Koninklijke Philips Corporation N.v.

Elekta, Inc.

Torbjoern Vik

Daniel Bystrov

Heinrich Schulz

Harald Sepp Heese

Alfonso Agatino Isola

Christoph Neukirchen

Vladimir Pekar

Rolf Juergen Weese

Rafael Wiemker

Michael Kaus

Nicole Schadewaldt

Rolf Jürgen Weese

Astrid Ruth Franz

Juergen Weese

Thomas Buelow

Julien Senegas

David Anthony Jaffray

Steffen Renisch

Prashant Kumar

Jens Wiegert

Dominik Benjamin Kutra

Maria Luiza Bondar

Matthieu Frédéric Bal

Yogish Mallya

Matthieu Frederic Bal

Nick Flaeschner

Ruediger Grewer

Stefan Matthias Florian Schmidt

Sebastian Dries

The invention relates to a system for determining a radiation therapy plan for a radiation therapy system (), comprising a multi-leaf collimator. The radiation therapy plan determination system () comprises a therapy system characteristics providing unit (), wherein the characteristics comprise possible leaf positions and possible radiation fluence values, a planning objectives providing unit (), wherein the planning objectives are indicative of a desired therapeutic radiation dose distribution, an optimization function providing unit (), wherein the optimization function is indicative of a deviation of a radiation dose distribution from the planning objectives and of an uncertainty of the radiation dose distribution at edges of the possible apertures, and a therapy plan optimization unit () adapted to determine a sequence of possible apertures and possible radiation fluence values for which the optimization function is optimized. Thus, an optimal therapy plan can be provided for each individual patient.

System, method and computer program for determining a radiation therapy plan for a radiation therapy system

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

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

A resource management system for better operation of a plurality of devices. The system comprises an input interface (IN) for receiving input data including one or more characteristics of at least one work object (P-P) and/or including context data. The at least one work object (P-P) can be processed by one or more processing devices (M). The said processing is specified in a respective work specification (S-S). A predictor component (PC) of the system is configured to predict, based on the input data, a change to the work specification. The system comprises an output interface (OUT) for providing output data that represents said predicted change.

Resource scheduling in adaptive radiation therapy planning

A system includes a computing system with a processor and computer readable storage medium with computer readable and executable instructions, including a radiation plan module, a radiation plan optimization module and a radiation plan visualization module. The processor is configured to execute the instructions, which causes the processor to construct and visually present, via a display monitor, a two-dimensional plot with three-dimensions of data from a radiation plan, and two dimensions along two axes of the plot and a third dimension represented through intensity.

Visualization of volumetric modulated arc therapy (VMAT) plans

The invention relates to a dynamic sliding-window-like initialization for, for example, iterative VMAT algorithms. Specifically, a dynamic sliding window conversion method is contemplated where typical dynamic VMAT constraints are taken into account to find an optimal set of suitable openings (i.e. binary masks) that can be used as quasi-feasible start initialization for any VMAT algorithm that can refine until a deliverable plan is reached. Here, a multileaf leaf tip trajectory least square constrained optimization is performed to find a set of optimal unidirectional trajectories for all MLC leaf pairs of all arc points. To ensure that a quasi-feasible (or better quasi-deliverable) solution is returned, for example, a maximum dose rate, a maximum gantry speed, a maximum leafs speed, and a maximum treatment time may be enforced.

Warm start initialization for external beam radiotherapy plan optimization

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.

Radiation therapy planning optimization and visualization

A system and a method are provided for enabling a user to interactively navigate through a set of slice images, the set of slice images jointly representing an image volume showing an anatomical structure of a patient. A user may be enabled to switch from a static viewing mode to a navigation mode based on navigation commands received from a user input device operable by the user. A display processor may be configured for, in the static viewing mode, generating an output image comprising one slice image of the set of slice images. The display processor may be configured for, in the navigation mode, replacing the said one slice image in the output image by a volume rendering of a slab of the image volume, the slab comprising more than one slice image. The system and method thus selectively switch to volume rendering, namely during navigation, whereas in a static (i.e., non-navigation) viewing mode, a slice image is shown. Advantageously, the user may thus follow structures more accurately when navigate through a volume image, thereby more quickly and accurately identifying slice images of interest.

Medical image navigation system

A method for segmenting image data includes identifying a 2D boundary start position corresponding to tissue of interest in a cross-section of volumetric image data, wherein the start position is identified by a current position of a graphical pointer with respect to the cross-section, generating a preview 2D boundary for the tissue of interest based on the start position, displaying the preview 2D boundary superimposed over the cross-section, and updating the displayed preview 2D boundary if the position of the graphical pointer changes with respect to the cross-section.

