Catheter-based intravascular imaging with identification of vessel blockage and / or stent irregularity

Abstract

An apparatus includes a processor circuit that controls an intravascular imaging catheter to obtain a sequence of intravascular images for live imaging while positioned within a diseased blood vessel. In one or multiple of the intravascular images, the processor identifies an anatomical feature associated with the disease and / or an irregularity associated with a stent positioned within the blood vessel for treatment of the disease. The processor outputs a screen display including the intravascular image and a visual representation of the anatomical feature or the irregularity associated with the stent. The identification is performed while the live imaging is ongoing, and the screen display is outputted while the live imaging is ongoing, such that the visual representation displayed simultaneously as the intravascular image is displayed to the user for the first time.

Description

CATHETER-BASED INTRAVASCULAR IMAGING WITH IDENTIFICATION OF VESSEL BLOCKAGE AND / OR STENT IRREGULARITYTECHNICAL FIELD

[0001] The present disclosure relates generally to intravascular imaging (e.g., intravascular ultrasound (IVUS), optical coherence tomography (OCT), etc.) using an intravascular imaging catheter for generating images of a blood vessel. In particular, intravascular imaging and automatic identification of calcium, stent irregularities, in-stent restenosis, thrombus, and / or thin cap fibroatheroma is provided, e.g., to provide user guidance associated with therapeutic treatment of disease in the blood vessel.BACKGROUND

[0002] Intravascular imaging (IVI) (such as intravascular ultrasound (IVUS) or optical coherence tomography (OCT) imaging) is widely used in interventional cardiology as a diagnostic tool for assessing a diseased vessel, such as an artery, within the human body to determine the need for treatment, to guide the intervention, and / or to assess its effectiveness. An IVI device including one or more ultrasound transducers is passed into the vessel and guided to the area to be imaged. The transducers emit ultrasonic energy in order to create an image of the vessel of interest. Ultrasonic waves are partially reflected by discontinuities arising from tissue structures (such as the various layers of the vessel wall), red blood cells, and other features of interest. Echoes from the reflected waves are received by the transducer and passed along to an IVI (e.g., IVUS or OCT) imaging system. The imaging system processes the received ultrasound echoes to produce a cross-sectional image of the vessel where the device is placed.

[0003] Many systems for intravascular imaging require a complete pullback before any features shown in the intravascular images can be identified.

[0004] The information included in this Background section of the specification, including any references cited herein and any description or discussion thereof, is included for technical reference purposes only and is not to be regarded as subject matter by which the scope of the disclosure is to be bound.SUMMARY

[0005] Disclosed herein are systems, devices, and methods for intravascular imaging and automatic identification of disease features of a blood vessel. The intravascular feature imaging and detection system disclosed herein provides automatic detection of intravascular features such as calcium, stent edge dissections, stent malapposition, plaque protrusion through a stent, and instent restenosis. First, this automatic detection can be performed in real time during live imaging, such as while a user selectively moves an intravascular imaging catheter through the blood vessel or keeps the catheter stationary. Second, this detection includes features that have not been previously automatically detected, such as calcified nodules, plaque protrusion through the stent, dissections, etc. Automating the detection and quantification of features visible by intravascular imaging saves procedure time and makes intravascular imaging easier to use.

[0006] This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to limit the scope of the claimed subject matter. A more extensive presentation of features, details, utilities, and advantages of the intravascular feature imaging and detection system, as defined in the claims, is provided in the following written description of various aspects of the disclosure and illustrated in the accompanying drawings.BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Illustrative aspects of the present disclosure will be described with reference to the accompanying drawings, of which:

[0008] Figure 1 is a diagrammatic schematic view of an intraluminal imaging system, according to aspects of the present disclosure.

[0009] Figure 2 is a schematic diagram of a processor circuit, according to aspects of the present disclosure.

[0010] Figure 3 is a lateral cross-sectional or tomographic view of a blood vessel, according to aspects of the present disclosure.

[0011] Figure 4 is a lateral cross-sectional or tomographic view of a blood vessel, according to aspects of the present disclosure.

[0012] Figure 5A is a lateral cross-sectional or tomographic view of a blood vessel, according to aspects of the present disclosure.

[0013] Figure 5B is a lateral cross-sectional or tomographic view of a blood vessel, according to aspects of the present disclosure.

[0014] Figure 6 is a lateral cross-sectional or tomographic view of a blood vessel, according to aspects of the present disclosure.

[0015] Figure 7 is a lateral cross-sectional or tomographic view of a blood vessel, according to aspects of the present disclosure.

[0016] Figure 8 is a lateral cross-sectional or tomographic view of a blood vessel, according to aspects of the present disclosure.

[0017] Figure 9 is a lateral cross-sectional or tomographic view of a blood vessel, according to aspects of the present disclosure.

[0018] Figure 10 is a lateral cross-sectional or tomographic view of a blood vessel, according to aspects of the present disclosure.

[0019] Figure 11 is a lateral cross-sectional or tomographic view of a blood vessel, according to aspects of the present disclosure.

[0020] Figure 12 is a schematic, diagrammatic representation, in block diagram form, of an intravascular feature imaging and detection system, according to aspects of the present disclosure.

[0021] Figure 13 is a schematic, diagrammatic representation, in block diagram form, of an intravascular feature imaging and detection system, according to aspects of the present disclosure.

[0022] Figure 14 is a schematic, diagrammatic representation, in block diagram form, of an intravascular feature imaging and detection system, according to aspects of the present disclosure.

[0023] Figure 15 is a schematic, diagrammatic representation, in block diagram form, of an intravascular feature imaging and detection system, according to aspects of the present disclosure.

[0024] Figure 16 is a schematic, diagrammatic representation, in block diagram form, of an intravascular feature imaging and detection system, according to aspects of the present disclosure.

[0025] Figure 17 is a schematic, diagrammatic representation, in block diagram form, of a training system for a machine learning model, according to aspects of the present disclosure.

[0026] Figure 18 is a schematic, diagrammatic representation, in block diagram form, of a training system for a machine learning model, according to aspects of the present disclosure.

[0027] Figure 19 is a diagrammatic schematic of a deep learning network configuration 1900, according to aspects of the present disclosure.

[0028] Figure 20 is an example screen display of a radial and / or tomographic cross-sectional view and longitudinal view with indicators for calcified nodule detection, according to aspects of the present disclosure.

[0029] Figure 21 is an example screen display of a radial and / or tomographic cross-sectional view and longitudinal view with indicators for cracked calcium detection, according to aspects of the present disclosure.

[0030] Figure 22 is an example screen display of a radial and / or tomographic cross-sectional view, longitudinal view, and x-ray frame with contrast with indicators for calcium detection, according to aspects of the present disclosure.

[0031] Figure 23 is an example screen display of a radial and / or tomographic cross-sectional view and longitudinal view with indicators for stent malapposition detection, according to aspects of the present disclosure.

[0032] Figure 24 is an example screen display of a radial and / or tomographic cross-sectional view and longitudinal view with indicators for plaque protrusion detection, according to aspects of the present disclosure.

[0033] Figure 25 is an example screen display of a radial and / or tomographic cross-sectional view and longitudinal view with indicators for dissection detection, according to aspects of the present disclosure.

[0034] Figure 26 is an example screen display of a radial and / or tomographic cross-sectional view and longitudinal view with indicators for in-stent restenosis detection, according to aspects of the present disclosure.

[0035] Figure 27 is an example screen display of a radial and / or tomographic cross-sectional view and longitudinal view with indicators for thrombus detection, according to aspects of the present disclosure.

[0036] Figure 28 is an example screen display of a radial and / or tomographic cross-sectional view and longitudinal view with indicators for thin cap fibroatheroma (TCFA) detection, according to aspects of the present disclosure.DETAILED DESCRIPTION

[0037] The present application provides devices, systems, and methods for automatic detection of intravascular features such as calcium, stent edge dissections, stent malapposition, plaque protrusion through a stent, and in-stent restenosis. Automating the detection and quantification of features visible by intravascular imaging saves procedure time and makes intravascular imaging easier to use. Additionally, current intravascular imaging automated feature identification systems work on recorded images or pullback image sequences. However, IVI is useful as a live imaging modality, as the user may make observations and decisions based on real-time imaging. The present disclosure provides automated feature detection and image annotation that function in real time, during live imaging. This may involve artificial intelligence (Al), classical image recognition or feature recognition techniques, or combinations thereof.

[0038] The present disclosure includes software-based calcium detection and quantification. Identification of the presence of calcium may be based on its characteristics, including brightness, saturation, oversaturation, presence of acoustic shadowing beyond the calcium, typical calcium geometry (arcs of calcium, possibly with the presence of calcified nodules), and measuring the arc size (angle and depth) within a single frame. In addition, the smoothness of the surface has been shown to be correlated with the age of the calcium. New calcium tends to be smoother, and also thinner (and therefore less likely to prevent stent expansion and / or to require pre-stent atherectomy. Smooth calcium produces unique reverberation patterns in IVUS that can be automatically detected.

[0039] The intravascular feature imaging and detection system can also detect whether or not the calcium is cracked (for example, when checking on the efficacy of atherectomy). Softwarebased tracking of the shape of the calcium throughout the cardiac motion can show that cracks based direct observation of the cracks or by observing disjointed motion of the calcium. Live imaging automation is particularly useful for detecting cracked calcium because it may be useful to view a location in real time throughout multiple cardiac cycles, check for cracking, and then move the catheter to a check a different location.

[0040] Post pullback, the intravascular feature imaging and detection system can measure the continuous length of calcium across multiple frames. The length information can come from a pullback device that records length or speed, and / or from angiographic co-registration.

