Intraluminal ultrasound imaging with automatic detection of target and reference regions

Abstract

An intraluminal ultrasound imaging system is presented that includes a processor circuit configured to communicate with an intraluminal ultrasound imaging catheter. The processor circuit is configured to receive a plurality of intraluminal ultrasound images obtained by an intraluminal ultrasound imaging catheter during movement within a body lumen of a patient. The body lumen includes a plurality of segments, and compression within at least one of the segments. The processor circuit is further configured to automatically determine a target location within the compression and a reference location including a healthy portion proximate the compressed lumen based on the image; and outputting a screen display to a display in communication with the processor circuit, the screen display including the target location, the reference location, and at least one quantity associated with the target location and the reference location.

Description

Technical Field

[0001] The subject matter described herein relates to a system for medical imaging. In particular, the disclosed system provides a system for identifying treatment target locations and reference locations in peripheral intravascular ultrasound or IVUS images during a pullback procedure. The system is particularly, but not exclusively, used for diagnosis and treatment of vascular disease. Background Art

[0002] IVUS can be used to evaluate diseases in peripheral vascular processes and deep venous system processes. Treatment may include stent implantation, IVC filter retrieval, thrombectomy and other processes. Different diseases or medical processes produce physical features with different sizes, structures, densities, water content and accessibility of imaging sensors. For example, deep vein thrombosis (DVT) produces blood cell clots, while post-thrombotic syndrome (PTS) produces netting or other residual structural effects in blood vessels, which have components similar to the vessel wall itself and may therefore be difficult to distinguish from the vessel wall. A stent is a dense (e.g., metal) object that can be placed in a blood vessel or lumen to keep the vessel or lumen open to a specific diameter. When anatomical structures outside the blood vessel or lumen act on the blood vessel or lumen, compression occurs, causing it to shrink.

[0003] In some cases, intraluminal medical imaging is performed using an IVUS device that includes one or more ultrasonic transducers. The IVUS device can be delivered into a blood vessel and guided to the area to be imaged. The transducer transmits ultrasonic energy and receives ultrasonic echoes reflected from the blood vessel. The ultrasonic echoes are processed to create an image of the blood vessel of interest. The image of the blood vessel of interest can include one or more lesions or blockages in the blood vessel. Stents can be placed in the blood vessel to treat these blockages, and intraluminal imaging can be performed to observe the placement of the stent in the blood vessel. Other types of treatments include thrombectomy, ablation, angioplasty, medications, etc.

[0004] Clinician interpretation of IVUS images of deep venous disease is critical in identifying areas of compression or other disease and corresponding non-diseased normal or reference areas and potential landing zones for stenting or other treatments.

[0005] However, there are considerable variations in the methods and thought processes that clinicians use to identify these target locations and reference locations. This lack of standardization may lead to different results, and may also make it difficult for novice clinicians to know which methods or thought processes to adopt. The influence of external factors (e.g., reference anatomical structures from literature, age and medical history of the patient, etc.) and internal factors (e.g., the state of the blood vessels around the diseased blood vessels) may add additional complexity to the clinician's thought process. For example, some studies show that one-third of doctors disagree with the "standard" or reference anatomical structures in a particular publication. Therefore, IVUS interpretation may be very complicated, so intermediate users and novice users may not be sure to withdraw by their own analysis. Therefore, in some scenarios, there may be an over-reliance on trained staff. The information included in this background technology part of this specification (including any references cited herein and any description or discussion thereof) is included only for technical reference purposes, and should not be considered as the subject matter to which the scope of the present disclosure will be constrained. Summary of the invention

[0006] An automatic target and reference detection system for an intravascular ultrasound (IVUS) procedure for treating a peripheral vein or other vessel is disclosed herein. The present disclosure provides a unified strategy for interpreting IVUS images of deep vein pullbacks while maintaining the flexibility for expert users to override the default performance of the system. The systems, devices, and methods disclosed herein include algorithms for presenting target and reference locations to the user, including (one or more) target frames and (one or more) reference frames for each segment of the iliofemoral pullback, as well as potential stent landing frames in the pre-treatment pullback, and / or clinical areas of interest in the post-treatment pullback. The automatic target and reference detection system can also provide the user with the option of changing various settings to support different thought processes. Therefore, while the system supports standardization of deep vein IVUS procedures, it retains the flexibility for experienced clinicians to use their own judgment.

[0007] Aspects of the present disclosure are particularly suitable for peripheral venous vasculature / systems. In some cases, the peripheral venous vasculature may include or may be the deep venous vasculature / system and / or the peripheral deep venous vasculature / system. The peripheral venous vasculature may include a continuous length of vein having differently named venous segments (established by medical institutions) that are fluidly connected to each other to transport blood from the legs to the heart. For example, the peripheral venous vasculature may include veins in the abdomen and / or legs of a patient. The segments of the peripheral vasculature may include: the inferior vena cava (IVC), the iliac veins (e.g., the common iliac vein, the internal iliac vein, the external iliac vein), the femoral veins (e.g., the common femoral vein, the femoral vein), the anterior foot vein, the popliteal vein, the tibial vein, the saphenous vein (e.g., the great saphenous vein, the small saphenous vein), and / or other veins (such as Figure 2With respect to peripheral venous vessels, intravascular ultrasound (IVUS) is used to assess iliofemoral disease, compression, etc., pre- and post-procedure (e.g., stent placement, IVC-filter retrieval, thrombectomy, and / or other procedures). Peripheral veins present different types of disease than coronary arteries. Thus, aspects of the present disclosure are particularly well suited for pre- and post-procedure assessment of disease that restricts blood flow in peripheral veins.

[0008] A system of one or more computers may be configured to perform specific operations or actions by installing software, firmware, hardware, or a combination thereof on the system, which software, firmware, hardware, or a combination thereof causes the system to perform actions in operation. For example, any one or more modules and / or steps described herein may be implemented by hardware and / or software in a processor circuit and / or a processor. One or more computer programs may be configured to perform specific operations or actions by including instructions that, when executed by a data processing device, cause the device to perform an action. A general aspect includes an intraluminal ultrasound imaging system comprising a processor circuit configured to communicate with an intraluminal ultrasound imaging catheter. The processor circuit is configured to: receive a first plurality of intraluminal ultrasound images obtained by the intraluminal ultrasound imaging catheter during movement of the intraluminal ultrasound imaging catheter within a body lumen of a patient, the body lumen including a plurality of segments and compression within at least one segment; for at least one segment, automatically determine a reference position and a target position within the compression based on the first plurality of intraluminal ultrasound images, the reference position including a healthy portion of the lumen proximate to the compression; and output a screen display to a display in communication with the processor circuit, the screen display including the target position, the reference position, and at least one quantity associated with the target position and the reference position. Other embodiments of this aspect include corresponding computer systems, apparatuses, and computer programs recorded on one or more computer storage devices, each of which is configured to perform the actions of the method.

[0009] Embodiments may include one or more of the following features. In some embodiments, the processor circuit is further configured to: receive a second plurality of intraluminal ultrasound images obtained by the intraluminal ultrasound imaging catheter during movement of the intraluminal ultrasound imaging catheter within a body lumen, the body lumen comprising a stent longitudinally aligned with the target position; automatically determine the proximal end of the stent, the distal end of the stent, and the position within the stent where the stent is most contracted based on the second plurality of intraluminal ultrasound images; and output the proximal end of the stent, the end of the stent, the position where the stent is most contracted, and at least one quantity associated with contraction to a screen display. In some embodiments, automatically determining at least one of the target position, the reference position, the proximal end of the stent, the distal end of the stent, or the position within the stent where the stent is most contracted involves a machine learning algorithm. In some embodiments, the body lumen comprises a peripheral vessel, and wherein the plurality of segments comprises at least one of a common iliac vein (CIV), an external iliac vein (EIV), a common femoral vein (CFV), or a femoral vein for popliteal access. In some embodiments, at least one segment comprises a plurality of segments. In some embodiments, the screen display simultaneously shows the target frame and reference frame of each segment in a plurality of segments. In some embodiments, the target position is determined at least in part by the change of the first lumen measurement along the segment, and the reference position is determined at least in part by the change of the second lumen measurement along the segment. In some embodiments, the first lumen measurement and the second lumen measurement are the same. In some embodiments, the first lumen measurement or the second lumen measurement includes the area, minimum diameter, maximum diameter, effective diameter, average diameter, aspect ratio or flow resistance of the lumen. In some embodiments, the target position or reference position is determined at least in part based on the intraluminal ultrasound imaging catheter. In some embodiments, the target position or reference position is determined at least in part based on the length of movement of the intraluminal ultrasound imaging catheter in the body lumen. In some embodiments, the target position or reference position is determined at least in part based on the segment where the compression is located. In some embodiments, the target position or reference position is determined at least in part based on the boundary of the second body lumen adjacent to the body lumen. In some embodiments, the screen display includes at least one of a roadmap image or an image longitudinal display (ILD). Implementations of the described technology may include hardware, methods or processes, or computer software on a computer-accessible medium.

[0010] A general aspect includes a method of intraluminal ultrasound imaging using a processor circuit in communication with an intraluminal ultrasound imaging catheter: receiving a first plurality of intraluminal ultrasound images obtained by an intraluminal ultrasound imaging catheter during movement of the intraluminal ultrasound imaging catheter within a body lumen of a patient, the body lumen including a plurality of segments and a compression within at least one segment; for at least one segment, automatically determining a reference position and a target position within the compression based on the first plurality of intraluminal ultrasound images, the reference position including a healthy portion of the lumen proximate to the compression; and outputting a screen display to a display in communication with the processor circuit, the screen display including the target position, the reference position, and at least one quantity associated with the target position and the reference position. Other embodiments of this aspect include corresponding computer systems, apparatuses, and computer programs recorded on one or more computer storage devices, each of which is configured to perform the actions of the method.

[0011] Implementations may include one or more of the following features. In some embodiments, the method further includes, using a processor circuit: receiving a second plurality of intraluminal ultrasound images obtained by the intraluminal ultrasound imaging catheter during movement of the intraluminal ultrasound imaging catheter within a body lumen, the body lumen including a stent longitudinally aligned with a target position; automatically determining a proximal end of the stent, a distal end of the stent, and a location within the stent where stent contraction is greatest based on the second plurality of intraluminal ultrasound images; and outputting to a screen display the proximal end of the stent, the distal end of the stent, the location where stent contraction is greatest, and at least one quantity associated with contraction. In some embodiments, the body lumen includes a peripheral vessel, and wherein the plurality of segments include at least one of a common iliac vein (CIV), an external iliac vein (EIV), or a common femoral vein (CFV). In some embodiments, at least one segment includes a plurality of segments, and the screen display simultaneously shows a target frame and a reference frame for each of the plurality of segments. In some embodiments, the target position is determined at least in part by a change in a first lumen metric along the segment, and the reference position is determined at least in part by a change in a second lumen metric along the segment, wherein the first lumen metric or the second lumen metric comprises an area, a minimum diameter, a maximum diameter, an effective diameter, an average diameter, an aspect ratio, or a flow resistance of the lumen. In some embodiments, the first lumen metric and the second lumen metric are the same. Implementations of the described techniques may include hardware, methods or processes, or computer software on a computer-accessible medium.

