WO2025129125A1

WO2025129125A1 - Thrombus removal systems and associated methods

Info

Publication number: WO2025129125A1
Application number: PCT/US2024/060213
Authority: WO
Inventors: Praveen Krishna DALA; Uday Illindala; Svyatoslav KORNEEV; Daniel Joseph RACHLIN; Amr Salahieh
Original assignee: Shifamed Holdings LLC
Current assignee: Shifamed Holdings LLC
Priority date: 2023-12-13
Filing date: 2024-12-13
Publication date: 2025-06-19
Anticipated expiration: 2026-06-13

Abstract

The present technology relates to systems and methods for removing a thrombus from a blood vessel of a patient. In some embodiments, the present technology is directed to systems including an elongated catheter having a distal portion configured to be positioned within the blood vessel of the patient, a proximal portion configured to be external to the patient, and a lumen extending therebetween. The systems and methods discussed herein provide 3D models of the pulmonary vasculature, clots within the pulmonary vasculature, and/or of medical devices within the patient, for pre and peri-operative planning and navigation.

Description

THROMBUS REMOVAL SYSTEMS AND ASSOCIATED METHODS

PRIORITY CLAIM

[0001] This patent application claims priority to U.S. provisional patent application no. 63/609,820, titled “NAVIGATION AND 3D MODELING FOR CATHETER-BASED SYSTEMS AND ASSOCIATED METHODS,” filed on December 13, 2023, U.S. provisional patent application no. 63/646,751, titled “NAVIGATION AND 3D MODELING FOR CATHETER-BASED SYSTEMS AND ASSOCIATED METHODS,” filed on May 13, 2024, U.S. provisional patent application no. 63/698,936, titled “NAVIGATION AND 3D MODELING FOR CATHETER-BASED SYSTEMS AND ASSOCIATED METHODS,” filed on September 25, 2024, and U.S. provisional patent application no. 63/699,003, titled “THROMBUS REMOVAL SYSTEMS AND ASSOCIATED METHODS,” filed on September 25, 2024, each of which are herein incorporated by reference in their entirety.

INCORPORATION BY REFERENCE

[0002] All publications and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication or patent application was specifically and individually indicated to be incorporated by reference.

FIELD

[0003] The present technology generally relates to medical devices and, in particular, to systems including aspiration and fluid delivery mechanisms and associated methods for removing a thrombus from a mammalian blood vessel.

BACKGROUND

[0004] Thrombotic material may lead to a blockage in fluid flow within the vasculature of a mammal. Such blockages may occur in varied regions within the body, such as within the pulmonary system, peripheral vasculature, deep vasculature, or brain. Pulmonary embolisms typically arise when a thrombus originating from another part of the body (e.g., a vein in the pelvis or leg) becomes dislodged and travels to the lungs.

[0005] Anti coagulation therapy is the current standard of care for treating pulmonary embolisms, but may not be effective in some patients. Additionally, conventional devices for removing thrombotic material may not be capable of navigating the tortuous vascular anatomy, may not be effective in removing thrombotic material, and/or may lack the ability to provide sensor data or other feedback to the clinician during the thrombectomy procedure. [0006] Existing thrombectomy devices operate based on simple aspiration which works sufficiently for certain clots but is largely ineffective for difficult, organized clots. Many patients presenting with deep vein thrombus (DVT) are left untreated as long as the risk of limb ischemia is low. In more urgent cases, they are treated with catheter-directed thrombolysis or lytic therapy to break up a clot over the course of many hours or days. [0007] Visualization of clots and the location of a thrombectomy device relative to the clot is also an issue during thrombectomy procedures. While positioning a thrombectomy catheter in the vicinity of a clot is fairly routine, is particularly difficult to orient a thrombectomy catheter at the clot locally using current imaging techniques. The further away the catheter is from the clot prior to turning on aspiration, the more blood is likely to be unnecessarily aspirated.

