Koninklijke Philips Corporation N.v.

Columbia University

Koninklike Philips N.v.

Molly Lara Flexman

David Paul Noonan

Ashish Panse

Paul Thienphrapa

Neriman Nicoletta Kahya

Aleksandra Popovic

Bharat Ramachandran

Torre Michelle Bydlon

Jochen Kruecker

Ameet Kumar Jain

Grzegorz Andrzej Toporek

Alexandru Patriciu

Aryeh Leib Reinstein

Shyam Bharat

Marcin Arkadiusz Balicki

Gregory Cole

Alyssa Torjesen

Kunal Vaidya

Sean Joseph Kyne

Raymond C Chan

Ramon Quido Erkamp

Martinus Bernardus Van Der Mark

Ehsan Dehghan Marvast

Alvin Chen

Andreas H Hielscher

Sander Hans Denissen

Sibo Li

Francois Guy Gerard Marie Vignon

Karen Irene Trovato

Amir Mohammad Tahmasebi Maraghoosh

Ahmet Ekin

Wilhelmus Henrica Gerarda Maria Van Den Boomen

Atul Gupta

Ayman Alalao

Xingchi He

Dirk Dijkkamp

Raoul Florent

Gereon Vogtmeier

Christopher Stephen Hall

Olivier Pierre Nempont

Jeroen Jan Lambertus Horikx

Keith Yeager

Eibert Gerjan Van Putten

Niels Nijhof

Pascal Yves Francois Cathier

Dirk Binnekamp

Ronaldus Frederik Johannes Holthuizen

Anna Hendrika Van Dusschoten

Hyun K Kim

Cornelius Antonius Nicolaas Maria Van Der Vleuten

Franciscus Reinier Antonius Van Der Linde

Ron Kroon

Elbert Gerjan Van Putten

Milan Jan Henri Marell

Pascal Yves François Cathier

Christopher J Fong

James M Masciotti

John Allan Bracken

Eike Gerhard Gegenmantel

Marissa Patricia Dreyer

Merel Danielle Leistikow

Benoit Jean-Dominque Bertrand Maurice Mory

Willhelmus Henrica Gerarda Maria Van Den Boomen

Alalao Ayman

Niels Nojhof

Christopher Martel

Merel Danielle Leistokow

Guillaume Leopold Theodoros Frederik Hautvast

William J Sgambati

Ronny Stoll

Yang Li

Andres Bur

A controller () for selecting images for presentation includes a memory (), a processor () and a receiver. The memory () stores instructions. The processor () executes the instructions. The receiver receives a stream of a plurality of images. When executed by the processor (), the instructions cause the controller () to apply trained artificial intelligence to the plurality of images and generate, based on the trained artificial intelligence, a 1-dimensional stream of a plurality of values corresponding respectively to the plurality of images. Each of the plurality of values of the 1-dimensional stream indicates a significance of a corresponding image of the plurality of images according to the trained artificial intelligence. The instructions also cause the controller () to output the plurality of values of the 1-dimensional stream for display with the plurality of images.

Image selection for presentation

System (SYS) and delated methods for supporting an imaging operation of an imaging apparatus (IA) capable of assuming different imaging geometries. The system comprises an input interface (IN) for receiving a current image acquired by the imaging apparatus (IA) of a current region of interest (ROI_1) at a current imaging geometry (p). A pre-trained machine learning component (MLC) computes output data that represents an imaging geometry change (Δp) for a next imaging geometry (p') in relation to a next region of interest (ROI_2). An output interface (OUT) outputs a specification of the imaging geometry change.

Patient model estimation for interventions

An OSS guiding and monitoring system employs an interventional device () including an integration of a OSS sensor () and one or more interventional tools (), the OSS sensor () for generating shape sensing data informative of a shape of the OSS sensor () as the interventional device () is navigated within an anatomical region. The OSS guiding and monitoring system further employs an OSS guiding controller () for controlling a reconstruction of a shape of the interventional device () within the anatomical region responsive to a generation of the shape sensing data by the OSS sensor (), and an OSS monitoring controller () for controlling a monitoring of a degree of folding and/or a degree of twisting of the interventional device () within the anatomical region.

OSS guiding and monitoring systems, controllers and methods

A system includes an interface () to an optical shape sensing device () with a shape conforming to a shape of an interventional medical device (01). The system also includes a controller () with a memory () that stores instructions and a processor () that executes the instructions. The instructions cause the system to identify a shape of the optical shape sensing device () using optical shape sensing signals received via the interface (), identify the interventional medical device (01) in disparate coordinate spaces of imaging systems that image the interventional medical device (01) in disparate imaging modes, and register the coordinate spaces to each other and to the interventional medical device (01). The instructions also cause the system to obtain a segmented representation () of the interventional medical device (01), and re-register the interventional medical device (01) to one of the coordinate space based on registering the interventional medical device (01) to another of the coordinate spaces using the segmented representation ().

Interventional medical device tracking

A system (SYS) and related method for facilitating collimator adjustment in X-ray imaging or a radiation therapy delivery. The system comprises an input interface (IN) for receiving input data including i) an input image and/or ii) user input data including a partial collimator setting for a collimator (COL) of an X-ray imaging apparatus (IA). A collimator setting estimator (CSE) of the system computes a complemented collimator setting for the collimator based the input data. Preferably, the system uses machine learning.

