US-12623093-B2 - Continuum radiotherapy treatment planning

Abstract

Systems and methods are disclosed for dynamic radiotherapy treatment planning in a continuous space of computation. Example operations for generating treatment plan data for a radiotherapy treatment include: obtaining data for a radiotherapy treatment of a human subject; generating a set of radiation controls from the data for the radiotherapy treatment, with at least one of the radiation controls being based on a mapping from a continuous (e.g., infinite dimensional) computational space; converting the generated set of radiation controls to a set of treatment delivery parameters, the set of treatment delivery parameters corresponding to capabilities of a radiotherapy treatment machine; and producing treatment plan data for the radiotherapy treatment based on the set of treatment delivery parameters.

Inventors

• Jens Olof Sjolund
• Carl Axel Håkan Nordström

Assignees

• ELEKTA AB (PUBL)

Dates

Publication Date

20260512

Application Date

20211025

Claims (20)

1. 1 . A computer-implemented method for radiotherapy treatment planning, the method comprising: obtaining data for a radiotherapy treatment of a human subject; generating a set of radiation controls from the data for the radiotherapy treatment, wherein at least one of the radiation controls is based on a mapping from a continuous computational space; converting the generated set of radiation controls to a set of treatment delivery parameters, the set of treatment delivery parameters corresponding to capabilities of a radiotherapy treatment machine; and producing treatment plan data for the radiotherapy treatment based on the set of treatment delivery parameters.
2. 2 . The method of claim 1 , wherein generating the set of radiation controls comprises solving an optimization problem for the radiotherapy treatment.
3. 3 . The method of claim 2 , wherein optimization variables of the optimization problem comprise a set of auxiliary variables related to at least one of the radiation controls, having a relation defined by a linear operator.
4. 4 . The method of claim 2 , wherein an objective function of the optimization problem comprises a functional that maps the radiation controls, based on the continuous computational space, into a scalar value.
5. 5 . The method of claim 1 , wherein generating the set of radiation controls comprises producing a simulation of a radiation dose distribution corresponding to a particular set of radiation controls.
6. 6 . The method of claim 5 , wherein producing the simulation of the radiation dose distribution corresponding to the particular set of radiation controls comprises applying a transform to produce simulated dose calculations, wherein the transform is defined in the continuous computational space as a convolution.
7. 7 . The method of claim 6 , wherein applying the transform comprises producing a convolution between a patient-specific dose deposition kernel or fluence deposition matrix and a radiation control corresponding to at least one of irradiation time or radiation intensity.
8. 8 . The method of claim 7 , wherein each dose deposition kernel or fluence deposition matrix represents a dose rate from a particular sector, a particular collimator, and a particular isocenter, to a particular location in a patient to receive the radiotherapy treatment.
9. 9 . The method of claim 6 , wherein applying the transform comprises applying a Fourier transform, and wherein the simulated dose calculations are represented as a multiplication in Fourier space.
10. 10 . The method of claim 6 , wherein applying the transform comprises applying one of a: wavelet, Laplace, Hankel, Mellin, or Hilbert transform.
11. 11 . The method of claim 1 , wherein converting the generated set of radiation controls to the set of treatment delivery parameters is based on minimizing a degradation of plan quality according to a clinically relevant objective.
12. 12 . The method of claim 1 , further comprising: collapsing the set of treatment delivery parameters to a path to perform the radiotherapy treatment, based on a type of the radiotherapy treatment machine.
13. 13 . The method of claim 12 , wherein the collapsing of the set of treatment delivery parameters is performed using curvelets.
14. 14 . The method of claim 1 , wherein converting the set of radiation controls to the set of treatment delivery parameters comprises discretizing at least a portion of the set of radiation controls into a set of finite-dimensional treatment delivery parameters.
15. 15 . The method of claim 1 , wherein generating the set of the radiation controls that belongs to the continuous computational space comprises using a probabilistic language of random fields, performing computations using a finite subset of points, and using interpolation to determine properties of each infinite-dimensional radiation control.
16. 16 . The method of claim 1 , wherein the data for the radiotherapy treatment comprises imaging data based on a defined two-dimensional or three-dimensional grid.
17. 17 . The method of claim 1 , wherein the data for the radiotherapy treatment comprises a definition of one or more volumes to receive the radiotherapy treatment from the radiotherapy treatment machine.
18. 18 . The method of claim 17 , wherein the definition of one or more volumes defines one or more organ at risk areas and one or more target areas.
19. 19 . The method of claim 1 , wherein the set of radiation controls are based on modulation of radiation using at least one of: focus position, directionality, irradiation time, flux, fluence, energy, or collimation, for the radiation.
20. 20 . The method of claim 1 , wherein the radiotherapy treatment is provided with a Gamma knife, and wherein the set of treatment delivery parameters comprises a set of isocenters used for delivery of the radiotherapy treatment.

