Classifications

• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
  • A61N5/00—Radiation therapy
  • A61N5/10—X-ray therapy; Gamma-ray therapy; Particle-irradiation therapy
  • A61N5/1042—X-ray therapy; Gamma-ray therapy; Particle-irradiation therapy with spatial modulation of the radiation beam within the treatment head
  • A61N5/1045—X-ray therapy; Gamma-ray therapy; Particle-irradiation therapy with spatial modulation of the radiation beam within the treatment head using a multi-leaf collimator, e.g. for intensity modulated radiation therapy or IMRT
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
  • A61N5/00—Radiation therapy
  • A61N5/10—X-ray therapy; Gamma-ray therapy; Particle-irradiation therapy
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
  • A61N5/00—Radiation therapy
  • A61N5/10—X-ray therapy; Gamma-ray therapy; Particle-irradiation therapy
  • A61N5/103—Treatment planning systems
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
  • A61N5/00—Radiation therapy
  • A61N5/10—X-ray therapy; Gamma-ray therapy; Particle-irradiation therapy
  • A61N5/103—Treatment planning systems
  • A61N5/1031—Treatment planning systems using a specific method of dose optimization
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
  • A61N5/00—Radiation therapy
  • A61N5/10—X-ray therapy; Gamma-ray therapy; Particle-irradiation therapy
  • A61N5/103—Treatment planning systems
  • A61N5/1039—Treatment planning systems using functional images, e.g. PET or MRI
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61N—ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY
  • A61N5/00—Radiation therapy
  • A61N5/10—X-ray therapy; Gamma-ray therapy; Particle-irradiation therapy
  • A61N5/103—Treatment planning systems
  • A61N5/1031—Treatment planning systems using a specific method of dose optimization
  • A61N2005/1034—Monte Carlo type methods; particle tracking
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
  • G06V2201/00—Indexing scheme relating to image or video recognition or understanding
  • G06V2201/03—Recognition of patterns in medical or anatomical images

Definitions

• Fluence map optimization (FMO) problems in radiation treatment planning are most often solved by interior point methods or by gradient-based methods such as the projected gradient method or quasi-Newton methods.
• the optimization problem is typically reformulated as either a linear program or a quadratic program, which is then solved using an interior point method.
• interior point methods perform very well for small and medium-size problems, they have the disadvantage that they require solving a large linear system of equations at each iteration. For large scale problems, including large fluence map optimization problems, this can become prohibitively computationally intensive.
• Gradient-based methods do not suffer from this limitation; however, they are unable to handle nondifferentiable objective functions and complex constraints. This puts significant restrictions on how the fluence map optimization problem can be formulated, and limits the quality of the resulting treatment plans.
• FMO radiation treatment optimization
• a method for FMO comprises a proximal gradient method (e.g., an accelerated proximal gradient method such as FISTA) with a smoothed-out nondifferentiable penalty function to compute a fluence map that may be used by a radiotherapy system to apply a specified dose of radiation to one or more regions of interest (ROI) or volumes of interest (VOI).
• the fluence map may comprise a set of radiation beamlet data (e.g., beamlet intensity data) derived from a prescribed radiation dose plan (e.g., a treatment plan).
• the fluence map may be used to position a radiation source at one or more selected angles with respect to the ROI and to adjust the beam intensity of the radiation source such that a desired radiation dose is applied to the ROI, while reducing radiation exposure of organs-at-risk (OAR).
• the methods described herein may compute a fluence map such that the radiation exposure of OARs is below a preselected threshold, while still delivering a selected dose of radiation to a ROI.
• Some variations may use one or more Li-type penalty or cost functions, while other variations may use one or more L 2 -type penalty or cost functions.
• a beamlet may be a portion of a full radiation beam that is defined by a multi-leaf collimator leaf opening (e.g., as depicted in FIG. IB).
• Each of the plurality of voxels may have an acceptable dose range (e.g., a maximum radiation dose level and a minimum radiation dose level), which may be defined by a treatment plan and/or a clinician.
• the method may comprise calculating a dose matrix A for the volume of interest based on the set of candidate beamlets b.
• the dose matrix A represents per-voxel dose delivered to each of the plurality of voxels by the set of candidate beamlets b.
• One example of a dose calculation matrix A for n candidate beamlets ⁇ bi ⁇ and for a VOI with k pre-selected voxels is a (k x n) matrix.
• An z ' -th column of the dose calculation matrix A (which has k elements) represents a dose contribution from a unity -weighted beamlet bi to each of the A: voxels.
• Dose matrix A is may be calculated column-by-column, for example, by ray -tracing each beamlet's aperture along the path through the patient's volume and calculating the contribution of a unity -weighted beamlet to each of the k voxels.
• dose calculation algorithms include Monte-Carlo simulation, collapsed-cone convolution superposition, pencil-beam convolution, and others.
• a fluence map generation method may also comprise calculating a fluence map having a final set of beamlet intensity weights x k by adjusting the initial beamlet intensity weights according to a proximal gradient method (x k_1 ⁇ x k ) with a penalty function containing one or more linear penalties.
• the proximal gradient method may be an accelerated proximal gradient method such as fast iterative shrinkage-thresholding algorithm (FISTA).
• FISTA fast iterative shrinkage-thresholding algorithm
• the proximal gradient method may iterate on the initial beamlet intensity weights until the adjusted beamlet intensity weights converge on a final set of beamlet intensity weights such that changes between iterations of the beamlet intensity weights are less than a predetermined residual criterion.
• the method may use any proximal methods.
• Solving an optimization problem involves finding an input value that minimizes or maximizes a real-valued function. When minimization is used, the function is often called a "cost function" or "penalty function".
• Convex optimization restricts the types of functions to so-called convex functions. Algorithms for convex optimizations guarantee convergence to a global minima, and may have other useful properties.
• a proximal algorithm or method is an algorithm for solving a convex optimization problem, and may be, in fluence map generation for example, an algorithm for minimizing a convex penalty function.
• Proximal algorithms use proximal operators of the components of the penalty functions.
• a proximal operator of a function involves solving a small convex optimization problem. For these small sub-problems, a closed form solution usually exists, making the overall algorithm efficient.
• a proximal gradient algorithm is one example of a proximal algorithm, and it assumes that a cost or penalty function can be split as f(x)+g(x), where f(x) is differentiable and g(x) has a simple closed form of the proximal operator.
• the optimization problem involves multiple beamlets and multiple voxels, but the penalty function must be a scalar real-valued function.
• a penalty function (or cost function) that is typically used may include a sum of multiple components, where each component directs the iterative process to a solution to satisfy a specific problem goal.
• the problem goal to be satisfied is the prescription target dose for a VOI (or a plurality of VOIs) in a patient.
• Each component in turn may also a summation across multiple beamlets of multiple voxels.
• a common choice for a penalty function component is a L2 penalty, also known as a quadratic penalty.
