Siemens Aktiengesellschaft

Siemens Healthcare Gmbh

Siemens Healthineers Ag

Bayer Pharma Aktiengesellschaft

Siemens Aktiengesell Schaft

Siemens Mobility Gmbh

Thomas Flohr

Herbert Bruder

Bernhard Schmidt

Karl Stierstorfer

Rainer Raupach

Bernd Ohnesorge

Stefan Schaller

Michael Grasruck

Thomas Allmendinger

Martin Sedlmair

Steffen Kappler

Dorin Comaniciu

Bernhard Krauss

Gabriel Haras

Daniel Niederlöhner

Hubertus Pietsch

Bogdan Georgescu

Puneet Sharma

Kwok C Tam

Lucian Mihai Itu

Michael Suehling

Klaus Klingenbeck-Regn

Grzegorz Soza

Thomas Von Der Haar

Youngjin Yoo

Gregor Jost

Harald Schöndube

Hanns-Joachim Weinmann

Sasa Grbic

Tiziano Passerini

Saikiran Rapaka

Michael Scheuering

Bjoern Heismann

Georg Wittmann

Bernhard Scholz

Zhoubing Xu

Christopher Rohkohl

Ute Feuerlein

Martin Petersilka

Philipp Hoelzer

Stefan Reichelt

Eli Gibson

Siqi Liu

Daniel Fischer

Jing Yan

Otto Sembritzki

Ernst Klotz

Matthias Niethammer

Michael Sühling

Karl Schwarz

Heinrich Wallschlaeger

Stefan Pflaum

Thomas Re

Katharine Lynn Rowley Grant

Bernhard Krauβ

Christoph Becker

Marcus Wagner

Marc Kachelriess

Brian Teixeira

Karlheinz Pauli

Sebastien Piat

Mark-Aleksi Keller-Reichenbecher

Guillaume Chabin

Heinrich Wallschläger

Arkadiusz Polacin

Juergen Wohlrab

Andrei Chekkoury

Annabella Rauscher

Christoph Süss

Jyotipriya Das

Eva Eibenberger

Raimund Martin

Shikha Chaganti

Rainer Kurt Rienmueller

Xavier Battle

Matthias Baer-beck

Hans-Christoph Becker

Ulrich Foerner

John Sunnegårdh

Roman Fischbach

Andreas Kopp

Juergen Simon

Valentin Ziebandt

Bernard Schmidt

Robert Mayer

Friederike Schoeck

Katharine Grant

Marcus Brehm

Pascal Ceccaldi

Barbara Brehm

Pierre-Hugo Trigan

Rüdiger Lawaczeck

Stefan Gleissmann

Peter Kuehs

Bernhard Krauß

A first image data set and at least one second image data set are captured, and a contrast-enhanced image data set and a non-contrast image data set are determined based on the first and the at least one second image data set. The perfusion image data set is calculated based on a ratio of image values of the contrast-enhanced image data set and locally corresponding image values of the non-contrast image data set, and the perfusion image data set is provided via a second interface.

Method and apparatus for providing a perfusion image data set of a patient

Systems and method for performing a medical imaging analysis task for making a clinical decision are provided. One or more input medical images of a patient are received. A medical imaging analysis task is performed from the one or more input medical images using a machine learning based network. The machine learning based network generates a probability score associated with the medical imaging analysis task. An uncertainty measure associated with the probability score is determined. A clinical decision is made based on the probability score and the uncertainty measure.

Machine learning for automatic detection of intracranial hemorrhages with uncertainty measures from medical images

A method is for actuating a medical imaging device including a radiation source, embodied during a rotational movement around an axis of rotation to irradiate an irradiation region from a plurality of angular positions. In an embodiment, the method includes capturing an object position of a moving object relative to the irradiation region. The Method further includes actuating the medical imaging device based upon the captured object position of the moving object, such that the intensity of the radiation emitted by the radiation source for a first partial number of the plurality of angular positions in a first angular range round the axis of rotation is reduced relative to a second partial number of the plurality of angular positions in a second angular range around the axis of rotation, the captured object position being included in the first angular range.

Method for actuating a medical imaging device and medical imaging device

A method is for noise reduction in image recordings. In an embodiment, the method includes providing an input image; de-noising the input image and producing a de-noised input image; and adapting noise texture of pixels of the de-noised input image via an adaptation method, noise amplitude of the de-noised input image being largely retained and the noise texture of the pixels of the de-noised input image being adapted to correspond largely to a defined noise texture. A corresponding device, a production method for an adaptation device, such an adaptation device, and a control facility and a computed tomography system are also disclosed.

Method and device for noise reduction in image recordings

In one embodiment, a method is for calculating an examination parameter for a computed tomography examination of an area of interest of a patient. The method includes receiving a topogram of the area of interest of the patient; determining fat distribution information by applying a trained machine learning algorithm onto the topogram; and calculating the examination parameter for the computed tomography examination of the area of interest of the patient based on the fat distribution information.

Topogram-based fat quantification for a computed tomography examination

A method is disclosed for generating an image. An embodiment of the method includes detecting a first projection data set via a first group of detector units, the first group including a first plurality of first detector units, each having more than a given number of detector elements; detecting a second projection data set via a second group of detector units, the second group including a second plurality of second detector units, each including, at most, the given number of detector elements; reconstructing first image data based on the first projection data set; reconstructing second image data based on the second projection data set; and combining the first image data and the second image data. A non-transitory computer readable medium, a data processing unit, and an imaging device including the data processing unit are also disclosed.

