Detection and visualization of intraluminal treatment anomalies based on intraluminal imaging

Abstract

Disclosed is an intravascular imaging system including processor circuitry configured to communicate with an intravascular imaging catheter designed and shaped for positioning within the lumen of a blood vessel. The processor circuitry is configured to receive multiple intravascular images acquired by the intravascular imaging catheter when positioned within the lumen, wherein the multiple intravascular images correspond to multiple locations along the length of the blood vessel. The processor circuitry is further configured to: determine a measurement associated with the lumen for each of the multiple intravascular images; generate a curve representing the variation of the measurement along the length of the blood vessel; detect the condition of the blood vessel based on the curve; and display a graphical representation of the condition.

Description

Technical Field

[0001] The subject matter described herein relates to systems for medical imaging and data collection. In particular, the disclosed system provides a method for detecting and managing abnormalities within a collection of intraluminal medical images. This system has a particular, but not exclusive, use in the diagnosis and management of vascular diseases. Background Technology

[0002] Various types of intraluminal (also known as intravascular) imaging and measurement systems are used in the diagnosis and treatment of diseases. For example, intravascular ultrasound (IVUS) imaging is widely used in interventional cardiology as a diagnostic tool for visualizing blood vessels within a patient's body. This can aid in assessing diseased blood vessels (e.g., arteries and veins) within the body to determine treatment needs, optimize treatment, and / or evaluate the effectiveness of treatments (e.g., angioplasty and stent placement, IVC filter retrieval, EVAR and FEVAR (which are similar in abdominal features) plaque resection, and thrombectomy). Different diseases or medical procedures produce physical characteristics with varying sizes, structures, densities, water content, and imaging sensor accessibility. For example, deep vein thrombosis (DVT) produces clots of blood cells, while post-thrombotic syndrome (PTS) produces webbing or other residual structural effects in the vessel that are compositionally similar to the vessel wall itself and therefore difficult to distinguish from it. Stents are dense (e.g., metallic) objects that can be placed in a blood vessel or lumen to keep the vessel or lumen open to a specific diameter. Compression occurs when an external anatomical structure impacts a blood vessel or lumen, causing it to constrict. A thrombus can form via plaque rupture or other pathological conditions, such as when blood pools inside a vessel due to compression. Compression, plaque formation, and thrombosis are all examples of narrowing (e.g., narrowing of a blood vessel).

[0003] In some cases, intraluminal medical imaging is performed using an IVUS catheter that includes one or more ultrasound transducers. The IVUS device can be delivered into the blood vessel, and the IVUS catheter can be guided to the area to be imaged. The transducer emits ultrasound energy and receives the ultrasound echoes reflected from the blood vessel. The ultrasound echoes are processed to create an image of the vessel of interest. The image of the vessel of interest can include one or more lesions or blockages in the vessel. Stents can be placed inside the vessel to treat these blockages, and intraluminal imaging can be performed to see where the stent is positioned within the vessel. Other types of treatment include thrombectomy, ablation, angioplasty, drug administration, etc.

[0004] Current intraluminal imaging systems cannot easily detect and visualize certain post-treatment conditions (e.g., stent dog-boning, suboptimal stent coverage, and insufficient stent expansion) and natural conditions (e.g., diffuse disease and tapering of anatomical structures).

[0005] Information included in the background section of this specification (including any references cited herein and any descriptions or discussions thereof) is for technical reference purposes only and should not be considered as the subject matter defining the scope of this disclosure. Summary of the Invention

[0006] What is disclosed is a system for advantageously detecting and displaying post-treatment abnormalities within a body lumen. The current disclosure provides systems, apparatus, and methods for detecting, for example, changes in the value of the luminal area, slope, and / or gradient along the length of the lumen, and using this gradient to detect the presence of post-treatment abnormalities (e.g., stent dog-boning, stent underexpansion, suboptimal stent coverage of the lesion) and / or natural conditions (e.g., diffuse disease and anatomical conization). Visual identification of such abnormalities can be difficult, subjective, and time-consuming, while automated detection is rapid, systematic, and repeatable. This system is hereinafter referred to as an intraluminal treatment abnormality detection system.

[0007] The intraluminal treatment anomaly detection system disclosed herein has a particular, but not exclusive, use in intraluminal ultrasound imaging procedures. A system of one or more computers can be configured to perform specific operations or actions by means of software, firmware, hardware, or a combination thereof installed on the system that causes the system to perform actions during operation. One or more computer programs can be configured to perform specific operations or actions by means of instructions that, when executed by a data processing device, cause the device to perform the actions. One general aspect of the intravascular treatment anomaly detection system includes an intravascular imaging system comprising processor circuitry configured to communicate with an intravascular imaging catheter designed and shaped for positioning within the lumen of a blood vessel. The processor circuitry is configured to: receive multiple intravascular images acquired by the intravascular imaging catheter when positioned within the lumen, wherein the multiple intravascular images correspond to multiple locations along the length of the blood vessel; calculate a dimension associated with the lumen or determine a measurement associated with the lumen for each of the multiple intravascular images; generate a curve or other graphical representation representing the variation of the measurement along the length of the blood vessel; detect the condition of the blood vessel based on the curve or other graphical representation; and output a graphical representation of the condition to a display communicating with the processor circuitry. Other embodiments of this aspect include corresponding computer systems, apparatuses, and computer programs recorded on one or more computer storage devices, all configured to perform the actions of the method.

[0008] Implementations may include one or more of the following features. In the system, the processor circuitry determines the measurement result by averaging the amount of the measurement result at one of the plurality of locations with the amount of the measurement result at another of the plurality of locations. In the system, the processor circuitry calculates the dimension or determines the measurement result by calculating or determining at least one of the cross-sectional area of ​​the lumen or the diameter of the lumen. In the system, the processor circuitry detects the condition by detecting at least one of the anatomical conization of the blood vessel or the presence of diffuse disease in the blood vessel. In the system, the condition includes the anatomical conization, and wherein the processor circuitry detects the condition by detecting that, for multiple locations within a segment of the blood vessel, the plaque burden of the blood vessel does not exceed a threshold. In the system, the condition includes the diffuse disease, and wherein the processor circuitry detects the condition by detecting that, for multiple locations within a segment of the blood vessel, the plaque burden of the blood vessel exceeds a threshold. In the system, one or more of the multiple intravascular images include a stent positioned within the lumen, and wherein the processor circuitry detects the condition of the vessel by detecting a post-treatment condition. In the system, the measurement results include the spacing between the struts of the stent. In the system, the processor circuitry detects the condition by detecting at least one of dog-boning of the stent, under-expansion of the stent, or incomplete coverage of the lesion by the stent. In the system, the condition is dog-boning of the stent, and wherein the processor circuitry detects the condition by determining that the rate of change of the measurement results within the stent exhibits an inflection point and that the rate of change of the measurement results within the stent exceeds a threshold proximal or distal to the inflection point. In the system, the condition is under-expansion of the stent, and wherein the processor circuitry detects the condition by determining that a first value of the measurement results for a distance beyond the edge of the stent exceeds a second value of the measurement results at the edge of the stent by an amount greater than a threshold. In the system, the condition is incomplete coverage of the lesion by the stent, and wherein the processor circuit detects the condition by detecting: a first value of the measurement result for a first distance beyond the edge of the stent being less than a second value of the measurement result at the edge of the stent by at least a threshold amount; and plaque burden for the second distance beyond the edge of the stent exceeding the threshold.In the system, the processor circuitry is configured to receive extravascular images of the blood vessel and co-register the plurality of intravascular images to the plurality of locations along the length of the blood vessel in the extravascular images. In the system, the processor circuitry outputting a graphical representation of the condition includes: the processor circuitry outputting an indication of the condition along the length of the blood vessel in the extravascular images. The system also includes: the intravascular imaging catheter, wherein the intravascular imaging catheter comprises an intravascular ultrasound (IVUS) imaging catheter. Embodiments of the described technology may include hardware, methods, or processes, or computer software on a computer-accessible medium.

