Methods and systems to control a cutting tool

• US 20050119783A1
• Filed: 06/24/2004
• Published: 06/02/2005
• Est. Priority Date: 05/03/2002
• Status: Abandoned Application

• Current Assignee: Carnegie Mellon University
• Original Assignee: Carnegie Mellon University
• Inventors: Brisson, Gabriel, Jaramaz, Branislav, DiGioia, Anthony III, Kanade, Takeo
• Application Number:
  US10/876,204
• Publication Number:
  US 20050119783A1
• Time in Patent Office: Days
• US Class Current: 700/186
• CPC Class Codes: A61B 17/155 Cutting femur
• CPC Class Codes: A61B 17/157 Cutting tibia
• CPC Class Codes: A61B 17/1675 for the knee
• CPC Class Codes: A61B 17/32 Surgical cutting instruments A61B18/042 takes precedence; suture cutters A61B17/0467; instruments for ligaturing or cutting A61B17/128; instruments for rupturing the amniotic membrane A61B17/4208; specially adapted knives for eye surgery A61F9/0133
• CPC Class Codes: A61B 17/32002 with continuously rotating, oscillating or reciprocating cutting instruments
• CPC Class Codes: A61B 17/3203 Fluid jet cutting instruments
• CPC Class Codes: A61B 17/3211 Surgical scalpels, knives; Accessories therefor
• CPC Class Codes: A61B 2017/320052 Guides for cutting instruments
• CPC Class Codes: A61B 2017/32007 with suction or vacuum means
• CPC Class Codes: A61B 2034/2055 Optical tracking systems
• CPC Class Codes: A61B 34/20 Surgical navigation systems; Devices for tracking or guiding surgical instruments, e.g. for frameless stereotaxis
• CPC Class Codes: A61B 90/10 for stereotaxic surgery, e.g. frame-based stereotaxis
• CPC Class Codes: A61B 90/37 Surgical systems with images on a monitor during operation
• CPC Class Codes: B33Y 50/02 for controlling or regulating additive manufacturing processes
• CPC Class Codes: B33Y 80/00 Products made by additive manufacturing
• CPC Class Codes: G05B 2219/35217 Cagd computer aided geometric design, sbgd scanning based geometric design
• CPC Class Codes: G05B 2219/37097 Marker on workpiece to detect reference position
• CPC Class Codes: G05B 2219/39219 Trajectory tracking
• CPC Class Codes: G05B 2219/42338 Position tracking control
• CPC Class Codes: G05B 2219/45118 Endoscopic, laparoscopic manipulator
• CPC Class Codes: G16H 30/40 : for processing medical images, e.g. editing
• CPC Class Codes: G16H 40/63 : for local operation
• CPC Class Codes: G16H 50/50 : for simulation or modelling of medical disorders
• CPC Class Codes: Y10T 83/698 : Including means to cause nonrectilinear tool infeed

Abstract

A method and system for providing control that include providing a workpiece that includes a target shape, providing a cutting tool, providing a 3-D image associated with the workpiece, identifying the target shape within the workpiece image, providing a 3-D image associated with the cutting tool, registering the workpiece with the workpiece image, registering the cutting tool with the cutting tool image, tracking at least one of the workpiece and the cutting tool, transforming the tracking data based on image coordinates to determine a relationship between the workpiece and the cutting tool, and, based on the relationship, providing a control to the cutting tool. In one embodiment, the workpiece image can be represented as volume pixels (voxels) that can be classified and/or reclassified based on target shape, waste, and/or workpiece.

Claims

1. A control method, the method comprising,

providing a workpiece that includes a target shape,

providing a cutting tool,

providing a 3-D image associated with the workpiece,

identifying the target shape within the workpiece image,

providing a 3-D image associated with the cutting tool,

registering the workpiece with the workpiece image,

registering the cutting tool with the cutting tool image,

tracking at least one of the workpiece and the cutting tool,

transforming the tracking data based on image coordinates to determine a relationship between the workpiece and the cutting tool, and,

based on the relationship, providing a control to the cutting tool.

2. A method according to claim 1, where the workpiece image is comprised of volume pixels (voxels).

3. A method according to claim 1, further comprising representing the workpiece image using volume pixels (voxels), and classifying the workpiece image voxels based on the target shape.

