Johns Hopkins University

Medtronic Navigation, Inc.

Globus Medical, Inc.

Ali Uneri

Jeffrey H Siewerdsen

Patrick A Helm

Xiaoxuan Zhang

Jean-Paul Wolinsky

Wojciech Zbijewski

Michael Ketcha

Joseph W Stayman, Iv

Yoshito Otake

Wathudurage Tharindu deSilva

Russell H Taylor

Nicholas A Theodore

Iulian Ioan Iordachita

Edward Frederick Ruppel, Iii

Peter Louis Gehlbach

Sritam Parashar Rout

Joseph Webster Stayman

Marcin Arkadiusz Balicki

James Tahara Handa

Amir Hossein Soltanianzadeh

Marc Chelala

Adam S Wang

David Michael Gullotti

Nicholas Griesmer Franconi

J Webster Stayman

Saki Fujita

Kyle Robert Cowdrick

Maria Fernanda Torres

Miguel Antonio Inserni

Sureerat Reaungamornrat

Jennifer Lin

Robert Li

Craig K Jones

Sarah Ouadah

Wathudurage Tharindu De Silva

Yixuan Huang

Edward Frederick Ruppel Iii

Amir Hossein Soltanianzaeh

Fujita Saki

A method and system is disclosed for analyzing image data of a subject. The image data can be collected with an imaging system in a selected manner and/or motion. The image data may include selected overlap and be acquired with an imaging system that generates a plurality of perspectives for more than one location. An automatic system and method may then define or identify various features and/or allow for registration for alternative image data.

System and method for identifying and classifying a feature in an image of a subject

Selected artifacts, which may be based on distortions or selected attenuation features, may be reduced or removed from a reconstructed image. Various artifacts may occur due to the presence of a metal object in a field of view. The metal object may be identified and removed from a data that is used to generate a reconstruction.

System and method for artifact reduction in an image

Some embodiments provide systems, assemblies, and methods of analyzing patient anatomy, including providing an analysis of a patient's spine, and also analyzing the biomechanical effects of implants. In some embodiments, the systems, assemblies, and/or methods can include obtaining initial patient data, acquiring spinal alignment and contour information, acquiring flexibility and/or biomechanical information, registering patient anatomical landmarks of interest relative to fiducial markers, analyzing databases of measurements and patient data to predict postoperative patient outcomes. Further, in some embodiments, the systems, assemblies, and/or methods can assess localized anatomical features of the patient, and obtain anatomical region data. In some embodiments, the systems, assemblies, and/or methods can also analyze the localized anatomy and therapeutic device location and contouring. Further, the systems, assemblies, and/or methods can output localized anatomical analyses and therapeutic device contouring data and/or imagery on a display according to some embodiments.

Intraoperative alignment assessment system and method

A method and system is disclosed for analyzing and evaluating image data of a subject. The image data can be collected with an imaging system in a selected manner and/or motion. More than one projection may be combined to generate and create a selected view of the subject. The evaluation may be a location determination of a member positioned within the subject.

System and method for displaying an image

An embodiment in accordance with the present invention provides a method for 3D-2D registration (for example, registration of a 3D CT image to a 2D radiograph) that permits deformable motion between structures defined in the 3D image based on a series of locally rigid transformations. This invention utilizes predefined annotations in 3D images (e.g., the location of anatomical features of interest) to perform multiple locally rigid registrations that yield improved accuracy in aligning structures that have undergone deformation between the acquisition of the 3D and 2D images (e.g., a preoperative CT compared to an intraoperative radiograph). The 3D image is divided into subregions that are masked according to the annotations, and the registration is computed simultaneously for each divided region by incorporating a volumetric masking method within the 3D-2D registration process.

Method for deformable 3D-2D registration using multiple locally rigid registrations

The present invention is directed to a method for enabling volumetric image reconstruction from unknown projection geometry of tomographic imaging systems, including CT, cone-beam CT (CBCT), and tomosynthesis systems. The invention enables image reconstruction in cases where it was not previously possible (e.g., custom-designed trajectories on robotic C-arms, or systems using uncalibrated geometries), and more broadly offers improved image quality (e.g., improved spatial resolution and reduced streak artifact) and robustness to patient motion (e.g., inherent compensation for rigid motion) in a manner that does not alter the patient setup or imaging workflow. The method provides a means for accurately estimating the complete geometric description of each projection acquired during a scan by simulating various poses of the x-ray source and detector to determine their unique, scan-specific positions relative to the patient, which is often unknown or inexactly known (e.g., a custom-designed trajectory, or scan-to-scan variability in source and detector position).

Self-calibrating projection geometry for volumetric image reconstruction

An embodiment in accordance with the present invention provides a technique for localizing structures of interest in projection images (e.g., x-ray projection radiographs or fluoroscopy) based on structures defined in a preoperative 3D image (e.g., MR or CT). Applications include, but are not limited to, spinal interventions. The present invention achieves 3D-2D image registration (and particularly allowing use with a preoperative MR image) by segmenting the structures of interest in the preoperative 3D image and generating a simulated projection of the segmented structures to be aligned with the 2D projection image. Other applications include various clinical scenarios involving 3D-2D image registration, such as image-guided cranial neurosurgery, orthopedic surgery, biopsy, and radiation therapy.

MR-levelcheck-2: method for localization of structures in projection images

A method and system for micro-force guided cooperative control that assists the operator in manipulating tissue in the direction of least resistance. A tool holder receives a surgical tool adapted to be held by a robot and a surgeon. A first sensor measures interaction forces between a tip of the surgical tool and tissue of a region of interest. A second sensor measures interaction forces between the surgeon and a handle to the surgical tool. A data processor is configured to perform an algorithm to actively guide the surgical tool by creating a bias towards a path of least resistance and limit directional tool forces of the surgical tool as a function of handle input forces and tip forces. This function offers assistance to challenging retinal membrane peeling procedures that require a surgeon to delicately delaminate fragile tissue that is susceptible to hemorrhage and tearing due to undesirable forces.

Micro-force guided cooperative control for surgical manipulation of delicate tissue

A method, an imaging apparatus and a computer readable medium are enabled for automatically registering medical images. The imaging apparatus may be an amended C-arm. The image acquisition apparatus has a primary x-ray source and at least one auxiliary x-ray source, a detector for receiving radiation of the primary and auxiliary x-ray source, and an interface to a registration unit. The registration unit computes a 3D/2D registration of a provided 3D image and at least two acquired 2D images. An output interface is configured to provide an output and to display the registered images on a monitor.

Method of image registration in a multi-source/single detector radiographic imaging system, and image acquisition apparatus

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Baltimore, MD, United States of America

Ali Uneri