[0012] This summary is provided to introduce a selection of concepts in a simplified form that will be further described in the following 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 automatic target and reference detection system as defined in the claims is provided in the following written description of various embodiments of the present disclosure and illustrated in the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Illustrative embodiments of the present disclosure will be described with reference to the accompanying drawings, in which:

[0014] Figure 1 is a diagrammatic illustration of an intraluminal imaging system according to aspects of the present disclosure.

[0015] Figure 2 Blood vessels (eg, arteries and veins) in the human body are illustrated.

[0016] Figure 3 The image shows the combined compressed vessels.

[0017] Figure 4 A blood vessel is illustrated incorporating compression and having a stent expanded inside the vessel to restore flow.

[0018] Figure 5 An example intraluminal imaging display screen is illustrated in accordance with at least one embodiment of the present disclosure.

[0019] Figure 6 is a schematic representation of a clinician's thought process in accordance with at least one embodiment of the present disclosure.

[0020] Figure 7 is a schematic representation in flowchart form of an example pre-treatment reference frame detection method in accordance with at least one embodiment of the present disclosure.

[0021] Figure 8 is a schematic representation in flow chart form of an exemplary post-treatment stent inspection method in accordance with at least one embodiment of the present disclosure.

[0022] Fig. 9 is a schematic representation in block diagram form of at least a portion of an exemplary pre-treatment automated target and reference detection system or method in accordance with at least one embodiment of the present disclosure.

[0023] Fig.10 is a schematic representation in block diagram form of at least a portion of an exemplary pre-treatment automated target and reference detection system or method in accordance with at least one embodiment of the present disclosure.

[0024] Fig.11is a schematic representation in block diagram form of at least a portion of an exemplary post-treatment automated stent assessment system or method in accordance with at least one embodiment of the present disclosure.

[0025] Fig.12 is a schematic representation in block diagram form of at least a portion of an exemplary post-treatment automated stent assessment system or method in accordance with at least one embodiment of the present disclosure.

[0026] Fig.13 is a schematic representation in block diagram form of at least a portion of an exemplary pre-treatment and post-treatment automatic target recognition, reference recognition, and stent assessment system or method in accordance with at least one embodiment of the present disclosure.

[0027] Fig.14 is a schematic diagram of a processor circuit according to at least one embodiment of the present disclosure.

[0028] Fig.15 is a schematic representation in flow chart form of an exemplary pre-treatment target zone and reference frame detection method in accordance with at least one embodiment of the present disclosure.

[0029] Fig.16 is a schematic illustration of analyzed IVUS image data in accordance with at least one embodiment of the present disclosure.

[0030] Fig.17 An example intraluminal imaging display screen is illustrated in accordance with at least one embodiment of the present disclosure.

[0031] Fig.18 is a schematic representation in flow chart form of an example pre-treatment target region and reference frame detection method in accordance with at least one embodiment of the present disclosure. DETAILED DESCRIPTION

[0032] The present disclosure generally relates to medical imaging, including imaging associated with a patient's body lumen using an intraluminal imaging device.For example, the present disclosure describes systems, devices, and methods for detecting treatment target locations and healthy reference locations in a peripheral vein or other vessel.

[0033] There is considerable variation in the methods and thought processes that clinicians use to identify these target and reference locations and landing zones for stents or other treatments. As a result, clinician training can be difficult and inconsistent. To address this need, the present disclosure provides a common strategy for interpreting IVUS images of deep venous withdrawal while maintaining the flexibility for expert users to override the default behavior of the system.

[0034] The application logic disclosed herein includes an algorithm for presenting a target position and a reference position (e.g., a specific IVUS image frame) to a user. The overall algorithm describes how to find a target frame (or frames) and a reference frame (or frames) for each segment of the iliofemoral withdrawal. In some embodiments, the analysis performed by the algorithm also includes finding a potential stent landing frame in the withdrawal before treatment, and / or finding a clinical area of ​​interest in the withdrawal after treatment. The automatic target and reference detection system can also provide users with the option of changing various settings to support different thinking processes. Therefore, although the system supports the standardization of deep vein IVUS procedures, it retains the flexibility of experienced clinicians to use their own judgment. Therefore, the goal of the system is to present relevant information about high-level withdrawal analysis to the user without telling the user what to treat. The algorithm can use reference values, ratios, averages, means or formulas from public studies to facilitate clinician acceptance while standardizing the results.

[0035] The devices, systems, and methods described herein may include one or more features described in U.S. Provisional Application No. 62 / 946 097 filed on December 10, 2019 (Attorney Docket No. 2018PF01110-44755.2066PV01), U.S. Provisional Application No. 63 / 250 498 filed on September 30, 2021 (Attorney Docket No. 2021PF00350-44755.2223PV01), U.S. Provisional Application No. 62 / 750 983 filed on October 26, 2018 (Attorney Docket No. 2018PF01112-44755.1996PV01), U.S. Provisional Application No. 62 / 750 498 filed on October 26, 2018 (Attorney Docket No. 2018PF01110-44755.2066PV01), 983 (Attorney Docket No. 2018PF01112-44755.2000PV01), U.S. Provisional Application No. 62 / 751 268, filed on October 26, 2018 (Attorney Docket No. 2018PF01160-44755.1997PV01), U.S. Provisional Application No. 62 / 751 289, filed on October 26, 2018 (Attorney Docket No. 2018PF01159-44755.1998PV01), U.S. Provisional Application No. 62 / 750 996, filed on October 26, 2018 (Attorney Docket No. 2018PF01145-44755.1999PV01), U.S. Provisional Application No. 62 / 751 289, filed on October 26, 2018 (Attorney Docket No. 2018PF01159-44755.1998PV01), 167 (attorney docket number 2018PF01115-44755.2000PV01), and U.S. Provisional Application No. 62 / 751 185 filed on October 26, 2018 (attorney docket number 2018PF01116-44755.2001PV01), the entire contents of which are incorporated herein by reference in their entirety, as if fully set forth herein.

[0036] The devices, systems, and methods described herein may also include one or more features described in U.S. Provisional Application No. 62 / 642,847, filed March 14, 2018 (Attorney Docket No. 2017PF02103) (and a non-provisional application filed therefrom as U.S. Serial No. 16 / 351,175, filed March 12, 2019), U.S. Provisional Application No. 62 / 712,009, filed July 30, 2018 (Attorney Docket No. 2017PF02296), U.S. Provisional Application No. 62 / 711,927, filed July 30, 2018 (Attorney Docket No. 2017PF02101), U.S. Provisional Application No. 62 / 643,009, filed March 15, 2018 (Attorney Docket No. 2017PF02103), and U.S. Provisional Application No. 62 / 643,009, filed March 15, 2018 (Attorney Docket No. 2017PF02104). No. 366 (Attorney Docket No. 2017PF02365) (and a nonprovisional application filed therefrom as U.S. Serial No. 16 / 354,970 on March 15, 2019), the entire contents of which are incorporated herein by reference as if fully set forth herein.

[0037] The present disclosure basically helps clinicians understand a large amount of intraluminal imaging data, as well as reporting and treatment planning, plus reduced case time and improved ease of use. The present disclosure achieves this by providing a fast and seamless process for identifying and marking the location of interest in a blood vessel or lumen along the length of the inspection in real time during the imaging process (e.g., IVUS withdrawal process). Implemented on a medical imaging console (e.g., IVUS imaging console) that communicates with a medical imaging sensor (e.g., an intraluminal ultrasound sensor), the automatic target and reference detection system disclosed herein provides both time savings and improvements in the accuracy of bookmarking the captured images. This improved imaging workflow converts the time-consuming process of imaging, image selection, review and clinical judgment into a streamlined repeatable process involving fewer steps and simpler steps on the part of the clinician. For example, this occurs without the normal routine need for clinicians to perform mathematical calculations or apply visual judgment when identifying targets, references and landing positions. This unconventional method improves the functionality of medical imaging consoles and sensors by automating the bookmarking steps that are usually performed manually by clinicians or other users.

[0038] The automatic target and reference detection system can be implemented as a set of logical branches and mathematical operations, the output of which is visible on a display and is operated by a control process executed on a processor, which accepts user input (e.g., from a user interface such as a keyboard, mouse, or touch screen interface) and communicates with one or more medical imaging sensors (e.g., an intraluminal ultrasound sensor). In this regard, the control process performs certain specific operations in response to different inputs or selections made by the user at the beginning of the imaging process, and may also respond to inputs made by the user during the process. Certain structures, functions, and operations of the processor, display, sensor, and user input system are known in the art, while other structures, functions, and operations are recorded herein to implement novel features or aspects of the present disclosure.

[0039] Various types of intraluminal imaging systems are used to diagnose and treat disease. For example, intravascular ultrasound (IVUS) imaging is used as a diagnostic tool to visualize blood vessels within a patient's body. This can help evaluate diseased or compressed blood vessels, such as arteries or veins, within the body to determine the need for treatment, optimize treatment, and / or evaluate the effectiveness of treatment (e.g., by imaging the vessel before and after treatment).

[0040] In some cases, intraluminal imaging is performed using an IVUS device that includes one or more ultrasonic transducers. The IVUS device can be delivered into a blood vessel and guided to the area to be imaged. The transducer transmits ultrasonic energy and receives ultrasonic echoes reflected from the blood vessel. The ultrasonic echoes are processed to create an image of the blood vessel of interest. The image of the blood vessel of interest can include one or more lesions or blockages in the blood vessel. Stents can be placed in the blood vessel to treat these blockages, and intraluminal imaging can be performed to observe the placement of the stent in the blood vessel. Other types of treatments include thrombectomy, ablation, angioplasty, medications, etc.

[0041] These descriptions are provided for exemplary purposes only and should not be considered to limit the scope of the automatic target and reference detection system. Certain features may be added, removed, or modified without departing from the spirit of the claimed subject matter.

[0042] In order to promote the understanding of the principles of the present disclosure, reference will now be made to the embodiments shown in the accompanying drawings, and specific language will be used to describe these embodiments. However, it should be understood that it is not intended to limit the scope of the present disclosure. Any changes and further modifications to the described devices, systems and methods and any further applications of the principles of the present disclosure are fully contemplated and included in the present disclosure, as would be normally expected by a person skilled in the art to which the present disclosure relates. In particular, it is fully conceivable that the features, components and / or steps described with respect to one embodiment can be combined with the features, components and / or steps described with respect to other embodiments of the present disclosure. However, for the sake of brevity, multiple iterations of these combinations will not be described separately.

[0043] Figure 1 is a diagrammatic representation of an intraluminal imaging system in combination with an automatic target and reference detection system according to aspects of the present disclosure. In some embodiments, the intraluminal imaging system 100 may 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, 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, in various embodiments, the intraluminal device 102 may be a catheter, a guidewire, a guide catheter, a pressure wire, and / or a flow wire. In some cases, the system 100 may include additional components and / or may be used without Figure 1 For example, the system 100 may omit the external imaging system 132.

[0044] The intraluminal imaging system 100 (or intravascular imaging system) can be any type of imaging system suitable for use in a lumen or vessel of a patient. In some embodiments, the intraluminal imaging system 100 is an intraluminal ultrasound (IVUS) imaging system. In other embodiments, the intraluminal imaging system 100 can include a system configured for forward-looking intraluminal ultrasound (FL-IVUS) imaging, intraluminal photoacoustic (IVPA) imaging, intracardiac echocardiography (ICE), transesophageal echocardiography (TEE), and / or other suitable imaging modalities.

