Siemens Healthcare Gmbh

Siemens Aktiengesellschaft

Schlumberger Technology Corporation

Siemens Healthineers Ag

Siemens Medical Solutions Usa, Inc.

Yale University

National Institutes of Health

Case Western Reserve University

Siemens Corporation

Siemens Corporate Research, Inc.

Siemens Canada Limited

Siemens Aktiengellschaft

Centre Hospitalier Universitaire Vaudois

Siemens Aktiengesellscaft

Julius-maximilians-universitaet Wuerzburg

Peter Speier

Martin E Poitzsch

Michaela Schmidt

Mathias Nittka

Krishnamurthy Ganesan

Markus Vester

Thorsten Feiweier

Julian J Pop

Mariappan S Nadar

Andreas Greiser

Swen Campagna

Thomas Benner

Bernd Kuehn

Marie-Pierre Jolly

Edgar Müller

Thorsten Speckner

Jan Bollenbeck

Edgar Mueller

Xiaoguang Lu

Daniel Nico Splitthoff

Gregor Koerzdoerfer

Carmel Hayes

Steven F Crary

Boris Mailhe

Matthias Fenchel

Peter Schmitt

Michael Zenge

Xiao Chen

Simon Arberet

Jens Gühring

Ti-chiun Chang

Shu-Kong Chang

Jens Guehring

Jaideva C Goswami

Christoph Forman

Ralf Heidler

Bruno Luong

Stefan Popescu

Yan Huang

Tobias Kober

Christoph Guetter

Arne Littmann

Hasan Ertan Cetingul

Michael Wullenweber

Christine Lorenz

Ning Jin

Davide Piccini

Jianing Pang

Robert Todd Constable

Wilfried Landschuetz

Peter Gatehouse

Gigi Galiana

Aurelien Stalder

Dorin Comaniciu

Bogdan Georgescu

Mark A Griswold

Reza Taherian

Frank Sauer

Marcel Dominik Nickel

Tong Fang

Robert L Kleinberg

Stephan Kannengiesser

Björn Heismann

Rainer Schneider

Matthias Gebhardt

Josef Pfeuffer

Charles Flaum

Johann Sukkau

Stefan Assmann

Wolfgang Gerhard Rehwald

Boqin Q Sun

Adrian Mark Thomas

Robert Rehner

Hui Xue

Georg Pirkl

Xiaoming Bi

Tom Hilbert

Yun Jiang

Patrick Liebig

Andre De Oliveira

Ralf Kartäusch

Ying Sun

Klaus J Kirchberg

Thomas Kluge

Shivraman Giri

Wolfgang Härer

Karsten Wicklow

Dingxin Wang

Jens Wetzl

Randall Kroeker

Christof Krellmann

Stephan Stoecker

Kai Tobias Block

Katrin Christel Sprung

Steffen Schröter

Kelvin Chow

Andreas Grimme

Christian Straley

Gregor Körzdörfer

Corinna Maier

A motion correction method may include: calculating a current motion-corrected MR image based on a current motion parameter of an imaging target and K-space measurement data of the imaging target; calculating current motion-corrected K-space data based on the current motion parameter of the imaging target and the current motion-corrected MR image; calculating a current K-space measurement data error based on the K-space measurement data of the imaging target and the current motion-corrected K-space data; and determining, based on the current K-space measurement data error, whether an iteration end condition is met. If so, using the current motion-corrected MR image as a final motion-corrected MR image to be used. Otherwise, updating the current motion parameter of the imaging target based on the current K-space measurement data error and the current motion-corrected MR image. The method advantageously provides an increased motion correction speed of an MR image.

Motion correction method and apparatus for magnetic resonance image, and magnetic resonance imaging system

Techniques are disclosed for acquiring data of an examination object in at least two slices by means of a pulse sequence. Time intervals between excitations of neighboring slices and associated minimum intervals are determined. From these, time intervals to be adapted between excitations of neighboring slices are determined and adapted before a measurement protocol is executed, with the adapted time intervals. Through the determination of a minimum time interval between excitations of neighboring slices and the adaptation of the time intervals between excitations of neighboring slices, a falsification of measurement results can be avoided, measurement time of the chosen measurement protocol is not increased, and the user is not restricted in their choice of the slices to be excited.