• a quadratic penalty or cost function is a sum of squares, for instance surra (di 2 ) is a penalty that would tend to minimize overall dose.
• a penalty or cost function may be a Li penalty, also known as a linear penalty, which is a simple sum, for example surra (di).
• a penalty or cost function may comprise one or more Li penalties and/or one or more L2 penalties.
• An accelerated proximal gradient method may include addition terms (such as a momentum term) to help direct and/or speed up (i.e., increase the rate of, decrease the number of iterations) convergence to a solution set.
• Generating or calculating a fluence map may comprise smoothing the penalty function such that it is differentiable.
• the penalty function may be smoothed using Moreau- Yosida regularization.
• the initial set of beamlet intensity weights ⁇ x 0 ⁇ is an all-zero vector.
• the beamlets in the set of candidate beamlets b are divided between a smaller set of firing positions or angles ⁇ fi ⁇ .
• a firing position is a location where a radiation source may be positioned (e.g., relative to a patient region) to fire a beamlet.
• a firing position may be a firing angle and identified by the locations around the circular gantry (from 0 to 360 degrees) around a patient treatment area.
• the set of firing angles ⁇ fi ⁇ may comprise a plurality of angles around a patient area of a radiation treatment device. The plurality of firing angles may be evenly distributed 360 degrees around the patient area.
• Any of the proximal gradient method penalty functions described herein may comprise one or more quadratic or L 2 penalties.
• Penalty functions may penalize voxel dose excursions outside of the acceptable dose range. That is, as part of a proximal gradient method iteration, the magnitude of the dose deviation outside of the acceptable dose range is incorporated into the calculation of a set of beamlet weights. Such penalty functions may discourage beamlets that result in a dose delivery to a voxel or VOI that is not within the acceptable dose range.
• Some methods may have a penalty function that comprises a single-value penalty function that aggregates the voxel dose excursions outside of the acceptable dose range of all of the voxels in the volume of interest.
• the acceptable dose range of each of the plurality of voxels may be determined at least in part by a treatment plan.
• Fluence map generation or calculation may comprise selecting a second volume of interest, selecting a second plurality of voxels within the second volume of interest where each of the voxels has an acceptable dose range, and calculating the dose matrix A to include the second volume of interest and second plurality of voxels. That is, the dose matrix A may represent per- voxel dose delivered to each of the first and second plurality of voxels by the set of candidate beamlets b.
• some methods may comprise segmenting a fluence map into a set of multi- leaf collimator and radiation source positioning instructions.
• Also described herein is a system for calculating or generating a fluence map for radiation therapy.
• the processor may also be configured to store the fluence map in a processor memory.
• a beamlet may be a portion of a full radiation beam that is defined by a multi-leaf collimator leaf opening (e.g., as depicted in FIG. IB).
• Each of the plurality of voxels may have an acceptable dose range (e.g., a maximum radiation dose level and a minimum radiation dose level), which may be defined by a treatment plan and/or a clinician.
• the dose matrix A represents per-voxel dose delivered to each of the plurality of voxels by the set of candidate beamlets b.
• a dose calculation matrix A for n candidate beamlets ⁇ bi ⁇ and for a VOI with k pre-selected voxels is a (k x n) matrix.
• An z ' -th column of the dose calculation matrix A (which has k elements) represents a dose contribution from a unity -weighted beamlet bi to each of the k voxels.
• Dose matrix A is may be calculated column-by-column, for example, by ray- tracing each beamlet' s aperture along the path through the patient's volume and calculating the contribution of a unity -weighted beamlet to each of the k voxels.
• Several well-known algorithms exist for this dose calculation process differing in their accuracy and speed. Examples of dose calculation algorithms that may be used in any of the methods described herein may include Monte- Carlo simulation, collapsed-cone convolution superposition, pencil-beam convolution, and others.
• a system processor may be configured to iterate on a proximal gradient method that may be an accelerated proximal gradient method such as fast iterative shrinkage-thresholding algorithm (FISTA).
• FISTA fast iterative shrinkage-thresholding algorithm
• the proximal gradient method may iterate on the initial beamlet intensity weights until the adjusted beamlet intensity weights converge on a final set of beamlet intensity weights such that changes between iterations of the beamlet intensity weights are less than a predetermined residual criterion. More generally, the method may use any proximal methods. Solving an optimization problem involves finding an input value that minimizes or maximizes a real-valued function. When minimization is used, the function is often called a "cost function" or "penalty function".
• Convex optimization restricts the types of functions to so-called convex functions. Algorithms for convex optimizations guarantee convergence to a global minima, and may have other useful properties.
• a proximal algorithm or method is an algorithm for solving a convex optimization problem, and may be, in fluence map generation for example, an algorithm for minimizing a convex penalty function.
• Proximal algorithms use proximal operators of the components of the penalty functions. Evaluating a proximal operator of a function involves solving a small convex optimization problem. For these small sub-problems, a closed form solution usually exists, making the overall algorithm efficient.
• a proximal gradient algorithm is one example of a proximal algorithm, and it assumes that a cost or penalty function can be split as f(x)+g(x), where f(x) is differentiable and g(x) has a simple closed form of the proximal operator.
• the optimization problem involves multiple beamlets and multiple voxels, but the penalty function must be a scalar real-valued function.
• a penalty function (or cost function) that is typically used may include a sum of multiple components, where each component directs the iterative process to a solution to satisfy a specific problem goal.
• the problem goal to be satisfied is the prescription target dose for a VOI (or a plurality of VOIs) in a patient.
• Each component in turn may also a summation across multiple beamlets of multiple voxels.
• a common choice for a penalty function component is a L2 penalty, also known as a quadratic penalty.
• a quadratic penalty or cost function is a sum of squares, for instance surra (di 2 ) is a penalty that would tend to minimize overall dose.
• Some components of a penalty or cost function may be a Li penalty, also known as a linear penalty, which is a simple sum, for example surra (di).
• a penalty or cost function may comprise one or more Li penalties and/or one or more L2 penalties.
• An accelerated proximal gradient method may include addition terms (such as a momentum term) to help direct and/or speed up (i.e., increase the rate of, decrease the number of iterations) convergence to a solution set.
• Some processors configured for generating a fluence map may be configured to smooth out the penalty function such that it is differentiable.
• the penalty function may be smoothed using Moreau-Yosida regularization.
• the initial set of beamlet intensity weights ⁇ x 0 ⁇ is an all-zero vector.
• the beamlets in the set of candidate beamlets b are divided between a smaller set of firing positions or angles ⁇ fi ⁇ .
• a firing position is a location where a radiation source may be positioned (e.g., relative to a patient region) to fire a beamlet.
• a firing position may be a firing angle and identified by the locations around the circular gantry (from 0 to 360 degrees) around a patient treatment area.
• the set of firing angles ⁇ fi ⁇ may comprise a plurality of angles around a patient area of a radiation treatment device. The plurality of firing angles may be evenly distributed 360 degrees around the patient area.
