Abstract: Fractionation optimization receives inputs including a radiation dose distribution to be delivered by fractionated radiation therapy, maximum and minimum number of fractions, and Biologically Effective Dose (BED) constraints for one or more organs-at-risk. A two-dimensional (2D) graph is displayed of a parameter X equal to or proportional to (I) versus a parameter Y equal to or proportional to (II) where N is the number of fractions, D is a total radiation dose to be delivered by the fractionated radiation therapy, and dt is the fractional dose in fraction t. A constraint BED lines are displayed on the 2D graph depicting each BED constraint. A marker is displayed at a location on the 2D graph defined by a current fractionation and a current total dose. A new value for the current fractionation and/or the current total dose is received, and the marker is updated accordingly.

Abstract: In providing radiation therapy (RT) support, a patient specific toxicity risk is estimated for a RT side effect using a Bayesian network that receives as inputs values of biomarkers of the patient. A patient-specific RT plan is optimized with respect to parameters including the patient-specific toxicity risk. During delivery of RT according to the plan, at least one updated value is received for the biomarkers of the patient, and an updated patient-specific toxicity risk is estimated using the Bayesian network with the updated value(s). The Bayesian network biomarker nodes and a toxicity risk node representing the patient specific toxicity risk, and directed arcs with arc weights representing strengths of interdependencies between the nodes connected by the directed arcs. A graphical user interface (GUI) is provided via which a clinician may interact with the Bayesian network.

Abstract: A device for optimizing a radiation therapy plan (30) for delivering therapeutic radiation to a patient using a therapeutic radiation source (16) while modulated by a multi-leaf collimator (MLC) (14) includes at least one electronic processor (25) connected to a radiation therapy device (12). A non-transitory computer readable medium (26) stores instructions readable and executable by the at least one electronic processor to perform a radiation therapy plan optimization method (102) including: optimizing MLC settings of the MLC respective to an objective function wherein the MLC settings define MLC leaf tip positions for a plurality of rows of MLC leaf pairs at a plurality of control points (CPs). The optimizing is performed in two or more iterations with a resolution of the MLC settings increasing in successive iterations.

Abstract: A system and a method for monitoring anatomical changes in a subject in radiation therapy are provided, as well as an arrangement for medical imaging and analysis and a computer program product for carrying out the method. For monitoring anatomical changes in a subject in radiation therapy, the following steps are performed. First anatomical image data and subsequent anatomical image data of the subject are received. The first anatomical image data and the subsequent anatomical image data are analyzed. This analysis comprises registering the subsequent anatomical data to the first anatomical data. Changes between the first anatomical image data and the subsequent anatomical image data are identified as change states, and the identified change states are matched to corresponding qualitative descriptions. A monitoring report is provided, which comprises the qualitative descriptions of the identified changes.

Abstract: A dose error determination device (100) for forecasting radiation dose error for a body volume comprising one or more regions of interest subject to radiation treatment based on a dose plan, the dose error determination device comprising a dose-error determining unit (106) configured to generate, using dose plan data for the respective region of interest and an input value of the spatial-error quantity, dose variation data indicative of a rate of change of the dose values in a respective modified region of interest that in comparison with the respective region of interest is expanded in volume by an amount corresponding with the value of the spatial-error quantity and to determine and provide a respective value of a dose-error quantity associated with the respective region of interest, using a calculation rule representing a positive correlation of the dose-error quantity with the determined dose variation data.

Abstract: The invention relates to a system and method for generating a radiotherapy treatment plan on the basis of treatment goals comprising optimization objectives and/or constraints. A planning unit (7) generates a first treatment plan including treatment parameters for fulfilling first treatment goals in a first optimization cycle, and a decision unit (8) receives second treatment goals and compares the first and second treatment goals to 5 determine a modification of the treatment goals, assigns to the modification a category from a plurality of predetermined categories, wherein to each category a strategy from a plurality of predetermined strategies for treatment plan generation is allocated, and instructs the planning unit (7) to generate the second treatment plan in accordance with a strategy allocated to the determined category of modifications in a second optimization cycle.

Abstract: The invention relates to a planning system (7) for planning a volumetric modulated arc radiation therapy procedure, wherein the volumetric modulated arc radiation therapy procedure includes rotating a radiation source (2), which is configured to emit a radiation beam (3) for treating a subject (4), along an arc around the subject, in order to apply the radiation beam to the subject in different treatment directions. A sequence of sub-arcs groups is provided, wherein a respective sub-arcs group comprises several sub-arcs of the arc, which are evenly distributed along the arc, wherein a treatment plan is generated by sequentially determining for each sub-arcs group treatment parameters, which define a property of the radiation beam, in the provided sequence. Determining the treatment parameters by using this sequence leads to an increased likelihood that really an optimal treatment plan is generated.

