Abstract: An ultrasound portable system (10) and method comprise acquiring ultrasound images, via a smart device (14) and ultrasound probe (20), over a portion of an ultrasound scan protocol and/or ultrasound exam and presenting at least one acquired ultrasound image in an image space portion (24) of a display (16). Digital images are acquired, via a camera (18), of at least a device operator's head pose, and left and right eyes within respective digital images. Eye-gaze focus point locations on the smart device are determined within a combination of image space and control space (26) portions of the display via an image processing framework (36) configured to track the device operator's gaze and eye movement determined from the acquired digital images.

Abstract: Ultrasound image devices, systems, and methods are provided. An ultrasound imaging system, comprising an array of acoustic elements configured to transmit ultrasound energy into an anatomy in accordance with a first preset acquisition setting, and to receive ultrasound echoes associated with the anatomy; and a processor circuit in communication with the array of acoustic elements and configured to receive, from the array, ultrasound channel data corresponding to the received ultrasound echoes; generate a first set of beamformed data by applying a predictive network to the ultrasound channel data, wherein the first set of beamformed data is associated with a second preset acquisition setting different than the first preset acquisition setting; generate an image of the anatomy from the first set of beamformed data; and output, to a display in communication with the processor circuit, the image of the anatomy.

Abstract: A system and method for ultrasound imaging may involve the use of an ultrasound probe and a processor coupled to the probe and to a source of previously-acquired ultrasound image data. The processor may be configured to receive patient identification information (e.g., responsive to user input and/or supplemented by additional information such a photo of the patient), to determine whether the patient identification information identifies a recurring patient, and if so, to retrieve, from the source of previously-acquired ultrasound image data, previous ultrasound images associated with the recurring patient. The processor may be further configured to generate a current ultrasound image based on signals received from the prove and to apply a neural network to the current ultrasound image and the previous ultrasound images to identify a matching pair of images, such that imaging settings from the matched previous image may be applied to the system for subsequent imaging.

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: A system and method for determining position information for a device within a body of a subject. The device outputs and/or routes an acoustic signal with a frequency of no more than 20 kHz, which is received by at least a first sensor, positioned externally to the body of the subject. A processing system receives the received signal from the sensor, obtains a value for one or more parameters of the received signal and processes the value(s) to determine position information for the device.

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 computer-implemented method of providing an endovascular coil specification of an endovascular coil for treating an aneurysm in a coil embolization procedure, includes: inputting (S120) X-ray image data (110), comprising one or more X-ray images including an aneurysm (120), into a neural network (130, 230) trained to predict, from the X-ray image data (110), endovascular coil data (140, 150) of an endovascular coil for treating the aneurysm (120); and outputting (S130) the endovascular coil data (140, 150) to provide the endovascular coil specification.

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 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: 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 force sensed surface scanning system (20) employs a scanning robot (41) and a surface scanning controller (50). The scanning robot (41) includes a surface scanning end-effector (43) for generating force sensing data informative of a contact force applied by the surface scanning end-effector (43) to an anatomical organ. In operation, the surface scanning controller (50) controls a surface scanning of the anatomical organ by the surface scanning end-effector (43) including the surface scanning end-effector (43) 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 (43) indicating a defined surface deformation offset of the anatomical organ.

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: 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: Ultrasound image devices, systems, and methods are provided. In one embodiment, a method of automated medical examination, comprising receiving, from an imaging device, a first image representative of a subject's body while the imaging device is positioned at a first imaging position with respect to the subject's body; determining a first motion control configuration for repositioning the imaging device from the first imaging position to a second imaging position based on a first predictive network, the first image, and a target image view including a clinical property; and repositioning, by a robotic system coupled to the imaging device, the imaging device to the second imaging position based on the first motion control configuration.

Abstract: Systems and methods of determining a second medical professional having experience relevant to a medical procedure being performed by a first medical professional. A method of determining a second medical professional having experience relevant to a medical procedure being performed by a first medical professional comprises: obtaining a first multi-dimensional embedding vector related to the medical procedure being performed by the first medical professional. The method then comprises obtaining a plurality of candidate multi-dimensional embedding vectors related to medical experience of a corresponding plurality of candidate medical professionals, and using a model to determine the second medical professional from the plurality of candidate medical professionals, based on the first multi-dimensional embedding vector and the plurality of candidate multi-dimensional embedding vectors.

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: Methods, Apparatuses, and Systems of operating a laser atherectomy system to perform an endoscopic atherectomy procedure within a vessel at a therapeutic region of an anatomical condition by use of an atherectomy laser device coupled to an ultrasound imaging probe. The atherectomy laser device operates to generate photoacoustic signals from a light source of the atherectomy laser device to for guidance within the vessel and to characterize tissue about the therapeutic region by delivery of pulsed wavelengths within the vessel, and to perform operations of tissue ablation directed to the anatomical condition. This enables guidance of the atherectomy laser device by feedback from the viewing of photoacoustic images based on photoacoustic signals generated the atherectomy laser device and created in response to changes in acoustic intensity due to changes of optical wavelength monitored by the ultrasound imaging probe.