[0045] It should be understood that the system 100 and / or the device 102 can be configured to obtain any suitable intraluminal imaging data. In some embodiments, the device 102 can include imaging components of any suitable imaging modality (such as optical imaging, optical coherence tomography (OCT), etc.). In some embodiments, the device 102 can 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. Generally speaking, the device 102 can include imaging elements to obtain intraluminal imaging data associated with the lumen 120. The size and shape of the device 102 can be set (and / or configured) for insertion into a patient's blood vessel or lumen 120.

[0046] System 100 can be deployed in the catheter room with control room.Processing system 106 can be located in the control room.Alternatively, processing system 106 can be located elsewhere, for example, in the catheter room itself.The catheter room can include a sterile area, and its associated control room can be or can not be sterile, depending on the process to be performed and / or depending on the medical care institution.The catheter room and control room can be used to perform any number of medical imaging processes, such as angiography, fluoroscopy, CT, IVUS, virtual histology (VH), forward IVUS (FL-IVUS), intraluminal photoacoustic (IVPA) imaging, blood flow reserve fraction (FFR) determination, coronary blood flow reserve (CFR) determination, optical coherence tomography (OCT), computed tomography, intracardiac echocardiography (ICE), forward ICE (FLICE), intraluminal pulse tracing, transesophageal ultrasound, fluoroscopy and other medical imaging modalities or combinations thereof.In certain embodiments, device 102 can be controlled from a remote location such as a control room, such as without requiring an operator to be very close to the patient.

[0047] The intraluminal device 102, PIM 104, monitor 108, and external imaging system 132 may be directly or indirectly communicatively coupled 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 an optical fiber link and / or via a wireless connection using the IEEE 802.11 Wi-Fi standard, an ultra-wideband (UWB) standard, wireless FireWire, wireless USB, or another high-speed wireless network standard. The processing system 106 may be communicatively coupled to one or more data networks, such as a TCP / IP-based local area network (LAN). In other embodiments, different protocols may be utilized, such as a synchronous optical network (SONET). In some cases, the processing system 106 may be communicatively coupled to a wide area network (WAN). The processing system 106 may utilize a network connection to access various resources. For example, the processing system 106 may communicate with a medical digital imaging and communications (DICOM) system, a picture archiving and communications system (PACS), and / or a hospital information system (HIS) via a network connection.

[0048] At a high level, the ultrasound imaging intraluminal device 102 transmits ultrasound energy from a transducer array 124 included in a scanner assembly 110 mounted near the distal end of the intraluminal device 102. The ultrasound energy is reflected by tissue structures in the medium surrounding the scanner assembly 110, such as the lumen 120, and the ultrasound echo signals are received by the transducer array 124. The scanner assembly 110 generates (one or more) electrical signals representing the ultrasound echoes. The scanner assembly 110 may include one or more individual ultrasound transducers and / or transducer arrays 124 in any suitable configuration, such as a planar array, a curved array, a circumferential array, an annular array, etc. For example, in some cases, the scanner assembly 110 may be a one-dimensional array or a two-dimensional array. In some cases, the scanner assembly 110 may be a rotating ultrasound device. The active area of ​​the scanner assembly 110 may include one or more sections (e.g., one or more rows, one or more columns, and / or one or more orientations) of one or more transducer materials and / or ultrasonic elements that may be uniformly or independently controlled and activated. The active area of ​​the scanner assembly 110 may be patterned or configured in a variety of basic or complex geometric shapes. The scanner assembly 110 may be configured in a side-looking orientation (e.g., ultrasonic energy emitted perpendicular to and / or orthogonal to the longitudinal axis of the intraluminal device 102) and / or a forward-looking orientation (e.g., ultrasonic energy emitted parallel to and / or along the longitudinal axis). In some cases, the scanner assembly 110 is structurally arranged to emit and / or receive ultrasonic energy in a proximal or distal direction at an oblique angle relative to the longitudinal axis. In some embodiments, ultrasonic energy emission may be electronically manipulated by selectively triggering one or more transducer elements of the scanner assembly 110.

[0049] The ultrasonic transducer(s) of the scanner assembly 110 may be a piezoelectric micromachined ultrasonic transducer (PMUT), a capacitive micromachined ultrasonic transducer (CMUT), a single crystal, lead zirconate titanate (PZT), a PZT composite, other suitable transducer types, and / or combinations thereof. In embodiments, the ultrasonic transducer array 124 may 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 greater and lesser.

[0050] The PIM 104 transmits the received echo signals to a processing system 106 where an ultrasound image (including flow information) is reconstructed and displayed on a monitor 108. The console or processing system 106 may 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 may execute computer-readable instructions stored on a non-transitory tangible computer-readable medium.

[0051] The PIM 104 facilitates communication of signals between the processing system 106 and the scanner assembly 110 included in the intraluminal device 102. The communication may include providing commands to the (one or more) integrated circuit controller chips within the intraluminal device 102, selecting (one or more) specific elements on the transducer array 124 for transmission and reception, providing a transmission trigger signal to the (one or more) integrated circuit controller chips to activate the transmitter circuit to generate an electrical pulse to excite the selected transducer array element, and / or accepting an amplified echo signal received from the selected transducer array element via an amplifier included on the (one or more) integrated circuit controller chips. In some embodiments, the PIM 104 performs preliminary processing of the echo data before relaying the data to the processing system 106. In an example of such an embodiment, the PIM 104 performs amplification, filtering, and / or aggregation of the data. In an embodiment, the PIM 104 also supplies high and low voltage DC power to support the operation of the intraluminal device 102 including the circuits within the scanner assembly 110.

[0052] The processing system 106 receives echo data from the scanner assembly 110 through the PIM 104 and processes the data to reconstruct an image of tissue structures in the medium surrounding the scanner assembly 110. In general, the device 102 can be used in any suitable anatomical structure and / or body lumen of a patient. The processing system 106 outputs image data so that an image of a blood vessel or lumen 120 (such as a cross-sectional IVUS image of the lumen 120) is displayed on the monitor 108. The lumen 120 can represent a structure filled with or surrounded by a fluid, whether natural or artificial. The lumen 120 can be in the patient's body. The lumen 120 can be a blood vessel, such as an artery or vein of the patient's vascular system, including cardiac vessels, peripheral vessels, neural vessels, renal vessels, and / or any other suitable lumen in the body. For example, the device 102 may be used to inspect any number of anatomical locations and tissue types, including, but not limited to: organs, including the liver, heart, kidneys, gallbladder, pancreas, lungs; ducts; intestines; nervous system structures, including the brain, thecal sac, spinal cord, and peripheral nerves; urinary tract; and 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 inspect artificial structures, such as, but not limited to, heart valves, stents, shunts, filters, and other devices.

[0053] The controller or processing system 106 may include a processing circuit having one or more processors communicating with a 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 the present 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 in a single component. For example, the system 100 may include a touch screen device, including a housing with a touch screen display and a processor. The system 100 may include any suitable input device, such as a touch-sensitive pad or a touch screen display, a keyboard / mouse, a joystick, a button, etc., for a user to select the options shown on the monitor 108. The processing system 106, the monitor 108, the input device and / or a combination thereof may be referred to as a controller of the system 100. The controller may communicate with other components of the device 102, the PIM 104, the processing system 106, the monitor 108, the input device and / or the system 100.

[0054] In some embodiments, the intraluminal device 102 includes features similar to those of a conventional solid-state IVUS catheter, such as the one available from Volcano Corporation. Catheters and those disclosed in U.S. Pat. No. 7,846,101, the entire contents of which are incorporated herein by reference. 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 a plurality of conductors, including one, two, three, four, five, six, seven or more conductors.

[0055] The transmission harness 112 terminates at 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 an embodiment, the intraluminal device 102 also includes a guidewire exit port 116. Thus, in some cases, the intraluminal device 102 is a rapid exchange catheter. The guidewire exit port 116 allows a guidewire 118 to be inserted toward the distal end in order to guide the intraluminal device 102 through a lumen 120.

[0056] Monitor 108 may be a display device, such as a computer monitor or other type of screen. Monitor 108 may be used to display optional prompts, instructions, and visualizations of imaging data to the user. In some embodiments, monitor 108 may be used to provide a user with a procedure-specific workflow to complete an intraluminal imaging procedure. The workflow may include performing a pre-stent implantation plan to determine the state of the lumen and the potential for stent implantation, as well as a post-stent implantation check to determine the state of the stent already positioned in the lumen. The workflow may be performed in a Figure 5-11 Any display or visualization shown is presented to the user.

[0057] The external imaging system 132 can be configured to obtain X-ray, radiographic, angiographic / venographic (e.g., with contrast agents), and / or fluoroscopic (e.g., without contrast agents) images of the patient's body (including the blood vessel 120). The external imaging system 132 can also be configured to obtain computed tomographic images of the patient's body (including the blood vessel 120). The external imaging system 132 can include an external ultrasound probe configured to obtain ultrasound images of the patient's body (including the blood vessel 120) when positioned outside the body. In some embodiments, the system 100 includes other imaging modality systems (e.g., MRI) to obtain images of the patient's body (including the blood vessel 120). The processing system 106 can utilize the images of the patient's body in conjunction with the intraluminal images obtained by the intraluminal device 102.

[0058] Figure 2Blood vessels (e.g., arteries and veins) in a human body are illustrated. For example, veins in a human body are labeled. Aspects of the present disclosure may relate to peripheral vasculature, such as veins in the torso or legs.

[0059] Blockage can occur in an artery or vein. Blockage can generally refer to any obstruction or other structural arrangement that results in a restriction of fluid flow through a lumen (e.g., an artery or vein), for example, in a manner that is detrimental to the patient's health. For example, a blockage narrows the lumen, such that the cross-sectional area of ​​the lumen and / or the space available for fluid flow through the lumen is reduced. In the case where the anatomical structure is a blood vessel, the blockage may be the result of narrowing due to compression (e.g., from external blood vessels), plaque accumulation (including but not limited to plaque components such as fiber, fibrolipid (fibrofat), necrotic core, calcification (dense calcium), blood, and / or different stages of thrombus (e.g., acute, subacute, chronic, etc.). In some cases, the blockage may be referred to as a thrombus, stenosis, and / or lesion. In general, the composition of the blockage will depend on the type of anatomical structure being evaluated. Healthier portions of the anatomical structure may have a uniform or symmetrical profile (e.g., a cylindrical profile with a circular cross-sectional profile). The blockage may not have a uniform or symmetrical profile. Thus, a diseased or compressed portion of the anatomical structure having a blockage will have an asymmetrical and / or otherwise irregular profile. The anatomical structure may have one blockage or multiple blockages.

[0060] The accumulation of blockage (e.g., thrombus, deep vein thrombosis or DVT, chronic complete blockage or CTO, etc.) is a way to reduce the cross-sectional area of ​​the vein in the peripheral vessels (e.g., trunk, abdomen, groin, leg). Other anatomical structures that contact the vein can also reduce its cross-sectional area, thereby limiting blood flow therethrough. For example, an artery or ligament in the trunk, abdomen, groin or leg can compress the vein, which changes the shape of the vein and reduces its cross-sectional area. This reduction in cross-sectional area caused by contact with other anatomical structures can be referred to as compression, in that the vein wall is compressed due to contact with an artery or ligament.

[0061] Figure 3 The blood vessel 300 is illustrated in conjunction with a compression 330. The compression 330 occurs external to the vessel wall 310 and may restrict the flow of blood 320. The compression may be caused by other anatomical structures external to the blood vessel 300 including, but not limited to, tendons, ligaments, or adjacent lumens.

