hmri_run_proc_dartel_norm.m

function out = hmri_run_proc_dartel_norm(job)
% function out = hmri_run_proc_dartel_norm(job)
% derived from spm_dartel_norm_fun_local
%
% It applies the "Dartel - Normalize to MNI" onto the tissue classes and
% parametric maps, from the job created in the batch.
%_______________________________________________________________________
% Copyright (C) 2017 Cyclotron Research Centre

% Written by Christophe Phillips

%  Build the output 'out' structure with created filenames in cell arrays
out.vols_mwc = {}; % filenames are organised as {1 x #mwc} of {#subj x 1}
out.vols_wc = {};  % filenames are organised as {1 x #wc} of {#subj x 1}
out.vols_wpm = {}; % filenames are organised as {1 x #pm} of {#subj x 1}

% MFC - Setting up feds structure which has a copy of the (reordered) subject info:
job = dartel_perimage_to_persubject(job);

feds.template = job.template;
feds.vox      = job.vox;
feds.bb       = job.bb;
feds.fwhm     = [0 0 0];
for m = 1:length(job.subjd)
    feds.data.subj(m).flowfield = job.subjd(m).flowfield;
    feds.data.subj(m).images    = job.subjd(m).tc_vols;
end

% Define output folder for SPM function
output = struct('outDir', [], 'option', 'same');
if isfield(job.output,'outdir') % -> everything in the same
    output.outDir = job.output.outdir{1};
    output.option = 'allin';
elseif isfield(job.output,'outdir_ps') % -> per subject organization
    output.outDir = job.output.outdir_ps{1};
    output.option = 'subjspec';
end
feds.output = output;

[~,ver] = spm('Ver');
if str2double(ver)>=7219
    % Knows how to handle output specification
    use_spm_output_handling = true;
else
    use_spm_output_handling = false;
end

% Define eTPM as the tissue probability maps to use for MNI space reference
feds.tpm = struct(...
    'fn', hmri_get_defaults('proc.TPM'),...
    'k', 6); % Need to provide #tissue classes considered

% Jacobian modulation correction to preserve total signal intensity:
feds.preserve = 1;

% Produce mwc* images, i.e. modulated, spatially normalised images.
% This produces w = |Dphi|t(phi), the product of the Jacobian determinants
% of deformation phi and the tissue class image warped by phi, as per
% Draganski 2011, NeuroImage.
if isempty(job.multsdata.vols_tc)
    fprintf('\nNo tissue segment images to normalize.\n');
else
    out_tc = spm_dartel_norm_fun(feds);
    
    % Specify the output with created file names, based on out_tc
    out.vols_mwc  = cell(1,numel(job.subjd(1).tc_vols));
    for ii=1:numel(job.subjd(1).tc_vols)
        out.vols_mwc{ii}  = cell(numel(job.subjd),1);
        for jj=1:numel(job.subjd)
            out.vols_mwc{ii}{jj} = out_tc{jj}{ii};
        end
    end
end

% No jacobian modulation
feds.preserve = 0;

% Now take the MPMs and c* iamges and do a regular normalisation
% -> produce ws and wc images
if isempty(job.multsdata.vols_tc)
    fprintf('\nNo tissue segment images to normalize.\n');
else
    out_tc = spm_dartel_norm_fun(feds);
    
    % Specify the output with created file names, based on out_tc
    out.vols_wc  = cell(1,numel(job.subjd(1).tc_vols));
    for ii=1:numel(job.subjd(1).tc_vols)
        out.vols_wc{ii}  = cell(numel(job.subjd),1);
        for jj=1:numel(job.subjd)
            out.vols_wc{ii}{jj} = out_tc{jj}{ii};
        end
    end
end

for mm = 1:length(job.subjd)
    feds.data.subj(mm).images = job.subjd(mm).mp_vols;
end
out_pm = spm_dartel_norm_fun(feds);

if ~use_spm_output_handling
    % Move things and update path of saved filenames
    % NOTE: to be removed if this ends up part of the native Dartel function!
    if isfield(job.output,'indir') && ...
            job.output.indir == 1
        % leave as it is and do nothing. :-)
    else
        for ii=1:numel(out_pm) % for each subject
            % find output directory
            if isfield(job.output,'outdir') % -> everything in the same
                dn_output = job.output.outdir{1};
            elseif isfield(job.output,'outdir_ps')
                % Get the subjects directory name, from maps
                data_path = spm_file(job.subjd(ii).mp_vols{1},'path');
                l_fsep = strfind(data_path,filesep);
                lp_fsep = [0 l_fsep length(data_path)+1];
                dn_subj = data_path(lp_fsep(end-1)+1:lp_fsep(end)-1);
                dn_output = fullfile(job.output.outdir_ps{1},dn_subj);
                if ~exist(dn_output,'dir')
                    % Create subject sub-directory if necessary
                    mkdir(dn_output);
                end
            else
                warning('hmri:norm2mni','Incorrect output directory specification.');
                break
            end
            % move files
            for jj=1:numel(out_pm{ii})
                movefile(out_pm{ii}{jj},dn_output)
            end
            % update path
            out_pm{ii} = spm_file(out_pm{ii},'path',dn_output);
        end
    end
end

% Specify the output with created file names, based on out_pm
out.vols_wpm  = cell(1,numel(job.subjd(1).mp_vols));
for ii = 1:numel(job.subjd(1).mp_vols)
    out.vols_wpm{ii} = cell(numel(job.subjd),1);
    for jj = 1:numel(job.subjd)
        out.vols_wpm{ii}{jj} = out_pm{jj}{ii};
    end
end

end
%_______________________________________________________________________

%% SUBFUNCTIONS
%_______________________________________________________________________
function job = dartel_perimage_to_persubject(job)
% Rearrange data per subject for dartel processing.
for i=1:numel(job.multsdata.vols_field)
    job.subjd(i).tc_vols = {};
    for j=1:numel(job.multsdata.vols_tc)
        job.subjd(i).tc_vols{j,1} = job.multsdata.vols_tc{j}{i};
    end
    job.subjd(i).flowfield = {};
    job.subjd(i).flowfield{1} = job.multsdata.vols_field{i};
    job.subjd(i).mp_vols = {};
    for j=1:numel(job.multsdata.vols_pm)
        job.subjd(i).mp_vols{j,1} = job.multsdata.vols_pm{j}{i};
    end
end
end
%_______________________________________________________________________