Adaptive collimation for interventional x-ray

A system for localizing a three-dimensional field of view of a beamforming ultrasound imaging probe based on a position indicator disposed within said field of view. The beamforming ultrasound imaging probe transmits and receives ultrasound signals within a three-dimensional field of view comprising a plurality of predetermined sub-volumes, each sub-volume being defined by a two dimensional array of beams. A controller causes the beamforming ultrasound imaging probe to scan the sub-volumes sequentially by transmitting and receiving ultrasound signals corresponding to each beam. A tracking system determines a position of the position indicator within the three-dimensional field of view; and determines a sub-volume in which the position indicator is located. The controller causes the beamforming ultrasound imaging probe to provide a localized field of view including the position of the position indicator by constraining the transmitting and receiving of ultrasound signals to a portion of the sub-volume in which the position indicator is located.

Ultrasound-based device localization

The following relates generally to systems and methods of transesophageal echocardiography (TEE) automation. Some aspects relate to a TEE probe with ultrasonic transducers on a distal end of the TEE probe. In some implementations, if a target is in a field of view (FOV) of the ultrasonic transducers, an electronic beam steering of the probe is adjusted; if the target is at an edge of the FOV, both the electronic beam steering and mechanical joints of the probe are adjusted; and if the target is not in the FOV, only the mechanical joints of the probe are adjusted.

Hybrid robotic-image plane control of a tee probe

A training data modification system (TDM) for machine learning and related methods. The system comprises a data modifier (DM) configured to perform a modification operation to modify medical training X-ray imagery of a patient. The modification operation causes image structures in the modified medical training imagery. The image structure is representative of a property of i) a medical procedure, ii) an image acquisition operation by an X-ray-based medical imaging apparatus (IA), iii) an anatomy of the patient.

System and methods for augmenting X-ray images for training of deep neural networks

A controller () for determining a shape of an interventional medical device in an interventional medical procedure based on a location of the interventional medical device includes a memory () that stores instructions and a processor () that executes the instructions. The instructions cause a system () that includes the controller () to implement a process that includes obtaining (S) the location of the interventional medical device () and obtaining (S) imagery of a volume that includes the interventional medical device. The process also includes applying (S), based on the location of the interventional medical device (), image processing to the imagery to identify the interventional medical device () including the shape of the interventional medical device (). The process further includes (S) segmenting the interventional medical device () to obtain a segmented representation of the interventional medical device (). The segmented representation of the interventional medical device () is overlaid (S) on the imagery.

Passive-ultrasound-sensor-based initialization for image-based device segmentation

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 controller for maintaining alignment of X-Ray imagery and ultrasound imagery includes a memory that stores instructions, and a processor that executes the instructions. When executed by the processor, the instructions cause the controller to execute a process that includes receiving data from an X-Ray system used to perform X-Ray imaging, and receiving data from an ultrasound imaging probe used to perform ultrasound imaging. The process executed by the controller also includes registering imagery based on X-Rays to imagery from the ultrasound imaging probe based on an X-Ray image of the ultrasound imaging probe among the imagery based on X-Rays, and detecting, from the data from the ultrasound imaging probe, movement of the ultrasound imaging probe.

Multi-modal imaging alignment

An electromagnetic navigation device for guiding and tracking an interventional tool () within an anatomical region. The electromagnetic navigation device employs a guidewire () insertable into the anatomical region, and a hub () translatable and/or rotatable in conjunction with the interventional tool () relative to the guidewire (). In operation, the guidewire () includes one or more guidance electromagnetic sensors generating guidance data informative of an electromagnetic sensing of a position and/or an orientation of the guidewire () relative to the anatomical region, and the hub () includes a tracking electromagnetic sensor () generating tracking data informative of an electromagnetic sensing of a position and/or an orientation of the hub () relative to the guidewire (). Responsive to the electromagnetic sensing data, a navigation controller () controls a determination of a position and/or an orientation of the interventional tool () relative to the guidewire ().

Electromagnetic navigation device for guiding and tracking an interventional tool

An optical shape sensing (OSS) system includes a launch fixture configured to receive and secure an optical fiber within a flexible OSS enabled instrument, where the launch fixture includes a docking interface; a launch fixture base configured to be connected to a support structure; and a docking device configured to secure the launch fixture onto the launch fixture base. The docking device includes a launch fixture slot passing through the docking device, and the launch fixture slot is configured to receive and secure the docking interface of the launch fixture through both a top side of the docking device and an opposing bottom side of the docking device.

Docking device for optical shape sensing launch fixtures

A shape sensing enabled instrument includes a flexible guide wire or support rod including an outer surface which encapsulates interior features. The interior features include an optical fiber lumen () configured to receive one or more optical fibers for optical shape sensing, and a guide wire support rod or the support rod forming a hollow extending longitudinally along the instrument. The guide wire support rod or the support rod is configured to receive the optical fiber lumen therein to permit rotation and translation of an optical fiber and to protect the optical fiber.

Multipurpose lumen design for optical shape sensing

Various embodiments of the present disclosure include a C-arm registration system employing a controller () for registering a C-arm () to a X-ray ring marker (). The X-ray ring marker () includes a coaxial construction of a chirp ring () and a centric ring () on an annular base (). In operation, the controller () acquires a baseline X-ray image illustrative of the X-ray ring marker () within a baseline X-ray projection by the C-arm () at a baseline imaging pose, derives baseline position parameters of the X-ray ring marker () within the baseline X-ray projection as a function of an illustration of the centric ring () within the baseline X-ray image, and derives a baseline twist parameter of the X-ray ring marker () within the baseline X-ray projection as a function of the baseline position parameters and of an illustration of the chirp ring () within the baseline X-ray image.

X-ray ring markers for X-ray calibration

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Melrose, MA, United States of America

Molly Lara Flexman