Abstract: 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.

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: An augmented reality interventional system which provides contextual overlays (116) to assist or guide a user (101) or enhance the performance of the interventional procedure by the user that uses an interactive medical device (102) to perform the interventional procedure. The system includes a graphic processing module (110) that is configured to generate at least one contextual overlay on an augmented reality display device system (106). The contextual overlays may identify a component (104) 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.

Abstract: A controller (220) 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 (221) that stores instructions and a processor (222) that executes the instructions. The instructions cause a system (200) that includes the controller (220) to implement a process that includes obtaining (S320) the location of the interventional medical device (201) and obtaining (S330) imagery of a volume that includes the interventional medical device. The process also includes applying (S340), based on the location of the interventional medical device (201), image processing to the imagery to identify the interventional medical device (201) including the shape of the interventional medical device (201). The process further includes (S350) segmenting the interventional medical device (201) to obtain a segmented representation of the interventional medical device (201).

Abstract: A robotic device (160) is configured to operate an interventional device (101) comprising an outer device and an inner device movably positioned with the outer device. A method for controlling the robotic device (160) includes receiving image data from an image of a portion of the interventional device (101) and a branched intersection of a plurality of branches of an anatomical structure. The method also includes analyzing the image data to measure at least one of a location or an orientation of a distal portion of the outer device and of a distal portion of the inner device in the image. The method further includes determining a path for the interventional device (101), and controlling, based on a plurality of modules of predefined motions stored in a memory (151), the robotic device (160) to operate the interventional device (101) through the path by a sequence of the predefined motions. The method further includes displaying the path on a display (180).

Abstract: A computer-implemented method of identifying a vascular access site for inserting an interventional device in order to reach a target site in a vasculature, is provided. The method includes computing a success metric for multiple potential vascular access sites. The success metric represents an ease of navigating the interventional device from the vascular access site to the target site via the vasculature, and is computed based on image data. A vascular access site is identified based on the computed success metrics.

Abstract: An augmented reality interventional system which provides contextual overlays (116) to assist or guide a user (101) or enhance the performance of the interventional procedure by the user that uses an interactive medical device (102) to perform the interventional procedure. The system includes a graphic processing module (110) that is configured to generate at least one contextual overlay on an augmented reality display device system (106). The contextual overlays may identify a component (104) 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.

Abstract: 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.

Abstract: Computer implemented system (C-SYS) and related methods for controlling one or more devices in a procedure performed in relation to a patient. A shape sensing system is used to acquire shape measurement data. A predictor logic (PL) predicts based on this shape measurements an anatomical location, procedure type, or phase of such a procedure. The System may support navigation of a device in the patient. No imagery need to be acquired during the procedure for navigation. The predictor logic (PL) may be based on a machine learning model. Systems (TS, TDS) and methods for training such as model and for generating training data are also described herein.

Abstract: A computer-implemented method of providing navigation guidance for navigating an interventional device within the anatomy, includes: receiving (S110) interventional device shape data (110) representing a shape of the interventional device (120) at one or more time steps (t1 . . . n), the time steps including at least a current time step (tn): inputting (S120) the interventional device shape data (110) into a neural network (130) trained to predict, from the interventional device shape data (110), a future position (140) of one or more portions of the interventional device (120) at one or more future time steps (tn+1 . . . n+k), and a corresponding confidence estimate (150) for the one or more future positions (140); and displaying (S130) the predicted one or more future positions (140), and the corresponding predicted confidence estimate (150).

Abstract: 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.

Abstract: Various embodiments of the present disclosure encompass an optical shape sensing registration system employing an optical shape sensing guidewire (30) translatable within an over-the-wire device (40), and further employing an optical shape sensing registration controller (20) for controlling an autonomous device registration of an over-the-wire device (40). In operation, the optical shape sensing registration controller (20) automatically detects one or more sensing features of the optical shape sensing guidewire (30) (e.g., shape, curvature, temperature, vibration, strain, etc.) from an optical shape sensing of a translation of the optical shape sensing guidewire (30) within the over-the-wire device (40), and then automatically determines one or more registration characteristics of the over-the-wire device (40) (e.g., a device type, a length, a diameter, a color, a hub type, treatment device(s), anatomical image(s), anatomical model(s), anatomical location(s), procedure type etc.

Abstract: An electromagnetic navigation device for guiding and tracking an interventional tool (40) within an anatomical region. The electromagnetic navigation device employs a guidewire (20) insertable into the anatomical region, and a hub (30) translatable and/or rotatable in conjunction with the interventional tool (40) relative to the guidewire (20). In operation, the guidewire (20) 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 (20) relative to the anatomical region, and the hub (30) includes a tracking electromagnetic sensor (31) generating tracking data informative of an electromagnetic sensing of a position and/or an orientation of the hub (30) relative to the guidewire (20). Responsive to the electromagnetic sensing data, a navigation controller (76) controls a determination of a position and/or an orientation of the interventional tool (40) relative to the guidewire (20).

Abstract: 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.

Abstract: 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 (105) 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.

Abstract: Various embodiments of the present disclosure encompass an optical shape sensing registration system employing an optical shape sensing guidewire (30) translatable within an over-the-wire device (40), and further employing an optical shape sensing registration controller (20) for controlling an autonomous device registration of an over-the-wire device (40). In operation, the optical shape sensing registration controller (20) automatically detects one or more sensing features of the optical shape sensing guidewire (30) (e.g., shape, curvature, temperature, vibration, strain, etc.) from an optical shape sensing of a translation of the optical shape sensing guidewire (30) within the over-the-wire device (40), and then automatically determines one or more registration characteristics of the over-the-wire device (40) (e.g., a device type, a length, a diameter, a color, a hub type, treatment device(s), anatomical image(s), anatomical model(s), anatomical location(s), procedure type etc.

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: An augmented reality trigger system (10) comprising a primary augmented reality device (30) and a trigger action controller (40) for implementing an augmented reality trigger method based on a medical tool (20) and/or a tool identifier (21) associated with the medical tool (20). In operation, the primary augmented reality device (30) generates a camera image of the real world, which may or may not at any time include the medical tool (20) and/or the tool identifier (21). The trigger action controller (40) recognizes a generation by the primary augmented reality device (30) of the camera image of the real world including the medical tool (20) and/or the tool identifier (21) and in response to such recognition, triggers a medical procedure action by the primary augmented reality device (30) and/or a medical device (50) in support of a medical procedure involving the medical tool (20).

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.