Siemens Healthcare Gmbh

Siemens Healthineers Ag

Siemens Aktiengesellschaft

Siemens Corporation

Jens Wetzl

Christoph Forman

Michaela Schmidt

Seung Su Yoon

Elisabeth Hoppe

Puneet Sharma

Shaohua Kevin Zhou

Peter Speier

Michal Sofka

Saikiran Rapaka

Edgar Mueller

Jingdan Zhang

Neil Birkbeck

Jerome Declerck

Christian Geppert

Jens Kaftan

Teodora Chitiboi

Fasil Gadjimuradov

Timo Kohlberger

Daniel Giese

Maria Monzon

Carola Fischer

Ivo Prochaska

A frequency offset is selected based on similarity measures of multiple MRI images obtained from frequency scout measurements associated with multiple frequency offsets from a reference frequency of a magnetization excitation pulse. The similarity measure for each respective MRI image of the multiple MRI images is determined based on a comparison between at least one reference image and the respective MRI image. The at least one reference image is determined from the multiple MRI images based on spectrum information of each of the multiple MRI images. Such methods facilitate automatically determining/selecting a more optimal frequency offset for an MRI scan following a frequency scout scan, in particular, for an SSFP or a bSSFP pulse sequence, and thereby banding artifacts and/or flow-related artifacts can be reduced for the MRI scan.

Selection of frequency offset for an MRI scan

The disclosure relates to techniques for determining a motion parameter of a heart. A subset of a sequence of cardiac MR images is applied as a first input to a first trained convolutional neural network configured to determine, as a first output, a probability distribution of at least 2 anatomical landmarks. The sequence of cardiac MR images is cropped and realigned based on the at least 2 anatomical landmarks to determine a reframed and aligned sequence of new cardiac MR images showing the same orientation of the heart. The reframed and aligned sequence of new cardiac MR images is applied to a second trained convolutional neural network configured to determine, as a second output, a further probability distribution of the at least 2 anatomical landmarks in each new MR image of the reframed and aligned sequence, the motion parameter of the heart is determined based on the second output.

Automatic determination of a motion parameter of the heart

A trained deep learning network is for determining a cardiac phase in magnet resonance imaging. In an embodiment, the trained deep learning network includes an input layer; an output layer; and a number of hidden layers between input layer and output layer, the layers processing input data entered into the input layer. In an embodiment, the deep learning network is designed and trained to output a probability or some other label of a certain cardiac phase at a certain time from entered input data. A method for determining a cardiac phase in magnet resonance imaging; a related device; a training method for the deep learning network; a control device and a related magnetic resonance imaging system are also disclosed.

Method and device for determining a cardiac phase in magnet resonance imaging

Systems and methods for predicting a location for acquiring a target view of an anatomical object of interest in an input image are provided. An input image of an anatomical object of interest of a patient is received. An output image is generated using a machine learning based network. The output image depicts a projection of a 3D image plane for acquiring a target view of the anatomical object of interest identified on the input image. The output image is output.

Automatic, dynamic, and adaptive slice planning for cardiac MRI acquisition

In a method for automatic motion detection in medical image-series, a dataset of a series of images is provided. The images can be of a similar region of interest that are recorded at consecutive points of time. The method can further include localizing a target in the images of the dataset and calculating a position of the target in the images to calculate localization data of the target, and calculating movement data of a movement of the target of temporal adjacent images of the images based on the localization data.

Method and device for automatic motion detection in medical image-series

In a method and magnetic resonance apparatus for recording diagnostic measurement data of a heart of an examination object, the magnetic resonance apparatus is operated by a control sequence wherein an RF pulse excites nuclear spins with a flip angle of at least 60&#xb0;, the diagnostic measurement data are recorded in a coordinate system independent of the heart, and the basic magnetic field produced by the magnetic resonance apparatus is smaller than 1.0 tesla.

Method and magnetic resonance apparatus for acquiring diagnostic measurement data of a heart

A method for generating at least one acquisition template for an acquisition of magnetic resonance signals, an acquisition template generating unit, a magnetic resonance apparatus and a computer program product. At least one acquisition template is generated with an acquisition template generating unit. The at least one acquisition template has a plurality of spiral-like spokes in a k-space, each spoke having a plurality of spiral points.

Spiral shaped MR acquisition template

In a method and apparatus for producing respiration-corrected MR images of an examination volume containing the heart of a patient during respiratory movement of MR signals are recorded continuously during multiple cardiac cycles, each cardiac cycle having multiple time segments. One 2D navigator image data record per cardiac cycle is recorded during a time segment of that cardiac cycle, with k-space being filled along a Cartesian trajectory such that a spatial resolution is achieved in two spatial directions of the examination volume. Also, multiple 3D image data records are recorded in the other time segments of that cardiac cycle, with Cartesian filling of raw data space such that a spatial resolution is achieved in all three spatial directions of the examination volume. The respiratory movement is then determined from these navigator data records. The determined respiratory movement is corrected in the recorded MR signals.

Magnetic resonance 2D navigator technique

A method is for determining a type of reconstruction of image data from a magnetic resonance measurement of an examination object by way of a magnetic resonance device. In an embodiment, the method includes determining at least one first parameter which includes information about a breathing process of the examination object; comparing the at least one first parameter with at least one second parameter from a maintained database; and determining a type of reconstruction of image data of the magnetic resonance measurement according to the comparison that has been made. In an example embodiment, at least one of the at least one first parameter and the at least one second parameter is derived from a histogram of respiratory phases and the maintained database includes a self-learning system.

Method for determining a type of a reconstruction of image data from a magnetic resonance measurement of an examination object by means of a magnetic resonance device

A method and system for automatic multi-organ segmentation in a 3D image, such as a 3D computed tomography (CT) volume using learning-base segmentation and level set optimization is disclosed. A plurality of meshes are segmented in a 3D medical image, each mesh corresponding to one of a plurality of organs. A level set in initialized by converting each of the plurality of meshes to a respective signed distance map. The level set optimized by refining the signed distance map corresponding to each one of the plurality of organs to minimize an energy function.

Growing community of inventors

Spardorf, Germany

Jens Wetzl