Abstract: A method, device, and system for evaluating a vessel of a patient, and in particular the hemodynamic impact of a stenosis within the vessel of a patient. Proximal and distal pressure measurements are made using first and second instrument while the first instrument is moved longitudinally through the vessel from a first position to a second position and the second instrument remains in a fixed longitudinal position within the vessel. A series of pressure ratio values are calculated, and a pressure ratio curve is generated. One or more stepped change in the pressure ratio curve are then identified and/or located using an Automatic Step Detection (ASD) process and/or algorithm.

Abstract: A method, device, and system for evaluating a vessel of a patient, and in particular the hemodynamic impact of a stenosis within the vessel of a patient. Proximal and distal pressure measurements are made using first and second instrument while the first instrument is moved longitudinally through the vessel from a first position to a second position and the second instrument remains in a fixed longitudinal position within the vessel. A series of pressure ratio values are calculated, and a pressure ratio curve is generated. One or more stepped change in the pressure ratio curve are then identified and/or located using an Automatic Step Detection (ASD) process and/or algorithm.

Abstract: Methods include capturing intravascular ultrasound images. A drive motor is used to actively drive an ultrasound transducer at a set rotation speed. A temporary sensing window is created in which the ultrasound transducer is driven with a fixed drive signal. A plurality of signals from are received the ultrasound transducer during the temporary sensing window.

Abstract: A method, device, and system for evaluating a vessel of a patient, and in particular the hemodynamic impact of a stenosis within the vessel of a patient. Proximal and distal pressure measurements are made using first and second instrument while the first instrument is moved longitudinally in the vessel and the second instrument remains in a fixed position. A series of pressure ratio values are calculated, and a pressure ratio curve is generated. The pressure ratio curve may be output to a display. One or more stepped change in the pressure ratio curve are identified using an Automatic Step Detection algorithm. An image is obtained showing the position of the first instrument within the vessel that corresponds to the identified stepped change. The image is registered to the identified stepped change in the pressure ratio curve. The image may be output to the display.

Abstract: The present disclosure provides apparatus and methods to train ML models to infer intravascular images with noisy artifacts removed from intravascular images comprising the noisy artifacts. Further, the disclosure provides a computing system that can be part of an intravascular image capture device configured to infer cleaned images from raw images captured by the intravascular imaging device.

Abstract: The present disclosure provides to match side branches from different imaging modalities. Side branches detected from a series of intravascular images can be matched with side branches detected from an extravascular image. The pairs of matches can be determined based on an initially identified reference pair and then a technique that accounts for the characteristics of the side branch, such as, the diameter, the orientation, the relative size, and/or the relative order.

Abstract: The present disclosure provides to co-register intravascular images of a vessel with one or more extravascular images of the vessel in real-time, such as, during acquisition of the extravascular images. Key points in a first frame of the extravascular images can be identified and then tracked across other frames of the extravascular images. The intravascular images can be co-registered to a frame (or frames) of the extravascular images based on the locations of the key points in the frame.

Abstract: The present disclosure provides to generate a side branch mask from an extravascular image of a vessel using an ensemble of machine learning (ML) models. The side branch mask can be generated by inferring, using several initial ML models, indications of side branches from an image frame and inferring, using a post-processing ML model, the side branch mask from the indication of side branches.

Abstract: The present disclosure provides to generate a 3D visualization of a vessel from intravascular ultrasound (IVUS) images. In particular, the present disclosure provides to reduce jitter between frames of an IVUS recording to provide a smoother appearance of a longitudinal view of the vessel from the IVUS image frames and to construct a 3D visualization of the vessel from the jitter compensated IVUS image frames.

Abstract: A method, device, and system for evaluating a vessel of a patient, and in particular the hemodynamic impact of a stenosis within the vessel of a patient. Proximal and distal pressure measurements are made using first and second instrument while the first instrument is moved longitudinally in the vessel and the second instrument remains in a fixed position. A series of pressure ratio values are calculated, and a pressure ratio curve is generated. The pressure ratio curve may be output to a display. One or more stepped change in the pressure ratio curve are identified using an Automatic Step Detection algorithm. An image is obtained showing the position of the first instrument within the vessel that corresponds to the identified stepped change. The image is registered to the identified stepped change in the pressure ratio curve. The image may be output to the display.

Abstract: The present disclosure provides to generate a 3D visualization of a vessel from intravascular ultrasound (IVUS) images. In particular, the present disclosure provides to reduce jitter between frames of an IVUS recording to provide a smoother appearance of a longitudinal view of the vessel from the IVUS image frames and to construct a 3D visualization of the vessel from the jitter compensated IVUS image frames.