[0008] Fluoroscopy imaging can be used to visualize clots and the positioning of the thrombectomy device within the anatomy, however this requires frequent injection of radiopaque dyes or contrast agent into the vasculature and pausing the procedure to obtain the fluoroscopy imaging. Additionally, the 2D imaging does not provide for accurate placement of a thrombectomy device in 3D space, particularly within the voluminous left and right pulmonary arteries (relative to the size of a thrombectomy catheter), which can result in clots that appear to be close to the thrombectomy catheter in the fluoroscopy images being distanced from the catheter out of the imaging plane. This deficiency with traditional 2D fluoroscopy imaging can result in larger than desirable volumes of blood being aspirated from the patient by initiating aspiration when the physician incorrectly believes that the clot is engaged with or near to the thrombectomy catheter. To correct for imaging out of plane with the clot and/or catheter, the physician must guess which direction the catheter is out of plane from the clot, requiring additional steering and manipulation of the catheter to try to engage with the targeted clot. This can increase treatment time and also lead to procedures in which no clot is removed.

[0009] There remains the need for systems to address these and other problems with existing venous thrombectomy including, but not limited to, visualization that provides for a fast, easy-to-use, and effective device for removing a variety of clot morphologies.

BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The novel features of the invention are set forth with particularity in the claims that follow. A better understanding of the features and advantages of the present invention will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the invention are utilized, and the accompanying drawings of which:

[0011] FIGS. 1 A-1B illustrate a medical device such as a thrombectomy catheter.

[0012] FIG. 2 is a schematic view of the pulmonary vasculature.

[0013] FIGS. 3A-3B show a system for generating a 3D anatomical model of a target anatomy.

[0014] FIGS. 4A-4B are schematic embodiments of a system for providing pre-operative assessment and/or real-time navigation of a medical device during an interventional procedure.

[0015] FIGS. 5-7 are flowcharts for providing an assessment or recommendation or navigation before or during an interventional procedure.

[0016] FIGS. 8A-8K illustrate views of a 3D anatomical model including a graphical representation of the medical device in the 3D model.

[0017] FIGS. 9A-9B illustrate another method of using a 3D anatomical model to navigate to a target location in the anatomy (e.g., a target clot).

[0018] FIGS. 10A-10F is an example of a navigation system that provides display of 3D pulmonary artery architecture and clot location.

[0019] FIGS. 11 A-l ID illustrate a pseudo fluoroscopy feature of a 3D pulmonary artery architecture.

[0020] FIGS. 12A-12F are examples of a 3D pulmonary artery architecture with segmented clots overlaid within the model or within medical imaging.

SUMMARY OF THE DISCLOSURE

[0021] A thrombus removal is provided, comprising an elongate shaft comprising a working end, at least one fluid lumen in the elongate shaft, and two or more apertures disposed at or near the working end, the two or more apertures in fluid communication with the least one fluid lumen and configured to generate two or more fluid streams to mechanically fractionate a target thrombus.

[0022] In certain examples, there is a system having: one or more processors; memory coupled to the one or more processors, in which the memory includes computer-program instructions that, when executed by the one or more processors, cause the device to perform operations including: acquiring two-dimensional (2D) and/or three-dimensional (3D) imaging data of at least a torso of a patient; segmenting the imaging data of the patient to identify 1) the pulmonary vasculature, and/or 2) one or more clots or lesions within the pulmonary vasculature; generating a 3D pulmonary vasculature model of the patient from the segmented imaging data; generating a 3D clot model of the patient from the segmented imaging data; characterizing and assessing the one or more clots or lesions within the 3D pulmonary vasculature model and the 3D clot model. Then, based on considering the characterizing and assessing, the system outputs a treatment recommendation of selected clots or lesions of the one or more clots or lesions to target for removal.

[0023] According to certain examples of the system, characterizing and assessing the one or more clots includes determining a clot type of the one or more clots.

[0024] According to certain examples of the system, characterizing and assessing the one or more clots includes determining a clot density of the one or more clots.

[0025] According to certain examples of the system, characterizing and assessing the one or more clots includes determining volume of the clot relative to a vessel diameter at a location of the clot.

[0026] According to certain examples of the system, characterizing and assessing the one or more clots in includes identifying clots that should be excluded from treatment based on their location within the 3D pulmonary vasculature model.

[0027] According to certain examples of the system, characterizing and assessing the one or more clots includes identifying clots that should not be accessed with a thrombectomy catheter.

[0028] According to certain examples of the system, characterizing and assessing the one or more clots includes identifying clots that can be accessed with a thrombectomy catheter.