[0041] The present disclosure also provides software-based stent irregularity detection. In an example, the software can automatically identify and measure gaps between the stent strut and the lumen border (e.g., malapposition). This may for example include the detection of a gap between the stent and the vessel wall, and / or the use of Doppler-based blood flow imaging or similar blood flow data to identify the presence of blood flow outside the stent.

[0042] The present disclosure also provides plaque protrusion detection within a stent. This may be done for example by software examination of the lumen area for tissue rather than blood flow. This may include the use of Doppler-based blood flow imaging or similar blood flow data to identify whether blood is flowing or if the protrusion is made up of plaque or tissue (e.g., no flow at the location of the protrusion).

[0043] The present disclosure also provides automatic detection of dissections (e.g., whether a dissection extends to the media of the blood vessel) by one or more of the following methods: lumen geometry analysis, blood flow visualization or blood flow data, and / or tracking the motion of the dissection (the flapping of the separated tissue), whether in real time or on a recorded image sequence.

[0044] The present disclosure also provides software-based analysis of in-stent restenosis. This may for example involve comparison of the stent size to the vessel size (e.g., either the local vessel size within an area of the stent, a nearby location where the vessel is more easily visible, or from reference area(s) beyond the end of the stent).

[0045] The present disclosure also provides automatic plaque morphology analysis, particularly looking for neointimal hyperplasia or neoatherosclerosis.

[0046] The present disclosure also provides software-based identification of thrombus and thin cap fibroatheroma, which can include automated comparison of grayscale IVUS and Doppler-based blood flow imaging (or similar blood flow data), and / or identification and / or measurement of a thin cap.

[0047] Examples of tissue characterization or virtual histology, such as to identify and / or distinguish between different types of plaque (e.g., necrotic core, dense calcium, fibrous, fibro- fatty), are described in U.S. Patent No. 8,449,465, U.S. Patent No. 9,615,878, U.S. Patent No. 9,592,027, each of which is hereby incorporated by reference as though fully set forth herein.

[0048] Aspects of detecting a lumen border and / or a vessel border and / or calculating lumen / vessel dimensions based on the detected borders are described in U.S. Publication No.2007 / 0201736, U.S. Patent No. 11,272,845, U.S. Patent No. 7,463,759, U.S. Patent No.9,295,447, U.S. Patent No. 11,744,527, U.S. Publication No. 2020 / 0029932, U.S. Publication No. 2019 / 0282211, each of which is hereby incorporated by reference as though fully set forth herein.

[0049] Examples of calculation or estimation of pullback speed can be found for example in U.S. Application No. 16 / 542,001, filed August 15, 2019, and U.S. Application No. 16 / 662,847, filed October 24, 2019, incorporated by reference as though fully set forth herein.

[0050] The devices, systems, and methods described herein can include one or more features described in U.S. Provisional App. No. 63 / 600,110, filed November 17, 2023, which is hereby incorporated by reference in its entirety as though fully set forth herein.

[0051] The devices, systems, and methods described herein can include one or more features described in U.S. Provisional App. No. 62 / 750,983, filed 26 October 2018, U.S. Provisional App. No. 62 / 751,268, filed 26 October 2018, U.S. Provisional App. No. 62 / 751,289, filed 26 October 2018, U.S. Provisional App. No. 62 / 750,996, filed 26 October 2018, U.S. Provisional App. No. 62 / 751,167, filed 26 October 2018, and U.S. Provisional App. No. 62 / 751,185, filed 26 October 2018, each of which is hereby incorporated by reference in its entirety as though fully set forth herein.

[0052] The devices, systems, and methods described herein can also include one or more features described in U.S. Provisional App. No. 62 / 642,847, filed March 14, 2018, U.S. Provisional App. No. 62 / 712,009, filed July 30, 2018, U.S. Provisional App. No. 62 / 711,927, filed July 30, 2018, and U.S. Provisional App. No. 62 / 643,366, filed March 15, 2018, each of which is hereby incorporated by reference in its entirety as though fully set forth herein.

[0053] The present disclosure aids substantially in diagnosing disease conditions of vascular tissue, by improving a clinician’s ability to identify, in real time, features of vascular anatomy or interventional devices that may require treatment or other attention. Implemented on a processor in communication with an intravascular imaging catheter, the intravascular feature imaging and detection system disclosed herein provides practical, real-time diagnosis or diagnostic assistance for the vascular conditions described herein, during live intravascular imaging. This unconventional approach improves the functioning of the intravascular imaging system, by providing a capability to automatically identify the imaged structures.

[0054] The intravascular feature imaging and detection system may be implemented as a process at least partially viewable on a display, and operated by a control process executing on a processor that accepts user inputs from a keyboard, mouse, or touchscreen interface, and that is in communication with one or more sensors. In that regard, the control process performs certain specific operations in response to different inputs or selections made at different times or sensor locations. Outputs of the intravascular feature imaging and detection system may be printed, shown on a display, or otherwise communicated to human operators. Certain structures, functions, and operations of the processor, display, sensors, and user input systems are known in the art, while others are recited herein to enable novel features or aspects of the present disclosure with particularity.

[0055] These descriptions are provided for exemplary purposes only, and should not be considered to limit the scope of the intravascular feature imaging and detection system. Certain features may be added, removed, or modified without departing from the spirit of the claimed subject matter.

[0056] For the purposes of promoting an understanding of the principles of the present disclosure, reference will now be made to the aspects illustrated in the drawings, and specific language will be used to describe the same. It is nevertheless understood that no limitation to the scope of the disclosure is intended. Any alterations and further modifications to the described devices, systems, and methods, and any further application of the principles of the present disclosure are fully contemplated and included within the present disclosure as would normally occur to one skilled in the art to which the disclosure relates. In particular, it is fully contemplated that the features, components, and / or steps described with respect to one aspect may be combined with the features, components, and / or steps described with respect to other aspects of the present disclosure. For the sake of brevity, however, the numerous iterations of these combinations will not be described separately.

[0057] Figure 1 is a diagrammatic schematic view of an intraluminal imaging system, according to aspects of the present disclosure. The intraluminal imaging system 100 can be an intravascular ultrasound (IVUS) imaging system in some aspects. The intraluminal imaging system 100 may include an intraluminal device 102, a patient interface module (PIM) 104, a console or processing system 106, a monitor 108, and an external imaging system 132 which may include angiography, ultrasound, X-ray, computed tomography (CT), magnetic resonanceimaging (MRI), or other imaging technologies, equipment, and methods. The intraluminal device 102 is sized and shaped, and / or otherwise structurally arranged to be positioned within a body lumen of a patient. For example, the intraluminal device 102 can be a catheter, guidewire, and / or guide catheter, in various aspects. In some circumstances, the system 100 may include additional elements and / or may be implemented without one or more of the elements illustrated in Figure 1. For example, the system 100 may omit the external imaging system 132.

[0058] The intraluminal imaging system 100 (or intravascular imaging system) can be any type of imaging system suitable for use in the lumens or vasculature of a patient. In some aspects, the intraluminal imaging system 100 is an intravascular ultrasound (IVUS) imaging system. In other aspects, the intraluminal imaging system 100 may include systems configured for forward looking intravascular ultrasound (FL-IVUS) imaging, intravascular photoacoustic (IVPA) imaging, intracardiac echocardiography (ICE), transesophageal echocardiography (TEE), and / or other suitable imaging modalities.

[0059] It is understood that the system 100 and / or device 102 can be configured to obtain any suitable intraluminal imaging data. In some aspects, the device 102 may include an imaging component of any suitable imaging modality, such as optical imaging, optical coherence tomography (OCT), etc. In some aspects, the device 102 may include any suitable non-imaging component, including a pressure sensor, a flow sensor, a temperature sensor, an optical fiber, a reflector, a mirror, a prism, an ablation element, a radio frequency (RF) electrode, a conductor, or combinations thereof. Generally, the device 102 can include an imaging element to obtain intraluminal imaging data associated with the lumen 120. The device 102 may be sized and shaped (and / or configured) for insertion into a vessel or lumen 120 of the patient.

[0060] The system 100 may be deployed in a catheterization laboratory having a control room. The processing system 106 may be located in the control room. Optionally, the processing system 106 may be located elsewhere, such as in the catheterization laboratory itself. The catheterization laboratory may include a sterile field while its associated control room may or may not be sterile depending on the procedure to be performed and / or on the health care facility. The catheterization laboratory and control room may be used to perform any number of medical imaging procedures such as angiography, fluoroscopy, CT, IVUS, virtual histology (VH), forward looking IVUS (FL-IVUS), intraluminal photoacoustic (IVPA) imaging, a fractional flow reserve (FFR) determination, a coronary flow reserve (CFR) determination,optical coherence tomography (OCT), computed tomography, intracardiac echocardiography (ICE), forward-looking ICE (FLICE), intraluminal palpography, transesophageal ultrasound, fluoroscopy, and other medical imaging modalities, or combinations thereof. In some aspects, device 102 may be controlled from a remote location such as the control room, such than an operator is not required to be in close proximity to the patient.