[0009] One general aspect includes an intravascular imaging method comprising: receiving, at a processor circuit in communication with an intravascular imaging catheter, multiple intravascular images acquired by the catheter when the catheter is positioned within the lumen of a blood vessel, wherein the multiple intravascular images correspond to multiple locations along the length of the blood vessel; calculating, by the processor circuit, a dimension associated with the lumen or determining a measurement associated with the lumen for each of the multiple intravascular images; generating, by the processor circuit, a curve or graphical representation of the variation of the measurement along the length of the blood vessel; detecting a condition of the blood vessel based on the curve or graphical representation; and outputting a graphical representation of the condition to a display in communication with the processor circuit. Other embodiments of this aspect include corresponding computer systems, apparatuses, and computer programs recorded on one or more computer storage devices, all configured to perform the actions of the method.

[0010] One general aspect includes an intravascular ultrasound (IVUS) imaging system comprising processor circuitry configured to communicate with an IVUS imaging catheter designed and shaped for positioning within the lumen of a blood vessel. The processor circuitry is configured to: receive multiple IVUS images acquired by the IVUS imaging catheter when positioned within the lumen, wherein the multiple IVUS images correspond to multiple locations along the length of the blood vessel; determine a measurement associated with the lumen for each of the multiple IVUS images; generate a curve representing the variation of the measurement along the length of the blood vessel; detect a condition of the blood vessel based on the curve, wherein the condition includes at least one of the following: dog-boning of a stent within the blood vessel, insufficient expansion of the stent, incomplete coverage of the lesion by the stent, diffuse disease, or tapering of an anatomy; and output a graphical representation of the condition to a display communicating with the processor circuitry.

[0011] This abstract is provided to present selected concepts in a simplified form, which are further described in the detailed embodiments below. This abstract is not intended to identify key or essential features of the claimed subject matter, nor is it intended to limit the scope of the claimed subject matter. The written description of various embodiments of this disclosure below provides a broader presentation of the features, details, uses, and advantages of the intraluminal treatment anomaly detection system as defined in the claims, and this presentation is illustrated in the accompanying drawings. Attached Figure Description

[0012] Illustrative embodiments of this disclosure will be described with reference to the accompanying drawings, in which:

[0013] Figure 1 This is an illustrative schematic diagram of an intraluminal imaging system based on various aspects of this disclosure.

[0014] Figure 2 The illustration shows a blood vessel containing a narrowing.

[0015] Figure 3 The illustration shows a blood vessel containing narrowing that has been expanded with a stent.

[0016] Figure 4 An example screen display of an intraluminal imaging system according to various aspects of this disclosure is shown.

[0017] Figure 5 An example screen display of an intraluminal imaging system according to at least one embodiment of the present disclosure is shown.

[0018] Figure 6An example screen display of an intraluminal imaging system according to at least one embodiment of the present disclosure is shown.

[0019] Figure 7 A schematic diagram of a blood vessel whose walls have been expanded with a stent having proximal and distal edges, according to various aspects of this disclosure, is shown.

[0020] Figure 8 This is a flowchart illustrating the steps of an exemplary intraluminal treatment anomaly detection system according to at least one embodiment of the present disclosure.

[0021] Figure 9 This is a flowchart of an example stent underexpansion detection algorithm according to at least one embodiment of the present disclosure.

[0022] Figure 10 This is a flowchart of an example dog-boning detection algorithm according to at least one embodiment of the present disclosure.

[0023] Figure 11 This is a flowchart of a different example dog-boning detection algorithm 870 according to at least one embodiment of the present disclosure.

[0024] Figure 12 This is a flowchart of an example suboptimal bracket placement detection algorithm 880 according to at least one embodiment of the present disclosure.

[0025] Figure 13 This is a flowchart of an example anatomical tapering and diffuse disease detection algorithm according to at least one embodiment of the present disclosure.

[0026] Figure 14 This is a schematic diagram of a processor circuit according to an embodiment of the present disclosure.

[0027] Figure 15 A schematic diagram is shown of a blood vessel whose wall has been expanded using a stent exhibiting dog-boning, according to various aspects of this disclosure. Detailed Implementation

[0028] This disclosure generally relates to medical imaging, including imaging associated with a patient's body lumens using an intraluminal imaging device. In some cases, intraluminal imaging is performed using an IVUS device comprising one or more ultrasound transducers. The IVUS device can be delivered into the blood vessel and the IVUS catheter can be guided to the area to be imaged. The transducer emits ultrasound energy and receives ultrasound echoes reflected from the blood vessel. The ultrasound echoes are processed to create an image of the vessel of interest. The image of the vessel of interest may include one or more lesions or blockages in the vessel. Stents can be placed within the vessel to treat these blockages, and intraluminal imaging can be performed to view the placement of the stent within the vessel. Other types of treatment include thrombectomy, ablation, angioplasty, drug administration, etc.

[0029] Disclosed are systems for the advantageous detection and visualization of post-treatment abnormalities within a body lumen (e.g., a blood vessel). The current disclosure provides systems, apparatus, and methods for detecting gradients in the luminal area along the length of the lumen and using these gradients to detect the presence of abnormalities, including dog-boning, suboptimal coverage, and diffuse disease. Abnormalities described in this disclosure (including stent dog-boning, suboptimal stent coverage (e.g., incomplete stent coverage of lesions), diffuse disease, and anatomical tapering) cannot be readily visualized in current intraluminal imaging devices, nor can these features be clearly visualized to clinicians and automatically represented as significant time savings, and may also fail to facilitate timely and effective treatment. Accurate disease or abnormality detection can influence not only stent placement decisions but also treatment procedures, such as the selection of a balloon or other treatment devices, including but not limited to plaque resection devices. The logic and algorithms disclosed herein can be used to review any automated measurement system, such as those used in pre-PCI and post-PCI case analysis. The system can also be used for the education and training of novice users. This system is hereinafter referred to as an intraluminal treatment abnormality detection system.

[0030] The apparatus, system, and method described herein can include one or more features described in the following documents: U.S. Provisional Application US 62 / 750983 (Representative File No. 2018PF01112-44755.2000PV01) (filed October 26, 2018), U.S. Provisional Application US 62 / 751268 (Representative File No. 2018PF01160-44755.1997PV01) (filed October 26, 2018), U.S. Provisional Application US 62 / 751289 (Representative File No. 2018PF01159-44755.1998PV01) (filed October 26, 2018), and U.S. Provisional Application US... The following are references: US 62 / 750996 (Representative File No. 2018PF01145-44755.1999PV01) (filed October 26, 2018), US Provisional Application US 62 / 751167 (Representative File No. 2018PF01115-44755.2000PV01) (filed October 26, 2018), and US Provisional Application US 62 / 751185 (Representative File No. 2018PF01116-44755.2001PV01) (filed October 26, 2018). Although this paper does not fully elaborate on the above-mentioned documents, each of the above documents is incorporated into this paper in its entirety through citation.

[0031] The apparatus, system, and method described herein may also include one or more features described in the following documents: U.S. Provisional Application US 62 / 642847 (Agency File No. 2017PF02103) (filed March 14, 2018) (and its non-provisional application US Serial No. 16 / 351175, filed March 12, 2019), U.S. Provisional Application US 62 / 712009 (Agency File No. 2017PF02296) (filed July 30, 2018), U.S. Provisional Application US 62 / 711927 (Agency File No. 2017PF02101) (filed July 30, 2018), and U.S. Provisional Application US 62 / 643366 (Agency Case No. 2017PF02365) (filed on March 15, 2018 (and from its non-provisional application with U.S. Serial No. US16 / 354970 filed on March 15, 2019), although the above-mentioned documents are not fully described herein, each of the above-mentioned documents is incorporated herein by reference in its entirety.

[0032] This disclosure substantially assists clinicians in identifying intravascular treatment abnormalities using data available in intraluminal pullback image sequences by calculating and plotting a filtering gradient associated with at least one per-frame metric. When implemented on a medical imaging console (e.g., an IVUS imaging console) communicating with a medical imaging sensor (e.g., an intraluminal ultrasound sensor), the intraluminal treatment abnormality detection system disclosed herein saves both time and improves the detection certainty and location certainty for specific abnormality types. This improved approach transforms an imprecise, judgment-driven process into a quantitative, repeatable procedure that requires fewer and simpler steps from clinicians or other users. For example, the improved approach described above is needed where human judgment or vision is typically required to estimate where an abnormality might be present in the lumen, whereas such a routine is not necessary. This unconventional approach improves the functionality of medical imaging consoles and sensors by standardizing and automating the detection criteria for abnormalities.