[0029] According to certain examples of the system, characterizing and assessing the one or more clots includes determining a pre-operative Miller Score or an equivalent index or metric based on the one or more clots to indicate an extent of obstruction to blood flow caused by the one or more clots.

[0030] According to certain examples of the system, characterizing and assessing the one or more clots includes determining a post-operative Miller Score or an equivalent index or metric based if selected clots are removed to indicate an extent of obstruction to blood flow caused by the one or more clots.

[0031] According to certain examples of the system, characterizing and assessing the one or more clots includes determining a pre-operative Miller Score based on the one or more clots, identifying one or more selected clots to be removed, and determining a post-operative Miller Score if the selected clots are removed.

[0032] According to certain examples of the system, the 2D or 3D imaging data includes X-ray imaging data. [0033] According to certain examples of the system, the 2D or 3D imaging data includes computed tomography (CT) imaging data.

[0034] According to certain examples of the system, the 2D or 3D imaging data includes magnetic resonance imaging (MRI) imaging data.

[0035] According to certain examples of the system, the 2D or 3D imaging data includes positron emission tomography (PET) imaging data.

[0036] According to certain examples of the system, the 2D or 3D imaging data includes a fusion of one or more sources of imaging data.

[0037] According to certain examples of the system, the 2D or 3D imaging data includes ultrasound imaging data.

[0038] In yet other examples, there is another system having: one or more processors; a memory coupled to the one or more processors, in which the memory includes computerprogram instructions that, when executed by the one or more processors, cause the device to perform operations including: acquiring two-dimensional (2D) and/or three-dimensional (3D) imaging data of at least a torso of a patient; segmenting the imaging data of the patient to identify 1) the pulmonary vasculature, and/or 2) one or more clots or lesions within the pulmonary vasculature; generating a 3D pulmonary vasculature model of the patient from the segmented imaging data; generating a 3D clot model of the patient from the segmented imaging data; characterizing and assessing the one or more clots or lesions within the 3D pulmonary vasculature model and the 3D clot model; determining if one or more clots or lesions within the 3D pulmonary vasculature model and the 3D clot model can be accessed with a thrombectomy device. Then, based on considering the characterizing and assessing, the system outputs a treatment recommendation of selected clots or lesions of the one or more clots or lesions to target for removal.

[0039] According to certain examples of this system, characterizing and assessing the one or more clots includes determining a clot type of the one or more clots.

[0040] According to certain examples of this system, characterizing and assessing the one or more clots includes determining a clot density of the one or more clots.

[0041] According to certain examples of this system, characterizing and assessing the one or more clots includes determining volume of the clot relative to a vessel diameter at a location of the clot.

[0042] According to certain examples of this system, characterizing and assessing the one or more clots includes identifying clots that should be excluded from treatment based on their location within the 3D pulmonary vasculature model. [0043] According to certain examples of this system, characterizing and assessing the one or more clots includes identifying clots that should not be accessed with a thrombectomy catheter.

[0044] According to certain examples of this system, characterizing and assessing the one or more clots includes identifying clots that can be accessed with a thrombectomy catheter.

[0045] According to certain examples of this system, characterizing and assessing the one or more clots includes determining a pre-operative Miller Score or an equivalent index or metric based on the one or more clots to indicate an extent of obstruction to blood flow caused by the one or more clots.

[0046] According to certain examples of this system, characterizing and assessing the one or more clots includes determining a post-operative Miller Score or an equivalent index or metric based if selected clots are removed to indicate an extent of obstruction to blood flow caused by the one or more clots.

[0047] According to certain examples of this system, characterizing and assessing the one or more clots includes determining a pre-operative Miller Score based on the one or more clots, identifying one or more selected clots to be removed, and determining a post-operative Miller Score if the selected clots are removed.

[0048] According to certain examples of this system, the 2D or 3D imaging data includes X-ray imaging data.

[0049] According to certain examples of this system, the 2D or 3D imaging data includes computed tomography (CT) imaging data.

[0050] According to certain examples of this system, the 2D or 3D imaging data includes magnetic resonance imaging (MRI) imaging data.

[0051] According to certain examples of this system, the 2D or 3D imaging data includes positron emission tomography (PET) imaging data.