Acquisition of data of an examination object by means of magnetic resonance with improved time management

Movement detection of at least one part of a subject located inside a magnetic resonance imaging (MRI) device is provided. A method includes performing an MR scan by executing a programmable MR sequence protocol. The sequence protocol includes MR excitation pulses to be transmitted via a parallel transmit system and receive time windows for receiving magnetic resonance signals via a receive system. The MR sequence protocol includes, in between the MR excitation pulses, the generation of multi-channel pilot tone signals that are transmitted via the parallel transmit system and an RF transmit coil array. During transmission of the multi-channel pilot tone signals, the pilot tone signals are received with an RF receive coil array. The received pilot tone signals are forwarded via the receive system to an analyzing unit, and movement of at least one part of the subject is determined by analyzing the received pilot tone signal.

Method and system for detecting movement of at least one part of a subject

Method acquiring a target physiological motion signal, including: acquiring multi-channel complex signals received by multiple channels; and acquiring, from the multi-channel complex signals using a motion signal synthesis vector corresponding to a target motion signal, a target motion complex signal with interference removed; acquiring data received by multiple channels, the data including data without external interference in a first sub-period and data with external interference in a second sub-period; acquiring an external interference suppression matrix based on the data, and acquiring external interference suppression data based on the data without external interference or the data with external interference and the external interference suppression matrix; acquiring a motion signal correlation matrix of the target motion signal in frequency domain based on a frequency range of the target motion signal; and using, as a motion signal synthesis vector, an eigenvector acquired according to an eigenvalue of the motion signal correlation matrix.

Target motion signal acquisition

A method includes determining a heart beat signal during acquisition of MR images obtained at a plurality of cardiac cycles; determining at least one physiological parameter of a heart obtained at the plurality of cardiac cycles; determining a model including, determining, in each of the cardiac cycles, a variable time interval of variable duration and at least one additional time interval based on the heart beat signal and the at least one physiological parameter, the at least one additional time interval having a lower variability in duration than the variable time interval; determining a duration of the variable time interval and a duration of the cardiac cycle for each of the cardiac cycles based on the heart beat signal and the at least one physiological parameter; and assigning the MR images to the different cardiac phases based on the variable time interval and each of the cardiac cycles.

Assignment of MR images to cardiac phases

A method for adapting, per cardiac cycle, the parameters governing interpolation of varying and non-interpolation of fixed fractions of each individual cardiac cycle is provided. A time series of data values associated with a cardiac cycle is received, and the time series is scaled to a reference cardiac cycle, wherein the scaling includes applying a model to the time series to generate a scaled time series of data values associated with the first cardiac cycle. The model is trained using the scaled time series.

Using cardiac motion for beat-to-beat optimisation of varying and constant fractions of cardiac cycles in segmented k-space MRI acquisitions

According to a method for characterizing a motion of an object, a reference signal is emitted into a target region and two or more receiver coil signals are generated in response to the reference signal by two or more receiver coils. A motion signal characterizing a motion of the object is determined by a computing unit depending on temporal modulations of the two or more receiver coil signals. A correlation coefficient of the motion signal and a receiver coil signal is computed by the computing unit. A reference correlation coefficient is determined by the computing unit depending on a location of the receiver coil based on a predetermined reference correlation map. The motion signal is corrected by the computing unit depending on a correlation coefficient and the reference correlation coefficient.

Characterizing a motion of an object

A pilot tone signal generator, a magnetic resonance tomograph, a method for transmission of a synchronization signal, and a computer program product are disclosed. The pilot tone signal generator includes a receive unit for receipt of a synchronization signal of a system control unit of a magnetic resonance tomograph. The synchronization signal may include a clock signal, and the pilot tone signal generator is configured to emit a pilot tone signal as a function of the synchronization signal.

Pilot tone signal generator, magnetic resonance tomograph, method for transmission of a synchronization signal and computer program product

A peripheral device for a magnetic resonance tomography unit. The peripheral device includes a first sensor for receiving an electromagnetic data signal from the environment of the peripheral device. The peripheral device is configured to execute signal processing in dependence on the electromagnetic data signal and a frequency of the electromagnetic data signal is greater than a Larmor frequency of the magnetic resonance tomography unit.