[0033] An intraluminal procedure anomaly detection system can be implemented as a collection of logical branches and mathematical operations, capable of displaying its output on a monitor and manipulating its output via a control process running on a processor that accepts user input from a user interface (e.g., a keyboard, mouse, or touchscreen interface) and communicates with one or more medical imaging sensors (e.g., an intraluminal ultrasound sensor). In this regard, the control process performs specific operations in response to different inputs or selections made by the user at the start of the imaging procedure, and can also respond to inputs made by the user during the procedure. Certain structures, functions, and operations of the processor, display, sensors, and user input system are known in the art, while other structures, functions, and operations are described herein to specifically implement novel features or aspects of this disclosure.

[0034] These descriptions are provided for illustrative purposes only and should not be considered as limiting the scope of the intraluminal treatment anomaly detection system. Features may be added, deleted, or modified without departing from the spirit of the subject matter for which protection is sought.

[0035] To facilitate an understanding of the principles of this disclosure, reference will now be made to embodiments illustrated in the accompanying drawings, and these embodiments will be described using specific language. Nevertheless, it should be understood that this disclosure is not intended to limit its scope. Any changes and further modifications to the described devices, systems, and methods, as well as any further applications of the principles of this disclosure, are fully contemplated and included within this disclosure, as will commonly conceived by those skilled in the art to which this disclosure pertains. In particular, it is fully contemplated that features, components, and / or steps described with respect to one embodiment may be combined with features, components, and / or steps described with respect to other embodiments of this disclosure. However, for the sake of brevity, numerous iterative forms of these combinations will not be described separately.

[0036] Figure 1 This is an illustrative schematic diagram of an intraluminal imaging system, including an intraluminal treatment anomaly detection system, according to various aspects of this disclosure. In some embodiments, the intraluminal imaging system 100 can be an intravascular ultrasound (IVUS) imaging system. 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 resonance imaging (MRI), or other imaging techniques, instruments, and methods. The intraluminal device 102 may be designed in size and shape and / or otherwise structurally arranged to be positioned within a patient's body lumen. For example, in various embodiments, the intraluminal device 102 can be a catheter, guidewire, guiding catheter, pressure guidewire, and / or flow guidewire. In some cases, the system 100 may include additional components and / or may be available without them. Figure 1 The system is implemented with one or more of the components shown. For example, system 100 may omit the external imaging system 132.

[0037] The intraluminal imaging system 100 (or intravascular imaging system) can be any type of imaging system suitable for use in a patient's lumen or vascular system. In some embodiments, the intraluminal imaging system 100 is an intraluminal ultrasound (IVUS) imaging system. In other embodiments, the intraluminal imaging system 100 may include a system configured for imaging modalities such as forward-looking intraluminal ultrasound (FL-IVUS) imaging, intraluminal photoacoustic (IVPA) imaging, intracardiac echocardiography (ICE), transesophageal echocardiography (TEE), and / or other suitable imaging modalities.

[0038] It should be understood that system 100 and / or device 102 can be configured to acquire any suitable intraluminal imaging data. In some embodiments, device 102 may include imaging components of any suitable imaging modality, such as optical coherence tomography (OCT). In some embodiments, device 102 may include any suitable non-imaging components, including pressure sensors, flow sensors, temperature sensors, optical fibers, reflectors, mirrors, prisms, ablation elements, radio frequency (RF) electrodes, conductors, or combinations thereof. Typically, device 102 can include imaging elements to acquire intraluminal imaging data associated with lumen 120. Device 102 may be sized and shaped (and / or configured) for insertion into a patient's blood vessel or lumen 120.

[0039] System 100 can be deployed in a catheterization laboratory with a control room. Processing system 106 can be located in the control room. Optionally, processing system 106 can be located elsewhere, such as within the catheterization laboratory itself. The catheterization laboratory may include a sterile site, while its associated control room may be sterile or non-sterile depending on the procedures to be performed and / or the healthcare facility. The catheterization laboratory and control room can be used to perform any number of medical imaging procedures, such as angiography, fluorescein imaging, CT, IVUS, virtual histology (VH), forward-looking IVUS (FL-IVUS), intraluminal photoacoustic (IVPA) imaging, fractional flow reserve (FFR) determination, coronary flow reserve (CFR) determination, optical coherence tomography (OCT), computed tomography, intracardiac echocardiography (ICE), forward-looking ICE (FLICE), intraluminal palpation imaging, transesophageal echocardiography, fluorescein imaging, and other medical imaging modalities or combinations thereof. In some embodiments, the device 102 can be controlled from a remote location (e.g., a control room), so that the operator does not need to be close to the patient.

[0040] Intraluminal device 102, PIM 104, monitor 108, and external imaging system 132 can be directly or indirectly communicatively coupled to processing system 106. These components can be communicatively coupled to medical processing system 106 via wired connections (e.g., standard copper or fiber optic links) and / or via wireless connections (using IEEE 802.11 Wi-Fi, Ultra-Wideband (UWB), FireWire, Wireless USB, or another high-speed wireless networking standard). Processing system 106 can be communicatively coupled to one or more data networks, such as a TCP / IP-based local area network (LAN). In other embodiments, different protocols can be utilized, such as Synchronous Optical Network (SONET). In some cases, processing system 106 can be communicatively coupled to a wide area network (WAN). Processing system 106 can utilize network connectivity to access various resources. For example, processing system 106 can communicate via network connectivity with a Medical Digital Imaging and Communication (DICOM) system, a Picture Archiving and Communication (PACS) system, and / or a Hospital Information System (HIS).

[0041] An intraluminal ultrasound imaging device 102 emits ultrasonic energy at a high level from a transducer array 124 included in a scanner assembly 110, which is mounted near the distal end of the intraluminal device 102. The ultrasonic energy is reflected by tissue structures in the medium surrounding the scanner assembly 110 (e.g., lumen 120), and the transducer array 124 receives the ultrasonic echo signals. The scanner assembly 110 generates one or more electrical signals representing the ultrasonic echoes. The scanner assembly 110 can include one or more individual ultrasonic sensors and / or transducer arrays 124 in any suitable configuration (e.g., planar array, curved array, circular array, ring array, etc.). For example, in some instances, the scanner assembly 110 can be a one-dimensional or two-dimensional array. In some instances, the scanner assembly 110 can be a rotating ultrasound device. The active region of the scanner assembly 110 can include one or more segments (e.g., one or more rows, columns, and / or one or more orientations) of one or more transducer materials and / or ultrasonic elements that can be uniformly or independently controlled and activated. The active region of the scanner assembly 110 can be patterned or structured on a variety of basic or complex geometries. The scanner assembly 110 can be configured for a side-view orientation (e.g., ultrasonic energy emitted perpendicular to and / or orthogonal to the longitudinal axis of the intraluminal device 102) and / or a front-view 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 in a proximal or distal direction at an angle relative to the longitudinal axis. In some embodiments, the ultrasonic energy emission can be electronically redirected by selectively triggering one or more transducer elements of the scanner assembly 110.

[0042] The ultrasonic sensors(s) of the scanner assembly 110 can be piezoelectric micromechanical ultrasonic transducers (PMUTs), capacitive micromechanical ultrasonic transducers (CMUTs), single crystals, lead zirconate titanate (PZT), PZT composites, other suitable transducer types, and / or combinations thereof. In embodiments, the ultrasonic transducer array 124 can include any suitable number of individual transducer elements or acoustic elements (between 1 and 1000 acoustic elements), for example, 2 acoustic elements, 4 acoustic elements, 36 acoustic elements, 64 acoustic elements, 128 acoustic elements, 500 acoustic elements, 812 acoustic elements, and / or more and fewer acoustic elements.

[0043] The received echo signal is transmitted from PIM 104 to processing system 106, where the ultrasound image (including flow information) is reconstructed and displayed on monitor 108. The console or processing system 106 may include a processor and memory. Processing system 106 is capable of operating features that facilitate the intraluminal imaging system 100 described herein. For example, the processor may execute computer-readable instructions stored on a transient tangible computer-readable medium.