[0052] According to certain examples of this system, the 2D or 3D imaging data includes a fusion of one or more sources of imaging data.

[0053] According to certain examples of this system, the 2D or 3D imaging data includes ultrasound imaging data.

[0054] In yet other examples, there is a system having: an elongate medical device insertable into a human subject; one or more processors; memory coupled to the one or more processors, in which the memory includes computer-program instructions that, when executed by the one or more processors, cause the device to perform operations including: acquiring two-dimensional (2D) and/or three-dimensional (3D) imaging data of at least a torso of a patient; segmenting the imaging data of the patient to identify 1) the pulmonary vasculature, and/or 2) one or more clots or lesions within the pulmonary vasculature; generating a 3D pulmonary vasculature model of the patient from the segmented imaging data; generating a 3D clot model of the patient from the segmented imaging data; generating a 3D model of the elongate medical device including a position and/or orientation within the subject; presenting the 3D pulmonary vasculature model, 3D clot model, and the 3D model of the medical device to a user.

[0055] According to certain examples, the system further outputs instructions to a user to navigate the 3D model of the medical device to a selected clot from the 3D clot model within the 3D pulmonary vasculature model.

[0056] According to certain examples of this system, characterizing and assessing the one or more clots includes determining a clot type of the one or more clots.

[0057] According to certain examples of this system, characterizing and assessing the one or more clots includes determining a clot density of the one or more clots.

[0058] According to certain examples of this system, characterizing and assessing the one or more clots includes determining volume of the clot relative to a vessel diameter at a location of the clot.

[0059] According to certain examples of this system, characterizing and assessing the one or more clots includes identifying clots that should be excluded from treatment based on their location within the 3D pulmonary vasculature model.

[0060] According to certain examples of this system, characterizing and assessing the one or more clots includes identifying clots that should not be accessed with a thrombectomy catheter.

[0061] According to certain examples of this system, characterizing and assessing the one or more clots includes identifying clots that can be accessed with a thrombectomy catheter.

[0062] According to certain examples of this system, characterizing and assessing the one or more clots includes determining a pre-operative Miller Score based on the one or more clots.

[0063] According to certain examples of this system, characterizing and assessing the one or more clots includes determining a post-operative Miller Score if selected clots are removed.

[0064] According to certain examples of this system, characterizing and assessing the one or more clots includes determining a pre-operative Miller Score based on the one or more clots, identifying one or more selected clots to be removed, and determining a post-operative Miller Score if the selected clots are removed. [0065] According to certain examples of this system, the 2D or 3D imaging data includes X-ray imaging data.

[0066] According to certain examples of this system, the 2D or 3D imaging data includes computed tomography (CT) imaging data.

[0067] According to certain examples of this system, the 2D or 3D imaging data includes magnetic resonance imaging (MRI) imaging data.

[0068] According to certain examples of this system, the 2D or 3D imaging data includes positron emission tomography (PET) imaging data.

[0069] According to certain examples of this system, the 2D or 3D imaging data includes a fusion of one or more sources of imaging data.

[0070] According to certain examples of this system, the 2D or 3D imaging data includes ultrasound imaging data.

[0071] In still other examples, there is a computer implemented method, including: acquiring two-dimensional (2D) and/or three-dimensional (3D) imaging data of at least a torso of a patient; segmenting the imaging data of the patient to identify 1) the pulmonary vasculature, and/or 2) one or more clots or lesions within the pulmonary vasculature; generating a 3D pulmonary vasculature model of the patient from the segmented imaging data; generating a 3D clot model of the patient from the segmented imaging data; characterizing and assessing the one or more clots or lesions within the 3D pulmonary vasculature model and the 3D clot model; and considering the characterizing and assessing, outputting a treatment recommendation of selected clots or lesions of the one or more clots or lesions to target for removal.

[0072] In still other examples, there is a computer implemented method, including: acquiring two-dimensional (2D) and/or three-dimensional (3D) imaging data of at least a torso of a patient; segmenting the imaging data of the patient to identify 1) the pulmonary vasculature, and/or 2) one or more clots or lesions within the pulmonary vasculature; generating a 3D pulmonary vasculature model of the patient from the segmented imaging data; generating a 3D clot model of the patient from the segmented imaging data; characterizing and assessing the one or more clots or lesions within the 3D pulmonary vasculature model and the 3D clot model; determining if one or more clots or lesions within the 3D pulmonary vasculature model and the 3D clot model can be accessed with a thrombectomy device; and considering the characterizing and assessing, outputting a treatment recommendation of selected clots or lesions of the one or more clots or lesions to target for removal.

