Siemens Healthineers Ag

Siemens Healthcare Gmbh

Seung Su Yoon

Jens Wetzl

Michaela Schmidt

Elisabeth Hoppe

Christoph Forman

Fasil Gadjimuradov

Daniel Giese

Maria Monzon

Carola Fischer

Jens Wetzi

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

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

Growing community of inventors

Erlangen, Germany

Seung Su Yoon