Abstract: A computer-implemented method of performing motion compensation on a temporal sequence of digital subtraction angiography, DSA, images includes: inputting (S120) a temporal sequence of DSA images (110) into a neural network (120) trained to predict, from the inputted temporal sequence (110), a composite motion-compensated DSA image (130) representing the inputted temporal sequence (110) and which includes compensation for motion of the vasculature between successive contrast-enhanced images in the temporal sequence, and which also includes compensation for motion of the vasculature between acquisition of contrast-enhanced images in the temporal sequence and acquisition of the mask image; and outputting (S130) the predicted composite motion-compensated DSA image (130).

Abstract: Various embodiments of the present disclosure include a C-arm registration system employing a controller (70) for registering a C-arm (60) to a X-ray ring marker (20). The X-ray ring marker (20) includes a coaxial construction of a chirp ring (40) and a centric ring (50) on an annular base (30). In operation, the controller (70) acquires a baseline X-ray image illustrative of the X-ray ring marker (20) within a baseline X-ray projection by the C-arm (60) at a baseline imaging pose, derives baseline position parameters of the X-ray ring marker (20) within the baseline X-ray projection as a function of an illustration of the centric ring (50) within the baseline X-ray image, and derives a baseline twist parameter of the X-ray ring marker (20) within the baseline X-ray projection as a function of the baseline position parameters and of an illustration of the chirp ring (40) within the baseline X-ray image.

Abstract: A computer-implemented method of reducing temporal motion artifacts in temporal intracardiac sensor data, includes: inputting (S120) temporal intracardiac sensor data (110), into a neural network (130) trained to predict, from the temporal intracardiac sensor data (110), temporal motion data (140, 150) representing the temporal motion artifacts (120); and compensating (S130) for the temporal motion artifacts (120) in the received 5 temporal intracardiac sensor data (110) based on the predicted temporal motion data (140, 150).

Abstract: A controller (150) for selecting images for presentation includes a memory (151), a processor (152) and a receiver. The memory (151) stores instructions. The processor (152)executes the instructions. The receiver receives a stream of a plurality of images. When executed by the processor (152), the instructions cause the controller (150) 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 (150) to output the plurality of values of the 1-dimensional stream for display with the plurality of images.

Abstract: A method is provided for determining whether a section of a lead has adhered to a blood vessel. The method comprises obtaining data corresponding to the blood vessel with a lead inside and determining motion vectors corresponding to the lead from the data corresponding to the blood vessel. The motion vectors are input into a machine learning algorithm trained to learn the correlation between the motion vectors and whether a section of a lead has adhered to a blood vessel and output an adherence level for segments of the lead.

Abstract: A controller (150) for interventional medical devices includes a memory (151) and a processor (152). The memory (151) stores instructions that the processor (152) executes. When the instructions are executed, the instructions cause the controller (150) to obtain at least one location of a distal end of the interventional medical device (101), identify motion at a proximal end of an interventional medical device (101), apply a first trained artificial intelligence to the motion at the proximal end of the interventional medical device (101) and to the at least one location of the distal end of the interventional medical device (101), and predict motion along the interventional medical device (101) towards a distal end of the interventional medical device (101) during the interventional medical procedure. The controller (150) also obtains images of the distal end of the interventional medical device (101) from a medical imaging system (120) to determine when the actual motion deviates from the predicted motion.

Abstract: A computer-implemented method of locating a vascular constriction in a temporal sequence of angiographic images, includes identifying (S130), from a temporal sequence of differential images, temporal sequences of a subset of sub-regions (120i,j) of the vasculature wherein contrast agent enters the sub-region, and the contrast agent subsequently leaves the sub-region; and inputting (S140) the identified temporal sequences of the subset into a neural network (130) trained to classify, from temporal sequences of angiographic images of the vasculature, a sub-region (120i,j) of the vasculature as including a vascular constriction (140).

Abstract: A computer-implemented method of providing a neural network for predicting a position of each of a plurality of portions of an interventional device (100), includes training (S130) a neural network (130) to predict, from temporal shape data (110) representing a shape of the interventional device (100) at one or more historic time steps i(t1 . . . tn-1) in a sequence, a position (140) of each of the plurality of portions of the interventional device (100) at a current time step (tn) in the sequence.

