Lead-DBS allows you to automatically normalize the patient images into standard stereotactic (MNI) space. All templates can be found in your installation under
lead_dbs/templates/. For ANTs-based and Schönecker normalizations, the ICBM 152 2009b Nonlinear Asymmetric is used (
mni_hires.nii). In case of DARTEL, a DARTEL template was generated based on the ICBM 152 2009c series and is supplied within Lead-DBS (
dartel/dartelmni_*.nii). The New Segment algorithm uses the enhanced tissue probability map by Lorio et al. (
TPM_Lorio_Draganski.nii file located under
There are several approaches to achieve a precise normalization of pre- and post-operative images. In most of the routines that come pre-installed with LEAD, post-operative images are first coregistered with each other and then coregistered to the pre-operative images. Finally, all coregistrations are normalized into the MNI space based on the transformation parameters found when co-registering the pre-operative image to the template.
Lead-DBS comes with the following built-in normalization protocols:
_Advanced Normalization Tools (ANTs):
_This protocol uses the nonlinear diffeomorphic normalization algorithms referred to as SyN or BSplineSyN (e.g. Avants 2011 or Tustison 2013). The deformation field is estimated based on a series of preoperative acquisitions (these can include any number of preoperative images, e.g.
anat_pd.nii, anat_fgatir.nii etc. as well as
dti.niiwhich will then produce
fa2anat.nii) and applied to all (co-registered) postoperative images later on. Please note that the dti.nii is used to export an fa.nii image which is subsequently co-registered to the anat.nii as fa2anat.nii.
This protocol uses the FSL FNIRT routine. There is no multispectral normalization support, meaning it will only use the anchor modality (usually the
anat_t1.nii volume depending on the space configuration).
_This protocol uses the Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra (DARTEL, Ashburner 2007) approach supplied with SPM12 to normalize the preoperative MR-image directly to the ICBM template (in MNI space). The estimated DARTEL flowfields are then applied to the coregistered postoperative versions. A DARTEL template was generated based on the
ICBM 152 2009b series and is supplied within Lead-DBS. Thus, other than the standard DARTEL workflow (which aims at generating a group-template and affine-registering that to MNI space), the DARTEL template used in the Lead-DBS setting is defined by the nonlinear MNI templates, directly. As pointed out in Klein 2009, DARTEL seems to perform equally well in pair-wise and group-wise normalizations.
_This protocol uses the SPM12 "Segment" (In SPM8 referred to as "New Segment") approach to segment and normalize the pre-operative image to the ICBM template (in MNI space). The estimated deformation fields are then applied to the coregistered postoperative versions. LEAD uses a slightly modified version of the New Segment approach in that it uses a higher spatial resolution of the warps. This leads to a higher processing time.
Three step affine normalization (Schönecker 2009): This protocol is based on the approach described in Schönecker 2009. It uses ANTs to linearly coregister the pre- or postoperative images into MNI space in three consecutive steps, each focusing more on the subcortical target region. The last step spares the ventricles, which may largely vary in the subject-specific anatomy. This is the only normalization routine that can handle the situation where you don't have pre-operative images and still should give precise results.
You can select the protocol to run depending on the image files that are available for processing.
After loading the patient directory and choosing the imaging technique (see Section 2), check the option
 Normalize and press
When available, Lead-DBS also gives you the option of normalizing fiber tracking images into MNI space. For processing of these images, the option
 Normalize Fibers under the
Lead-Connectome panel should be checked.
 Check Normalization opens several viewers that are built to check co-registration of the normalized images with the MNI space at the end of the process.