Non-linear correlation models for internal target movement

• US 20060074299A1
• Filed: 09/29/2005
• Published: 04/06/2006
• Est. Priority Date: 10/02/2004
• Status: Active Grant

• Current Assignee: Accuray Incorporated
• Original Assignee: Accuray Incorporated
• Inventors: Sayeh, Sohail
• Granted Patent: US 8,027,715 B2
• Time in Patent Office: Days
• US Class Current: 600/426
• CPC Class Codes: A61B 5/1127 using markers
• CPC Class Codes: A61B 5/113 occurring during breathing
• CPC Class Codes: A61B 6/4458 the source unit or the detector unit being attached to robotic arms
• CPC Class Codes: A61N 2005/1061 using an x-ray imaging system having a separate imaging source
• CPC Class Codes: A61N 5/1037 taking into account the movement of the target, e.g. 4D-image based planning
• CPC Class Codes: A61N 5/1049 for verifying the position of the patient with respect to the radiation beam
• CPC Class Codes: A61N 5/1064 for adjusting radiation treatment in response to monitoring
• CPC Class Codes: A61N 5/1067 in real time, i.e. during treatment
• CPC Class Codes: A61N 5/107 in real time, i.e. during treatment

Abstract

A method and apparatus to track non-linear internal target movement based on movement of an external marker.

Claims

1. A method, comprising:

acquiring a plurality of data points representative of a corresponding plurality of positions over time of an external marker associated with a patient,

acquiring a plurality of images of a target internal to the patient, the plurality of images corresponding to the plurality of data points; and

identifying a non-linear path of movement of the target based on the plurality of data points and the plurality of images.

2. The method of claim 1, further comprising:

identifying an inspiration interval of the non-linear path of movement of the target, the inspiration interval associated with an approximate time during which the patient breathes in; and

identifying an expiration interval of the non-linear path of movement of the target, the expiration interval associated with an approximate time during which the patient breathes out.

3. The method of claim 2, wherein the non-linear path of movement of the target comprises:

an inspiration path associated with the inspiration interval; and

an expiration path associated with the expiration interval.

4. The method of claim 3, further comprising comparing the inspiration path and the expiration path to identify a difference between the inspiration path and the expiration path.

5. The method of claim 1, further comprising developing a non-linear model of the non-linear path of movement of the target.

6. The method of claim 5, wherein the non-linear model comprises a polynomial approximation of the non-linear path of movement of the target.

7. The method of claim 5, wherein the non-linear model comprises a multi-linear model having a plurality of linear approximations comprising a first linear approximation connected to a second linear approximation at a non-zero angle.

8. The method of claim 5, wherein the plurality of positions of the external marker defines an external path of movement of the external marker, the external path of movement having a respiratory period associated with a respiratory cycle of the patient.

9. The method of claim 8, further comprising generating a derivative from the plurality of data points at a selected one of the plurality of data points to identify an inspiration interval and an expiration interval of the respiratory period, the derivative comprising a directional indicator.

10. The method of claim 9, wherein the non-linear model comprises:

a first polynomial approximation associated with the non-linear path of movement of the target over the inspiration interval; and

a second polynomial approximation associated with the non-linear path of movement of the target over the expiration interval.

11. The method of claim 10, wherein the non-linear model further comprises one or more matching approximations to link the first polynomial approximation and the second polynomial approximation.

12. The method of claim 10, wherein the first polynomial approximation and the second polynomial approximation are derived from at least partially overlapping data sets.

13. The method of claim 8, wherein the non-linear model comprises a multi-linear model comprising:

a first linear approximation associated with an inspiration interval of the respiratory period; and

a second linear approximation associated with an expiration interval of the respiratory period.

14. The method of claim 13, wherein the multi-linear model further comprises at least one linear matching approximation to link the first linear approximation and the second linear approximation.

15. The method of claim 5, further comprising deriving a target position of the target based on the non-linear model of the path of movement of the target.

16. The method of claim 15, further comprising:

sending a position signal associated with the target position to a beam generator controller; and

controlling a beam generator to direct a beam at the target.

17. The method of claim 5, further comprising updating the non-linear model in response to an acquisition of a new data point.

18. The method of claim 5, wherein the non-linear model comprises a two-dimensional model or a three-dimensional model.

19. An apparatus, comprising:

an data storage device to store a plurality of displacement points of an external marker and a corresponding plurality of images of a target internal to a patient; and

a digital processing device coupled to the data storage device, the digital processing device to identify a non-linear path of movement of the target and to develop a non-linear model of the non-linear path of movement.

20. The apparatus of claim 19, wherein the non-linear model comprises:

a first polynomial approximation associated with the non-linear path of movement of the target over an inspiration interval of the patient; and

a second polynomial approximation associated with the non-linear path of movement of the target over an expiration interval of the patient.