Apparatus and method for transferring clock signals from a magnetic resonance tomography unit to a peripheral device

Determining parameter values in image points of an examination object in an MR system by an MRF technique. Comparison signal waveforms, established using predetermined recording parameters, and each assigned to predetermined values of the parameters to be determined, are loaded. An image point time series of the examination object is acquired with an MRF recording method such that the acquired image point time series are comparable with the loaded comparison signal waveforms. A signal comparison of a section of the respective signal waveform of the acquired one image point time series is carried out with a corresponding section of loaded comparison signal waveforms to establish similarity values. The values of the parameters to be determined on the basis of the most similar comparison signal waveforms determined are determined, and then stored or output.

Magnetic resonance fingerprinting method

A computer-implemented method is provided for evaluating a pilot tone signal. In the method, the pilot tone signal is recorded using a high-frequency coil arrangement of a magnetic resonance facility and describes a movement of a patient. The method also includes extracting movement information assigned to a movement component, (e.g., a respiratory movement). A breakdown or decomposition of the pilot tone signal is effected on a basis of signal components having assigned weightings and for the purpose of determining the movement information, a part of a base which is assigned to the movement component is selected by a selection criterion. For the purpose of determining the base and the weightings, a non-negative matrix factorization is performed, in the context of which a signal matrix, which is formed from the pilot tone signal and is in particular non-negative, is formulated as a product of a non-negative signal component matrix that describes the base and a non-negative weighting matrix that describes the weightings.

Method for evaluating a pilot tone signal in a magnetic resonance facility, magnetic resonance facility, computer program and electronically readable data medium

In a method and apparatus for determining parameter values in voxels of an examination object using magnetic resonance fingerprinting (MRF), a first signal comparison is made of signal characteristics of established voxel time series with first comparison signal characteristics. Further synthetic comparison signal characteristics are generated from the first comparison signal characteristics and values determined in the first signal comparison. The generated further comparison signal characteristics are used to perform a further signal comparison, with which values of at least a first and a second further parameter are determined. From the further comparison signal characteristics, a value of at least one further parameter is determined that could not necessarily already be determined in the first signal comparison.

Magnetic resonance fingerprinting method and apparatus

A device for NMR spectroscopy includes a magnet arrangement, configured to produce a magnetic probe field within a magnet field of view external to the magnet arrangement. In a embodiment, the device includes a coil arrangement, configured to generate an electromagnetic excitation field within a coil field of view and a controller, configured to control the coil arrangement. The device includes a magnet adjustment arrangement, configured and arranged to modify at least one parameter of the magnet arrangement to change a spatial position of the magnet field of view.

Device and method for nuclear magnet resonance spectroscopy

For machine training and application of a trained complex-valued machine learning model, an activation function of the machine learning model, such as a neural network, includes a learnable parameter that is complex or defined in a complex domain with two dimensions, such as real and imaginary or magnitude and phase dimensions. The complex learnable parameter is trained for any of various applications, such as MR fingerprinting, other medical imaging, or non-medical uses.

Complex-valued neural network with learnable non-linearities in medical imaging

In a method and magnetic resonance (MR) apparatus for pulse wave velocity (PWV) measurement along the aorta of a subject using MR imaging, a multislice cardio synchronized segmented cin&#xe9; MR data acquisition sequence is optimized in order to enhance inflow representation in the slice images, in order to make the multislice MR data acquisition sequence viable for clinical uses, so as to acquire intensity-based MR data from two transverse slices spaced from each other along the descending aorta. The respective intensities of relevant pixels in at least two respective slice images are analyzed in order to identify the arrival of a pulse wave in the respective slices by the onset of flow enhancement in the slices, represented by intensity changes in the pixels. From the onset of flow enhancement in the respective slice images, PWV is calculated. An electronic signal representing the calculated PWV is then provided from a computer.

Inflow-based pulse wave velocity profiling along the aorta using magnetic resonance imaging

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Erlangen, Germany

Peter Speier