Who Falls After a Stroke? Evidence From a Prospective Stroke Cohort.

1. function varargout=ea_normalize_fsl(options)
2. % This is a function that normalizes both a copy of transversal and coronal
3. % images into MNI-space. The goal was to make the procedure both robust and
4. % automatic, but still, it must be said that normalization results should
5. % be taken with much care because all reconstruction results heavily depend
6. % on these results. Normalization of DBS-MR-images is especially
7. % problematic since usually, the field of view doesn't cover the whole
8. % brain (to reduce SAR-levels during acquisition) and since electrode
9. % artifacts can impair the normalization process. Therefore, normalization
10. % might be best archieved with other tools that have specialized on
11. % normalization of such image data.
12. %
13. % The procedure used here uses the ANTs Syn approach to map a patient's
14. % brain to MNI space directly.
15. % __________________________________________________________________________________
16. % Copyright (C) 2015 Charite University Medicine Berlin, Movement Disorders Unit
17. % Andreas Horn
20. if ischar(options) % return name of method.
21. varargout{1}='FNIRT (Andersson 2010)';
22. varargout{2}=1; % dummy output
23. varargout{3}=1; % hassettings.
24. varargout{4}=0; % is multispectral
25. return
26. end
28. % FSL FNIRT nonlinear registration
29. ea_fnirt([ea_space, options.primarytemplate, '.nii'],...
30. options.subj.coreg.anat.preop.(options.subj.AnchorModality),...
31. options.subj.norm.anat.preop.(options.subj.AnchorModality));
33. % Clean up FNIRT log file
34. logFileBase = ea_niifileparts(options.subj.coreg.anat.preop.(options.subj.AnchorModality));
35. ea_delete([logFileBase, '_to_*.log']);
37. % Move transformation file
38. transformBase = ea_niifileparts(options.subj.norm.anat.preop.(options.subj.AnchorModality));
39. transform = dir([transformBase, 'WarpField*']);
40. ext = regexp(transform(end).name, '(?<=WarpField)\.nii(\.gz)?$', 'match', 'once');
41. movefile([transformBase, 'WarpField', ext], [options.subj.norm.transform.forwardBaseName, 'fnirt', ext]);
42. movefile([transformBase, 'InverseWarpField', ext], [options.subj.norm.transform.inverseBaseName, 'fnirt', ext]);
44. % Apply registration
45. ea_apply_normalization(options)
47. %% add methods dump:
48. [scit, lcit] = ea_getspacedefcit;
49. cits = {
50. 'Andersson JLR, Jenkinson M, Smith S (2010) Non-linear registration, aka spatial normalisation. FMRIB technical report TR07JA2'
51. 'Woolrich MW, Jbabdi S, Patenaude B, Chappell M, Makni S, Behrens T, Beckmann C, Jenkinson M, Smith SM (2009) Bayesian analysis of neuroimaging data in FSL. NeuroImage, 45:S173-86'
52. };
54. if ~isempty(lcit)
55. cits = [cits;{lcit}];
56. end
58. ea_methods(options,['Pre- (and post-) operative acquisitions were spatially normalized into ',ea_getspace,' space ',scit,' based on the preoperative acquisition (',options.subj.AnchorModality,') using the'...
59. ' FNIRT approach as implemented in the FMRIB Software Library (Andersson 2010; Woolrich 2009 https://fsl.fmrib.ox.ac.uk/).'],cits);

ea_normalize_fsl.m at commit 5b1008d, under GPL-3.0 · at the source