Siemens Healthcare Gmbh

University of Utah Research Foundation

The United States of America, as Represented by the Secretary, Department of Health and Human Services

Siemens Aktiengesellschaft

Siemens Healthineers Ag

Sunil Goraksha Patil

Dennis Lee Parker

Henrik Odeen

Himanshu Bhat

Rainer Schneider

Klaus J Kirchberg

Bradley Drake Bolster, Jr

Daniel H Reich

Waqas Majeed

Adrienne Campbell

Julien Christian Barbot

John A Roberts

Steven Michael Shea

Di Xu

Bhat Himanshu

Pascal Sati

John Roberts

A method for performing magnetic resonance-guided thermal therapy includes selecting a first set of sampling characteristics for acquiring a first set of slabs covering a first anatomical region of interest. Additionally, a second set of sampling characteristics is selected for acquiring a second set of slabs covering a second anatomical region of interest. This second set of sampling characteristics is distinct from the first set of sampling characteristics. An interleaved acquisition of the first set of slabs and the second set of slabs may then be performed using the first set of sampling characteristics and the second set of sampling characteristics.

Simultaneous multi-slab thermometry during MR-guided thermal therapy

A method, system and article of manufacture is disclosed. The method includes providing a spatial navigator outside of a thermal therapy region; receiving a plurality of analog-to-digital conversion (ADC) readouts from an MRI device at a plurality of time points, wherein the ADC readouts comprise a first ADC readout acquired at a first time point, and one or more additional ADC readouts acquired at subsequent time points; processing the ADC readouts to obtain a frequency of the spatial navigator at each of the time points; obtaining a main magnetic field (B) drift of the MRI device based on the frequency of the spatial navigator at a particular time point and the frequency of the spatial navigator at the first time point; and obtaining the temperature change at the particular time point based on the Bdrift.

Temperature measurement in thermal therapy

A computer-implemented method for automatically identifying subjects at risk of Multiple Sclerosis (MS) includes acquiring a plurality of images of a subject's brain using a Magnetic Resonance Imaging (MRI) scanner. A contrast enhancement process is applied to each image to generate a plurality of contrast-enhanced images. An automated lesion detection algorithm is applied to detect one or more lesions present in the contrast-enhanced images. An automated central vein detection algorithm is applied to detect one or more central veins present in the contrast-enhanced images. An automated paramagnetic rim detection algorithm is applied to detect one or more paramagnetic rims present in the contrast-enhanced images. The patient's risk for MS may then be determined based on the one or more of the lesions, central veins, and paramagnetic rims present in the contrast-enhanced images.

Automatic identification of subjects at risk of multiple sclerosis

A method for decomposing noise into white and spatially correlated components during MR thermometry imaging includes acquiring a series of MR images of an anatomical object and generating a series of temperature difference maps of the anatomical object. The method further includes receiving a selection of a region of interest (ROI) within the temperature difference map and estimating total noise variance values depicting total noise variance in the temperature difference map. Each total noise variance value is determined using a random sampling of a pre-determined number of voxels from the ROI. A white noise component and a spatially correlated noise component of the total noise variance providing a best fit to the total noise variance values for all of the random samplings are identified. The white noise component and the spatially correlated noise component are displayed on a user interface.

Guiding protocol development for magnetic resonance thermometry

An MRI system uses a Cartesian-radial hybrid k-space trajectory to capture three-dimensional k-space data and reconstruct an image of an area of interest of a subject. The MRI system performs a series of k-space acquisitions to collect the data. A first k-space acquisition includes acquiring a two-dimensional EPI projection in a first plane parallel to a frequency-encoding direction and acquiring additional two-dimensional EPI projections in planes that are radially shifted about a center axis parallel to the frequency-encoding direction with respect to the first plane, until a selected number of projections are acquired. Each subsequent k-space acquisition includes acquiring an additional set of two-dimensional EPI projections in all of the planes in which an EPI projection was acquired during the first k-space acquisition, each additional set of EPI projections being shifted along a respective plane in a direction perpendicular to the frequency-encoding direction.

Cartesian-radial hybrid k-space trajectory for volumetric imaging

A method and apparatus for Bcorrection in echo-planar (EP) based magnetic resonance image (MRI) is disclosed. Two phase images are obtained from each of a first echo-planar imaging (EPI) acquisition and a second EPI acquisition. A first susceptibility map is generated based on the two phase images obtained from the first EPI acquisition and a second susceptibility map is generated based on the two phase images obtained from the second EPI acquisition. A smooth polynomial function for modeling the Bdrift and respiratory motion between the first EPI acquisition and the second EPI acquisition is initialized based on the first and second susceptibility maps. A compensated temperature map is then iteratively reconstructed based on the smooth polynomial function.

Method and system for B0 drift and respiratory motion compensation in echo-planar based magnetic resonance imaging

Localization of a coil is provided for magnetic resonance (MR)-guided intervention. A multi-scale decomposition and characteristic transitions in the power spectra for the coil are used to determine a distribution of likelihood of the coil being at each of various locations and/or to determine a confidence in the position determination. For example, the power spectra along each axis is used to generate a likelihood distribution of the location of the coil. The power spectra are decomposited at different scales. For each scale, the modulus maxima reflecting transitions in the power spectra are matched using various criteria. A likelihood is calculated for each of the matched candidates from characterizations of the matched candidates. The likelihood distribution is determined from a combination of the likelihoods from the various scales.

Localization of a device for MR-guided intervention

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Ellicott City, MD, United States of America

Sunil Goraksha Patil