Siemens Healthcare Gmbh

Siemens Aktiengesellschaft

Siemens Healthineers Ag

Siemens Medical Solutions USA, Inc.

Markus Kowarschik

Christian Kaethner

Christopher Rohkohl

Sebastian Schafer

Michael Manhart

Kevin Royalty

Annette Birkhold

Sonja Gehrisch

Marcus Pfister

Frank Dennerlein

Benno Heigl

Oliver Hornung

Anton Nekovar

Sai Gokul Hariharan

Jurgen Endres

Thomas Redel

Andreas Meyer

Klaus Klingenbeck

Andreas Maier

Andreas Fieselmann

Wei Wei

Alexander Preuhs

Katharina Breininger

Stefan Hoppe

Holger Scherl

Julie DiNitto

Elisabeth Preuhs

Tim Horz

Reinhard Stadler

Anne Tjorven Büßen

A system for monitoring a physiological state of an object under examination in remotely controlled procedures includes a remote control unit for remote control of a remote manipulation unit, the remote manipulation unit for performing a procedure step, a capture unit for capturing state data of the object under examination, and a provider unit for classifying the state of the object under examination. When an expected state is present, the remote manipulation unit performs the procedure step. When a deviating state is present, the provider unit provides a remote signal to the remote control unit and a local signal to the output unit, the output unit outputs to local operating personnel a first workflow notice, and the remote control unit adjusts the remote control of the remote manipulation unit, outputs a second workflow notice to the remote operating personnel, and/or provides an alternative remote control signal.

System for monitoring a physiological state of an object under examination in remotely controlled procedures, method for signal provision according to a physiological state of an object under examination, and computer program product

A method for providing a comparison dataset is disclosed. The method includes providing a time-resolved first dataset that maps a first contrast medium flow in a region of interest of an examination object in a first period of time; providing a time-resolved second dataset that maps a second contrast medium flow in the region of interest in a second period of time after the first period of time; spatially registering the first and second datasets; identifying a mapping of at least one vessel section of the region of interest in the first and second datasets; temporally registering the first and second datasets; identifying a difference between the first and second contrast medium flows by a comparison of the registered first and second datasets; and providing the comparison dataset based on the registered first and second datasets, wherein the comparison dataset has at least one parameter characterizing the difference.

Provision of a comparison dataset

In an X-ray imaging method, a first time length that corresponds to a time length for a contrast agent to flow from a defined contrast-agent injection location to a defined proximal location of a hollow organ, and a second time length that corresponds to a time length for a contrast agent to flow from the proximal location to a defined distal location of the hollow organ are obtained. During a first acquisition phase, a first sequence of projection images is produced. Each projection image at least partially represents the hollow organ under a different respectively defined projection direction. The first acquisition phase begins the first time length after an injection start time for injecting the contrast agent, and a duration of the first acquisition phase equals the second time length. A time-resolved three-dimensional reconstruction of the hollow organ is produced based on the projection images in the first sequence.

X-ray imaging method, X-ray imaging system, and computer program product

Angiographic recordings are to be made more informative. To this end, a method for spatiotemporal fusion of time-resolved angiographic data sets is proposed. Respective 4D reconstructions are obtained from angiographic 3D data sets acquired from contrast agents administered at different sites. In both 4D reconstructions, a common vascular region is identified. For each contrast agent bolus, the corresponding time point or time course in the common vascular region is determined. Finally, the two 4D reconstructions are synchronized and fused.

Spatiotemporal fusion of time-resolved angiographic data sets

A method for actuating an X-ray device during robot-assisted navigation of at least two objects introduced into a body, such as a hollow organ, of a patient by at least one robotic system, includes providing a first selection criterion for selecting one of the objects, and providing a second selection criterion for selecting one of the objects. One of the at least two objects is selected based on the first selection criterion and the second selection criterion, and recording parameters of the X-ray device are automatically set, such that the selected object in an X-ray image to be recorded is highlighted compared to the at least one other object. The selected object may be highlighted with respect to image quality, image flavor, positioning on the X-ray image, and/or a collimator setting of a collimator of the X-ray device. An X-ray image is recorded with the set recording parameters.

Actuating an x-ray device and medical system

A computer-implemented method for providing a vascular image data record includes receiving a plurality of projection-X-ray images recorded in temporal succession. The plurality of projection-X-ray images at least partially map a common examination region of an examination object. The plurality of projection-X-ray images map a temporal change in the examination region of the examination object. A change image data record is determined in each case based on at least one region of interest of the plurality of projection-X-ray images. The at least one region of interest includes a plurality of image points. The change image data record in each case includes a time-intensity curve for each of the image points. The vascular image data record is generated based on the change image data record, and the vascular image data record is provided.

Providing a vascular image data record

A method is for determining a three-dimensional image dataset. In an embodiment, the method includes a first X-ray dataset of the examination volume being received, the first X-ray dataset including a two-dimensional first X-ray projection of the examination volume with respect to a first projection direction; and a second X-ray dataset of the examination volume being received, the second X-ray dataset including a second two-dimensional X-ray projection of the examination volume with respect to a second projection direction. Furthermore, a first three-dimensional image dataset of the examination volume is determined based on the two-dimensional first X-ray projection and the two-dimensional second X-ray projection. An effect of overlaps of vessels with respect to the first projection direction or the second projection direction can be reduced as a result of determining the first three-dimensional image dataset based on the first X-ray dataset and the second X-ray dataset.