• a system processor configured to generate a fluence map may use any of the proximal gradient method penalty functions described herein with one or more quadratic or L 2 penalties.
• Penalty functions may penalize voxel dose excursions outside of the acceptable dose range. That is, as part of a proximal gradient method iteration, the magnitude of the dose deviation outside of the acceptable dose range is incorporated into the calculation of a set of beamlet weights.
• Such penalty functions may discourage beamlets that result in a dose delivery to a voxel or VOI that is not within the acceptable dose range.
• Some methods may have a penalty function that comprises a single-value penalty function that aggregates the voxel dose excursions outside of the acceptable dose range of all of the voxels in the volume of interest.
• the acceptable dose range of each of the plurality of voxels may be determined at least in part by a treatment plan.
• a system for generating a fluence map may comprise a processor configured to select a second volume of interest, select a second plurality of voxels within the second volume of interest where each of the voxels has an acceptable dose range, and calculate the dose matrix A to include the second volume of interest and second plurality of voxels. That is, the dose matrix A may represent per-voxel dose delivered to each of the first and second plurality of voxels by the set of candidate beamlets b.
• a radiation system may further comprise a multi-leaf collimator disposed in a beam path of the therapeutic radiation source, and the processor may be configured to segment the fluence map into a set of multi-leaf collimator instructions and to transmit the instructions to the radiation therapy system.
• the radiation therapy system may comprise one or more PET detectors.
• the therapeutic radiation source of a radiation therapy system may be movable about the patient area at a speed of at least about 40 RPM.
• Fluence map generation methods comprising a proximal gradient method with a penalty function (also known as a cost function) having one or more smoothed linear penalties (e.g., regularized Li penalties) may have advantages over fluence map generation methods that use weighted quadratic penalties.
• Fluence map generation methods typically use weighted quadratic penalties (e.g., L 2 penalties) derived from user-specified dose constraints and weghts as components in an overall penalty function. The use of quadratic penalties to model minimum and maximum dose constraints on voxels, often results in solutions with lots of small magnitude violations of the desired dose constraint (e.g.
• min-dose or max-dose or other
• Alternative strategy that is employed by users is manually tuning the penalty function component weights.
• Quadratic penalties may also require a greater number of iterations before converging on a set of beamlet weights.
• a fluence map generation method that includes a quadratic penalty or cost function may generate a set of beamlet weights that results in a greater number of violations of user-imposed constraints.
• the fluence map generation methods described herein which comprise a proximal gradient method (such as an accelerated proximal gradient method, for example, FIST A) having linear (e.g., Li penalty) components in the penalty function may converge on a set of beamlet weights faster (i.e., in fewer iterations), may result in a solution that has fewer violations of clinical dose and other constraints with fewer required interventions (such as weight tuning) by the user, may be simpler and less-computationally intensive to implement in a processor, and/or may be more amenable to parallelization on multi-core CPUs and/or GPUs.
• a proximal gradient method such as an accelerated proximal gradient method, for example, FIST A
• linear penalty e.g., Li penalty
• a fluence map generation method comprising a proximal gradient method with a penalty or cost function having one or more Li penalties may promote better plan conformance to user-specified planning goals, i.e., minimum target ROI or VOI dose, maximum OAR dose as compared to methods with a penalty or cost function having one or more L 2 penalties.
• FIGS. 1 A and IB are schematic depictions of the fluence map optimization problem.
• FIG. 1C depicts one variation of a method for generating a fluence map.
• FIG. ID depicts one example of a fluence map and
• FIG. IE depicts an axial slice of simulated dose delivered to a patient based on the fluence map of FIG. ID.
• FIG. 2 depicts Table 1 : Examples of penalty functions.
• FIG. 3 depicts Table 2: Notation and Definitions.
• FIG. 4 depicts Table 3 : Prox-operator calculus rules.
• FIG. 5 depicts a variation of a proximal gradient method with fixed step size (Algorithm 1).
• FIG. 6 depicts a variation of a proximal gradient method with line search (Algorithm 2).
• FIG. 7 depicts a variation of a FISTA method with fixed step size (Algorithm 3).
• FIG. 8 depicts a variation of a FISTA method with line search (Algorithm 4).
• FIG. 9 depicts a variation of a Chambolle-Pock method with overrelaxation (Algorithm 5).
• FIG. 10 depicts a dose distribution that results from a fluence map generation method comprising the Chambolle-Pock algorithm.
• FIG. 11 is a dose-volume histogram of the dose distribution of FIG. 10.
• a radiation therapy system may be used by a radiation therapy system to position a radiation source and to control the intensity of the generated radiation beam such that a selected/prescribed dose of radiation is applied to the ROI (e.g., target volume, irradiation target volume such as tumor regions) while limiting the amount of radiation applied to one or more organs-at-risk or OARs (e.g., irradiation-avoidance volumes).
• ROI e.g., target volume, irradiation target volume such as tumor regions
• OARs e.g., irradiation-avoidance volumes
• a FMO or fluence map generation method computes a set of beamlet intensities and angles that deliver the prescription dose to the target(s) while meeting OAR dose limits and other constraints.
• a system configured to generate a fluence map may be in communication with a radiation therapy system.
• a radiation therapy system may comprise a gantry that is movable (e.g., rotatable) about a patient treatment area, a radiation source mounted on the gantry, and a controller that is in communication with the gantry and the radiation source.
• a radiation therapy system may comprise a detector located opposite the radiation source that is also in communication with the controller.
• the controller may provide signals to a gantry motion system to position the radiation source at a particular location with respect to the radiation treatment area and may provide a sequence of radiation beamlet data (e.g., pulse intensity, width, duration, etc.) to the radiation source based on a fluence map (e.g., a fluence map generated by any of the fluence map generation methods described herein).
• the radiation source may comprise a multi-leaf collimator to shape the radiation beam.
• a system controller may be configured to convert a fluence map into a set of gantry motion and/or multi-leaf collimator instructions (using segmentation methods, for example).
• FIGS. lA and IB A schematic depiction of a patient 102 located within the treatment area of a radiotherapy system 100 is provided in FIGS. lA and IB.
• a beamlet 107 may be a portion of a full radiation beam 105 that is defined by a multi-leaf collimator leaf 101 opening at a particular firing position (e.g., a firing position 106a with respect to a patient area 111).
• a firing position e.g., a firing position 106a with respect to a patient area 111
• a set of all possible beamlets a subset of which is represented by 106a, 106b, 106c, 106d
• a radiation therapy system 100 having m firing positions may comprise a multi-leaf collimator 103 that may be positioned at each of the m firing positions.
• the MLC may have n leaves, and as such, there may be a total oim x n possible beamlets.
• a radiation therapy system comprising a binary multi-leaf collimator having 64 leaves that is located on a circular or rotatable gantry having 100 firing positions may have a total of 6400 possible beamlets.
