Koninklijke Philips Corporation N.v.

Koninklijke Philips Electronics N.v.

Philips Intellectual Property & Standards Gmbh

Vladimir Pekar

Michael Kaus

Daniel Bystrov

Todd R McNutt

Stewart Young

Juergen Weese

Heinrich Schulz

Torbjoern Vik

Rafael Wiemker

Thomas Netsch

Arish A Qazi

Jens Von Berg

David Anthony Jaffray

Thomas Blaffert

Kirsten Regina Meetz

Sherif Makram-Ebeid

Jean-Michel Rouet

Olivier Gerard

Lothar Spies

Himanshu P Shukla

Ingwer Curt Carlsen

Maxim Fradkin

Matthias Wolfgang Meyer

Holger Timinger

Arianne Van Muiswinkel

Stephane Allaire

Ruediger Grewer

R Keith Tipton

Todd Mcnutt

Sebastian Dries

Cornelis Johannes Fransiscus Maria Bergmans

Celine Saint Olive

Anja Hennemuth

Todd R Mcnutt

When delineating anatomical structures in a medical image of a patient for radiotherapy planning, a processor () detects landmarks () in a low-resolution image (e.g., MRI or low-dose CT) and maps the detected landmarks to reference landmarks () in a reference contour of the anatomical structure. The mapped landmarks facilitate adjusting the reference contour to fit the anatomical structure. The adjusted reference contour data is transformed and applied to a second image using a thin-plate spline, and the adjusted high-resolution image is used for radiotherapy planning.

Automated anatomy delineation for image guided therapy planning

A system and method for developing radiation therapy plans and a system and method for developing a radiation therapy plan to be used in a radiation therapy treatment is disclosed. A radiation therapy plan is developed using a registration of medical images. The registration is based on identifying landmarks located within inner body structures.

Deformable registration of images for image guided radiation therapy

A system for segmenting current diagnostic images includes a workstation () which segments a volume of interest in previously generated diagnostic images of a selected volume of interest generated from a plurality of patients. One or more processors () are programmed to register the segmented previously generated images and merge the segmented previously generated images into a probability map that depicts a probability that each voxel represents the volume of interest () or background () and a mean segmentation boundary (). A segmentation processor () registers the probability map with a current diagnostic image () to generate a transformed probability map (). A previously-trained classifier () classifies voxels of the diagnostic image with a probability that each voxel depicts the volume of interest or the background. A merge processor () merges the probabilities from the classifier and the transformed probability map. A segmentation boundary processor () determines the segmentation boundary for the volume of interest based on the current image based on the merge probabilities.

Probabilistic refinement of model-based segmentation

An imager () and a reconstruction processor () generate an intensity image (). A region containing a target volume is identified () and limited (). A classifier () operates on the voxels in the limited volume with an enhancement filter to generate an enhanced image, such as an image whose voxel values represent a probability that each voxel is in the target volume. A segmentation processor () segments the enhanced image to identify the surface of the target volume to generate a segmented image which is displayed on a monitor () or used in a radiation therapy planning system ().

Organ-specific enhancement filter for robust segmentation of medical images

When registering multiple multidimensional images based on landmarks, the system improves the distribution and density of the points in correspondence across images, which are of crucial importance for the accuracy and reliability of the resulting registration transform. Projection of input corresponding point landmarks is performed in order to automatically generate additional point correspondences. The input existing landmarks may have been manually or automatically located in the input multiple images. Projection is performed from each source landmark along one or more determined projection directions onto one or more determined projection targets in each image. The projection target(s) can be explicitly materialized or implicitly defined. Candidate new points are identified at the locations where the projection ray paths intersect with the projection target(s). Correspondence between the new points across images is transferred from the input landmarks from which they have been projected, with further distinction by projection direction and/or projection target in the case where a plurality of these was used. Subsequent registration can use all or a selected subset of all combined input landmark correspondences and correspondences between additional projected landmark points. The additional correspondences between projected landmark points contribute in refining the image registration. Such accurate, efficient and robust tools for image registration and any downstream processing, such as contour propagation or image fusion, are highly demanded for various medical applications, such as adaptive radiotherapy.

