Abstract: A computing system comprising a central processing unit (CPU), and memory coupled to the CPU and having stored therein instructions that, when executed by the computing system, cause the computing system to execute operations to generate a radiation treatment plan. The operations include accessing a minimum prescribed dose to be delivered into and across the target, determining a number of beams and directions of the beams, and determining a beam energy for each of the beams, wherein the number of beams, the directions of the beams, and the beam energy for each of the beams are determined such that the entire target receives the minimum prescribed dose. A quantitative time-dependent model-based charged particle pencil beam scanning optimization is then implemented for FLASH therapy.

Abstract: Nominal values of parameters, and perturbations of the nominal values, that are associated with previously defined radiation treatment plans are accessed. For each treatment field of the treatment plans, a field-specific planning target volume (fsPTV) is determined based on those perturbations. At least one clinical target volume (CTV) and at least one organ-at-risk (OAR) volume are also delineated. Each OAR includes at least one sub-volume that is delineated based on spatial relationships between each OAR and the CTV and the fsPTV for each treatment field. Dose distributions for the sub-volumes are determined based on the nominal values and the perturbations. One or more dose prediction models are generated for each sub-volume. The dose prediction model(s) are trained using the dose distributions.

Abstract: Embodiments of the present invention provide an integrated solution to radiotherapy treatment planning that enables accurate recording and accumulation of physical parameters as input (e.g., dose, dose rate, irradiation time per voxel, etc.). User-defined functions are evaluated to correlate the input parameters with 4D biological outcomes. The resulting biological parameters can be visualized as a biological outcome map to evaluate decisions, support decisions, and optimize decisions regarding the parameters of the radiotherapy treatment plan, for example, for supporting clinical trials and related clinical research.

Abstract: An apparatus for determining material property, includes: an interface configured to obtain a first HU value associated with a first image of an object, and to obtain a second HU value associated with a second image of the object, wherein the first image is created using a first energy having a first energy level, and wherein the second image is created using a second energy having a second energy level that is different from the first energy level; and a processing unit configured to determine a weighted property value for the object based at least in part on the first HU value and the second HU value.

Abstract: A search space used for multicriteria optimization in radiation treatment planning can be expanded using extrapolation. For instance, given an initial set of base plans, one or more virtual plans can be generated by assigning a weight to each base plan such that at least one of the weights is less than zero and/or such that the sum of the weights is not normalized to 1. The dose distribution for the virtual plan is computed as the weighted sum of the dose distributions of the base plans. Virtual plans criteria can be used together with the initial set of base plans to define an expanded search space within which interpolation can be performed to identify an optimal treatment plan.

Abstract: Multi-criteria optimization of radiation therapy tools for receiving a plurality of criteria including one or more sets of competing objectives. The competing objective can include delivery time criteria, fractionation criteria or similar time-based criteria. One or more radiation therapy plans can be determined based on a multi-criteria optimization of the plurality of criteria for the radiation therapy treatment. The one or more optimized radiation therapy plans can be output for consideration by one or more members of a radiation oncology team.

Abstract: A computer implemented method of determining a resultant treatment plan for a proton radiation therapy system based on given dose volume constraints, wherein the resultant treatment plan is optimized for treatment time comprises accessing the dose volume constraints and range information, wherein the range information indicates acceptable deviations from the dose volume constraints. Based on the proton radiation therapy system, the method further comprises accessing machine configuration information comprising a plurality of machine parameters that define a maximum resolution achievable in irradiating a patient. Further, the method comprises iteratively adjusting the plurality of machine parameters to values which decrease the maximum resolution and simulating a plurality of candidate treatment plans to generate a plurality of treatment plan results, wherein each treatment plan result comprises: a respective treatment time and a respective plan quality.

Abstract: A clinical goal for radiation treatment of a patient is set. A dose prediction model is selected from a number of dose prediction models based on the clinical goal. A radiation treatment plan is then generated for the patient using the dose prediction model that was selected based on the clinical goal.

Abstract: A computer implemented method of determining a resultant treatment plan for a proton radiation therapy system based on given dose volume constraints, wherein the resultant treatment plan is optimized for treatment time comprises accessing the dose volume constraints and range information, wherein the range information indicates acceptable deviations from the dose volume constraints. Based on the proton radiation therapy system, the method further comprises accessing machine configuration information comprising a plurality of machine parameters that define a maximum resolution achievable in irradiating a patient. Further, the method comprises iteratively adjusting the plurality of machine parameters to values which decrease the maximum resolution and simulating a plurality of candidate treatment plans to generate a plurality of treatment plan results, wherein each treatment plan result comprises: a respective treatment time and a respective plan quality.

Abstract: Embodiments of the present invention provide an integrated solution to radiotherapy treatment planning that enables accurate recording and accumulation of physical parameters as input (e.g., dose, dose rate, irradiation time per voxel, etc.). User-defined functions are evaluated to correlate the input parameters with 4D biological outcomes. The resulting biological parameters can be visualized as a biological outcome map to evaluate decisions, support decisions, and optimize decisions regarding the parameters of the radiotherapy treatment plan, for example, for supporting clinical trials and related clinical research.