[0044] PIM 104 facilitates signal communication between processing system 106 and scanner components included in in-lumen device 102. This communication may include: providing commands to one or more integrated circuit controller chips within in-lumen device 102; selecting one or more specific elements on transducer array 124 for transmission and reception; providing a transmit trigger signal to one or more integrated circuit controller chips to activate transmitter circuitry to generate electrical pulses that excite the selected one or more transducer array elements; and / or receiving echo signals received from the selected one or more transducer array elements via amplifiers included on one or more integrated circuit controller chips. In some embodiments, PIM 104 performs preliminary processing on the echo data before relaying the data to processing system 106. In examples of such embodiments, PIM 104 performs amplification, filtering, and / or aggregation of the data. In embodiments, PIM 104 also supplies high-voltage and low-voltage DC power to support the operation of in-lumen device 102, including circuitry within scanner component 110.

[0045] Processing system 106 receives echo data from scanner assembly 110 via PIM 104 and processes the data to reconstruct an image of tissue structures in the medium surrounding scanner assembly 110. Typically, device 102 can be used within any suitable anatomical structure and / or body lumen of the patient. Processing system 106 outputs image data such that an image of a blood vessel or lumen 120, e.g., a cross-sectional IVUS image of lumen 120, is displayed on monitor 108. Lumen 120 can represent naturally fluid-filled or fluid-surrounded structures and artificial fluid-filled or fluid-surrounded structures. Lumen 120 can be within the patient's body. Lumen 120 can be a blood vessel, e.g., an artery or vein of the patient's vascular system (including the cardiac vascular system, peripheral vascular system, neurovascular system, renal vascular system, and / or any other suitable lumen within the body). For example, device 102 can be used to examine any number of anatomical locations and tissue types, including but not limited to organs (including the liver, heart, kidneys, gallbladder, pancreas, and lungs), ducts, intestines, nervous system structures (including the brain, dura mater, spinal cord, and peripheral nerves); the urinary tract; and valves within the blood vessels, chambers, or other parts of the heart and / or other systems of the body. In addition to natural structures, device 102 can also be used to examine artificial structures, such as, but not limited to, heart valves, stents, shunts, filters, and other devices.

[0046] The controller or processing system 106 may include processing circuitry having one or more processors that communicate with memory and / or other suitable tangible computer-readable storage media. The controller or processing system 106 may be configured to perform one or more aspects of this disclosure. In some embodiments, the processing system 106 and the monitor 108 are separate components. In other embodiments, the processing system 106 and the monitor 108 are integrated into a single component. For example, system 100 may include a touchscreen device, including a housing with a touchscreen display and a processor. System 100 may include any suitable input device (e.g., a touchpad or touchscreen display, keyboard / mouse, joystick, buttons, etc.) for a user to select options displayed on the monitor 108. The processing system 106, monitor 108, input devices, and / or combinations thereof may be referred to as the controller of system 100. This controller is capable of communicating with device 102, PIM 104, processing system 106, monitor 108, input devices, and / or other components of system 100.

[0047] In some embodiments, the intraluminal device 102 includes features similar to those of conventional solid-state IVUS catheters (e.g., EagleEye® catheters available from Volcano Corporation and those disclosed in U.S. Patent 7,846,101, which is incorporated herein by reference in its entirety). For example, the intraluminal device 102 may include a scanner assembly 110 near the distal end of the intraluminal device 102 and a transmission harness 112 extending along the longitudinal body of the intraluminal device 102. The cable or transmission harness 112 may include multiple conductors, including one, two, three, four, five, six, seven, or more conductors.

[0048] The transmission harness 112 terminates in a PIM connector 114 at the proximal end of the intraluminal device 102. The PIM connector 114 electrically couples the transmission harness 112 to the PIM 104 and physically couples the intraluminal device 102 to the PIM 104. In embodiments, the intraluminal device 102 also includes a guidewire outlet 116. Thus, in some instances, the intraluminal device 102 is a quick-change catheter. The guidewire outlet 116 allows distal insertion of a guidewire 118 to guide the intraluminal device 102 through the lumen 120.

[0049] Monitor 108 may be a display device, such as a computer monitor or other type of screen. Monitor 108 can be used to display optional prompts, instructions, and visualizations of imaging data to a user. In some embodiments, monitor 108 can be used to provide a user with a process-specific workflow to complete an intraluminal imaging procedure. This workflow may include performing pre-stent placement planning to determine lumen status and stent potential, and may also include post-stent placement checks to determine the status of the stent that has been positioned in the lumen. It can be displayed or visualized in any of a variety of different ways (e.g., as shown in the image). Figure 4-6 (As shown) present the workflow to the user.

[0050] External imaging system 132 can be configured to acquire X-ray images, radiographic images, angiographic images (e.g., with contrast), and / or fluorescence examination images (e.g., without contrast) of a patient's body (including blood vessels 120). External imaging system 132 can also be configured to acquire computed tomographic images of a patient's body (including blood vessels 120). External imaging system 132 may include an external ultrasound probe configured to acquire ultrasound images of the patient's body (including blood vessels 120) when positioned outside the body. In some embodiments, system 100 includes other imaging modalities (e.g., MRI) to acquire images of the patient's body (including blood vessels 120). Processing system 106 can combine intraluminal images acquired by intraluminal device 102 to utilize images of the patient's body.

[0051] Figure 2 The illustration shows a blood vessel 200 containing a stenosis 230. The stenosis 230 can occur inside the vessel wall (e.g., a thrombus, clot, or plaque) or outside the vessel wall 210 (e.g., compression) and can restrict the flow of blood 220. Compression can be caused by other anatomical structures outside the blood vessel 200, including but not limited to tendons, ligaments, or adjacent lumens.

[0052] Figure 3 The illustration shows a blood vessel 200 containing a stenosis 230 and dilated with a stent 340. The stent 340 replaces and blocks the stenosis 230, thereby pushing the vessel wall 210 outward and reducing the restriction on blood flow 220. Other treatment options for relieving the blockage may include, but are not limited to, thrombectomy, ablation, angioplasty, and drug administration. However, in most cases, accurate and timely intravascular imaging of the affected area and accurate and detailed knowledge of the location, orientation, length, and volume of the affected area before, during, or after treatment are highly desirable.

[0053] Figure 4 An example screen display 400 of an intraluminal imaging system 100 according to various aspects of this disclosure is shown. The screen display 400 includes a tomographic intravascular image 410 of the vessel 200 (e.g., an IVUS image), a route map image 420 of the same vessel 200 acquired from an external source (e.g., an X-ray fluorescence image), and an image longitudinal display (ILD) 430 including longitudinal cross-sections of multiple tomographic intravascular images 410. In this example, both the route map image 420 and the ILD 430 include location markers 425 indicating the current (co-registered) position of the intravascular probe 102 (and therefore the tomographic images 410) within the vessel 200. The ILD 430 also includes region of interest markers 435, which can, for example, identify the location of diseased segments of the vessel 200. Co-registration of the intraluminal image 410 with the route map image 420 allows clinicians or other users to see at a glance precisely where the intraluminal imaging probe 102 is currently imaging within the vessel 200. This locational certainty can be associated with improved clinical outcomes. Aspects of co-registration are described, for example, in U.S. Patents US 7,930,014 and US 8,298,147, which are incorporated herein by reference in their entirety.

[0054] This example also shows multiple user interface controls 440.

[0055] Figure 5An example screen display 500 of an intraluminal imaging system 100 according to at least one embodiment of the present disclosure is shown. The screen display 500 includes three cross-sectional images (e.g., axial or radial cross-sectional images, also referred to as tomographic images): a proximal reference frame 510, a target frame 520, and a distal reference frame 530. In the example, the proximal and distal reference frames 510 and 530 represent healthy tissue proximal and distal to a narrowing or other constriction in a lumen 120 (e.g., a blood vessel 200), and the health status of the tissue is represented by a diameter measurement 540, which can be used to determine the cross-sectional area of ​​the lumen 120. In the example, the target frame 520 represents the narrowest portion of a diseased segment of the lumen 120, as indicated by a detected lumen boundary or perimeter 550, which can be used to determine the diameter or cross-sectional area of ​​the lumen 120.