[0062] Figure 4A blood vessel 300 is illustrated incorporating compression 330 and having a stent 440 expanded within it to restore flow. Stent 440 displaces and blocks compression 330, thereby pushing vessel wall 310 outward, thereby reducing flow restriction to blood 320. Other treatment options for alleviating blockage may include, but are not limited to, thrombectomy, ablation, angioplasty, and medication. However, in most cases, it may be highly desirable to obtain accurate and timely intravascular images of the affected area, as well as accurate and detailed knowledge of the location, orientation, length, and volume of the affected area before, during, or after treatment.

[0063] Figure 5 An example intraluminal imaging display screen 500 according to at least one embodiment of the present disclosure is illustrated. In this example, the screen display 500 includes a current tomographic IVUS image 510 from a series of continuous tomographic images, an image longitudinal display (ILD) 520 containing stacked longitudinal cross-sections of a series of continuous tomographic images, and a graphical roadmap 530. Bookmarks 540a, 540b, 540c, 540d, 540e, and 540f associated with both the graphical roadmap 530 and the ILD 520 are also visible. Bookmark 540d is also associated with the current IVUS image 510, such as a label 550 containing information about the location and properties of the IVUS image 510. In this example, the IVUS image is identified as a reference image of the left external iliac vein. In addition, the bookmark information can be saved in an automatically generated report. If the bookmark is changed at any of these locations, the automatic target and reference detection system updates the bookmarks at all these locations, thereby saving time and simplifying the process of identifying targets and reference frames.

[0064] Bookmark 540a indicates a reference location within the left common iliac vein (CIV). Bookmark 540b indicates a target location within the left CIV. Bookmark 540c indicates a target location within the left external iliac vein (EIV). Bookmark 540d indicates a reference location within the left EIV. Bookmark 540e indicates a reference location within the left common femoral vein (CFV). Bookmark 540f indicates a target location within the left CFV. Other segments may be identified by the system, including, but not limited to, the inferior vena cava (IVC), femoral vein (e.g., femoral vein for popliteal access), iliac vein, etc.

[0065] It should be understood that in some cases, one or more segments of a vein (e.g., CIV, EIV, CFV, etc.) may have multiple targets and / or reference locations, for example, if there is more than one compression or other blockage in the segment. Similarly, in some cases, one or more segments of a vein may not have compression or other blockage, and therefore may not have an identified target or reference location. In some cases, the reference location may also mark the landing zone of the stent, although this may not always be the case.

[0066] Other vascular segments or lumen segments may be identified in other areas of the body. In some embodiments, identification of vascular segments is automatically performed by an automatic target and reference detection system (e.g., using image recognition, velocity tracking, and position estimation). In other embodiments, bookmarks are predictively suggested to clinicians or other users. Predicting the next bookmark that a user will need advantageously avoids the requirement for the user to review a list of bookmarks to find the correct bookmark or type in a manual bookmark. In other embodiments, identification of vascular segments is performed by a clinician or other user with the assistance of an automatic target and reference detection system. For example, a bookmark or label can be applied to the location where a segment begins or ends, or the location where another segment begins or ends.

[0067] The bookmark can be automatically associated with the intraluminal image 510 that appears at that location, and also associated with the corresponding location on the ILD 520 and the graphical roadmap 530. In addition, the automatic target and reference detection system can automatically populate a label 590 that is automatically associated with the bookmark and the associated intraluminal image, and can include, for example, bookmark information, segment name, frame number, and image type (e.g., reference, pre-treatment target, post-treatment target, etc.). Based on image recognition to track known bifurcations of blood vessels or lumens as anatomical landmarks, these steps can be automatically performed by the automatic target and reference detection system without requiring any type of user input. Bookmarks can also be suggested or automatically placed based on automatic image recognition of problems such as thrombus, webbing, and compression (venous) or stenosis (arterial).

[0068] Examples of boundary detection, image processing, image analysis, and / or pattern recognition include: U.S. Patent No. 6,200,268, entitled “VASCULAR PLAQUE CHARACTERIZATION,” issued on March 13, 2001, with D. Geoffrey Vince, Barry D. Kuban, and Anuja Nair as inventors; U.S. Patent No. 6,381,350, entitled “INTRAVASCULAR ULTRASONIC ANALYSIS USING ACTIVE CONTOUR METHOD AND SYSTEM,” issued on April 30, 2002, with Jon D. Klingensmith, D. Geoffrey Vince, and Raj Shekhar as inventors; U.S. Patent No. 7,437,666, entitled “SYSTEM AND METHOD OF CHARACTERIZING VASCULAR TISSUE,” issued on July 11, 2006, with Anuja Nair, D. Geoffrey Vince, Jon D. Klingensmith, and Barry D. Kuban as inventors; 074 188; U.S. Patent No. 7 175 597, entitled “NON-INVASIVE TISSUE CHARACTERIZATION SYSTEM AND METHOD,” published on February 13, 2007, with D. Geoffrey Vince, Anuja Nair and Jon D. Klingensmith as inventors; U.S. Patent No. 7 215 802, entitled “SYSTEM AND METHOD FOR VASCULAR BORDER DETECTION,” published on May 8, 2007, with Jon D. Klingensmith, Aniya Nair, Barry D. Kuban and D. Geoffrey Vince as inventors; U.S. Patent No. 7 359, entitled “SYSTEM AND METHOD FOR IDENTIFYING A VASCULAR BORDER,” published on April 15, 2008, with Jon D. Klingensmith, D. Geoffrey Vince, Anuja Nair and Barry D. Kuban as inventors 554; published on December 9, 2008 by Jon D. Klingensmith, Anuja Nair, Barry D. Kuban, and D.U.S. Patent No. 7 463 759 entitled “SYSTEM AND METHOD FOR VASCULAR BORDER DETECTION” with Geoffrey Vince as inventor.

[0069] To perform the calculations, determinations, and logical operations disclosed herein, the automatic target and reference detection system may employ a number of variables with default values. However, if desired, the values ​​of one or more of these variables may be edited by the user to values ​​other than the default values ​​in order to replace, in whole or in part, the user's judgment of the assumptions embedded in the automatic target and reference detection system. Some example variables are shown in Table 1.

[0070] Table 1: User-modifiable variables

[0071]

[0072]

[0073] APPSET 4 and APPSET 5 can be used to identify target and reference positions using standardized or "normal" anatomy from the literature, but may not be suitable for calculating target and reference positions using the novel methods disclosed herein. Table 2 includes user-editable standard anatomical measurements that can be used with APPSET 4 and APPSET 5, with default values ​​from the medical literature.

[0074] Table 2: Example "standard" vein sizes that can be modified by the user

[0075] ID describe scope unit default Data Types Step Length APPSET5 A CIV standard area 0-300mm2 mm2 200 Fraction Any APPSET5 B EIV standard area 0-300mm3 mm2 150 Fraction Any APPSET5 C CFV standard area 0-300mm4 mm2 125 Fraction Any APPSET5 D CIV standard effective radius 0-20mm mm 16 Fraction Any APPSET5 E EIV standard effective radius 0-20mm mm 14 Fraction Any APPSET5 F CFV standard effective radius 0-20mm mm 12 Fraction Any

[0076] In an example, depending on the implementation and the selections made by the user, the intraluminal imaging display screen 500 can display a reference zone and a landing zone calculated based on the lumen area, the lumen minimum diameter, the lumen effective diameter, other lumen dimensions (e.g., lumen aspect ratio, flow resistance, etc.), or a stenosis formula. In an example stenosis formula, such as:

[0077] % Area Stenosis = (Normal Area - Lesion Area) / Normal Area x 100 (Equation 1)

[0078] In a clinically representative example, the threshold for % area stenosis of a lesion considered significant is approximately 54%. Another stenosis formula is:

[0079]

[0080] In a clinically representative example, the threshold for % diameter stenosis of a lesion considered significant is approximately 61%.

[0081] Note that the lumen size shown at the bookmarked location along ILD 520 may be based on variable APPSET10 such that it shows lumen area by default. However, other options selectable by the user may include, for example, effective diameter, minimum diameter, maximum diameter, aspect ratio, flow resistance, or other lumen metrics, depending on the implementation.

[0082] Figure 6 is a schematic diagrammatic representation of a method 600 for selecting a target frame, a reference frame, and a stent landing zone in accordance with at least one embodiment of the present disclosure. In selecting 610 a target location, a reference location, a stent landing zone, or other region of interest, the clinician must first consider the patient's medical history 620 and the classification of vascular disease that the patient is known to have. For example, the selection 610 of the target location may be performed by the processing system 106 (see Figure 1 ) or processor circuit 1450 (see Fig.14 ) as described below. The patient history 620 can be obtained, for example, from a medical record stored in a memory accessible to the intraluminal imaging system. For example, if the vein segment is healthy or contains a focal lesion, then 0 or 1 target frame and 0 or 1 reference frame can be expected. However, if the segment includes a diffuse lesion, then 1 reference frame can be expected, but no identifiable target frame. If the segment includes excessive compression, then 1 target frame and 1 reference frame can be expected (e.g., in pre-stenotic dilation). Therefore, there can be zero, one, or more target and reference frames within a given segment.

[0083] Additionally, the method 600 may receive patient-specific anatomy 630, including branches or confluences, proximity of adjacent vessels, location of lesions, etc., and specific properties 640 of the lumen of interest, such as lumen area, flatness / roundness (e.g., aspect ratio), etc. For example, both the patient-specific anatomy 630 and specific properties 640 may be obtained from extraluminal (e.g., X-ray, CT images) and / or intraluminal (IVUS) images.

[0084] Additionally, the method 600 considers other factors 650, including, for example, cost, time, age and overall health of the patient, or reference values ​​for vessel size. In a clinically relevant example, anatomically normal "theoretical values" for area and diameter (which may vary by up to + / - 10%) include:

[0085] IVC: 250mm2, 18mm

[0086] CIV: 200mm2, 16mm

[0087] EIV: 150mm2, 14mm

[0088] CFV: 125mm2, 12mm

[0089] However, not all patients conform to these standardized dimensions, not all clinicians agree on them, and calculations based on these values ​​do not always achieve optimal results. In addition, clinicians can choose the IVUS image frame that is most relevant in their minds and is not necessarily the target frame or reference frame in the usual sense.

[0090] As a result of method 600, clinical judgment (which can vary widely, resulting in significant differences in the selected reference positions, target positions, and landing positions) can be enhanced with an algorithmic real-time process that is simple, easy to understand, easy to communicate, and produces repeatable results, so that the user knows exactly how the algorithm picks target frames and reference frames in each segment.

[0091] In addition, the automatic target and reference detection system can allow the user to override the default values ​​of multiple variables so that the logical operations described herein can be performed again using different assumptions. Thus, the clinician can check in real time any changes in the target or landing position that occur when, for example, a lumen metric (e.g., variable APPSET 3 or APPSET 10) changes from "area" to "aspect ratio" or when a threshold for a lumen metric (e.g., variable APPSET 2 or APPSET 9) is increased or decreased. This real-time adjustability can allow an experienced practitioner to determine a stable solution that increases the likelihood of a successful treatment with a higher confidence in the outcome (which is possible using traditional methods) and in the time frame (which is not possible using traditional methods).

