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US-12623092-B2 - Dynamic adaptation of radiotherapy treatment plans

US12623092B2US 12623092 B2US12623092 B2US 12623092B2US-12623092-B2

Abstract

A reference radiotherapy treatment plan having a plurality of control points, such as a sequence of gantry angles, aperture leaf positions, and intensity weights, can be adapted during the radiotherapy treatment session. Treatment imaging data of a patient may be obtained during the radiotherapy treatment session and one or more parameters may be determined using the treatment imaging data. Then, a current radiotherapy treatment plan may be generated based on the parameter(s). The reference radiotherapy treatment plan for the radiotherapy treatment session may be modified during the radiotherapy treatment session by updating one of the plurality of control points of the reference radiotherapy treatment plan with a control point of a current radiotherapy treatment plan, which may compensate for patient deformations during the delivery of radiotherapy treatment, or intrafraction patient deformations and thereby improve the delivery accuracy and efficacy of radiation doses to a patient undergoing radiotherapy treatment.

Inventors

  • Martin Emile Lachaine
  • Tony Falco

Assignees

  • ELEKTA LTD.

Dates

Publication Date
20260512
Application Date
20230308
Priority Date
20220309

Claims (20)

  1. 1 . A computer-implemented method for adaptation of a reference radiotherapy treatment plan having a plurality of control points, performed on a subject in a radiotherapy treatment session, the computer-implemented method comprising: obtaining treatment imaging data of the subject during the radiotherapy treatment session; determining at least one parameter using the treatment imaging data; generating a current radiotherapy treatment plan based on the at least one parameter; modifying the reference radiotherapy treatment plan for the radiotherapy treatment session during the radiotherapy treatment session by updating one of the plurality of control points of the reference radiotherapy treatment plan with a control point of the current radiotherapy treatment plan; and using a trained model that includes: generating at least two training images representing potential anatomical states of the subject, the at least two training images corresponding to at least two training parameters: generating at least two training radiotherapy treatment plans corresponding to the at least two training images; and generating a training regression between the at least two training parameters and the at least two training radiotherapy treatment plans.
  2. 2 . The computer-implemented method of claim 1 , comprising: generating a current image of the subject using the at least one parameter and a reference image.
  3. 3 . The computer-implemented method of claim 2 , comprising: generating a current structure set of the subject using the at least one parameter and a reference structure set.
  4. 4 . The computer-implemented method of claim 3 , comprising: generating a current dose distribution using the current radiotherapy treatment plan and the current image.
  5. 5 . The computer-implemented method of claim 4 , comprising: generating a dose volume histogram using the current dose distribution and the current structure set.
  6. 6 . The computer-implemented method of claim 1 , wherein generating the current radiotherapy treatment plan based on at least one parameter includes: generating, using the training regression, the current radiotherapy treatment plan from the at least one parameter.
  7. 7 . The computer-implemented method of claim 1 , wherein generating the at least two training radiotherapy treatment plans corresponding to the at least two training images includes: modifying the reference radiotherapy treatment plan to the at least two training images using segment-aperture morphing.
  8. 8 . The computer-implemented method of claim 1 , comprising: generating at least two reference images of the subject; selecting one of the at least two reference images as a primary reference image; generating a deformation vector field between the primary reference image and each of the other reference images; and determining at least one principal component of the deformation vector field.
  9. 9 . The computer-implemented method of claim 8 , wherein generating the at least two training images includes: generating at least two deformation vector fields using the at least one principal component and at least two principal component weights; and generating the at least two training images by deforming the primary reference image using the at least two deformation vector fields.
  10. 10 . The computer-implemented method of claim 8 , wherein determining the at least one parameter includes: determining principal component weights that generate a deformation vector field that, when used to deform the primary reference image, are consistent with the treatment imaging data, wherein the principal component weights are assigned as the at least one parameter.
  11. 11 . The computer-implemented method of claim 8 , wherein generating the at least two reference images of the subject includes: generating the at least two reference images from a 4D dataset acquired with the subject set up for treatment, and prior to beam-on.
  12. 12 . The computer-implemented method of claim 1 , wherein an individual one of the control points includes aperture information.
  13. 13 . The computer-implemented method of claim 1 , wherein an individual one of the control points includes multi-leaf collimator leaf position information.
  14. 14 . The computer-implemented method of claim 1 , wherein an individual one of the control points includes dose rate information.
  15. 15 . The computer-implemented method of claim 1 , wherein obtaining the treatment imaging data of the subject during the radiotherapy treatment session includes: obtaining the treatment imaging data of the subject during the radiotherapy treatment session using at least one of a kV imaging data, 2D MR slices, surface camera imaging data, cone-beam CT (CBCT) imaging data, or MV imaging data.
  16. 16 . A radiotherapy system configured to perform the computer-implemented method of claim 1 .
  17. 17 . A tangible or non-tangible computer readable medium encoded with instructions that, when executed by a processor, cause the processor to perform the computer-implemented method of claim 1 .
  18. 18 . A radiotherapy system for adaptation of a reference radiotherapy treatment plan having a plurality of control points, performed on a subject in a radiotherapy treatment session, the radiotherapy system comprising: an image acquisition device configured to acquire measurements of the subject during the radiotherapy treatment session; a processor configured to: perform, at a first rate, a first computational loop to generate at least one parameter using the acquired measurements, wherein the at least one parameter represents an anatomical state of the subject; perform, at a second rate that is independent of the first rate, a second computational loop to generate a current radiotherapy treatment plan based on the at least one parameter, wherein the current radiotherapy treatment plan includes a plurality of control points; and modify a reference radiotherapy treatment plan for the radiotherapy treatment session during the radiotherapy treatment session by updating a control point of the reference radiotherapy treatment plan with the control point of the current radiotherapy treatment plan; and a radiotherapy device configured to deliver a dose of radiation to an anatomical region of interest using the current radiotherapy treatment plan.
  19. 19 . The radiotherapy system of claim 18 , wherein the processor is configured to: repeatedly perform the first computational loop; and repeatedly perform the second computational loop.
  20. 20 . The radiotherapy system of claim 19 , comprising: a memory storage device configured to store sequentially generated parameters while the processor is repeatedly performing the first computational loop, wherein the processor is configured to perform the second computational loop based on sequentially generated parameters.