[0056] In step 720, the system identifies the lumen boundary or blood vessel boundary 410 through image processing and image recognition algorithms. Examples of boundary detection, image processing, image analysis, and / or pattern recognition include: U.S. Patent 6,200,268 (titled "VASCULAR PLAQUE CHARACTERIZATION", granted March 13, 2001, inventors D. Geoffrey Vince, Barry D. Kuban, and Anuja Nair), U.S. Patent 6,381,350 (titled "INTRAVASCULAR ULTRASONIC ANALYSIS USING ACTIVE CONTOUR METHOD AND SYSTEM", granted April 30, 2002, inventors Jon D. Klingensmith, D. Geoffrey Vince, and Raj Shekhar), U.S. Patent 7,074,188 (titled "SYSTEM AND METHOD OF CHARACTERIZING VASCULAR TISSUE", granted July 11, 2006, inventors Anuja Nair, D. Geoffrey Vince, Jon D. Klingensmith, and Barry D. Kuban), and U.S. Patent US... US Patent No. 7175597 (titled "NON-INVASIVE TISSUE CHARACTERIZATION SYSTEM AND METHOD", granted February 13, 2007, inventors: D. Geoffrey Vince, Anuja Nair, and Jon D. Klingensmith), US Patent No. 7215802 (titled "SYSTEM AND METHOD FOR VASCULAR BORDER DETECTION", granted May 8, 2007, inventors: Jon D. Klingensmith, Anuja Nair, Barry D. Kuban, and D. Geoffrey Vince), and US Patent No. 7359554 (titled "SYSTEM AND METHOD FORIDENTIFYING A VASCULAR BORDER", granted April 15, 2008, inventors: Jon D. Klingensmith, D. Geoffrey Vince, Anuja Nair, and Barry D.Kuban), and U.S. Patent 7463759 (titled "SYSTEM AND METHOD FOR VASCULAR BORDER DETECTION", granted December 9, 2008, inventors Jon D. Klingensmith, Anuja Nair, Barry D. Kuban, and D. Geoffrey Vince), whose teachings are incorporated herein by reference in their entirety.

[0057] Also visible is the longitudinal display 555, which includes a stent probability map 560, where the Y-axis represents probability and the X-axis represents longitudinal position. A low stent probability value or a zero stent probability value for a given position indicates that no high-density (e.g., metallic) object was detected in the tomographic frame captured at that position, while a high value indicates multiple detections of high-density points that may represent struts of the metallic stent 340. Pattern recognition can be used to detect the proximal and distal edges of the stent 340 that has been placed within the lumen 120, both in this map and in the tomographic image itself. In the example shown in the figures, location markers 510a, 520a, and 530a mark the positions of the proximal reference frame 510, the target frame 520, and the distal reference frame 530 captured within the vessel on the longitudinal display 555, along with a symmetrical graphic diameter or area indicator 570 around an invisible horizontal centerline. The diameter or area indicator 570 indicates the lumen diameter at each point, determined based on tomographic images 410 taken at each location within the lumen 120. In the example, the diameter or area indicator 570 is smoothed (e.g., showing the average of the current frame and 2, 4, or 6 surrounding frames) to reduce the effect of normal frame-to-frame variation in diameter or area measurements. In some instances, the longitudinal display may be a stack of tomographic image frames, thus showing the actual lumen profile. In this case, the lumen profile does not need to be symmetrical, as it follows the actual contour of the blood vessel, e.g. Figure 4 As shown in ILD 430. In other instances, the longitudinal display may be a graphical representation of the actual lumen profile, which may again not necessarily be symmetrical.

[0058] Below the diameter indicator 570, which is displayed vertically, a vertical gradient indicator 580 can also be seen. The vertical gradient indicator 580 indicates the slope or gradient of the diameter indicator at a point along the vertical display 555. In the example, the gradient indicator 580 uses a change in the shading, brightness, or intensity of a first color to indicate the value of the diameter indicator 570 exceeding a positive threshold, while a change in the shading, brightness, or intensity of a second color is used to indicate the slope or gradient value exceeding a negative threshold.

[0059] Dog-boning is a condition where the diameter expansion of the stent 340 at its ends is greater than that at its center, and depending on the severity, this can be considered an abnormal or suboptimal outcome requiring correction. Figure 5 In the example shown, the stent probability map indicates the presence of stent 340, and both the diameter or area indicator 570 and the gradient indicator 555 in the longitudinal display 555 indicate dog-boning within stent 340. This is because the diameter or area 570 at the target frame 520 appears narrower than the diameter or area 570 at the proximal reference frame 510 or the distal reference frame 530, and the gradient 580 is negative between the proximal reference frame 510 and the target frame 520, but positive between the target frame 520 and the distal reference frame 530. Therefore, in this example, a shaded dog-boning warning indicator 590 has been overlaid on the longitudinal display 555.

[0060] For example, based on the dog-boning warning indicator 590 or the color presented by the gradient display 580 or the diameter shown in the diameter indicator 570, clinicians can see at a glance whether the dog-boning is very severe and requires correction (e.g., by reinserting the non-compliant balloon and expanding it near the center of the stent or at multiple locations along the length of the stent).

[0061] In some embodiments, stent detection (e.g., detecting the spacing between bright spots that may represent stent struts in a tomographic image) can be used instead of the lumen area or diameter, or as a proxy for the lumen area or diameter, or as a check of the lumen area or diameter, or as a method of calculating the lumen area or diameter. In some embodiments, plaque burden (PB) can be tracked instead of the lumen diameter or area, or as a supplementary scheme to the lumen diameter or area. Plaque burden is the percentage of the total vascular area containing plaque and is calculated as the difference between the vascular wall area and the vascular lumen area, expressed as a fraction of the total vascular area. In some embodiments, the diameter, slope, gradient, or inflection point value that triggers a dog-boning warning is a user-editable parameter, but default values ​​may also be provided.

[0062] Figure 6 An example screen display 500 illustrates an intraluminal imaging system 100 according to at least one embodiment of the present disclosure. Figure 5Similarly, screen display 500 includes three tomographic images: a proximal reference frame 510, a target frame 520, and a distal reference frame 530. Also visible is a longitudinal display 555, which includes a stent probability map 560, markers 510a, 520a, and 530a indicating the positions of the proximal reference frame 510, the target frame 520, and the distal reference frame 530, and a graphic diameter or area indicator 570.

[0063] Stent expansion is accomplished by placing a non-compliant balloon inside the stent 340 and expanding it section by section until it expands uniformly along its length. Suboptimal coverage occurs when the stent 340 is mispositioned and the clearance from the underlying lesion is as short as 2-3 mm.

[0064] The longitudinal display 555 shows the area of ​​the catheter sheath 610, which can be detected based on the area of ​​high stent probability value 560. The area of ​​the catheter sheath 610 is coupled with a constant diameter or area that is significantly smaller than the diameter or area of ​​the proximal reference frame 510 and the distal reference frame 530, resulting in a larger lumen diameter outside the edge of the sheath. The sheath is generally not considered in clinical decision-making, therefore, tomographic images including the sheath can optionally be removed from the pull-back sequence, and its graphical representation can optionally be removed from the longitudinal display 555.

[0065] Longitudinal display 555 also shows two regions of healthy tissue 620 that demonstrate evidence of stent underexpansion. This condition is detectable because healthy tissue 620 has a low or zero stent probability and a larger diameter or cross-sectional area compared to stent region 630, while stent region 630 has a narrower diameter and intermittently high stent probability. For example, stent underexpansion can occur if the clinician has not expanded the stent or has expanded it to a suboptimal state. Stent underexpansion can be corrected by inserting a non-compliant balloon into the underexpansion segment of the stent and inflating the non-compliant balloon to the desired diameter.

[0066] Longitudinal view 555 also shows evidence of tapering of anatomical structures, as this is the case if the healthy tissue 620 on the left side of longitudinal view 555 has a larger diameter or cross-sectional area compared to the healthy tissue 620 on the right side of longitudinal view. The detection of tapering of anatomical structures is discussed below.

[0067] Figure 7 A schematic diagram of a blood vessel 200, whose vessel wall 210 has been dilated with a stent 340 having a proximal edge 712 and a distal edge 714, is shown according to various aspects of this disclosure. Also visible (e.g., below in...) Figure 12 In step 1240 or in Figure 13 The graphical representation 730 of the plaque burden detection threshold covering the vessel wall 210 (applied in step 1330). In this example, the placement of the stent 340 can be considered suboptimal or inadequate because there is a constriction 735 outside the distal edge 714 of the stent 340, such that the diameter and cross-sectional area of ​​the vessel lumen outside the edge of the stent 340 are smaller than the diameter and cross-sectional area of ​​the vessel lumen at the edge of the stent, and the plaque burden is higher than the threshold; Plaque burden (%) = 100 × (vascular measurement result - lumen measurement result) / vascular measurement result. This suboptimal stent placement indicates the diseased portion of the vessel 200 (e.g., as in...). Figure 2 and Figure 3 The stenosis (230) seen in the image is not yet fully covered by the stent (340), either because the stent (340) is too short or because the stent (340) has not been correctly placed within the blood vessel (200). For example, suboptimal stent placement can be corrected by placing an additional stent near the incorrectly placed stent. As shown below... Figure 12 As described, the intraluminal treatment anomaly detection system is able to detect this condition proximal or distal to the stent 340.

