Siemens Aktiengesellschaft

Siemens Healthcare Gmbh

Siemens Healthineers Ag

Siemens Aktiengesell Schaft

Rainer Raupach

Herbert Bruder

Thomas Flohr

Karl Stierstorfer

Bernhard Schmidt

Ute Feuerlein

Thomas Allmendinger

Martin Petersilka

Harald Schöndube

Christiane Koch

Andreas Krauss

Stefan Popescu

Martin Hoheisel

Steffen Kappler

Grzegorz Soza

Eckhard Hempel

Christian Hofmann

Michael Grasruck

Martin Sedlmair

Bernhard Krauss

Ernst Klotz

Andre Ritter

Robert Lapp

Johannes Georg Korporaal

Bernhard Krauβ

Matthias Baer-Beck

Stefan Kaepplinger

Sebastian Gehrsitz

Tong Fang

Bjoern Heismann

Gabriel Haras

Yunqiang Chen

Andreas Wimmer

Stefan Schaller

Roland Barthel

Christian Tietjen

Daniel Fischer

Otto Sembritzki

Karl Schwarz

Katharine Lynn Rowley Grant

Anja Borsdorf

Martin Hupfer

Marc Kachelriess

Heiko Wolf

Sebastian Dennert

Esther Meyer

Heinrich Wallschläger

Gerhard Lechsel

Johan Sunnegardh

Nora Hünemohr

André Ritter

Andreas Schaller

Christoph Süss

Andreas Prokein

Nicole Haag

Stephan Arens

Sebastian Faby

Johan Sunnegaardh

Achim Hillenbrand

Jens-Peter Scharnagl

Eberhard Ten Weges

Marco Schuster

Danilo Stitz

Christopher Dennl-Ortega Arrieta

Ralf Gutjahr

Niklas Rehfeld

Mayank Patwari

Gerd Mayer

Stefan Muehleck

Robert Mayer

John Sunnegårdh

Matthias Baer

Andre Hartung

In a computer-implemented method of iteratively reconstructing images or a volume using deep learning and a computer-implemented method of training, projection filter parameter(s) of a projection (joint) bilateral filter, (J)BF, are automatically tuned by a first trained neural network based on data of projections of an imaging procedure. Volume filter parameter(s) of a volume (J)BF are automatically tuned by a second trained neural network based on data of a volume backward-projected from projections filtered by the projection (joint) bilateral filter using the automatically tuned projection filter parameter(s). A volume filtered by the volume (J)BF is output as the reconstructed images or volume, in case the filtered volume meets a predefined quality criterion.

Process for iteratively reconstructing images using deep learning

A method is for the transmission of patient-specific data to an examination log adjustment unit. In an embodiment, the method includes providing a user interface via a software application; receiving patient-specific data of a patient via the user interface, the patient-specific data being based on an input into the user interface; storing the patient-specific data received; and transmitting the patient-specific data to the examination log adjustment unit, the examination log adjustment unit being embodied to adjust an examination log for a medical imaging examination of the patient based upon the patient-specific data.

Method for the transmission of patient-specific data to an examination protocol adjustment unit and patient data transmission unit

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

A method is disclosed for recording medical images of the human body or that of an animal. The method includes scanning for acquiring data and signaling the start and/or end of a manual administration of a contrast medium in temporal relation to the scan for acquiring data.

Method and device for recording medical images

A method is for checking a characteristic variable of an application procedure of an X-ray based medical imaging application, each patient model, of a plurality of patient models stored in a database, is characterized by at least one model parameter. In addition, a subset of the plurality of patient models is selected based upon the at least one model parameter assigned to a patient model or based upon the X-ray based medical imaging application. In addition, a number of simulated application procedures of the X-ray based imaging application are performed based upon the selected subset of patient models, at least one patient model being input into each performed simulated application procedure. Furthermore, a value of the characteristic variable is ascertained for each performed simulated application procedure. In addition, the value of the characteristic variable ascertained is output or is automatically evaluated based upon a specified target for the characteristic variable.

Method for checking a characteristic variable of an application procedure of an X-ray based medical imaging application

A method for correction of an input dataset is disclosed. In an embodiment, the method includes acquisition of an input dataset comprising at least one data error; determination of a correction function; creation of a corrected output dataset by application of the correction function to the input dataset; and outputting of the corrected output dataset. The correction function is embodied to bring about a reduction of at least two data errors that mutually influence one another in the input dataset.

Data correction in X-ray imaging

A method and a system are for calculating an output from a tomographic scrollable image stack, including a large number of generated sectional images of a tissue to be examined. In this context, a tomographic scrollable image stack is received. An output for a display is calculated. The output includes a primary image that is intended for representing the received image stack, and the output includes a secondary image with additional information. The secondary image is displayed overlaid on the primary image once an unhide signal or hide signal is received. In this context, a reference may be provided between the additional information of the secondary image and the slice of tissue for examination that is shown in the sectional image of the image stack.

