Siemens Aktiengesellschaft

Siemens Medical Solutions Usa, Inc.

Siemens Healthcare Gmbh

Siemens Corporate Research, Inc.

Siemens Corporation

Other

The Ohio State University

Ohio State Innovation Foundation

Siemens Canada Limited

National Institutes of Health

Siemens Corporation Research, Inc.

Marie-Pierre Jolly

Jens Gühring

Christoph Guetter

Hui Xue

Xiaoguang Lu

Ying Sun

Peter Speier

Michaela Schmidt

Jens Guehring

Bogdan Georgescu

Sven Zuehlsdorff

Arne Littmann

Carmel Hayes

Dorin Comaniciu

Leo John Grady

Ali Kamen

Tommaso Mansi

Tiziano Passerini

Mehmet Akif Gulsun

Orlando P Simonetti

Alok Gupta

Saurabh Shah

Yuri Boykov

Philipp Seegerer

Roch Mollero

Mariappan S Nadar

Frank Sauer

Andreas Greiser

Benjamin L Odry

Yu Ding

Christophe Chefd'hotel

Edgar Mueller

Thomas J O'Donnell

Xiaoming Bi

Hasan Ertan Cetingul

Nikolaos Paragios

Christopher Vincent Alvino

Christine Lorenz

Davi Geiger

Ning Jin

Nicolae Duta

Edgar Müller

Peter Weale

José M F Moura

Davide Piccini

Randall Kroeker

Andreas Grimme

Manuela Rick

Jordin D Green

Jeremy Collins

Yixun Liu

Julien Abi-Nahed

Maxime G Taron

Methods and systems for estimating patient-specific cardiac electrical properties from medical image data and non-invasive electrocardiography measurements of a patient are disclosed. A patient-specific anatomical heart model is generated from medical image data of a patient. Patient-specific cardiac electrical properties are estimated by simulating cardiac electrophysiology over time in the patient-specific anatomical heart model using a computational cardiac electrophysiology model and adjusting cardiac electrical parameters based on the simulation results and the non-invasive electrocardiography measurements. A patient-specific cardiac electrophysiology model with the patient-specific cardiac electrical parameters can then be used to perform virtual cardiac electrophysiology interventions for planning and guidance of cardiac electrophysiology interventions.

System and method for characterization of electrical properties of the heart from medical images and body surface potentials

A method for computing global longitudinal strain from cine magnetic resonance (MR) images includes automatically detecting landmark points in each MR long axis image frame included in a cine MR image sequence. A deformation field is determined between every pair of frames based on the landmark points. Myocardial pixels in the frames are labeled using a deep learning framework to yield myocardium masks. These myocardium masks are propagated to each frame using the deformation fields. A polar transformation is performed on each of the masked frames. The contours of the myocardium in each transformed frame are computed using a shortest path algorithm. Next, longitudinal strain is calculated at every pixel in the myocardium for the polar frames using the contours of the myocardium. Then, global longitudinal strain is computed by averaging the longitudinal strain at every pixel in the myocardium of the transformed frames.

Global longitudinal strain from cine magnetic resonance images

A novel free-breathing myocardial T2* mapping combining multiple single-shot black-blood GRE-EPI images with automatic non-rigid motion correction. The present disclosure describes a method of accurate myocardial T2* measurements that is insensitive to respiratory motion, and is likely to reduce sensitivity to arrhythmia as well since each image is acquired in a single heart beat. The T2*-weighted GRE-EPI images are motion corrected using, e.g., automatic non-rigid motion correction to reduce mis-registration due to respiratory motion. A T2* map is calculated using the motion-corrected T2*-weighted images by fitting pixel intensities to a, e.g., two-parameter mono-exponential model.

Free-breathing myocardial T2* mapping using single-heartbeat, multi-shot, gradient echo-echo planar imaging (GRE-EPI) and automatic non-rigid motion correction

Phase unwrapping is provided for phase contrast magnetic resonance (MR) imaging. The velocity values are unaliased. For a given location over time, a path over time through a directed graph of possible velocities at each time is determined by minimization of derivatives over time. The possible velocities are based on the input velocity, the input velocity wrapped in a positive direction, and the input velocity wrapped in a negative direction, so the selection to create the minimum cost path represents unaliasing of any aliased velocities.

Phase unwrapping in phase contrast MR imaging

A method of magnetic resonance (MR) imaging of a volume undergoing repetitive motion includes obtaining source slice data indicative of a plurality of source slices during the repetitive motion, and obtaining anchor slice data indicative of an anchor slice during the repetitive motion. The anchor slice intersects the plurality of source slices. The source slice data and the anchor slice data are reconstructed. A three-dimensional image assembly procedure is implemented to generate, for each phase of the repetitive motion, volume data based on a respective subset of the reconstructed source slice data. For each phase of the repetitive motion, the respective subset of slices is selected based on a correlation of the source slice data and the anchor slice data along an intersection between each source slice and the anchor slice. The source slice data of the selected subset is corrected for misalignment with the anchor slice data.