Determining an image dataset

An inverse visualization of a time-resolved angiographic image data set of a vascular system of a patient that was recorded by a medical imager during the flow of a contrast medium through the vascular system is provided. The time-resolved angiographic image data set of the vascular system has a temporal sequence of frames of the vascular system corresponding to the contrast medium filling process. A data set from bolus arrival times for each pixel or voxel is determined. The bolus arrival time corresponds to the time in the temporal sequence at which a predetermined contrast enhancement due to the contrast medium filling first occurs. A data set of temporally inverted bolus arrival times with respect to the contrast medium filling is determined for each pixel or voxel, resulting in a temporally inverted sequence of frames with respect to the contrast medium filling. The time-resolved angiographic image data set in the temporally inverted sequence is visualized.

Visualization method and apparatus

Some embodiments relate to solutions to providing a result image data set. At least one embodiment is based on an input image data set of a first examination volume being received. A result image parameter is received or determined. A result image data set of the first examination volume is determined by application of a trained generator function to input data. Input data is based on the input image data set and the result image parameter, and the result image parameter relates to a property of the result image data set. A parameter of the trained generator function is based on a GA algorithm (acronym for the English technical term 'generative adversarial'). Finally, the result image data set is provided. Some embodiments relate to solutions for providing a trained generator function and/or a trained classifier function, in particular for use in solutions for providing a result image data set.

Providing a result image data set

A computer-implemented method is for providing a difference image data record. In an embodiment, the method includes a determination of a first real image data record of an examination volume in respect of a first X-ray energy, and a determination of a multi-energetic real image data record of the examination volume in respect of a first X-ray energy and a second X-ray energy, the second X-ray energy differing from the first X-ray energy. The method further includes the determination of the difference image data record of the examination volume by applying a trained function to input data, wherein the input data is based upon the first real image data record and the multi-energetic real image data record, as well as the provision of the difference image data record.

Providing a difference image data record and providing a trained function

A computer-implemented method includes, in an embodiment, receiving first X-ray projections of an examination volume in respect of a first X-ray energy and second X-ray projections in respect of a second X-ray energy, the first and second X-ray energies differing. The method further includes determination of a multienergetic real image data record of the examination volume based upon the first and second X-ray projections; selection of first voxels of the multienergetic real image data record based upon the multienergetic real image data record; selection of second voxels of the multienergetic real image data record based upon the first X-ray projections and the second X-ray projections, the first voxels including the second voxels and the second voxels mapping contrast medium in the examination volume. The method further includes provision of a constraint image data record and/or a difference image data record based upon the second voxels.

Providing a constraint image data record and/or a difference image data record

In an embodiment, a first real image dataset of an examination volume is received. The examination volume includes a vessel here, and the first real image dataset maps the examination volume includes contrast medium. Furthermore a differential image dataset of the examination volume is determined by application of a first trained generator function to input data. Here the input data includes the first real image dataset and a parameter of the trained generator function based on a GA algorithm. Furthermore the differential image dataset is provided.

Provision of a differential image dataset and a trained generator function

For a particularly comprehensive identification of hollow organ systems, a method is provided for producing a digital subtraction angiography of a hollow organ system of a patient. The method includes: providing mask image data recorded by an X-ray device; providing at least first fill image data recorded by the X-ray device, which has been recorded during an at least partial filling of the hollow organ system with a contrast agent; starting from a first intravenous and a second intraarterial contrast agent injection following in time; ascertaining at least first subtraction image data by subtracting the mask image data from the at least first fill image data; ascertaining final subtraction image data from the at least first subtraction image data; and segmenting the final subtraction image data and assigning the pixels or voxels of the final subtraction image data to at least two different intensity classes based on their respective intensity value.

Method for producing a digital subtraction angiography and apparatus

Described herein are technologies for facilitating reconstruction of flow data. In accordance with one aspect, the framework receives a four-dimensional projection image dataset and registers one or more pairs of temporally adjacent projection images in the image dataset. Two-dimensional flow maps may be determined based on the registered pairs. The framework may then sort the two-dimensional flow maps according to heart phases, and reconstruct a three-dimensional flow map based on the sorted two-dimensional flow maps.

Reconstruction of flow data

A method is provided for adapting an image impression of an image, in particular of an image acquired in the context of medical imaging. The method includes: providing an image or input data from which the image is acquired; specifying a respective ideal image impression class for the image and/or for at least one image segment of the image specified directly or by specifying at least one segment type, wherein a connection between the image data in the image and/or in the respective image segment and an assigned image impression class is specified by a classification algorithm; and modifying the image or the input data by a modification algorithm, in order to adapt the image impression class that has been assigned to the resulting modified image data pertaining to the image or pertaining to the respective image segment to match the respective ideal image impression class, wherein at least one modification parameter of the modification algorithm is or becomes specified as a function of the classification algorithm.

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Nuremberg, Germany

Markus Kowarschik