Method and system for calculating an output from a tomographic scrollable image stack

An embodiment of the invention relates to a method for calculating an image matrix size N for reconstructing image data of an examination subject from projection data. The method includes acquiring projection data obtained during a relative rotational movement between a radiation source of a computed tomography system and the examination subject; calculating the image matrix size N as a function of an extent of an axial field of view of the computed tomography system and a sharpness value in the image data to be reconstructed; and making the calculated image matrix size available to a reconstruction unit to reconstruct the image data from the projection data acquired.

Calculating an image matrix size

A method is for determining a patient weight and/or a body mass index of a patient. The method includes acquiring image data containing depth information of the patient; generating a surface model of the patient based upon the image data acquired; determining density information or X-ray attenuation information of at least part of the patient; and determining at least one of the total patient weight and the body mass index of the patient using the surface model generated and the density information or X-ray attenuation information determined.

Method and apparatus for determining a patient weight and/or a body mass index

An adaptive method for generating CT image data is described. In the method, projection measurement data of an examination region of an examination object is acquired. Furthermore, uncorrected image data of the examination region is generated. Artifact-affected subregions of the examination region are determined on the basis of at least one part of the uncorrected image data. An artifact-reduced image reconstruction is carried out in the artifact-affected subregions of the examination region. Only artifact-reduced subimage data of the artifact-affected subregions is generated. Finally, artifact-reduced image data of the entire examination region is generated by combining at least one part of the uncorrected image data and the artifact-reduced subimage data. A reconstruction device is also described. Moreover, a computed tomography system is described.

Adaptive method for generating artifact-reduced CT image data

A method is for generating image data using a computer tomography device. CT raw data is provided. An initial image is produced from the CT raw data. The initial image is segmented into regions based upon anatomical features. An image mask corresponding to the regions in each case is produced for the initial image, the image mask defining an effective region for a respectively assigned mapping rule. The mapping rules are applied in the effective regions defined by the respective image masks, and the image data is generated based upon the initial image processed via the mapping rules.

Method for generating image data using a computer tomography device, image generating computer, computer tomography device, computer program product and computer-readable data medium

A method is for checking a medical image with regard to a processing of the medical image via an image processing module. The method includes providing at least one input requirement of the image processing module relating to at least one image parameter of the medical image; providing the at least one image parameter of the medical image; and checking whether the at least one image parameter fulfills the at least one input requirement.

Method for checking a medical image and method for adapting an examination protocol during a medical imaging examination

A method is described for the determination of a calcium score for a patient to be examined with the aid of a CT system. The method is used to define patient-specific CT-acquisition parameters. In addition, material parameters for a model method for the generation of synthetic image data for virtual CT-acquisition parameters are calibrated using phantom image data recorded with reference CT-acquisition parameters. A calcium score assigned to synthetic phantom image data corresponds to a calcium score determined with phantom image data recorded with reference CT-acquisition parameters. Next, CT-projection-measurement data is acquired for a region of interest using the patient-specific CT-acquisition parameters. The acquired CT-projection-measurement data is used to generate synthetic image data using the calibrated model method. Finally, a calcium score is determined using a standard method on the basis of the synthetic image data. Also described is a calcium-score-determining device. Also described is a computed tomography system.

Spectrum-neutral determination of calcareous deposits in blood vessels

A diaphragm apparatus for the collimation of an X-ray bundle of an X-ray device is provided for scanning an examination object. An X-ray device including the diaphragm apparatus is also provided. In an embodiment, the diaphragm apparatus includes two diaphragms in the form of slotted diaphragms arranged in series in the direction of the X-rays and mounted to be positionable with respect to one another. Each of the diaphragms includes a fixed diaphragm aperture corresponding to maximum collimation of the X-ray bundle and a region that is impermeable to X-rays, which in each case includes an extension corresponding to the diaphragm aperture corresponding to the maximum collimation.

Diaphragm apparatus for the collimation of an X-ray bundle of an X-ray device

A method for determining a spatial distribution of a material property value in an examination region of an examination object is described. With an embodiment of the method, projection scan data is acquired which has been produced with the aid of a single-energy CT scan with a defined X-ray energy spectrum from the examination region of the examination object using a defined scan projection geometry. Furthermore, a target function is established which includes a spectral forward projection of the sought spatial distribution and the acquired projection data. Finally, a spatial distribution of a material property value is determined for which the target function assumes an extremal value. A material property distribution-determining device is also described. A computer tomography system is described, moreover.

Determining a spatial distribution of material property values on the basis of a single-energy CT scan with the aid of an iterative optimization method

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Heroldsbach, Germany

Rainer Raupach