Siemens Aktiengesellschaft

Siemens Healthcare Gmbh

Duke University

University of Virginia

Siemens Healthineers Ag

Other

The Johns Hopkins University

Siemens Medical Solutions Usa, Inc.

Siemens Aktiengesell Schaft

Siemens Aktiengesellchaft

Berthold Kiefer

Marcel Dominik Nickel

Stephan Kannengiesser

Robert Grimm

Ali Kamen

Mamadou Diallo

Heinrich Von Busch

Shaohua Kevin Zhou

Xiaodong Zhong

Dorin Comaniciu

Alto Stemmer

Martin Requardt

Peter Gall

David Jean Winkel

Xin Yu

Tongbai Meng

Brian Dale

Xiang Sean Zhou

Atilla Peter Kiraly

Craig H Meyer

David D Liu

Matthias Fenchel

Josef Pfeuffer

Yiqiang Zhan

David A Feinberg

John P Mugler, Iii

Bin Lou

Jingdan Zhang

Andre De Oliveira

Clifford R Weiss

Samuel W Fielden

Parmeshwar Khurd

Bibo Shi

Mustafa R Bashir

Nicolas Von Roden

Leo John Grady

David Grodzki

Thorsten Feiweier

Tommaso Mansi

Wolfgang Renz

Benjamin L Odry

Gerardo Hermosillo Valadez

Jianmin Wang

Swen Campagna

Mathias Nittka

Franz Schmitt

Ralf Nanke

Edgar Mueller

Orlando P Simonetti

Jens Fehre

Martin Harder

Lars Lauer

Gerhard Laub

Heiko Meyer

Neil Birkbeck

Vibhas S Deshpande

Xiaoming Bi

Lothar Schön

Robert Krieg

Clemens Otte

Thomas Pheiffer

Simon Bauer

Peter Heubes

Karlheinz Glaser-Seidnitzer

Gudrun Ruyters

Wolfgang Wein

Stefan Roell

Kirstin Jattke

Cecile Mohr

Li Pan

Katrin Wohlfarth

Ahmet Tuysuzoglu

Werner Hauptmann

Stephan Kannengieβer

Elmar Rummert

Niels Oesingmann

G Wilson Miller, Iv

Shaolei Feng

Rainer Kaim

Qiang Zhang

Gerardo Hermosillo-Valadez

Sonia Nielles-Vallespin

Marianne Vorbuchner

Ralf Loeffler

Kareem Abdelrahman

Jiamin Wang

Talissa A Altes

Kalpitkumar Gajera

Sebastian Fürst

Isabel Dregely

Clement Jad Abi Nader

Claudia Fellner

Ihab Kamel

David Liu

Systems and methods are provided for optimizing a deep learning model. A multi-site dataset associated with different clinical sites and a deployment dataset associated with a deployment clinical site are received. A deep learning model is trained based on the multi-site dataset. The trained deep learning model is optimized based on the deployment dataset. The optimized trained deep learning model is output.

Localization and classification of abnormalities in medical images

In a method for an actuation of a magnetic resonance device for capturing image data from an examination region of an examination object, at least one first control sequence is provisioned, the magnetic resonance device is actuated according to the at least one first control sequence to capture first data from the examination object, the first data is analyzed with respect to a property to generate a result, and, based on the result, a selective performance of one of: a selection of a further control sequence, and termination of the actuation of the magnetic resonance device, is performed.

Method for an adaptive actuation of a magnetic resonance device

The disclosure relates to techniques for automatically characterizing liver tissue of a patient, comprising receiving morphological magnetic resonance image data set and at least one magnetic resonance parameter map of an imaging region comprising at least partially the liver of the patient, each acquired by a magnetic resonance imaging device, via a first interface. The techniques further include applying a trained function comprising a neural network to input data comprising at least the image data set and the parameter map. At least one tissue score describing the liver tissue is generated as output data, which is provided using a second interface.

Techniques for automatically characterizing liver tissue of a patient

In a method for generating a surrogate marker based on medical image data mapping an image region, the medical image data is detected using a first interface, a first subregion of the image region is selected by segmenting a first structure included in the image region, a first property of the first subregion is extracted, the surrogate marker is determined based on the first property, and the surrogate marker is provided using a second interface.

Methods and systems for generating surrogate marker based on medical image data

In a computer-implemented method of training a machine learning based processor, the processor can be trained to derive image data from signal data sets of multiple spin echo sequences. The trained processor can be configured to perform image processing for Magnetic Resonance Imaging (MRI) to derive the image data.

Trained image processing for diffusion weighted imaging and/or turbo spin echo sequences with focus on body applications

Systems and methods are provided for classifying an abnormality in a medical image. An input medical image depicting a lesion is received. The lesion is localized in the input medical image using a trained localization network to generate a localization map. The lesion is classified based on the input medical image and the localization map using a trained classification network. The classification of the lesion is output. The trained localization network and the trained classification network are jointly trained.

