Streamlined, guided on-couch adaptive workflow

1. A method for radiation treatment, comprising:

positioning a patient on a treatment couch of a radiation treatment system and moving the patient to an imaging position;

acquiring one or more treatment session images of the patient by irradiating the patient with radiation beams;

applying a series of automated steps guided by a set of previously determined directives including information representing a planned treatment for the patient to generate a treatment session patient model;

generating a plurality of treatment plans based on the generated treatment session patient model;

selecting an appropriate treatment plan from the plurality of generated treatment plans to be delivered to the patient; and

delivering the selected treatment plan onto the patient.

2. The method 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.

3. The method of claim 2 , wherein the applying of the series of automated steps includes:

generating a treatment session image of a portion of the patient, the treatment session image containing an anatomy of interest;

generating one or more influencer structures from the list of influencer structures on the treatment session image;

evaluating the generated influencer structures based on one or more directives of the set of directives;

propagating target structures of a reference image to the treatment session image upon acceptance of the generated influencer structures;

evaluating the propagated target structures based on one or more directives of the set of directives; and

upon acceptance of the propagated target structures, accepting the treatment session image including the influencer structures and the propagated target structures as the session patient model.

4. The method 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 method of claim 4 , wherein the generating of the one or more influencer structures includes applying 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 method of claim 5 , wherein the propagating of the target structures includes propagating from the reference image to the treatment session image using structure-guided deformable registration, wherein the structure-guided deformable registration is deformable registration that is guided by one or more of the influencer structures.

7. The method of claim 3 , wherein the evaluating, accepting, and selecting is done by a single user.

8. The method of claim 7 , 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.

9. The method of claim 8 , further including displaying contouring/segmentation guidelines to the user to be used for the verifying.

10. The method of claim 9 , 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.

11. The method of claim 10 , wherein the determining further includes one or more of:

synchronizing the reference image with the treatment session image prior to the verifying;

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;

using information relating to radiation dose representations in the reference image and the treatment session images;

using automated tools to detect irregularities in the compared first set of target structures; and

using automated tools to guide the user to locations where irregularities are detected.

12. The method of claim 11 , further comprising allowing the user to select to correct the propagated first set of target structures or to default to another user for correction upon a determination that the propagated first set of target structures are not acceptable.

13. The method of claim 12 , wherein the generating of a 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.

14. The method of claim 13 , wherein the generating of a 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,

wherein the optimization parameters are automatically modified and automatically selected without the user's input.

15. The method of claim 14 , wherein the selecting of the appropriate treatment plan includes:

evaluating whether the first treatment plan is acceptable for the current treatment session using the clinical goals from the set of directives;

selecting the second treatment plan when determined that the first treatment plan is not acceptable;

evaluating whether the second treatment plan is acceptable; and

selecting a contingency plan when determined that the second treatment plan is not acceptable.

16. The method of claim 15 , wherein the selecting of the appropriate treatment session further includes:

presenting, to the user, the first and second treatment plans in a comparison view;

illustrating isodose distribution of the reference plan on the reference patient model, isodose distribution of the first treatment plan on the treatment session model, and isodose distribution of the second treatment plan on the treatment session model;

illustrating dose volume histograms of the reference plan, the first treatment plan and the second treatment plan;

presenting the planned clinical goal values;

presenting actual clinical values for the first and second treatment plans; and

selecting the treatment plan that provides the clinical values closest to the planned clinical goal values,

wherein the determining further includes selecting, by the user, a contingency plan when neither the first and second treatment plans achieve the planned clinical goals.

17. The method of claim 3 , further comprising evaluating, using a reference image from the set of directives, the reference image containing a reference bone structure, the treatment session image being accepted when a bone structure of interest in the treatment session image matches the bone structure in the reference image.

18. A radiation treatment system, comprising:

a treatment couch configured to support a patient;

a radiation source to irradiate the patient with radiation beams;

an imaging device configured to generate treatment session images of the patient;

a computer processing system configured to:

obtain a set of directives, the set of directives including information representing a planned treatment for the patient;

apply a series of automated steps guided by the set of directives to generate a treatment session patient model;

generate a plurality of treatment plans based on the generated treatment session patient model; and

allow a user to select an appropriate treatment plan from the plurality of generated treatment plans to be delivered to the patient; and

a controller configured to deliver the selected treatment plan onto the patient.

19. The system of claim 18 ,

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; and

the applying of the series of automated steps includes:

generating a treatment session image of a portion of the patient, the treatment session image containing an anatomy of interest;

generating one or more influencer structures from the list of influencer structures on the treatment session image;

evaluating the generated influencer structures based on one or more directives of the set of directives;

propagating target structures of a reference image to the treatment session image upon acceptance of the generated influencer structures;

evaluating the propagated target structures based on one or more directives of the set of directives; and

upon acceptance of the propagated target structures, accepting the treatment session image including the influencer structures and the propagated target structures as the session patient model,

the target structures including:

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 including:

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.

20. The system of claim 19 , wherein the generating of the plurality of treatment plans includes generating a first treatment plan and a second treatment plan,

the generating of the first treatment plan including:

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 a first treatment plan; and

the generating of the second treatment plan including:

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 a second treatment plan by optimizing the generated plan using information relating to the reference treatment plan included in the set of directives, wherein the optimization parameters are automatically modified and automatically selected without the user's input.