[0092] In the example, if the vein contains a position with 53.6% of the area or 61.2% of the diameter of the nearby healthy part of the vein, the vein can be considered to be compressed or suffering from other diseases. In some cases, any part of the vein with a value equal to or lower than these values ​​can be considered a "target area", within which a specific target position or target frame can be selected, as described below. In other cases, a combination of values ​​can be considered to determine the presence, location and severity of compression, lesions or other diseases. Therefore, there are multiple target candidates from which to select the target area and identify the target frame. The algorithm uses reference values ​​derived from the IVUS measurements obtained during the withdrawal process (e.g., a reference frame identified in real time as described below). However, the automatic target and reference detection system can include user-selectable settings to allow clinicians to calculate targets and reference frames based on standardized theoretical anatomical averages obtained from published literature.

[0093] Figure 7 is a diagrammatic representation in the form of a flow chart of an exemplary pre-treatment target region and reference frame detection method 700 in accordance with at least one embodiment of the present disclosure. It should be understood that the steps of method 700 may be performed in the same manner as described above. Figure 7The method 700 may be performed in a different order than shown, additional steps may be provided before, during, and after the steps, and / or some of the steps described may be replaced or eliminated in other embodiments. One or more steps of the method 700 may be performed by one or more devices and / or systems described herein, such as components of the intraluminal imaging system 100, the PIM 194, the processing system 106, and / or the processor circuit 1450.

[0094] In step 710, method 700 includes receiving a plurality of IVUS images (e.g., from an IVUS pullback), determining whether compression or other disease is present in the IVUS images, and identifying a particular IVUS image frame within the pullback sequence that includes compression or other disease. This identification can be based on image recognition or other determination of the area, shape, or aspect ratio of the lumen of the vessel, or based on other factors as described herein. The logic described herein can identify both the lumen and sheath of the vessel in order to determine whether compression or other disease is present in the vessel and to what extent.

[0095] In step 720, method 700 includes determining whether the detected compression or other disease is located near (e.g., within a certain number of frames or millimeters) the confluence of the inferior vena cava (IVC). If so, execution proceeds to step 730. If not, execution proceeds to step 740.

[0096] At step 730 , method 700 includes determining that the reference frame should be located distally rather than proximally of the compression region. Execution then proceeds to step 790 .

[0097] In step 740, method 700 includes determining whether compression exists within the current segment of the blood vessel, thereby defining a target zone. If yes, execution proceeds to step 750. If no, execution proceeds to step 780.

[0098] In step 750 , method 700 includes identifying an optimal reference frame that is proximal to the compression or other lesion and within the same vessel segment. Execution then proceeds to step 760 .

[0099] In step 760 , method 700 includes identifying an optimal reference frame distal to the compression or other disease and within the same vessel segment. Execution then proceeds to step 770 .

[0100] In step 770, method 700 includes reporting the reference frames (eg, by displaying them on a display) so that the clinician can confirm the presence and location of the compression and determine appropriate treatment. The method is now complete.

[0101] In step 780 , method 700 includes finding an optimal reference frame that is proximal to the compression or other disease and not necessarily within the current segment.

[0102] In step 790 , method 700 includes finding an optimal reference frame that is distal to the compression or other disease and not necessarily within the current segment.

[0103] In some cases, the proximal reference frame and the distal reference frame can define a landing zone for stent implantation such that, for example, the proximal end of the stent is aligned with the proximal reference frame and the distal end of the stent is aligned with the distal reference frame. However, clinical judgment on the part of the clinician may dictate alternative placement of the stent.

[0104] Note that the terms "target" and "reference" are merely exemplary, and other terms may be used alternatively or additionally, including but not limited to: target frame, compression frame, frame of interest, treatment frame, reference frame, healthy frame, etc. The reference frame can be on either or both sides of the compression. The automatic target and reference detection system can identify one or more reference frames for each segment. The reference frame can be used for comparison with the target frame (e.g., to assess the extent of disease / compression before treatment to inform clinical decisions about treatment). One or more reference frames in the screen display can be used and identified as potential proximal / distal landing zones for the stent. In other embodiments, it is not necessary to use or identify one or more reference frames as potential landing zones for the stent. The automatic target and reference detection system can identify one target frame and one reference frame for each segment, or multiple target frames and / or multiple reference frames for each segment.

[0105] Note that all flowcharts disclosed herein are provided for exemplary purposes; those of ordinary skill in the art will recognize countless variations that still fall within the scope of the present disclosure. For example, the logic of the flowchart may be shown as sequential. However, similar logic may be parallel, massively parallel, object-oriented, real-time, event-driven, cellular automata, etc., while implementing the same or similar functions. In order to perform the methods described herein, the processor may divide each step described herein into multiple machine instructions, and these instructions may be executed at a rate of hundreds, thousands, millions, or billions per second in a single processor or across multiple processors. Such rapid execution may be necessary in order to perform the methods described herein in real time or near real time. For example, in order to provide accurate, repeatable identification of target frames, reference frames, landing zones, etc., the system may perform the steps disclosed herein during the IVUS withdrawal process, where shorter process times may be associated with improved patient outcomes. Therefore, if the pre-treatment target and reference frame detection method 700 does not reduce the time required to perform the process and / or the accuracy of the results, clinicians may be reluctant to adopt it.

[0106] Figure 8 is a graphical representation in the form of a flow chart of an exemplary post-treatment stent inspection method or minimum stent area calculation method 800 in accordance with at least one embodiment of the present disclosure.

[0107] In step 810, method 800 includes receiving multiple IVUS images of the stent (e.g., captured and stored during IVUS pullback), identifying the proximal and distal ends of the stent in the images, and evaluating the aspect ratio (the ratio between the maximum and minimum diameters of the ellipse) to determine the roundness of the stent in the cross section. Other variables may also be evaluated, such as lumen cross-sectional area, maximum diameter, minimum diameter, flow resistance, etc.

[0108] In step 820, method 800 includes, for each stent segment of the vessel, determining whether the stent lumen area (or aspect ratio, etc.) is within expected parameters for successful treatment (e.g., whether it will support increased blood flow to the expected degree). If yes, execution proceeds to step 830. If no, execution proceeds to step 850.

[0109] In step 830, method 800 includes determining whether the stent transition is smooth. For example, if the diameter of the vessel is similar both inside and outside the stent, the transition may be acceptably smooth, and execution proceeds to step 850. If the diameter outside the stent is significantly different than the diameter inside the stent, the transition may not be acceptably smooth, and execution proceeds to step 850.

[0110] In step 840, the method includes steps 810-820 of reporting the results (eg, displaying them on a display). The method 800 is complete.

[0111] At step 850, method 800 includes determining, at the clinician's discretion, that further treatment is needed or that an existing treatment is optimized.

[0112] Fig. 9 is a diagrammatic representation in block diagram form of at least a portion of an exemplary pre-treatment automatic target and reference detection system or method 900 in accordance with at least one embodiment of the present disclosure. It should be understood that the steps, modules, inputs, or outputs of the method 900 may be in the same manner as described above. Fig. 9 The steps of method 900 may occur or be performed in a different order than shown, additional steps, modules, inputs or outputs may be provided before, during, and after the steps, and / or some of the steps described may be replaced or eliminated in other embodiments. One or more of the steps, modules, inputs or outputs of method 900 may be performed by one or more devices and / or systems described herein (such as components of intraluminal imaging system 100, PIM 194, processing system 106, and / or processor circuit 1450).

[0113] In step 910, method 900 includes prompting a user to select which limb is to be treated.

[0114] In step 920, method 900 includes selecting which side of the body the limb is on (eg, left or right), and which access point will be used to access a target vessel in the limb.

[0115] In step 930, method 900 includes prompting the user to turn on the IVUS system and insert the intraluminal imaging device into the patient's target blood vessel and capture live IVUS images of the interior of the blood vessel. For example, the clinician can advance the imaging device through a compressed or otherwise diseased portion of the blood vessel and prepare to perform a retraction procedure.

[0116] In step 940, the method 900 includes accepting input from a user to switch to a recording mode, wherein the IVUS images captured by the intraluminal imaging device are such that, when a retraction procedure is performed, images of the diseased portion of the vessel and healthy portions surrounding the diseased portion are stored for analysis. The system records images of the sheath 950 and the vessel lumen boundary 960, and calculates the retraction speed 970 by analyzing the recorded images thereof. For example, the sheath may refer to the outermost portion of the imaging catheter. In some embodiments, the method may include measuring a value relative to the sheath (e.g., a distance between the sheath and the lumen boundary, an area between the sheath and the lumen boundary, etc.).

[0117] Within the captured image, the system then identifies the anatomical segment 980 in the vessel through which the intravascular imaging probe has passed based on the retraction velocity 970 and the size of the sheath 950 and lumen boundary 960 and the image-identified per-frame measurements and derived quantities thereof. The per-frame measurements and derived quantities may, for example, include every possible lumen metric that may be specified by [APPSET 3] and [APPSET 10].

[0118] In step or module 990, the application logic receives images and per-frame measurements of the sheath 950 and lumen boundary 960, as well as the retraction speed 970 and the identified anatomical segments 980. The application logic then calculates the target position and reference position within the vessel.

[0119] In step 995 , method 900 includes displaying or otherwise presenting the target location(s), reference location(s), IVUS images, anatomical segments, and per-frame measurements to the clinician for review.

[0120] The application logic described in step 990 may, for example, perform the following steps:

[0121] 990A: Identify vessel segments and exclude confluences

[0122] 990B: In each segment, exclude keep-out areas (convergence) that cannot be used as target or reference locations.

[0123] 990C: Exclude the last 15 frames of the tail window between the start of each segment and the start of the next branch or join. For example, a CIV segment may be defined as CIV start to EIV start - 15. Similarly, an EIV segment = EIV start to CFV start or jacket start - 15, and a CFV segment = CFV start to jacket start - 15. Another example implementation may exclude a specified percentage (e.g., 5% or a user-selectable value) of both the start and end of each segment.

[0124] 990D: Find the average lumen metric for the segment, where the lumen metric has been specified by the user (e.g., via variables APPSET 3 and / or APPSET 10). Each frame of the segment can be analyzed so that the lumen metric is measured and stored, and then the stored values ​​are averaged. Optionally, the method can filter out single frame outliers (e.g., using a median filter). For example, the method can calculate a histogram of the lumen metric and remove the top 25% and bottom 25% of the curve before calculating the average. The system can then also calculate the standard deviation of the remaining data points.

[0125] Reference Frame

[0126] 990E: Identify a reference region that satisfies the following criteria: Window center of [APPSET 7] frames, where at least [APPSET 7] consecutive frames satisfy a primary condition (e.g., aspect ratio < 1.5), a secondary condition (e.g., lumen metric > 100% of the segment's average), and / or an optional tertiary condition (e.g., maximum and minimum lumen metric) defined by variables APPSET 6-APPSET 9. The start frame can be defined as the location where all specified conditions are passed, and the stop frame can be defined as the location where at least one of the specified conditions is not passed. Note that there can be multiple reference regions in a segment, even though the method will ultimately only show one reference frame to the user. Note also that the location of the reference frames is user editable.

[0127] The aspect ratio can be the maximum lumen diameter divided by the minimum lumen diameter. The use of aspect ratio is particularly relevant to peripheral venous disease because aspect ratio is a measure of compression (e.g., to what extent another anatomical structure squeezes the peripheral veins and blocks blood flow in the periphery). Aspect ratio is not usually used to assess coronary artery disease because compression is less of a problem in the coronary arteries. In contrast, plaque load is used to assess blood flow obstruction in the coronary arteries because plaque accumulation usually blocks blood flow in the coronary arteries. In some embodiments, aspects of the present disclosure do not utilize plaque load to assess blood flow obstruction, but rather aspect ratio, which is a better measure of peripheral venous vascular disease (e.g., compression). An aspect ratio of 1 (e.g., a vein cross section shaped like a circle) will be considered healthy / normal. An aspect ratio greater than 1 indicates that the shape of the vein cross section is like an ellipse and is affected by compression. That is, another anatomical structure presses against the vein, thereby changing the cross-sectional shape from a circle to an ellipse.

