Streamlined, guided on-couch adaptive workflow

Patent metadata

Field	Value
Publication number	US-10799716-B2
Application number	US-201816233360-A
Country	US
Kind code	B2
Filing date	Dec 27, 2018
Priority date	Oct 18, 2018
Publication date	Oct 13, 2020
Grant date	Oct 13, 2020

Abstract

Systems and methods for implementing an adaptive therapy workflow that minimizes time needed to create a session patient model, select an appropriate plan for the treatment session, and treat the patient.

First claim

The invention claimed is:

1. An automated workflow for an adaptive radiation therapy session, comprising:
obtaining a set of directives, the set of directives including information representing a planned treatment for a patient; and
using the set of directives to perform a series of automated steps to:
generate a session patient model in a step-wise fashion;
generate a first and a second treatment plan for the session patient model; and
select a treatment plan that is appropriate for a current treatment session.

2. The workflow of claim 1 , wherein the set of directives includes information regarding planned radiation dose, planned clinical goals, planned clinical goal values, reference patient model, reference treatment plan, list of influencer structures, and one or more reference images, wherein the influences structures include anatomical structures that influence other anatomical structures of the patient.

3. The workflow of claim 2 , wherein the generating of the session patient model in a step-wise fashion includes:
a first step wherein a treatment session image of a portion of the patient is generated, the treatment session image containing an anatomy of interest;
a second step wherein one or more influencer structures from the list of influencer structures are generated on the treatment session image;
a third step wherein the generated influencer structures are evaluated based on one or more directives of the set of directives;
a fourth step wherein, upon acceptance of the generated influencer structures, target structures of the reference image are propagated to the treatment session image; and
a fifth step wherein the propagated target structures are evaluated based on one or more directives of the set of directives,
wherein, upon acceptance of the propagated target structures, the treatment session image including the influencer structures and the propagated target structures is accepted as the session patient model.

4. The workflow of claim 3 , wherein
the target structures include:
a first set of target structures representing contours of a primary tumor; and
a second set of target structures representing contours of one or more primarily affected organs (OARs), and

the influencer structures include:
a first set of influencer structures representing contours of one or more organs that affect one or more of a shape, size or location of one or more of the target structures; and
a second set of influencer structures representing contours of non-volumetric structures.

5. The workflow of claim 4 , wherein the one or more influencer structures are generated by one of a manual, automatic, or a combination of manual and automatic segmentation, or by propagating the one or more influencer structures from the reference image to the treatment session image by deformable and/or rigid deformation.

6. The workflow of claim 4 , wherein the target structures are propagated from the reference image to the treatment session image using structure-guided deformable registration.

7. The workflow of claim 6 , wherein the structure-guided deformable registration is deformable registration that is guided by one or more of the influencer structures.

8. The workflow of claim 4 , wherein the evaluating, accepting, and selecting is done by a single user.

9. The workflow of claim 8 , wherein the determining whether the one or more influencer structures are acceptable includes:
presenting, to the user, the treatment session image including the generated influencer structures and the reference image including corresponding reference influencer structures for comparison; and
the user verifying that the generated influencer structures correspond to the reference influencer structures.

10. The workflow of claim 9 , further includes displaying contouring/segmentation guidelines to the user to be used for the verifying.

11. The workflow of claim 8 , wherein the determining whether the propagated target structures are acceptable includes:
presenting, to the user, the treatment session image including the propagated target structures and the reference image including corresponding reference target structures for comparison; and
the user verifying that the propagated first set of target structures on the treatment session image represent same anatomical regions of the patient as the reference first set of target structures in the reference image.

12. The workflow of claim 11 , wherein the determining further includes synchronizing the reference image with the treatment session image prior to the verifying.

13. The workflow of claim 12 , wherein the determining further includes using information relating to shapes and positions of the propagated and reference first set of target structures in the treatment session image and the reference image.

14. The workflow of claim 13 , wherein the determining further includes using information relating to radiation dose representations in the reference image and the treatment session images.

15. The workflow of claim 14 , wherein the determining further includes using automated tools to detect irregularities in the compared first set of target structures.

16. The workflow of claim 15 , wherein the determining further includes using automated tools to guide the user to locations where irregularities are detected.

17. The workflow of claim 11 , wherein when the user determines that the propagated first set of target structures are not acceptable, the user can select to correct the propagated first set of target structures or to default to another user for correction.

18. The workflow of claim 8 , wherein the generating of the first treatment plan for the session patient model, includes:
obtaining a reference isocenter location for the reference patient model from the set of directives;
determining an acquisition isocenter location for the session patient model;
aligning the accepted propagated first set of target structures in the session patient model with the corresponding reference first set of target structures in the reference patient model;
determining a difference between the location of the reference isocenter and the location of the acquisition isocenter;
determining a treatment session isocenter location by applying the determined difference to the acquisition isocenter location; and
using the treatment session isocenter location as an input to a plan generation algorithm to generate the first treatment plan.

19. The workflow of claim 18 , wherein the generating of the second treatment plan includes:
generating a synthetic image for the patient by registering the treatment session image with the reference image;
using the synthetic image and the propagated target structures as input to a treatment plan generation algorithm to generate a treatment plan, wherein the plan generation algorithm includes optimization parameters which are automatically generated based on the planned clinical goals included in the set of directives; and
generating the second treatment plan by optimizing the generated plan using information relating to the reference treatment plan included in the set of directives.

20. The workflow of claim 19 , wherein the optimization parameters are automatically modified and automatically selected without the user's input.

21. The workflow of claim 8 , wherein the selecting of the appropriate treatment plan includes:
evaluating whether the first treatment plan is acceptable for the current treatment session using the cli

Assignees

• Varian Medical Systems Int Ag

Inventors

• MORGAS Tomasz
• Lessard Marco
• Gass Tobias
• Haas Benjamin
• Coradi Thomas
• Honegger Jonas
• Boylan Christopher

Classifications

• A61N2005/1074

  Details of the control system, e.g. user interfaces · CPC title

• A61N5/1048

  Monitoring, verifying, controlling systems and methods · CPC title

• A61N5/1038

  taking into account previously administered plans applied to the same patient, i.e. adaptive radiotherapy · CPC title

• A61N2005/1061

  using an x-ray imaging system having a separate imaging source · CPC title

• A61B5/4836

  Diagnosis combined with treatment in closed-loop systems or methods (A61B5/0036 takes precedence) · CPC title