Koninklijke Philips Corporation N.v.

Christopher Stephen Hall

Shyam Bharat

Jan Frederik Suijver

Jochen Kruecker

Stein Kuiper

Chien Ting Chin

Bernardus Hendrikus Wilhelmus Hendriks

Aleksandra Popovic

Anna Teresa Fernandez

Raymond C Chan

Gert Wim 't Hooft

William Tao Shi

Vijay Parthasarathy

Bharat Ramachandran

Haytham Elhawary

Cynthia Ming-fu Kung

Kongkuo Lu

Shunmugavelu D Sokka

Guy Shechter

Francois Guy Gerard Marie Vignon

Rafael Wiemker

Nevenka Dimitrova

Ameet Kumar Jain

Molly Lara Flexman

Ralph Kurt

Marcel Rene Bohmer

Jens Von Berg

James Robertson Jago

Balasundar Iyyavu Raju

Hua Xie

Stewart Young

Robert Manzke

Vijay Thakur Shamdasani

Amir Mohammad Tahmasebi Maraghoosh

Andrew Lee Robinson

Falk Uhlemann

Ralf Seip

Douglas Allen Stanton

Fabian Wenzel

Axel Saalbach

Gary Cheng-How Ng

Jingping Xu

Walid Ali

Sandeep M Dalal

Nijs Cornelis Van Der Vaart

Tim Philipp Harder

Nagaraju Bussa

Tom Brosch

Krishnamoorthy Palanisamy

Pingkun Yan

Jinnan Wang

Adrien Emmanuel Desjardins

Yan Shi

Adrien Desjardins

Xin Liu

Balasundara I Raju

Dinesh Mysore Siddu

Gary Lee Andrews

Todd Nicholas Erpelding

David Louis Marie Savery

Chien-Ting Chin

Ronald Tabaksblat

Lyubomir Georglev Zagorchev

Gert 'T Hooft

Bernardus H W Hendriks

Freek Suijver

A method for multi-modal tissue classification of an anatomical tissue involves a generation of a tissue classification volume () of the anatomical tissue derived from a spatial registration and an image extraction of one or more MRI features of the anatomical tissue and one or more ultrasound image features of the anatomical tissue. The method further involves a classification of each voxel of the tissue classification volume () as one of a plurality of tissue types including a healthy tissue voxel and an unhealthy tissue voxel.

Method and system for multi-modal tissue classification

A system for providing a perspective for a virtual image includes an intraoperative imaging system () having a transducer () configured to generate an image data set for a region. A shape sensing enabled device () is configured to have at least a portion of the shape sensing enabled device positioned relative to the region. The shape sensing enabled device has a coordinate system registered with a coordinate system of the intraoperative imaging system. An image generation module () is configured to render a virtual image () of at least a portion of the region using the image data set wherein the virtual image includes a vantage point relative to a position on the shape sensing enabled device.

Virtual image with optical shape sensing device perspective

A system with integrated tracking includes a procedure-specific hardware component (or) disposed at or near a region of interest. A field generator () is configured to generate a field with a field of view covering the region of interest. A mounting device () is connected to the field generator and is coupled to the procedure-specific hardware. The field generator is fixedly positioned by the mounting device to permit workflow access to the region of interest without interfering with the field generator and to provide a known position of the field generator relative to the region of interest. A tracking device () is configured to be inserted in or near the region of interest to be tracked within the field of view of the field generator to generate tracking data.

Interventional guidance system with integrated tracking setup

Presented are concepts for detecting subject motion in medical imaging of a subject. One such concept obtains a motion classification model representing relationships between motion of image features and subject motion values. For each of a plurality of medical slice images of an imaged volume of the subject, an image feature of the medical slice image is extracted. Based on the extracted image feature for each of the plurality of medical slice images, motion information for the image feature is determined. Based on the motion information for the image feature and the obtained motion classification model, a subject motion value is determined.

Detecting subject motion in medical imaging

There is provided a computer-implemented method and system () for determining regions of hyperdense lung parenchyma in an image of a lung. The system () comprises a memory () comprising instruction data representing a set of instructions and a processor () configured to communicate with the memory and to execute the set of instructions. The set of instructions, when executed by the processor (), cause the processor () to locate a vessel in the image, determine a density of lung parenchyma in a region of the image that neighbours the located vessel, and determine whether the region of the image comprises hyperdense lung parenchyma based on the determined density, hyperdense lung parenchyma having a density greater than &#x2212;800 HU.

Determining regions of hyperdense lung tissue in an image of a lung

An ultrasound system includes a 3D imaging probe and a needle guide which attaches to the probe for guidance of needle insertion into a volumetric region which can be scanned by the 3D imaging probe. The needle guide responds to the insertion of a needle through the guide by identifying a plane for scanning by the probe which is the insertion plane through which the needle will pass during insertion. The orientation of the insertion plane is communicated to the probe to cause the probe to scan the identified plane and produce images of the needle as it travels through the insertion plane.