[0061] The intraluminal device 102, PIM 104, monitor 108, and external imaging system 132 may be communicatively coupled directly or indirectly to the processing system 106. These elements may be communicatively coupled to the medical processing system 106 via a wired connection such as a standard copper link or a fiber optic link and / or via wireless connections using IEEE 802.11 Wi-Fi standards, Ultra Wide-Band (UWB) standards, wireless FireWire, wireless USB, or another high-speed wireless networking standard. The processing system 106 may be communicatively coupled to one or more data networks, e.g., a TCP / IP-based local area network (LAN). In other aspects, different protocols may be utilized such as Synchronous Optical Networking (SONET). In some cases, the processing system 106 may be communicatively coupled to a wide area network (WAN). The processing system 106 may utilize network connectivity to access various resources. For example, the processing system 106 may communicate with a Digital Imaging and Communications in Medicine (DICOM) system, a Picture Archiving and Communication System (PACS), and / or a Hospital Information System (HIS) via a network connection.

[0062] At a high level, an ultrasound imaging intraluminal device 102 emits ultrasonic energy from a transducer array 124 included in scanner assembly 110 mounted near a distal end of the intraluminal device 102. The ultrasonic energy is reflected by tissue structures in the medium (such as a blood vessel or other body lumen 120) surrounding the scanner assembly 110, and the ultrasound echo signals are received by the transducer array 124. The scanner assembly 110 generates electrical signal(s) representative of the ultrasound echoes. The scanner assembly 110 can include one or more single ultrasound transducers and / or a transducer array 124 in any suitable configuration, such as a planar array, a curved array, a circumferential array, an annular array, etc. For example, the scanner assembly 110 can be a one-dimensional array or a two- dimensional array in some instances. In some instances, the scanner assembly 110 can be a rotational ultrasound device. The active area of the scanner assembly 110 can include one or more transducer materials and / or one or more segments of ultrasound elements (e.g., one or morerows, one or more columns, and / or one or more orientations) that can be uniformly or independently controlled and activated. The active area of the scanner assembly 110 can be patterned or structured in various basic or complex geometries. The scanner assembly 110 can be disposed in a side-looking orientation (e.g., ultrasonic energy emitted perpendicular and / or orthogonal to the longitudinal axis of the intraluminal device 102) and / or a forward-looking looking orientation (e.g., ultrasonic energy emitted parallel to and / or along the longitudinal axis). In some instances, the scanner assembly 110 is structurally arranged to emit and / or receive ultrasonic energy at an oblique angle relative to the longitudinal axis, in a proximal or distal direction. In some aspects, ultrasonic energy emission can be electronically steered by selective triggering of one or more transducer elements of the scanner assembly 110.

[0063] The ultrasound transducer(s) of the scanner assembly 110 can be a piezoelectric micromachined ultrasound transducer (PMUT), capacitive micromachined ultrasonic transducer (CMUT), single crystal, lead zirconate titanate (PZT), PZT composite, other suitable transducer type, and / or combinations thereof. In an aspect the ultrasound transducer array 124 can include any suitable number of individual transducer elements or acoustic elements between 1 acoustic element and 1000 acoustic elements, including values such as 2 acoustic elements, 4 acoustic elements, 36 acoustic elements, 64 acoustic elements, 128 acoustic elements, 500 acoustic elements, 812 acoustic elements, and / or other values both larger and smaller.

[0064] The PIM 104 transfers the received echo signals to the processing system 106 where the ultrasound image (including the flow information) is reconstructed and displayed on the monitor 108. The console or processing system 106 can include a processor and a memory. The processing system 106 may be operable to facilitate the features of the intraluminal imaging system 100 described herein. For example, the processor can execute computer readable instructions stored on the non-transitory tangible computer readable medium.

[0065] The PIM 104 facilitates communication of signals between the processing system 106 and the scanner assembly 110 included in the intraluminal device 102. This communication may include providing commands to integrated circuit controller chip(s) within the intraluminal device 102, selecting particular element(s) on the transducer array 124 to be used for transmit and receive, providing the transmit trigger signals to the integrated circuit controller chip(s) to activate the transmitter circuitry to generate an electrical pulse to excite the selected transducer array element(s), and / or accepting amplified echo signals received from the selected transducerarray element(s) via amplifiers included on the integrated circuit controller chip(s). In some aspects, the PIM 104 performs preliminary processing of the echo data prior to relaying the data to the processing system 106. In examples of such aspects, the PIM 104 performs amplification, filtering, and / or aggregating of the data. In an aspect, the PIM 104 also supplies high- and low- voltage DC power to support operation of the intraluminal device 102 including circuitry within the scanner assembly 110.

[0066] The processing system 106 receives echo data from the scanner assembly 110 by way of the PIM 104 and processes the data to reconstruct an image of the tissue structures in the medium surrounding the scanner assembly 110. Generally, the device 102 can be utilized within any suitable anatomy and / or body lumen of the patient. The processing system 106 outputs image data such that an image of the vessel or lumen 120, such as a cross-sectional IVUS image of the lumen 120, is displayed on the monitor 108. Lumen 120 may represent fluid filled or fluid-surrounded structures, both natural and man-made. Lumen 120 may be within a body of a patient. Lumen 120 may be a blood vessel, such as an artery or a vein of a patient’s vascular system, including cardiac vasculature, peripheral vasculature, neural vasculature, renal vasculature, and / or or any other suitable lumen inside the body. For example, the device 102 may be used to examine any number of anatomical locations and tissue types, including without limitation, organs including the liver, heart, kidneys, gall bladder, pancreas, lungs; ducts; intestines; nervous system structures including the brain, dural sac, spinal cord and peripheral nerves; the urinary tract; as well as valves within the blood, chambers or other parts of the heart, and / or other systems of the body. In addition to natural structures, the device 102 may be used to examine man-made structures such as, but without limitation, heart valves, stents, shunts, filters and other devices.

[0067] The controller or processing system 106 may include a processing circuit having one or more processors in communication with memory and / or other suitable tangible computer readable storage media. The controller or processing system 106 may be configured to carry out one or more aspects of the present disclosure. In some aspects, the processing system 106 and the monitor 108 are separate components. In other aspects, the processing system 106 and the monitor 108 are integrated in a single component. For example, the system 100 can include a touch screen device, including a housing having a touch screen display and a processor. The system 100 can include any suitable input device, such as a touch sensitive pad or touch screendisplay, keyboard / mouse, joystick, button, etc., for a user to select options shown on the monitor 108. The processing system 106, the monitor 108, the input device, and / or combinations thereof can be referenced as a controller of the system 100. The controller can be in communication with the device 102, the PIM 104, the processing system 106, the monitor 108, the input device, and / or other components of the system 100.

[0068] In some aspects, the intraluminal device 102 includes some features similar to traditional solid-state IVUS catheters, such those disclosed in U.S. Patent No. 7,846,101, which is incorporated by reference herein in its entirety. For example, the intraluminal device 102 may include the scanner assembly 110 near a distal end of the intraluminal device 102 and a transmission line bundle 112 extending along the longitudinal body of the intraluminal device 102. The cable or transmission line bundle 112 can include a plurality of conductors, including one, two, three, four, five, six, seven, or more conductors.

[0069] The transmission line bundle 112 terminates in a PIM connector 114 at a proximal end of the intraluminal device 102. The PIM connector 114 electrically couples the transmission line bundle 112 to the PIM 104 and physically couples the intraluminal device 102 to the PIM 104. In an aspect, the intraluminal device 102 further includes a guidewire exit port 116. Accordingly, in some instances the intraluminal device 102 is a rapid-exchange catheter. The guidewire exit port 116 allows a guidewire 118 to be inserted towards the distal end in order to direct the intraluminal device 102 through the lumen 120.

[0070] The monitor 108 may be a display device such as a computer monitor or other type of screen. The monitor 108 may be used to display selectable prompts, instructions, and visualizations of imaging data to a user. In some aspects, the monitor 108 may be used to provide a procedure-specific workflow to a user to complete an intraluminal imaging procedure. This workflow may include performing a pre-stent plan to determine the state of a lumen and potential for a stent, as well as a post-stent inspection to determine the status of a stent that has been positioned in a lumen.

[0071] The external imaging system 132 can be configured to obtain x-ray, radiographic, angiographic / venographic (e.g., with contrast), and / or fluoroscopic (e.g., without contrast) images of the body of a patient (including the vessel 120). External imaging system 132 may also be configured to obtain computed tomography images of the body of the patient (including the vessel 120). The external imaging system 132 may include an external ultrasound probeconfigured to obtain ultrasound images of the body of the patient (including the vessel 120) while positioned outside the body. In some aspects, the system 100 includes other imaging modality systems (e.g., MRI) to obtain images of the body of the patient (including the vessel 120). The processing system 106 can utilize the images of the body of the patient in conjunction with the intraluminal images obtained by the intraluminal device 102.

[0072] Figure 2 is a schematic diagram of a processor circuit 250, according to aspects of the present disclosure. The processor circuit 250 may be implemented in the intraluminal imaging system 100, or other devices or workstations (e.g., third-party workstations, network routers, etc.), or on a cloud processor or other remote processing unit, as necessary to implement the method. As shown, the processor circuit 250 may include a processor 260, a memory 264, and a communication module 268. These elements may be in direct or indirect communication with each other, for example via one or more buses.

[0073] The processor 260 may include a central processing unit (CPU), a digital signal processor (DSP), an ASIC, a controller, or any combination of general-purpose computing devices, reduced instruction set computing (RISC) devices, application-specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or other related logic devices, including mechanical and quantum computers. The processor 260 may also comprise another hardware device, a firmware device, or any combination thereof configured to perform the operations described herein. The processor 260 may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.