[0073] In still other examples, there is a computer implemented method, including: acquiring two-dimensional (2D) and/or three-dimensional (3D) imaging data of at least a torso of a patient; segmenting the imaging data of the patient to identify 1) the pulmonary vasculature, and/or 2) one or more clots or lesions within the pulmonary vasculature; generating a 3D pulmonary vasculature model of the patient from the segmented imaging data; generating a 3D clot model of the patient from the segmented imaging data; generating a 3D model of a medical device including a position and/or orientation within the subject; presenting the 3D pulmonary vasculature model, 3D clot model, and the 3D model of the medical device to a user.

[0074] In certain examples of this method, characterizing and assessing the one or more clots includes determining a clot type of the one or more clots.

[0075] In certain examples of this method, characterizing and assessing the one or more clots includes determining a clot density of the one or more clots.

[0076] In certain examples of this method, characterizing and assessing the one or more clots includes determining volume of the clot relative to a vessel diameter at a location of the clot.

[0077] In certain examples of this method, characterizing and assessing the one or more clots includes identifying clots that should be excluded from treatment based on their location within the 3D pulmonary vasculature model.

[0078] In certain examples of this method, characterizing and assessing the one or more clots includes identifying clots that should not be accessed with a thrombectomy catheter. [0079] In certain examples of this method, characterizing and assessing the one or more clots includes identifying clots that can be accessed with a thrombectomy catheter.

[0080] In certain examples of this method, characterizing and assessing the one or more clots includes determining a pre-operative Miller Score or an equivalent index or metric based on the one or more clots to indicate an extent of obstruction to blood flow caused by the one or more clots.

[0081] In certain examples of this method, characterizing and assessing the one or more clots includes determining a post-operative Miller Score or an equivalent index or metric based if selected clots are removed to indicate an extent of obstruction to blood flow caused by the one or more clots.

[0082] In certain examples of this method, characterizing and assessing the one or more clots includes determining a pre-operative Miller Score based on the one or more clots, identifying one or more selected clots to be removed, and determining a post-operative Miller Score if the selected clots are removed.

[0083] In certain examples of this method, the 2D or 3D imaging data includes X-ray imaging data.

[0084] In certain examples of this method, the 2D or 3D imaging data includes computed tomography (CT) imaging data.

[0085] In certain examples of this method, the 2D or 3D imaging data includes magnetic resonance imaging (MRI) imaging data.

[0086] In certain examples of this method, the 2D or 3D imaging data includes positron emission tomography (PET) imaging data.

[0087] In certain examples of this method, the 2D or 3D imaging data includes a fusion of one or more sources of imaging data.

[0088] In certain examples of this method, the 2D or 3D imaging data includes ultrasound imaging data.

DETAILED DESCRIPTION

[0089] This application is related to disclosure in International Application No. PCT/US2021/020915, filed March 8, 2021 (the ‘915 application), and International Application No. PCT/US2022/033028, filed June 10, 2022 (the ‘028 application), the disclosures of which are incorporated by reference herein for all purposes. The ‘915 and ‘028 applications describe general mechanisms for capturing and removing a clot. By example, multiple fluid streams are directed toward the clot to fragment the material.

[0090] The present technology is generally directed to thrombus removal systems and associated methods. A system configured in accordance with an embodiment of the present technology can include, for example, an elongated catheter having a distal portion configured to be positioned within a blood vessel of the patient, a proximal portion configured to be external to the patient, a fluid delivery mechanism configured to fragment the thrombus with pressurized fluid, an aspiration mechanism configured to aspirate the fragments of the thrombus, and one or more lumens extending at least partially from the proximal portion to the distal portion..

