Siemens Healthcare Gmbh

Siemens Healthineers Ag

University of California

University of Minnesota

The General Hospital Corporation

Duke University

Mayo Foundation for Medical Education and Research

Vibhas S Deshpande

Xiaodong Zhong

Marcel Dominik Nickel

Stephan Kannengiesser

Dingxin Wang

Peter Kollasch

Kamil Ugurbil

Li Pan

Holden H Wu

Xiufeng Li

Fei Han

Tess Armstrong

Mario Zeller

Puneet Sharma

Berthold Kiefer

Himanshu Bhat

Steen Moeller

Xiaoguang Lu

Stephen Farman Cauley

Lars Lauer

Heiko Meyer

Gerhard Laub

Joel P Felmlee

Xiaoming Bi

Thomas Benkert

Gregory John Metzger

Keith Aaron Heberlein

Venkata Veerendranadh Chebrolu

Thomas Vahle

Mustafa R Bashir

John William Grinstead

Mahesh Bharath Keerthivasan

Michael Bush

Abraham Padua, Jr

Kimberly K Amrami

Jacob Stolk

Dieter Faust

Matthew A Frick

Systems and methods include segmentation of a first image of a subject to identify locations of anatomical structures of the subject, determination of a region of interest of the subject based on the locations of anatomical structures, determination of a coil-mixing matrix based on the region of interest, control of an MR scanner to acquire radial trajectory k-space data of the subject from each of a plurality of RF coils of the MR scanner, application of the coil-mixing matrix to the radial trajectory k-space data of the subject acquired from each of the plurality of RF coils to generate first radial trajectory k-space data, reconstruction of a second image of the subject based on the first radial trajectory k-space data, and display of the second image.

De-streaking algorithm for radial k-space data

For imaging an object subject to a cyclic motion, two or more imaging repetitions are carried out. Each of the imaging repetitions includes a sequence of equally spaced imaging events, wherein each imaging event has an event number, which corresponds to a respective predefined imaging parameter. A cycle duration of the cyclic motion is determined, a number of events per cycle is determined based on the cycle duration and a shift number is determined at least in part randomly. For a first imaging repetition, a starting number is determined depending on the number of events per cycle and the shift number. The first imaging repetition is carried out, wherein the respective sequence is started with an imaging event, whose event number is given by the starting number.

Imaging an object subjected to a cyclic motion

A computer implemented method of processing a medical image is disclosed. The method includes receiving a medical image comprising a first plurality of pixels each having an initial pixel value. For each of the first plurality of pixels, a filtering operation is applied to the pixel to generate a filtered pixel value for the pixel based on the initial pixel values of pixels that surround the pixel in the medical image. For each of the first plurality of pixels, a comparison of the initial pixel value with the filtered pixel value is performed. The method comprises, for each of the first plurality of pixels, determining, based on the comparison, whether or not to categorize the pixel as an erroneous pixel; and for each of the first plurality of pixels for which it is determined to categorize the pixel as an erroneous pixel, categorizing the pixel as an erroneous pixel.

Method and apparatus for processing a medical image

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

A system and method for performing accelerated k-space shift correction calibration scans for non-Cartesian trajectories is provided. The method can include applying an MRI sequence, performing a calibration scan based on the MRI sequence using the non-Cartesian trajectory to acquire k-space shift data, wherein one or more partitions are skipped during the calibration scan, interpolating the skipped one or more partitions using the k-space shift data from adjacent partitions, and calibrating the MRI system using the k-space shift data and the interpolated k-space shift data. In some embodiments, an acceleration factor Acc can be defined and the calibration scan acquires k-space shift data for only one partition in every Acc partitions.

Method and apparatus for improved efficiency of non-cartesian imaging using accelerated calibration scan for k-space shift correction

Systems and methods for generating images with a magnetic resonance imaging ('MRI') system, in which the images have been corrected for receive coil nonuniformities are described. Improved data acquisition schemes for fat saturation are also described.