[0128] 990F: Identify a reference frame within the reference region(s). In one example, the criterion for the reference frame is the frame with the largest lumen metric within the reference region(s). In the event that multiple frames can meet this criterion, the last of these frames is selected. Alternatively, the reference frame can be selected as the last single frame of at least [APPSET 7] consecutive frames in the reference region(s).

[0129] Target Frame

[0130] 990G: Identify a target zone that includes all frames that meet the following criteria: Center of a window of [APPSET 1] frames where at least [APPSET 1] consecutive frames are less than a specified threshold for a lumen metric (e.g., APPSET 2 or APPSET 3). For example, the threshold may default to 50% for an area metric, or 62% for a diameter metric. A secondary metric may optionally be specified. The start frame of the target zone is where both conditions are passed (e.g., the frame continuation condition or the threshold condition). The stop frame of the target zone is where at least one condition fails to pass. Note that even if a single target frame is ultimately selected, there may be multiple target zones in a segment. All identified target zones may be shown to the user (e.g., on an ILD or roadmap image, such as Figure 5 ). Note also that the location of the target frame is user editable.

[0131] 990H: Identify the target frame, which is the frame with the smallest lumen metric within the target region(s). In the case of multiple frames that can meet this criterion, select the headmost frame.

[0132] 990I: Calculate and display the difference metric associated with the target frame.

[0133] The difference measure can be calculated based on a narrow formula such as:

[0134] % Difference Area: (reference area - target area) / reference area x 100 (Equation 3)

[0135]

[0136] As an alternative to steps 990A to 990I (hereinafter referred to as complex calculations), the application logic 990 may optionally calculate the reference frame and the target frame by a simpler method as follows. For each segment, find the frame with the maximum value of the lumen metric [APPSET 3] and the frame with the minimum lumen metric [APPSET 3]. The minimum frame is identified as the target, and the maximum frame is identified as the reference.

[0137] In some embodiments, the user can switch between the complex and simplified calculations to see if their results have different meanings. In addition, in both the complex and simplified calculations, the user can change [APPSET 3] to switch lumen metrics between values ​​such as "Area", "Minimum Diameter", "Effective Diameter", "Flow Resistance", etc. to determine if different lumen metrics result in meaningfully different positions for the reference and target frames. Thus, the clinician can "play" with the settings in order to gain confidence that the results are correct and robust.

[0138] It should be noted that resistance to flow can be expressed in terms of area (Ae) and aspect ratio (AR). Thus, the diameter, area, aspect ratio, etc. of a vessel at a given location is related to the flow or resistance to flow at that location. Resistance is inversely proportional to the square of the area and approximately linear with the aspect ratio as shown below:

[0139]

[0140] Ae=pi ab Formula 8

[0141] AR=a / b Formula 9

[0142] Ae*AR=pi ab*a / b=pi a^2 Formula 10

[0143] Ae / AR=pi ab / (a / b)=pib^2 Formula 11

[0144] Steps similar to those described above can be used to determine post-stenotic dilation, for example, changes in lumen contour at the caudal end of the withdrawal. The method can exclude frames at the head of the caudal edge of the segment if the detected compression exceeds 60% of the criterion.

[0145] As yet another alternative, the target zone may be calculated using theoretical "normal" values ​​from the open literature, as follows: The target region includes at least [APPSET 1] consecutive frames that satisfy the primary condition of [APPSET2] compression of theoretical values ​​[APPSET 5a-APPSET 5f] of lumen metrics [APPSET 3] within a specified range [APPSET 4]. In some embodiments, secondary criteria may also be selected to be checked along with the primary condition. If the primary condition is not applicable for the current retraction, the secondary condition may become the primary condition. For example, the aspect ratio is implicitly used in the calculation of the fluid, as described below.

[0146] This option allows the clinician to revert to an alternative target area calculation in order to verify whether this produces a different result than the complex calculation or the simplified calculation. The following equations can be used to determine values ​​such as area and / or flow resistance R using the diameter, lumen radius r (e.g., an average value, such as mean, median, or mode), minimum lumen radius a and maximum lumen radius b, viscosity η, length L of the volume through which flow is considered, and flow rate. Any one or more values ​​(e.g., measured, calculated, derived, etc.) can be lumen metrics.

[0147] A c =πr 2 Formula 8

[0148] R c =8ηL / πr 4 Formula 9

[0149] =8πηL / A c 2 ~ηL / A 2 Formula 10

[0150] A e =πab Formula 11

[0151]

[0152] The area can be calculated by pixel counting or by other geometric techniques and the diameter derived therefrom. The diameter can be found from the area or by finding the distance between relative points on a contour passing through the centroid. Similarly, the flow resistance can be calculated from the area. The lumen metric(s) can be selected by the user via settings.

[0153] In some embodiments, one or more support landing frames can be selected based on certain characteristics of the identified reference zones and target zones. For example, in the case where the target appears at a 1VC junction, the nearest non-target frame can be the head support landing frame. The tail support landing frame can be, for example, the support landing frame of the reference frame that is closest to the tail of the target area (excluding branches). Other examples include finding a single support landing frame at each end of an area covering multiple target zones and reference zones, either within a segment or across a boundary between two segments. User-configurable thresholds can be used to define how large the gap between zones can be in order for it to be considered a support zone. These support landing frames can also be edited by the user.

[0154] All block diagrams disclosed herein are provided for exemplary purposes; those of ordinary skill in the art will recognize numerous variations that still fall within the scope of the present disclosure. For example, a block diagram may illustrate a specific arrangement of components, modules, services, steps, processes, or layers that result in a particular data flow. It should be understood that some embodiments of the systems disclosed herein may include additional components, some components shown may not be present in some embodiments, and the arrangement of components may be different than shown, resulting in different data flows while still performing the methods described herein.

[0155] Fig.10 is a diagrammatic representation in block diagram form of at least a portion of an exemplary pre-treatment automated target and reference detection system or method 1000 in accordance with at least one embodiment of the present disclosure. In some cases, method 1000 may be similar to method 900, but may involve a greater number of measurements of a lumen and its surroundings.

[0156] In step 1010, method 1000 includes prompting a user to select which limb is to be treated.

[0157] In step 1020, method 1000 includes selecting which side of the body the limb is on (eg, left or right), and which access point will be used to access a target vessel in the limb.

[0158] In step 1030, method 1000 includes prompting the user to turn on the IVUS system and insert the intraluminal imaging device into the patient's target blood vessel and capture real-time IVUS images of the interior of the blood vessel. For example, the clinician can advance the imaging device through a compressed or otherwise diseased portion of the blood vessel and prepare to perform a retraction procedure.

[0159] In step 1040, the method 1000 includes accepting input from a user to switch to a recording mode, wherein the IVUS images captured by the intraluminal imaging device allow images of the diseased portion of the vessel as well as healthy portions surrounding the diseased portion to be stored for analysis while the retraction process is performed. The system records images of the sheath 1070 and vessel lumen boundary 1074, and calculates the retraction speed 1050 by analyzing the recorded images thereof. In addition, if a guidewire or catheter is present in the IVUS image, the system identifies and measures arterial boundaries 1076 and venous boundaries 1078 of any neighboring vessels of the vessel of interest, as well as the guidewire or catheter boundary 1079.

[0160] The system then identifies the anatomical segment 1085 in the vessel through which the intravascular imaging probe is traversed based on the per-frame measurements and derivatives thereof of the retraction speed 1050, retraction length 1080, and the sizes of the sheath 1070, stent 1072 (if present), lumen boundary 1074, artery boundary 1076, vein boundary 1078, and guidewire or catheter boundary 1079 and image recognition. The per-frame measurements and derivatives may, for example, include every possible lumen metric that may be specified by [APPSET 3] and [APPSET 10].

[0161] In step or module 1090, the application logic receives the images and per-frame measurements of the sheath 1070 and lumen boundary 1060, as well as the retraction speed 1050 and the identified anatomical segment 1085. The application logic then calculates the retraction speed according to the above-described method. Fig. 9 One or more of the methods described or related methods that produce the same or similar results are used to calculate the compression zone 1094 (including both the target zone and the reference zone as described above) and the target location and reference location 1092 within the vessel.

[0162] At step 1096, method 1000 includes updating and / or annotating the graphical ILD and / or roadmap display (e.g., Figure 5 ), to include at least one of (one or more) target locations, (one or more) reference locations, IVUS images, anatomical segments, and per-frame measurement results for viewing by a clinician.

[0163] In step 1098, method 1000 comprises prompting the clinician to check ILD and / or roadmap display, and / or waits for additional input when the clinician checks the data displayed. For example, such input can include the command of changing lumen measurement and / or calculating the method for reference frame and target frame. If such input is received, execution returns to step or module 1090.

[0164] In step 1099, the method includes generating a report including at least one of the target location(s), reference location(s), IVUS images, anatomical segments, and per-frame measurements. The method is now complete.

[0165] Fig.11 is a graphical representation in block diagram form of at least a portion of an exemplary post-treatment automated stent assessment system or method 1100 in accordance with at least one embodiment of the present disclosure.

[0166] In step 1110 , method 1100 includes prompting the user to select which limb is to be treated.

[0167] In step 1130, method 1100 includes prompting the user to reinsert the intraluminal imaging device into the patient's target vessel and again capture live IVUS images of the interior of the vessel. For example, the clinician may advance the imaging device past a stent or other treated portion of the vessel and prepare to perform a retrieval procedure.

[0168] In step 1140, method 1100 includes accepting input from a user to switch to a recording mode, wherein IVUS images captured by the intraluminal imaging device enable images of the stented portion of the vessel and the healthy portion surrounding the stented portion to be stored for analysis while the retraction process is performed. The system records images of the stent 1155, sheath 1150, and vessel lumen boundary 1160, and calculates a retraction speed 1170 by analyzing the recorded images thereof.

[0169] In step or module 1190, the application logic receives images and per-frame measurements of the sheath 1150 and lumen boundary 1160, as well as the retraction speed 1170. The application logic also receives pre-treatment data 1180 that has been stored in a database, including images and calculations from pre-treatment analysis (see Fig. 9 and 10 ). Then, the application logic evaluates the bracket as follows:

[0170] 1190A: Identify the proximal and distal ends of the stent. Note that the location of the proximal and distal stent edges are user editable.

[0171] 1190B: Verify that at least 5 consecutive frames conform to the expected dimensions of the bracket.

[0172] 1190C: Identify the maximum shrinkage of the stent, eg, the frame within the stent that exhibits the most undesirable luminal metric (eg, minimum stent area or MSA). Note that the location of the frame with the greatest shrinkage is user editable.

[0173] 1190D: Calculate the aspect ratio (AR) of the maximum contraction frame, if different from the lumen metric. It should be noted that both lumen metric and AR may be clinically important in assessing whether the stent has been successfully deployed and has sufficiently opened the vessel to allow healthy blood flow.

