Siemens Aktiengesellschaft

Siemens Healthcare Gmbh

Siemens Healthineers Ag

National Institutes of Health

Siemens Aktiengesellscaft

Michael Zenge

Mariappan S Nadar

Edgar Mueller

Qiu Wang

Peter Schmitt

Jun Liu

Ti-chiun Chang

Michaela Schmidt

Edgar Müller

Peter Speier

Alban Lefebvre

Thorsten Feiweier

Marcel Dominik Nickel

Andreas Greiser

Jens Gühring

Carsten Prinz

Davide Piccini

Kai Tobias Block

Nirmal Janardhanan

Zhili Yang

Michael Schneider

Boris Mailhe

Stephan Kannengiesser

Stefan Assmann

Sven Zuehlsdorff

Hui Xue

Xiaoming Bi

Arne Littmann

Christoph Forman

Simon Bauer

Daniel Nicolas Splitthoff

Christopher Glielmi

Peter Weale

Christof Krellmann

Jonathan Sperl

Jeremy Rapin

Thomas Blum

Axel Loewe

Aurelien Stalder

Andrea Hopf

Aurélien Stalder

Derya Gol Gungor

Michael S Hansen

Christian Köglmeier

Jens Kaftan

At least one example embodiment relates to a computer-implemented method comprising receiving a medical image dataset; receiving at least one medical comparison image dataset; extracting biometric data based on the medical image dataset; extracting biometric comparison data based on the medical comparison image dataset; determining a measure of difference between the biometric comparison data and the biometric data; and assigning the medical image dataset to the medical comparison image dataset when the measure of difference does not exceed a threshold value.

Method for assigning a medical image dataset to a medical comparison image dataset depending on biometric data

The present disclosure relates to an imaging system for creating an image of an examination object comprising a system component and a control component. The system component has a component property that is able to assume a value in a first value range established by the imaging system. A corresponding system component of another imaging system comprises a corresponding component property with another value range with an overlap range with the first value range. The control component is embodied to control the use of the system component on the creation of the image, wherein the control component can be operated in a compatibility mode. In the compatibility mode, the control component is configured, on the creation of the image, to only allow values that lie within the overlap range for the component property of the system component.

Method, system, and medical imaging system for creating an image of an examination object and the use of such images

A method for operating a medical diagnostic system that is configured to use a system component of the diagnostic system to generate examination data of a person under examination during an examination procedure is provided. The examination procedure with control of the system component is controlled by a piece of control software, and a component driver exchanges control commands of the control software with the system component in order to control the system component. The method includes providing an event driver that communicates with the control software via an interface of the control software. Via the event driver, a first event is detected in the examination procedure and reported to the event driver. When the first event is detected in the examination procedure, the use of the system component in the examination procedure is modified to a first type defined by the event driver.

Medical diagnostic system with event drivers

Techniques are described for generating setting parameters for an MRI sequence to be performed, wherein a conditional generative artificial neural network is used that has been trained as part of a conditional generative adversarial net. The techniques also include establishing a conditional generative artificial neural network. The conditioning of the conditional generative artificial neural network can be used to specify a specific medical application or question to obtain setting parameters for an MRI apparatus that are optimized with respect to said application or question.

Generating setting parameters for an MRI sequence

A method for estimating a coil sensitivity map for a magnetic resonance (MR) image includes providing a matrix A of sliding blocks of a 3D image of coil calibration data, calculating a left singular matrix Vfrom a singular value decomposition of A corresponding to &#x3c4; leading singular values, calculating P=VV, calculating a matrix that is an inverse Fourier transform of a zero-padded matrix P, and solving Mc=(S)cfor c, where cis a vector of coil sensitivity maps for all coils at spatial location r, and 

Eigen-vector approach for coil sensitivity maps estimation

A method for acquiring a three-dimensional image volume using a magnetic resonance imaging device includes performing a multi-slice or multi-slab acquisition process to acquire a plurality of slices or three-dimensional slabs corresponding to an imaged object. Each respective slice or three-dimensional slab included in the plurality of slices or three-dimensional slabs comprises k-space data. An iterative compressed-sensing reconstruction process is applied to jointly reconstruct the plurality of three-dimensional slabs as a single consistent volume. The iterative compressed-sensing reconstruction process solves a cost function comprising a summation of individual data fidelity terms corresponding to the plurality of three-dimensional slabs.

Compressed sensing reconstruction for multi-slice and multi-slab acquisitions

In a method for computing MR images of an examination object that performs a cyclic movement, MR signals are detected over at least two cycles of the cyclic movement. In each of these cycles, data for multiple MR images are recorded. Over these cycles, a magnetization of the examination object that influences the MR images approaches a state of equilibrium in a second of these cycles is closer to the state of equilibrium than in a first of these cycles. Movement information for various movement phases of the cyclic movement of the examination object is determined using the MR images from the second cycle, with movement information of the examination object determined for each of the various movement phases. Movement correction of the examination object is carried out in the MR images of the first cycle using the movement information determined in the second cycle.