• the total number of possible beamlets may also take into account patient platform movement through the therapy system, such that a system with p patient platform positions may have a total oim x n xp possible beamlets.
• the radiation therapy system described above may have from about 10 to about 100 patient platform positions, which may result in a number of possible beamlets from about 64,000 (64 x 100 x 10) to about 640,000 (64 x 100 x 100).
• an individual beamlet may be uniquely identified by its firing position or angle, collimator leaf index, and optionally, patient platform position.
• a set of candidate beamlets for fluence map generation may be a subset of the total number of possible beamlets.
• a set of candidate beamlets may be derived by removing the beamlets that do not intersect a volume of interest (e.g., an irradiation target volume, etc.) from the total number of possible beamlets.
• the beamlets where all beamlet weights (i.e., beamlet intensities) are 0 can be omitted.
• a mathematical optimization problem may be solved based on the set of candidate beamlets to calculate the beamlet weights (e.g., beamlet intensities) that apply a prescribed dose of radiation to a target region/ROI 108.
• Fluence map optimization is the method by which a set of "optimal" (i.e., satisfying the imposed constraints) beamlet weights are found.
• a FMO or fluence map generation method may comprise computing a set of beamlet weights that deliver the prescribed dose to the target while limiting radiation dose to OARs 110a, 110b, 110c.
• the shading of the beamlets 106a-106d may represent the weight of that beamlet (e.g., intensity), where a darker shade represents a higher beamlet weight (i.e., greater beamlet intensity).
• the fluence map generated by a FMO method may result in the application of radiation according to the profile depicted in FIG. 1 A, where the target 108 may receive the prescribed dose of radiation while the radiation exposure of the OARs 110a, 110b, 110c is reduced (e.g., below a selected threshold).
• volumes of interest may be divided into a plurality of voxels.
• each voxel may have an acceptable dose range.
• a voxel in an irradiation target region may have a minimum dose threshold for the treatment session to meet treatment goals and a maximum dose threshold above which a patient may be subject to undesired radiation risk.
• a voxel in an irradiation-avoidance region may have a maximum dose threshold above which undesired tissue damage may be expected to occur.
• this maximum dose threshold may be lower than the maximum dose threshold for the irradiation target region(s), since tissue in the irradiation-avoidance region may be particularly sensitive or prone to radiation damage.
• the acceptable dose range of a voxel may be calculated based on the prescribed dose for that volume of interest as specified by a treatment plan.
• the dose constraints on the voxels in the volumes of interest (VOIs) may be used in the fluence map generation methods described herein to derive a set of beamlet weights that meet these voxel dose constraints.
• fluence map generation methods may utilize the acceptable dose range per voxel as a constraint for evaluating whether a set of beamlet weights meets clinical goals.
• fluence map generation methods may aggregate the acceptable dose ranges of all of the voxels of a volume of interest as a single-value constraint (e.g., a single-value penalty or cost function) for evaluating whether a set of beamlet weights meets clinical goals.
• a single-value constraint e.g., a single-value penalty or cost function
• a fluence map generation method may comprise an iterative method including assigning a set of beamlet weights to a set of initial values (e.g., zero or a baseline value), calculating the dose per voxel based on the current value of the set of beamlet weights, comparing the calculated dose per voxel with the acceptable dose range per voxel to determine whether the current set of beamlet weights meets clinical goals and/or whether the current set of beamlet weights meets one or more stopping criteria, and if not, updating the set of beamlet weights to a new set of values.
• a set of beamlet weights to a set of initial values (e.g., zero or a baseline value)
• calculating the dose per voxel based on the current value of the set of beamlet weights
• comparing the calculated dose per voxel with the acceptable dose range per voxel to determine whether the current set of beamlet weights meets clinical goals and/or whether the current set of beamlet weights meets one
• stopping criteria may include, but are not limited to, the set of beamlet weights converging to across iterations (e.g., the difference between the set of beamlet weights of the current iteration and the set of beamlet weights of a previous iteration is less than a predetermined threshold; residual r is less than less than a threshold ⁇ ), and/or attaining an upper bound or number of iterations.
• Updating the set of beamlet weights from a previous iteration (x k_1 ) to a new set of beamlet weights for the current iteration (x k ) may be based on an accelerated proximal gradient method (such as FISTA), or any proximal algorithms (such as Chambolle-Pock methods), with one or more linear penalty functions.
• the fluence map generation methods described herein may be used to calculate beamlet weights for delivering a dose to one or more VOIs within the acceptable dose range of each VOL Examples of VOIs may include irradiation target regions, irradiation-avoidance regions (e.g., organs-at-risk, areas of particular radiation sensitivity), and/or any combination of such regions. Fluence mapping methods that are described in the context of generating a set of beamlet weights based on acceptable dose ranges for a single VOI may be expanded to generate a set of beamlet weights based on acceptable dose ranges for multiple VOIs.
• the dose range limits of a VOI and/or a plurality of voxels in the generation of a fluence map may be represented by a penalty function.
• a penalty function may comprise a plurality of penalties that represent VOI or voxel criteria or conditions that a fluence map generation method seeks to fulfill.
• a penalty function that may be included with a proximal gradient method e.g., an accelerated proximal gradient method such as FISTA
• FISTA accelerated proximal gradient method
• a penalty function may comprise one or more linear or nonlinear (e.g., quadratic) penalties that represent constraints based on acceptable dose ranges per voxel and/or VOI (e.g., as may be extracted from a treatment plan), as well as one or more linear or nonlinear (e.g., quadratic) penalties that represent constraints based on the smoothness of a set of beamlet weights.
• linear or nonlinear penalties e.g., quadratic penalties that represent constraints based on acceptable dose ranges per voxel and/or VOI (e.g., as may be extracted from a treatment plan)
• linear or nonlinear penalties e.g., quadratic penalties that represent constraints based on the smoothness of a set of beamlet weights.
• a linear penalty may be one in which deviations from a desired set of constraints are linearly weighted when evaluating whether a solution satisfies a set of requirements, while a nonlinear penalty (e.g., a quadratic penalty) may be one in which deviations from a desired set of constraints are nonlinearly weighted by a higher-order multiplicative factor.
• a nonlinear penalty e.g., a quadratic penalty
• a quadratic (or L2) type, penalty may amplify, or weight heavily, large deviations from a desired set of constraints (e.g., large deviations from acceptable dose ranges per voxel or VOI) when evaluating whether a solution (e.g., a set of beamlet weights) satisfies a set of requirements (e.g., dose as specified in a treatment plan).
• Linear (or Li type) penalties that may be included in a fluence map generation method may help converge on a set of beamlet weights that reduce the number of voxels and/or VOIs where the delivered dose exceeds the acceptable dose range.
• the penalty function may be a linear penalty function (e.g., having only linear penalties) while in other variations, the penalty function may be a nonlinear penalty function (e.g., comprising one or more nonlinear penalties).
