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CT-based imaging for stereotactical neurosurgery planning

Learn about the history, technology, and stages of operative planning in CT-based imaging for stereotactical neurosurgery. Explore the determination of intracerebral landmarks, trajectory calculation, risk assessment, and more.

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CT-based imaging for stereotactical neurosurgery planning

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  1. Kharkiv National University of Radioelectronics CT-based imaging for stereotactical neurosurgery planning Department of Biomedical Engineering French-CERN-Ukrainian Workshop on Medical Physics and Imaging Kharkiv 2017 M. Tymkovych, O. Avrunin, V. Pyatikop

  2. Neurosurgical Interventions – Diseases (Parkinsons, epilepsy, local tumors and other); – Dangerous; – Experience; – Unpredictability. CT-based imaging for stereotactical neurosurgery planning

  3. Stereotaxy History Zernov apparatus (1889) Horsley & Clarke (1908) CT-based imaging for stereotactical neurosurgery planning

  4. CT Imaging Siemens CT apparatus Axial slice CT-based imaging for stereotactical neurosurgery planning

  5. Modern Stereotaxis Frame systems Frameless stereotaxis CT-based imaging for stereotactical neurosurgery planning

  6. Planning Software Stealth (Medtronic) Leksell SurgiPlan (Elekta) Stereotactic Planning Software (BrainLab) Neuroinspire (Renishaw) 6 CT-based imaging for stereotactical neurosurgery planning

  7. Main Stages of Operative Planning CT-based imaging for stereotactical neurosurgery planning

  8. Image processing tasks • Intracerebral landmarks determination; • Cranial landmarks determination; • Brain segmentation. CT-based imaging for stereotactical neurosurgery planning

  9. Intracerebral landmarks CA – commissura anterior; CP – commissura posterior ; V3 – third ventricle; Om – orbito-metal plane. CT-based imaging for stereotactical neurosurgery planning

  10. Intracerebral landmarks slices СА СP 10 CT-based imaging for stereotactical neurosurgery planning

  11. Example Ventricle region Head region Initial slice Segmented Ventricular system Third ventricle position

  12. Densitogram analysis of skull borders I1,I2 – intensity; R – sharpness. It is advisable to use the inner boundaries of the skull as skull marks 12 CT-based imaging for stereotactical neurosurgery planning

  13. Illustration of the navigation binding of the intracerebral coordinate system to the cranial landmarks 13 CT-based imaging for stereotactical neurosurgery planning

  14. Trajectory of surgical access An trajectory of surgical access Blood vessel target 14 CT-based imaging for stereotactical neurosurgery planning

  15. The indices of risk of damage (IDSΣ) of anatomical and functional structures 15 CT-based imaging for stereotactical neurosurgery planning

  16. Determination of risk indices of damage (IDSΣ) Slice CTA 3D map of Anatomical Atlas Illustration 16 CT-based imaging for stereotactical neurosurgery planning

  17. Anatomical atlases Talairach atlas CT-based imaging for stereotactical neurosurgery planning

  18. Computed Tomography Angiography 3D Visualization Axial slice CT-based imaging for stereotactical neurosurgery planning

  19. Slice classification task CT-based imaging for stereotactical neurosurgery planning

  20. Slice classification task CT-based imaging for stereotactical neurosurgery planning

  21. Preliminary traitement CT-based imaging for stereotactical neurosurgery planning

  22. Features extraction for slice classification а, b – semiaxes of ellipses. Ellipse model hr – local histogram of region r; Fr – feature of regionr. Rm CT-based imaging for stereotactical neurosurgery planning

  23. Feature example CT-based imaging for stereotactical neurosurgery planning

  24. The CT-slices classification scheme The part of training set The graphical representation of the position of slices The graphical representation of state machine for CT-slices classification CT-based imaging for stereotactical neurosurgery planning

  25. Logistic regression with respect to the classification – logistic function; θ – vector-column of regression parameters; x – vector-column of independent paramters (x0=1); n – number of independent variables. m – number of elements in the training set; y(i)–classification answer і from the training set. – probability of transition from state 1 to 2. θ regression parameters S1S2 CT-based imaging for stereotactical neurosurgery planning

  26. Optimal trajectory calculation Trajectory of surgical access: wheren– is element of trajectory; x(n),y(n),z(n) – coordiantes of element-n of trajectory; xM,yM,zM – coordinates of target; xT,yT,zT – coordiantes of intact point; Δt – step. Invasiveness function: wherem – number of surgical access; N – count of elements in the trajectory. Optimal trajectory: 26 CT-based imaging for stereotactical neurosurgery planning

  27. – low risk; – high risk; 3D Visualization 3D Map of risks Risk visualization in the form of a hemisphere 27 CT-based imaging for stereotactical neurosurgery planning

  28. – access is not possible. Surgical Instrument V – volume of surgical access l – length of trajectory of surgical access. Appearance of surgical instrument Shematic representation of surgical instrument 28 CT-based imaging for stereotactical neurosurgery planning

  29. Illustration of stereotactic calculations using the trajectory planning tool for neurosurgical interventions Axial CT cut at zero stereotaxic plane Axial CT-slice at the level of the surgical intervention 3D Map of Risks CT-based imaging for stereotactical neurosurgery planning

  30. Illustration of stereotactic biopsy of a tumor of thalamic localization Topogram in the sagittal projection CT-slice at the distal side of the instrument 3D Map of risks CT-slices along the surgical instrument CT-based imaging for stereotactical neurosurgery planning

  31. Steps of virtual modelling Turn the carriage at an angle of 23 ° in the frontal plane Position of the surgical instrument after penetration by 50 mm Turn the frame by 11 ° in the sagittal plane The starting position of surgical instrument CT-based imaging for stereotactical neurosurgery planning

  32. Conclusion – CT is an indispensable method for surgical guidance for stereotaxis; – The development of planning systems requires the use of increasingly sophisticated methods of image analysis; – Necessary to use: machine learning, computer vision techniques, more training datasets, new methods of imaging... CT-based imaging for stereotactical neurosurgery planning

  33. Cooperation Kharkiv National University of Radioelectronics, Department of Biomedical Engineering Kharkiv Regional Clinical Hospital, Depertment of Neurosurgery Kharkiv National Medical University, Department of Neurosurgery CT-based imaging for stereotactical neurosurgery planning

  34. Thank you for attention! Maksym Tymkovych maksym.tymkovych@nure.ua International Neuroscience Institute, Hannover, Germany

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