[0174] In step 1195, method 1100 includes displaying or otherwise presenting the location of the maximum contraction frame, lumen metrics and aspect ratio, IVUS images, proximal and distal ends of the stent, and measurements per frame for the clinician to view. The system can then wait for additional input while the clinician views the displayed data. For example, such input can include a command to change lumen metrics (e.g., minimum stent area, minimum effective diameter, maximum flow resistance, etc.). If such input is received, execution returns to step or module 1190. Based on the presented data, the clinician can choose further treatment, such as further expansion of the stent, placement of additional stents, or other appropriate treatment.

[0175] Fig.12 is a graphical representation in block diagram form of at least a portion of an exemplary post-treatment automated stent assessment system or method 1200 in accordance with at least one embodiment of the present disclosure.

[0176] In step 1210, method 1200 includes prompting the user to select which limb is to be treated.

[0177] In step 1220, method 1200 includes selecting which side of the body the limb is on (eg, left or right), and which access point will be used to access a target vessel in the limb.

[0178] In step 1230, method 1200 includes prompting the user to reinsert the intraluminal imaging device into the patient's target vessel and again capture live IVUS images of the interior of the vessel. For example, the clinician may advance the imaging device past a stent or other treated portion of the vessel and prepare to perform a retrieval procedure.

[0179] In step 1240, method 1200 includes accepting input from a user to switch to a recording mode, wherein IVUS images captured by an intraluminal imaging device enable images of the stented portion of the vessel and the healthy portion surrounding the stented portion to be stored for analysis while the retraction process is being performed. The system records images of the stent 1272, sheath 1270, and vessel lumen boundary 1274, and calculates a retraction speed 1250 by analyzing the recorded images thereof. In some embodiments, the retraction speed 1250 may be displayed in real time on a retraction speed indicator 1260 visible to a clinician during the retraction.

[0180] In step or module 1290, the application logic receives images and per-frame measurements of the sheath 1270 and lumen boundary 1274, as well as the retraction speed 1250. The application logic then evaluates the stent as follows:

[0181] 1290A: Identify the proximal and distal edges of the stent. Note that the locations of the proximal stent edge and distal stent edge are user editable.

[0182] 1290C: Identify the frame within the stent that exhibits the minimum stent area (MSA). Note that the position of the MSA frame is user editable.

[0183] 1290D: Calculate the aspect ratio (AR) of the MSA frame. Note that MSA and AR may be clinically important in assessing whether the stent has been successfully deployed and has sufficiently opened the vessel to allow healthy blood flow.

[0184] At step 1296, method 1200 includes updating and / or annotating the graphical ILD and / or roadmap display (e.g., Figure 5 ) to include the MSA position, the aspect ratio of the MSA position, the IVUS image, the proximal edge of the stent and the distal edge of the stent, and at least one of the measurement results in each frame for the clinician to review, and prompt the clinician to view the ILD and / or roadmap display.

[0185] In step 1298, method 1200 includes waiting for additional input while the clinician reviews the displayed data. For example, such input may include a command to change lumen metrics (e.g., other variables other than minimum stent area, such as minimum effective diameter, maximum flow resistance, etc.). If such input is received, execution returns to step or module 1290. Based on the presented data, the clinician may select further treatment, such as further expansion of the stent, placement of additional stents, or other appropriate treatment.

[0186] In step 1299, the method includes generating a report including at least one of the MSA location, the aspect ratio of the MSA location, the IVUS image, the stent proximal edge, and the stent distal edge, and the per-frame measurements. The method is now complete.

[0187] Fig.13 is a graphical representation in block diagram form of at least a portion of an exemplary pre-treatment and post-treatment automatic target recognition, reference recognition, and stent assessment system or method 1300 in accordance with at least one embodiment of the present disclosure.

[0188] In step 1310, method 1300 includes prompting the user to select which limb is to be treated.

[0189] In step 1330, method 1300 includes prompting the user to insert the intraluminal imaging device into the patient's target blood vessel and then capture real-time IVUS images of the interior of the blood vessel. For example, the clinician can advance the imaging device past the region of interest within the blood vessel and prepare to perform a retraction procedure.

[0190] In step 1340, method 1300 includes accepting input from a user to switch to a recording mode and then storing IVUS images captured by the intraluminal imaging device so that when a pullback procedure is performed, images of the region of interest of the vessel and healthy portions surrounding the region of interest can be analyzed.

[0191] The IVUS images captured from the withdrawal are then analyzed in real time by detecting anatomical segments 1392, segmenting lumens 1394, detecting stents 1396, detecting sheaths 1398, and / or other image analysis 1399. Various aspects of modules / steps 1392, 1394, 1396, 1398, and / or 1399 can be similar to those described in the patent applications, patents, and / or publications mentioned herein. For example, modules / steps 1392, 1394, 1396, 1398, and / or 1399 can be automatically generated using the techniques in the patent applications, patents, and / or publications mentioned herein. In some cases, modules / steps 1392, 1394, 1396, 1398, and / or 1399 are from manually drawn inputs and do not need to be automatically generated. Modules / steps 1392, 1394, 1396, 1398, and / or 1399 can also use image processing, machine learning, or deep learning. In some cases, one or more modules / steps 1392, 1394, 1396, 1398 and / or 1399 are executed only after another one or more modules / steps 1392, 1394, 1396, 1398 and / or 1399. For example, one or more modules / steps 1392, 1394, 1396, 1398 and / or 1399 use the output of another one or more modules / steps 1392, 1394, 1396, 1398 and / or 1399.

[0192] In an example, modules / steps 1392, 1394, 1396, 1398 and / or 1399 are machine learning (ML) algorithms trained on annotations from experts using data from multiple positions and sub-populations. Modules / steps 1392, 1394, 1396, 1398 and / or 1399 can also be derived from benchtop experiments (e.g., withdrawal speed measurement experiments), animal experiments, etc. Training. Modules / steps 1392, 1394, 1396, 1398 and / or 1399 are selected so that their accuracy falls within the normal variation of the judgment of clinicians about lumen measurement, derived amount and other clinical decisions. Therefore, modules / steps 1392, 1394, 1396, 1398 and / or 1399 can be statistically at least as accurate as the clinician population using the system. The acceptability of modules / steps 1392, 1394, 1396 and / or 1398 can be tested by user evaluation. In some embodiments, based on the use of the system in a clinical setting, self-training of modules / steps 1392, 1394, 1396, 1398, and / or 1399 is ongoing. For example, the output of modules / steps 1392, 1394, 1396, 1398, and / or 1399 can be a reference region, a reference frame, a target region, a target frame, per-frame measurements, derived volumes, stent assessments, or other.

[0193] In step or module 1390, the application logic receives the output of modules / steps 1392, 1394, 1396, 1398 and / or 1399. For example, the application logic can provide a target frame, a reference frame, and a stent assessment based on the output. For example, in the case where the output is a per-frame measurement result or a derived quantity, the application logic can process the information according to any of the above methods. In the case where the output is a reference frame, a target frame, or a stent assessment, the application logic can combine the outputs of different modules / steps through a weighted process, wherein the weights are determined theoretically, empirically, based on expert opinion, or a combination thereof. Dissimilar output types can be combined through a Kalman filter or other statistical process. Such application logic can be direct and can produce fast, repeatable results. The application logic 1390 automatically determines at least one of a target position, a reference position, a proximal end of a stent, a distal end of a stent, or a position within a stent where stent contraction is greatest.

[0194] In step 1395, method 1300 includes displaying or otherwise presenting at least one of the target frame, reference frame, IVUS image, anatomical segment, and per-frame measurements (or in the case of a stent, the location of the most contracted frame, lumen metrics and aspect ratio, and the proximal and distal ends of the stent) for review by the clinician. The system can then wait for additional input while the clinician reviews the displayed data. For example, such input can include a command to change lumen metrics or calculation methods. If such input is received, execution returns to step or module 1390. Otherwise, the method is completed.

[0195] Fig.14 14 is a schematic diagram of a processor circuit 1450 according to an embodiment of the present disclosure. The processor circuit 1450 may be implemented in the 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 needed to implement the method. As shown, the processor circuit 1450 may include a processor 1460, a memory 1464, and a communication module 1468. These elements may communicate with each other directly or indirectly, for example, via one or more buses.

[0196] The processor 1460 may include any combination of a central processing unit (CPU), a digital signal processor (DSP), an ASIC, a controller or a general purpose computing device, a reduced instruction set computing (RISC) device, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other related logic devices (including mechanical and quantum computers). The processor 1460 may also include another hardware device, a firmware device, or any combination thereof configured to perform the operations described herein. The processor 1460 may also be implemented as a combination of computing devices, for example, a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors combined with a DSP core, or any other such configuration.

[0197] 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 drives, other forms of volatile and non-volatile memory, or a combination of different types of memory. In an embodiment, memory 1464 includes a non-transitory 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 "instructions" and "code" should be interpreted broadly to include any type of computer-readable statements. For example, the terms "instructions" and "code" may refer to one or more programs, routines, subroutines, functions, processes, etc.

[0198] The communication module 1468 may include any electronic circuitry and / or logic circuitry to facilitate direct or indirect data communication between the processor circuit 1450 and other processors or devices. In this regard, the communication module 1468 may be an input / output (I / O) device. In some cases, the communication module 1468 facilitates direct or indirect communication between the processor circuit 1450 and / or various elements of the system 100. The communication module 1468 may communicate within the processor circuit 1450 via a variety of methods or protocols. Serial communication protocols may include, but are not limited to, the United States Serial Protocol Interface (US SPI), the Inter-Integrated Circuit (IIC), the Serial Serial Protocol Interface (SPI ... 2 C), 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), a Universal Synchronous Receiver Transmitter (USART), or other appropriate subsystem.

[0199] External communications (including but not limited to software updates, firmware updates, preset sharing between the processor and the central server, or readings from an intraluminal imaging device) can be achieved 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 a cellular data connection (such as 2G / SM (Global System for Mobile), 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 a connection with a cloud service for transmitting data and for receiving software patches. The controller can be configured to communicate with a remote server or a local device (such as a laptop, tablet or handheld device), or can include a display capable of showing state variables and other information. Information can also be transmitted on a physical medium such as a USB flash drive or memory stick.

[0200] Fig.15 is a graphical representation in the form of a flow chart of an exemplary pre-treatment target region and reference frame detection method 1500 in accordance with at least one embodiment of the present disclosure.

[0201] In step 1510, the method includes obtaining intraluminal image data, which may be, for example, a plurality of IVUS images stored during a pullback procedure.

[0202] At step 1520, the method includes using the intraluminal imaging data to automatically determine one or more lumen metrics without requiring user input to measure or verify the lumen metrics.

[0203] In step 1530, the method includes using the intraluminal imaging data to automatically identify the vessel segment without requiring user input to locate or verify the vessel segment.

[0204] In step 1540, the method includes automatically determining one or more reference regions and / or one or more reference frames within the reference region(s) based on the intraluminal imaging data without requiring user input to locate or confirm the reference regions and / or reference frames. The determination may be made:

[0205] (a) using only lumen metrics from intraluminal imaging data,

[0206] (b) using the maximum lumen measurement from intraluminal imaging data, or

[0207] (c) Use of luminal metrics from intraluminal imaging data and luminal metrics from clinical / literature sources.

[0208] In step 1550, the method includes automatically determining one or more target regions and / or one or more target frames within the target region(s) without requiring user input to locate or confirm the target regions and / or target frames. This determination may be made:

[0209] (a) Using only lumen metrics from intraluminal imaging data

[0210] (b) Using the maximum lumen metric from intraluminal imaging data

[0211] (c) Use of luminal metrics from intraluminal imaging data and luminal metrics from clinical / literature sources.