• Some variations of a method for generating a fluence map may comprise an accelerated proximal gradient method having a single-value penalty function, which may be derived by aggregating the dose constraints of each voxel in a volume of interest.
• constraints imposed by a VOI may be represented by a single-value penalty function, and an accelerated proximal gradient method may iterate on a set of beamlet weights based on the single-value penalty function of each of the VOIs in a patient.
• penalties that may be included in a penalty function are described in greater detail below and/or tabulated in Table 1 of FIG. 2.
• Any of the fluence map generation methods described herein may optionally include a step of generating one or more penalty functions (e.g., multiple single-value penalty functions that correspond to multiple VOIs, multiple penalty functions for multiple sets of voxels) that represents the clinical and/or mathematical constraints that may be relevant to a particular patient and/or set of VOIs outlined in the treatment plan.
• a smoothing function e.g., convex regularization, Moreau-Yosida regularization
• convex optimization techniques may be used to address fluence map generation problems that may arise during radiation planning.
• proximal algorithms may be used for solving very large scale, constrained convex optimization problems with nondifferentiable objective functions.
• Some variations may comprise the use of proximal algorithms to address fluence map generation issues during treatment planning.
• a method for fluence map generation may comprise the use of the Alternating Direction Method of
• a method for fluence map generation may comprise the use of a proximal algorithm such as the Chambolle-Pock method.
• the Chambolle-Pock method may be able to handle nonquadratic dose-penalty terms, including nondifferentiable Ji-based penalties in the objective, and also hard constraints on the amount of radiation delivered to the planning target volume (PTV) and OARs.
• Another class of algorithms accelerated proximal gradient methods (including FISTA, for example) may also be used in a fluence map generation method. These accelerated methods have a convergence rate which has been shown to be in some sense optimal for first-order methods.
• a fluence map comprising a set of beamlet intensities that delivers radiation dose to every VOI within the acceptable dose range for that VOI.
• these methods may generate a fluence map such that the prescribed radiation dose to irradiation target regions (e.g., planning target volumes PTV, tumor regions, etc.) while not exceeding a maximum dose to irradiation-avoidance regions (e.g., organs at risk OARs).
• the acceptable radiation dose range for a VOI (and/or for the individual voxels within the VOI) may be determined at least in part by a treatment plan.
• a treatment plan may be generated based on images of the patient that have been acquired before a treatment session (e.g., during a diagnostic imaging session) and/or during a previous treatment session or fraction.
• Some variations for generating a fluence map may comprise generating a set of beamlet weights or intensities using an accelerated proximal gradient method, such as FISTA or a proximal method, such as the Chambolle-Pock method.
• FISTA has been used for solving inverse problems in signal or image processing and in particular, for compression, denoising, image restoration, sparse approximation of signals, compressed sensing and the like.
• Accelerated proximal gradient methods may include a penalty function having a linear or Li penalty term to promote signal sparsity so that signals can be compressed.
• accelerated proximal gradient methods such as FISTA and proximal methods such as Chambolle-Pock have not been considered for generating a set of beamlet weights or intensities for radiation treatment plans and systems.
• accelerated proximal gradient methods such as FISTA may be able to provide a computationally efficient method to generate a fluence map.
• the fluence map generation methods described herein may comprise using an accelerated proximal gradient method such as FISTA with one or more linear or Li penalty terms.
• the Li penalty terms may be smoothed by a regularization method to help reduce discontinuities in the FISTA method.
• FISTA with linear penalty functions having one or more Li penalty terms may be readily implemented on multi-core processors (CPUs and/or GPUs), facilitate faster convergence to a final set of beamlet weights, and/or may promote better plan conformance to user or clinician-specified planning goals or treatment plans.
• FISTA with Li penalty terms may result in a set of beamlet weights that deliver dose to VOIs that conform better to treatment plan specifications as compared to accelerated proximal gradient methods have L2 penalty terms.
• Fluence map generation methods that have L2 penalty terms may result in a set of beamlet weights that deliver fluence such that a higher proportion of VOIs or voxels receive dose levels that are outside of their acceptable dose ranges (as compared to methods with Li penalty terms).
• Fluence map generation methods with L2 penalties may require users or clinicians to over-constrain the problem or iteratively tuning the objective weights, etc., resulting in higher computational load on the processor and a fluence map that does not conform as closely to treatment plan specifications
• FIG. 1C depicts one variation of a method for generating a fluence map.
• the method 150 may comprise selecting 152 one or more volumes of interest (VOIs).
• a VOI may include one or more irradiation target (e.g., PTV, tumor region, etc.) and/or may include one or more irradiation- avoidance regions).
• the method 150 may comprise selecting 154 voxels in the one or more VOIs. In some variations, the selected voxels may, cumulatively, approximate the overall size, shape and location of each of the VOIs.
• the method 150 may comprise calculating 158 a dose matrix A for each volume of interest based on the set of candidate beamlets b.
• the dose matrix A may represent per-voxel dose delivered to each of the plurality of voxels by the set of candidate beamlets b.
• the method may comprise initializing 160 beamlet intensity weights to zero (e.g., the initial set of beamlet weights x° may be an all-zero vector).
• the method may comprise calculating 162 a set of beamlet intensity weights x k by adjusting the initial beamlet intensity weights according to a proximal gradient method update (x k" l ⁇ x k ) with a penalty function containing one or more linear penalties, such as an accelerated proximal gradient method.
• a proximal gradient method update x k" l ⁇ x k
• a penalty function containing one or more linear penalties such as an accelerated proximal gradient method.
• This may be an iterative method where a set of beamlet intensity weights is adjusted based on the penalty function until one or more stopping criteria are met (step 164), and may be any of the methods described below.
• the accelerated proximal gradient method may be a FISTA method having one or more Li cost or penalty functions (e.g., as explained further below and represented in FIGS. 7-8).
• calculating 162 a set of beamlet intensity weights may comprise adjusting beamlet intensity weights according to a proximal gradient method or more generally, a proximal method.
• the stopping criteria may include convergence of the set of beamlet intensity weights to a set of intensity values. Convergence from one iteration to the next may be determined, for example, by comparing the set of beamlet intensity weights of iteration x to the set from iteration x-1 and taking the difference (e.g., a residual) in values between the two sets. If the difference is smaller than a predetermined threshold the stopping criterion may be met and the iteration may stop. Other stopping criteria are described below.
• iteration may stop if the number of iterations attains an upper bound.
• the method 150 may comprise calculating a fluence map comprising a final set of beamlet intensity weights x k .
• FIG. ID depicts one example of a fluence map (i.e., set of beamlet intensities) that has been generated using the FISTA method having a smoothed Li penalty function.
• This fluence map was generated for a radiation therapy system having 100 firing positions (e.g., 100 firing angles around a circular gantry) and a multi-leaf collimator having 60 leaves.