[0091] The terminology used in the description presented below is intended to be interpreted in its broadest reasonable manner, even though it is being used in conjunction with a detailed description of certain specific embodiments of the present technology. Certain terms may even be emphasized below; however, any terminology intended to be interpreted in any restricted manner will be overtly and specifically defined as such in this Detailed Description section. Additionally, the present technology can include other embodiments that are within the scope of the examples but are not described in detail with respect to the figures. [0092] Reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present technology. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features or characteristics may be combined in any suitable manner in one or more embodiments.

[0093] Reference throughout this specification to relative terms such as, for example, "generally," "approximately," and "about" are used herein to mean the stated value plus or minus 10%.

[0094] Although some embodiments herein are described in terms of thrombus removal, it will be appreciated that the present technology can be used and/or modified to remove other types of emboli that may occlude a blood vessel, such as fat, tissue, or a foreign substance. Additionally, although some embodiments herein are described in the context of thrombus removal from a pulmonary artery (e.g., pulmonary embolectomy), the technology may be applied to removal of thrombi and/or emboli from other portions of the vasculature (e.g., in neurovascular, coronary, or peripheral applications). Moreover, although some embodiments are discussed in terms of maceration of a thrombus with a fluid, the present technology can be adapted for use with other techniques for breaking up a thrombus into smaller fragments or particles (e.g., ultrasonic, mechanical, enzymatic, etc.).

[0095] The headings provided herein are for convenience only and do not interpret the scope or meaning of the claimed present technology.

Systems for Thrombus Removal

[0096] As provided above, the present technology is generally directed to thrombus removal systems. Such systems include an elongated catheter having a distal portion positionable within a blood vessel of the patient (e.g., an artery or vein), a proximal portion positionable outside the patient's body, a fluid delivery mechanism configured to fragment the thrombus with pressurized fluid, an aspiration mechanism configured to aspirate the fragments of the thrombus, and one or more lumens extending at least partially from the proximal portion to the distal portion. In some embodiments, the systems herein are configured to engage a thrombus in a patient's blood vessel, break the thrombus into small fragments, and aspirate the fragments out of the patient's body. The pressurized fluid streams (e.g., jets) function to cut or macerate thrombus, before, during, and/or after at least a portion of the thrombus has entered the aspiration lumen or a funnel of the system. Fragmentation helps to prevent clogging of the aspiration lumen and allows the thrombus removal system to macerate large, firm clots that otherwise could not be aspirated. As used herein, “thrombus” and “embolism” are used somewhat interchangeably in various respects. It should be appreciated that while the description may refer to removal of “thrombus,” this should be understood to encompass removal of thrombus fragments and other emboli as provided herein.

[0097] According to embodiments of the present technology, a fluid delivery mechanism can provide a plurality of fluid streams (e.g., jets) to fluid apertures of the thrombus removal system for macerating, cutting, fragmenting, pulverizing and/or urging thrombus to be removed from a proximal portion of the thrombus removal system. The thrombus removal system can include an aspiration lumen extending at least partially from the proximal portion to the distal portion of the thrombus removal system that is adapted for fluid communication with an aspiration pump (e.g., vacuum source). In operation, the aspiration pump may generate a volume of lower pressure within the aspiration lumen near the proximal portion of the thrombus removal system, urging aspiration of thrombus from the distal portion.

[0098] FIGS. 1 A-1B illustrate a vascular access and treatment system 100 that can include an introducer catheter 102 and a medical device 108 disposed within a lumen of the introducer catheter. The introducer catheter can include an elongate, steerable, flexible shaft and a distal end 103 at the end of one or more lumens that runs along the shaft of the introducer catheter. The introducer catheter can include one or more sensors 105 disposed along, in, or within the shaft 101, including but not limited to pressure sensors, flow sensors, electrical sensors (electrodes), or any other sensor useful for measuring patient parameters during an intravascular procedure. In the example of FIGS. 1 A-1B, the sensor 105 can comprise a pressure sensor disposed near the distal end 103.

[0099] The medical device 108 can comprise any elongate medical device insertable into the lumen of the introducer catheter, including but not limited to balloon angioplasty catheters, dilators, or thrombectomy devices. As shown in FIG. 1 A, the medical device 108 comprises a thrombectomy device with an elongate shaft 106 and an expandable element or funnel 108 on a distal end of the shaft. The funnel and/or shaft can include one or more lumens 107, e.g., for aspiration of thrombus material and/or delivery of fluid or jets from the thrombectomy device. The funnel 108 can be advanced out of the distal end 103 of the introducer catheter during a thrombectomy procedure.

