Brainlab Ag

Other

Pascal Bertram

Birte Domnik

Kajetan Berlinger

Elisa Garcia Corisco

Claus Promberger

Elisa Garcia Corsico

Dirk Staneker

Thomas Flurschuetz

Ivana Ivanovska

Birte Dominik

Thomas Flurschütz

A computer implemented method for determining a two dimensional DRR referred to as dynamic DRR based on a 4D-CT, the 4D-CT describing a sequence of three dimensional medical computer tomographic images of an anatomical body part of a patient, the images being referred to as sequence CTs, the 4D-CT representing the anatomical body part at different points in time, the anatomical body part comprising at least one primary anatomical element and secondary anatomical elements, the computer implemented method comprising the following steps: acquiring the 4D-CT; acquiring a planning CT, the planning CT being a three dimensional image used for planning of a treatment of the patient, the planning CT being acquired based on at least one of the sequence CTs or independently from the 4D-CT, acquiring a three dimensional image, referred to as undynamic CT, from the 4D-CT, the undynamic CT comprising at least one first image element representing the at least one primary anatomical element and second image elements representing the secondary anatomical elements; acquiring at least one trajectory, referred to as primary trajectory, based on the 4D-CT, the at least one primary trajectory describing a path of the at least one first image element as a function of time; acquiring trajectories of the second image elements, referred to as secondary trajectories, based on the 4D-CT; for the image elements of the undynamic CT, determining trajectory similarity values based on the at least one primary trajectory and the secondary trajectories, the trajectory similarity values respectively describing a measure of similarity between a respective one of the secondary trajectories and the at least one primary trajectory; determining the dynamic DRR by using the determined trajectory similarity values, and, in case the planning CT is acquired independently from the 4D-CT, further using a transformation referred to as planning transformation from the undynamic CT to the planning CT, at least a part of image values of image elements of the dynamic DRR being determined by using the trajectory similarity values.

Determination of dynamic DRRs

A medical data processing method, performed by a computer (), for determining error analysis data describing the registration accuracy of a first elastic registration between first and second image data (A, B) describing images of an anatomical structure of a patient, comprising the steps of: &#x2014;acquiring the first image data (A) describing a first image of the anatomical structure, &#x2014;acquiring the second image data (B) describing a second image of the anatomical structure, &#x2014;determining first registration data describing a first elastic registration of the first image data (A) to the second image data (B) by mapping the first image data (A) to the second image data (B) using a registration algorithm, &#x2014;determining second registration data describing a second elastic registration of the second image data (B) to the first image data (A) by mapping the second image data (B) to the first image data (A) using the registration algorithm, &#x2014;determining error analysis data describing the registration accuracy of the first elastic registration based on the first registration data and the second registration data.

Determination of registration accuracy

Determination of dynamic DRRS

A medical data processing method and system determines the position of an artifact in patient image data describing a set of tomographic slice images of an anatomical structure of a patient. The images are described by color Values. Color value difference data describing differences in color values for image elements in adjacent slice images is determined. At least one of positive or negative difference data, describing a subset of the differences and consisting of differences having a positive or negative value are determined. Smoothed difference data describing a smoothing of the differences contained in the positive or negative difference data are determined and, based on the positive or negative difference data and the smoothed difference data, artifact position data is determined describing the position of an artifact in the patient image data.

Automatic detection of an artifact in patient image

A method for generating planning data or control data for a radiation treatment, comprising the following steps: acquiring segmented data of an object which contains a treatment volume and a non-treatment volume; modelling at least some or all of the volume or surface of the treatment volume as a source of light or rays exhibiting a predefined or constant initial intensity; modelling the non-treatment volume as comprising volumetric elements or voxels which each exhibit an individually assigned feature or attenuation or transparency value (t&#x2264;t&#x2264;t) for the light or rays which feature is assigned to the light or ray or which attenuation or transparency maintains or reduces the intensity of the light or ray as it passes through the respective volumetric element or voxel, wherein the feature or attenuation or transparency value is individually assigned to each volumetric element or voxel of the non-treatment volume; defining a map surface which surrounds the treatment volume or the object; calculating an accumulated intensity value for points or areas on the map surface, the accumulated intensity being the sum of the intensities of all the rays which exhibit the predefined or constant initial intensity and are emitted from the volume or surface of the treatment volume and reach a respective point on the map surface preferably by following a straight line, wherein if the ray passes through a non-treatment volume or voxel, the intensity of the respective ray is reduced or attenuated by a factor which is determined by the individual feature or attenuation or transparency value of the respective non-treatment volume or voxel; and generating an intensity distribution on the map surface using the calculated accumulated intensities.

Method and apparatus for planning or controlling a radiation treatment

A medical image data processing method for determining a set of medical image data to be displayed, the data processing method being constituted to be executed by a computer and comprising the following steps: acquiring medical image data comprising three-dimensional medical image information describing an anatomical structure, and displaying the medical image information; acquiring navigation display feature data comprising navigation display feature information describing at least one graphical navigation feature; displaying the navigation display information simultaneously with the medical image information; acquiring viewing direction data comprising viewing direction information describing a spatial relationship of a viewing direction of a user relative to the position of the at least one graphical navigation feature; determining, based on the viewing direction data, image information subset data comprising image data subset information describing a subset of the medical image information to be selected for display.

3D-volume viewing by controlling sight depth

A medical image data processing method for determining a set of medical image data to be displayed, the data processing method being constituted to be executed by a computer and comprising the following steps: a) acquiring medical image data comprising three-dimensional medical image information describing an anatomical structure, and displaying the medical image information; b) acquiring navigation display feature data comprising navigation display feature information describing at least one graphical navigation feature; c) displaying the navigation display information simultaneously with the medical image information; d) acquiring viewing direction data comprising viewing direction information describing a spatial relationship of a viewing direction of a user relative to the position of the at least one graphical navigation feature; e) determining, based on the viewing direction data, image information subset data comprising image data subset information describing a subset of the medical image information to be selected for display.

The present invention relates to method for generating planning data or control data for a radiation treatment, comprising the following steps: acquiring segmented data of an object which contains a treatment volume and a non-treatment volume; modelling at least some or all of the volume or surface of the treatment volume as a source of light or rays exhibiting a predefined or constant initial intensity; modelling the non-treatment volume as comprising volumetric elements or voxels which each exhibit an individually assigned feature or attenuation or transparency value (t&#x2266;t&#x2266;t) for the light or rays which feature is assigned to the light or ray or which attenuation or transparency maintains or reduces the intensity of the light or ray as it passes through the respective volumetric element or voxel, wherein the feature or attenuation or transparency value is individually assigned to each volumetric element or voxel of the non-treatment volume; defining a map surface which surrounds the treatment volume or the object; calculating an accumulated intensity value for points or areas on the map surface, the accumulated intensity being the sum of the intensities of all the rays which exhibit the predefined or constant initial intensity and are emitted from the volume or surface of the treatment volume and reach a respective point on the map surface preferably by following a straight line, wherein if the ray passes through a non-treatment volume or voxel, the intensity of the respective ray is reduced or attenuated by a factor which is determined by the individual feature or attenuation or transparency value of the respective non-treatment volume or voxel; and generating an intensity distribution on the map surface using the calculated accumulated intensities.

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Munich, Germany

Pascal Bertram