Image data segmentation

The present invention relates to a device for segmenting an image () of a subject () comprising a data interface () for receiving an image () of said subject () and at least one contour () or at least one part of a contour (), said contour () indicating a structure () within said image (), a selection unit () for selecting a region () in said image () divided into a first and a second disjoint part () by said contour () or said part of said contour (), said selected region () comprising a drawn region and/or a computed region, a classifier () for classifying said selected region () based on at least one parameter for image segmentation, an analysis unit () for defining an objective function based on the classification result, an optimizer () for optimizing said parameter set by varying an output of said objective function and an image segmentation unit () for segmenting said image () using said optimized parameter set.

Optimization of parameters for segmenting an image

A method includes obtaining a single training image from a set of training images in a data repository. The method further includes generating an initial tissue class atlas based on the obtained single training image. The initial tissue class atlas includes two or more different tissue class images corresponding to two or more different tissue classes. The method further includes registering the remaining training images of the set of training images to the initial tissue class atlas. The method further includes generating a quality metric for each of the registered images. The method further includes evaluating the quality metric of each of the registered image with a predetermined evaluation criterion. The method further includes identifying a sub-set of images from the set of training images that satisfy the evaluation criterion. The method further includes generating a subsequent tissue class atlas based on the identified sub-set of the set of training images.

Unsupervised training for an atlas-based registration

Interactive mesh deformation for in-plane 3D segmentation/delineation for radiation therapy planning done on a slice by slice basis of a region/a volume of interest (VOI, ROI). Segmentation starts by some automatic 3D algorithm approximating the organ surface roughly by some triangular surface mesh which mesh is afterwards manually refined by the user who deforms it to bring it closer to the region of interest. The deformation is an invertible, i.e. one-to-one, mapping avoiding self-intersections of the deformed mesh thereby preserving the topology of the anatomy. The deformation mapping involves a Gaussian function (Gaussian deformation kernel) restricting the deformation to a local region. The user picks with the pointer a start point on a selected image slice through the volume and moves it to some end point. The distance the mesh vertices move decreases exponentially with the distance to the start point. Additionally, surface mesh resolution is increased by iteratively subdividing mesh triangles in the vicinity of a user-selected contour in a surface mesh until every pixel or voxel contains at least one triangle vertex.

In-plane and interactive surface mesh adaptation

The present application is directed to the idea of using sampling techniques to propagate segmentation uncertainty in order to evaluate variability in radiation planning. A radiotherapy planning apparatus () creates diagnostic image data of a region of interest of a subject. Image data from other sources can also be used. The image data is segmented () and combined with previously imaged model data. Target measures such as dose volume histograms are produced for each of the segmentations of the image data. These measures are later combined into a statistical quantification of the target measure (FIG.). This information is presented () to the user to give the user possible outcomes of the radiotherapy plan, and, e.g., confidence levels in those outcomes.

Treatment plan evaluation in radiotherapy by stochastic analysis of delineation uncertainty

When modeling anatomical structures in a patient for diagnosis or therapeutic planning, an atlas () of segmented co-registered CT and MRI anatomical structure reference images can be accessed, and an image of one or more such structures can be selected and overlaid with an MRI image of corresponding structure(s) in a clinical image of a patient. A user can click and drag landmarks or segment edges on the reference MRI image to deform the reference MRI image to align with the patient MRI image. Registration of a landmark in the patient MRI image to the reference MRI image also registers the patient MRI image landmark with a corresponding landmark in the co-registered reference CT image, and electron density information from the reference CT image landmark is automatically attributed to the corresponding registered patient MRI landmark.

CT surrogate by auto-segmentation of magnetic resonance images

When adapting models of anatomical structures in a patient for diagnosis or therapeutic planning, an atlas () of predesigned anatomical structure models or image volumes can be accessed, and a segmentation of one or more such structures can be selected and overlaid on an a 3D image of corresponding structure(s) in a clinical image () of a patient. A user can click on an initially unapproved segmentationlandmark () on the patient image (), reposition the unapproved landmark, and approve the repositioned landmark. Remaining unapproved landmarks () are then repositioned as a function of the position of the approved landmark () using one or more interpolation techniques to adapt the model to the patient image on the fly.

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Hamburg, Germany

Torbjoern Vik