System and method for patient-specific motion management
A medical method includes: determining a first probability density function related to a first uncertainty in hitting a target in a treatment of the target; determining a second probability density function related to a second uncertainty, wherein the first uncertainty is attributable to a first source of uncertainty, and the second uncertainty is attributable to a second source of uncertainty that is different from the first source of uncertainty; processing at least the first probability density function and the second probability density function using a processing unit; and outputting a result of the processing.
1. A method for management of treatment strategy, comprising:
determining, using a processing unit, a first probability density function related to a first uncertainty in hitting a target in a treatment of the target;
determining, using the processing unit, a second probability density function related to a second uncertainty, wherein the first uncertainty is attributable to a first source of uncertainty, and the second uncertainty is attributable to a second source of uncertainty that is different from the first source of uncertainty;
processing at least the first probability density function and the second probability density function using the processing unit to obtain a result for management of the treatment strategy, wherein the management of the treatment strategy is for determining one or more parameters for controlling a treatment system; and
outputting the result of the processing.
2. The method of claim 1 , wherein the act of processing comprises combining the first and second probability density functions by convolution to determine a composite probability density function.
3. The method of claim 2 , further comprising determining a margin based on the composite probability density function.
4. The method of claim 2 , further comprising determining a volume that encompasses a level of certainty derived from the composite probability density function.
5. The method of claim 4 , wherein the act of determining the volume comprises determining a margin expansion.
6. The method of claim 2 , wherein the result comprises the composite probability density function.
7. The method of claim 1 , wherein the act of processing comprises performing a calculation using a margin as an optimization variable.
8. The method of claim 1 , wherein the first probability density function is based on a motion trace.
9. The method of claim 8 , wherein the motion trace is created using one or more external markers.
10. The method of claim 8 , wherein the motion trace is created using one or more internal markers.
11. The method of claim 8 , wherein the motion trace represents a breathing motion, a cardiac motion, or an irregular motion.
12. The method of claim 8 , wherein the motion trace represents a motion of a patient for which the first probability density function is determined.
13. The method of claim 8 , wherein the first probability density function is determined for a first patient, and the motion trace represents a motion of a second patient or a modeled motion.
14. The method of claim 1 , wherein the first uncertainty is attributable to an accuracy of a device that measures positions.
15. The method of claim 1 , wherein the first uncertainty is attributable to an accuracy of a correlation between a marker position and a tissue position.
16. The method of claim 1 , wherein the first uncertainty is attributable to an accuracy in a prediction of a future position.
17. The method of claim 1 , wherein the first uncertainty is attributable to an energy delivery accuracy of a treatment machine.
18. The method of claim 1 , wherein the first uncertainty relates to a gating window.
19. The method of claim 1 , wherein the result is output during a treatment planning session.
20. The method of claim 1 , wherein the result is output during a treatment session.
21. The method of claim 1 , wherein the result comprises a ranking of different motion management strategies.
22. The method of claim 21 , wherein the different motion management strategies are ranked based on different respective margins, or based on different volumes derived from the different respective margins.
23. The method of claim 1 , wherein the result comprises a margin.
24. The method of claim 1 , wherein the result comprises information regarding a beam aperture margin.
25. The method of claim 24 , further comprising:
obtaining motion data during a treatment session; and
adjusting a beam aperture based on the information regarding the beam aperture margin.
26. The method of claim 1 , wherein the result comprises a volume to be treated.
27. The method of claim 1 , wherein the result comprises a volume of healthy tissue.
28. The method of claim 1 , further comprising adjusting a dose rate based on the result during a treatment session.
29. An apparatus for management of treatment strategy, comprising:
a processing unit configured for:
determining a first probability density function related to a first uncertainty in hitting a target in a treatment of the target;
determining a second probability density function related to a second uncertainty, wherein the first uncertainty is attributable to a first source of uncertainty, and the second uncertainty is attributable to a second source of uncertainty that is different from the first source of uncertainty;
processing at least the first probability density function and the second probability density function to obtain a result for management of the treatment strategy, wherein the management of the treatment strategy is for determining one or more parameters for controlling a treatment system; and
outputting the result of the processing.
