Video-Based Breathing Monitoring Without Fiducial Tracking

• US 20100061596A1
• Filed: 09/05/2008
• Published: 03/11/2010
• Est. Priority Date: 09/05/2008
• Status: Abandoned Application

• Current Assignee: Varian Medical Systems Incorporated (Siemens AG)
• Original Assignee: Varian Medical Systems Incorporated (Siemens AG)
• Inventors: Povzner, Sergey, Mostafavi, Hassan
• Application Number:
  US12/205,431
• Publication Number:
  US 20100061596A1
• Time in Patent Office: Days
• US Class Current: 382/107
• CPC Class Codes: A61B 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00
• CPC Class Codes: A61B 5/113 occurring during breathing
• CPC Class Codes: A61B 5/4818 Sleep apnoea
• CPC Class Codes: A61B 5/7285 for synchronising or triggering a physiological measurement or image acquisition with a physiological event or waveform, e.g. an ECG signal A61B6/5205, A61B6/541, A61B8/5207, A61B8/543 take precedence
• CPC Class Codes: A61B 5/7289 Retrospective gating, i.e. associating measured signals or images with a physiological event after the actual measurement or image acquisition, e.g. by simultaneously recording an additional physiological signal during the measurement or image acquisition
• CPC Class Codes: A61B 6/5288 involving retrospective matching to a physiological signal
• CPC Class Codes: A61B 6/541 involving acquisition triggered by a physiological signal
• CPC Class Codes: G06F 2218/10 by analysing the shape of a waveform, e.g. extracting parameters relating to peaks
• CPC Class Codes: G06T 2207/10016 Video; Image sequence
• CPC Class Codes: G06T 2207/30004 Biomedical image processing
• CPC Class Codes: G06T 2207/30076 Plethysmography
• CPC Class Codes: G06T 2207/30204 Marker
• CPC Class Codes: G06T 7/248 involving reference images or patches
• CPC Class Codes: G06V 2201/03 Recognition of patterns in medical or anatomical images

Abstract

A method of determining a similarity with a portion of a physiological motion, includes obtaining a first image of an object, obtaining a second image of the object, determining a level of similarity between the first and second images, and correlating the determined level of similarity between the first and second images with a portion of the physiological motion. A computer product having a set of instructions, an execution of which causes a method of determining a similarity with a portion of a physiological motion to be performed, the method includes obtaining a first image of an object, obtaining a second image of the object, determining a level of similarity between the first and second images, and correlating the determined level of similarity between the first and second images with a portion of the physiological motion.

Claims

1. A method of determining a similarity with a portion of a physiological motion, comprising:

obtaining a first image of an object;

obtaining a second image of the object;

determining a level of similarity between the first and second images; and

correlating the determined level of similarity between the first and second images with a portion of the physiological motion.

2. The method of claim 1, wherein the first and second images are obtained using a camera.

3. The method of claim 2, wherein the camera has an auto-focus feature.

4. The method of claim 1, wherein the first and second images are obtained using a radiation machine.

5. The method of claim 1, wherein the first image is a subset of a first image frame.

6. The method of claim 5, wherein the second image is a subset of a second image frame, and wherein a position of the first image in the first image frame is the same as a position of the second image in the second image frame.

7. The method of claim 5, further comprising obtaining the first image frame, wherein the first image is obtained by:

determining a portion in the first image frame, wherein the portion of the first image frame contains an image of a portion of the object that was undergoing a relatively high motion when the first image frame was obtained; and

using the portion as the first image.

8. The method of claim 5, further comprising obtaining the first image frame, wherein the first image frame is obtained when the object is at an end of an exhale motion.

9. The method of claim 5, further comprising obtaining the first image frame, wherein the first image frame is obtained when the object is at an end of an inhale motion.

10. The method of claim 5, further comprising:

determining a first time point t₁ at which a level of correlation between a reference image and a first input image is highest;

determining a second time point t₂ at which a level of correlation between a reference image and a second input image is highest; and

determining a period T that is between the first and second time points;

wherein the first image is obtained at a time that is T/2 after a detected peak.

11. The method of claim 10, wherein the reference image is obtained before the first input image, the first input image is obtained before the second input image, the second input image is obtained before the first image, and the first image is obtained before the second image.

12. The method of claim 11, wherein the reference image, the first input image, the second input image, the first image, and the second image are obtained using a same imaging device.

13. The method of claim 1, further comprising obtaining a new image for use as the first image when the level of determined correlation is below a prescribed threshold.

14. The method of claim 1, further comprising:

obtaining a third image of the object;

determining a level of correlation between the first and third images; and

correlating the determined level of correlation between the first and third images with another portion of the physiological motion.