Description

CLAIM FOR PRIORITY This application claims the benefit of priority of British Application No. 2203310.4, filed Mar. 9, 2022, which is hereby incorporated by reference in its entirety. TECHNICAL FIELD Embodiments of the present disclosure pertain generally to radiotherapy treatment sessions and specifically to imaging techniques. BACKGROUND Radiation therapy (or “radiotherapy”) may be used to treat cancers or other ailments in mammalian (e.g., human and animal) tissue. One such radiotherapy technique involves irradiation with a Gamma Knife, whereby a patient is irradiated by a large number of low-intensity gamma ray beams that converge with high intensity and high precision at a target (e.g., a tumor). In another embodiment, radiotherapy is provided using a linear accelerator, whereby a tumor is irradiated by high-energy particles (e.g., electrons, protons, ions, high-energy photons, and the like). The placement and dose of the radiation beam must be accurately controlled to ensure the tumor receives the prescribed radiation, and the placement of the beam should be such as to minimize damage to the surrounding healthy tissue, often called the organ(s) at risk (OARs). Radiation is termed “prescribed” because a physician orders a predefined amount of radiation to the tumor and surrounding organs similar to a prescription for medicine. Generally, ionizing radiation in the form of a collimated beam is directed from an external radiation source toward a patient. A specified or selectable beam energy may be used, such as for delivering a diagnostic energy level range or a therapeutic energy level range. Modulation of a radiation beam may be provided by one or more attenuators or collimators (e.g., a multi-leaf collimator (MLC)). The intensity and shape of the radiation beam may be adjusted by collimation to avoid damaging healthy tissue (e.g., OARs) adjacent to the targeted tissue by conforming the projected beam to a profile of the targeted tissue. The treatment planning procedure may include using a three-dimensional (3D) image of the patient to identify a target region (e.g., the tumor) and to identify critical organs near the tumor. Creation of a treatment plan may be a time-consuming process where a planner tries to comply with various treatment objectives or constraints (e.g., dose volume histogram (DVH), overlap volume histogram (OVH)), taking into account their individual importance (e.g., weighting) in order to produce a treatment plan that is clinically acceptable. This task may be a time-consuming trial-and-error process that is complicated by the various OARs because as the number of OARs increases (e.g., up to thirteen for a head-and-neck treatment), so does the complexity of the process. OARs distant from a tumor may be easily spared from radiation, while OARs close to or overlapping a target tumor may be difficult to spare. Traditionally, for each patient, the initial treatment plan may be generated in an “offline” manner. The treatment plan may be developed well before radiation therapy is delivered, such as using one or more medical imaging techniques. Imaging information may include, for example, images from X-rays, computed tomography (CT), nuclear magnetic resonance (MR), positron emission tomography (PET), single-photon emission computed tomography (SPECT), or ultrasound. A health care provider, such as a physician, may use 3D imaging information indicative of the patient anatomy to identify one or more target tumors along with the OARs near the tumor(s). The health care provider may delineate the target tumor that is to receive a prescribed radiation dose using a manual technique, and the health care provider may similarly delineate nearby tissue, such as organs, at risk of damage from the radiation treatment. Alternatively, or additionally, an automated tool (e.g., ABAS provided by Elekta AB, Sweden) may be used to assist in identifying or delineating the target tumor and organs at risk. A radiation therapy treatment plan (“treatment plan”) may then be created using an optimization technique based on clinical and dosimetric objectives and constraints (e.g., the maximum, minimum, and fraction of dose of radiation to a fraction of the tumor volume (“95% of target shall receive no less than 100% of prescribed dose”), and like measures for the critical organs). The optimized plan is comprised of numerical parameters that specify the direction, cross-sectional shape, and intensity of each radiation beam. The treatment plan may then be later executed by positioning the patient in the treatment machine and delivering the prescribed radiation therapy directed by the optimized plan parameters. The radiation therapy treatment plan may include dose “fractioning,” whereby a sequence of radiation treatments is provided over a predetermined period of time (e.g., 30-45 daily fractions), with each treatment including a specified fraction of a total prescribed dose. However, during treatment, the position of