A method of generating biomarker parameters includes acquiring imaging data depicting a patient using a MRI system. The imaging data is acquired for a plurality of contrasts resulting from application of a pulse on the patient's anatomy. A process is executed to generate a MoCoAve image for each contrast. This process includes dividing the imaging data for the contrast into bins corresponding to one of a plurality of respiratory motion phases, and reconstructing the imaging data in each bin to yield bin images. The process further includes selecting a reference bin image from the bin images, and warping the bin images based on the reference bin image. The warped bin images and the reference bin image are averaged to generate the MoCoAve image for the contrast. One or more biomarker parameter maps are calculated based on the MoCoAve images generated for the contrasts.

Quality and signal-to-noise ratio of free-breathing quantitative measurement of magnetic resonance imaging parameters and related biomarkers

In a method for displaying quantitative magnetic resonance image data, and a processor, and a magnetic resonance (MR) apparatus that implement such a method, first quantitative MR image data of an examination object are provided to the processor, the first quantitative MR image having been obtained using an MR scanner with a first basic magnetic field strength. The first quantitative magnetic resonance image data are converted in the processor from the first basic magnetic field strength to a second basic magnetic field strength, thereby generating second quantitative MR image data, which are then displayed.

Method, processor and magnetic resonance apparatus for displaying quantitative magnetic resonance image data

Tissue is characterized using machine-learnt classification. The prognosis, diagnosis or evidence in the form of a similar case is found by machine-learnt classification from features extracted from frames of medical scan data. The texture features for tissue characterization may be learned using deep learning. Using the features, therapy response is predicted from magnetic resonance functional measures before and after treatment in one example. Using the machine-learnt classification, the number of measures after treatment may be reduced as compared to RECIST for predicting the outcome of the treatment, allowing earlier termination or alteration of the therapy.

Tissue characterization based on machine learning in medical imaging

Some aspects of the present disclosure relate to ultrashort-echo-time (UTE) imaging. In one embodiment, a method includes acquiring UTE imaging data associated with an area of interest of a subject. The acquiring comprises applying an imaging pulse sequence with a three-dimensional (3D) spiral acquisition and a nonselective excitation pulse. The method also includes reconstructing at least one image of the area of interest from the acquired UTE imaging data.

3D UTE imaging using variable-TE stack-of-spirals acquisition

In a method and magnetic resonance (MR) apparatus for performing an adjustment of the MR system, an examination object under is divided into at least one excitation volume. First adjustment parameters for the at least one excitation volume of the object, and second adjustment parameters for the at least one excitation volume of the object, which differ from the first adjustment parameters are determined. First MR signals are acquired from the at least one excitation volume using the first adjustment parameters. Second MR signals are acquired from an excitation volume using the second adjustment parameters. A first MR image of the at least one excitation volume is reconstructed using the first MR signal. A second MR image of the at least one excitation volume is reconstructed using the second MR signal.

Magnetic resonance apparatus and method for dynamic adjustment thereof with multiple adjustment parameters

A magnetic resonance imaging system and method are provided for improved determination of noise bias effects in calculating fitted parameters for quantitative MRI procedures. The system and method includes selecting a range for the SNR and fitted parameter values, and for each of a plurality of base pairs of these values and for a plurality of b values, adding a random noise term to the real and imaginary components of a plurality of corresponding signal terms, fitting magnitudes of the resulting 'noisy' signals to determine a 'noisy' fitted parameter value, and compare the &#x201c;noisy&#x201d; and base fitted parameter values to determine a noise-based error for each pair of base values. The noise-based errors can be used to generate an error map, modify imaging parameters to reduce such errors, or correct fitted parameters directly.

Systems and methods for predicting errors and optimizing protocols in quantitative magnetic resonance imaging

In a magnetic resonance (MR) apparatus and a method for operating such an apparatus, a T1 parameter map is generated with fat fraction correction, by using a model in which the fat fraction of acquired MR data is used as a known parameter. The T1 values from the acquired MR data are fat fraction-corrected in such a manner, so as to generate fat fraction-corrected entries for the T1 parameter map according to the model.

Method and magnetic resonance apparatus for generating a fat fraction-corrected T1 parameter map

In some aspects, the disclosed technology relates to magnetic field monitoring of spiral echo train imaging. In one embodiment, a method for spiral echo train imaging of an area of interest of a subject includes measuring k-space values and field dynamics corresponding to each echo of a spiral echo pulse train, using a dynamic field camera and a magnetic resonance imaging (MRI) system. The dynamic field camera is configured to measure characteristics of fields generated by the MRI system; the characteristics include at least one imperfection associated with the MRI system. The spiral echo pulse train corresponds to a spiral trajectory scan from the MRI system that obtains magnetic resonance imaging data using a pulse sequence which applies spiral gradients in-plane with through-plane phase encoding. The method also includes generating, based on the characteristics of the fields measured by the dynamic field camera and based on the obtained magnetic resonance imaging data, a model of the k-space trajectory corresponding to each echo of the spiral echo pulse train; and, based on the generated model of the k-space trajectory, reconstructing images that correspond to the area of interest and that are compensated for the at least one imperfection associated with the MRI system.

Magnetic field monitoring of spiral echo train imaging

Growing community of inventors

Erlangen, Germany

Berthold Kiefer