[0068] Figure 8 This is a flowchart illustrating the steps of an example intraluminal treatment anomaly detection system 800 operating according to at least one embodiment of the present disclosure. In step 810, a complete set of intraluminal images is captured along the entire length of the retraction.

[0069] In step 820, the system detects stent edges (if any) within the imaged portion of the lumen. This can be accomplished, for example, by detecting possible stent struts within the tomographic image frame using machine learning, image recognition, or pattern recognition, and assigning a stent probability value between 0.0 (absolutely no stent) and 1.0 (absolutely certain stent presence) to each frame. In the example, values ​​0.0 and 1.0 may be relatively rare due to image noise and inter-frame noise, while regions where the smoothed stent probability value is consistently below a lower threshold (e.g., 0.3) (e.g., the lowest 10 consecutive frames) may indicate the absence of a stent in that region, and regions where the smoothed stent probability value is consistently above a higher threshold (e.g., 0.5) may indicate the presence of a stent in that region. The proximal stent edge and distal stent edge can then be defined as locations where the stent probability ranges from indicating no stent to indicating the presence of a stent, and the distal stent edge can be defined as locations where the stent probability ranges from indicating a stent to indicating no stent.

[0070] In step 830, the system calculates and / or otherwise determines per-frame statistics, which may include, but are not limited to, measurements and / or dimensions associated with the lumen, including lumen diameter, lumen cross-sectional area, stent strut spacing, or plaque load. In this example, this is done for each frame pulled back (e.g., for each of multiple locations along the vessel).

[0071] In step 840, the system calculates at least one curve of a filtering gradient relative to location for at least one per-frame statistic (e.g., lumen diameter) (e.g., a curve representing the change in measurement result / dimension along the length of the vessel). In some embodiments, graphical representations other than curves (e.g., bar charts, non-realistic drawings, cartoons, or inline longitudinal displays) may be used instead of curves or as a supplement to curves. Filtering (e.g., averaging the current frame with 2, 4, or 6 frames or other numbers of frames before, after, or on either side of it) may tend to smooth the gradient curve (or other graphical representation) and prevent image noise or inter-frame measurement noise from creating spurious inflection points or gradient values. Other types of filtering may include, but are not limited to, calculations based on sampled frames or gated frames. Gating is a means of selecting frames corresponding to specific moments in successive cardiac cycles, or some other way of ensuring that frame representations select regions of their frames.

[0072] In step 850, some embodiments of the system also calculate a filtering curve for the detected support strut spacing.

[0073] In step 860, some embodiments of the system detect unexpanded stents. Unexpanded or underexpanded stents can be detected based on regions with high stent probability values ​​coupled to a diameter or area within the stent that is significantly smaller than the lumen diameter (or area, etc.) outside the stent's edge.

[0074] In step 870, the system detects dog-boning in any detected scaffold (if present). For example, dog-boning can be detected by detecting the presence of inflection points in the filtered diameter, area, or scaffold strut detection results across the length of the scaffold, where the slope on either side of the inflection point exceeds a threshold absolute value. In embodiments where the graphical representation is not a curve, other parameters can be used instead of the slope, such as the difference between two bars in a bar chart and / or the sign (positive or negative) of the difference. Alternatively or additionally, dog-boning can be detected via analysis of scaffold strut detection: First, the system identifies scaffold struts in each slice using image recognition. Second, the system creates a scaffold strut profile for the slice by calculating the distance between struts. Third, the system calculates a 3D scaffold model by comparing scaffold strut distance profiles across multiple frames. This model indicates the areas where scaffold struts extend and the areas where scaffold struts do not extend. The 3D visualization can be binary (e.g., based solely on whether the distance exceeds a threshold) or continuous, and can be visualized, for example, via a color map, similar to... Figure 5 The gradient in the graph is 580. By studying the properties of the mapping graph, the system is able to determine whether dog-boning has occurred at a level exceeding a threshold, for example, near the stent edge region, the stent expansion is greater than the expansion in the middle of the stent, i.e., the stent edge is separated by a greater distance than the stent middle.

[0075] In step 880, the system detects suboptimal coverage (if any) in any detected stents. To do this, the system compares the start and end frames of the stent (e.g., the proximal and distal edges of the stent) to the contours of the disease near the frames. Specifically, the algorithm compares the stent area to the lumen area of ​​±N frames closest to the stent edge. If the lumen area is smaller than the stent edge area and the plaque burden (PB) exceeds a threshold amount on a specified number of frames M, suboptimal coverage occurs on this side of the stent.

[0076] If length measurement can be performed automatically, the M frames can be quantified by the speed of pullback, for example, in the case of co-registration with angiographic images. In the example, M corresponds to a specified distance, such as 2-3 mm.

[0077] In step 890, the system detects the difference between anatomical conicalization and diffuse disease. Anatomical conicalization naturally occurs in body lumens (e.g., blood vessels) and can be observed as a gradual decrease in diameter in a more distal frame within a pull-back image set compared to a more proximal frame in the pull-back. In anatomically conical lumens, the filter curve for diameter or area relative to location within the lumen typically does not show an abrupt change in value, but rather a gradual decrease from proximal to distal location. Conversely, diffuse disease or diffuse lesions occur when intermittent or increasing plaque burden is observed within the lumen. For example, diffuse disease or diffuse lesions can occur in the presence of plaque buildup along a larger vessel length compared to focal (e.g., localized) lesions. The degree of constriction (e.g., narrowing of the lumen) can sometimes be relatively smaller than what is seen in focal lesions, but the constriction extends relatively long along the length of the vessel and may therefore have an equal or greater effect on vessel volume constriction and blood flow constriction. For tapered lumens, if plaque burden follows a decreasing trend or never increases to exceed a specified threshold (e.g., 50%), then tapering is an anatomical structure (e.g., healthy or normal). If plaque burden (whether continuous or intermittent) for a total of P frames (e.g., 20 frames) within a segment is above the threshold (e.g., 50%), then tapering indicates diffuse disease. If plaque burden is increasing or intermittent when you move distally, then tapering is an anatomical structure (e.g., healthy or normal) if plaque burden never exceeds the threshold amount (e.g., 50%).

[0078] In step 895, the system outputs a graphical representation of one or more detected vascular conditions to a display. For example, the system (e.g., on monitor 108) creates and displays a longitudinal display 555, which includes graphics, markers, highlighted content, color coding, text, and / or numerical values ​​sufficient to indicate the suspected presence and location of the various abnormalities described above. Clinicians or other users can then use the longitudinal display to evaluate the status of the lumen after treatment, thereby determining whether the treatment has been completed or, conversely, whether any additional intervention is required.

[0079] The method completes its execution once the system displays a portrait orientation.

[0080] Figure 9This is a flowchart 900 of an example stent underexpansion detection algorithm 860 according to at least one embodiment of the present disclosure. In step 910, the algorithm determines whether, for a specified number of frames, the amount by which the lumen area outside the stent exceeds the lumen area at the stent edge exceeds a threshold amount. More generally, other measurements (including but not limited to measured or calculated vessel diameter) can be used instead of area for detection. If so, the algorithm proceeds to step 920, where it determines that there is no stent underexpansion on this side of the stent. If not, the algorithm proceeds to step 930, where it determines that there is stent underexpansion on this side of the stent. In some embodiments, instead of comparing the areas inside and outside the stent, step 910 examines the filtering gradient of the areas of N frames outside the stent edge to see if the stent is expanding. If so, the algorithm proceeds to step 920. If not, the algorithm proceeds to step 930.

[0081] In other words, insufficient expansion is detected when a first value for a lumen dimension (e.g., area or diameter) that extends beyond a specified distance from the stent edge exceeds a second value for that dimension at the stent edge.