Abstract: A disinfection robot, including a top plate, a bottom plate and a swinging mechanism. The top plate is located above the bottom plate. The swinging mechanism is located between the top plate and the bottom plate, and includes a swinging arm, a hinge shaft, a swinging gear, two swinging units and a driving unit. The swinging arm is hingedly connected to the bottom plate through the hinge shaft. The two swinging units are symmetrically arranged on both sides of the swinging gear. Each of the two swinging units includes a swinging shaft, an incomplete gear and a linear driver. The driving unit is configured to simultaneously drive swinging shafts of the two swinging units to rotate in opposite directions. The disinfection robot can adjust a swing amplitude of the swinging arm according to a size of a disinfection site.

Abstract: The present disclosure provides devices and methods to identify locations of side branches in a series of intravascular images (e.g., a pre-treatment IVUS pullback, a post-treatment IVUS pullback, or the like) to assist with co-registering the IVUS images with an extravascular image (e.g., angiogram, or the like) or with another set of IVUS images. The present disclosure further provides devices and methods for training a machine learning (ML) model to infer side branch locations from IVUS images and an analytic algorithm for extracting frames from the IVUS images representing side branches.

Abstract: This disclosure provides alternative medical imaging systems and methods for vascular imaging co-registration. Methods include obtaining extravascular imaging data of a portion of a blood vessel including an extravascular image showing an intravascular imaging device disposed within the vessel with an imaging element disposed at a starting location for a translation procedure. The extravascular image also includes an extravascular contrast image showing the portion of the blood vessel with contrast showing a visualized anatomical landmark. Intravascular imaging data is obtained during the translation procedure that includes one or more intravascular images showing a detected anatomical landmark. The starting location and the ending location of the imaging element on the extravascular imaging data is marked, and the predicted location of the detected anatomical landmark on the extravascular imaging data is marked.

Abstract: The present disclosure provides to process intravascular ultrasound (IVUS) images from different runs through a vessel to generate a mapping between frames of each IVUS run and to generate a graphical user interface (GUI) to graphically present the IVUS runs in relationship to each other. In some examples, a vessel fiducial is identified in a frame of each IVUS run and one or both runs are offset in time, distance, and/or angle to align the frames with the identified vessel fiducial. Further, the disclosure provides to angularly align intravascular images to a viewing perspective of an external image of the vessel.

Abstract: The present disclosure provides to process intravascular ultrasound (IVUS) images from different runs through a vessel to generate a mapping between frames of each IVUS run and to generate a graphical user interface (GUI) to graphically present the IVUS runs in relationship to each other. In some examples, a vessel fiducial is identified in a frame of each IVUS run and one or both runs are offset in time, distance, and/or angle to align the frames with the identified vessel fiducial. Further, the disclosure provides to angularly align intravascular images to a viewing perspective of an external image of the vessel.

Abstract: The present disclosure provides devices and methods to process intravascular images of a vessel of one imaging modalities and to generate, extract and adapt features from another imaging modality to generate a hybrid image comprising features from both modalities. The disclosure provides devices and methods to train deep generative models to adapt domain specific features from one intravascular imaging modality (e.g., OCT, or the like) to another intravascular imaging modality (e.g., IVUS) and integrate the adapted features into the images from the other intravascular imaging modality.

Abstract: A disinfection robot, including a top plate, a bottom plate and a swinging mechanism. The top plate is located above the bottom plate. The swinging mechanism is located between the top plate and the bottom plate, and includes a swinging arm, a hinge shaft, a swinging gear, two swinging units and a driving unit. The swinging arm is hingedly connected to the bottom plate through the hinge shaft. The two swinging units are symmetrically arranged on both sides of the swinging gear. Each of the two swinging units includes a swinging shaft, an incomplete gear and a linear driver. The driving unit is configured to simultaneously drive swinging shafts of the two swinging units to rotate in opposite directions. The disinfection robot can adjust a swing amplitude of the swinging arm according to a size of a disinfection site.

Abstract: The present disclosure provides apparatus and methods to generate a three-dimensional (3D) model of the physiology of a vessel from a single angiographic image and a series of intravascular images as well as another physical characteristic of the vessel, such as, for example, pressure.

Abstract: This disclosure provides alternative medical imaging systems and methods for vascular imaging co-registration. Methods include obtaining extravascular imaging data of a portion of a blood vessel including an extravascular image showing an intravascular imaging device disposed within the vessel with an imaging element disposed at a starting location for a translation procedure. The extravascular image also includes an extravascular contrast image showing the portion of the blood vessel with contrast showing a visualized anatomical landmark. Intravascular imaging data is obtained during the translation procedure that includes one or more intravascular images showing a detected anatomical landmark. The starting location and the ending location of the imaging element on the extravascular imaging data is marked, and the predicted location of the detected anatomical landmark on the extravascular imaging data is marked.