[0074] The memory 264 may include a cache memory (e.g., a cache memory of the processor 260), random access memory (RAM), magnetoresistive RAM (MRAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read only memory (EPROM), electrically erasable programmable read only memory (EEPROM), flash memory, solid state memory device, hard disk drives, other forms of volatile and non-volatile memory, or a combination of different types of memory. In an aspect, the memory 264 includes a non-transitory computer-readable medium. The memory 264 may store instructions 266. The instructions 266 may include instructions that, when executed by the processor 260, cause the processor 260 to perform the operations described herein. Instructions 266 may also be referredto as code. The terms “instructions” and “code” should be interpreted broadly to include any type of computer-readable statement(s). For example, the terms “instructions” and “code” may refer to one or more programs, routines, sub-routines, functions, procedures, etc. “Instructions” and “code” may include a single computer-readable statement or many computer-readable statements.

[0075] The communication module 268 can include any electronic circuitry and / or logic circuitry to facilitate direct or indirect communication of data between the processor circuit 250, and other processors or devices. In that regard, the communication module 268 can be an input / output (I / O) device. In some instances, the communication module 268 facilitates direct or indirect communication between various elements of the processor circuit 250 and / or the intraluminal imaging system 100. The communication module 268 may communicate within the processor circuit 250 through numerous methods or protocols. Serial communication protocols may include but are not limited to United States Serial Protocol Interface (US SPI), Inter- Integrated Circuit (I2C), Recommended Standard 232 (RS-232), RS-485, Controller Area Network (CAN), Ethernet, Aeronautical Radio, Incorporated 429 (ARINC 429), MODBUS, Military Standard 1553 (MIL-STD-1553), or any other suitable method or protocol. Parallel protocols include but are not limited to Industry Standard Architecture (ISA), Advanced Technology Attachment (ATA), Small Computer System Interface (SCSI), Peripheral Component Interconnect (PCI), Institute of Electrical and Electronics Engineers 488 (IEEE-488), IEEE- 1284, and other suitable protocols. Where appropriate, serial and parallel communications may be bridged by a Universal Asynchronous Receiver Transmitter (UART), Universal Synchronous Receiver Transmitter (USART), or other appropriate subsystem.

[0076] External communication (including but not limited to software updates, firmware updates, preset sharing between the processor and central server, or readings from the annular ultrasound imaging array) may be accomplished using any suitable wireless or wired communication technology, such as a cable interface such as a universal serial bus (USB), micro USB, Lightning, or FireWire interface, Bluetooth, Wi-Fi, ZigBee, Li-Fi, or cellular data connections such as 2G / GSM (global system for mobiles) , 3G / UMTS (universal mobile telecommunications system), 4G, long term evolution (LTE), WiMax, or 5G. For example, a Bluetooth Low Energy (BLE) radio can be used to establish connectivity with a cloud service, for transmission of data, and for receipt of software patches. The controller may be configuredto communicate with a remote server, or a local device such as a laptop, tablet, or handheld device, or may include a display capable of showing status variables and other information. Information may also be transferred on physical media such as a USB flash drive or memory stick.

[0077] It will also be understood that one or more of the steps of the methods described above can be performed by one or more components of an ultrasound imaging system, such as the processing system, a multiplexer, a beamformer, a signal processing unit, an image processing unit, or any other suitable component of the system. For example, activating the scan sequences may be carried out by a processor in communication with a multiplexer configured to select or activate one or more elements of an ultrasound transducer array. In some aspects, generating the ultrasound images may include beamforming incoming signals from the ultrasound imaging device and processing the beamformed signals by an image processor. The processing components of the system can be integrated within the ultrasound imaging device, contained within an external console, or may be a separate component.

[0078] Figure 3 is a lateral cross-sectional or tomographic view of a blood vessel 120, according to aspects of the present disclosure. Visible is an intravascular device 102 within a blood vessel lumen 320. The lumen 320 is surrounded by a vessel wall 330 (e.g., the space between the lumen border and the vessel border), which is surrounded by muscle tissue or other tissue 310. Within the vessel wall, there is can be healthy tissue (e.g., the intima, media, adventitia, etc.) and / or plaque (e.g., necrotic core, calcium, fibrous, or fibro-fatty plaque).

[0079] In the example shown in Figure 3, the vessel wall 330 includes calcium 340 (e.g., a calcified plaque), which is positioned a distance D from the lumen boundary 325 and extends for an angle 0 around the vessel wall 330.

[0080] Figure 4 is a lateral cross-sectional or tomographic view of a blood vessel 120, according to aspects of the present disclosure. Visible are the intravascular device 102, vessel lumen 320, vessel wall 330, and surrounding tissue 310. In the example shown in Figure 4, the vessel wall 330 includes a calcified nodule, which is a particular type of calcified plaque.

[0081] Figure 5A is a lateral cross-sectional or tomographic view of a blood vessel 120, according to aspects of the present disclosure. Visible are the intravascular device 102, vessel lumen 320, vessel wall 330, and surrounding tissue 310. In the example shown in Figure 5A, the vessel wall 330 includes a calcified plaque 340.

[0082] Figure 5B is a lateral cross-sectional or tomographic view of a blood vessel 120, according to aspects of the present disclosure. Visible are the intravascular device 102, vessel lumen 320, vessel wall 330, and surrounding tissue 310. In the example shown in Figure 5B, the calcified plaque of Figure 5A has been cracked (e.g., by angioplasty or other intervention or therapeutic procedure designed to crack calcified lesions), and is now identifiable by the intravascular feature imaging and detection system as cracked calcium 540.

[0083] Figure 6 is a lateral cross-sectional or tomographic view of a blood vessel 120, according to aspects of the present disclosure. Visible are the intravascular device 102, vessel lumen 320, vessel wall 330, and surrounding tissue 310. In the example shown in Figure 6, a stent 610 (shown here in an unexpanded state) has been introduced into the lumen 320. However, a gap 620 exists between the stent 610 and the vessel wall 330. The gap 620 may be detected visually (e.g., through image recognition, classification, and / or segmentation techniques as described below), and / or through detection or visualization of blood flow in between the stent 610 and vessel wall 330. If the stent 610 is in an expanded state, then the presence of the gap 620 may be indicative of malapposition (e.g., inadequate expansion of the stent) - a type of stent irregularity (e.g., the stent is not structurally arranged within the vessel to properly maintain the expanded diameter / cross-sectional area of the vessel and / or the improved blood flow that results therefrom). In general, a stent irregularity can be a non-optimal structure, non-optimal positioning, non-optimal operation of the stent (e.g., expansion), and / or non-optimal relationship between the stent and the vessel, which prevents the stent from restoring, improving, and / or maintaining a more open (less blocked / compressed) lumen for blood flow in the manner that a physician intends for treatment of the patient. The stent irregularity can also be referred to as a non-optimal stent, a stent irregularity, a stent deficiency, etc.

[0084] Figure 7 is a lateral cross-sectional or tomographic view of a blood vessel 120, according to aspects of the present disclosure. Visible are the intravascular device 102, vessel lumen 320, vessel wall 330, and surrounding tissue 310. In the example shown in Figure 7, a plaque protrusion 740 is projecting inward through the structure of an expanded stent 610. Depending on the implementation, the plaque protrusion may be detected directly (e.g., through classical or Al-based image recognition, classification, or segmentation techniques) and / or indirectly (e.g., by using Doppler flow imaging or other flow data to detect that the flow of blood 720 through the vessel lumen 320 is interrupted at the location of the plaque protrusion 740). Anexample of blood flow imaging is ChromaFlo, available from Philips. In some cases, plaque protrusion may be considered a type of stent irregularity (e.g., the stent is not properly maintaining the enlarged diameter / cross-sectional area of the vessel for improved blood flow).

[0085] Figure 8 is a lateral cross-sectional or tomographic view of a blood vessel 120, according to aspects of the present disclosure. Visible are the intravascular device 102, vessel lumen 320, vessel wall 330, and surrounding tissue 310. In the example shown in Figure 8, the vessel wall 330 includes a dissection 840 (e.g., a flap of vessel wall tissue that has partially separated from the vessel wall). Depending on the implementation, the dissection 840 may be detected directly through classical or Al-based image recognition, classification, or segmentation techniques, or by detecting movement of the dissection 840 (e.g., movement of the flap of tissue) between subsequent image frames, or indirectly by using Doppler flow imaging or other flow data to detect that the flow of blood through the vessel lumen 320 is interrupted at the location of the dissection 840, or that the dissection 840 has blood flowing behind it.

[0086] Figure 9 is a lateral cross-sectional or tomographic view of a blood vessel 120, according to aspects of the present disclosure. Visible are the intravascular device 102, vessel lumen 320, vessel wall 330, and surrounding tissue 310. In the example shown in Figure 9, an expanded stent 610 is in circumferential contact with the vessel wall 330. However, an in-stent restenosis 940, such as a neointimal hyperplasia (e.g., new intimal tissue) or neoatherosclerosis (e.g., new plaque) has formed inside the stent 610 and the vessel lumen 320. Depending on the implementation, the restenosis 940 may be detected directly (e.g., through classical or Al-based image recognition, classification, or segmentation techniques) and / or indirectly (e.g., by using Doppler flow imaging or other flow data to detect that the flow of blood through the vessel lumen 320 is interrupted at the location of the restenosis 940). In some cases, restenosis may be considered a type of stent irregularity (e.g., the stent lumen, which was supposed to allow improved blood vessel, is being blocked).

