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

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

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

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

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

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

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

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

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

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

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

Densitogram analysis of skull borders I1,I2 – intensity; R – sharpness. Parameter R, r.u/mm. Internal border 0.28 External border 0.5 It is advisable to use the inner boundaries of the skull as skull marks 12 CT-based imaging for stereotactical neurosurgery planning

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

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

The indices of risk of damage (IDSΣ) of anatomical and functional structures Anatomical or functional structure or region IDSΣ External objects of the scan area (air, navigation marks, the head fixation elements) The air in the paranasal sinuses 1 Ventricles, CSF 2 Bone structures 3 Gray and white matter of the cerebral hemispheres 4 The nuclei of the diencephalon 5 Internal capsule 6 Nucleus of the hypotalamus and brain stem 7 Blood vessels 8 15 CT-based imaging for stereotactical neurosurgery planning

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

CT-based imaging for stereotactical neurosurgery planning Anatomical atlases Item Id Structure Emptiness 1 Left Cerebellum.Posterior Lobe.Inferior Semi-Lunar Lobule.Gray Matter 69 Right Cerebellum.Sub-lobar.Fourth Ventricle.Cerebro-Spinal Fluid 71 Left Brainstem.Pons 191 Left Cerebrum.Frontal Lobe.Inferior Frontal Gyrus.Gray Matter.Brodmann area 273 White Matter.Optic Tract 438 Right Brainstem.Midbrain.Thalamus.Gray Matter.Medial Geniculum Body Talairach atlas CT-based imaging for stereotactical neurosurgery planning

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

Slice classification task CT-based imaging for stereotactical neurosurgery planning

Slice classification task CT-based imaging for stereotactical neurosurgery planning

Preliminary traitement CT-based imaging for stereotactical neurosurgery planning

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

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

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

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 θ0 θ1 θ2 θ3 θ4 θ5 θ6 θ7 θ8 θ9 θ10 θ11 θ12 -0.04 -0.02 -0.31 -0.15 -0.03 0.11 0.28 0.37 -0.01 θ13 θ14 θ15 θ16 θ17 θ18 θ19 θ20 θ21 θ22 θ23 θ24 θ25 0.02 0.03 -0.11 CT-based imaging for stereotactical neurosurgery planning

Optimal trajectory calculation Trajectory of surgical access: where n – 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: where m – number of surgical access; N – count of elements in the trajectory. Optimal trajectory: 26 CT-based imaging for stereotactical neurosurgery planning

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

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

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

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

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 The starting position of surgical instrument Turn the frame by 11 ° in the sagittal plane CT-based imaging for stereotactical neurosurgery planning

CT-based imaging for stereotactical neurosurgery planning 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

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

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