Siemens Aktiengesellschaft

Siemens Medical Solutions Usa, Inc.

Other

Siemens Healthcare Diagnostics Gmbh

Beth Israel Deaconess Medical Center, Inc.

Fred S Azar

Frank Sauer

Ali Khamene

John V Frangioni

Leo John Grady

Ali Kamen

Norbert Rahn

Chenyang Xu

Bruce J Tromberg

Yiyong Sun

Sebastian Vogt

Ravi Teja Seethamraju

Albert E Cerussi

Kijoon Lee

Christian P Schultz

Tina Ehtiati

Arjun G Yodh

Regine Choe

Mukesh G Harisinghani

Vida Kianzad

Benoit deRoquemaurel

Benoit De Roquemaurel

A system and method for instrument placement using an image based navigation system is disclosed. A target of interest is identified in a medical image of a patient. An image plane is displayed that goes through a center of the target. The image plane has a configurable orientation. The image plane is used to select a path for an instrument from a position on the patient's skin to the center of the target. A trajectory plane is viewed from a tip of the instrument to the center of the target. The trajectory plane reflects an orientation of the instrument. A particular trajectory plane is selected that is representative of a desired orientation of the instrument. An image of the particular trajectory plane is frozen. The instrument can then be inserted using a virtual guide and is navigated toward the target.

Method for augmented reality instrument placement using an image based navigation system

An endo-robot can be tracked and a reconstructed 3D volume of data can be created for virtual endoscopy. The 3D position and orientation of each image captured by the endo-robot is determined. The longitudinal distance traveled inside the structure of interest is determined. The position, orientation and longitudinal distance are used to register the position of the endo-robot to a corresponding position inside the 3D volume of data. Virtual endoscopy can be used to locate areas of interest and correlate clinical findings with the images of the areas of interest.

Registered video endoscopy and virtual endoscopy

A method for coregistration of multi-modal images obtained in different geometries includes acquiring multi-modal image data, wherein the multi-model image data includes image data of a first modality and image data of a second modality, wherein the image data of the respective modalities have different geometries, defining a volume of interest in the multi-modal image data, segmenting the image data of the first modality and incorporating segmentation data of the first modality into a reconstruction of the second modality, and applying a registration of the second modality image data to the first modality image data according to a similarity measure through the volume of interest, wherein an output of the registration comprises superimposed multi-modal image data.

Coregistration and analysis of multi-modal images obtained in different geometries

A system detects malignant lymph nodes using an MR imaging device, for, in the absence of a contrast agent, acquiring in a patient anatomical volume of interest including lymph nodes, (a) a first image using a variable flip angle, lymph node enhanced contrast, MR image acquisition process, (b) a second image using a susceptibility weighting imaging acquisition process and (c) a third image using a diffusion weighting imaging acquisition process. In the presence of a contrast agent absorbed by benign lymph nodes, MR imaging device acquires in the patient anatomical volume of interest, (d) a fourth image using a susceptibility weighting imaging acquisition process. A display processor processes data representing the first, second, third and fourth images for display of malignant and benign lymph nodes on a reproduction device.

System for detecting malignant lymph nodes using an MR imaging device

Macroscopic imaging data, such as from a CT, MR, PET, or SPECT scanner, is obtained. Microscopic imaging data of at least a portion of the same tissue is obtained. To align the microscopic imaging data with the macroscopic imaging data, intermediate data is also obtained. For example, photographic data is acquired at an intermediary stage of a process of preparing tissue for microscopic scan. The macroscopic and microscopic data are registered to the intermediary photographic data. Once registered to the intermediary data, the spatial relationship between the macroscopic and microscopic data is known and may be used for imaging or quantification.

Integration of micro and macro information for biomedical imaging

A diffuse optical spectroscopy system comprises a laser breast scanner, a handheld probe connected to the laser breast scanner for scanning a breast, and a tracking device coupled to the handheld probe, wherein the tracking device determines locations of the handheld probe relative to the breast. The tracking device comprises a magnetic tracking device, an optical tracking device or a laser tracking device.

Three-dimensional breast anatomy imaging system

A method for unsupervised classification of histological images of prostatic tissue includes providing histological image data obtained from a slide simultaneously co-stained with NIR fluorescent and Hematoxylin-and-Eosin (H&E) stains, segmenting prostate gland units in the image data, forming feature vectors by computing discriminating attributes of the segmented gland units, and using the feature vectors to train a multi-class classifier, where the classifier classifies prostatic tissue into benign, prostatic intraepithelial neoplasia (PIN), and Gleason scale adenocarcinoma grades 1 to 5 categories.

System and method for unsupervised detection and gleason grading of prostate cancer whole mounts using NIR fluorscence

A computer implemented method for differentiating between elements of an image and a background includes inputting an image comprising pixels forming a view of the elements and a background, providing a model for assigning a probability of belonging to a predefined class to each of the pixels, assigning a probability to each of the pixels of belonging to the predefined class, labeling each of the pixels according to a corresponding probability and a predefined threshold, determining boundaries between groups of like-labeled pixels, and outputting a visualization of the boundaries.

Cell analysis using isoperimetric graph partitioning

A computer readable medium is provided embodying instructions executable by a processor to perform a method for generating a volumetric mask. The method includes providing a closed surface mesh, determining a discrete volume representation of the closed surface mesh, determining a temporary surface mask based on the closed surface mesh and a slice direction chosen from the discrete volume representation to produce a contour of the closed surface mesh for each slice of the discrete volume representation, and filling a surface defined by the contours to generate the volumetric mask.

Generating a 3D volumetric mask from a closed surface mesh

A method for determining whether a point is contained in a sub-volume of a digitized medical image, includes providing a tetrahedron volume mesh (TVM) representing a sub-volume of a digital image volume and a point M, finding a vertex P of said TVM that is closest to point M, finding a tetrahedron Tin said TVM that defines a solid angle around point P where point M is located, wherein if M is inside T, then M is inside the TVM, if M is not inside T, finding a facet F of tetrahedron Tthrough which a line PM connecting points M and P exits T, and determining whether a next tetrahedron Tof said TVM exists along line PM, wherein if no such tetrahedron Texists, then point M is outside the TVM, and wherein if Texists, determining whether point M is inside tetrahedron T.

System and method for efficient real-time technique for point localization in and out of a tetrahedral mesh

A system and method for automatically registering a three dimensional (3D) pre-operative image of an anatomical structure with intra-operative electrophysiological (EP) points of a 3D electro-anatomical (EA) image map of the anatomical structure is disclosed. The pre-operative image is displayed in a first supporting view. The intra-operative EA image map is displayed in a second supporting view. An alignment of the pre-operative image with the intra-operative map is performed by identifying at least one corresponding point on each image. The view of the pre-operative image is integrated with the EA map based on the alignment.

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Princeton, NJ, United States of America

Fred S Azar