[0087] Figure 10 is a lateral cross-sectional or tomographic view of a blood vessel 120, according to aspects of the present disclosure. Visible are the intravascular device 102, vessel lumen 320, vessel wall 330, and surrounding tissue 310. In the example shown in Figure 10, a thrombus 1040 has formed within the lumen 320, in partial contact with the vessel wall 330. Depending on the implementation, the thrombus 1040 may be detected directly (e.g., through classical or Al-based image recognition, classification, or segmentation techniques) and / orindirectly (e.g., by using Doppler flow imaging or other flow data to detect that the flow of blood through the vessel lumen 320 is interrupted at the location of the thrombus 1040). While a plaque may be treated by expanding the vessel with a balloon and / or a stent, a thrombus is typically treated with devices to ablate or remove the thrombus. Thus, automatically distinguishing between a plaque and a thrombus provides helpful feedback to the clinician during live imaging.

[0088] Figure 11 is a lateral cross-sectional or tomographic view of a blood vessel 120, according to aspects of the present disclosure. Visible are the intravascular device 102, vessel lumen 320, vessel wall 330, and surrounding tissue 310. In the example shown in Figure 1, a thin-cap fibroatheroma 1140 has formed within the lumen 320, in partial contact with the vessel wall 330. The fibroatheroma 1140 extends across an angle 0 around the vessel, and includes a cap 1145 with a thickness T. Depending on the implementation, the fibroatheroma 1140 or cap 1145 may be detected and / or measured directly (e.g., through classical or Al-based image recognition, classification, or segmentation techniques) and / or indirectly, whether for flowlimiting or non-flow-limiting plaques. Detecting the thickness of the cap is important, as is detecting an appearance of necrotic core close to the lumen, and of a certain / threshold concentration of the necrotic core relative to the plaque as a whole. Alternative markers of vulnerable plaque can also be used where presence above a certain / threshold concentration indicates vulnerability. While plaque may be treated by expanding the vessel with a balloon and / or a stent, standard treatment for a thin-cap fibroatheroma 1140 is a developing area of clinical knowledge. Currently operators may decide to avoid landing stent edges in this type of plaque, intensify anti-cholesterol drug treatments, increase frequency of follow-up, or in the setting of research, prophylactically treat with a stent. As the science develops, automatically identifying a thin-cap fibroatheroma provides helpful feedback to the clinician during live imaging.

[0089] Figure 12 is a schematic, diagrammatic representation, in block diagram form, of an intravascular feature imaging and detection system 1200, according to aspects of the present disclosure. System 1200 may detect various features associated with disease states and generate indicators to identify the feature in a display for review by a user, such as a physician. The intravascular feature imaging and detection system 1200 may include live intravascular imaging1210, feature(s) detection 1220, and indicator(s) 1230 for detected feature(s) 1222, 1224, 1226,1228.

[0090] Live intravascular imaging 1210 may include use of an intraluminal imaging system 100 to gather intravascular imaging data to generate live intravascular images 1212 (e.g., radial and / or tomographic cross-sectional views). Intraluminal imaging system 100 may process the live intravascular images 1212 into live longitudinal views 1214 (e.g., image-based longitudinal view as depicted in the displays of Figs. 20-21 and 23-28 and / or a graphical longitudinal view). Longitudinal locations along an intravascular cross-sectional longitudinal view can be respectively correspond to individual intravascular images (radial and / or tomographic cross- sectional views). Longitudinal views are described in, for example, U.S. Publication No. 2020 / 0129158, U.S. Publication No. 2022 / 0079563, U.S. Patent No. 9,367,965, and U.S. Patent No. 10,070,827, each of which is incorporated by reference herein in its entirety. Live intravascular images 1212 and live longitudinal views 1214 may be used in feature(s) detection 1220 and / or screen display 1240. Images may be generated in real time or near real time.Similarly, real time or near real time generated images may be displayed to a user, such as a physician, in real time or near real time.

[0091] Feature(s) detection 1220 uses artificial intelligence or machine learning models to detect features related to disease states and / or treatment devices in live intravascular images 1212 and live longitudinal views 1214. Object detection may involve artificial intelligence (Al), machine learning (ML), deep learning (DL), or classical image processing techniques (e.g., pixel-based analysis, border detection, edge detection, etc.), or combinations thereof. In some embodiments, a convolutional neural network may be used for object detection in live intravascular images 1212 and / or live longitudinal views 1214. Machine learning model used for feature(s) detection 1220 is further described in Figs. 17-19. Various features may be detected, including calcium 1222 (e.g., a calcified stenosis, calcified lesion, calcium nodule, cracked calcium, smooth (new) calcium, rough (established) calcium, etc.), stent irregularities 1224 (e.g., malapposition, plaque protrusion, etc.), in-stent restenosis 1226 (e.g., neointimal hyperplasia, neoatherosclerosis, etc.), or thrombus / thin cap fibroatheroma 1228.

[0092] Indicators(s) 1230 for detected feature(s) 1222, 1224, 1226, 1228 may be generated based on which of the detected features 1222, 1224, 1226, 1228 have been detected by feature(s) detection 1220. Indicators 1230 may take many different forms. Flags, text descriptions, and / orthe use of partially transparent, colored shapes as overlays on an image in a display may be used. Multiple indicators may be used simultaneously for the same feature. For example, both a flag and text description could be used to indicate a proximal cap detection. Other combinations are also conceived of and encompassed by the present disclosure. Some non-limiting examples of indicators are depicted in Figs. 20-28.

[0093] Screen display 1240 may provide various images with indicators to a clinician to identify detected features. Screen display 1240 may include live intravascular images 1212, live longitudinal view 1214, and indicator(s) 1230 for detected feature(s) 1222, 1224, 1226, 1228. Figs. 20-28 depict some example displays of images including indicators for features.

[0094] In one example, an intravascular image frame 1212 is not shown to the user until realtime feature detection 1220 has been performed, such that the user sees the intravascular image frame 1212, in real time, highlighted or annotated based on the feature detection. Thus, it may be desirable for feature detection 1220 and annotation (e.g., the development of indicators 1230) to be performed at a frequency of 10 Hz or faster, to avoid an impression of lag or latency on the part of the user.

[0095] Block diagrams are provided herein for exemplary purposes; a person of ordinary skill in the art will recognize myriad variations that nonetheless fall within the scope of the present disclosure. For example, any of the steps described herein may optionally include an output to a user of information relevant to the step, and may thus represent an improvement in the user interface over existing art by providing information not otherwise available. Block diagrams may show a particular arrangement of components, modules, services, steps, processes, or layers, resulting in a particular data flow. It is understood that some embodiments of the systems disclosed herein may include additional components, that some components shown may be absent from some embodiments, and that the arrangement of components may be different than shown, resulting in different data flows while still performing the methods described herein.

[0096] Figure 13 is a schematic, diagrammatic representation, in block diagram form, of an intravascular feature imaging and detection system 1300, according to aspects of the present disclosure. The imaging catheter 102 emits imaging energy to and receives imaging reflections from the tissue 1310, and generates B-mode imaging data 1320 (e.g., analog data, digital data, radio frequency (RF) data, image quality (IQ) data, beamformed data, and / or combinations thereof), from which radial B-mode A-lines 1330 are constructed by the processor. A pluralityof radial A-lines can then be used by the processor to construct the live B-mode image 1212. It is noted that feature detection 1220 may make use of either the B-mode imaging data 1320, the A-lines 1330, the B-mode image 1212, or combinations thereof. The feature detection 1220 then yields the detected features 1222, 1224, 1226, and / or 1228.

[0097] The longitudinal view 1214 is constructed in real time, based on the B-mode images 1212. Each time a B-mode image is generated, a cross-section of it is added to the longitudinal view at the current location of the ultrasound imaging array, as shown below in Figures 20-28.

[0098] A screen display 1240 is shown to the user (e.g., on a display) that includes the most recent live B-mode image 1212 for which feature detection 1220 has been completed, as well as indicators 1230 annotating the B-mode image 1212 for any detected features. The screen display 1240 may also include the live longitudinal view 1214, which may also include indicators 1230 annotating it for any detected features, as shown below in Figures 20-28. One advantage of the longitudinal view 1214 is that it can show features that were previously detected at past positions of the imaging catheter 102. The indicators 1230 may be overlaid on, or proximate to, either or both of the B-mode image 1212 or the longitudinal view 1214.

[0099] Figure 14 is a schematic, diagrammatic representation, in block diagram form, of an intravascular feature imaging and detection system 1400, according to aspects of the present disclosure. The imaging catheter 102 emits imaging energy to and receives imaging reflections from the tissue 1310, and generates both B-mode images 1320 and flow mode images 1412, which are used by the feature detector 1220 to detect features 1222, 1224, 1226, and / or 1228. The screen display 1240 now includes the B-mode image 1212 and the flow mode image 1412, along with their respective indicators 1230 for any detected features 1222, 1224, 1226, 1228. Depending on the implementation, the flow-mode image 1412 may be separate from, or superimposed onto, the B-mode image 1212. The indicators 1230 may be overlaid on or proximate to the B-mode image 1212, flow mode image 1412, or combination B-mode and flow mode image.

[0100] Figure 15 is a schematic, diagrammatic representation, in block diagram form, of an intravascular feature imaging and detection system 1500, according to aspects of the present disclosure. The detection of certain types of features, such as dissections and cracked calcium, may be facilitated by comparing their shapes and / or positions in subsequent image frames (e.g., in order to detect movement). In the example shown in Figure 15, detection is based on thecurrent live image frame 1212 (at a time represented as t3) as well as the previous two live image frames 1212 (at times represented as t2 and tl). The three image frames 1212 are stored in the memory 264 of the processor, where they can be accessed by a predictive network 1510, which yields detected features 1222, 1224, 1226, and / or 1228, if such features are present in the images 1212. These features are then used to generate the indicators 1130 which are shown on the screen display 1240, along with the most recent live image frame 1212, from time t3.

