Siemens Aktiengesellschaft

Siemens Medical Solutions Usa, Inc.

Siemens Corporation

Siemens Healthcare Gmbh

The Ohio State University

Siemens Aktiengesellscaft

Arne Littmann

Bogdan Georgescu

Marie-Pierre Jolly

Xiaoguang Lu

Hui Xue

Christoph Guetter

Dorin Comaniciu

Andreas Greiser

Jens Gühring

Edgar Müller

Peter Speier

Yefeng Zheng

Edgar Mueller

Michaela Schmidt

Sven Zuehlsdorff

Jens Guehring

Mariappan S Nadar

Frank Sauer

Yu Ding

Mehmet Akif Gulsun

Michael Zenge

Orlando P Simonetti

Hasan Ertan Cetingul

Gunnar Krueger

Carmel Hayes

Davide Piccini

Katrin Christel Sprung

Brett Cowan

Alistair Young

Manuela Rick

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

In a method and a magnetic resonance tomography system, at least two temporally separate original data sets are acquired with one phase measurement value being acquired for each pixel in each original image data set. An optimization technique for the shared calculation of corrected phase values for the pixels in the data sets is implemented in a computer, wherein the corrected phase values of the pixels in a first of the data sets is in each case dependent at least on the phase measured value of the pixel at the same location in a second of the data sets which is recorded beforehand or afterwards, and the corrected phase values of the pixels in the second data set are in each case dependent at least on the phase measured value of the pixel at the same place in the first data set. Corrected image data sets are generated from the corrected phase values.

Magnetic resonance method and tomography system for acquiring image data sets

In a method and magnetic resonance (MR) apparatus for determination of movement of an examination subject during the acquisition of (MR) measurement data using at least two antenna elements that exhibit respectively different spatial positions, after each radiated excitation pulse a navigator signal is acquired in the measurement data and movement of the examination subject between two excitation pulses during the acquisition of the measurement data is determined from a change of the signal strength of the navigator signal in the at least two antenna elements and based on the respective spatial positions of the antenna elements.

Method and magnetic resonance apparatus for determination of patient movement during data acquisition

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 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

In a method and magnetic resonance (MR) apparatus for combining MR signals that were acquired with different acquisition coils from a region of an examination subject at least two MR signals that are based on MR signals acquired with at least two different acquisition coils are provided to a processor. Due to the spatially differing arrangement of the respective acquisition coils, the at least two MR signals image the region of the examination subject with different sensitivity profiles. The provided MR signals are combined, such that unwanted MR signal portions are suppressed, to form a combined MR signal with the suppression of unwanted MR signal portions being implemented by MR signal portions that were acquired with an acquisition coil that detects the unwanted MR signal portions with increased sensitivity in comparison to other acquisition coils being weighted less in the combined MR signal than other MR signal portions.

Method and magnetic resonance system for combining signals acquired from different acquisition coils

A method of deriving blood flow parameters from a moving three-dimensional (3D) model of a blood vessel includes determining a reference vascular cross-sectional plane through a location of a lumen in a moving 3D model of the blood vessel at one time within the model, determining a plurality of target vascular cross-sectional planes at multiple times via temporal tracking of the reference plane based on a displacement field, determining a plurality of contours based on an intersection of the target vascular cross-sectional planes with the moving 3D vessel model at multiple times within the model, and determining a blood flow parameter of the vessel from intersections of each contour of a given one of the times with a phase contrast magnetic resonance (PC-MRI) image of the blood vessel from the corresponding time.

Generation of moving vascular models and blood flow analysis from moving vascular models and phase contrast MRI

In a magnetic resonance (MR) device and method to determine a background phase curve in MR image data, in first MR image data and in second MR image data that respectively represent different segments (for example different slices) of an examination subject, first and second pixels are identified that represent essentially stationary tissue, and the associated phase values are determined. Phase correction values for the first MR image data are determined depending on the phase values determined for the first and second pixels that represent essentially stationary tissue.

Method and magnetic resonance device to determine a background phase curve

A method and system for left ventricle (LV) detection in 2D magnetic resonance imaging (MRI) images is disclosed. In order to detect the LV in a 2D MRI image, a plurality of LV candidates are detected, for example using marginal space learning (MSL) based detection. Candidates for distinctive anatomic landmarks associated with the LV are then detected in the 2D MRI image. In particular, apex candidates and base candidates are detected in the 2D MRI image. One of the LV candidates is selected as a final LV detection result using component-based voting based on the detected LV candidates, apex candidates, and base candidates.

Method and system for left ventricle detection in 2D magnetic resonance images

A method and system for left ventricle (LV) detection in 2D magnetic resonance imaging (MRI) images is disclosed. In order to detect the LV in a 2D MRI image, a plurality of LV candidates are detected, for example using marginal space learning (MSL) based detection. Candidates for distinctive anatomic landmarks associated with the LV are then detected in the 2D MRI image. In particular, apex candidates and base candidates are detected in the 2D MRI image. One of the LV candidates is selected as a final LV detection result by ranking the LV candidates based on the LV candidates, the apex candidates, and the base candidates using a trained ranking model.

Method and system for left ventricle detection in 2D magnetic resonance images using ranking based multi-detector aggregation

In a method to determine an inversion time value for contrast improvement between different tissue in a contrast agent-supported magnetic resonance imaging, a series of magnetic resonance images of an imaging area is acquired using an inversion recovery sequence with different inversion times. A structure in the magnetic resonance images is segmented and a time response of the signal intensity of image elements corresponding to one another in the magnetic resonance images of the segmented structure is automatically determined. Minima of the signal intensity in the segmented structure are determined automatically and associated with the associated inversion time values. The optimal inversion time value for contrast improvement is automatically determined from the inversion time values that have been associated with the minima of the signal intensity in the segmented structure.

Method and device to determine an inversion time value of tissue by means of magnetic resonance technology

A method and system for detecting anatomic landmarks in medical images is disclosed. In order to detect multiple related anatomic landmarks, a plurality of landmark candidates are first detected individually using trained landmark detectors. A joint context is then generated for each combination of the landmark candidates. The best combination of landmarks in then determined based on the joint context using a trained joint context detector.

Method and system for automatic landmark detection using discriminative joint context

The invention relates to a method for determining midpoint coordinates of an image of a point-symmetrical structure stored in a volume data record, comprising: extracting a partial volume data record from the volume data record which contains the image of the point-symmetrical structure, selecting a set of points whose coordinates lie in the partial volume data record, determining a measure for each point of the set wherein the measure for each of the points of the set characterizes in each case the symmetry of the partial volume data record in relation to this point, selecting a point as a midpoint of the point-symmetrical structure that point of the set in relation to which the partial volume data record has the greatest symmetry on the basis of the previously determined measures, and storing the coordinates of the midpoint in a computer unit.

Method for determining midpoint coordinates of an image of a point-symmetrical structure

In a method for evaluation of two images of an organ system acquired at different points in time to establish a pathological variation, the organ system exhibits a first region and a second region that are present in and that are differentiated in each of the images, with the second region of the pathological variation being acquired more strongly than the first region. A registration is determined that causes the respective first region in two images to be in registration with each other. Imaging system-dependent differences, in particular deformations, are determined and compensated between the two images. After an extension of the registration such that the second region is encompassed as well by the extended registration, via a transformation one of the two images to the other can be made, presented and/or stored that includes the second region, using the extended registration. A medical imaging system with a computer can implement the method.

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Erlangen, Germany

Arne Littmann