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Minimally-Invasive Approach to Pelvic Osteolysis

Minimally-Invasive Approach to Pelvic Osteolysis. Srinivas Prasad, Ming Li, Nicholas Ramey Final Presentation May 10, 2001. Project Overview. Project Overview. Project Components. Project 1 Pre-Operative Modeling Trajectory Planning Intra-Operative Registration

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Minimally-Invasive Approach to Pelvic Osteolysis

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  1. Minimally-Invasive Approach to Pelvic Osteolysis Srinivas Prasad, Ming Li, Nicholas Ramey Final Presentation May 10, 2001

  2. Project Overview

  3. Project Overview

  4. Project Components Project 1 • Pre-Operative Modeling • Trajectory Planning • Intra-Operative Registration • Robotically Controlled Cannula Placement • Project 2 • Lesion Evacuation • Cavity Filling

  5. Completed Tasks Pending Tasks Deferred/Deleted Tasks Deliverables Minimal: • Pre-Op Pelvic Modeling and Trajectory Planning Software • Intra-Operative Pelvis Registrationand Tracking Software/Hardware • Robotically Controlled Drill Guidewith Trackable Drill • 1.0 – 1.5 cm diameter manually introduced cannula with retractable spear-tip • Passive cannula holder Expected: 3a. Robotically Inserted Cannula with Robot-Cannula Interface Maximal: 1b. Pre-Op GUI-Based Trajectory Planning Software 6. Methods and Instruments for lesion visualization, excavation and filling. 7. Real-Time pelvic motion tracking and compensatory cannula position control.

  6. Revised Schedule 2/22 Project Proposal Submission 2/23 Meet with Andy, Jianhua, Rajesh for Reg. Strategy 2/25 Completion of Pelvis Preparation and Mounting 2/26-2/30 CT-scan of pelvis 4/2 Completion of Image Processing 4/22 Completion of 2D-3D Registration Software, F 4/22 Completion of Robot Registration Software, F 4/29 Completion of Cannula Design and Implementation 4/29 Completion of Robot Control Software 4/29-5/11 System Integration and Testing 5/11 Final Presentation Robot Fluoro Robot CT Completed before CheckpointCompleted after Checkpoint Pending Tasks

  7. Final Presentation Outline • Developed Technologies • 2D-3D Registration • Robot Registration • Robot Control • Component Simulations • Slicer Interface • Image Processing and Registration • Conclusions • Remaining Tasks • Obstacles/Dependencies • What We’ve Learned • Potential Future Evolution

  8. Input: Fiducial positions in 2D image (Fluoro) and 3D space (CT) Pick a 2D triangle abc and find all 3D triangles a’b’c’ that might match Compute the frames mapping a’b’c’ to abc Compute errors associated with each mapping Choose frame with minimal error and iterate to reduce error 2D-3D Registration

  9. Robot Registration • Purpose: • To relate the CT Coordinate Space to the Robot Coordinate System. • This is represented by the transformation • General Principles: • We use Fluoro Space as the common coordinate system. • We use two Fluoro poses to decrease registration error • We directly compute the best rigid transformation between points in robot space and their corresponding points in CT space.

  10. Robot Registration

  11. Pose 1 Robot CT p p p p 1 line 1 n n 1 1 1 line n Robot Registration

  12. Pose 1 Robot CT p p p p p p 2 1 line line 1 n 1 n 1 n 1 1 1 2 line line n n Pose 2 Robot CT Robot Registration

  13. p i 2 2 1 1 line line line line i i i i [ p1 ]Robot [ p1 ]CT [ p2 ]Robot [ p2 ]CT • = CBRT , . . . . . . [ p3 ]Robot [ p3 ]CT CBRT( ) = ComputeBestRigidTransformation( ) Robot Registration • For any given 1 ≤ i ≤ n : [ pi ]Robot = (xi, yi, zi)Robot [ pi ]CT = intersect( , )

  14. Robot Control MRC Server and Neuromate Server provide kinematics class which can control the robot on Frame level Instrument holder Can be attached to the fifth joint of the Neuromate Goal: Move the robot to let the tip of the holder to the specific position and the holder itself in the orientation which meets the planned trajectory in robot coor. Keep the gesture of the holder *

  15. d P A T Robot Control What we know: T: the target position T in CT coor. A: a point A on the planned trajectory line in CT coor. d: distance of T to P (the target position of the tip of the holder in CT coor.) • Transfer all the coordinate in robot coor. We got 2. Position

  16. P T 0 x z Robot Control y 3. Rotation The orientation of the holder as z axis in our target frame (1) (2) (3) (4) (5) Rotation: Target Frame: F(R,P)

  17. Slicer Simulation • Pre-Operative CT Volume Processing: • Pelvis Segmentation: (Thresholding Method) • Lesion Characterization • Trajectory Definition:Target and Origin Coordinates in CT coordinates, mm units. • Fiducial Finding:All fiducial coordinates in CT Coordinate system, mm units • Output Saved to text files for intra-op use: • Fiducial Coordinates • Trajectory Coordinates

  18. GUI for 2D Image Processing, built from Jianhua Yao’s Sample Interface Added Functionality Bzostek Transformation Fiducial Finding 2D-3D Registration file setup 2D-3D Registration Intra-Operative Image Processing Simulation

  19. Remaining Tasks • Human Pelvis Preparation • Create artificial osteolytic defects • Place multiple widely distributed pelvic fiducials • Obtain high quality CT and Fluoro Studies • System Integration • Consolidate various software components into a single Workspace/Application • System Testing • Evaluate System performance experimentally

  20. Pelvis Preparation – Dr. Frassica Orthopaedic Instrumentation – Dr. Frassica CT and High-Resolution Fluoro Access –Neuroradiology JHH Experimental Apparatus – ERC Fluoroscopy Access – ERC Neuromate Robot – ERC Dependencies and Obstacles

  21. Academically Code writing and integration (Slicer, MFC, MRC, CIS, etc.) Proper development technique (code maintenance, etc.) A little Linear Algebra, a lot of CIS Project Management Communication Do your part, follow up Stay involved with other components of the project Be persistent Use resources wisely and appreciatively What We’ve Learned

  22. Potential Future Evolution • True 2D-3D RegistrationIncorporation of fiducial-free 2D-3D registration algorithms would obviate the need for pre-imaging fiducial placement • Component TrackingIncorporation of intra-operative optical or magnetic tracking systems would allow real-time component tracking, making the experimental apparatus more realistic • Robotic Cannula InsertionEmployment of Active Robots capable of inserting the cannula might increase consistency and accuracy of cannula placement

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