Koninklijke Philips Corporation N.V.

Koninklike Philips N.v.

Ashish Panse

Molly Lara Flexman

Jochen Kruecker

Paul Thienphrapa

Grzegorz Andrzej Toporek

Alexandru Patriciu

Aleksandra Popovic

Torre Michelle Bydlon

Sean Joseph Kyne

Marcin Arkadiusz Balicki

Atul Gupta

Alyssa Torjesen

Ayman Alalao

Neriman Nicoletta Kahya

Pascal Yves Francois Cathier

Ronaldus Frederik Johannes Holthuizen

Benoit Jean-Dominque Bertrand Maurice Mory

Alalao Ayman

Christopher Martel

A position tracker driven display system for an interventional device () including an integration of one or more position trackers and one or more interventional tools. The position tracker driven display system employs a monitor () and a display controller () for controlling a real-time display on the monitor () of a tracking image illustrative of a navigation of a tracking node of the interventional device () within an anatomical region. The display controller () autonomously selects the tracking image among a plurality of registered spatial images illustrative of the anatomical region within an image space based on position tracking data informative of a position of the at least one position tracker within the anatomical region. The display controller () derives the autonomous selection of the tracking image from a position of the tracking node of the interventional device () relative to the image space as indicated by the position tracking data.

Automatic field of view updating of position tracked interventional device

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 (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 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

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

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

An augmented reality trigger system () comprising a primary augmented reality device () and a trigger action controller () for implementing an augmented reality trigger method based on a medical tool () and/or a tool identifier ()associated with the medical tool (). In operation, the primary augmented reality device () generates a camera image of the real world, which may or may not at any time include the medical tool () and/or the tool identifier (). The trigger action controller () recognizes a generation by the primary augmented reality device () of the camera image of the real world including the medical tool () and/or the toolidentifier () and in response to such recognition, triggers a medical procedure action by the primary augmented reality device () and/or a medical device () in support of a medical procedure involving the medical tool ().

Augmented reality triggering of devices

A controller for augmenting reality in a three-dimensional (3D) space includes a memory that stores instructions; and a processor that executes the instructions. The controller controls a display system configured to present virtual objects in the 3D space. When executed by the processor, the instructions cause the controller to perform a process. The process includes detecting a first action by a user relative to an object or a virtual object in the 3D space. The process also includes selectively enabling or disabling a virtual operation member based on detecting the first action between the user and the object or virtual object in the 3D space. The virtual operational member is configured, when operated, to control operation of a machine in the 3D space.

Virtual cover for user interaction in augmented reality

A torque detection vascular therapy system employing a vascular therapy device () and a torque detection controller (). The vascular therapy device () is operable to be transitioned from a pre-deployed state to a post-deployed state, and includes a matrix of imageable markers representative of a geometry of the vascular therapy device (). The torque detection controller () controls a detection of a non-torsional deployment or a torsional deployment of the vascular therapy device () subsequent to a transition of the vascular therapy device () from the pre-deployed state to the post-deployed state by deriving a vector indication of the non-torsional deployment or the torsional deployment of the vascular therapy device () from a matrix orientation similarity or a matrix orientation dissimilarity between a baseline device geometry of the vascular therapy device () represented by the matrix of the imageable markers and an imaged device geometry of the vascular therapy device () represented by the matrix of imageable markers.

Torsional deployment detection of a vascular therapy

Augmented reality interventional system providing contextual overlays

A medical instrument includes a first device () including shape-sensed flexible instrument, a second device () disposed over the first device and a third device () disposed over the first device and a portion of the second device. The second and third devices include a geometric relationship such that a position of the second device and the third device is determined from shape sensing information of the first device and the geometric relationship.

Shape sensing of multiple over-the-wire devices

A mixed reality device () employing a mixed reality display () for visualizing a virtual augmentation of a physical anatomical model, and a mixed reality controller () for controlling a visualization by the mixed reality display () of the virtual augmentation of the physical anatomical model including a mixed reality interaction between the physical anatomical model and a virtual anatomical model. The mixed reality controller () may employ a mixed reality registration module () for controlling a spatial registration between the physical anatomical model within a physical space and the virtual anatomical model within a virtual space, and a mixed reality interaction module for controlling the mixed reality interaction between the physical anatomical model and the virtual anatomical model based on the spatial registration between the physical anatomical model within the physical space and the virtual anatomical model within the virtual space.

Virtual augmentation of anatomical models

An augmented reality system includes a head-mountable augmented reality platform () having a display to provide guidance to a user. A prediction module () is trained in a procedure and is configured to predict a next activity in the procedure based on a current condition. An image generation module () is responsive to the prediction module to provide a visual cue of a predicted next activity to the user through the head-mountable augmented reality platform.

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Burlington, MA, United States of America

Ashish Panse