[0101] Figure 16 is a schematic, diagrammatic representation, in block diagram form, of an intravascular feature imaging and detection system 1600, according to aspects of the present disclosure. The detection of certain types of features, such as stent irregularities, restenosis, thrombus, or thin-cap fibroatheroma, may require only a single image frame. In the example shown in Figure 16, detection is based on the current live image frame 1212 (at a time represented as t3). The image frame 1212 is received by the predictive network 1510, which yields detected features 1222, 1224, 1226, and / or 1228, if such features are present in the image 1212. These features are then used to generate the indicators 1130 which are shown on the screen display 1240, along with the most recent live image frame 1212, from time t3.

[0102] Figure 17 is a schematic, diagrammatic representation, in block diagram form, of a training system 1700 for a machine learning model 1730, according to aspects of the present disclosure. Training system 1700 trains the machine learning model 1730 to accurately detect features, e.g., machine learning model 1730 may be included in feature(s) detection 1220. In some aspects, machine learning model 1730 is trained by providing historic intravascular image and / or longitudinal views alongside user-annotations of those images identifying salient features. For example, training may done be with input comprising some or all the output of intravascular imaging 1210. In some embodiments, the machine learning model 1730 may be trained to do border identification, object detection, classification, and / or image segmentation. Training machine learning model 1730 aims to improve the performance of the model at detecting features. Training system 1700 includes training data 1710, machine learning model 1730, and model objectives / functions 1750.

[0103] Training data 1710 may include data for a plurality of patient records 1720. Each patient record may contain a plurality of images or views and user annotated features therein. In some embodiments, patient record 1720 may include intravascular images (radial / tomographic) 1221, user annotation of calcium 1723 (e.g., calcified plaque, calcium nodule, cracked calcium,smooth calcium, and / or rough calcium), user annotation of stent irregularities 1725 (e.g., malapposition, plaque protrusion), user annotation of in-stent restenosis 1727 (e.g., neointimal hyperplasia, neoatherosclerosis, etc.), and user annotation of thrombus and / or thin cap fibroatheroma 1729. It should be appreciated that not all the patient records 1720 are needed to train the machine learning model 1730. For example, fewer than the four listed user annotations may be available. Furthermore, additional medical records from a patient’s medical history may be included in the training data 1710.

[0104] Machine learning model 1730 may receive the training data 1710. From the training data 1710, machine learning model 1730 may output predicted feature(s) 1740. For example, the output may be a selection of pixels in the intravascular images 1721. In some embodiments, machine learning model may receive intravascular images 1721 from the training data 1710. From the intravascular images 1721, machine learning model 1730 may generate detections of Features, e.g., 1222, 1224, 1226, 1228.

[0105] Using model objectives / functions 1750, training system 1700 compares the prediction 1740 with associated ground truth predictions. For example, in some embodiments, user annotations 1723, 1725, 1727, 1729 of features comprise ground truth predictions.

[0106] In some aspects, model objectives / functions 1750 may be used to compare predicted feature(s) 1740 from a machine learning model 1730 with user annotated features 1723, 1725, 1727, 1729, which represent the ground truth labels.

[0107] Model objectives / functions 1750 may include objectives / functions which penalize to a greater extent predicted features 1740 which are further from the ground truth labels. In some embodiments, model objectives / functions 1750 may include objectives / functions which penalize deviations of predicted feature(s) 1740 from user-annotated vessel measurements 1723, 1725, 1727, 1729.

[0108] Comparisons from the model objectives / functions 1750 may be used to update parameters 1760 of the machine learning model 1730. In some instances, updating may be accomplished using a gradient of the objective functions and backpropagation to update the parameters of the machine learning model. The structure of machine learning models is further described with respect to Fig. 19.

[0109] Figure 18 is a schematic, diagrammatic representation, in block diagram form, of a training system 1800 for a machine learning model 1880, according to aspects of the presentdisclosure. After training, as described in Fig. 17, is complete, an untrained machine learning model with parameters A 1880 is transformed into a trained machine learning model with parameters B 1890. Parameters A and B differ between the trained and untrained models because over the course of training, parameters in the machine learning model are updated (e.g., 1760 in Fig. 12) based on comparisons between ground truth labels and predictions.

[0110] Figure 19 is a diagrammatic schematic of a deep learning network configuration 1900, according to aspects of the present disclosure. The configuration 1900 can be implemented by a deep learning network. The configuration 1900 includes a deep learning network 1910 including one or more convolutional neural networks (CNNs) 1912. The deep learning network 1910 and / or the CNNs 1912 can be referred to as a predictive network. For simplicity of illustration and discussion, Fig. 19 illustrates one CNN 1912. However, the embodiments can be scaled to include any suitable number of CNNs 1912 (e.g., about 2, 3 or more). The configuration 1900 can be trained for identification of various features, including calcium, stent irregularities, restenosis, thrombuses, and / or thin cap fibroatheromas as described in greater detail below.

[0111] The CNN 1912 may include a set of N convolutional layers 1920 followed by a set of K fully connected layers 1930, where N and K may be any positive integers. The convolutional layers 1920 are shown as 1920(1) to 1920(N). The fully connected layers 1930 are shown as 1930(1) to 1930(K). Each convolutional layer 1920 may include a set of filters 1922 configured to extract features from an input 1902 (e.g., one or more intravascular image frames, imagebased longitudinal view, graphical longitudinal view, etc.). The values N and K and the size of the filters 1922 may vary depending on the embodiments. In some instances, the convolutional layers 1920(1) to 1920(N) and the fully connected layers 1930(1) to 1930(K-l) may utilize a leaky rectified non-linear (ReLU) activation function and / or batch normalization. The fully connected layers 1930 may be non-linear and may gradually shrink the high-dimensional output to a dimension of the prediction result (e.g., the classification output 1940). Thus, the fully connected layers 1930 may also be referred to as a classifier. In some embodiments, the fully convolutional layers 1920 may additionally be referred to as perception or perceptive layers.

[0112] The classification output 1940 may indicate a confidence score for each class 1942 based on the input image 1902. The classes 1942 are shown as 1942a, 1942b, ... , 1942c. When the CNN 1912 is trained for predicting feature detections, the classes 1942 may indicate pixelsfor calcium 1942a, stent irregularities 1942b, any other outputs 1942c of machine learning models as described herein, or any other suitable class. A class 1942 indicating a high confidence score indicates that the input image 1902 or a section or pixel of the image 1902 is likely to include an anatomical object / feature of the class 1942. Conversely, a class 1942 indicating a low confidence score indicates that the input image 1902 or a section or pixel of the image 1902 is unlikely to include an anatomical object / feature of the class 1942.

[0113] The CNN 1912 can also output a feature vector 1950 at the output of the last convolutional layer 1920(N). The feature vector 1950 may indicate objects detected from the input image 1902 or other data. For example, the feature vector 1950 may indicate regions associated with features identified from the image 1902. The feature vector 1950 may indicate the pixels associated with features, as described herein.

[0114] The deep learning network 1910 may implement or include any suitable type of learning network. For example, in some aspects, the deep learning network 1910 could include a convolutional neural network 1912. In addition, the convolutional neural network 1910 may additionally or alternatively be or include a multi-class classification network, an encoderdecoder type network, or any suitable network or means of identifying features within an image.

[0115] In an embodiment in which the deep learning network 1910 includes an encoderdecoder network, the network may include two paths. One path may be a contracting path, in which a large image, such as the image 1902, may be convolved by several convolutional layers 1920 such that the size of the image 1902 changes in depth of the network. The image 1902 may then be represented in a low dimensional space, or a flattened space. From this flattened space, an additional path may expand the flattened space to the original size of the image 1902. In some embodiments, the encoder-decoder network implemented may also be referred to as a principal component analysis (PCA) method. In some embodiments, the encoder-decoder network may segment the image 1902 into patches.

[0116] In an additional embodiment of the present disclosure, the deep learning network 1910 may include a multi-class classification network. In such an aspect, the multi-class classification network may include an encoder path. For example, the image 1902 may be of a high dimensional image. The image 1902 may then be processed with the convolutional layers 1920 such that the size is reduced. The resulting low dimensional representation of the image 1902 may be used to generate the feature vector 1950 shown in Fig. 19. The low dimensionalrepresentation of the image 1902 may additionally be used by the fully connected layers 1930 to regress and output one or more classes 1942. In some regards, the fully connected layers 1930 may process the output of the encoder or convolutional layers 1920. The fully connected layers 1930 may additionally be referred to as task layers or regression layers, among other terms.

[0117] Any suitable combination or variations of the deep learning network 1910 described is fully contemplated. For example, the deep learning network may include fully convolutional networks or layers or fully connected networks or layers or a combination of the two. In addition, the deep learning network may include a multi-class classification network, an encoderdecoder network, or a combination of the two. Other network architectures may be used instead or in addition without departing from the spirit of the present disclosure.

[0118] Figure 20 is an example screen display 2000 of a radial and / or tomographic cross- sectional view 2040 and longitudinal view 2005 with indicators for calcified nodule detection, according to aspects of the present disclosure. Example screen display 2000 may be provided to a physician during a therapeutic procedure in real time. Screen display 2000 notifies the physician with a text display, “LIVE”, 2017 that the images being shown are in real time. Screen display 2000 may include toggleable option 2020 to turn on / activate or off / deactivate the graphical overlay indicators for feature detections, e.g., 2010, 2015, 2020. The graphical overlays can be included in screen display while the live imaging is ongoing and can make a user aware, indicate to the user, and / or alert the user of a presence of the feature detections (e.g., an anatomical feature) in the intravascular image(s). Display 2000 includes the radial and / or tomographic cross-sectional view 2040 of Fig. 4 and a longitudinal view 2005.

