Koninklijke Philips Corporation N.v.

The General Hospital Corporation

Elekta, Inc.

Massachusetts General Hospital Corporation

Philips Gmbh

Matthieu Frédéric Bal

Alfonso Agatino Isola

Christoph Neukirchen

Maria Luiza Bondar

Jan Unkelbach

Juergen Weese

Prashant Kumar

Rolf Jürgen Weese

Torbjoern Vik

Harald Sepp Heese

Thomas Bortfeld

David J Papp

Ali Ajdari

Zoltán Perkó

Tom Brosch

Daniel Bystrov

Zoltán PerkÓ

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. A test recommendation may be initiated or updated for scheduling of a patient test based on the updated patient-specific toxicity risk.

Systems and methods to support personalization of cancer treatment for patients undergoing radiation therapy

A device for optimizing a radiation therapy plan () for delivering therapeutic radiation to a patient using a therapeutic radiation source () while modulated by a multi-leaf collimator (MLC) () includes at least one electronic processor () connected to a radiation therapy device (). A non-transitory computer readable medium () stores instructions readable and executable by the at least one electronic processor to perform a radiation therapy plan optimization method () 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.

Fast generation of multi-leaf collimator (MLC) openings using hierarchical multi-resolution matching

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.

Automated qualitative description of anatomical changes in radiotherapy

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 dis 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. Alternatively a second marker is displayed showing the new fractionation scheme along with its comparative advantages and disadvantages with respect to the current fractionation.

Fractionation selection tool in radiotherapy planning

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 () generates a first treatment plan including treatment parameters for fulfilling first treatment goals in a first optimization cycle, and a decision unit () receives second treatment goals and compares the first and second treatment goals todetermine 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 () to generate the second treatment plan in accordance with a strategy allocated to the determined category of modifications in a second optimization cycle.

Automatic selection of optimization strategy in iterative treatment planning

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.

Volumetric modulated arc therapy (VMAT) with non-coplanar trajectories

The invention relates to a device for determining illumination distributions () 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. This leads to an improved IMRT, wherein a post-processing of the determined illumination distributions for ensuring that the determined illumination distributions are really realizable is not necessarily required.

Device for determining illumination distributions for IMRT

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Geldrop, Netherlands

Matthieu Frédéric Bal