Siemens Medical Solutions Usa, Inc.

Siemens Corporate Research, Inc.

Siemens Aktiengesellschaft

Seimens Medical Solutions Usa, Inc.

The University of Arizona

Siemens Corporation

Siemens Information Systems, Ltd.

Arun Krishnan

Xiang Sean Zhou

Kazunori Okada

Dorin Comaniciu

Venkat Raghavan Ramamurthy

Yiqiang Zhan

Alok Gupta

Ali Reza Bani-Hashemi

James P Williams

Anna Jerebko

Jonathan Stoeckel

Juergen Soldner

Axel Platz

Visvanathan Ramesh

Luca Bogoni

Maneesh Kumar Singh

Zhigang Peng

Michael Wesley Marcellin

Darrell Burckhardt

Diane S Paine

Xiaolei Sharon Huang

Bhavani Duggirala

Stefan Käpplinger

Peggy Hawman

Christian Beldinger

Umut Akdemir

Vaclav Potesil

Maxim Mamin

Axel Barth

Michael Gluth

Mariappan S Nadar

Gerardo Hermosillo Valadez

Marcos Salganicoff

Michael Kelm

Glenn Fung

Ute Feuerlein

Jens Guehring

Martin Huber

Senthil Periaswamy

Shashi Kumar

Toshiro Kubota

Steven Charles Dilliplane

Jörg Freund

Karthikeyan Kaliyamoorthi

Joerg Freund

Haiying Guan

Venkata Tumuluri

Karthik Krishnan

Ramamani Ramraj

Yimo Tao

Stefan Kapplinger

Darrell Dennis Burckhardt

A method for bolus tracking includes acquiring one or more baseline images. One or more trigger regions are automatically established within the baseline images. A bolus is administered. The automatically established trigger regions are monitored for bolus arrival at the one or more trigger regions. Bolus arrival at a volume of interest is forecasted based on the bolus arrival at the one or more trigger regions. A diagnostic scan of the volume of interest is acquired at the forecasted time.

System and method for automatic trigger-ROI detection and monitoring during bolus tracking

Systems and methods for supporting a diagnostic workflow from a computer system are disclosed herein. In accordance with one implementation, a set of pre-identified anatomical landmarks associated with one or more structures of interest within one or more medical images are presented to a user. In response to a user input selecting at least one or more regions of interest including one or more of the pre-identified anatomical landmarks, the user is automatically navigated to the selected region of interest. In another implementation, a second user input selecting one or more measurement tools is received. An evaluation may be automatically determined based on one or more of the set of anatomical landmarks in response to the second user input.

Systems and methods for viewing and analyzing anatomical structures

A method for synchronizing patient data between at least two independent applications in a distributed environment includes capturing screen information from a display window of a first application client that is displaying a medical image of a patient, analyzing the screen information captured from the first application client display to extract patient identifying information, and synchronizing a display of information of the patient on a second application system display screen with the first application display window using the extracted patient identification information.

System and method for patient synchronization between independent applications in a distributed environment

A system for providing automatic diagnosis and decision support includes: a medical image database; generative learning and modeling modules that build distributional appearance models and spatial relational models of organs or structures using images from the medical image database; a statistical whole-body atlas that includes one or more distributional appearance models and spatial relational models of organs or structure, in one or more whole-body imaging modalities, built by the generative learning and modeling modules; and discriminative learning and modeling modules that build two-class or multi-class classifiers for performing at least one of organ, structure or disease detection or segmentation.

Systems and methods for computer aided diagnosis and decision support in whole-body imaging

A method for detecting lymph nodes in a medical image includes receiving image data. One or more regions of interest are detected from within the received image data. One or more lymph node candidates are identified using a set of predefined parameters that is particular to the detected region of interest where each lymph node candidate is located. The identifying unit may identify the one or more lymph node candidates by performing DGFR processing. The method may also include receiving user-provided adjustments to the predefined parameters that are particular to the detected regions of interest and identifying the lymph node candidates based on the adjusted parameters. The lymph node candidates identified based on the adjusted parameters may be displayed along with the image data in real-time as the adjustments are provided.

Automatic detection of lymph nodes

Systems and methods for performing a medical imaging study include acquiring a preliminary scan. A set of local feature candidates is automatically detected from the preliminary scan. The accuracy of each local feature candidate is assessed using multiple combinations of the other local feature candidates and removing a local feature candidate that is assessed to have the lowest accuracy. The assessing and removing steps are repeated until only a predetermined number of local feature candidates remain. A region of interest (ROI) is located from within the preliminary scan based on the remaining predetermined number of local feature candidates. A medical imaging study is performed based on the location of the ROI within the preliminary scan.