Ultrasonically guided biopsies in three dimensions

A system and method for receiving a medical image, receiving an adaptation of a model of a physical structure, the adaptation relating to the medical image, determining an image quantity of the medical image at each of a plurality of vertices of the adaptation and aggregating the plurality of image quantities to determine an evaluation metric.

Method and system for quantitative evaluation of image segmentation

The following relates generally to image segmentation. In one aspect, an image is received and preprocessed. The image may then be classified as segmentable if it is ready for segmentation; if not, it may be classified as not segmentable. Multiple, parallel segmentation processes may be performed on the image. The result of each segmentation process may be marked as a potential success (PS) or a potential failure (PF). The results of the individual segmentation processes may be evaluated in stages. An overall failure may be declared if a percentage of the segmentation processes marked as PF reaches a predetermined threshold.

Self-aware image segmentation methods and systems

An image registration system employs an endoscope and an endoscope controller. In operation, the endoscope generates an intra-operative endoscopic image of a vessel tree (e.g., an arterial tree or a venous tree) within an anatomical region, and the endoscope controller image registers the intra-operative endoscopic image of the vessel tree to a pre-operative three-dimensional image of the vessel tree within the anatomical region. The image registration includes an image matching of a graphical representation of each furcation of the vessel tree within the intra-operative endoscopic image of the vessel tree to a graphical representation of each furcation of the vessel tree within the pre-operative three-dimensional image of the vessel tree.

Endoscopic registration of vessel tree images

Systems and methods for surgical robotic guidance include a robotic system () having a robot () configured to pass to a target through a port (). The robotic system includes a visual component () employed in guiding the robot along a path to a location. The location is defined in accordance with a position and orientation of the robot. An ultrasonic probe () is guided by the robot to the location to permit engagement of the probe to collect ultrasonic images at the location.

Robotic guidance of ultrasound probe in endoscopic surgery

The invention relates to a method of calculating a displacement of an object of interest comprising a step of calculating () a displacement model of said object of interest from adjacent images of a set of pre-acquired images of said object of interest, said displacement model reflects the position of said object of interest along the time. The method is characterized in that the method further comprises the following. A step of determining () a first sub-set of images (S) from said set of pre acquired images within one periodical time cycle of said set of pre-acquired images on the basis of the displacement model. A first step of identifying () a second sub-set of images (S) from newly-acquired images, wherein images in said second sub-set of images (S) are consecutive and have the same most similar image in said first sub-set of images (S), wherein a first set of similarity levels is determined by comparing a given image in said newly acquired images with each image of said first sub-set of images (S), and wherein said most similar image has the largest similarity level in said first set of similarity levels. A first step of selecting () a given image in said second sub-set of images (S) as a first reference image (I). A second step of identifying () a third sub-set of images (S) from said newly-acquired images, wherein images in said third sub-set of images (S) are consecutive and have the same most similar image in said first sub-set of images (S), wherein a set of similarity levels is determined by comparing a given image in said newly acquired images with each image of said first sub-set of images (S), and wherein said most similar image has the largest similarity level in said set of similarity levels. A second step of selecting () a given image in said third sub-set of images (S) as a second reference image (I). A step of calculating () the displacement between said second reference image (I) and said first reference image (I). The invention also relates to a corresponding system of displacement calculation.

Method and system for calculating a displacement of an object of interest

A system and method for perfusion imaging includes an imaging device () configured to collect perfusion information from a target area. A processing module () is configured to determine perfusion levels of the target area based on the perfusion information. A planning module () is configured to provide a treatment plan for the target area by correlating the perfusion levels with treatment activities for the target area, wherein the treatment activities are adjusted based upon characteristics of the perfusion levels in the target area.

Real-time tumor perfusion imaging during radiation therapy delivery

A system for radiation therapy include an imaging device () configured to scan an area of interest for tissue undergoing radiation therapy to collect one or more images of the tissue. An interpretation module () is configured to receive the one or more images of the tissue to determine a burn status of the tissue and provide adjustments for a radiation treatment plan in accordance with the burn status.

Real-time quantification of skin burns in external beam radiation therapy

An apparatus, system and method for determining a position includes a transducer device () configured to receive signals from a console () and generate images based upon reflected waves. A flexible cable () is coupled to the transducer device to provide excitation energy to the transducer device from the console. An optical fiber () has a shape and position corresponding to a shape and position of the cable during operation. A plurality of sensors () is in optical communication with the optical fiber. The sensors are configured to measure deflections and bending in the optical fiber such that the deflections and bending in the optical fiber are employed to determine positional information about the transducer device.

Apparatus, system and method for imaging and treatment using optical position sensing

A device for delivery of a substance () using energy to protect, at a site of activation, against a side effect of another substance () that was delivered, is being delivered, and/or will be delivered, at another site. The activation may be non-invasive, remote and the energy beam () may be an ultrasound beam. A first of the substances can be activated at a particular energy level, and the second is then activated at a lower level so that a population of particles bearing the first substance is not inadvertently activated during activation of the second substance. The device may comprise a system to control the levels of energy applied.

Ultrasound mediated delivery with critical-organ protection

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Kirkland, WA, United States of America

Christopher Stephen Hall