• the multi- leaf collimator is a binary multi-leaf collimator.
• the fluence map in FIG. ID may represent the beamlet intensity for a single patient platform position, e.g., a single tomographic slice.
• the intensity of a pixel in the plot may be proportional to the beamlet intensity.
• FIG. IE depicts an axial slice of a simulated patient body where radiation beamlets were delivered based on the fluence map of FIG. ID.
• the fluence map of FIG. ID resulted in a dose delivery that concentrates on a ROI 130, with little or no irradiation of other patient regions.
• the Chambolle-Pock algorithm and accelerated proximal gradient methods may be advantageous in that they perform matrix-vector multiplications with the dose calculation matrix at each iteration. These methods may not include solving a linear system at each iteration, and hence may not include decomposition methods to make such a calculation manageable.
• the fluence map generation methods described herein may be effective algorithms that parallelize naturally and are particularly easy to implement in a controller of a radiotherapy system.
• the matrices Ai are the dose-calculation matrices for one or more VOIs (e.g., planning target volume PTV, irradiation target regions or volumes, OARs, irradiation avoidance regions or volumes, the tumor, etc.), Nis the number of OARs, the matrix D represents a discrete derivative or gradient operator, and the functions ⁇ , ⁇ ⁇ ( ⁇ and ⁇ , are convex penalty functions.
• ⁇ ( ⁇ ) encourages or defines a minimum level of radiation to be delivered to the PTV, while the terms (A/x) encourage or require the radiation delivered to the PTV and OARs not to exceed a maximum dosage.
• the regularization terms ⁇ ( ⁇ ) ⁇ ) and ⁇ ( ⁇ ) encourage smooth or pi ecewise- smooth nonnegative fluence maps.
• Problem (1) may contain most standard fluence map optimization formulations as special cases, with simple and convenient choices of the convex penalty functions.
• the optimization algorithms described herein may be able to handle nonquadratic and nondifferentiable penalty terms ⁇ , ⁇ ⁇ and ⁇ in fluence map optimization. By taking the penalty functions to be indicator functions (defined in equation (4)), hard constraints may be enforced on the amount of radiation delivered to the PTV and OARs.
• the advantages of the Li-norm may apply in fluence map optimization, as will be described further below.
• fluence map generation methods that comprise proximal algorithms with one or more penalties, such as Li penalties.
• the Chambolle-Pock algorithm may be used to solve problem (1) in the case where the penalty functions ⁇ , ⁇ ⁇ ( ⁇ and ⁇ have proximal operators that may be evaluated efficiently. This may include most fluence map optimization problems encountered in practice, including those with hard constraints and nondifferentiable objective functions.
• a fluence map generation method may comprise a smoothing technique from convex analysis, such as the Moreau-Yosida regularization, to smooth out the penalty functions ⁇ , ⁇ ⁇ and ⁇ , and an accelerated proximal gradient method (such as FISTA) to solve the smoothed out problem (which may involve nonquadratic penalties).
• the smoothed problem may contain, as a special case, all fluence map generation problems considered in the unified approach to inversion problems in intensity modulated radiation therapy (IMRT).
• Fluence map generation methods may include the selection of penalty functions
• Equation (1) may contain most standard FMO models as special cases, with simple and convenient choices of the penalty functions. Examples of penalty functions that may be used in the fluence map generation methods described herein are summarized in table 1, depicted in FIG. 2.
• £ is a vector that lists the prescribed doses that are to be delivered to each voxel in the tumor, but an arguably superior option is to take ⁇ to be a one-sided Ll-norm-based penalty:
• an J2-based penalty tends to allow a large number of voxels to be slightly underdosed, which is undesirable, and on practical FMO problems, consistenyly leads to slight underdosing of the target, and slight overdosing of the OAR.
• an Ji-based penalty is discourages the presence of small residuals, and encourages most residuals to be 0.
• a third important option is to take ⁇ to be an indicator function:
• This indicator function penalty may be able to enforce the hard constraint that A t
• the method may include the penalty function
• fluence map optimization methods and/or fluence map generation methods may include a quadratic regularization term where Mis the number of beamlets in the IMRT system, x is the vector of beamlet intensities, x m is the mth block of x (consisting of beamlet intensities for the mth beamlet), and each matrix D m represents a discrete derivative or gradient operator.
• This regularization term encourages adjacent beamlets to have similar intensities, thereby leading to less chaotic fluence maps.
• the regularization term may facilitate (i.e., speed up) convergence of optimization algorithms so that a solution (e.g., a set of beamlet weights or intensities) may be attained in fewer iterations.
• a quadratic penalty function due to the use of a quadratic penalty function, large components of Dx may be penalized severely (because they get squared), and as a result this regularization term tends not to allow any sharp jumps in intensity between adjacent beamlets. This may detract from the creation of treatment plans that are highly conformable to the tumor.
• a similar problem may be encountered in image restoration and reconstruction problems, where the use of a quadratic regularization term does not allow sharp edges in the image to be preserved.
• a regularization term may be the total variation regularization term
• ⁇ may be the indicator function for an L ⁇ -norm ball. A hard upper bound on the change in intensity between adjacent beamlets may be enforced. These choices of ⁇ may facilitate computations that are able to handle nonquadratic and nondifferentiable penalty terms.
• ⁇ is chosen to be the indicator function for the nonnegative orthant, denoted by / >0 .
• the term ⁇ ( ⁇ ) in the objective simply enforces the constraint that x > 0.
• ⁇ ( ⁇ ) / >0 (x) + (e/2)
• 2 or ⁇ ( ⁇ ) / >0 (x) + e ⁇ x W i_ .
• These choices of ⁇ control the size of x while also enforcing x > 0.
• Penalizing the Ji-norm of x could be useful, for example, to limit the number of beamlets that fire during treatment delivery (in other words, to promote sparsity in the fluence map). Penalizing the J2-norm of x may help to limit the total energy delivered during treatment.
• problem (1) may be solved using the Chambolle-Pock algorithm, under the assumption that the penalty functions ⁇ , ⁇ ⁇ , ⁇ and ⁇ have proximal operators that can be evaluated efficiently. This may include one or more (e.g., all) of the penalty functions described herein. Also disclosed are numerical results for the FMO problem
• C c E n be a closed convex set.
• the indicator function of C is the convex function Ic : E n — > E U ⁇ defined by si x (..'.
• Indicator functions are useful in convex optimization (including any of the methods described herein) for enforcing hard constraints on the optimization variable x.
• the problem of minimizing/ (x) subject to the constraint that x G C, is equivalent to the problem of minimizing f(x) + I c (x) - Indicator functions are highly nondifferentiable, but this poses no problem for proximal algorithms, which are able to handle indicator functions naturally.
• Conjugate The convex conjugate of a function / : E n — > E U ⁇ is the function * : E n ⁇ E U ⁇ defined by [0064]
• Proximal operator Let / : E n — > R U ⁇ be a proper closed convex function.
