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reformatting

FIGURE 1. Iconic Registration methods. transformation. dataset1. reformatted dataset. registration. reformatting. dataset2. Fiducials-based Registration methods. transformation. dataset1. reformatted dataset. Fiducials segmentation. registration. reformatting. dataset2.

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reformatting

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  1. FIGURE 1. Iconic Registration methods transformation dataset1 reformatted dataset registration reformatting dataset2 Fiducials-based Registration methods transformation dataset1 reformatted dataset Fiducials segmentation registration reformatting dataset2

  2. FIGURE 2. :Example MR / PET registration CASE 1 Workstation Registration server Selection of datasets to register call registration Calculation of Geometric transformation return geometric transformation Dataset reformatting Display CASE 2 Selection of datasets to register call registration Calculation of Geometric transformation return reformatted dataset Dataset reformatting Display

  3. FIGURE 3. : Example MR / MR template registration (anatomical standardization of fMRI data) CASE 1 Workstation Registration server (e.g. SPM) Selection of dataset to register call registration (transfo object -> target) Calculation of Geometric transformation return geometric transformation (12 param affine transform + nonlinear transform) Apply transfo To fMRI image series Statistical analysis CASE 2 Selection of datasets to register call registration (compute and apply transfo) Calculation of Geometric transformation Apply transfo To fMRI image series return reformatted datasets Statistical analysis

  4. FIGURE 4. : Example headshape / MR registration (e.g. MEG/EEG / MR) CASE 1 Workstation Registration server (e.g. SPM) Selection of datasets to register call registration Detection of skin in MR Surface-based Registration return geometric transformation (rigid transform) Visual Control of accuracy CASE 2 Selection of datasets to register call registration (feature extraction + surface matching) Detection of skin in MR return geometric transformation (rigid transform) Surface-based Registration Visual Control of accuracy

  5. FIGURE 5: Example pre-op images / intra-op images registration (e.g. neuronavigation) Neuronavigation workstation Registration Server Definition Of fiducials Fiducials-based Registration (patient, Pre-op images) Acquisition of intra- op images (e.g. US) Call registration (intra-op images –> pre_op images) Compute Non-linear transfo Return non-linear transfo Apply transfo To match pre-op data with intra-op data

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