[0119] Cross-sectional view 2040 includes a flag 2010 and graphical overlay 2030. Flag 2010 notifies the user that the displayed view includes a feature. Text description near the flag, “calcified nodule” specifies the type of feature detected. Overlay 2030 is located on view 2040 where the feature was detected, i.e., the location of the calcified nodule 440. Overlay 2030 may be partially transparent, colored, and / or variously shaped. The frame number of the cross- sectional view 2040 may also be shown, e.g., “Frame 130.”

[0120] Longitudinal view 2005, since it is displayed in real time (live), has a completed portion 2005A and a grayed-out portion 2005B, which will be updated as new images are processed during live imaging. Longitudinal view 2005 includes a flag 2015. Flag 2015 notifiesthe user that a feature has been detected in the frame where the flag is placed. Text description near the flag, “calcified nodule” specifies the type of feature detected.

[0121] The flag 2010 and associated text may be proximate to, or overlaid on, the tomographic image 2040. The overlay 2030 shown in Figure 20 may be a detection box, whose outline does not match the outline of the detected feature (e.g., the calcified nodule). However, if the predictive network provides segmentation, then the overlay 2030 may match the outline of the detected feature (e.g., the calcified nodule).

[0122] Figure 21 is an example screen display 2100 of a radial and / or tomographic cross- sectional view 2040 and longitudinal view 2005 with indicators for cracked calcium detection, according to aspects of the present disclosure. Visible are the tomographic image 2040 from Figure 5B, as well as the completed portion 2005A and grayed-out portion 2005B of the longitudinal view, a tomographic image flag 2010 and longitudinal image flag 2015, each with nearby text identifying the detected feature as cracked calcium, a “LIVE” indicator 2017, a feature detection overlay control 2020, and a calcium arc descriptor 2050 that lists the angle and depth of the calcium within the vessel wall.

[0123] Figure 22 is an example screen display 2200 of a radial and / or tomographic cross- sectional view 2040, longitudinal view 2205, and x-ray frame with contrast 2207 with indicators for calcium detection, according to aspects of the present disclosure. Example screen display 2200 may be provided to a clinician for review. Screen display 2200 notifies the clinician with a text display, “REVIEW”, 2217 that the images being shown are not in real time (i.e., not live). A user, such as a clinician, may select a frame of interest from the longitudinal view 2205 to show as the cross-sectional radial and / or tomographic view 2040. Screen display 2200 may include toggleable option 2220 to turn on / activate or off / deactivate the graphical overlay indicators for feature detections, e.g., 2210, 2215. The graphical overlays can make a user aware, indicate to the user, and / or alert the user of a presence of the feature detections (e.g., an anatomical feature) in the intravascular image(s). Screen display 2200 includes the radial and / or tomographic cross- sectional view 2040 of Fig. 4A, along with a longitudinal view 2205, and x-ray frame with contrast (angiogram) 2207.

[0124] Cross-sectional view 2240 includes a flag 2210. Flag 2210 notifies the user that the displayed view includes a feature. Text description near the flag, “Calcium” specifies the type offeature detected. The frame number of the cross-sectional view 2040 may also be shown, e.g.,“Frame 130.”

[0125] Longitudinal view 2205 includes a flag 2215 and graphical overlay 2225. Flag 2215 notifies the user that a feature has been detected in the frame where the flag is placed. Text description near the flag, “Calcium” specifies the type of feature detected. Overlay 2225 is located on longitudinal view 2205 where the feature was detected, e.g., location of calcium 340, across multiple frames. Overlay 2220 may be partially transparent, colored, and / or variously shaped.

[0126] X-ray frame with contrast 2207 includes a graphical overlay 2226 corresponding to overlay 2225 in the longitudinal view 2205, e.g.., the length of the overlay 2225 in x-ray frame 2207 corresponds to the width of the overlay 2225 in the longitudinal view 2205. To generate the two corresponding overlays, the longitudinal view 2205 and x-ray frame with contrast 2207 may be co-registered. The overlay 2226 in the x-ray frame 2207 includes a text description 2230, “Calcium length,” specifying the type of feature detected, as well as its length.

[0127] Figure 23 is an example screen display 2300 of a radial and / or tomographic cross- sectional view 2040 and longitudinal view 2005 with indicators for stent malapposition detection, according to aspects of the present disclosure. Visible are the tomographic image 2040 from Figure 6, as well as the completed portion 2005A and grayed-out portion 2005B of the longitudinal view, a tomographic image flag 2010 and longitudinal image flag 2015, each with nearby text identifying the detected feature as malapposition, a “LIVE” indicator 2017, and a feature detection overlay control 2020. Also visible is an overlay 2030 showing the location and extent of the gap 620 between the stent 610 and the vessel wall 330. The overlay 2030 may be translucent and / or colored to highlight the presence of the gap.

[0128] Figure 24 is an example screen display 2400 of a radial and / or tomographic cross- sectional view 2040 and longitudinal view 2005 with indicators for plaque protrusion detection, according to aspects of the present disclosure. Visible are the tomographic image 2040 from Figure 7, as well as the completed portion 2005A and grayed-out portion 2005B of the longitudinal view, a tomographic image flag 2010 and longitudinal image flag 2015, each with nearby text identifying the detected feature as a plaque protrusion, a “LIVE” indicator 2017, and a feature detection overlay control 2020. Also visible is an overlay 2030 showing the locationand extent of the plaque protrusion 740 between the stent 610 and the lumen 320. The overlay 2030 may be translucent and / or colored to highlight the presence of the plaque protrusion.

[0129] Figure 25 is an example screen display 2500 of a radial and / or tomographic cross- sectional view 2040 and longitudinal view 2005 with indicators for dissection detection, according to aspects of the present disclosure. Visible are the tomographic image 2040 from Figure 8, as well as the completed portion 2005A and grayed-out portion 2005B of the longitudinal view, a tomographic image flag 2010 and longitudinal image flag 2015, each with nearby text identifying the detected feature as a dissection, a “LIVE” indicator 2017, and a feature detection overlay control 2020. Also visible is an overlay 2030 showing the location and extent of the dissection 840 between the vessel wall 330 and the lumen 320. The overlay 2030 may be translucent and / or colored to highlight the presence of the dissection.

[0130] Figure 26 is an example screen display 2600 of a radial and / or tomographic cross- sectional view 2040 and longitudinal view 2005 with indicators for in-stent restenosis detection, according to aspects of the present disclosure. Examples of in-stent restenosis include neointimal hyperplasia and neoatherosclerosis. Visible are the tomographic image 2040 from Figure 9, as well as the completed portion 2005A and grayed-out portion 2005B of the longitudinal view, a tomographic image flag 2010 and longitudinal image flag 2015, each with nearby text identifying the detected feature as a neointimal hyperplasia, a “LIVE” indicator 2017, and a feature detection overlay control 2020. Also visible is an overlay 2030 showing the location and extent of the neointimal hyperplasia 940 between the stent 610 and the lumen 320. The overlay 2030 may be translucent and / or colored to highlight the presence of the neointimal hyperplasia. When neoatherosclerosis is detected, the visual representations can be for and / or labeled neoatherosclerosis.

[0131] Figure 27 is an example screen display 2700 of a radial and / or tomographic cross- sectional view 2040 and longitudinal view 2005 with indicators for thrombus detection, according to aspects of the present disclosure. Visible are the tomographic image 2040 from Figure 10, as well as the completed portion 2005 A and grayed-out portion 2005B of the longitudinal view, a tomographic image flag 2010 and longitudinal image flag 2015, each with nearby text identifying the detected feature as a thrombus, a “LIVE” indicator 2017, and a feature detection overlay control 2020. Also visible is an overlay 2030 showing the location andextent of the thrombus 1040 between the vessel wall 330 and the lumen 320. The overlay 2030 may be translucent and / or colored to highlight the presence of the thrombus.

[0132] Figure 28 is an example screen display 2800 of a radial and / or tomographic cross- sectional view 2040 and longitudinal view 2005 with indicators for thin cap fibroatheroma (TCFA) detection, according to aspects of the present disclosure. Visible are the tomographic image 2040 from Figure 11, as well as the completed portion 2005 A and grayed-out portion 2005B of the longitudinal view, a tomographic image flag 2010 and longitudinal image flag 2015, each with nearby text identifying the detected feature as a TCFA, a “LIVE” indicator 2017, and a feature detection overlay control 2020. Also visible is a TCFA metric indicating the angle and cap thickness of the TCFA. Also visible is an overlay 2030 showing the location and extent of the TCFA 1140 between the vessel wall 330 and the lumen 320. The overlay 2030 may be translucent and / or colored to highlight the presence of the TCFA. Also visible is a TCFA descriptor 2850 that lists the percentage of the lipid content of the TCFA within the vessel wall (e.g., concentration or percentage of lipid relative to plaque area as a whole), as well as the thickness of the cap. In some aspects, necrotic core content may also be shown (e.g., concentration or percentage of necrotic core to plaque area as a whole).

[0133] A system of one or more computers can be configured to perform particular operations or actions by virtue of having software, firmware, hardware, or a combination of them installed on the system that in operation causes or cause the system to perform the actions. One or more computer programs can be configured to perform particular operations or actions by virtue of including instructions that, when executed by data processing apparatus, cause the apparatus to perform the actions.