[0212] Steps 1540 and 1550 are performed for each segment until all segments have been analyzed. Execution then proceeds to step 1560.

[0213] In step 1560, the method includes generating an output to a display screen, the display screen including at least one of: segment(s), reference region(s), reference frame(s), target region(s), and / or target frame(s). The method is now complete.

[0214] Fig.16 1 is a schematic diagrammatic representation of analyzed IVUS image data 1600 according to at least one embodiment of the present disclosure. The analyzed IVUS image data includes a plurality of cross-sectional image frames 1610 of a vessel, which have been divided (by the method described above) into a plurality of segments 1620. Each segment may or may not include a reference region 1630, which includes a reference frame 1640. Similarly, each segment may or may not include a target region 650, which includes a target frame 1660.

[0215] Fig.17 An example intraluminal imaging display screen 1700 according to at least one embodiment of the present disclosure is shown. In this example, screen display 1700 includes current tomography IVUS image 1710, image longitudinal display (ILD) 1720 and graphic roadmap 1730 from a series of continuous tomographic images. ILD 1720 can be an ILD based on an image or a graphic / stylized ILD. Graphic roadmap 1730 can be a cartoon or illustration of a representative anatomical structure (e.g., peripheral venous vessels), rather than (one or more) actual images of the patient's anatomical structure. In some cases, the graphic roadmap may include (one or more) actual images of the patient's anatomical structure, such as X-ray, computed tomography (CT), ultrasound and / or (magnetic resonance imaging) MRI images. Lumen measurement window 1735 displays various lumen measurements of current image 1710.

[0216] In the example shown in diagram 1700, segment 1740 is highlighted and labeled in both ILD 1720 and roadmap 1730. Reference frame 1750 and target frame 1760 are also labeled on ILD 1720 and roadmap 1730, along with corresponding values ​​1770 of the selected lumen metric at these locations. Fig.17 , value 1770 is the lumen diameter from the reference frame in each different segment. Labeled on the ILD is an expression or quantity 1780 that compares the magnitude of the selected lumen metric at the reference frame and the target frame for each segment 1740. The width of the ILD 1720 varies with position, representing the magnitude of the selected lumen metric in each image frame for the IVUS image data. Note that the target frame 1760 is typically close to the narrowest portion of each segment 1740, although this may not always be the case. Similarly, the reference frame 1750 is typically close to the widest portion of each segment, although this may not always be the case.

[0217] Fig.18 is a graphical representation in the form of a flowchart of an exemplary pre-processed object region and reference frame detection method 1800 in accordance with at least one embodiment of the present disclosure.

[0218] In step 1810, the method includes receiving an IVUS image of a blood vessel and finding lumen contours and lumen metrics in each frame.

[0219] At step 1820, the method includes identifying an anatomical segment of a blood vessel based on the lumen contour and the lumen metric.

[0220] In step 1830, the method includes starting application logic as follows:

[0221] In step 1840, the method includes selectively removing frames from each segment whose lumen metrics and / or lumen contours are statistical outliers.

[0222] In step 1845, the method includes filtering outliers from the remaining distribution.

[0223] In step 1850, the method includes identifying candidate frames, wherein a candidate frame is a frame having an aspect ratio less than 1.8, or a frame that meets different user-selectable criteria.

[0224] In step 1860, the method includes determining whether a candidate frame is found. If not, execution proceeds to step 1870. If yes, execution proceeds to step 1880.

[0225] In step 1870, the method includes defining all filtered frames as candidate frames.

[0226] At step 1880, the method includes identifying a median or user-selected lumen metric for one or more lumen metrics in the candidate frame.

[0227] In step 1890, the method includes finding a 0-1 reference frame within the segment.

[0228] In step 1895, the method includes finding the 0-1 target frame within the segment.

[0229] An alternative to steps 1840-1895 for applying the logic (step 1830) includes step 1897. In step 1897, the intraluminal image frame having the smallest lumen metric is identified as the target frame, and the intraluminal image frame having the largest lumen metric is identified as the reference.

[0230] The method includes reporting the reference frame and the target frame to the user (e.g., by Fig.17 identify them on the screen display shown).

[0231] Many variations are possible for the above examples and embodiments. For example, other lumen metrics and other computational methods other than those described herein may be used without departing from the spirit of the present disclosure. The system may include any logic for computing and displaying target frames, reference frames, stent landing zones, and bookmarks in pre-treatment assessments and displaying MSA frames and other bookmarks in post-treatment assessments. The present application is primarily directed to peripheral vein and deep vein applications, but the same or similar logic may be applied to inspections of other body lumens.

[0232] The logical operations constituting the embodiments of the technology described herein are variously referred to as operations, steps, objects, elements, components or modules. In addition, it should be understood that these can occur or be arranged or executed in any order, unless otherwise clearly stated, or the claim language inherently requires a specific order. All directional references (e.g., upper, lower, inner, outer, upward, downward, left, right, lateral, front, rear, top, bottom, above, below, vertical, horizontal, clockwise, counterclockwise, near and far) are only used for identification purposes, to help readers understand the claimed subject matter, and do not generate restrictions, particularly with respect to the position, orientation or use of automatic targets and reference detection systems. Connection references (e.g., attachment, connection, connection and engagement) should be interpreted broadly, and may include intermediate members between element sets and relative motion between elements, unless otherwise stated. Therefore, connection references do not necessarily mean that two elements are directly connected and are in a fixed relationship to each other. The term "or" should be interpreted as meaning "and / or" rather than "exclusive or". Unless otherwise indicated in the claims, the values ​​described should be interpreted as illustrative only and should not be regarded as limiting.

[0233] The foregoing specification, examples, and data provide a complete description of the structure and use of an exemplary embodiment of the automatic target and reference detection system defined in the claims. Although various embodiments of the claimed subject matter have been described above with a certain degree of particularity or reference to one or more, a person skilled in the art may make many changes to the disclosed embodiments without departing from the spirit or scope of the claimed subject matter. Other embodiments are also contemplated. It is intended that all matter contained in the above description and shown in the accompanying drawings shall be construed as illustrative only of the particular embodiments and not as a limitation. Changes may be made in detail or structure without departing from the basic elements of the subject matter defined in the appended claims.

Claims

1. An intraluminal ultrasound imaging system, comprising: A processor circuit configured to communicate with an intraluminal ultrasound imaging catheter, wherein the processor circuit is configured to: receiving a first plurality of intraluminal ultrasound images acquired by the intraluminal ultrasound imaging catheter during movement of the intraluminal ultrasound imaging catheter within a body lumen of a patient, the body lumen comprising a plurality of segments and a compression within at least one segment; For at least one zone, automatically determining a target position within the compression and a reference position including a healthy portion of the lumen based on the first plurality of intraluminal ultrasound images; and A screen display is output to a display in communication with the processor circuit, the screen display including the target position, the reference position, and at least one quantity associated with the target position and the reference position.

2. The system according to claim 1, wherein: The processor circuit is further configured to: receiving a second plurality of intraluminal ultrasound images acquired by the intraluminal ultrasound imaging catheter during movement of the intraluminal ultrasound imaging catheter within the body lumen, the body lumen including a stent longitudinally aligned with the target location; Based on the second plurality of intraluminal ultrasound images, automatically determining the proximal end of the stent, the distal end of the stent, and the position in the stent where the stent shrinks to the greatest extent; as well as The proximal end of the stent, the distal end of the stent, the position where the stent is most contracted, and at least one quantity associated with the contraction are output to the screen for display.

3. The system according to claim 2, wherein: Automatically determining at least one of the target position, the reference position, the proximal end of the stent, the distal end of the stent, or the position within the stent where the stent is most contracted involves a machine learning algorithm.

4. The system according to claim 1, wherein: The body lumen comprises a peripheral vessel, and wherein the plurality of segments comprises at least one of: a common iliac vein (CIV), an external iliac vein (EIV), a common femoral vein (CFV), or a femoral vein for popliteal access.

5. The system according to claim 1, wherein: The at least one segment includes a plurality of segments.

6. The system according to claim 5, wherein: The screen display simultaneously shows a target frame and a reference frame for each of the plurality of segments.

7. The system according to claim 1, wherein: The target location is determined at least in part by a change in a first luminal metric along the segment, and the reference location is determined at least in part by a change in a second luminal metric along the segment.

8. The system according to claim 7, wherein: The first lumen measurement and the second lumen measurement are the same.

9. The system according to claim 7, wherein: The first lumen metric or the second lumen metric comprises an area, a minimum diameter, a maximum diameter, an effective diameter, a mean diameter, an aspect ratio, or a flow resistance of the lumen.

10. The system according to claim 1, wherein: The target location or the reference location is determined based at least in part on the intraluminal ultrasound imaging catheter.

11. The system according to claim 1, wherein: The target location or the reference location is determined based at least in part on the length of the movement of the intraluminal ultrasound imaging catheter within the body lumen.

12. The system of claim 1, wherein: The target position or the reference position is determined based at least in part on the zone where the compression is located.

13. The system of claim 1, wherein: The target location or the reference location is determined based at least in part on a boundary of a second body lumen adjacent to the body lumen.

14. The system of claim 1, wherein: The screen display includes at least one of a roadmap image or an image longitudinal display (ILD).

15. A method for intraluminal ultrasound imaging, comprising: Using a processor circuit in communication with an intraluminal ultrasound imaging catheter: receiving a first plurality of intraluminal ultrasound images acquired by the intraluminal ultrasound imaging catheter during movement of the intraluminal ultrasound imaging catheter within a body lumen of a patient, the body lumen comprising a plurality of segments and a compression within at least one segment; For at least one zone, automatically determining a target position within the compression and a reference position of a healthy portion of the lumen including a vicinity of the compression based on the first plurality of intraluminal ultrasound images; as well as A screen display is output to a display in communication with the processor circuit, the screen display including the target position, the reference position, and at least one quantity associated with the target position and the reference position.

16. The method of claim 15, further comprising, using the processor circuit: receiving a second plurality of intraluminal ultrasound images acquired by the intraluminal ultrasound imaging catheter during movement of the intraluminal ultrasound imaging catheter within the body lumen, the body lumen including a stent longitudinally aligned with the target location; Based on the second plurality of intraluminal ultrasound images, automatically determining the proximal end of the stent, the distal end of the stent, and the position in the stent where the stent shrinks to the greatest extent; as well as The proximal end of the stent, the distal end of the stent, the position where the stent is most contracted, and at least one quantity associated with the contraction are output to the screen for display.

17. The method according to claim 15, wherein: The body lumen comprises a peripheral vessel, and wherein the plurality of segments comprises at least one of: a common iliac vein (CIV), an external iliac vein (EIV), or a common femoral vein (CFV).

18. The method according to claim 15, wherein: The at least one segment includes a plurality of segments, and wherein the screen display simultaneously shows the target frame and the reference frame for each segment of the plurality of segments.

19. The method according to claim 15, wherein: The target position is determined at least in part by a change in a first luminal measurement along the segment, and the reference position is determined at least in part by a change in a second luminal measurement along the segment, wherein the first luminal measurement or the second luminal measurement includes an area, a minimum diameter, a maximum diameter, an effective diameter, an average diameter, an aspect ratio, or a flow resistance of the lumen.

20. The method according to claim 19, wherein: The first lumen measurement and the second lumen measurement are the same.

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