Method for uniform reconstruction of multi-channel surface-coil magnetic resonance data without use of a reference scan

A method for using a multi-echo magnetic resonance imaging (MRI) simultaneously quantify T1 and fat fraction in an anatomical region of interest includes performing a radial single shot multi-echo acquisition of the anatomical region of interest. The radial single shot multi-echo acquisition comprises applying a preparation pulse to invert longitudinal magnetization of the anatomical region of interest, and acquiring a plurality of radial readouts at different echo times (TE). A magnetization recovery curve is continuously sampled using the plurality of radial readouts to yield a plurality of radial spokes. The radial spokes for each TE are ground together to generate under-sampled k-space data for each TE. The under-sampled k-space data is reconstructed into a plurality of multi-echo images corresponding to the different echo times. One or more fitting algorithms are applied to the multi-echo images to generate a water-only T1 map and a proton density fat fraction (PDFF) measurement.

Multi-echo radial look-locker imaging for simultaneous T1 and fat fraction quantification

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

A computer-implemented method for using machine learning to suppress fat in acquired MR images includes receiving multi-echo images from an anatomical area of interest acquired using an MRI system. A first subset of the multi-echo images is acquired prior to application of contrast to the anatomical area of interest and a second subset of the multi-echo images is acquired after application of contrast to the anatomical area of interest. Next, data is generated including water images, fat images, and effective R*maps from the multi-echo images. The water images, the fat images, and the effective R*maps are used to create synthetic fat suppressed images. A neural network is trained to use the multi-echo images as input and the synthetic fat suppressed images as ground truth. A plurality of components of the neural network are saved to allow later deployment of the neural network on a computing system.

Artificial intelligence based suppression of chemical species in magnetic resonance imaging

A system comprises presentation of a user interface on the display for inputting a first set of parameter values for a magnetic resonance scan, reception of the first set of parameter values for the magnetic resonance scan from a user via the displayed user interface, and automatic determination, based on the first set of parameter values, of first additional parameter values for the magnetic resonance scan.

Efficient determination of MR scan parameters

A system uses multiple RF coils in MR imaging and an RF (Radio Frequency) signal generator generates RF excitation pulses in anatomical regions of interest and enables subsequent acquisition of associated RF echo data. A magnetic field gradient generator generates anatomical volume select magnetic field gradients for phase encoding and readout RF data acquisition. The RF signal generator and the gradient generator substantially concurrently acquire first and second volumes of first and second different anatomical regions by providing, a first RF pulse having a first asymmetric shape followed by a successive second RF pulse substantially having the first asymmetric shape but reversed in time, to substantially reduce echo time (TE) differences between acquisition of the first and second volumes and a phase encoding magnetic field gradient prepares for acquisition of data representing the first and second volumes.

System for simultaneous dual-slab acquisition of MR images with asymmetric and time-reversed asymmetric, concatenated pulses

A system for generating medical image scanner configurations includes a scanner configuration database and a simulation component. The database stores a scanner configuration dataset corresponding to a medical image scanner. The simulation component includes a display module which is configured to present a graphical user interface (GUI) utilized by the medical image scanner, and an editing module which is configured to create a modified scanner configuration dataset based on commands received from a user via the GUI. Additionally, the simulation component includes a simulation module which is configured to (i) perform a simulation of the medical image scanner using the modified scanner configuration dataset to yield simulated results, (ii) use the display module to present the simulated results in the GUI, and (iii) in response to receiving user approval of the simulated results via the GUI, save the modified scanner configuration dataset to the database.

Enterprise protocol management

A computer-implemented method for performing multi-slice magnetic resonance imaging with comparable contrast between simultaneously excited slices includes applying a first pulse sequence to a volume of interest to acquire a first k-space dataset. This first pulse sequence comprises a plurality of single-band slice-selective pulses applied in a first predefined order. One or more additional pulse sequences are also applied to the volume of interest to acquire one or more additional k-space datasets. Each additional pulse sequence comprises the plurality of single-band slice-selective pulses applied in one or more additional predefined orders that are distinct from the first predefined order. One or more final images are reconstructed using the first k-space dataset and the one or more additional k-space datasets.

Shifted pulses for simultaneous multi-slice imaging

Systems and methods for determining optimized imaging parameters for imaging a patient include learning a model of a relationship between known imaging parameters and a quality measure, the known imaging parameters and the quality measure being determined from training data. Optimized imaging parameters are determined by optimizing the quality measure using the learned model. Images of the patient are acquired using the optimized imaging parameters.

System and method for determining optimal operating parameters for medical imaging

Growing community of inventors

Austin, TX, United States of America

Vibhas S Deshpande