Automatic projection of landmarks to generate additional correspondences in image registration

An image segmentation method segments a plurality of image features in an image. The plurality of image features are segmented non-simultaneously in succession. The segmenting of each image feature includes adapting an initial mesh to boundaries of the image feature. The segmenting of each image feature further includes preventing the adapted mesh from overlapping any previously adapted mesh.

Region-competitive deformable mesh adaptation

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

A method of data processing is provided for estimating a position of an object in an image from a position of a reference object in a reference image. The method includes learning the position of the reference object in the reference image and its relation to a set of reference landmarks in the reference image, accessing the image, accessing the relation between the position of the reference object and the set of the reference landmarks, identifying a set of landmarks in the image corresponding to the set of the reference landmarks, and applying the relation to the set of landmarks in the image for estimating the position of the object in the image.

Method, an apparatus and a computer program for data processing

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.

Interactive image segmentation

A method, an apparatus and a computer program for transferring scan geometry between a first region and a second region, similar to the first region includes identifying the first and second regions in an overview image, followed by determining of the first scan geometry corresponding to the first region. Then, the first scan geometry is transferred into the second scan geometry corresponding to the second region using information on geometrical correspondence between the first and second regions. The transferring includes establishing corresponding mappings between similar regions and their respective scan geometries.

Method, an apparatus, a system and a computer program for transferring scan geometry between subsequent scans

When modeling anatomical structures in a patient for diagnosis or therapeutic planning, an atlas () of predesigned anatomical structure models can be accessed, and model of one or more such structures can be selected and overlaid on an a 3D image of corresponding structure(s) in a clinic image of a patient. A user can click and drag a cursor on the model to deform the model to align with the clinical image. Additionally, a processor () can generate a volumetric deformation function using splines, parametric techniques, or the like, and can deform the model to fit the image in real time, in response to user manipulation of the model.

Interactive atlas to image registration

An imaging system () includes imaging modalities such as a PET imaging system () and a CT scanner (). The CT scanner () is used to produce a first image () which is used for primary contouring. The PET system () is used to provide a second image (), which provides complementary information about the same or overlapping anatomical region. After first and second images () are registered with one another the first and second images () are concurrently segmented to outline a keyhole (). The keyhole portion of the second image () is inserted into the keyhole () of the first image (). The user can observe the composite image and deform a boundary () of the keyhole () by a mouse () to better focus on the region of interest within previously defined keyhole.

Data representation for rtp

The invention relates to a registration method () of registering a second image dataset with a first image dataset on the basis of a set of landmarks, said registration method () comprising a weight-assigning step () for assigning a weight to each coordinate of each landmark from the set of landmarks and a registering step () for registering the second image dataset with the first image dataset on the basis of the weight assigned to the each coordinate. Choosing an appropriate set of landmarks and assigning an appropriate weight to each coordinate of each landmark from the set of landmarks can be used for optimizing selected displacements of a designated anatomical structure comprising elastic bodies and/or a plurality of independent rigid bodies in a sequence of image datasets. This enables rendering a sequence of views wherein the designated anatomical structure is not displaced off a viewing plane and/or a selected part of the designated anatomical structure is not displaced in a viewing plane.

Compensating in-plane and off-plane motion in medical images

A method for selecting vertices for performing deformable registration of imaged objects is provided. The selected vertices form corresponding pairs, each pair including a vertex from a first imaged object and a vertex from a second imaged object. The corresponding vertex pairs are sorted in order of distance between the vertices making up the corresponding vertex pair. The corresponding vertex pair with the greatest distance is given top priority. Corresponding vertex pairs that lie within a selected distance from the selected corresponding vertex pair are discarded. In this manner, the number of vertex pairs used for deformable registration of the imaged objects is reduced and therefore allows for processing times that are clinically acceptable.

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Hamburg, Germany

Vladimir Pekar