21. The apparatus of claim 20, wherein the non-linear model further comprises one or more matching approximations to link the first polynomial approximation and the second polynomial approximation.

22. The apparatus of claim 19, wherein the non-linear model comprises:

a first linear approximation associated with an inspiration interval of the patient; and

a second linear approximation associated with an expiration interval of the patient.

23. The apparatus of claim 22, wherein the non-linear model further comprises one or more linear matching approximations to link the first linear approximation and the second linear approximation.

24. An apparatus, comprising:

means for acquiring a plurality of data points representative of a corresponding plurality of positions over time of an external marker associated with a patient;

means for acquiring a plurality of images of a target internal to the patient, the plurality of images corresponding to the plurality of data points; and

means for developing a non-linear model of a non-linear path of movement of the target.

25. The apparatus of claim 24, further comprising:

means for associating a first approximation with the non-linear path of movement of the target over an inspiration interval; and

means for associating a second approximation with the non-linear path of movement of the target over an expiration interval, wherein at least the first approximation or the second approximation is non-linear.

26. The apparatus of claim 24, further comprising means for linking the first approximation and the second approximation with one or more matching approximations.

27. A machine readable medium having instructions thereon, which when executed by a digital processing device, cause the digital processing device to perform one or more operations comprising:

acquiring a plurality of data points representative of a corresponding plurality of positions over time of an external marker associated with a patient;

acquiring a plurality of images of a target internal to the patient, the plurality of images corresponding to the plurality of data points; and

developing a non-linear model of a non-linear path of movement of the target.

28. The machine readable medium of claim 27, wherein the one or more operations further comprise:

associating a first approximation with the non-linear path of movement of the target region over an inspiration interval; and

associating a second approximation with the non-linear path of movement of the target region over an expiration interval, wherein at least the first approximation or the second approximation is non-linear.

29. The machine readable medium of claim 27, wherein the one or more operations further comprise linking the first approximation and the second approximation with one or more matching approximations.

30. A method, comprising:

receiving a plurality of displacement points over time of an external marker attached to a patient;

receiving a plurality of direction indicators of the external marker, the plurality of direction indicators corresponding to the plurality of position points; and

developing a correlation model of an internal path of movement of a target based on the plurality of position points and the plurality of direction indicators.

31. The method of claim 30, wherein the plurality of direction indicators comprises two types of direction indicators:

a positive direction indicator and a negative direction indicator.

32. The method of claim 31, wherein one of the two types of direction indicators corresponds to an inspiration interval of the patient and the other of the two types of direction indicators corresponds to an expiration interval of the patient.

33. The method of claim 30, further comprising deriving the plurality of direction indicators from the plurality of displacement points of the external marker.

34. The method of claim 30, wherein the correlation model of the internal path of movement of the target comprises a multi-poly correlation model.

35. The method of claim 34, wherein the multi-poly correlation model comprises:

a first polynomial approximation associated with the internal path of movement of the target over an inspiration interval; and

a second polynomial approximation associated with the internal path of movement of the target over an expiration interval.

36. The method of claim 35, wherein the multi-poly correlation model further comprises at least one polynomial matching approximation to link the first polynomial approximation and the second polynomial approximation.

37. The method of claim 30, wherein the correlation model of the internal path of movement of the target comprises a multi-linear correlation model.

38. The method of claim 37, wherein the multi-linear correlation model comprises:

a first linear approximation associated with the internal path of movement of the target over an inspiration interval; and

a second linear approximation associated with the internal path of movement of the target over an expiration interval.

39. The method of claim 38, wherein the multi-linear correlation model further comprises at least one linear matching approximation to link the first linear approximation and the second linear approximation.

40. An apparatus, comprising:

an data storage device to store a plurality of displacement points of an external marker and a corresponding plurality of direction indicators; and

a digital processing device coupled to the data storage device, the digital processing device configured to develop a non-linear correlation model of an internal path of movement of a target based on the plurality of position points and the plurality of direction indicators.

41. The apparatus of claim 40, wherein the digital processing device is further configured to derive the plurality of direction indicators from the plurality of displacement points of the external marker.

42. The apparatus of claim 40, wherein the digital processing device is further configured to associate a first polynomial approximation with the internal path of movement of the target over an inspiration interval and to associate a second polynomial approximation with the internal path of movement of the target over an expiration interval.

43. The apparatus of claim 42, wherein the digital processing device is further configured to link the first polynomial approximation and the second polynomial approximation with at least one linear or polynomial matching approximation.

44. The apparatus of claim 40, wherein the digital processing device is further configured to associate a first linear approximation with the internal path of movement of the target region over an inspiration interval and to associate a second linear approximation with the internal path of movement of the target region over an expiration interval.

45. The apparatus of claim 44, wherein the digital processing device is further configured to link the first polynomial approximation and the second polynomial approximation with at least one linear or polynomial matching approximation.