Koninklijke Philips Corporation N.v.

Clearpoint Neuro, Inc.

Lyubomir Georgiev Zagorchev

Jochen Peters

Martin Bergtholdt

Fabian Wenzel

Carsten Meyer

Thomas Heiko Stehle

Juergen Weese

Andrew J Buckler

Chen Li

Damon Hyde

Sabine Mollus

Yogish Mallya

Eric Jean

Narayanan Ayyakad Krishnan

Shiv Sabesan

Joel Haaf

Jochen Kruecker

Raymond C Chan

Stewart Young

Yuechen Qian

Amir Mohammad Tahmasebi Maraghoosh

Ivan Salgo

Douglas Allen Stanton

Nicole Schadewaldt

Arne Ewald

Kamal Vij

Reinhard Kneser

Ravindra Bhat

Scott Holland Settlemier

Dieter Geller

Robert M Manzke

Dennis Erwin Bos

Nick Flaeschner

Christian Richard Osswald

Irina Wächter-Stehle

Shivakumar Kunigal Ramaswamy

Michael Cardinale

Prescott Peter Klassen

Antonio T Latto

Katerina Georgopoulou

Kevin CamHong Quan

Nicolas Gallo

Miranda Rasmussen

Jeffrey Miller

Houchun Hu

Fabien Wenzel

Philip Bradley Hotte

Timothy Neil Orr

Alex Saalbach

Elizabeth Anne Morre

Matthew A Garlinghouse

Robert M Roth

Thomas W McAllister

Dennis E Bos

Examples of the presently disclosed technology provide new systems and methods for performing direct targeting for DBS procedures (and other related procedures) using a single pre-operative scan of a patient's brain. Examples can achieve this single-scan direct targeting by leveraging mesh vertex-based correspondences between patient-specific 3D mesh brain structure representations across prospective and historical DBS procedures. Examples can leverage such correspondences to accurately predict DBS lead placement coordinates for a prospective DBS procedure based on historical DBS lead placement coordinates from the historical DBS procedures. Accordingly, a clinician performing the prospective DBS procedure can utilize the predicted DBS lead placement coordinates for surgical planning purposes.

Automatic, patient-specific targeting for deep brain stimulation

Systems and methods are provided that automatically determine whether a prospective surgical trajectory will collide with a patient's skull using a 3D representation of the patient's cranial region (including the patient's scalp, skull, and brain) adapted from imaging data of the patient's cranial region (e.g., MRI data or CT data). Taking a particular patient's varied skull thickness into account, examples can determine bone collision during a trajectory planning stage before or after a stereotactic frame is mounted to the patient. Accordingly, examples may preemptively alert a clinician to a potential bone collision before the prospective surgical trajectory is underway/has been executed, thereby reducing the risk of bone collision during the surgical procedure.

Method for bone collision detection using a stereotactic image-guided navigation system

Systems and methods provide automated systems for accurately estimating infusion coverage within a target brain region in real-time (or approximately real-time) during surgical procedures. Such accurate and real-time infusion coverage estimation enables intraoperative monitoring and adjustment of infusion parameters (e.g., cannula tip location, infusate delivery flow rate, etc.) for achieving optimal/improved infusion coverage for a given drug therapy. Accordingly, examples of the presently disclosed technology can improve the efficacy and safety of drug therapies delivered to the brain.

Patient-specific infusion coverage estimates using segmented 3D brain representations

A method () that tessellates a cortical surface of a brain with a plurality of dipole patches, wherein a quantity of the plurality of dipole patches is variable and dependent upon a head size.

Patient-specific cortical surface tessellation into dipole patches

Methods and systems that provide quantitative assessments of in vivo thermal treatments, such as ablations, during image-guided surgeries using a high-resolution pre-operative MRI image segmented with a shape constrained and deformable mesh representations of brain structures and generating 3-D visualizations of thermally treated volumes during the thermal treatment that can provide near real time visual and quantitative feedback to a clinician.

Image-guided surgical systems with quantitative evaluation of in vivo thermal treatments and related methods

Systems and methods for magnetic resonance (MR) examination are provided. In an embodiment, a method for MR examination includes receiving, at a computing device in communication with a magnetic resonance imaging (MRI) device, MR data of a patient body comprising a plurality of anatomical structures, the plurality of anatomical structures including a region of interest, segmenting, by the computing device, the MR data to obtain geometries of the plurality of anatomical structures, receiving, at the computing device, a report comprising text descriptions representative of the plurality of anatomical structures, associating, by the computing device, the text descriptions with respective geometries of the plurality of anatomical structures, identifying, by the computing device, the text descriptions associated with anatomical structures within or outside of the region of interest; and outputting, by the computing device, a graphical representation based on the identified text descriptions associated with anatomical structures within or outside of the region of interest.

Incidental finding augmentation system for medical radiology

In one embodiment, a method that determine parameters corresponding to shape and volume of the one or more brain structures from the one or more meshes () and provides the parameters to a classifier to estimate a probability of a brain abnormality based on the parameters.

