Abstract: A method of obtaining an interpolated treatment plan is based on interpolating between associated dose distributions through optimization with respect to an optimization problem comprising optimization functions based on deviations from clinical goals. The method may suitably be used to improve navigated plans resulting from multi-criteria optimization.

Abstract: A ripple filter unit comprises a first and a second ripple filter, substantially identical, are arranged so that they overlap each other in a beam, with substantially the same orientation and movable relative to each other in such a way as to vary the filter characteristics dynamically. In this way, the modulation characteristics of the ripple filter unit can be varied.

Abstract: The present disclosure generally relates to the field of radiation treatment. More specifically, the present disclosure generally relates to methods and radiation treatment systems for facilitating a multimodal radiation therapy treatment plan, in particular a multimodal radiation therapy plan employing a combined photon beam and electron beam radiation treatment. According to one example embodiment described in the disclosure, a method may comprise obtaining information related to a set of candidate beam types for the combined photon beam and electron beam radiation treatment; comparing the set of candidate beam types against a selection criterion to establish a subset of beam types from the candidate beam types; and generating the combined photon beam and electron beam radiation treatment plan utilizing the thus established subset of beam types.

Abstract: A method of correcting a first CT image for shading artefacts using another CT image as reference, comprising the steps of creating a difference image between the images and a correction map based on the difference image. The correction map is used to correct the first CT image. A method of converting a CT image to the value range of another CT image is also disclosed.

Abstract: In accordance with one or more embodiments herein, a system for generating an optimized parametrized conversion function T for converting an original medical image of a first image type into a converted medical image of a second image type is provided.

Abstract: A system plans a radiation therapy treatment of a target volume based on inputs in the form of: a set of candidate beams (B), where each beam defines an arrangement of a therapeutic beam relative to the target volume; a treatment plan (x) for the radiation therapy treatment that uses a subset of the candidate beams (B); an objective function (F) describing a quality of the treatment plan (x); and a feasible region (X) describing requirements on the treatment plan (x) that must be fulfilled. The objective function (F) and/or the feasible region (X) also reflect a first complexity criterion (?(x)?{circumflex over (?)}) limiting a first complexity measure (?(x)) to be less than or equal to a maximum first complexity ({circumflex over (?)}). An optimization step is executed repeatedly; whereby, in each iteration, an updated treatment plan (x?) is calculated by optimizing the treatment plan (x) with respect to the objective function (F) and the feasible region (X).

Abstract: According to a first aspect, it is presented a method for determining a treatment plan comprising a distribution of spots for use with ion beam therapy for providing the spots in a target volume. The method comprises the steps of: selecting energy layers to be used in the treatment plan; determining a number of spot sizes to use; generating, for each energy layer, one copy of the energy layer for each spot size to use and populating each copy with spots of the spot size for that copy; optimizing spots of all copies of all energy layers, by repeatedly varying a weight of at least a subset of the spots and calculating an effect on a performance measurement, wherein the performance measurement is calculated by combining a plurality of evaluation criteria, comprising a first criterion related to total treatment time and a second criterion related to a desired dose distribution.

Abstract: It is provided a method for determining ripple filter settings for an ion therapy beam being capable of providing ions of different energy levels to a target volume. The method is performed in a treatment planning system and comprises the steps of: determining at least one beam direction to use to cover a target volume; and assigning a ripple filter setting to each one of a plurality of sub-beams of each one of the at least one beam direction such that each sub-beam is assigned a different ripple filter setting, wherein each ripple filter setting results in a different effect on a Bragg peak width in a direction along the beam, and each energy level is assigned to one of the plurality of sub-beams. The step of assigning a ripple filter setting comprises optimizing based on different filter settings for different sub-beams for each beam direction.

Abstract: It is provided a method for obtaining an energy spectrum of a focused ion beam when a Bragg peak chamber is used to measure an integrated depth dose, IDD. The method comprises the steps of: simulating doses of a set of nominally mono energetic focused ion beams; determining a lateral extension of a Bragg peak chamber to evaluate; calculating a set of theoretic component IDD curves, CIDDs, by laterally integrating the dose of the simulated set of the nominally mono energetic focused ion beams, over the lateral extension of the Bragg peak chamber; storing calculated CIDDs; obtaining a measured IDD of a focused ion beam with a nominal energy using the Bragg peak chamber; and performing a fit of a linear combination of CIDDs to the measured IDD, to determine an energy spectrum for the focused ion beam with the nominal beam energy.

Abstract: A scenario-based treatment plan optimization method for radiotherapy treatment is proposed, in which a first and a second possible scenario are defined. Different optimization functions are defined for the scenarios and the treatment plan is optimized applying the first optimization function under the first scenario and the second optimization function under the second scenario, thereby obtaining a first optimized radiotherapy treatment plan.

