Koninklijke Philips Corporation N.V.

Koninklijkle Philips N.v.

Aleksandra Popovic

David Paul Noonan

Haytham Elhawary

Molly Lara Flexman

Paul Thienphrapa

Grzegorz Andrzej Toporek

Aryeh Leib Reinstein

Raymond C Chan

Robert Manzke

Torre Michelle Bydlon

Christopher Stephen Hall

Ashish Panse

Alexandru Patriciu

Marcin Arkadiusz Balicki

Ameet Kumar Jain

Balasundar Iyyavu Raju

Ralf Seip

Bharat Ramachandran

Sean Joseph Kyne

Bernardus Hendrikus Wilhelmus Hendriks

Shyam Bharat

Karen Irene Trovato

Martinus Bernardus Van Der Mark

Gert Wim 't Hooft

Douglas Allen Stanton

Drazenko Babic

Christian Reich

Willem Verkruijsse

Ahmet Ekin

Jurgen Jean Louis Hoppenbrouwers

Sitharthan Kamalakaran

Wilhelmus Henrica Gerarda Maria Van Den Boomen

Michael Chun-Chieh Lee

Ayman Alalao

Heinrich Von Busch

Ron Kroon

Jarich Willem Spliethoff

Xingchi He

Prasad Vagdargi

Benoit Jean-Dominque Bertrand Maurice Mory

John Allen Bracken

Johannes Petrus Withagen

Elliot Eliyahu Greenblatt

A motion dependency surgical robotic system () employs an independent robotic arm (), a dependent robotic arm (), and a motion dependency robot controller (). In operation, the motion dependency robot controller () controls an independent motion of the independent robotic arm () within a coordinate space responsive to an input signal indicative of the motion of the independent robotic arm () within the coordinate space, and further controls a motion of the dependent robotic arm () within the coordinate space as a function of a spatial geometric relationship between the independent robotic arm () and the dependent robotic arm () within the coordinate space.

Automatic motion control of a dependent surgical robotic arm

A robot controller () includes a first input () configured to receive images () from an imaging device for a region of interest. A target identification device () is configured to identify a target region in the images. A control system () is coupled to a robotically controlled treatment device to generate control signals to control the treatment device to treat the target region when the treatment device is positioned corresponding to the target region.

Image guided robotic system for tumor aspiration

A training data collection method for an interventional device () including a portion of an interventional device () and sensors () adapted to provide position and/or orientation and/or shape information with at least a part of the sensors being affixed to said portion of device (). The method involves controlling one or more motion variables of the interventional device () in accordance with a pre-defined data point pattern, and determining, from shape data derived from said information, an estimating of a pose of said device portion () and an estimating of a positioning motion of the interventional device (). The method further involves a storage of a temporal data sequence for the interventional device () derived from the estimated pose of the end-effector () for each data point, the estimated positioning motion of the interventional device () for each data point, and the motion variable(s) of the interventional device () for each data point.

Training data collection for machine learning models

A control unit for a robot system including a robot with a rigid proximal portion having a remote center of motion (RCM), a flexible distal portion, and an image acquisition device. The control unit includes a processor that receives images from the image acquisition device; generates a first deployment path to a first target position based on the images; generates a second deployment path to a second target position from the first target position based on the images; generates first guidance information for positioning the rigid proximal portion along the first deployment path; generates second guidance information for positioning the flexible distal portion along the second deployment path; and deploys the first guidance information to the rigid proximal portion for guiding the rigid proximal portion to the first target position, and deploys the second guidance information to the flexible distal portion for guiding the flexible interface distal portion to the second target position.

Control unit, system and method for controlling hybrid robot having rigid proximal portion and flexible distal portion

A image guided motion scaled surgical robotic system () employs a surgical robotic arm () and an image guided motion scaled surgical controller (). In operation, responsive to an input signal indicative of a user defined motion of the surgical robotic arm () within an anatomical region, the image guided motion scaled surgical controller () controls an actuated motion of the surgical robotic arm () within the anatomical region based on a map () of a motion scale delineated within an imaging of the anatomical region.

Image guided motion scaling for robot control

An augmented reality interventional system which provides contextual overlays () to assist or guide a user () or enhance the performance of the interventional procedure by the user that uses an interactive medical device () to perform the interventional procedure. The system includes a graphic processing module () that is configured to generate at least one contextual overlay on an augmented reality display device system (). The contextual overlays may identify a component () or control of the interactive medical device. The contextual overlays may also identify steps of a procedure to be performed by the user and provide instructions for performance of the procedure. The contextual overlays may also identify a specific region of the environment to assist or guide the user or enhance the performance of the interventional procedure by identifying paths or protocols to reduce radiation exposure.

Augmented reality interventional system providing contextual overylays

A positioning controller () including an imaging predictive model () and inverse control predictive model (). In operation, the controller () applies the imaging predictive model () to imaging data generated by an imaging device () to render a predicted navigated pose of the imaging device (), and applies the control predictive model () to error positioning data derived from a differential aspect between a target pose of the imaging device () and the predicted navigated pose of the imaging device () to render a predicted corrective positioning motion of the imaging device () (or a portion of the interventional device associated with this imaging device) to the target pose. From the predictions, the controller () further generates continuous positioning commands controlling a corrective positioning by the interventional device () of the imaging device () (or said portion of interventional device) to the target pose based on the predicted corrective positioning motion of the interventional device ().