[0082] Figure 10 This is a flowchart 1000 of an example dog-boning detection algorithm 870 according to at least one embodiment of the present disclosure. In step 1010, the algorithm examines the filter area gradient between the proximal and distal edges of the stent and determines whether an inflection point exists within that location range. If no inflection point exists, the algorithm proceeds to step 1020. If an inflection point exists, the algorithm proceeds to step 1030. In step 1020, the algorithm determines that no dog-boning exists for that particular stent, and the algorithm run for that stent has been completed. In step 1030, the algorithm determines whether the magnitude or absolute value of the filter area gradient between the proximal and distal edges of the stent exceeds a threshold on either side of the inflection point. If it does not exceed the threshold, the algorithm proceeds to step 1020. If it does exceed the threshold, the algorithm proceeds to step 1040, where the algorithm determines that dog-boning exists for that particular stent.

[0083] Figure 11This is a flowchart 1100 of a different example dog-boning detection algorithm 870 according to at least one embodiment of the present disclosure. In step 1110, the algorithm determines whether the extension of the support strut at the edge of the support is greater than the extension at one or more points within the support. If not, it proceeds to step 1120. If so, it proceeds to step 1130. In step 1120, the algorithm determines that there is no dog-boning for that particular support, and the algorithm run for that support has been completed. In step 1130, the algorithm determines whether the difference in the extension of the support strut exceeds a threshold. If not, it proceeds to step 1120. If it does exceed the threshold, it proceeds to step 1140, where the algorithm determines that dog-boning exists for that particular support.

[0084] Figure 12 This is a flowchart 1200 of an example suboptimal stent placement detection algorithm 880 according to at least one embodiment of the present disclosure. In step 1210, the algorithm determines whether the lumen area for at least N frames outside the stent edge is less than the lumen area of ​​the stent edge. If not, it proceeds to step 1220. If so, it proceeds to step 1230. In step 1220, the algorithm determines that suboptimal placement was not detected for a particular side of the particular stent, and the algorithm run for that side of the stent has been completed. In step 1230, the algorithm determines whether the difference between the area outside the stent and the area of ​​the stent edge exceeds a threshold (e.g., 0.3 mm). 2 If the threshold is not exceeded, the algorithm proceeds to step 1220. If the threshold is exceeded, the algorithm proceeds to step 1240. In step 1240, the algorithm determines whether, for at least M frames (e.g., 20 frames), the plaque burden outside the stent edge exceeds a threshold (e.g., 50%). If the threshold is not exceeded, the algorithm proceeds to step 1220. If the threshold is exceeded, the algorithm proceeds to step 1250, where the algorithm determines that there is a suboptimal placement of the stent on this side.

[0085] In other words, suboptimal stent placement or incomplete coverage of the lesion is detected when the first value of the dimension for a first distance beyond the edge of the stent is less than the second value of that dimension at the edge of the stent by at least a threshold amount, and when the plaque burden for the second distance beyond the edge of the stent exceeds the threshold.

[0086] Figure 13This is a flowchart 1300 of an example anatomical conicalization and diffuse disease detection algorithm 890 according to at least one embodiment of the present disclosure. In step 1310, the algorithm determines whether the smoothed area gradient across a frame of a threshold percentage (e.g., 51%) for the length of unsupported tissue is negative. If it is not negative, the algorithm proceeds to step 1320. If it is negative, the algorithm has detected diffuse disease or anatomical conicalization in the vessel and proceeds to step 1330. In step 1320, the algorithm determines that no anatomical conicalization or diffuse disease is detected for that particular luminal segment, and the algorithm run for that luminal segment has been completed. In step 1330, the algorithm determines whether plaque burden exceeds a threshold (e.g., 50%) within a frame of a threshold number or percentage within that segment. If it does not exceed this threshold, the algorithm proceeds to step 1340, where the algorithm determines that the vessel is narrowing due to anatomical conicalization. If the limit is exceeded, the algorithm will continue to step 1350, where it determines that the blood vessel is narrowing due to diffuse disease.

[0087] Figure 14 This is a schematic diagram of a processor circuit 1450 according to an embodiment of the present disclosure. The processor circuit 1450 can be implemented in an ultrasound imaging system 100 or other device or workstation (e.g., a third-party workstation, network router, etc.) necessary for implementing the present method. As shown, the processor circuit 1450 may include a processor 1460, a memory 1464, and a communication module 1468. These components can communicate directly or indirectly with each other (e.g., via one or more buses).

[0088] Processor 1460 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 computers and quantum computers). Processor 1460 may also include another hardware device, firmware device, or any combination thereof configured to perform the operations described herein. Processor 1460 may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, a combination of multiple microprocessors, one or more microprocessors used in conjunction with a DSP core, or any other such configuration.

[0089] Memory 1464 may include cache memory (e.g., cache memory of processor 1460), 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 devices, hard disk drives, other forms of volatile and non-volatile memory, or combinations of different types of memory. In embodiments, memory 1464 includes a non-transient computer-readable medium. Memory 1464 may store instructions 1466. Instructions 1466 may include instructions that, when executed by processor 1460, cause processor 1460 to perform the operations described herein. Instructions 1466 may also be referred to as code. The terms “instruction” and “code” should be interpreted broadly to include any type of computer-readable statement(s). For example, the terms “instruction” and “code” may refer to one or more programs, routines, subroutines, functions, flows, etc. “Instruction” and “code” may include a single computer-readable statement or a number of computer-readable statements.

[0090] Communication module 1468 can include any electronic circuitry and / or logic circuitry to facilitate direct or indirect data communication between processor circuitry 1450 and other processors or devices. In this regard, communication module 1468 can be an input / output (I / O) device. In some instances, communication module 1468 facilitates direct or indirect communication between processor circuitry 1450 and / or various components of ultrasound imaging system 100. Communication module 1468 can communicate within processor circuitry 1450 via various methods or protocols. Serial communication protocols may include, but are not limited to, US SPI, I... 2 C. Serial communication may be transmitted via RS-232, RS-485, CAN, Ethernet, ARINC 429, MODBUS, MIL-STD-1553, or any other suitable method or protocol. Parallel protocols include, but are not limited to, ISA, ATA, SCSI, PCI, IEEE-488, IEEE-1284, and other suitable protocols. Where appropriate, serial and parallel communication may be bridged by UART, USART, or other suitable subsystems.

[0091] External communication (including, but not limited to, software updates, firmware updates, preset sharing between the processor and a central server, or readings from an ultrasound device) can be accomplished using any suitable wireless or wired communication technology, such as cable interfaces (e.g., USB, Micro USB, Lightning, or FireWire), Bluetooth, Wi-Fi, ZigBee, Li-Fi, or cellular data connections (e.g., 2G / GSM, 3G / UMTS, 4G / LTE / WiMax, or 5G). For example, Bluetooth Low Energy (BLE) radios can be used to establish connections with cloud services for data transfer and to receive software patches. The controller can be configured to communicate with a remote server or local device (e.g., a laptop, tablet, or handheld device) or may include a display capable of showing status variables and other information. Information can also be transferred over a physical medium (e.g., a USB flash drive or memory stick).

[0092] Figure 15 A schematic diagram is shown of a blood vessel whose vessel wall 210 has been expanded using a stent exhibiting dog-boning, according to various aspects of this disclosure. By comparing the slope of the stent at various points along the contour (e.g., slope 1 at point 1, slope 2 at point 2, slope 3 at point 3, slope 4 at point 4, and slope 5 at point 5), the algorithm is able to identify inflection points within the stent, thereby detecting dog-boning, as shown above. Figure 10 As described in [the text]. In this example, slopes 1 and 5 are approximately equal, while slopes 2 and 4 have opposite signs (negative and positive slopes), and point 3 (whose absolute slope value is less than slopes 2 and 4) is an inflection point. This pattern illustrates dog-boning.

[0093] The examples and embodiments described above can have many variations. For example, in addition to the systems described, endoluminal treatment anomaly detection systems can be used in anatomical systems within the body, or for imaging other disease types, object types, or process types besides those described. The techniques described herein can be applied to various types of endoluminal imaging sensors, whether currently existing or developed in the future. The above analyses can also be performed using the measured or calculated average diameter or intrinsic diameter (instead of area or volume) or any other variable representing the vascular dimension at different points along the vessel. Such analyses can be performed using a uniform or regular sample subset of measurements instead of all measurements, provided the measurements do not reflect high local variability (in which case a smoothing filter may be included). Co-registration with different modalities, such as angiography, can be used to indicate the location or severity of the anomalies identified above on the angiographic image itself. Any thresholds, ranges, or numbers described above can be user-editable quantities, but the system can also provide default values. To speed up operation or reduce computational burden, the system can work using only frame sampling (e.g., every five frames) instead of the entire image dataset.