Mesial temporal lobe epilepsy classifier based on volume and shape of subcortical brain regions

A cortical malformation identification method includes quantitatively evaluating, using a processor of a computer that includes the processor and a memory, digital image data from a magnetic resonance imaging (MRI) scan on a cerebral cortex to produce quantified scan data. The method also includes automatically detecting a cortical malformation based on the quantified scan data. An image of the cerebral cortex may be color-coded so that the cortical malformation is shown in a different color than the remainder of the cerebral cortex in the image, based on the quantified scan data. Additionally or alternatively, a 3-dimensional representation of the cerebral cortex may be mapped to the quantified scan data to produce a mapped image of the cerebral cortex including the detected cortical malformation.

Cortical malformation identification

A method () that encodes electrode locations to a mean scalp mesh for adaptation to subsequent image scans.

Automatic EEG sensor registration

Systems and methods for obtaining anatomical-structure-specific activation data in a brain of a patient are provided. In an embodiment, a method for obtaining anatomical-structure-specific activation data in a brain of a patient includes receiving magnetic resonance (MR) data of the brain obtained by use of a magnetic resonance imaging (MRI) device; segmenting the MR data of the brain to delineate a plurality of geometries, each of the plurality of geometries corresponding to a respective anatomical structure in the brain; receiving functional magnetic resonance (fMRI) data of the brain obtained by use of an MRI device; aligning the MR data and the fMRI data; determining a plurality of activation levels, each of the activation levels corresponding to respective delineated geometries based on the aligned MR data and fMRI data; and outputting a graphical representation of a dynamic activity in the brain corresponding to the delineated geometries of the anatomical structures.

Functional magnetic resonance imaging systems and methods

Devices, systems, methods for validating ablation results in a patient's brain are provided. In some embodiments, the method for validating ablation result in a patient's brain includes obtaining magnetic resonance (MR) data of the patient's brain, by use of a magnetic resonance imaging (MRI) device; obtaining first imaging data of the patient's brain, by use of the MRI device; extracting, by use of computing device in communication with the MRI device, first fiber tracts passing through an anatomy in the patient's brain based on the first imaging data; obtaining, by use of the MRI device, second imaging data of the patient's brain after ablation of the anatomy in the patient's brain has started; extracting second fiber tracts passing through the anatomy in the patient's brain based on the second imaging data; and outputting a graphical representation of a comparison between the first fiber tracts and the second fiber tracts.

Ablation result validation system

Treatment trajectory guidance systems and methods are provided. In one embodiment, the method for treatment trajectory guidance in a patient's brain includes obtaining a three- dimensional (3D) brain model that includes a model of an anatomy, the model of the anatomy including a plurality of feature points; modifying the 3D brain model based on magnetic resonance (MR) data of the patient's brain from a magnetic resonance imaging (MRI) device to obtain a plurality of modified feature points on a modified model of the patient's anatomy in the patient's brain; displaying on a display a first planned trajectory for treating the patient's anatomy based on the plurality of modified feature points; and displaying, on the display, a first estimated treatment result for the first planned trajectory.

Treatment trajectory guidance system

The present invention relates to medical image editing. In order to facilitate the medical image editing process, a medical image editing device () is provided that comprises a processor unit (), an output unit (), and an interface unit (). The processor unit () is configured to provide a 3D surface model of an anatomical structure of an object of interest. The 3D surface model comprises a plurality of surface sub-portions. The surface sub-portions each comprise a number of vertices, and each vertex is assigned by a ranking value. The processor unit () is further configured to identify at least one vertex of vertices adjacent to the determined point of interest as an intended vertex. The identification is based on a function of a detected proximity distance to the point of interest and the assigned ranking value. The output unit () is configured to provide a visual presentation of the 3D surface model. The interface unit () is configured to determine a point of interest in the visual presentation of the 3D surface model by interaction of a user. The interface unitis further configured to modify the 3D surface model by displacing the intended vertex by manual user interaction. In an example, the output unit () is a display configured to display the 3D surface model directly to the user ().

Medical image editing

An ultrasound system for planning a surgical implantation of a prosthetic aortic valve produces three dimensional images of the aortic root region of a patient. An electronic model of an aortic root is accessed and fitted to the aortic root in a three dimensional ultrasound image. Preferably the aortic root model exhibits closed contour cross-sections which are fitted to the endothelial lining of the aortic root in the ultrasound image. A medial axis of the fitted model is identified and radii measured from the medical axis to the border of the fitted model. The radii are joined to identify a surface forming a mesh model fitted to the aortic root anatomy of the patient. The shape and dimensions of the fitted model may be used to fabricate a custom prosthetic valve for aortic valve replacement.

Automated three dimensional aortic root measurement and modeling

A system and method are provided for brain tissue classification, which involves applying an automated tissue classification technique to an image of a brain based on a prior probability map, thereby obtaining a tissue classification map of the brain. A user is enabled to, using a user interaction subsystem, provide user feedback which is indicative of a) an area of misclassification in the tissue classification map and b) a correction of the misclassification. The prior probability map is then adjusted based on the user feedback to obtain an adjusted prior probability map, and the automated tissue classification technique is re-applied to the image based on the adjusted prior probability map. An advantage over a direct correction of the tissue classification map may be that the user does not need to indicate the area of misclassification or the correction of the misclassification with a highest degree of accuracy. Rather, it may suffice to provide an approximate indication thereof.

Brain tissue classification

Growing community of inventors

Burlington, MA, United States of America

Lyubomir Georgiev Zagorchev