• the proximal operator (also known as "prox-operator") of/ with parameter t > 0, is defined by
• Proximal algorithms are iterative algorithms that require the evaluation of various prox- operators at each iteration.
• the prox-operator has a simple closed-form expression and can be evaluated very efficiently, at a computational cost that is linear in m.
• the term "proximable" may describe a function whose prox-operator can be evaluated efficiently.
• proximal gradient method solves optimization problems of the form
• the proximal gradient method is known as the projected gradient method.
• a proximal gradient method may be used with a fixed step size
• a significant advantage of the proximal gradient method is that there is a simple and effective line search procedure that may be used to select the step size adaptively at each iteration.
• One variation of a proximal gradient method with line search is represented in algorithm 2 (FIG. 6).
• One variation of a fluence map generation method may comprise accelerated versions of the projected gradient method and the proximal gradient method for radiation treatment planning.
• FISTA short for "fast iterative shrinkage-thresholding algorithm”
• FISTA is an accelerated version of the proximal gradient method for solving problem (7), where (as before) / and g are closed convex functions, and /is differentiable with a Lipschitz continuous gradient (with Lipschitz constant J > 0).
• One variation of a FISTA iteration for a fixed step size t > 0 is
• the Chambolle-Pock algorithm is a primal-dual algorithm, meaning that it simultaneously solves the primal problem (9) and the dual problem, which is [0077]
• algorithm 5 At each iteration we perform matrix- vector multiplications by A and ⁇ 4 r are performed, but are not required to solve linear systems involving ⁇ . This is an advantage of the Chambolle-Pock algorithm over Douglas-Rachford-based methods such as ADMM.
• Algorithm 5 (FIG. 9) is an overrelaxed version of the Chambolle-Pock algorithm.
• step sizes s and t are required to satisfy st ⁇ A ⁇ 2 ⁇ 1, where II ⁇ II is the matrix norm induced by the J2-norm.
• FISTA accelerated proximal gradient methods
• the difficulty in using the proximal gradient method for fluence map optimization is that one must first express the optimization problem in the form (7), with a differ entiable function / and a simple (i.e., proximable) function g, and this is not always possible. This challenge may be addressed by smoothing out the nondifferentiable penalty functions appearing in problem (1).
• Convex analysis provides an elegant way to smooth out a nondifferentiable convex function—the Moreau-Yosida regularization.
• Moreau-Yosida regularization Let ⁇ p: E n ⁇ E U ⁇ be closed and convex.
• the parameter ⁇ controls the amount of smoothing— for small values of y, there is little smoothing and is a close approximation to ⁇ .
• the Lipschitz constant L is large.
• ⁇ ⁇ ⁇ -x i ⁇ prox... ,.. ⁇ ;r i ! -i iiprox, ⁇ ) — xm .
• each iteration may comprise computing the gradient of / and the prox-operator of g.
• prox-operator of / is just the prox-operator of ⁇ , and in some variations, may be evaluated efficiently.
• g is a separable sum.
• the Moreau decomposition theorem expresses the prox-operators of the functions ⁇ * , ⁇ and ⁇ * in terms of the prox-operators of the functions ⁇ , ⁇ ⁇ ( and ⁇ . These prox-operators are assumed to be inexpensive. Thus, it may be that the prox-operator of g* may be evaluated efficiently.
• Table 1 depicted in FIG. 2 gives formulas for the prox-operators of the most typical penalty functions ⁇ , ⁇ ⁇ ( ⁇ , and ⁇ . These formulas follow from the results listed in section 3, including the scaling and shifting rule (3.2). Numerical results
• the Chambolle-Pock algorithm may be used to solve the fluence map optimization problem minimize u 10 )
• the PTV is delineated by contour 1000.
• the region that receives at least 50 gray delineated by contour 1002 coincides almost exactly with the PTV contour 1000.
• the iterative portion of the fluence map generation method may exit once stopping criteria are attained (step 164 of the method 150).
• Fluence map generation or optimization methods comprising FISTA may have stopping criteria as described below.
• a proximal gradient method seeks to solve the primal problem: minimize
• a proximal gradient method may iterate to find r such that r ⁇ Vg(x) + dh(x) and r is nearly equal to 0, then this means that (13) is nearly satisfied and x is close to optimal.
• equation (12) may be equivalent to
• Condition (15) almost has the desired form, which may be rewritten equivalently as the following:
• a possible stopping criterion may be
• a stopping criterion may include a "relative" residual, in which case the stopping criterion may be approximately: ll* + ll
• Equati on ( 17) may b e equi val ent to : y- tVg(y)-x k
• a less computationally- intensive stopping criterion (i.e., which may not require evaluating the gradient of g at x k ) may include: y-Xk
• Fluence map generation or optimization methods comprising Chambolle-Pock may have stopping criteria as described below.
• Chambolle-Pock solves the primal problem: minimize
• a stopping criterion may be based on the duality gap.
• G is the indicator function for the nonnegative orthant, and it can be shown that G * is the indicator function for the nonpositive orthant. If the condition -K z ⁇ 0 is met, z may be dual feasible. In some variations, this may not be satisfied by z n+l .
• a stopping criterion may be based on residuals in KKT conditions.
• the Karush-Kuhn-Tucker (KKT) conditions are the set of necessary conditions for a solution of an optimization problem to be optimal, which can be expressed as a system of equations and inequalities involving the cost function and constraints on inputs. This may be used in certain variations where a stopping criterion based on the duality gap may be computationally intensive and/or where the conditions for a stopping criterion based on the duality gap are not met.
• the KKT conditions for the problem (18) can be written as
• equation (19) may be rewritten as follows: z n + ⁇ ⁇ -z n+1
• equation (25) may be expressed equivalently as:
• This stopping criterion may include an extra multiplication by K each iteration (e.g., K(x n — x n+1 )).
• K the number of iterations
• one variation of a method for generating a fluence map comprising a Chambolle-Pock method may be to check the stopping criteria or residuals at some iterations but not others.
• the residuals may be calculated once every 20 or so iterations instead of every iteration.
• the residuals may be calculated once every 2 iterations, once every 5 iterations, once every 10 iterations, once every 12 iterations, once every 25 iterations, once every 30 iterations, once every 50 iterations, etc.
• the residuals may be calculated at every iteration.
• the FMO or fluence map generation methods described herein may be implemented by a computer or controller, for example, in a computer program or software stored in a machine- readable storage medium.
• the computer or controller may be part of a radiation therapy system such that a fluence map generated by the FMO or fluence map generation methods described herein may be used, for example, to position a radiation source at a particular angle with respect to the ROI or patient (by rotating a gantry on which the radiation source is mounted) and to adjust the beam intensity generated by the radiation source in accordance with the fluence map.
• the FMO or fluence map generation methods described herein may be implemented by a computer or controller that is separate from a radiation therapy system.