Abstract: The present invention relates to controlling a 2D application in a mixed reality application. In order to provide further simplification in terms of operating devices and equipment for the medical personnel, a device (10) for controlling a 2D application in a mixed reality application is provided. The device comprises a 2D application interface (12), a processor (14), a mixed reality application interface (16) and a control interface (18). The 2D application interface receives data representative of a 2D application. The application comprises interactive elements providing commands for user control of the 2D application. The processor identifies and extracts the interactive elements based on the data representative of the 2D application. The processor also translates the interactive elements to user interface features of a mixed reality display application.

Abstract: System and related method for supporting a balloon catheter (BC) procedure. The system comprises an input interface (IN) for receiving input data. The input data comprises i) image data acquired of a balloon catheter in a vessel of a patient (PAT), and ii) one or more pressure readings collected by a pressure sensor (S) of the balloon catheter (BC). A trained machine learning module (MLM) is configured to predict, based on the input data, a prediction result including an event in relation to i) the balloon catheter and/or ii) a section of a vessel in which the balloon catheter is residable.

Abstract: A computer-implemented method of providing a neural network for predicting a three-dimensional shape of an interventional device disposed within a vascular region, includes: training (S140) a neural network (140) to predict, from received X-ray image data (120) and received volumetric image data (110), a three-dimensional shape of the interventional device constrained by the vascular region (150). The training includes constraining the adjusting of parameters of the neural network such that the three-dimensional shape of the interventional device predicted by the neural network (150) fits within the three-dimensional shape of the vascular region represented by the received volumetric image data (110).

Abstract: A system and method for providing guidance in an ultrasound imaging procedure involves presenting graphical user interface elements overlaid onto an external image of the subject's body. The system includes at least one camera for capturing the external image of the subject. The system may utilize trained artificial neural network(s) to identify locations in the external image that correspond to scanning zones of a scanning protocol. A probe placement marker is overlaid at each identified location to indicate the position of the probe's face onto the subject's body for acquiring a target view of the internal anatomy. Additional graphical elements may be presented via the guidance interface and results findings may optionally be overlaid onto the external image of the subject at the completion of the scan. In embodiments herein, imaging guidance is provided solely via the external images of the subject without displaying the ultrasound image data.

Abstract: The present disclosure relates to a method for a federated learning system in hybrid operating room environment, in which a global machine-learning model learns from a subset of local participants selected based on predefined selection criteria. The local participants amongst the subset respectively provide an update matrix and a quality score to train the global machine-learning model. A combination of the update matrix and the quality score is applied to the global machine-learning model, in which each update matrix is weighed based on the quality score of the local participant.

Abstract: A method and system are provided for performing an interventional procedure by a user on a subject.

Abstract: To improve noise situation for staff during medical procedures, a device (10) for sound management in an operating room is provided that comprises an input (12), a processor (14) and a sound generation output (16). Besides audio data from a plurality of sources positioned in the operating room receiving audio input of the operating room, also environmental context information of the plurality of sources within the operating room is provided together with priority weighting for the sources within the operating room for at least one user. Sound parts of the audio data are identified and assigned to the sources based on the environmental context information. Some sound parts are modified based on the priority weighting to generate user-assigned modified audio data for sound generation. The user-assigned modified audio data is provided to the at least one user for providing an adapted user-specific sound input.

Abstract: A system includes a processor circuit configured for communication with an external imaging device. The processor circuit receives, from the external imaging device, an image comprising a blood vessel within a patient. The processor circuit determines, using the image, a first location of the blood vessel with a restriction in blood flow caused by compression of the blood vessel by an anatomical structure within the patient and different than the blood vessel. The processor circuit generates a first graphical representation associated with the restriction. The processor circuit outputs, to a display in communication with the processor circuit, a screen display. The screen display includes the image and the first graphical representation at the first location of the blood vessel in the image.

Abstract: 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 (?p) 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.

Abstract: 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.

Abstract: Various embodiments of the present disclosure include a C-arm registration system employing a controller (70) for registering a C-arm (60) to a X-ray ring marker (20). The X-ray ring marker (20) includes a coaxial construction of a chirp ring (40) and a centric ring (50) on an annular base (30). In operation, the controller (70) acquires a baseline X-ray image illustrative of the X-ray ring marker (20) within a baseline X-ray projection by the C-arm (60) at a baseline imaging pose, derives baseline position parameters of the X-ray ring marker (20) within the baseline X-ray projection as a function of an illustration of the centric ring (50) within the baseline X-ray image, and derives a baseline twist parameter of the X-ray ring marker (20) within the baseline X-ray projection as a function of the baseline position parameters and of an illustration of the chirp ring (40) within the baseline X-ray image.