[0094] Therefore, the logical operations constituting embodiments of the technology described herein are referred to differently as operations, steps, objects, elements, components, or modules. Furthermore, it should be understood that these can occur or be performed in any order unless the order is otherwise expressly defined or a particular order is inherently required by the language of the claims. These steps can be added, deleted, combined, or rearranged without departing from the spirit of this disclosure. All directional references (e.g., up, down, inside, outside, upward, downward, left, right, lateral, front, back, top, bottom, above, below, vertical, horizontal, clockwise, counterclockwise, proximal, and distal) are used only for identification purposes to assist the reader in understanding the claimed subject matter and do not impose limitations, particularly regarding the location, orientation, or use of the intraluminal treatment anomaly detection system. Connection references (e.g., attachment, coupling, connection, and engagement) are to be interpreted broadly and can include intermediate members between sets of elements and relative movement between elements, unless otherwise stated. Therefore, connection references do not necessarily imply that two elements are directly connected and have a fixed relationship. The term "or" should be interpreted as "and / or" rather than "exclusive or". Unless otherwise stated in the claims, the stated values ​​should be interpreted as illustrative only and should not be considered limiting.

[0095] The foregoing description, examples, and data provide a complete description of the structure and use of exemplary embodiments of the intraluminal treatment anomaly detection system as defined in the claims. While various embodiments of the claimed subject matter have been described above to some degree or with reference to one or more individual embodiments, many modifications can be made to the disclosed embodiments by those skilled in the art without departing from the spirit or scope of the claimed subject matter. Other embodiments are also contemplated. All subject matter contained in the foregoing description and shown in the accompanying drawings is intended to be illustrative of particular embodiments only and not to be limiting. Changes may be made to details or structure without departing from the essential elements of the subject matter as defined in the claims.

Claims

1. An intravascular imaging system, comprising: A processor circuit configured to communicate with an intravascular imaging catheter, the catheter being sized and shaped to be positioned within the lumen of a blood vessel, wherein the processor circuit is configured to: Receive multiple intravascular images obtained by the intravascular imaging catheter when the intravascular imaging catheter is positioned within the lumen, wherein the multiple intravascular images correspond to multiple locations along the length of the blood vessel, and one or more of the multiple intravascular images include a stent positioned within the lumen; For each of the multiple intravascular images, a measurement result associated with the lumen is determined; Generate a first graphical representation of the change in the measurement along the length of the blood vessel; The condition of the blood vessel is detected based on the first graphical representation, wherein the condition includes dog-boning of the stent, and wherein, when the condition is dog-boning of the stent, the processor circuit detects the condition by: the processor circuit determining that the rate of change of the measurement result within the stent exhibits an inflection point and that the rate of change of the measurement result within the stent exceeds a threshold proximal or distal to the inflection point; and A second graphical representation of the situation is output to a display that communicates with the processor circuitry.

2. The system of claim 1, wherein, The processor circuit determines the measurement result by including: For each of the plurality of locations, the quantity of the measurement result at that location is averaged with the quantity of the measurement result at another location among the plurality of locations.

3. The system according to claim 1, wherein, The processor circuit determines the measurement result by calculating the cross-sectional area of ​​the cavity or the diameter of the cavity.

4. The system according to claim 1, wherein, The processor circuit's detection of the condition further includes: the processor circuit detecting at least one of the following: tapering of the anatomical structure of the blood vessel or diffuse disease of the blood vessel.

5. The system according to claim 4, wherein, The condition includes tapering of the anatomical structure, and wherein the processor circuit detects the condition by detecting that the plaque burden of the blood vessel does not exceed a threshold at multiple locations within a segment of the blood vessel.

6. The system according to claim 4, wherein, The condition includes the diffuse disease, and wherein the processor circuitry detects the condition by detecting that the plaque burden on the blood vessel exceeds a threshold at multiple locations within a segment of the blood vessel.

7. The system according to claim 1, wherein, The processor circuit's detection of the condition of the blood vessel also includes: detecting the post-treatment condition.

8. The system according to claim 1, wherein, The measurement results include the spacing between the struts of the support.

9. The system according to claim 1, wherein, The processor circuit's detection of the condition also includes: detecting insufficient expansion of the stent or incomplete coverage of the lesion by the stent.

10. The system according to claim 9, wherein, The condition is that the expansion of the support is insufficient, and wherein the processor circuit detects the condition by: the processor circuit determining that a first value of the measurement result for the distance beyond the edge of the support exceeds a second value of the measurement result at the edge of the support by a greater than a threshold amount.

11. The system according to claim 9, wherein, The condition is that the stent does not completely cover the lesion, and wherein the processor circuit detects the condition by detecting: The amount by which the first value of the measurement result for a first distance beyond the edge of the support is less than the second value of the measurement result at the edge of the support is at least a threshold amount; and The plaque burden exceeds a threshold at a second distance beyond the edge of the support.

12. The system according to claim 1, in, The processor circuit is configured to: receive an extravascular image of the blood vessel, and co-register the plurality of intravascular images to the plurality of locations along the length of the blood vessel in the extravascular image, and Wherein, the processor circuit outputs a second graphical representation of the condition, including: the processor circuit outputs an indication of the condition along the length of the blood vessel in the extravascular image.

13. The system according to claim 1, further comprising: The intravascular imaging catheter includes an intravascular ultrasound (IVUS) imaging catheter.

14. A computer program product including instructions that, when executed on a computer including processor circuitry, cause the computer to perform an intravascular imaging method, the intravascular imaging method comprising: The processor circuit, in communication with the intravascular imaging catheter, receives multiple intravascular images obtained by the intravascular imaging catheter when the intravascular imaging catheter is positioned within the lumen of a blood vessel, wherein the multiple intravascular images correspond to multiple locations along the length of the blood vessel, and one or more of the multiple intravascular images include a stent positioned within the lumen. The processor circuit determines the measurement results associated with the lumen for each of the multiple intravascular images; The processor circuit generates a first graphical representation of the change in the measurement along the length of the blood vessel; The processor circuit detects the condition of the blood vessel based on the first graphical representation, wherein the condition includes dog-boning of the stent, and wherein, when the condition is dog-boning of the stent, the processor circuit detects the condition by: determining that the rate of change of the measurement result within the stent exhibits an inflection point and that the rate of change of the measurement result within the stent exceeds a threshold proximal or distal to the inflection point; and A second graphical representation of the situation is output to a display that communicates with the processor circuitry.

15. An intravascular ultrasound (IVUS) imaging system, comprising: IVUS imaging catheters are designed and shaped to be positioned within the lumen of blood vessels; as well as A processor circuit configured to communicate with the IVUS imaging catheter, wherein the processor circuit is configured to: Receive multiple IVUS images obtained by the IVUS imaging catheter when the IVUS imaging catheter is positioned within the lumen, wherein the multiple IVUS images correspond to multiple locations along the length of the blood vessel, and one or more of the multiple IVUS images include a stent positioned within the lumen. For each of the multiple IVUS images, determine the measurement result associated with the lumen; Generate a curve representing the change in the measurement along the length of the blood vessel; The condition of the blood vessel is detected based on the curve, wherein the condition includes dog-boning of the stent within the blood vessel, and wherein, when the condition is dog-boning of the stent, the processor circuit detects the condition by: the processor circuit determining that the rate of change of the measurement result within the stent exhibits an inflection point and that the rate of change of the measurement result within the stent exceeds a threshold proximal or distal to the inflection point; and A graphical representation of the situation is output to a display that communicates with the processor circuitry.

16. The IVUS imaging system according to claim 15, wherein, The condition also includes at least one of the following: insufficient expansion of the stent, incomplete coverage of the lesion of the blood vessel by the stent, diffuse disease of the blood vessel, and tapering of the anatomical structure of the blood vessel.

17. The IVUS imaging system according to claim 16, wherein, When the condition is that the stent does not completely cover the lesion, the processor circuit detects the condition by detecting: The amount by which the first value of the measurement result for a first distance beyond the edge of the support is less than the second value of the measurement result at the edge of the support is at least a threshold amount; and The plaque burden exceeds a threshold at a second distance beyond the edge of the support.

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