MRI 3D cine imaging based on intersecting source and anchor slice data

Magnetic resonance reconstruction includes motion compensation. Inverse-consistent non-rigid registration is used to determine motion between shots. The motion is incorporated into reconstruction. The incorporation compensates for the motion resulting from the period over which the MR data is acquired.

Motion compensated magnetic resonance reconstruction in real-time imaging

A method and system for automated view planning for cardiac magnetic resonance imaging (MRI) acquisition is disclosed. The method and system automatically generate a full scan prescription using a single 3D MRI volume. The left ventricle (LV) is segmented in the 3D MRI volume. Cardiac landmarks are detected in the automatically prescribed slices. A full scan prescription, including a short axis stack and 2-chamber, 3-chamber, and 4-chamber views, is automatically generated based on cardiac anchors provided by the segmented left ventricle and the detected cardiac landmarks in the 3D MRI volume.

Method and system for automatic view planning for cardiac magnetic resonance imaging acquisition

A method for correcting the background phase in magnetic resonance phase contrast flow images includes providing a time series of velocity encoded magnetic resonance images of a patient, where the time series of velocity encoded images comprises for each time point a phase contrast image where a pixel intensity is proportional to a flow velocity, measuring a change of intensity for each pixel over the time series of phase contrast images, identifying pixels with a low measure of temporal change as stationary pixels, and calculating a correction field for the stationary pixels, where the correction field represents a background phase to be subtracted from the phase contrast image.

System and method for background phase correction for phase contrast flow images

A method for performing motion compensation in a series of magnetic resonance (MR) images includes acquiring a set of MR image frames spanning different points along an MR recovery curve. A motion-free synthetic image is generated for each of the acquired MR image frames using prior knowledge pertaining to an MR recovery curve. Each of the acquired MR images is registered to its corresponding generated synthetic images. Motion within each of the acquired MR image is corrected based on its corresponding generated synthetic image that has been registered thereto.

Automatic registration of image series with varying contrast based on synthetic images derived from intensity behavior model

A method including receiving an image sequence, wherein the image sequence includes a plurality of two-dimensional (2D) image frames of an organ arranged in a time sequence; constructing a three-dimensional (3D) volume by stacking a plurality of the 2D image frames in time order; detecting a best bounding box for a target of interest in the 3D volume, wherein the best bounding box is specified by a plurality of parameters including spatial and temporal information contained in the 3D volume; and determining the target of interest from the best bounding box.

Detection of landmarks and key-frames in cardiac perfusion MRI using a joint spatial-temporal context model

A method for identifying a region of interest within a time sequence of images includes acquiring a time sequence of images comprising a plurality of image frames. Image segmentation is performed to segment a region of interest (ROI) from within each of the plurality of image frames of the time sequence of images. Manual edits are received for the ROI within one or more of the plurality of image frames. The manual edits are propagated to other image frames of the plurality of images. An extent to which each of the manual edits are propagated to other image frames is dependent upon a transformation function or deformation field used to propagate the manual edits and a weighing factor that is influenced by a distance in time between the other image frames and the frames that have been manually edited.

Interaction method for regions of-interest in time series images

A method for clinical parameter derivation and adaptive flow acquisition within a sequence of magnetic resonance images includes commencing an acquisition of a sequence of images. One or more landmarks are automatically detected from within one or more images of the sequence of images. The detected one or more landmarks are propagated across subsequent images of the sequence of images. A plane is fitted to the propagation of landmarks. The positions of landmarks or alternatively the position of the fitted plane within the sequence of images is used for derivation of clinical parameters such as tissue velocities and/or performing adaptive flow acquisitions to measure blood flow properties.

Motion tracking for clinical parameter derivation and adaptive flow acquisition in magnetic resonance imaging

The aorta and left atrium are localized from magnetic resonance data. The locations of the aorta and left atrium are detected jointly. The aorta and the left atrium are, at least in part, treated as one object. The detection may be from data representing a two-dimensional region. The two-dimensional region may be determined by first detecting the left ventricle from data representing a volume.

Localization of aorta and left atrium from magnetic resonance imaging

A boundary in a medical image is segmented. To increase reproducibility, a multi-level segmentation approach is used. A boundary is detected based on a seed point. The boundary is used to construct a banded graph. Local segmentation is performed using the banded graph. Based on the local segmentation, a new seed point is found. The local segmentation identifies a consistent location for the seed point. The boundary detection is performed again using the new seed point.

Reproducible segmentation of elliptical boundaries in medical imaging

Growing community of inventors

Hillsborough, NJ, United States of America

Marie-Pierre Jolly