Siemens Healthcare Gmbh

The General Hospital Corporation

Siemens Healthineers Ag

Bryan Clifford

Thorsten Feiweier

Stephen Farman Cauley

Tom Hilbert

Daniel Polak

Daniel Nicolas Splitthoff

Julian Hossbach

Steffen Bollman

Wei-Ching Lo

A method for controlling a magnetic resonance imaging system, including: selecting a plurality of spatially non-selective initial RF-pulses each having a predefined pulse shape and a predefined frequency; determining a combined RF-pulse from the initial RF-pulses by choosing a time-offset comprising a relative application time-shift between the initial RF-pulses, wherein this time-offset is chosen such that the initial RF-pulses overlap; and including the combined RF pulse in a pulse sequence applied in a magnetic resonance imaging system.

Method and system for controlling a magnetic resonance imaging system

In a method for improving the contrast of magnetization-transfer-prepared magnetic resonance imaging (MRI), an acquisition scheme comprising a plurality of inversion-recovery (IR)-imaging modules in an interleaved arrangement is selected, a number of magnetization-transfer (MT)-preparation modules is selected, a pulse sequence is generated by arranging at least one MT-preparation module of the number of MT-preparation modules between two successive IR-preparation modules of the interleaved IR-imaging modules or in front of the first IR-preparation module of a group of interleaved IR-imaging modules, and the pulse sequence for an MRI examination is applied or saved. Each IR-imaging module may include an IR-preparation module and a slice acquisition module.

Method and system for improving the contrast of magnetization-transfer-prepared MRI

A system and method include generation of modified imaging data and modified acquisition parameters of a training example based on the target imaging data and target acquisition parameters of each of a plurality of target examples, and generation, for each training example, of an initial reconstructed image based on the modified imaging data and modified acquisition parameters of the training example. A first network stage of a multi-stage network is trained based on the modified imaging data, modified acquisition parameters and initial reconstructed image of each training example, a first output image is generated for each training example by inputting the modified imaging data, modified acquisition parameters and initial reconstructed image of the training example to the trained first network stage, and a second network stage of the multi-stage network is trained based on the modified imaging data, modified acquisition parameters and first output image of each training example.

Training a multi-stage network to reconstruct MR images

Systems and Methods that identify the effect of motion during a medical imaging procedure. A neural network is trained to translate motion induced deviations of a coil-mixing matrix relative to a reference acquisition into a motion score. This score can be used for the prospective detection of the most corrupted echo trains for removal or triggering a replacement by reacquisition.

Coil mixing error matrix and deep learning for prospective motion assessment

Growing community of inventors

Malden, MA, United States of America

Bryan Clifford