Siemens Aktiengesellschaft

Siemens Healthcare Gmbh

University of California

The Johns Hopkins University

The General Hospital Corporation

Li Pan

Marcel Dominik Nickel

Stephan Kannengiesser

Xiaodong Zhong

Vibhas S Deshpande

Christine Lorenz

Holden H Wu

Tess Armstrong

Shaohua Kevin Zhou

Atilla Peter Kiraly

Benjamin L Odry

David D Liu

Berthold Kiefer

Fei Long

Robert Grimm

Xiao Chen

Junichi Tokuda

Ming Jie

Glenn A Meredith

Ihab Kamel

Detlef Pauly

A method for acquiring magnetic resonance imaging data with respiratory motion compensation using one or more motion signals includes acquiring a plurality of gradient-delay-corrected radial readout views of a subject using a free-breathing multi-echo pulse sequence, and sampling a plurality of data points of the gradient-delay-corrected radial readout views to yield a self-gating signal. The self-gating signal is used to determine a plurality of respiratory motion states corresponding to the plurality of gradient-delay-corrected radial readout views. The respiratory motion states are used to correct respiratory motion bias in the gradient-delay-corrected radial readout views, thereby yielding gradient-delay-corrected and motion-compensated multi-echo data. One or more images are reconstructed using the gradient-delay-corrected and motion-compensated multi-echo data.

Free-breathing MRI with motion compensation

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

A system and method for modifying an imaging plane of a medical imaging device is presented. An electronic device includes a tilt sensor. The electronic device is configured to monitor a tilt of the electronic device. A computer is configured to receive an indication of the tilt of the electronic device, translate the indication of the tilt of the electronic device into a corresponding movement of the imaging plane of the medical imaging device, and transmit an instruction to the medical imaging device to cause the corresponding movement of the imaging plane. The medical imaging device may include an magnetic resonance imaging (MRI) system.

Interface for medical imaging device

A method for reconstructing an image includes acquiring raw image data during a scan of an area, estimating an image from the raw image data, separating the estimated image into a region of interest (ROI) and a background region, and applying compressed sensing to iteratively update only the ROI and maintain the background region to reconstruct an image.

Compressed sensing using regional sparsity

An automatic control system capable of executing a control program in parallel is described. The system includes more than one unit controller, each executing in parallel at least a part of the program to be executed by the automatic control system; a compiler, connected to one of the unit controllers, for converting the program to be executed by the automatic control system into tasks executed in parallel by the unit controllers; an interconnection network, for connecting the unit controllers, such that information on one of the unit controllers is transferred to another one via the interconnection network.

Automatic control system and method for executing control program in parallel

A method for selecting the b-values for diffusion weighted magnetic resonance scans. The method includes: identifying a single reference slice within a volume of interest (VOI); progressively changing b-values over a plurality of scans of the reference slice wherein each one of the scans has a different b-value to obtain a plurality of diffusion weighted images; calculating ADC maps for combinations of b-values from the obtained plurality of diffusion weighted images; evaluating the calculated ADC maps; selecting from the evaluation optimal b-values; and using the selected b-values for subsequent scans.

b-value optimization for diffusion weighted magnetic resonance imaging

Growing community of inventors

Perry Hall, MD, United States of America

Li Pan