Siemens Aktiengesellschaft

Siemens Medical Solutions Usa, Inc.

Other

Siemens Healthineers Ag

National Institutes of Health

Jens Gühring

Marie-Pierre Jolly

Hui Xue

Christoph Guetter

Ying Sun

Peter Speier

Christophe Chefd'hotel

Xiaoguang Lu

Michaela Schmidt

Arne Littmann

Mariappan S Nadar

Michael Zenge

Stefan Thesen

Ti-chiun Chang

Sven Zuehlsdorff

Carmel Hayes

Sebastian Vogt

Kai Tobias Block

Bogdan Georgescu

Frank Sauer

Andreas Greiser

Benjamin L Odry

Bernhard Geiger

Peter Schmitt

Chenyang Xu

Edgar Mueller

Gareth Funka-Lea

Ali Khamene

Lars Lauer

Yiyong Sun

Ravi Kumar

Christopher Vincent Alvino

Christine Lorenz

Edgar Müller

Peter Weale

Davide Piccini

Xiaolei Sharon Huang

Saurabh Shah

Manuela Rick

Peter Gall

René Kartmann

Jordin D Green

Tina Ehtiati

Benedikt Krüger

Jeremy Collins

Yixun Liu

Michael S Hansen

Christophe Lenglet

Techniques are described for providing image-based operational decision support. The technique includes providing initial imaging data from a preceding examination of a patient, determining clinical findings by automated processing of the initial imaging data, generating decision data at least comprising a decision whether a further recording of a number of images is necessary, and generating a suggested set of imaging parameter values for recording this number of images. The decision data may be based on the determined clinical findings, and the technique may further include outputting the suggested set of imaging parameter values for recording a number of images of the patient. Also described are a related clinical decision system, a decision module, and a related medical imaging system.

Method and system for image-based operational decision support

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 reconstructed image is rendered of a patient by a processor from a set of undersampled MRI data by first subtracting two repetitions of the acquired data in k-space to create a third dataset. The processor reconstructs the image by minimizing an objective function under a constraint related to the third dataset, wherein the objective function includes applying a Karhunen-Loeve Transform (KLT) to a temporal dimension of data. The objective function under the constraint is expressed as arg min{&#x2225;&#x3c6;(f)&#x2225;subject to &#x2225;Af&#x2212;y&#x2225;&#x2266;&#x3b5;}. The reconstructed image is an angiogram which may be a 4D angiogram. The angiogram is used to diagnose a vascular disease.

Non-contrast-enhanced 4D MRA using compressed sensing reconstruction

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 reconstructed image is rendered from a set of MRI data by first estimating an image with an area which does not contain artifacts or has an artifact with a relative small magnitude. Corresponding data elements in the estimated image and a trial image are processed, for instance by multiplication, to generate an intermediate data set. The intermediate data set is transformed and minimized iteratively to generate a reconstructed image that is free or substantially free of artifacts. In one embodiment a Karhunen-Loeve Transform (KLT) is used. A sparsifying transformation may be applied to generate the reconstructed image. The sparsifying transformation may be also not be applied.

Prior enhanced compressed sensing (PRINCE-CS) reconstruction for dynamic 2D-radial cardiac MRI

A method for automatic femur segmentation and condyle line detection. The method includes: scanning a knee of a patient with medical imaging equipment to obtain 3D imaging data with such equipment; processing the obtained 3D imaging data in a digital processor to determine two lines tangent to the bottom of the knee condyles in an axial and a coronal plane; and automatically scanning the patient in the defined plane. The processing includes: determining an approximate location of the knee; using the determined the location to define a volume of interest; segmenting the femur in the defined volume of interest; and determining a bottom point on the femur portion on a right side and a left side of the segmented femur in an axial and a coronal slice to determine the two lines.

Automatic femur segmentation and condyle line detection in 3D MR scans for alignment of high resolution MR

A method for automatically determining a field of view for performing a subsequent medical imaging study includes acquiring one or more preliminary images. A body mask is generated by thresholding the preliminary images and identifying a largest connected component. A boundary mask is obtained from the boundary of the generated body mask. A rectangular bounding box is fit to the obtained boundary mask. The rectangular bounding box is used as a field of view for performing a subsequent medical imaging study.

Automatic determination of field of view in cardiac MRI

Disclosed is a method for determining and displaying at least one piece of information on a target volume, especially in a human body, the information being obtained from an image record. At least one embodiment of the method includes: a first and at least one second image record of a target zone encompassing the target volume are recorded, the first image record having a higher contrast regarding the boundaries of the target volume, and the first and the second image record being registered together; the target volume is segmented in the first image record; target volume image data.

Method for determining and displaying at least one piece of information on a target volume

A method for automatically localizing left ventricle in medical image data includes acquiring a sequence of three-dimensional medical images spanning a cardiac cycle. Each of the images includes a plurality of two-dimensional image slices, one of which is defined as a template slice. The template slice of each medical image of the sequence is automatically cropped to include the heart and a margin around the heart based on temporal variations between pixels of the template slice throughout the sequence of medical images. The template slice of each medical image of the sequence is automatically contoured to determine the endo-cardial and epi-cardial boundaries for at least the end-diastolic and end-systolic phases. Localization information is generated for the left ventricle based on the determined endo-cardial and epi-cardial boundaries for at least the end-diastolic and end-systolic phases.

Automatic localization of the left ventricle in cardiac cine magnetic resonance imaging

A method for generating a positron emission tomography (PET) attenuation correction map from magnetic resonance (MR) images includes segmenting a 3-dimensional (3D) magnetic resonance (MR) whole-body image of a patient into low-signal regions, fat regions, and soft tissue regions; classifying the low-signal regions as either lungs, bones, or air by identifying lungs, identifying an abdominal station, and identifying a lower body station; and generating an attenuation map from the segmentation result by replacing the segmentation labels with corresponding representative attenuation coefficients.

System and method for the generation of attenuation correction maps from MR images

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Erlangen, Germany

Jens Gühring