Siemens Aktiengesellschaft

Siemens Medical Solutions Usa, Inc.

Siemens Healthcare Gmbh

Other

The Ohio State University

Siemens Corporation

Friedrich-Alexander-Universitat Erlangen-Nürnberg

Christoph Guetter

Marie-Pierre Jolly

Hui Xue

Jens Gühring

Xiaoguang Lu

Sven Zuehlsdorff

Jens Guehring

Arne Littmann

Frank Sauer

Chenyang Xu

Christophe Chefd'hotel

Peter Speier

Michaela Schmidt

Carmel Hayes

Saurabh Shah

Bogdan Georgescu

Mariappan S Nadar

Andreas Greiser

Li Zhang

Rui Liao

Yu Ding

Mehmet Akif Gulsun

Orlando P Simonetti

Joachim Hornegger

Ali Khamene

Hasan Ertan Cetingul

Christoph Vetter

Yiyong Sun

Klaus J Kirchberg

Edgar Müller

Davide Piccini

Peter Weale

Manuela Rick

Jeremy Collins

Yixun Liu

Guyu Li

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 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

A system receives cardiac cine MR images consists of multiple slices of the heart over time. A series of short axis images slices are received. Long axis images are also received by the system, wherein a base plane defined by landmark points is detected. An intersection of the base plane with a contour of a heart chamber is determined for a plurality of slices in the short axis image. A volume for each of the contour slices covering the heart chamber, including for contours that are limited by base plane intersections, is evaluated. All slice volumes are summed to determine a total volume of the chamber. In one embodiment the chamber is a left ventricle and the landmark is a mitral valve. An ejection factor is determined.

Cardiac chamber volume computation from contours and base plane in cardiac MR Cine images

A method and system for propagation of myocardial infarction from delayed enhanced magnetic resonance imaging (DE-MRI) to cine MRI is disclosed. A reference frame is selected in a cine MRI sequence. Deformation fields are calculated within the cine MRI sequence to register the frames of the cine MRI sequence to the reference frame. A DE-MRI image having an infarction region is registered to the reference frame of the cine MRI sequence. The DE-MRI image may be registered to the infarction region using a hybrid registration algorithm that unifies both intensity and feature points into a single cost function. Infarction information in the DE-MRI image is then propagated cardiac phases of the frames in the cine MRI sequence based on the registration of the DE-MRI image to the reference frame and the plurality of deformation fields calculated within the cine MRI sequence.

Method and system for propagation of myocardial infarction from delayed enhanced cardiac imaging to cine magnetic resonance imaging using hybrid image registration

A method and system for retrospective image combination for free-breathing magnetic resonance (MR) images is disclose. A free-breathing cardiac MR image acquisition including a plurality of frames is received. A key frame is selected of the plurality of frames. A deformation field for each frame to register each frame with the key frame. A weight is determined for each pixel in each frame based on the deformation field for each frame under a minimum total deformation constraint. A combination image is then generated as a weighted average of the frames using the weight determined for each pixel in each frame.

Method and system for retrospective image combination under minimal total deformation constrain for free-breathing cardiac magnetic resonance imaging with motion correction

A method for validating non-rigid image registration includes acquiring a source image and a target image. Registration is performed from source image to target image using a non-rigid registration technique to produce forward transformation map. Registration is performed from the target image back to the source image using the non-rigid registration technique to produce a backward transformation map. Consistency registration error is measured as an indication of a change in local volume of the source with respect to the target image using the produced forward transformation map and the produced backward transformation map. The non-rigid registration technique is validated based on the measured consistency registration error.

Method and system for validating image registration

A method for symmetric and inverse-consistent registration of a pair of digital images includes calculating a first update of a forward transformation of a first digital image to a second digital image from a previous update of the forward transformation and a gradient of a cost function of the first and second digital images, calculating a first update of a backward transformation of the second digital image to the first digital image from an inverse of the first update of the forward transformation, calculating a second update of the backward transformation from first update of the backward transformation and the gradient of a cost function of the second and first digital images, and calculating a second update of the forward transformation from an inverse of the second update of the backward transformation.

Symmetric and inverse-consistent deformable registration

A method for cardiac segmentation in magnetic resonance (MR) cine data, includes providing a time series of 3D cardiac MR images acquired at a plurality of phases over at least one cardiac cycle, in which each 3D image includes a plurality of 2D slices, and a heart and blood pool has been detected in each image. Gray scales of each image are analyzed to compute histograms of the blood pool and myocardium. Non-rigid registration deformation fields are calculated to register a selected image slice with corresponding slices in each phase. Endocardium and epicardium gradients are calculated for one phase of the selected image slice. Contours for the endocardium and epicardium are computed from the gradients in the one phase, and the endocardium and epicardium contours are recovered in all phases of the selected image slice. The recovered endocardium and epicardium contours segment the heart in the selected image slice.

System and method for cardiac segmentation in MR-cine data using inverse consistent non-rigid registration

Progress monitoring of lesions is done automatically by segmentation and registration of lesions in multi-phase medical images. A parametric level-set framework includes a model optimization for any number of lesions. The user specifies lesions in a baseline volume by clicking inside of them. The apparatus segments the lesions automatically in the baseline and follow-up volumes. The segmentation optimization compensates for lesion motion between baseline and follow-up volumes. 2D and 3D medical patient data can be processed by the methods.

Combined segmentation and registration framework for parametric shapes

A method of registering 3-dimensional digitized images to 2-dimensional digitized images during a medical procedure includes providing a pair of correctly-registered training images L={l, l} and their joint intensity distribution p(i, i), wherein iand iare reference and floating images, respectively, providing a pair of observed images O={o, o} and their joint intensity distribution p(i, i), mapping a marginal intensity distribution of the observed pair O={o, o} to a marginal intensity distribution of the training pair L={l, l}, and estimating a set of parameters T that registers image oto image oby maximizing a weighted sum of a Jensen-Shannon divergence (JSD) of a joint intensity distribution of the observed pair and a joint intensity distribution of the training pair and a similarity measure between the observed images.

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Lawrenceville, NJ, United States of America

Christoph Guetter