Here I copied the ea_normalize_ants file:
function varargout=ea_normalize_ants(options,includeatlas)
% This is a function that normalizes both a copy of transversal and coronar
% images into MNI-space. The goal was to make the procedure both robust and
% automatic, but still, it must be said that normalization results should
% be taken with much care because all reconstruction results heavily depend
% on these results. Normalization of DBS-MR-images is especially
% problematic since usually, the field of view doesn’t cover the whole
% brain (to reduce SAR-levels during acquisition) and since electrode
% artifacts can impair the normalization process. Therefore, normalization
% might be best archieved with other tools that have specialized on
% normalization of such image data.
%
% The procedure used here uses the ANTs Syn approach to map a patient’s
% brain to MNI space directly.
% __________________________________________________________________________________
% Copyright (C) 2015 Charite University Medicine Berlin, Movement Disorders Unit
% Andreas Horn
if ischar(options) % return name of method.
varargout{1}=’Advanced Normalization Tools (ANTs) SyN (Avants 2008)’;
varargout{2}=1;
return
end
usefa=1;
usebrainmask=0;
if ~exist(‘includeatlas’,’var’)
includeatlas=0;
end
if ~includeatlas % second run from maget-brain segment
ea_checkcoregallmri(options,usebrainmask)
end
directory=[options.root,options.patientname,filesep];
cnt=1;
if usebrainmask
bprfx=’b’;
else
bprfx=”;
end
spacedef=ea_getspacedef; % get definition of current space we are working in
if usefa && spacedef.hasfa % first put in FA since least important (if both an FA template and an fa2anat file is available)
if exist([directory,options.prefs.fa2anat],’file’) % recheck if now is present.
disp(‘Including FA information for white-matter normalization.’);
to{cnt}=[ea_space(options),’fa.nii’];
from{cnt}=[directory,bprfx,options.prefs.fa2anat];
weights(cnt)=0.5;
metrics{cnt}=’MI’;
cnt=cnt+1;
end
end
[~,anatpresent]=ea_assignpretra(options);
anatpresent=flip(anatpresent); % reverse order since most important transform should be put in last.
% The convergence criterion for the multivariate scenario is a slave to the last metric you pass on the ANTs command line.
for anatf=1:length(anatpresent)
disp([‘Including ‘,anatpresent{anatf},’ data for (grey-matter) normalization’]);
to{cnt}=[ea_space(options),ea_det_to(anatpresent{anatf},spacedef),’.nii’];
if usebrainmask && (~includeatlas) % if includeatlas is set we can assume that images have been coregistered and skulstripped already
ea_maskimg(options,[directory,anatpresent{anatf}],bprfx);
end
from{cnt}=[directory,bprfx,anatpresent{anatf}];
weights(cnt)=1.25;
metrics{cnt}=’MI’;
cnt=cnt+1;
end
if includeatlas % append as last to make criterion converge on this one.
to{cnt}=[ea_space(options),’atlas.nii’];
from{cnt}=[directory,’anat_atlas.nii.gz’];
weights(cnt)=1.5;
metrics{cnt}=’MI’; % could think about changing this to CC
cnt=cnt+1;
end
ea_ants_nonlinear(to,from,[directory,options.prefs.gprenii],weights,metrics,options);
ea_apply_normalization(options);
function masks=segmentall(from,options)
directory=[fileparts(from{1}),filesep];
for fr=1:length(from)
[~,fn,ext]=fileparts(from{fr});
switch [fn,ext]
case options.prefs.fa2anat
if ~exist([directory,’tc2′,options.prefs.prenii_unnormalized],’file’)
ea_newseg(directory,options.prefs.prenii_unnormalized,0,options);
% assume that tc2 doesn’t exist
nii=ea_load_nii([directory,’c2′,options.prefs.prenii_unnormalized]);
nii.img=nii.img>0.7;
nii.fname=[directory,’tc2′,options.prefs.prenii_unnormalized];
spm_write_vol(nii,nii.img);
end
masks{fr,1}=[ea_space(options),’c2mask.nii’];
masks{fr,2}=[directory,’tc2′,options.prefs.prenii_unnormalized];
otherwise
if ~exist([directory,’tc1′,options.prefs.prenii_unnormalized],’file’)
ea_newseg(directory,options.prefs.prenii_unnormalized,0,options);
% assume that tc1 doesn’t exist
nii=ea_load_nii([directory,’c1′,options.prefs.prenii_unnormalized]);
nii.img=nii.img>0.3;
nii.fname=[directory,’tc1′,options.prefs.prenii_unnormalized];
spm_write_vol(nii,nii.img);
end
masks{fr,1}=[ea_space(options),’c1mask.nii’];
masks{fr,2}=[directory,’tc1′,options.prefs.prenii_unnormalized];
end
end
if ~exist([directory,’tc1c2′,options.prefs.prenii_unnormalized],’file’)
Vc1=ea_load_nii([directory,’tc1′,options.prefs.prenii_unnormalized]);
Vc2=ea_load_nii([directory,’tc2′,options.prefs.prenii_unnormalized]);
Vc1.img=Vc1.img+Vc2.img;
Vc1.fname=[directory,’tc1c2′,options.prefs.prenii_unnormalized];
spm_write_vol(Vc1,Vc1.img);
end
function ea_genbrainmask(options)
directory=[options.root,options.patientname,filesep];
ea_newseg(directory,options.prefs.prenii_unnormalized,0,options);
c1=ea_load_nii([directory,’c1′,options.prefs.prenii_unnormalized]);
c2=ea_load_nii([directory,’c2′,options.prefs.prenii_unnormalized]);
c3=ea_load_nii([directory,’c3′,options.prefs.prenii_unnormalized]);
bm=c1;
bm.img=c1.img+c2.img+c3.img;
bm.fname=[directory,’brainmask.nii’];
bm.img=bm.img>0.6;
spm_write_vol(bm,bm.img);
function ea_maskimg(options,file,prefix)
directory=[options.root,options.patientname,filesep];
if ~exist([directory,’brainmask.nii’],’file’)
ea_genbrainmask(options);
end
[pth,fn,ext]=fileparts(file);
if ~exist([pth,filesep,prefix,fn,ext],’file’)
nii=ea_load_nii(file);
bm=ea_load_nii([directory,’brainmask.nii’]);
nii.img=nii.img.*double(bm.img);
nii.fname=[pth,filesep,prefix,fn,ext];
spm_write_vol(nii,nii.img);
end
function template2use=ea_det_to(anatfile,spacedef)
anatfile=strrep(anatfile,’anat_’,”);
anatfile=strrep(anatfile,’.nii’,”);
for avtpl=1:length(spacedef.templates)
if ismember(anatfile,spacedef.norm_mapping{avtpl})
template2use=spacedef.templates{avtpl};
return
end
end
% template still hasn’t been assigned, use misfit template if not empty:
if ~isempty(spacedef.misfit_template)
template2use=spacedef.misfit_template;
else
template2use=”;
end
