System and method for optimization of a radiation therapy plan in the presence of motion

• US 7,856,082 B2
• Filed: 02/27/2008
• Issued: 12/21/2010
• Est. Priority Date: 02/27/2007
• Status: Active Grant

• Current Assignee: Wisconsin Alumni Research Foundation (University of Wisconsin System)
• Original Assignee: Wisconsin Alumni Research Foundation (University of Wisconsin System)
• Inventors: Flynn, Ryan T., Mackie, Thomas R.
• Primary Examiner(s): Glick; Edward J
• Assistant Examiner(s): Artman; Thomas R
• Application Number:
  US12/440,055
• Publication Number:
  US 20100189220A1
• Time in Patent Office: 1,028 Days
• Field of Search: 378/65, 378/69, 378/95, 250/492.1
• US Class Current: 378/65
• CPC Class Codes: A61N 2005/1041 using a library of previously administered radiation treatment applied to other patients
• CPC Class Codes: A61N 5/103 Treatment planning systems
• CPC Class Codes: A61N 5/1037 taking into account the movement of the target, e.g. 4D-image based planning
• CPC Class Codes: A61N 5/1042 with spatial modulation of the radiation beam within the treatment head
• CPC Class Codes: G21K 1/046 varying the contour of the field, e.g. multileaf collimators

Abstract

A computer-implemented method for optimizing a radiation treatment plan for a radiotherapy machine providing independently controlled radiation along a plurality of rays j directed toward a patient and configured to account for the effects of patient motion. The method includes generating a probability distribution function quantitatively expressing patient motion, identifying a prescribed total dose D_i^p at the voxels i in a treatment area, assigning a fluence value w_j for each ray j based on an iterative function, calculating an actual total dose D_i^d produced each voxel i within the assigned fluence values and calculating an expectation value of the dose per energy fluence, d_ij based on the actual total dose D_i^d and the probability distribution function.

Claims

1. A computer-implemented method for optimizing a radiation treatment plan for a radiotherapy machine providing independently controlled radiation along a plurality of rays j directed toward a patient configured to account for the effects of patient motion, comprising:

a) generating a probability distribution function quantitatively expressing patient motion;

b) identifying a prescribed total dose D_i^p at the voxels i in a treatment area;

c) assigning a fluence value w_j for each ray j, based on an iterative function;

2. The method of claim 1, further including calculating an expectation value of the delivered dose D_i^d based on an aggregation of the expectation value of the dose per energy fluence, d_ij.

3. The method of claim 1, wherein the probability distribution function is generated based on at least one of calculated daily patient set up uncertainty, quantified internal patient motion due to respiratory, cardiac, and/or digestive processes, detected changes in tumor size, and detected changes in patient weight.

4. The method of claim 1, wherein the probability distribution function is generated based on a correlation between one or more attributes of the patient and a representative population of patients.

5. The method of claim 1, wherein the probability distribution function is generated based on one or more detected attributes of the patient.

6. The method of claim 5, wherein the attributes of the patient are detected based on at least one of computed tomography imaging, the four dimensional computed tomography imaging, magnetic resonance imaging, and fiducial marker tracking.

7. The method of claim 1, wherein calculating expectation value of the dose per energy fluence, d_ij may be implemented via an integration of p_i,i′

into the scatter kernels.

8. A system for optimizing a radiation treatment plan by providing independently controlled radiation along a plurality of rays j directed toward a patient configured to account for the effects of patient motion, comprising:

a radiation source configured to generate the plurality of rays;

a shutter system configured to attenuate the rays generated by the radiation source; and

a computer system configured to control the radiation source and the shutter system to implement a radiation treatment plan to account for the effects of patient motion, including the steps of

a) generating a probability distribution function quantitatively expressing patient motion;

b) identifying a prescribed total dose D_i^p at the voxels i in a treatment area;

c) assigning a fluence value w_j for each ray j, based on an iterative function;

9. The system of claim 8, wherein the steps further include calculating an expectation value of the delivered dose D_i^d based on an aggregation of the expectation value of the dose per energy fluence, d_ij.

10. The system of claim 8, wherein the probability distribution function is generated based on at least one of calculated daily patient set up uncertainty, quantified internal patient motion due to respiratory, cardiac, and/or digestive processes, detected changes in tumor size, and detected changes in patient weight.

11. The system of claim 8, wherein the probability distribution function is generated based on a correlation between one or more attributes of the patient and a representative population of patients.

12. The system of claim 8, wherein the probability distribution function is generated based on one or more detected attributes of the patient.

13. The system of claim 12, wherein the attributes of the patient are detected based on at least one of computed tomography imaging, the four dimensional computed tomography imaging, magnetic resonance imaging, and fiducial marker tracking.

14. The system of claim 8, wherein calculating expectation value of the dose per energy fluence, d_ij may be implemented via an integration of p_i,i′

into the scatter kernels.

15. A computer-implemented method for optimizing a radiation treatment plan for a radiotherapy machine providing independently controlled radiation along a plurality of rays j directed toward a patient configured to account for the effects of patient motion, comprising:

a) generating a probability distribution function quantitatively expressing patient motion;

b) identifying a prescribed total dose D_i^p at the voxels i in a treatment area;

c) assigning a fluence value w_j for each ray j, based on an iterative function;

16. The method of claim 15, further including calculating an expectation value of the delivered dose D_i^d based on an aggregation of the expectation value of the dose per energy fluence, d_ij.

17. The method of claim 15, wherein the probability distribution function is generated based on a correlation between one or more attributes of the patient and a representative population of patients.

18. The method of claim 15, wherein the probability distribution function is generated based on one or more detected attributes of the patient.

19. The method of claim 18, wherein the attributes of the patient are detected based on at least one of computed tomography imaging, the four dimensional computed tomography imaging, magnetic resonance imaging, and fiducial marker tracking.

20. The method of claim 15, wherein calculating expectation value of the dose per energy fluence, d_ij may be implemented via an integration of p_i,i′

into the scatter kernels.