Leland Stanford Junior University

Siemens Medical Solutions Usa, Inc.

H. Lee Moffitt Cancer Center and Research Institute, Inc.

Other

General Electric Company

Acuson Corporation

Stichting Maastricht Radiation Oncology “Maastro-Clinic”

Sandy A Napel

David Seungwon Paik

Geoffrey D Rubin

Christopher Allen Beaulieu

Raghav Raman

Markus Kukuk

Burak Acar

Bhargav Raman

Philipp K Lang

Salih Burak Gokturk

Thilaka S Sumanaweera

Robert J Gillies

Philippe Lambin

Thomas P Andriacchi

Carlo Tomasi

Eugene J Alexander

Steven A Eschrich

Daniel L Rubin

Christopher Frederick Beaulieu

Sylvia K Plevritis

Robert A Gatenby

R Brooke Jeffrey, Jr

Padmavathi Sundaram

Andreas L A J Dekker

Shaohua Sun

John Alexander Hossack

Norbert Joseph Pelc

Gary H Glover

John R Adler

Robert Tremaine Whalen

Norbert Strobel

Anming He Cai

Rebecca Fahrig

Thomas A Gentles

Denny Wingchung Lau

Vijaykalyan Yeluri

Yaseen Samara

Adam K Galant

Robert John Herfkens

Samuel M Song

Acar Burak

David Paik

Brooke Jeffrey, Jr

Smadar Shiffman

Chye Hwang Yan

Afra J Zomorodlan

Feng Zhuge

Ender Konukoglu

An example method for analyzing quantitative information obtained from radiological images includes identifying a ROI or a VOI in a radiological image, segmenting the ROI or the VOI from the radiological image and extracting quantitative features that describe the ROI or the VOI. The method also includes creating a radiological image record including the quantitative features, imaging parameters of the radiological image and clinical parameters and storing the radiological image record in a data structure containing a plurality of radiological image records. In addition, the method includes receiving a request with the patient's radiological image or information related thereto, analyzing the data structure to determine a statistical relationship between the request and the radiological image records and generating a patient report with a diagnosis, a prognosis or a recommended treatment regimen for the patient's disease based on a result of analyzing the data structure.

Systems, methods and devices for analyzing quantitative information obtained from radiological images

Disclosed are method and apparatus for guiding a needle during an interventional radiology procedure. The target and skin entry point are pre-determined in three-dimensional medical imaging scans. The needle trajectory is defined by the target and skin entry point, which is physically marked on the skin. A dual-headed laser emitting two laser beams is positioned and aligned such that one laser beam is aimed at the skin entry point and the other laser beam is aimed at an external reference point, which is determined such that the axis of the laser beam aimed at the skin entry point is substantially collinear with the needle trajectory. The needle is inserted while maintaining the axis of the needle substantially collinear with the axis of the laser beam aimed at the skin entry point.

Needle guidance with a dual-headed laser

Methods are disclosed for assessing the condition of a cartilage in a joint, particularly a human knee. The methods include converting an image such as an MRI to a three dimensional map of the cartilage. The cartilage map can be correlated to a movement pattern of the joint to assess the affect of movement on cartilage wear. Changes in the thickness of cartilage over time can be determined so that therapies can be provided. Information on thickness of cartilage and curvature of cartilage or subchondral bone can be used to plan therapy. Information on movement pattern can be used to plan therapy.

Assessing the condition of a joint and preventing damage

A pointing light device includes a light pointer, such as a laser pointer; and an elongated pointing member detachably associated with the light pointer. The pointing member contains at least two bead-like members. Also, a method for aligning a light pointer with a predetermined medical interventional device trajectory. The method includes attaching a pointing member to an output end the light pointer to form a movable pointing light device; imaging two or more bead-like members of the pointing member to create live projection images or shadows in the live projection image; projecting at least first and second points associated with the predetermined medical interventional device trajectory onto the live projection image; and moving the pointing light device until the live projection images of the two members are aligned with corresponding ones of the projected first and second points in the live projection image.

Apparatus and method for aligning a light pointer with a medical interventional device trajectory

An improved method is disclosed for imaging in an interventional medical procedure that is more advanced than a conventional 2D image processing application and less restrictive than a 3D reconstruction image processing application. In contrast to the prior art 2D imaging application in which a single 2D image is acquired, the inventive method acquires and stores a set of 2D anatomical images while rotating a C-arm/X-ray source using a single injection of contrast agent. In lieu of performing a 3D reconstruction, the multiplicity of anatomical views provided by the set of 2D anatomical images adequately serve as a visual 'rotatable roadmap' to perform classical 2D-roadmapping navigation. The 'rotatable roadmap' assists a user to locate an ideal working view of the patient's region of operative interest.