[0134] One general aspect includes an apparatus with a processor circuit configured for communication with an intravascular imaging catheter, where the processor circuit is configured to: control the intravascular imaging catheter to obtain a plurality of intravascular images of a blood vessel for live imaging while positioned within the blood, where the blood vessel may include a disease; in an intravascular image of the plurality of intravascular images, identify at least one of: an anatomical feature associated with the disease; or an irregularity associated with a stent positioned within the blood vessel for treatment of the disease; and output, to a display in communication with the processor circuit, a screen display that may include: the intravascular image; and a visual representation of at least one of the anatomical feature or the irregularityassociated with the stent, where the identification is performed while the live imaging is ongoing, and where the screen display is outputted while the live imaging is ongoing such that the visual representation displayed simultaneously as the intravascular image is displayed to the user for a first time. Other examples of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.

[0135] Implementations may include one or more of the following features. In some aspects, the visual representation may include a graphical overlay located on the intravascular image. In some aspects, the visual representation may include a text description located on or proximate to the intravascular image. In some aspects, the anatomical feature may include at least one of calcium, cracked calcium, a calcium nodule, a dissection, a thrombus, or a thin cap fibroatheroma. In some aspects, the irregularity associated with the stent may include at least one of a restenosis, a plaque protrusion, or a malapposition. In some aspects, the processor circuit is configured to identify at least one of the anatomical feature or the irregularity associated with the stent based on a location of blood flow relative to at least one of the anatomical feature or the stent. In some aspects, the processor circuit is configured to identify at least one of the anatomical feature or the irregularity associated with the stent based on a gap between the stent and a wall of the vessel. In some aspects, to identify at least one of the anatomical feature or the irregularity associated with the stent, the processor circuit is configured to detect movement of the anatomical feature from one intravascular image of the plurality of intravascular images to the next intravascular image of the plurality of intravascular images. In some aspects, the screen display may include a user-selectable option to activate or deactivate an appearance of the visual representation in the screen display. In some aspects, to identify at least one of the anatomical feature or the irregularity associated with the stent, the processor circuit is configured to: provide at least one image of the plurality of intravascular images as an input to a predictive network trained using a further plurality of intravascular images annotated with a plurality of locations of at least one of the anatomical feature or the stent; and generate, in real time, a detection of at least one of the anatomical feature or the irregularity associated with the stent as an output of the predictive network. In some aspects, the predictive network may include a convolutional neural network (CNN). In some aspects, the processor circuit is configured to perform co-registration between the plurality of intravascular images and an x-rayimage of the blood vessel with contrast, where the screen display further may include the x-ray image, where the visual representation is overlaid on the x-ray image and configured to provide the indication to the user. In some aspects, the apparatus may include the intravascular imaging catheter. In some aspects, the intravascular imaging catheter may include an intravascular ultrasound (IVUS) catheter or an optical coherence tomography (OCT) catheter.Implementations of the described techniques may include hardware, a method or process, or computer software on a computer-accessible medium.

[0136] One general aspect includes a method. The method includes controlling, with a processor circuit, an intravascular imaging catheter to obtain a plurality of intravascular images of a blood vessel for live imaging while positioned within the blood, where the blood vessel may include a disease; identifying, with the processor circuit while the live imaging is going, in an intravascular image of the plurality of intravascular images, identify at least one of: an anatomical feature associated with the disease; or an irregularity associated with a stent positioned within the blood vessel for treatment of the disease; outputting, to a display in communication with the processor circuit while the live imaging is ongoing, a screen display that may include: the intravascular image; and a visual representation of at least one of the anatomical feature or the irregularity associated with the stent, where the visual representation displayed simultaneously as the intravascular image is displayed to the user for a first time. Other examples of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.

[0137] A number of variations are possible on the examples and embodiments described above. For example, other features may be detected than those described herein, and / or other types of object detectors may be used. The technology described herein may be applied to other body lumens than blood vessels, and may be applied to any intravascular imaging (IVI) technology, including IVUS and OCT.

[0138] Accordingly, the logical operations making up the embodiments of the technology described herein are referred to variously as operations, steps, objects, elements, components, or modules. Furthermore, it should be understood that these may occur, or be performed or arranged, in any order, unless explicitly claimed otherwise or a specific order is inherently necessitated by the claim language.

[0139] All directional references e.g., upper, lower, inner, outer, upward, downward, left, right, lateral, front, back, top, bottom, above, below, vertical, horizontal, clockwise, counterclockwise, proximal, and distal are only used for identification purposes to aid the reader’s understanding of the claimed subject matter, and do not create limitations, particularly as to the position, orientation, or use of the intravascular feature imaging and detection system. Connection references, e.g., attached, coupled, connected, joined, or “in communication with” are to be construed broadly and may include intermediate members between a collection of elements and relative movement between elements unless otherwise indicated. As such, connection references do not necessarily imply that two elements are directly connected and in fixed relation to each other. The term “or” shall be interpreted to mean “and / or” rather than “exclusive or.” The word “comprising” does not exclude other elements or steps, and the indefinite article “a” or “an” does not exclude a plurality. Unless otherwise noted in the claims, stated values shall be interpreted as illustrative only and shall not be taken to be limiting.

[0140] The above specification, examples and data provide a complete description of the structure and use of exemplary embodiments of the intravascular feature imaging and detection system as defined in the claims. Although various embodiments of the claimed subject matter have been described above with a certain degree of particularity, or with reference to one or more individual embodiments, those skilled in the art could make numerous alterations to the disclosed embodiments without departing from the spirit or scope of the claimed subject matter.

[0141] Still other aspects are contemplated. It is intended that all matter contained in the above description and shown in the accompanying drawings shall be interpreted as illustrative only of particular aspects and not limiting. Changes in detail or structure may be made without departing from the basic elements of the subject matter as defined in the following claims.

Claims

CLAIMSWhat is claimed is:

1. An apparatus, comprising: a processor circuit configured for communication with an intravascular imaging catheter, wherein the processor circuit is configured to: control the intravascular imaging catheter to obtain a plurality of intravascular images of a blood vessel for live imaging while positioned within the blood, wherein the blood vessel comprises a disease; in an intravascular image of the plurality of intravascular images, identify at least one of: an anatomical feature associated with the disease; or an irregularity associated with a stent positioned within the blood vessel for treatment of the disease; and output, to a display in communication with the processor circuit, a screen display comprising: the intravascular image; and a visual representation of at least one of the anatomical feature or the irregularity associated with the stent, wherein the identification is performed while the live imaging is ongoing, and wherein the screen display is outputted while the live imaging is ongoing such that the visual representation displayed simultaneously as the intravascular image is displayed to a user for a first time.

2. The apparatus of claim 1, wherein the visual representation comprises a graphical overlay located on the intravascular image.

3. The apparatus of claim 1, wherein the visual representation comprises a text description located on or proximate to the intravascular image.

4. The apparatus of claim 1, wherein the anatomical feature comprises at least one of calcium, cracked calcium, a calcium nodule, a dissection, a thrombus, or a thin cap fibroatheroma.

5. The apparatus of claim 1, wherein the irregularity associated with the stent comprises at least one of a restenosis, a plaque protrusion, or a malapposition.

6. The apparatus of claim 1, wherein the processor circuit is configured to identify at least one of the anatomical feature or the irregularity associated with the stent based on a location of blood flow relative to at least one of the anatomical feature or the stent.

7. The apparatus of claim 1, the processor circuit is configured to identify at least one of the anatomical feature or the irregularity associated with the stent based on a gap between the stent and a wall of the vessel.

8. The apparatus of claim 1, wherein, to identify at least one of the anatomical feature or the irregularity associated with the stent, the processor circuit is configured to detect movement of the anatomical feature from one intravascular image of the plurality of intravascular images to the next intravascular image of the plurality of intravascular images.

9. The apparatus of claim 1, wherein the screen display comprises a user-selectable option to activate or deactivate an appearance of the visual representation in the screen display.

10. The apparatus of claim 1 , wherein, to identify at least one of the anatomical feature or the irregularity associated with the stent, the processor circuit is configured to: provide at least one image of the plurality of intravascular images as an input to a predictive network trained using a further plurality of intravascular images annotated with a plurality of locations of at least one of the anatomical feature or the stent; and generate, in real time, a detection of at least one of the anatomical feature or the irregularity associated with the stent as an output of the predictive network.

11. The apparatus of claim 10, wherein the predictive network comprises a convolutional neural network (CNN).

12. The apparatus of claim 1, wherein the processor circuit is configured to perform co-registration between the plurality of intravascular images and an x-ray image of the blood vessel with contrast, wherein the screen display further comprises the x-ray image, wherein the visual representation is overlaid on the x-ray image.

13. The apparatus of claim 1, further comprising the intravascular imaging catheter.

14. The apparatus of claim 13, wherein the intravascular imaging catheter comprises an intravascular ultrasound (IVUS) catheter or an optical coherence tomography (OCT) catheter.

15. A method, comprising: controlling, with a processor circuit, an intravascular imaging catheter to obtain a plurality of intravascular images of a blood vessel for live imaging while positioned within the blood, wherein the blood vessel comprises a disease; identifying, with the processor circuit while the live imaging is going, in an intravascular image of the plurality of intravascular images, identify at least one of: an anatomical feature associated with the disease; or an irregularity associated with a stent positioned within the blood vessel for treatment of the disease; outputting, to a display in communication with the processor circuit while the live imaging is ongoing, a screen display comprising: the intravascular image; and a visual representation of at least one of the anatomical feature or the irregularity associated with the stent, wherein the visual representation displayed simultaneously as the intravascular image is displayed to a user for a first time.

Images