Electromagnetic tracking using a discretized numerical field model

1. A method for electromagnetic tracking, the method comprising:

determining a discretized numerical field model associated with a particular distortion source;

acquiring mutual inductance signals between electromagnetic sensors and the particular distortion source, the sensors being rigidly attached to a tracked object;

estimating an initial position for the tracked object in the presence of the particular distortion source;

refining the estimated position of the tracked object; and

estimating an orientation of the tracked object.

2. The method as recited in claim 1 , further comprising simultaneously refining estimates of both position and orientation.

3. The method as recited in claim 1 , wherein the refining is performed iteratively.

4. The method as recited in claim 1 , wherein the estimating an initial position comprises direct seed-searching and refining results of the direct seed-searching.

5. The method as recited in claim 1 , wherein the particular distortion source is comprises a C-arm of a fluoroscope, a surgical table, surgical equipment, or other surgical instrument.

6. The method as recited in claim 1 , wherein the acquiring further comprises generating the discretized numerical field model associated with the particular distortion source.

7. The method as recited in claim 6 , wherein the generating comprises:

measuring “undistorted” sensor position and orientation at multiple positions and orientations in an designated volume without the presence of the particular distortion source;

measuring “distorted” mutual inductance between electromagnetic sensors at multiple positions and orientations in the same designated volume with the presence of the particular distortion source; and

mapping the “undistorted” sensor position and orientation and the “distorted” mutual inductance between electromagnetic sensors.

8. A method for electromagnetic tracking, the method comprising:

determining a discretized numerical field model associated with a particular distortion source;

acquiring mutual inductance signals between electromagnetic sensors and the particular distortion source, the sensors being rigidly attached to a tracked object;

estimating an initial position for the tracked object in the presence of the particular distortion source;

refining the estimated position of the tracked object; and

estimating an orientation of the tracked object;

wherein the estimating an initial position comprises direct seed-searching and refining results of the direct seed-searching, wherein the direct seed-searching comprises:

generating an orientation-independent mutual-inductance squared vector map (“mapped Lm^2”) based on acquired mutual inductance measurements associated with the particular distortion source;

measuring the acquired mutual inductance signals between the electromagnetic sensors and the particular distortion source;

computing mutual-inductance squared vector Lm^2 of the tracked object;

determining which hemisphere contains positional (“x, y, and z”) components of the Lm^2 of the tracked object;

computing the difference from the Lm^2 to each node of the mapped Lm^2; and

using, as the direct seed, the node position in the mapped Lm^2 having the smallest Lm^2 difference.

9. A computer-readable media having computer-executable instructions thereon that, when executed by a computer, perform a method for electromagnetic tracking, the method comprising:

determining a discretized numerical field model associated with a particular distortion source;

acquiring mutual inductance signals between electromagnetic sensors and the particular distortion source, the sensors being rigidly attached to a tracked object;

estimating an initial position for the tracked object in the presence of the particular distortion source;

refining the estimated position of the tracked object; and

estimating an orientation of the tracked object.

10. The computer-readable media as recited in claim 9 , further comprising simultaneously refining estimates of both position and orientation.

11. The computer-readable media as recited in claim 9 , wherein the refining is performed iteratively.

12. The computer-readable media as recited in claim 9 , wherein the particular distortion source comprises a C-arm of a fluoroscope, a surgical table, surgical equipment, or other surgical instrument.

13. The computer-readable media as recited in claim 9 , wherein the acquiring further comprises generating the discretized numerical field model associated with the particular distortion source.

14. The computer-readable media as recited in claim 13 , wherein the generating comprises:

measuring “undistorted” sensor position and orientation at multiple positions and orientations in an designated volume without the presence of the particular distortion source;

measuring “distorted” mutual inductance between electromagnetic sensors at multiple positions and orientations in the same designated volume with the presence of the particular distortion source; and

mapping the “undistorted” position and the “distorted” mutual inductance between electromagnetic sensors.

15. A method for electromagnetic tracking, the method comprising:

determining a discretized numerical field model associated with a particular distortion source;

acquiring mutual inductance signals between electromagnetic sensors and the particular distortion source, the sensors being rigidly attached to a tracked object;

measuring a “distorted” mutual inductance between the electromagnetic sensors in the presence of the particular distortion source; and

compensating for distortion caused by the particular distortion source by adjusting the measured “distorted” position and orientation for the tracked object, wherein the compensation is based upon the acquired discretized numerical field model.

16. The method as recited in claim 15 , wherein the compensating comprises:

estimating a initial position for the tracked object in the presence of the particular distortion source;

refining the estimated position of the tracked object; and

estimating an orientation of the tracked object.

17. The method as recited in claim 15 , wherein the acquiring further comprises generating the discretized numerical field model associated with the particular distortion source.

18. The method as recited in claim 15 , wherein the particular distortion source is comprises a C-arm of a fluoroscope, a surgical table, surgical equipment, or other surgical instrument.

19. A method for electromagnetic tracking, the method comprising:

determining a discretized numerical field model associated with a particular distortion source;

acquiring mutual inductance signals between electromagnetic sensors and the particular distortion source, the sensors being rigidly attached to a tracked object;

measuring a “distorted” mutual inductance between the electromagnetic sensors in the presence of the particular distortion source; and

compensating for distortion caused by the particular distortion source by adjusting the measured “distorted” position and orientation for the tracked object, wherein the compensation is based upon the acquired discretized numerical field model;

wherein the compensating comprises:

estimating a initial position for the tracked object in the presence of the particular distortion source;

refining the estimated position of the tracked object; and

estimating an orientation of the tracked object;

wherein the estimating an initial position comprises direct seed-searching and refining results of the direct seed-searching, wherein the direct seed-searching comprises:

generating an orientation-independent mutual-inductance squared vector map (“mapped Lm^2”) based on acquired mutual inductance measurements associated with the particular distortion source;

measuring the acquired mutual inductance signals between the electromagnetic sensors and the particular distortion source;

computing mutual-inductance squared vector Lm^2 of the tracked object;

determining which hemisphere contains positional (“x, y, and z”) components of the Lm^2 of the tracked object;

computing the difference from the Lm^2 to each node of the mapped Lm^2; and

using, as the direct seed, the node position in the mapped Lm^2 having the smallest Lm^2 difference.