Abstract: An anatomic structure of interest is contoured in 3D source data by selecting a first subset of data in a first image slice at a first axial position (z1). 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 at a second axial position (z2); and 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: An output radiation treatment plan for at least one target in a treatment volume is determined. Each target is associated with a prescribed radiation dose. An updated treatment plan causes the prescribed radiation dose to be delivered to the target when implemented by a radiation therapy machine. The updated treatment plan requires an updated delivery time to complete and is calculated by: [i] receiving a numerical value designating an upper bound on modulation efficiency of the updated treatment plan, [ii] receiving a current treatment plan, [iii] calculating a current delivery time for the current treatment plan, [iv] calculating an un-modulated delivery time for the current treatment plan, and [v] calculating the updated treatment plan by executing an optimization process while satisfying the upper bound on the modulation efficiency. Steps [ii] to [v] are traversed a predetermined number of times. Thereafter, the output radiation treatment plan is generated.

Abstract: A method for generating a radiotherapy treatment plan for a patient is provided. The treatment plan is optimized using an optimization objective which is at least partly based on an initially planned dose to the patient.

Abstract: An anatomic structure of interest is contoured in 3D source data by selecting a first subset of data in a first image slice at a first axial position (z1). 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 at a second axial position (z2); and 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: An optimization method for ion based radiotherapy includes inverse planning based on optimization variables related to the particle energy, a range modulator or ridge filter, a block and/or a range compensator. This enables automatic optimization of complex cases.

Abstract: A method for generating a radiation treatment plan is provided where uncertainties in the definition of regions of interest are incorporated and utilized by a treatment planning system for optimizing a treatment plan.

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: A method of optimizing a radiotherapy treatment plan is disclosed, comprising the steps of: a. obtaining a deliverable input treatment plan; b. optimizing the deliverable input treatment plan to obtain an optimized treatment plan, using an objective function and at least one constraint, wherein i. the objective function is related to reducing the plan complexity in terms of minimizing the machine output (MU) and/or minimizing the time required to deliver the plan and/or maximizing the segment area, and/or minimizing jaggedness of the MLC shapes, ii. to ensure that the quality is maintained, the at least one constraint is based on the dose distribution of the input plan, related to maintaining an acceptable dose distribution.

Abstract: A radiation treatment plan is determined by: [1] receiving a current fluence map defining a radiation dose; [2] receiving a current control-point sequence describing machine settings for a collimator associated with a radiation source; [3] determining an updated fluence map and an updated control-point sequence based on the current fluence map; [4] determining a further updated control-point sequence based on the updated control-point sequence and the updated fluence map; [5] determining a further updated fluence map based on the updated fluence map, the updated control-point sequence and the further updated control-point sequence; [6] checking if a stopping criterion is fulfilled; if so: stopping the process, and producing an output radiation treatment plan based on the further updated control-point sequence; and otherwise: setting the further updated fluence map, or zero, to the current fluence map; setting the further updated control-point sequence to the current control-point sequence; and returning to st

Abstract: A method for generating a robust radiotherapy treatment plan, using scenario-based robust optimization, is provided. Weights which are dependent on the overlap of different scenario-specific mappings of a region of interest is used in the optimization.

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 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: A method and computer program for dose calculation include using information from a fraction image to update contour information from a planning image and also includes using density information from the fraction image and the planning image for performing dose calculation.

Abstract: An interpolation of deliverable radiotherapy treatment plans is facilitated by restricting the movement of the multi-leaf collimator leaves between optimization steps.

Abstract: A data processing unit receives a reference image (IMG13D) of a deformable physical entity, a target image (IMG23D) of said physical entity, and a first region of interest (ROI13D) defining a first volume in the reference image (IMG13D) representing a reference image element. The reference image (IMG13D), the target image (IMG23D) and the first region of interest (ROI13D) all contain 3D datasets. In response to user commands (c1; c2), the data processing unit defines a first contour (C12D) in a first plane through the target image (IMG23D), which is presented to a user via a display unit together with graphic data reflecting the reference image (IMG13D), the target image (IMG23D) and the first region of interest (ROI13D). The first contour (C12D) is aligned with at least a portion of a first border (IEB1) of a target image element (IE3D) in the target image (IMG23D). The target image element (IE3D) corresponds to the reference image element in the reference image (IMG13D).

Abstract: A method for generating a robust radiotherapy treatment plan, using scenario-based robust optimization, is provided. Weights which are dependent on the overlap of different scenario-specific mappings of a region of interest is used in the optimization.

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.

Abstract: An optimization method for ion based radiotherapy includes inverse planning based on optimization variables related to the particle energy, a range modulator or ridge filter, a block and/or a range compensator. This enables automatic optimization of complex cases.

Abstract: A radiation treatment plan is determined by: [1] receiving a current fluence map defining a radiation dose; [2] receiving a current control-point sequence describing machine settings for a collimator associated with a radiation source; [3] determining an updated fluence map and an updated control-point sequence based on the current fluence map; [4] determining a further updated control-point sequence based on the updated control-point sequence and the updated fluence map; [5] determining a further updated fluence map based on the updated fluence map, the updated control-point sequence and the further updated control-point sequence; [6] checking if a stopping criterion is fulfilled; if so: stopping the process, and producing an output radiation treatment plan based on the further updated control-point sequence; and otherwise: setting the further updated fluence map, or zero, to the current fluence map; setting the further updated control-point sequence to the current control-point sequence; and returning to st

Abstract: A method of obtaining a 3D image of a part of a patient's body is disclosed, based on a fraction image having a limited field-of-view and complementing this with information from a planning image having a greater field-of-view. In the area outside of the fraction image field-of-view, contour and anatomical data from the planning image are used to complement the fraction image, by means of a contour-guided deformable registration between the planning image and the fraction image.