%----------------------------------------------------------------------
%----------------------------------------------------------------------
%% =======================================================================
% VOUT function
% =======================================================================
% Collect and prepare output
function dep = vout_proc_pipeline(job)
% This depends on job contents, which may not be present when virtual
% outputs are calculated.
% There should be one series of images per parametric map and tissue class,
% e.g. in the usual case of 4 MPMs and GM/WM -> 8 series of image
n_pams = numel(job.vols_pm); % #parametric image types
n_TCs = 2; % #tissue classes = 2, by default
cdep = cfg_dep;
for ii=1:n_TCs
for jj=1:n_pams
cdep(end+1) = cfg_dep; %#ok<*AGROW>
cdep(end).sname = sprintf('c#%d, pMap #%d', ii, jj);
cdep(end).src_output = substruct('.', 'tc', '{}', {ii,jj});
cdep(end).tgt_spec = cfg_findspec({{'filter','image','strtype','e'}});
end
end
dep = cdep(2:end);
end
%% =======================================================================
% CHECKING the data
% ========================================================================
function t = check_data(job)
% Checking that the data are consistent.
t = {};
nSubj = numel(job.s_vols); % number of subjects from #struct images
nPara = numel(job.vols_pm); % number of maps type
% Check number of structurals matches the number of parametric maps per
% type
if nPara>0
for ii=1:nPara
if numel(job.vols_pm{ii})~=0 && numel(job.vols_pm{ii})~=nSubj
t{1} = 'Number of maps not matching number of structural images/subjects!';
warndlg(t,'Maps numbers');
return
end
end
end
end