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hMRI-toolbox-public/tbx_scfg_hmri_proc_smooth.m
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function proc_smooth = tbx_scfg_hmri_proc_smooth | |
% Configuration file for the "smoothing", weighted averaging, of | |
% quantitative maps | |
%_______________________________________________________________________ | |
% Copyright (C) 2017 Cyclotron Research Center | |
% Written by Christophe Phillips | |
% NOTE: | |
% data are selected in a 'many subject' style, i.e. all the images of one | |
% type are selected from many subjects at once! | |
% | |
% It could be advantageous to define the TPM in a definition file and use | |
% it when ever we need it. Right now, this is hard-coded in the cfg file! | |
% Same goes for a bunch of parameters for each tissue class (e.g. number of | |
% Guassians, what is written out, bias correction, etc.) which ar enow +/- | |
% hard-coded in this file. | |
% --------------------------------------------------------------------- | |
% vols_pm Parametric volumes | |
% --------------------------------------------------------------------- | |
vols_pm = cfg_files; | |
vols_pm.tag = 'vols_pm'; | |
vols_pm.name = 'Volumes'; | |
vols_pm.help = {['Select whole brain parameter maps (e.g. MT, R2*, ',... | |
'FA etc) warped into MNI space.']}; | |
vols_pm.filter = 'image'; | |
vols_pm.ufilter = '^w.*'; | |
vols_pm.num = [1 Inf]; | |
% --------------------------------------------------------------------- | |
% m_pams Parameter maps, used for 'many subjects' | |
% --------------------------------------------------------------------- | |
m_pams = cfg_repeat; | |
m_pams.tag = 'm_pams'; | |
m_pams.name = 'Warped parameter maps'; | |
m_pams.values = {vols_pm }; | |
m_pams.val = {vols_pm }; | |
m_pams.num = [1 Inf]; | |
m_pams.help = {['Select whole brain parameter maps (e.g. MT, ',... | |
'R2*, FA etc) warped into MNI space.']}; | |
% --------------------------------------------------------------------- | |
% vols_mwc Modulated warped tissue segement volumes | |
% --------------------------------------------------------------------- | |
vols_mwc = cfg_files; | |
vols_mwc.tag = 'vols_mwc'; | |
vols_mwc.name = 'mwc images'; | |
vols_mwc.help = {'Select the modulated warped tissue segements (mwc*).', ... | |
'Pick only one type of mwc* images across all subjects!.'}; | |
vols_mwc.filter = 'image'; | |
vols_mwc.ufilter = '^mwc.*'; | |
vols_mwc.num = [1 Inf]; | |
% --------------------------------------------------------------------- | |
% m_MWCs Modulate warped tissue segement (MWC) maps | |
% --------------------------------------------------------------------- | |
m_MWCs = cfg_repeat; | |
m_MWCs.tag = 'm_MWCs'; | |
m_MWCs.name = 'Modulated warped tissue segements'; | |
m_MWCs.values = {vols_mwc }; | |
m_MWCs.val = {vols_mwc }; | |
m_MWCs.num = [1 Inf]; | |
m_MWCs.help = {['Select the modulated warped tissue segments ',... | |
'of interest from all subjects.'], ... | |
['For the typical case of GM and WM, you would selectall the mwc1* images ', ... | |
'in one set of ''mwc_images'' and the mwc2* ones in second set of ', ... | |
'''mwc_images''!']}; | |
% --------------------------------------------------------------------- | |
% tpm Tissue Probability Maps | |
% --------------------------------------------------------------------- | |
% use the hMRI specific TPMs. | |
fn_tpm = hmri_get_defaults('proc.TPM'); | |
tpm = cfg_files; | |
tpm.tag = 'tpm'; | |
tpm.name = 'Tissue probability maps'; | |
tpm.help = {'Select the TPM used for the segmentation.'}; | |
tpm.filter = 'image'; | |
tpm.ufilter = '.*'; | |
tpm.num = [1 1]; | |
tpm.val = {{fn_tpm}}; | |
% --------------------------------------------------------------------- | |
% 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.']}; | |
% --------------------------------------------------------------------- | |
% 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}; | |
%% EXEC function | |
% --------------------------------------------------------------------- | |
% proc_smooth Processing hMRI -> smoothing | |
% --------------------------------------------------------------------- | |
proc_smooth = cfg_exbranch; | |
proc_smooth.tag = 'proc_smooth'; | |
proc_smooth.name = 'Proc. hMRI -> Smoothing'; | |
proc_smooth.val = {m_pams m_MWCs tpm fwhm output}; | |
proc_smooth.check = @check_proc_smooth; | |
proc_smooth.help = { | |
'Applying tissue specific smoothing, aka. weighted averaging, ', ... | |
'in order to limit partial volume effect.'}; | |
proc_smooth.prog = @hmri_run_proc_smooth; | |
proc_smooth.vout = @vout_smooth; | |
end | |
%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% | |
% SUBFUNCTIONS | |
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% | |
% Collect and prepare output | |
function dep = vout_smooth(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 = numel(job.vols_mwc); % #tissue classes | |
cdep = cfg_dep; | |
for ii=1:n_TCs | |
for jj=1:n_pams | |
cdep(end+1) = cfg_dep; | |
cdep(end).sname = sprintf('TC #%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 | |
% Check number of files match | |
function chk = check_proc_smooth(job) | |
% ensure they are the same for each list of files, one of each per subject. | |
n_pams = numel(job.vols_pm); | |
n_TCs = numel(job.vols_mwc); | |
chk = ''; | |
if n_pams>1 | |
ni_pams = numel(job.vols_pm{1}); | |
for ii=2:n_pams | |
if ni_pams ~= numel(job.vols_pm{ii}) | |
chk = [chk 'Incompatible number of maps. ']; | |
break | |
end | |
end | |
end | |
if n_TCs>1 | |
ni_TC = numel(job.vols_mwc{1}); | |
for ii=2:n_TCs | |
if ni_TC ~= numel(job.vols_mwc{ii}) | |
chk = [chk 'Incompatible number of tissue segments. ']; %#ok<*AGROW> | |
break | |
end | |
end | |
end | |
if n_pams>0 && n_TCs>0 | |
if numel(job.vols_pm{1}) ~= numel(job.vols_mwc{1}) | |
chk = [chk 'Incompatible number of maps & tissue segments.']; | |
end | |
end | |
end | |