4. A method according to claim 3, further comprising re-classifying the voxels based on the relationship.

5. A method according to claim 1, where providing an image associated with the workpiece includes providing at least one of:

CT scan data, X-ray data, MRI data, fluoroscopy data, and ultrasound data.

6. A method according to claim 3, where classifying includes distinguishing between target shape voxels and workpiece voxels.

7. A method according to claim 6, where distinguishing includes associating target shape voxels with the target shape and associating non-target shape voxels as waste.

8. A method according to claim 6, where distinguishing includes color-coding at least target shape voxels associated with the target shape.

9. A method according to claim 3, where classifying includes,

identifying mixture voxels that include part workpiece and part target shape,

subdividing the mixture voxels, and,

iteratively returning to identifying mixture voxels to a predetermined voxel resolution.

10. A method according to claim 9, where subdividing the mixture voxels includes subdividing based on an octree.

11. A method according to claim 9, further comprising recombining voxels having the same classification.

12. A method according to claim 1, further including calibrating a probe.

13. A method according to claim 12, where at least one of registering the workpiece and registering the cutting tool includes employing the calibrated probe to identify at least one location on at least one of the workpiece and the cutting tool.

14. A method according to claim 1, where tracking includes providing at least one marker on at least one of the workpiece and the cutting tool.

15. A method according to claim 1, where tracking includes determining at least one position and at least one angle associated with at least one of the workpiece and the cutting tool.

16. A method according to claim 1, where transforming the tracking data includes performing at least one of collision detection and interference detection.

17. A method according to claim 1, where identifying includes classifying voxels associated with the workpiece based on the tracking data.

18. A method according to claim 17, where classifying includes re-classifying voxels based on the tracking data.

19. A method according to claim 18, where re-classifying includes identifying voxels associated with the workpiece that are eliminated by the cutting tool.

20. A method according to claim 18, where at least one of classifying and re-classifying includes,

identifying mixture voxels,

subdividing the mixture voxels, and,

iteratively returning to identifying mixture voxels, until reaching a predetermined voxel resolution.

21. A method according to claim 20, where identifying mixture voxels includes identifying voxels having more than one classification.

22. A method according to claim 20, where subdividing the mixture voxels includes subdividing based on an octree.

23. A method according to claim 20, further comprising recombining voxels having the same classification.

24. A method according to claim 1, where providing a control includes determining a distance between the cutting tool image and the target shape.

25. A method according to claim 1, where providing a control includes increasing the size of the cutting tool image to determine whether the increased size cutting tool image intersects with the target shape in the workpiece image.

26. A method according to claim 25, where increasing the size includes at least one of increasing the size by a fixed amount, and increasing the size based on tracking data associated with the cutting tool.

27. A method according to claim 1, where providing a control includes providing a control based on the relationship between a cutting element associated with the cutting tool image, and voxels classified based on the target shape.

28. A method according to claim 1, where providing a workpiece image comprises,

providing a three-dimensional grid of voxels,

incorporating the workpiece image into the grid, and,

identifying grid voxels associated with the workpiece.

29. A method according to claim 28, where identifying grid voxels associated with the workpiece includes associating at least one of the grid voxels with at least one of the workpiece and the target shape.

30. A method according to claim 1, where providing a control includes at least one of:

providing an analog signal, providing a digital signal, providing a control to at least partially retract a cutting element associated with the cutting tool, providing a control to reduce the speed of a cutting element associated with the cutting tool, and providing a control to stop a cutting element associated with a cutting tool.

31. A method according to claim 1, where providing a control to the cutting tool, includes performing at least one of collision detection and intersection detection.

32. A method according to claim 1, where providing a control to the cutting tool includes performing at least one of collision detection and intersection detection between at least part of the cutting tool and the target shape of the workpiece image.

33. A method according to claim 1, where identifying the target shape includes classifying voxels associated with the workpiece image as at least one of workpiece and target shape.

34. A method according to claim 33, where providing control to the cutting tool includes performing at least one of collision detection and intersection detection between at least part of the cutting tool and the target shape voxels.

35. A method according to claim 1, where providing a control includes providing a control based on a threshold distance between the workpiece image and the cutting tool image.