Determining uncertainty in radiation therapy delivered dose accumulation

Patent metadata

Field	Value
Publication number	US-12318633-B2
Application number	US-202217710975-A
Country	US
Kind code	B2
Filing date	Mar 31, 2022
Priority date	Mar 31, 2022
Publication date	Jun 3, 2025
Grant date	Jun 3, 2025

Abstract

A computer-implemented method of generating dose information for a region of patient anatomy includes determining a first set of dose values for a first three-dimensional (3D) image of the region, wherein each value in the first set of dose values is associated with a different voxel of the first 3D image, and wherein the first 3D image is associated with a specific application of dose to the region; determining a second set of dose values for a representative 3D image of the region, wherein each value in the second set of dose values is associated with a different voxel of the representative 3D image; determining a set of geometric error models for the representative 3D image of the region, wherein each geometric error model in the set of geometric error models indicates a geometric error between a voxel of the representative 3D image and one or more voxels of a treatment fraction 3D image of the region; and based on the second set of dose values and the set of geometric error models, determining a set of dose probability values for each voxel of the representative 3D image, wherein each set of dose probability values includes at least one dose value and a probability value that corresponds to the dose value.

First claim

We claim:

1. A computer-implemented method of generating dose information for a region of patient anatomy, the method comprising:
determining a first set of dose values for a day-of-treatment three-dimensional (3D) image of the region, wherein each value in the first set of dose values is associated with a different voxel of the first 3D image, and wherein the day-of-treatment 3D image is associated with a specific application of dose to the region for a treatment fraction;
determining a second set of dose values for a representative 3D image of the region, wherein the representative 3D image corresponds to a treatment planning image of the region obtained prior to the day-of-treatment 3D image, and each value in the second set of dose values is associated with a different voxel of the representative 3D image;
determining a set of geometric error models for the representative 3D image of the region, wherein each geometric error model in the set of geometric error models indicates a geometric error between a voxel of the representative 3D image and one or more voxels of the day-of-treatment 3D image of the region for the treatment fraction;
based on the second set of dose values and the set of geometric error models, determining a set of dose probability values for each voxel of the representative 3D image, wherein each set of dose probability values includes at least one dose value and a probability value that a voxel of the representative 3D image corresponds to the dose value; and
presenting a delivered dose distribution for a portion of the representative 3D image and uncertainty information associated with the delivered dose distribution to a clinician, wherein the uncertainty information is based on the sets of dose probability values.

2. The computer-implemented method of claim 1 , wherein determining the set of dose probability values for each voxel of the representative 3D image comprises:
determining a sampling region of the representative 3D image that is proximate the voxel of the representative 3D image; and
sampling a dose value from the second set of dose values for each voxel within the sampling region.

3. The computer-implemented method of claim 1 , wherein each set of dose probability values comprises a probabilistic dose distribution.

4. The computer-implemented method of claim 1 , wherein each geometric error model in the set of geometric error models indicates one of a measured geometric error between a particular voxel of the representative 3D image and one or more voxels of the day-of-treatment 3D image of the region for the treatment fraction or an assumed geometric error between the particular voxel and one or more voxels of the day-of-treatment 3D image of the region for the treatment fraction.

5. The computer-implemented method of claim 1 , further comprising generating a dose-volume histogram for the region that is based on the sets of dose probability values determined for the voxels of the representative 3D image.

6. The computer-implemented method of claim 1 , wherein the portion of the representative 3D image comprises a region of interest included in the representative 3D image.

7. The computer-implemented method of claim 1 , wherein the delivered dose distribution for the portion of the representative 3D image comprises dose delivered over a specific single treatment fraction.

8. The computer-implemented method of claim 1 , wherein the delivered dose distribution for the portion of the representative 3D image comprises dose accumulated over multiple treatment fractions.

9. The computer-implemented method of claim 1 , wherein determining the second set of dose values for the representative 3D image comprises propagating the first set of dose values to the voxels of the representative 3D image.

10. The method of claim 1 , wherein the uncertainty information is associated with geometric error between one or more voxels of the representative 3D image and one or more voxels of the day-of-treatment 3D image of the region for the treatment fraction.

11. The computer-implemented method of claim 2 , wherein determining the sampling region comprises determining the sampling region based on a geometric error model for the voxel of the representative 3D image.

12. The computer-implemented method of claim 2 , wherein determining the set of dose probability values for each voxel of the representative 3D image further comprises determining a probability value for each dose bin in the set of dose probability values based on the dose values sampled from the second set of dose values.

13. The computer-implemented method of claim 4 , wherein a geometric error model in the set of geometric error models indicates a specific measured geometric error between the particular voxel of the representative 3D image and one or more voxels of the day-of-treatment 3D image of the region for the treatment fraction when the particular voxel is included in an interpretable feature within the representative 3D image.

14. The computer-implemented method of claim 4 , wherein a geometric error model in the set of geometric error models indicates a specific assumed geometric error between the particular voxel of the representative 3D image and one or more voxels of the day-of-treatment 3D image of the region for the treatment fraction when the particular voxel is not included in an interpretable feature within the representative 3D image.

15. The computer-implemented method of claim 4 , wherein the assumed geometric error is represented by a region of the representative 3D image that includes a plurality of voxels proximate the particular voxel.

16. The computer-implemented method of claim 5 , wherein the dose-volume histogram includes, for each dose bin of the dose-volume histogram, probability information for the dose bin.

17. The computer-implemented method of claim 12 , wherein determining the probability value for each dose bin in the set of dose probability values comprises applying a weighting function to the dose values sampled from the second set of dose values.

18. The computer-implemented method of claim 15 , wherein a size of the region is a function of a distance of the particular voxel from an interpretable feature included in the representative 3D image.

19. The computer-implemented method of claim 16 , wherein the probability information for the dose bin includes one or more of a mean dose value that corresponds to a mean of the probabilistic dose distribution for the dose bin, a highest likely dose value that corresponds to a high percentile of the probabilistic dose distribution for the dose bin, or a lowest likely dose value that corresponds to a high percentile of the probabilistic dose distribution for the dose bin.

20. A non-transitory computer-readable medium having instructions stored thereon, which in response to execution by one or more processors, cause the one or more processors to perform or control performance of a method of generating dose information for a region of patient anatomy, wherein the method comprises:
determining a first set of dose values for a day-of-treatment three-dimensional (3D) image of the region, wherein each value in the first set of dose values is associated with a different voxel of the first 3D image, and wherein the day-of-treatment 3D image is associated with a specific application of dose to the region for a treatment fraction;
determining a second set of dose values for a representative 3D image of the region, wherein the representative 3D image corresponds to a treatment p

Assignees

• Siemens Healthineers Int Ag

Inventors

• MORGAS Tomasz
• Gass Tobias
• Coradi Thomas
• Franco Fernando
• Waschbüesch Michael

Classifications

• A61N5/1071 Primary

  for verifying the dose delivered by the treatment plan · CPC title

• A61N5/1031 Primary

  using a specific method of dose optimization · CPC title