30. The apparatus of claim 29 , wherein the processing unit is configured to perform the act of processing by combining the first and second probability density functions by convolution to determine a composite probability density function.
31. The apparatus of claim 30 , wherein the processing unit is further configured for determining a margin based on the composite probability density function.
32. The apparatus of claim 30 , wherein the processing unit is further configured for determining a volume that encompasses a level of certainty derived from the composite probability density function.
33. The apparatus of claim 32 , wherein the processing unit is configured for determining a margin expansion for determining the volume.
34. The apparatus of claim 30 , wherein the result comprises the composite probability density function.
35. The apparatus of claim 29 , wherein the processing unit is configured to perform the act of processing comprises performing a calculation using a margin as an optimization variable.
36. The apparatus of claim 29 , wherein the processing unit is configured to receive a motion trace, and the processing unit is configured to determine the first probability density function based on a motion trace.
37. The apparatus of claim 36 , wherein the motion trace is created using one or more external markers.
38. The apparatus of claim 36 , wherein the motion trace is created using one or more internal markers.
39. The apparatus of claim 36 , wherein the motion trace represents a breathing motion, a cardiac motion, or an irregular motion.
40. The apparatus of claim 36 , wherein the motion trace represents a motion of a patient for which the first probability density function is determined.
41. The apparatus of claim 36 , wherein the first probability density function is for a first patient, and the motion trace represents a motion of a second patient or a modeled motion.
42. The apparatus of claim 29 , wherein the first uncertainty is attributable to an accuracy of a device that measures positions.
43. The apparatus of claim 29 , wherein the first uncertainty is attributable to an accuracy of a correlation between a marker position and a tissue position.
44. The apparatus of claim 29 , wherein the first uncertainty is attributable to an accuracy in a prediction of a future position.
45. The apparatus of claim 29 , wherein the first uncertainty is attributable to an energy delivery accuracy of a treatment machine.
46. The apparatus of claim 29 , wherein the first uncertainty relates to a gating window.
47. The apparatus of claim 29 , wherein the processing unit is configured to output the result during a treatment planning session.
48. The apparatus of claim 29 , wherein the processing unit is configured to output the result during a treatment session.
49. The apparatus of claim 29 , wherein the result comprises a ranking of different motion management strategies.
50. The apparatus of claim 49 , wherein the processing unit is configured to rank the different motion management strategies based on different respective margins.
51. The apparatus of claim 29 , wherein the result comprises a margin.
52. The apparatus of claim 29 , wherein the result comprises information regarding a beam aperture margin.
53. The apparatus of claim 52 , wherein the processing unit is further configured for:
obtaining motion data during a treatment session; and
adjusting a beam aperture based on the information regarding the beam aperture margin.
54. The apparatus of claim 29 , wherein the result comprises a volume to be treated.
55. The apparatus of claim 29 , wherein the result comprises a volume of healthy tissue.
56. The apparatus of claim 29 , wherein the processing unit is further configured to adjust a dose rate based on the result during a treatment session.
57. A computer product having a non-transitory medium storing a set of instructions, an execution of which causes an image processing method for management of treatment strategy to be performed, the method comprising:
determining a first probability density function related to a first uncertainty in hitting a target in a treatment of the target;
determining a second probability density function related to a second uncertainty, wherein the first uncertainty is attributable to a first source of uncertainty, and the second uncertainty is attributable to a second source of uncertainty that is different from the first source of uncertainty;
processing at least the first probability density function and the second probability density function to obtain a result for management of the treatment strategy, wherein the management of the treatment strategy is for determining one or more parameters for controlling a treatment system; and
outputting the result of the processing.