15. The method of claim 1, wherein the physiological motion comprises a respiratory motion.

16. The method of claim 1, wherein the physiological motion comprises a cardiac motion.

17. The method of claim 1, wherein the object comprises at least a portion of a blanket.

18. The method of claim 1, wherein the object comprises at least a portion of a person'"'"'s clothes or a person'"'"'s skin.

19. The method of claim 1, wherein the portion of the physiological motion comprises an amplitude of the motion at a time point.

20. The method of claim 1, wherein the portion of the physiological motion comprises a phase of the motion at a time point.

21. The method of claim 1, wherein the level of correlation represents how far the object is away from a reference location.

22. The method of claim 21, wherein the reference location comprises a location of the object as captured in the first image.

23. The method of claim 1, wherein the act of determining a level of correlation between the first and second images does not require a detection of a marker in the second image.

24. The method of claim 1, wherein the object is a patient, and wherein the patient does not have a marker that is specifically designed to be detected by an imaging device.

25. The method of claim 1, further comprising performing a spectral analysis using the determined level of similarity.

26. The method of claim 25, wherein the spectral analysis is performed to detect position shift, lack of periodicity, or lack of motion.

27. A computer product having a set of instructions, an execution of which causes a method of determining a similarity with a portion of a physiological motion to be performed, the method comprising:

obtaining a first image of an object;

obtaining a second image of the object;

determining a level of similarity between the first and second images; and

correlating the determined level of similarity between the first and second images with a portion of the physiological motion.

28. The computer product of claim 27, wherein the first and second images are obtained using a camera.

29. The computer product of claim 28, wherein the camera has an auto-focus feature.

30. The computer product of claim 27, wherein the first and second images are obtained using a radiation machine.

31. The computer product of claim 27, wherein the first image is a subset of a first image frame.

32. The computer product of claim 31, wherein the second image is a subset of a second image frame, and wherein a position of the first image in the first image frame is the same as a position of the second image in the second image frame.

33. The computer product of claim 31, the method further comprising obtaining the first image frame, wherein the first image is obtained by:

determining a portion in the first image frame, wherein the portion of the first image frame contains an image of a portion of the object that was undergoing a relatively high motion when the first image frame was obtained; and

using the portion as the first image.

34. The computer product of claim 31, the method further comprising obtaining the first image frame, wherein the first image frame is obtained when the object is at an end of an exhale motion.

35. The computer product of claim 31, the method further comprising obtaining the first image frame, wherein the first image frame is obtained when the object is at an end of an inhale motion.

36. The computer product of claim 31, the method further comprising:

determining a first time point t₁ at which a level of correlation between a reference image and a first input image is highest;

determining a second time point t₂ at which a level of correlation between a reference image and a second input image is highest; and

determining a period T that is between the first and second time points;

wherein the first image is obtained at a time that is T/2 after a detected peak.

37. The computer product of claim 36, wherein the reference image is obtained before the first input image, the first input image is obtained before the second input image, the second input image is obtained before the first image, and the first image is obtained before the second image.

38. The computer product of claim 37, wherein the reference image, the first input image, the second input image, the first image, and the second image are obtained using a same imaging device.

39. The computer product of claim 27, the method further comprising obtaining a new image for use as the first image when the level of determined correlation is below a prescribed threshold.

40. The computer product of claim 27, the method further comprising:

obtaining a third image of the object;

determining a level of correlation between the first and third images; and

correlating the determined level of correlation between the first and third images with another portion of the physiological motion.

41. The computer product of claim 27, wherein the physiological motion comprises a respiratory motion.

42. The computer product of claim 27, wherein the physiological motion comprises a cardiac motion.

43. The computer product of claim 27, wherein the object comprises at least a portion of a blanket.

44. The computer product of claim 27, wherein the object comprises at least a portion of a person'"'"'s clothes.

45. The computer product of claim 27, wherein the portion of the physiological motion comprises an amplitude of the motion at a time point.

46. The computer product of claim 27, wherein the portion of the physiological motion comprises a phase of the motion at a time point.

47. The computer product of claim 27, wherein the level of correlation represents how far the object is away from a reference location.

48. The computer product of claim 47, wherein the reference location comprises a location of the object as captured in the first image.

49. The computer product of claim 27, wherein the act of determining a level of correlation between the first and second images does not require a detection of a marker in the second image.

50. The computer product of claim 27, wherein the object is a patient, and wherein the patient does not have a marker that is specifically designed to be detected by an imaging device.

51. The computer product of claim 27, wherein the process further comprises performing a spectral analysis using the determined level of similarity.

52. The computer product of claim 51, wherein the spectral analysis is performed to detect position shift, lack of periodicity, or lack of motion.

53. A system for determining a similarity with a portion of a physiological motion, comprising:

means for obtaining a first image of an object and a second image of the object;

means for determining a level of similarity between the first and second images; and

means for correlating the determined level of similarity between the first and second images with a portion of the physiological motion.