Feedback continuous positioning control of end-effectors

A robotic surgical system employing a treatment catheter () (e.g., a thermoablation catheter or a cyroablation catheter), and an articulated robot () including a plurality of linkages and one or more joints interconnecting the linkages for navigating the treatment catheter () within an anatomical region. The robotic surgical system further employs a robot controller () for controlling a navigation by the articulated robot () of the treatment catheter () along a intraoperative treatment path within the anatomical region relative to the anatomical structure derived from a planned treatment path within the anatomical region relative to the anatomical structure based on a registration between the articulated robot () and a preoperative image () illustrative of the planned treatment path within the anatomical region relative to the anatomical structure.

Single-port surgical procedure using image guided articulated robot

A multi-stage robot () comprises a sequential series arrangement of a flexible robot arm (), a robot platform (), a snake robot arm () and an end-effector (). In operation, the multi-state robot () is introduced through an incision or an anatomical opening into an anatomical region enclosing an anatomical structure, and the snake robot arm () and the end-effector () are further introduced through an incision or an anatomical opening into the anatomical structure. The robot platform () is thereafter attached to the incision or the anatomical opening of the anatomical structure to facilitate an actuation of the snake robot arm () relative to the robot platform () to thereby target position the end-effector () within the anatomical structure. The multi-stage robot () may further comprise a robot base at the proximal end of the multi-stage robot () and attachable to the incision or the anatomical opening into the anatomical region.

Multi-stage robot for anatomical structure interventions

A system for visualizing an anatomical target includes an imaging device () configured to collect real-time images of an anatomical target. A three-dimensional model () is generated from pre- or intra-operative images and includes images of structures below a surface of the anatomical target not visible in the images from the scope. An image processing module () is configured to generate an overlay () registered to the real-time images and to indicate the structures below the surface and a depth of the structures below the surface. A display device () is configured to concurrently display the real-time images and the overlay.

Visualization of depth and position of blood vessels and robot guided visualization of blood vessel cross section

Various embodiments of the present disclosure encompass a visual endoscopic guidance device employing an endoscopic viewing controller () for controlling a display of an endoscopic view () of an anatomical structure, and a visual guidance controller () for controlling of a display one or more guided manipulation anchors (-) within the display of the endoscopic view () of the anatomical structure. A guided manipulation anchor (-) is representative of location marking and/or a motion directive of a guided manipulation of the anatomical structure. The visual guidance controller () further controls a display of a hidden feature anchor relative to the display of the endoscopic view () of the anatomical structure. The hidden feature anchor () being representative of a position (e.g., a location and/or an orientation) of a guided visualization of the hidden feature of the anatomical structure.

Guided anatomical visualization for endoscopic procedures

An imaging device positioning system for monitoring an anatomical region (). The imaging device positioning system employs an imaging device () for generating an image () of an anatomical region (). The imaging device positioning system further employs an imaging device controller () for controlling a positioning of the imaging device () relative to the anatomical region (). During a generation by the imaging device () of the image () of the anatomical region (), the imaging device controller () adapts the positioning of the imaging device () relative to the anatomical region () to a physiological condition of the anatomical region () extracted from the image () of the anatomical region ().

Control of anatomical image acquisition using physiological information

A force sensed surface scanning system () employs a scanning robot () and a surface scanning controller (). The scanning robot () includes a surface scanning end-effector () for generating force sensing data informative of a contact force applied by the surface scanning end-effector () to an anatomical organ. In operation, the surface scanning controller () controls a surface scanning of the anatomical organ by the surface scanning end-effector () including the surface scanning end-effector () generating the force sensing data, and further constructs an intraoperative volume model of the anatomical organ responsive to the force sensing data generated by the surface scanning end-effector () indicating a defined surface deformation offset of the anatomical organ.

Force sensed surface scanning systems, devices, controllers and methods

An optical shape sensing system includes an attachment mechanism () being configured to secure an optical shape sensing fiber to an instrument, the optical shape sensing fiber being connected to the instrument and configured to identify a position and orientation of the instrument. An optical shape sensing module () is configured to receive feedback from the optical shape sensing fiber and register the position of the instrument relative to an operating environment. A position response module () is configured to provide feedback to an operator based on position or orientation of the instrument to guide usage of the instrument.

Optical shape sensing for instrument tracking in orthopedics

An imaging device positioning system for monitoring an anatomical region (). The imaging device positioning system employs an imaging device () for generating an image () of an anatomical region (). The imaging device positioning system further employs a imaging device controller () for controlling a positioning of the imaging device () relative to the anatomical region (). During a generation by the imaging device () of the image () of the anatomical region (), the imaging device controller () adapts the control of the positioning of the imaging device () relative to the anatomical region () to a physiological condition of the anatomical region () extracted from the image () of the anatomical region ().

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Boston, MA, United States of America

Aleksandra Popovic