Dynamic magnetic resonance imaging with variable contrast

In a method to control a magnetic resonance imaging system to generate magnetic resonance image data of an examination subject, raw magnetic resonance data are acquired that include measurement values at multiple readout points in k-space. The readout points are arranged along a readout axis in k-space as readout pairs with a predetermined pair spacing relative to one another. Readout pairs that are adjacent in k-space along the readout axis have a sampling interval that is different than the pair spacing, which sampling interval varies along the readout axis. A control sequence determination system is designed to determine a control sequence for a magnetic resonance imaging system that is designed to control the magnetic resonance imaging system according to this method, and a magnetic resonance imaging system that has a control device designed to control the magnetic resonance imaging system according to such a method.

Controlling a magnetic resonance imaging system to generate magnetic resonance image data of an examination subject

In a method and apparatus, magnetic resonance angiography images of an examination volume of a patient are obtained using time-of-flight angiography in a magnetic resonance scanner. By continuous recording, a number of two-dimensional slice images covering the examination volume along an axial direction are acquired in a slice-by-slice layer-wise, such as with overlapping. The slice images are divided into groups of, in each case, a predetermined number of consecutive slice images in the axial direction. A maximum intensity projection image is determined for each group, and the angiography images are determined as the maximum intensity projection images and/or dependent on the maximum intensity projection images.

Method and magnetic resonance apparatus for determination of magnetic resonance angiography images using time-of-flight angiography

Magnetic resonance imaging uses regularized SENSE reconstruction for a reduced field of view, but minimizes folding artifacts. A reference scan is oversampled relative to the reduced field of view. The oversampling provides coil sensitivity information for a region greater than the reduced field of view. The reconstruction of the object for the reduced field of view using the coil sensitivities for the larger region may have fewer folding artifacts.

Reference oversampling in sense-type magnetic resonance reconstruction

In a method and apparatus for the generation of image data of a moving subject, raw data are initially acquired for a region comprising the subject at different measurement points in time in different movement phases of the subject. A reconstruction then takes place of multiple interim image data sets of the subject from the raw data that are respectively associated with different movement phases of the subject. Deviation data are then determined between the interim image data sets of the different movement phases of the subject, and the reconstruction of image data from raw data of different movement phases then takes place under consideration of the deviation data.

Method and apparatus to generate image data

A computer-implemented method of selecting a Magnetic Resonance Imaging (MRI) sampling strategy includes selecting a base variable-density sampling pattern and determining a scan time associated with the base variable-density sampling pattern. A modified variable-density sampling pattern is created by modifying one or more parameters of the base variable-density sampling pattern to maximize a sampled k-space area without increasing the scan time. Next, a scan is performed on an object of interest using the modified variable-density sampling pattern to obtain a sparse MRI dataset. Then a sparse reconstruction process is applied to the sparse MRI dataset to yield an image of the object of interest.

Sampling strategies for sparse magnetic resonance image reconstruction

A computer-implemented method for learning a tight frame includes acquiring undersampled k-space data over a time period using an interleaved process. An average of the undersampled k-space data is determined and a reference image is generated based on the average of the undersampled k-space data. Next, a tight frame operator is determined based on the reference image. Then, a reconstructed image data is generated from the undersampled k-space data via a sparse reconstruction which utilizes the tight frame operator.

Dynamic image reconstruction with tight frame learning

A method of image reconstruction for a magnetic resonance imaging (MRI) system having a plurality of coils includes obtaining k-space scan data captured by the MRI system, the k-space scan data being representative of an undersampled region over time, determining a respective coil sensitivity profile for the region for each coil of the plurality of coils, and iteratively reconstructing dynamic images for the region from the k-space scan data via an optimization of a minimization problem. The minimization problem is based on the determined coil sensitivity profiles and redundant Haar wavelet transforms of the dynamic images.

MRI reconstruction with incoherent sampling and redundant haar wavelets

In the generation of MR angiography images of a predetermined three-dimensional volume segment of a living examination subject using means a magnetic resonance system, MR data in the volume segment are acquired by radial acquisition of k-space. The MR data are analyzed in order to subdivide the MR data into groups, with each group including only the MR data that correspond to a specific heart beat phase of the heat of the examination subject. MR angiography images are generated based only on the MR data of one of these groups.

Method and magnetic resonance system to generate angiography images

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Nuremberg, Germany

Michael Zenge