Siemens Aktiengesellschaft

Siemens Medical Solutions Usa, Inc.

Siemens Corporation

Christophe Chefd'hotel

Bernhard Geiger

Li Zhang

Jens Gühring

Christoph Guetter

James G Reisman

Atilla Peter Kiraly

Rui Liao

Shun P Miao

Hui Xue

Sandra Sudarsky

Jens Guehring

Sebastian Vogt

Chen-Rui Chou

Kinda Anna Saddi

Guillaume Bousquet

Mert Rory Sabuncu

Mariappan S Nadar

Ali Kamen

Frank Sauer

Andreas Greiser

Benjamin L Odry

Michael Scheuering

Matthias Fenchel

Marie-Pierre Jolly

Chenyang Xu

Ying Zhu

Sven Zuehlsdorff

Lutz Gündel

Yiyong Sun

Ernst Klotz

Wolfgang Wein

Alban Lefebvre

Francisco Pereira

Xiaolei Sharon Huang

Liron Yatziv

Saurabh Shah

Sajan Goud Lingala

Tina Ehtiati

Julian Ibarz

David Thivierge-Gaulin

Razik Yousfi

Guyu Li

Mathews Jacob

Alexis Sebbane

Christophe Lenglet

Wanmei Ou

Razvan Chisu

Sheshadri Thiruvenkadam

Mathieu Chartouni

Helene Chopinet

A method for reconstructing a three-dimension image includes receiving a plurality of two-dimensional images and projection information of the two-dimensional images, projecting a plurality of rays onto the plurality of two-dimensional images, determining correspondence information between pixels of different ones of the plurality of two-dimensional images, determining a value of each of the pixels, and reconstructing a three-dimension image by integrating the plurality of rays, wherein a position on each ray can be associated to one pixel of the plurality of two-dimensional images.

Image reconstruction

A method and system for magnetic resonance imaging (MRI) based motion correction in position emission tomography (PET) images is disclosed. A static 3D magnetic resonance (MR) image of a patient is received. PET image data of the patient and a series of 2D MR images of the patient acquired simultaneous to the acquisition of the PET image data are received. A 3D+t motion field is estimated by registering the series of 2D MR images acquired at the plurality of time points to the static 3D MR image. A motion corrected PET image is generated based on the estimated 3D+t motion field using motion corrected PET reconstruction.

System and method for magnetic resonance imaging based respiratory motion correction for PET/MRI

A method () that generates attenuation correction maps for the reconstruction of PET data using MR images, such as, MR ultra-fast TE (UTE) images, Dixon MR images, as well as MR images obtained using other MR imaging methods.

Method for creating attenuation correction maps for PET image reconstruction

A method () that registers a 3D heart volume () obtained from either a pre-operative MR image or CT image () to an intra-operative fluoroscopic image using a mesh of the heart structure () as the basis for the registration.

2D/3D image registration method

A method for automatic initialization of 2D to 3D image registration includes acquiring a 3D model. A plurality of shape descriptor features is calculated from the acquired 3D model representing a plurality of poses of the 3D model. A 2D image is acquired. The plurality of shape descriptors is matched to the acquired 2D model. An optimum pose of the 3D model is determined based on the matching of the plurality of shape descriptors to the acquired 2D model. An initial registration is generated, in an image processing system, between the 3D model and the 2D image based on the determined optimum pose.

Automatic initialization for 2D/3D registration

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 for generating a pseudo-computed tomography (CT) image volume includes acquiring a first magnetic resonance (MR) image volume (UTE) using an ultra-short echo time and acquiring a second MR image volume (UTE) using a conventional echo time that is longer than the ultra-short echo time. The acquired UTEand UTEimage volumes are normalized. A mask for an anatomical structure featured in the normalized UTEand UTEimage volumes is created and bone regions are segmented from the normalized UTEand UTEimage volumes using the created mask and one or more trained classifiers. A pseudo-CT image is constructed from the normalized UTEand UTEimage volumes, the created mask, and the segmented bone regions.

Generating pseudo-CT image volumes from ultra-short echo time MR

A method and system for data dependent multi phase image visualization, includes: acquiring a plurality of series of image data acquisitions; registering the plurality of series of image data acquisitions to a same reference series to create a plurality of registered series; combining information from the registered series to create a new series; creating a further new series by a selection decision based on combination rules from information from the plurality of registered series and the new series; and displaying the further new series.

Method and system for data dependent multi phase visualization

An exemplary embodiment of the present invention includes a method of registering images. The method includes: for each image, determining an optimum intensity threshold set from a plurality of intensity threshold sets that maximizes a variance between classes of each set, segmenting each image using the corresponding determined optimum intensity threshold set, generating mutual information from a joint histogram of at least two of the segmented images, and registering the at least two images using the mutual information. The joint histogram may be generated using a geometry shader of a graphical processing unit.

Multilevel thresholding for mutual information based registration and image registration using a GPU

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

Methods and a system to unify reconstruction and motion estimation steps in first pass cardiac perfusion MRI include a global objective function that meets data consistency, spatial smoothness, motion and contrast dynamics constraints. The global objective decomposed into simpler sub-problems which include low pass filtering of a deformed object, TV shrinkage, analytical Fourier replacement and an lminimizing problem. A registration tool based on the local cross-correlation similarity measure and enabled to perform both rigid and flexile deformations, is applied. Registration parameters are tuned by rigid, semi rigid and flexible models at different stages of iterations. A system to perform the methods is also disclosed.

Unifying reconstruction and motion estimation in first pass cardiac perfusion imaging

A method for enhancing stent visibility includes acquiring a set of image frames including multiple test frames. A set of measurement points uniformly distributed within an image of a stent is defined in the test frames. A local image context is defined around each measurement point. A non-rigid deformation field relating the local image contexts of the test frames to local image contexts of a reference image is calculated. The non-rigid deformation field is optimized by maximizing a similarity function between the local image contexts of the test frames and the local image contexts of the reference image. The optimized non-rigid deformation field is used to deform images of a stent in the multiple test frames and combine the non-rigidly deformed images of the stent from the test frames. An image frame with the combined image of the stents superimposed thereon is displayed.

Deformable motion correction for stent visibility enhancement

A method of verifying a registration of a digital image includes receiving a reference image and a moving image registered to the reference image representing the same object, selecting one or more landmarks in the images, defining a neighborhood in the domain of each image near each selected landmark, extracting feature sets from the neighborhoods for the reference image and the registered moving image, calculating a total quality measure of the registration of the moving image from a distance metric applied to the feature sets extracted from the neighborhoods of the reference image and the registered moving image, comparing the total quality measure to a threshold determined from a reference atlas for the object represented in the image, and determining whether to accept or reject the registration of the moving image based on the comparison result.

System and method for verifying registration accuracy in digital medical images

A method for correcting registration misalignment in magnetic resonance perfusion images includes receiving a time series of magnetic resonance images of an anatomical organ, each image comprising a plurality of intensities defined on an N-dimensional domain of points, performing a first group-wise registration of all images in the time series of images using a pair-wise registration as an initialization, detecting a misaligned image from the group-wise registration by analyzing a total variation of intensity differences between consecutive images, and correcting the misaligned image by optimizing a second group-wise energy function over registration parameters of the misaligned image.

System and method for detecting and correcting registration misalignment of MR perfusion images

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Jersey City, NJ, United States of America

Christophe Chefd'hotel