• the resultant fluence map may then be transferred to the controller of a radiation therapy system using a machine-readable transmission medium, such as (but not limited to) electrical, optical, acoustical, or other type of medium suitable for transmitting electronic information.
• a system e.g., a treatment planning system that may be configured to generate fluence maps based on treatment plan parameters may comprise a controller in communication with a radiation therapy system and/or a clinician and/or operator.
• the controller may comprise one or more processors and one or more machine-readable memories in communication with the one or more processors.
• the controller may be connected to a radiation therapy system and/or other systems by wired or wireless communication channels.
• the controller of a treatment planning system may be located in the same or different room as the patient.
• the controller may be coupled to a patient platform or disposed on a trolley or medical cart adjacent to the patient and/or operator.
• the controller may be implemented consistent with numerous general purpose or special purpose computing systems or configurations.
• environments, and/or configurations that may be suitable for use with the systems and devices disclosed herein may include, but are not limited to software or other components within or embodied on personal computing devices, network appliances, servers or server computing devices such as routing/connectivity components, portable (e.g., hand-held) or laptop devices,
• Examples of portable computing devices include smartphones, personal digital assistants (PDAs), cell phones, tablet PCs, phablets (personal computing devices that are larger than a smartphone, but smaller than a tablet), wearable computers taking the form of smartwatches, portable music devices, and the like.
• PDAs personal digital assistants
• phablets personal computing devices that are larger than a smartphone, but smaller than a tablet
• wearable computers taking the form of smartwatches, portable music devices, and the like.
• a processor may be any suitable processing device configured to run and/or execute a set of instructions or code and may include one or more data processors, image processors, graphics processing units, physics processing units, digital signal processors, and/or central processing units.
• the processor may be, for example, a general purpose processor, Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), or the like.
• the processor may be configured to run and/or execute application processes and/or other modules, processes and/or functions associated with the system and/or a network associated therewith (not shown).
• MOSFET metal-oxide semiconductor field-effect transistor
• CMOS complementary metal-oxide semiconductor
• ECL emitter-coupled logic
• polymer technologies e.g., silicon-conjugated polymer and metal-conjugated polymer-metal structures
• mixed analog and digital or the like.
• memory may include a database (not shown) and may be, for example, a random access memory (RAM), a memory buffer, a hard drive, an erasable
• EPROM programmable read-only memory
• EPROM electrically erasable read-only memory
• the memory may store instructions to cause the processor to execute modules, processes and/or functions associated with the system , such as the calculation of fluence maps based on treatment plan and/or clinical goals, segmentation of fluence maps into radiation therapy system instructions (e.g., that may direct the operation of the gantry, therapeutic radiation source, multi-leaf collimator, and/or any other components of a radiation therapy system and/or diagnostic or treatment planning system), and image and/or data processing associated with treatment planning and/or delivery.
• modules, processes and/or functions associated with the system such as the calculation of fluence maps based on treatment plan and/or clinical goals, segmentation of fluence maps into radiation therapy system instructions (e.g., that may direct the operation of the gantry, therapeutic radiation source, multi-leaf collimator, and/or any other components of a radiation therapy system and/or diagnostic or treatment planning system), and image and/or data processing associated with treatment planning and/or delivery.
• Some embodiments described herein relate to a computer storage product with a non- transitory computer-readable medium (also may be referred to as a non-transitory processor- readable medium) having instructions or computer code thereon for performing various computer- implemented operations.
• the computer-readable medium (or processor-readable medium) is non- transitory in the sense that it does not include transitory propagating signals per se (e.g., a propagating electromagnetic wave carrying information on a transmission medium such as space or a cable).
• the media and computer code also may be referred to as code or algorithm
• non-transitory computer-readable media include, but are not limited to, magnetic storage media such as hard disks, floppy disks, and magnetic tape; optical storage media such as Compact Disc/Digital Video Discs (CD/DVDs); Compact Disc-Read Only Memories (CD-ROMs), and holographic devices; magneto- optical storage media such as optical disks; solid state storage devices such as a solid state drive (SSD) and a solid state hybrid drive (SSFID); carrier wave signal processing modules; and hardware devices that are specially configured to store and execute program code, such as Application- Specific Integrated Circuits (ASICs), Programmable Logic Devices (PLDs), Read-Only Memory (ROM), and Random-Access Memory (RAM) devices.
• ASICs Application- Specific Integrated Circuits
• PLDs Programmable Logic Devices
• ROM Read-Only Memory
• RAM Random-Access Memory
• a user interface may serve as a communication interface between an operator or clinician and the treatment planning system.
• the user interface may comprise an input device and output device (e.g., touch screen and display) and be configured to receive input data and output data from one or more of the support arm, external magnet, sensor, delivery device, input device, output device, network, database, and server.
• Sensor data from one or more sensors may be received by user interface and output visually, audibly, and/or through haptic feedback by one or more output devices.
• operator control of an input device may be received by user and then processed by processor and memory for user interface to output a control signal to one or more support arms, external magnets, intracavity devices, and delivery devices.
• a treatment planning system for generating fluence maps may comprise a display device that may allow an operator to view graphical and/or textual representations of fluence maps, and/or dose distributions, and/or regions of interest, and/or volumes of interest, and/or patient anatomical images, and/or patient data (e.g., physiological and/or biological), and the like.
• an output device may comprise a display device including at least one of a light emitting diode (LED), liquid crystal display (LCD), electroluminescent display (ELD), plasma display panel (PDP), thin film transistor (TFT), organic light emitting diodes (OLED), electronic paper/e-ink display, laser display, and/or holographic display.
• LED light emitting diode
• LCD liquid crystal display
• ELD electroluminescent display
• PDP plasma display panel
• TFT thin film transistor
• OLED organic light emitting diodes
• a treatment planning system may be in communication with other computing devices (not shown) via, for example, one or more networks, each of which may be any type of network (e.g., wired network, wireless network).
• a wireless network may refer to any type of digital network that is not connected by cables of any kind. Examples of wireless communication in a wireless network include, but are not limited to cellular, radio, satellite, and microwave communication.
• a wireless network may connect to a wired network in order to interface with the Internet, other carrier voice and data networks, business networks, and personal networks.
• a wired network is typically carried over copper twisted pair, coaxial cable and/or fiber optic cables.
• network refers to any combination of wireless, wired, public and private data networks that are typically interconnected through the Internet, to provide a unified networking and information access system.
• Cellular communication may encompass technologies such as GSM, PCS, CDMA or GPRS, W-CDMA, EDGE or CDMA2000, LTE, WiMAX, and 5G networking standards. Some wireless network deployments combine networks from multiple cellular networks or use a mix of cellular, Wi-Fi, and satellite communication.
• the systems, apparatuses, and methods described herein may include a radiofrequency receiver, transmitter, and/or optical (e.g., infrared) receiver and transmitter to communicate with one or more devices and/or networks.

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