Abstract: A method of radiotherapy treatment planning comprises optimizing a treatment plan based on at least one proposed dose map according to a set of clinical goals The resulting dose distribution is compared to the at least one clinical goal, and if the optimized dose distribution does not fulfil the at least one clinical goal, continuing with step d the dose map is adjusted before a new treatment plan is optimized. When the optimized dose distribution fulfils the clinical goals, the treatment plan is accepted.

Abstract: An anatomic structure of interest is contoured in 3D source data by selecting a first subset of data in a first image slice, which has a first orientation in the source data. A first set of instructions identifies a first edge (E1) of the anatomic structure of interest in the first image slice. Then, a second subset of data is selected in a second image slice, which has a second orientation in the source data. A second set of instructions identifies a second edge (E2) of the anatomic structure of interest in the second image slice. A three-dimensional shell (3DS) is calculated based on the first and second edges (E1; E2) and the source data (SD). The three-dimensional shell (3DS) represents an approximation of a delimiting surface that separates the anatomic structure of interest from adjoining tissues in the 3D source data.

Abstract: A method of radiotherapy treatment planning for creating a plan for delivery of radiation to a patient in at least one particle-based arc is proposed. The delivery time for the plan is reduced by including a penalty in the objective function, designed to limit the number of energy layers and/or the number of energy layer changes.

Abstract: An optimization method for a radiotherapy plan using robust optimization to handle different scenarios that may occur during one treatment session because of patient movement. The optimization is based on the period and amplitude of the movement, the starting point of the treatment session within a period and the delivery time structure.

Abstract: Radiation therapy treatment planning methods facilitate predicting achievable dose distributions by training a prediction model for clinical DVH curves based on simplified DVH curves obtained using standardized methods. Machine learning applied on pairs of clinical and simplified DVH curves enables predicting actual clinical DVH curves based on simplified DVH curves.

Abstract: An anatomic structure of interest is contoured in 3D source data by selecting a first subset of data in a first image slice, which has a first orientation in the source data. A first set of instructions identifies a first edge (E1) of the anatomic structure of interest in the first image slice. Then, a second subset of data is selected in a second image slice, which has a second orientation in the source data. A second set of instructions identifies a second edge (E2) of the anatomic structure of interest in the second image slice. A three-dimensional shell (3DS) is calculated based on the first and second edges (E1; E2) and the source data (SD). The three-dimensional shell (3DS) represents an approximation of a delimiting surface that separates the anatomic structure of interest from adjoining tissues in the 3D source data.

Abstract: The present disclosure generally relates to the field of radiation treatment. More specifically, the present disclosure generally relates to methods and radiation treatment systems for generating a radiation treatment plan. According to one example embodiment, a method may comprise dividing at least one dose characteristic related to a dose distribution for a ROI into a plurality of subintervals. The method may further comprise partitioning the ROI into a plurality of different partitions based on the subintervals. All voxels of the ROI with values within the same subinterval are partitioned into the same partition. For each of the plurality of different partitions, the method may comprise establishing a weight and specifying an optimization function for an obtainable dose distribution based on the respective subinterval of dose characteristics. The method may further comprise generating the radiation treatment plan based on said established weights and specified optimization functions.

Abstract: A scenario-based radiotherapy treatment plan optimization method is used to define an extended region of interest for treatment planning purposes, based on the union of the positions of a region of interest in a number of different scenarios, each scenario representing a realization of a possible location of the region of interest. Preferably a number of scenarios are defined and some of these scenarios are used to define the extended region.

Abstract: The present disclosure relates to a system, a method, a computer program and a computer readable medium for determining the thickness of a range shifter to attain target dose coverage in ion beam treatment, wherein the range shifter is for use in a machine for radiation treatment of a target volume, by: receiving, in a processor, input parameters comprising a radiation energy parameter, a range shifter material parameter, object geometry information, and beam characteristic parameters; and further calculating, for each of at least one delivery direction, a range shifter thickness, based on the input parameters, which will deliver the optimum dose conformity.

Abstract: A database stores pre-calculated solutions each of which defines a radiation therapy treatment plan for a treatment volume associated with at least one target and at least one organ-at-risk. The pre-calculated solutions are divided into at least two groups each of which includes at least one pre-calculated solution. The pre-calculated solutions in a given group represent radiation therapy treatment plans which share a common beam configuration. A radiation therapy treatment plan (sc) is established based on the pre-calculated solutions as follows. A first user interface receives operator commands specifying criteria for selecting radiation therapy treatment plans from the database, the criteria defining a set of parameters for the treatment volume. In particular, the operator commands jointly specify criteria for selecting radiation therapy treatment plans from more than one of the at least two groups.