Description

PRIORITY APPLICATIONS This application is a U.S. National Stage Filing under 35 U.S.C. § 371 from International Application No. PCT/EP2021/079551, filed on Oct. 25, 2021, and published as WO2023/072364 on May 4, 2023; the benefit of priority of which is hereby claimed herein, and which application and publication is hereby incorporated herein by reference in its entirety. TECHNICAL FIELD Embodiments of the present disclosure pertain generally to processing and optimization techniques used in connection with a radiation therapy planning and treatment system. In particular, the present disclosure pertains to the use of computationally tractable methods for continuum treatment planning and delivery in connection with planning for a radiation therapy session. BACKGROUND Radiation therapy (or “radiotherapy”) can be used to treat cancers or other ailments in mammalian (e.g., human and animal) tissue. One such radiotherapy technique is provided using a Gamma Knife, by which a patient is irradiated by a large number of low-intensity gamma rays that converge with high intensity and high precision at a target (e.g., a tumor). Another such radiotherapy technique is provided using an 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). In radiotherapy, treatment planning is typically performed in a sequential manner, where the degrees of freedom (e.g., the isocenter locations in Gamma Knife) are determined prior to solving an optimization problem. However, determining the degrees of freedom necessarily restricts the search space of the optimization, and thereby bounds the quality of treatment plans that can be found by the optimization—sometimes even to the point where no acceptable plan exists and the degrees of freedom have to be redetermined and the optimization must be re-calculated. Some limited approaches have been proposed to improve treatment planning for the paths used to deliver treatment with a Gamma Knife. However, the resulting path that is produced by such approaches may not be fully optimized. OVERVIEW In some embodiments, methods, systems, and computer-readable mediums are provided for evaluations and optimizations performed on a radiotherapy treatment plan designed for a radiotherapy treatment session. An initial step in conventional treatment planning is to determine the degrees of freedom that is later used to solve an optimization problem. The following provides an expanded approach for radiotherapy treatment planning optimization by removing the initial step of determining the degrees of freedom, and instead, performing a radiotherapy treatment planning in a continuum limit as an initial step of optimization. In various examples, operations for such evaluations and optimizations in a radiotherapy treatment planning setting include: obtaining data for a radiotherapy treatment of a human subject; generating a set of radiation controls from the data for the radiotherapy treatment, where at least one of the radiation controls is based on (e.g., defined by) a mapping from a continuous computational space; converting the generated set of radiation controls to a set of treatment delivery parameters, with the set of treatment delivery parameters corresponding to capabilities of a radiotherapy treatment machine; and producing treatment plan data for the radiotherapy treatment based on the set of treatment delivery parameters. Various techniques may be used to produce the set of radiation controls in the continuous computational space (e.g., an infinite-dimensional space). In an example, generating such radiation controls comprises solving an optimization problem for the radiotherapy treatment, and optionally, using optimization variables in the optimization problem such as a set of auxiliary variables related to at least one of the radiation controls, having a relation defined by a linear operator. The simulation of the radiation dose may include applying a transform in the continuous computational space as a convolution. Such operations are done by convolving a patient-specific dose deposition kernel or fluence deposition matrix and a radiation control corresponding to at least one of irradiation time (e.g., for Gamma knife treatment) or radiation intensity (e.g., for Linac treatment). In this example, each dose deposition kernel or fluence deposition matrix represents a dose rate from a particular sector, a particular collimator, and a particular isocenter, to a particular location in a patient to receive the radiotherapy treatment. In another example, applying a transform to determine dose includes applying a Fourier transform, such that the simulated d