Rotational stereo roadmapping

A system and method are disclosed for reconstructing an instrument in 3 dimensions for use during interventional medical procedures to provide enhanced instrument visualization with respect to a patient's vasculature. A patient vessel tree volume is co-registered with a live fluoroscopic image of a percutaneously-inserted instrument, such as a guidewire. The fluoroscopic image is segmented to eliminate images of surrounding tissue and to retain the guidewire image. The black pixels of the image are backprojected to the focal point of the x-ray source, through the co-registered vessel tree. The vessel tree is divided into segments that are scored based on proximity to the backprojected black pixels. Candidate instrument-containing vessel paths are identified based on the scores of the segments, and errant candidate vessel paths are eliminated to produce a refined list of candidate paths. Thresholding and visualization are performed to further refine the candidate vessel paths. 3D curve fitting is used to reconstruct an image of the instrument along with a 3D rendering of the final vessel path.

System for three-dimensional medical instrument navigation

Improved surface feature recognition in CT images is provided by extracting a triangulated mesh representation of the surface of interest. Shape operators are computed at each vertex of the mesh from finite differences of vertex normals. The shape operators at each vertex are smoothed according to an iterative weighted averaging procedure. Principal curvatures at each vertex are computed from the smoothed shape operators. Vertices are marked as maxima and/or minima according to the signs of the principal curvatures. Vertices marked as having the same feature type are clustered together by adjacency on the mesh to provide candidate patches. Feature scores are computed for each candidate patch and the scores are provided as output to a user or for further processing.

Polyp detection using smoothed shape operators

A Learning Enhanced Simulated Annealing (LESA) method is provided. Based on a Simulated Annealing (SA) framework, this method adds a Knowledge Base (KB) initialized at the beginning of the search and updated at each iteration, which memorizes a portion of the search history and guides the further search through a KB trial generator. The basic idea of LESA is that its search history is stored in a KB, and a KB trial generator extracts information from it and uses it to generate a new trial. The next move of the search is the weighted sum of the trial generated by the KB trial generator and the trial generated by the usual SA trial generator. The knowledge base is then updated after each search iteration.

Learning enhanced simulated annealing

Certain embodiments of the present invention provide systems and methods for correlating an acquired image with an historical image. Certain embodiments include registering an acquired image and at least one historical image according to a coordinate system, applying a metric to the acquired image and each of the historical image(s), and identifying a correlation between the acquired image and one of the historical image(s) based on the metric. Certain embodiments further include storing the correlation between the acquired image and an historical image for automatic linking of the acquired image and the historical image upon display. In certain embodiments, the acquired image and the historical image may be displayed based on the correlation. In certain embodiments, the acquired image and the historical image may be automatically linked based on the correlation. In certain embodiments, the metric analyzes the acquired image and at least one historical image based on anatomy.

System and method for linking current and previous images based on anatomy

A method for detecting and identifying structures of interest such as colonic polyps or similar structures like lung nodules in volumetric (medical) images data is provided. The method includes obtaining a heat diffusion field (HDF) by applying a heat diffusion scheme to a volume of interest that includes structures. The obtained heat diffusion field is then used for identifying a structure of interest from the structures in the volume of interest using a geometrical analysis of the heat diffusion field. The heat diffusion scheme is, at least partly, governed by non-linear diffusion parameters. The identification includes two parts: (i) the computation of a spherical symmetry parameter, and (ii) the performance of a local analysis of the volume of interest and computation of a triangulization parameter.

Heat diffusion based detection of structures of interest in medical images

A nodule registration system useful for tracking lung nodules in computed tomography (CT) scans is presented. The system registers a small sphere centered on a detected nodule in one scan with another scan under a rigid transformation assumption using a fast registration scheme. The registration scheme employs very fast simulated annealing (VFSA) with constraints to maximize a tunable cross-correlation (TCC) coefficient, enabling the system to register, with minimal registration error, all nodules within their maximum diameter. The system achieves an average registration time of 10 seconds or less on a 3.06 GHz computer programmed to implement the present invention.

Registration system and method for tracking lung nodules in medical images

A method of identifying polyps and in a medical image is provided. In a first step, a 3-dimensional model is made of the medical image that contains both polyps (if any were present in the original medical image) and folds. Next, a second 3-dimensional model of the medical image, which is a filtered version of the first model, is constructed in which folds are preserved, but polyps are minimized or eliminated. In a third step, any polyps that were contained in the medical image are identified by subtracting the second 3-dimensional model from the first 3-dimensional model. This subtraction results in a third 3-dimensional model, in which polyps are preserved but folds are minimized or eliminated. With the present inventive method, polyps may be easily and quickly identified without interference from folds.

Polyp identification through subtraction of models of medical images

A method to define a curved slab region of interest that includes vessels while maximally excluding surrounding soft tissue and bone is provided. The thickness of the curved slab is automatically adapted to the thickness of the vessel and follows the tortuous vessel(s) so that an increase in tortuousity does not result in a disproportionate increase in the region of interest required to enclose the vessel. A plurality of boundary pairs is determined in the view plane to define a vessel. Vessel-intensities are determined for each one of the boundary pairs. The boundary pairs with associated intensities define the view of the vessel in the projection plane. Context-intensity could be defined in the area surrounding the boundary pairs in the projection and/or transverse plane. The method also includes several steps that will result in a better isolation and removal of non-vessel structures and view of the vessel(s) and its(their) branches.

Curved-slab maximum intensity projections

Growing community of inventors

Menlo Park, CA, United States of America

Sandy A Napel