Abstract: Fractionation optimization receives inputs including a radiation dose distribution to be delivered by fractionated radiation therapy, maximum and minimum number of fractions, and Biologically Effective Dose (BED) constraints for one or more organs-at-risk. A two-dimensional (2D) graph is displayed of a parameter X equal to or proportional to (I) versus a parameter Y equal to or proportional to (II) where N is the number of fractions, D is a total radiation dose to be delivered by the fractionated radiation therapy, and dt is the fractional dose in fraction t. A constraint BED lines are displayed on the 2D graph depicting each BED constraint. A marker is displayed at a location on the 2D graph defined by a current fractionation and a current total dose. A new value for the current fractionation and/or the current total dose is received, and the marker is updated accordingly.

Abstract: The invention relates to a system and method for generating a radiotherapy treatment plan on the basis of treatment goals comprising optimization objectives and/or constraints. A planning unit (7) generates a first treatment plan including treatment parameters for fulfilling first treatment goals in a first optimization cycle, and a decision unit (8) receives second treatment goals and compares the first and second treatment goals to 5 determine a modification of the treatment goals, assigns to the modification a category from a plurality of predetermined categories, wherein to each category a strategy from a plurality of predetermined strategies for treatment plan generation is allocated, and instructs the planning unit (7) to generate the second treatment plan in accordance with a strategy allocated to the determined category of modifications in a second optimization cycle.

Abstract: A radiation planning system includes a predictor-corrector optimizer unit which computes a predicted dose based on a collection of control points with a current approximate dose, each control point with a corresponding set of leaf positions, and determines an additional control point with a corresponding set of leaf positions based on a difference of the predicted fluence and the current approximate fluence through a least cost or shortest path in a layered graph structure of realizable leaf positions. Tools are described to help a planner to evaluate the effect of parameter changes to the current plan based on an identified zone of influence. The planner interactively views the current plan based on a visualization of the plan objectives and correlations between the objectives.

Abstract: A method includes determining a set of candidate beam directions. The radiation therapy method further includes selecting a sub-set of non-coplanar beam directions of interest from the set of candidate beam directions based on a fluence optimization using a beam angle selection algorithm. The radiation therapy method further includes determining a set of delivery options based on a beam trajectory algorithm, wherein the delivery options at least include a non-coplanar trajectory during radiation treatment delivery. The radiation therapy method further includes optimizing the delivery options to generate a VMAT radiation plan with non-coplanar beam trajectories. The optimizing of the delivery options includes optimizing at least one machine parameter.

Abstract: A radiation planning system includes a predictor-corrector optimizer unit which computes a predicted dose based on a collection of control points with a current approximate dose, each control point with a corresponding set of leaf positions, and determines an additional control point with a corresponding set of leaf positions based on a difference of the predicted fluence and the current approximate fluence through a least cost or shortest path in a layered graph structure of realizable leaf positions. Tools are described to help a planner to evaluate the effect of parameter changes to the current plan based on an identified zone of influence. The planner interactively views the current plan based on a visualization of the plan objectives and correlations between the objectives.

Abstract: The invention relates to a device for determining illumination distributions (18) for IMRT. A layered graph structure is used, which considers which extensions of an illumination distribution along a respective line and thus which illumination distributions are realizable, for determining extensions for the illumination distributions. Moreover, second weights defining fluences of the illumination distributions are determined such that a deviation between a provided fluence map and a fluence map formed by a combination of illumination distributions, which are defined by the respective determined extensions and the respective second weight, is minimized. This determination procedure leads to illumination distributions, which very well correspond to the provided fluence map and which are automatically realizable by the radiation device.

Abstract: The invention relates to a device for determining illumination distributions (18) for IMRT. A layered graph structure is used, which considers which extensions of an illumination distribution along a respective line and thus which illumination distributions are realizable, for determining extensions for the illumination distributions. Moreover, second weights defining fluences of the illumination distributions are determined such that a deviation between a provided fluence map and a fluence map formed by a combination of illumination distributions, which are defined by the respective determined extensions and the respective second weight, is minimized. This determination procedure leads to illumination distributions, which very well correspond to the provided fluence map and which are automatically realizable by the radiation device.

Abstract: A method for reviewing a treatment plan including displaying list of at least one of a selected plurality of patients, institutions, and treatment plans associated with a treatment planning system, selecting at least one of the one or more patients, institutions, and treatment plans, querying treatment plan parameters associated with the selected one or more patients, institutions, and treatment plans, and generating a report file of the queried treatment plan parameters

Abstract: A classification system for systems of n mirrors, whereby systems of mirrors are classified by a number C, is defined as follows: C = ∑ i = 1 n ⁢ a i · 2 ( n - i ) ⁢ ( M &LeftBracketingBar; M &RightBracketingBar; ) where: ai=1 if the angle of incidence of the chief ray at mirror i is negative, ai=0 if the angle of incidence of the chief ray at mirror i is positive, M is the magnification of the projection system, and index i numbers the mirrors of the system in series.