Method for generating an image

An embodiment relates to a computer-implemented method for generating a combined tissue-vessel representation. The method includes receiving imaging data of a tissue; receiving imaging data of a vessel; generating a tissue representation based on the imaging data of the tissue; generating a vessel representation based on the imaging data of the vessel; and generating a combined tissue-vessel representation based on the vessel representation and the tissue representation, the vessel representation being overlaid over the tissue representation.

Method and computer system for generating a combined tissue-vessel representation

Systems and methods for assessing a disease are provided. Medical imaging data of lungs of a patient is received. The lungs are segmented from the medical imaging data and abnormality regions associated with a disease are segmented from the medical imaging data. An assessment of the disease is determined based on the segmented lungs and the segmented abnormality regions. The disease may be COVID-19 (coronavirus disease 2019) or diseases, such as, e.g., SARS (severe acute respiratory syndrome), MERS (Middle East respiratory syndrome), or other types of viral and non-viral pneumonia.

Assessment of abnormality regions associated with a disease from chest CT images

A method for the automatic determination of a contrast agent protocol for a contrast agent-enhanced medical imaging method is described. The method includes an overview recording of a region of interest of an object under examination. In addition, an image recording protocol including a plurality of recording parameters is defined on the basis of the overview recording. Furthermore, recording time points are determined for a plurality of z-positions of the region of interest on the basis of the image recording protocol. In addition, the structures acquired with the overview recording are assigned to the recording time points determined. Finally, a contrast agent protocol including the temporal course of a contrast agent injection curve is defined as a function of the acquired structures and the assigned recording time points. A contrast agent protocol determination device is also described, as well as an imaging medicinal device.

Determination of a time-dependent contrast agent injection curve as a function of CT scan parameters

A method is for the characterization of plaque in a region of interest inside an examination subject by way of a plurality of image data sets. The image data sets have been reconstructed from a plurality of projection data sets, which have been acquired via a CT device using different X-ray energy spectra. The method includes: acquiring the image data sets, which include a plurality of pixels. Spectral parameter values are acquired on a pixel by pixel basis using at least two image data sets. Character parameter values are then acquired on a pixel by pixel basis to characterize plaques on the basis of the spectral parameter values. An analysis unit and a computed tomography system are also disclosed.

Characterization of plaque

A method is for producing a CT image data set via a detection unit with a photon-counting X-ray detector and configured to convert detected X-rays into measurement signals resolved at least into a first and a second energy range and, via an X-ray source unit, configured to emit X-rays having a first energy spectrum and having a different second energy spectrum. The method includes adapting the first and the second energy ranges, at least one limiting energy of each energy range being adapted; emitting X-rays of the first and second energy spectrum; detecting the emitted X-rays, at least one measurement signal being generated as a function of each respective energy range and the respective energy spectrum; producing the spectral CT image data set based upon the generated first and second measurement signals with the assistance of a spectral image processing technique; and outputting the spectral CT image data set.

Method and apparatus for producing a spectral computed tomography image data set

A method is for controlling an x-ray imaging device, in particular a computed tomography device. The x-ray imaging device includes an x-ray source and a photon counting detector as an x-ray detector. The methods includes, for an image acquisition process of a patient: determining at least one input parameter relating to at least one of an attenuation property of the patient and a purpose of the image acquisition; at least one of determining or adapting at least one operation parameter of the x-ray detector, dependent upon the at least one input parameter determined; and performing the image acquisition using the at least one operation parameter determined or adapted.

Method for controlling a x-ray imaging device, x-ray imaging device, computer program and electronically readable storage medium

An X-ray imaging method is for generating image data of a field of view of an object to be examined. In the method, firstly an individual imaging protocol is determined for imaging of the object to be examined. Furthermore, first X-ray projection measurement data with a first X-ray energy spectrum and at least one set of second contrast medium-influenced X-ray projection measurement data with a second X-ray energy spectrum, are acquired from the field of view. A third X-ray energy spectrum with a third mean energy is then determined on the basis of the determined individual imaging protocol. Subsequently, preferably pseudo-monoenergetic image data, associated with the third X-ray energy spectrum, is reconstructed on the basis of the acquired first and at least second X-ray projection measurement data as well as the determined imaging protocol. An image data-generating device is also described. A computerized tomography system is described, moreover.

Individually adjusted generation of virtual image data on the basis of multi-energy x-ray imaging

A method is for determining at least one first characteristic value of blood in a patient. In an embodiment, the method includes acquiring, via a computed tomography device, computed tomography data of the patient for at least two energy levels of radiation using multi-energy computed tomography; defining a region of interest including blood, in at least one image data set reconstructed from the computed tomography data acquired; determining, at least in the region of interest defined, attenuation coefficients for each energy level of the at least two energy levels; performing material decomposition into at least two materials, one material of the two materials being iron, using the attenuation coefficients determined, yielding at least a fraction of iron in the region of interest defined; and determining the at least one first characteristic value, at least one of as and from the fraction of iron yielded.

Method for determining a characteristic blood value, computed tomography device, computer program and electronically readable storage medium

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Uehlfeld, Germany

Thomas Flohr