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Advanced Registration Techniques for Transforming Medical Imaging Data

This document explores various advanced registration methods utilized to transform and align diverse medical imaging datasets, including MR, PET, and fMRI. It details fiducial-based and surface-based registration processes, emphasizing the geometric transformations involved and their applications in clinical settings, such as neuronavigation. Case studies illustrate the workflow, including dataset selection, geometric transformation calculations, and the application of transformations for accurate statistical analysis. Each figure presents its own methodology and results, enhancing our understanding of anatomical standardization in imaging.

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Advanced Registration Techniques for Transforming Medical Imaging Data

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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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