Abstract: A computing system comprising a central processing unit (CPU), and memory coupled to the CPU and having stored therein instructions that, when executed by the computing system, cause the computing system to execute operations to generate a radiation treatment plan. The operations include accessing a minimum prescribed dose to be delivered into and across the target, determining a number of beams and directions of the beams, and determining a beam energy for each of the beams, wherein the number of beams, the directions of the beams, and the beam energy for each of the beams are determined such that the entire target receives the minimum prescribed dose. A quantitative time-dependent model-based charged particle pencil beam scanning optimization is then implemented for FLASH therapy.

Abstract: System includes a beam generator to generate beam(s) for patient treatment and a computing device that obtains three-dimensional image(s) of a target structure that repositions with respect to surrounding tissue of the patient. The computing device creates plan(s) including a first three-dimensional probability distribution of patient's position and a second three-dimensional probability distribution of the repositioned structure's internal position. The computing device combines the first distribution with the second distribution to generate a joint distribution and selects a probability level from the joint distribution. The probability level defines an enclosed surface. A distance defined between the surface and a point of origin in at least one direction is equal to a threshold value of a parameter of the repositioned target in the direction.

Abstract: Computer-implemented methods and systems can be used to facilitate generation of VMAT treatment plans for providing radiation therapy to patients. Starting from a seed plan, a library of alternative plans, each with an associated outcome for a set of treatment objectives, can be generated via a set of parallel optimization operations performed on the seed plan. A graphical user interface is provided, via which the user can navigate within a treatment space defined by the alternative plans to identify a satisfactory outcome. Based on the satisfactory outcome and a candidate plan selected from the alternative plans, a deliverable VMAT treatment plan can be generated using another optimization operation.

Abstract: A control circuit uses a (possibly self-generated) seed radiation treatment plan to identify a portion (possibly only a point) of a multi-criteria optimization (MCO)-based Pareto surface. The control circuit then selects a sampling plan set for MCO planning by enlarging that portion of the Pareto surface region to thereby facilitate developing an optimized radiation treatment plan. A radiation treatment platform then uses that optimized radiation treatment plan to treat a patient by administering the radiation in accordance with the plan.

Abstract: A computer implemented method of determining a resultant treatment plan for a proton radiation therapy system based on given dose volume constraints, wherein the resultant treatment plan is optimized for treatment time comprises accessing the dose volume constraints and range information, wherein the range information indicates acceptable deviations from the dose volume constraints. Based on the proton radiation therapy system, the method further comprises accessing machine configuration information comprising a plurality of machine parameters that define a maximum resolution achievable in irradiating a patient. Further, the method comprises iteratively adjusting the plurality of machine parameters to values which decrease the maximum resolution and simulating a plurality of candidate treatment plans to generate a plurality of treatment plan results, wherein each treatment plan result comprises: a respective treatment time and a respective plan quality.

Abstract: Multi-criteria optimization of radiation therapy tools for receiving a plurality of criteria including one or more sets of competing objectives. The competing objective can include delivery time criteria, fractionation criteria or similar time-based criteria. One or more radiation therapy plans can be determined based on a multi-criteria optimization of the plurality of criteria for the radiation therapy treatment. The one or more optimized radiation therapy plans can be output for consideration by one or more members of a radiation oncology team.

Abstract: A control circuit utilizes patient information and treatment-platform information to optimize a radiation-treatment plan by permitting isocenters of various radiation-treatment fields as comprise parts of a same treatment plan to not be coincidental with one another to thereby yield an optimized treatment plan. The patient information can pertain to one or more physical aspects of the patient as desired. By one approach, the foregoing can comprise scattering the isocenters of the various radiation-treatment fields around a predetermined point (such as, for example, the center of the treatment volume and/or some or all of the beams). This approach can comprise causing an area of highest energy flux for a given field to be non-coincident for at least some of the radiation-treatment fields as are specified by the radiation-treatment plan.

Abstract: Target values for quality metrics associated with the radiation treatment plan are accessed. Cost function contours are generated; each of the cost function contours includes a respective first set of values of the quality metrics calculated with a respective cost function based on a respective one of the target values. A region that includes a second set of values of the quality metrics is defined; the region is bounded by the cost function contours. A final (optimized) radiation treatment plan is selected from a set of radiation treatment plans that have values of the quality metrics that are within the region.

Abstract: A control circuit utilizes patient information and treatment-platform information to optimize a radiation-treatment plan by permitting isocenters of various radiation-treatment fields as comprise parts of a same treatment plan to not be coincidental with one another to thereby yield an optimized treatment plan. The patient information can pertain to one or more physical aspects of the patient as desired. By one approach, the foregoing can comprise scattering the isocenters of the various radiation-treatment fields around a predetermined point (such as, for example, the center of the treatment volume and/or some or all of the beams). This approach can comprise causing an area of highest energy flux for a given field to be non-coincident for at least some of the radiation-treatment fields as are specified by the radiation-treatment plan.

Abstract: Computer-implemented methods and systems can be used to facilitate generation of VMAT treatment plans for providing radiation therapy to patients. Starting from a seed plan, a library of alternative plans, each with an associated outcome for a set of treatment objectives, can be generated via a set of parallel optimization operations performed on the seed plan. A graphical user interface is provided, via which the user can navigate within a treatment space defined by the alternative plans to identify a satisfactory outcome. Based on the satisfactory outcome and a candidate plan selected from the alternative plans, a deliverable VMAT treatment plan can be generated using another optimization operation.