Systems and methods for robust learning based annotation of medical radiographs

A system and method for linking volumes of interest (VOIs) across timepoints are provided. The method comprises: loading an image dataset of a first timepoint and an image dataset of a second timepoint; registering the image dataset of the first timepoint and the image dataset of the second timepoint; displaying the image dataset of the first timepoint and the image dataset of the second timepoint; selecting a VOI in the image dataset of the first timepoint and the image dataset of the second timepoint; and linking the VOIs in the image dataset of the first timepoint and the image dataset of the second timepoint.

System and method for linking VOIs across timepoints for analysis of disease progression or response to therapy

A method for performing a medical imaging study includes acquiring a preliminary scan. A set of local feature candidates is automatically detected from the preliminary scan. The accuracy of each local feature candidate is assessed using multiple combinations of the other local feature candidates and removing a local feature candidate that is assessed to have the lowest accuracy. The assessing and removing steps are repeated until only a predetermined number of local feature candidates remain. A region of interest (ROI) is located from within the preliminary scan based on the remaining predetermined number of local feature candidates. A medical imaging study is performed based on the location of the ROI within the preliminary scan.

Systems and methods for automatic robust anatomy detection through local voting and prediction

A computer system for automatic selection of a computer-aided detection (CAD) algorithm including a database storing image data, a browser for navigating the data and selecting image data, an application receiving image data selected by the browser, and a selector selecting a CAD algorithm for processing the image data according to at least one of fixed attributes of the image data and an indication of a subject of the image data.

Automatic CAD algorithm selection

A system for computer aided detection and decision support includes an ontology of image representations for injecting meaning into and adding relationships among image contents, an image understanding and parsing module in communication with the ontology of image representations for extracting structures from an image including the image contents, and a reasoning engine based in communication with the ontology and the image understanding and parsing module for classifying the image contents, wherein the system receives the image and corresponding descriptive information.

Computer aided detection and decision support

A method for registering a medical image includes acquiring a first medical image of a subject. One or more simulated medical images are synthesized based on the acquired first medical image. One or more matching functions are trained using the first medical image and the simulated medical images. A second medical image of the subject is acquired. The first medical image and the second medical image are registered using the one or more trained matching functions.

Registration of medical images using learned-based matching functions

A method for lesion detection includes acquiring pre-therapy medical image data from a first modality. Post-therapy medical image data is acquired from a second modality. A transformation matrix for transforming from an image space of the first modality to an image space of the second modality is calculated. A volume of interest is defined from the medical image data of the first modality. The volume of interest includes one or more lesions. The volume of interest is automatically copied to the medical image data of the second modality using the calculated transformation matrix. Treatment is directed to the lesion using the medical image data of the second modality including the copied volume of interest data.

Lesion quantification and tracking using multiple modalities

By testing for nodule segmentation errors based on the scan data, juxtapleural cases are identified. Once identified, the scan data or subsequent estimation may be altered to account for adjacent rib, tissue, vessel or other structure effecting segmentation. One alteration is to shape a filter as a function of the scan data. For example, an originally estimated ellipsoid for the nodule segmentation defines the filter. The filter is used to identify the undesired information, and masking removes the undesired information for subsequent estimation of the nodule segmentation. Another possible alteration biases the subsequent estimation away from the incorrect information, such as the rib, tissue or vessel information influencing the original estimation. For example, a negative prior or probability is assigned to data corresponding to the originally estimated segmentation for the subsequent estimation.

Refined segmentation of nodules for computer assisted diagnosis

A method for training a computer system for automatic detection of regions of interest includes receiving patient records. For each of the received patient records a text field and a medical image are identified from within the patient record and the medical image is automatically segmented to identify a structure of interest. The text field is searched for one or more keywords indicative of a particular abnormality associated with the structure of interest. The medical image is added to a grouping representing the particular abnormality when the text field indicates that the patient has the particular abnormality and the medical image is added to a grouping representing the absence of the particular abnormality when the text field does not indicate that the patient has the particular abnormality. The groupings of medical images are used to automatically train a computer system for the subsequent detection of the particular abnormality.

Automatic learning of image features to predict disease

Disclosed is robust click-point linking, defined as estimating a single point-wise correspondence between data domains given a user-specified point in one domain or as an interactive localized registration of a monomodal data pair. To link visually dissimilar local regions, Geometric Configuration Context (GCC) is introduced. GCC represents the spatial likelihood of the point corresponding to the click-point in the other domain. A set of scale-invariant saliency features are pre-computed for both data. GCC is modeled by a Gaussian mixture whose component mean and width are determined as a function of the neighboring saliency features and their correspondences. This allows correspondence of dissimilar parts using only geometrical relations without comparing the local appearances. GCC models are derived for three transformation classes: pure translation, scaling and translation, and similarity transformation. For solving the linking problem, a variable-bandwidth mean shift method is adapted for estimating the maximum likelihood solution of the GCC.

Robust click-point linking with geometric configuration context: interactive localized registration approach

Growing community of inventors

Exton, PA, United States of America

Arun Krishnan