Koninklijke Philips Corporation N.v.

Nick Flaeschner

Jens Wiegert

Arne Ewald

Harald Sepp Heese

Rolf Juergen Weese

Maria Luiza Bondar

Bernhard Gleich

Peter Boernert

Johannes Adrianus Overweg

Lyubomir Georgiev Zagorchev

Fabian Wenzel

Alfonso Agatino Isola

Ingmar Graesslin

Ingo Schmale

Torbjoern Vik

Shiv Sabesan

Kay Sun

Katerina Georgopoulou

The invention relates a system for assisting in planning a radiation therapy treatment provided using a treatment plan comprising irradiation parameters for controlling a delivery of radiation. The system is configured to (i) receive a first dose distribution, (ii) obtain a first objective function, which depends upon at least one parameter and a dose distribution, (iii) determine a first value of the parameter such that the first objective function fulfills a predefined criterion when being evaluated for the first value of the parameter and for a second dose distribution derived from the first dose distribution, (iii) provide the first objective function in connection with the first value of the at least one parameter to a user for modifying the first objective function to generate a second objective function, and (v) determine the treatment plan using the second objective function. Further, the invention relates to a corresponding method and computer program.

Radiation therapy planning

The invention relates to a planning apparatus for planning a radiation therapy. A medical image, in which a target to be irradiated is indicated, is reformatted based on ray geometries to be used during the radiation therapy to be planned, resulting in several reformatted medical images. Radiation therapy parameters being indicative of intensities of raysto be used for irradiating a targetare determined based on the reformatted medical images by using a neural network unit. This allows to determine high quality radiation therapy parameters and hence allows for an improved planning of a radiation therapy. In particular, radiation and absorptions physics can be captured better, which can lead to the improved quality.

Planning apparatus for planning a radiation therapy

The invention provides for a medical imaging system () comprising a memory () storing machine executable instructions () and a configured artificial neural network (). The medical imaging system further comprises a processor () configured for controlling the medical imaging system. Execution of the machine executable instructions causes the processor to receive () magnetic resonance imaging data (), wherein the magnetic resonance imaging data is BOLD functional magnetic resonance imaging data descriptive of a time dependent BOLD signal () for each of a set of voxels. Execution of the machine executable instructions further causes the processor to construct () a set of initial signals () by reconstructing the time dependent BOLD signal for each of the set of voxels using the magnetic resonance imaging data. Execution of the machine executable instructions further causes the processor to receive () a set of modified signals () in response to inputting the set of initial signals into the configured artificial neural network. The configured artificial neural network is configured for removing physiological artifacts from the set of initial signals.

Functional magnetic resonance imaging artifact removal by means of an artificial neural network

A method () that encodes electrode locations to a mean scalp mesh for adaptation to subsequent image scans.

Automatic EEG sensor registration

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Hamburg, Germany

Nick Flaeschner