[0100] In FIG. 1 A, a hub assembly 110 such as a Touhy Borst is shown which can provide access for the medical device 108 into the lumen of the steerable introducer catheter 102 and include an injection port for fluidic connection a fluid or contrast source 112. The injection port can direct the fluid or contrast into the lumen(s) of the introducer catheter. In the illustrated embodiment, the fluid or contrast source 112 can comprise a contrast injector configured to automatically or manually deliver a controlled volume (e.g., bolus) of a contrast agent into the patient’s vasculature via the introducer catheter 102. In some examples, injection of contrast from the injector into the hub assembly 110 provides the contrast agent into the annular space between the introducer catheter 102 and the medical device 108 (e.g., within the lumen of the introducer catheter, between the introducer catheter shaft and the shaft 106 of the medical device).

[0101] FIG. IB shows the funnel 108 of the medical device 108 axially disposed out of a distal end 103 of the introducer catheter 102. When the medical device is a thrombectomy or aspiration catheter, aspiration or vacuum generated in lumen 107 can pull thrombus material into the funnel 108 and out of the device via lumen 107. In some embodiments, jets or fluid streams can also be delivered into the funnel or aspiration lumen to interact with and/or macerate the thrombus material. In this example, contrast delivered by the fluid or contrast source 112 into the lumen of the introducer catheter can still be delivered into the patient, even when the funnel is in an expanded state. In some examples, the funnel can disperse the contrast agent as it’ s delivered past the funnel from the introducer catheter. Alternatively, a dilator device or other medical device can be inserted into the introducer catheter, as will be described below.

[0102] Additionally, FIG. IB shows the medical device 108 with a plurality of 3D sensors 150 disposed on or in the device. The 3D sensors can comprise, for example, spatial position sensors, or spatial mapping sensors that can provide position and orientation information of the device, including approximations or calculations about the curvature of the device. The 3D sensors on the device can work in conjunction with an external transmitter/receiver (external device 352 in FIG. 3 A) to generate 3D maps and pinpoint the exact location and orientation of the medical device in the pulmonary arteries during diagnosis and therapeutic procedures for patients with pulmonary embolisms. In some embodiments, the 3D sensors comprise magnetic sensors. While the sensors 150 are shown on the medical device in FIG. IB, they could also be on the introducer sheath shaft 101, or on both the introducer sheath and the medical device. The sensors can be positioned at selected points on either the medical device and/or the introducer sheath, including at or near a distal end of the medical device or sheath to provide information on the relative position between the two devices.

[0103] One or more of the 3D sensors can be used to define a path and curvature of the medical device, which will improve potential position estimation errors/ speed up rendering. In some examples, the 3D sensors can use position at the catheter/ sheath in a region of the heart such as the right atrium (RA), right ventricle (RV), root of the pulmonary artery (PA), and/or left pulmonary artery (LPA) or right pulmonary artery (RPA) to capture fiducial positions to register 3D models of the pulmonary arteries, CT or other imaging modalities with the 3D sensors and/or medical device. The sensors can also be coupled with real-time fluoroscopy captures to scale/register fluoroscopy to magnetic/CT as well.

[0104] The 3D sensors can provide a minimum-fluoroscopy/fluoroscopy-free thrombectomy procedure in real-time, especially the system utilizes pressure waveform morphology to validate position of catheter/sheath.

[0105] The fluid or contrast source 112 (e.g., contrast injector) can be configured to automatically inject or deliver selected volumes or boluses of any contrast agent into the thrombus removal system to assist with imaging of the thrombus removal device and/or a target thrombus. In some embodiments, while the volumes and timing of contrast to be delivered by the injector are selected by a user or pre-selected, the injector can be configured to automatically and/or continuously deliver contrast at the selected volumes and frequency. In the illustrated embodiment, the fluid or contrast source can comprise a cradle assembly configured to receive one or more contrast injection syringe(s). The cradle assembly can include an automatic pusher or other mechanism configured to engage with the syringe to inject a contrast agent into the lumen(s) of the introducer catheter.

[0106] The system 100 can employ control algorithms or protocols to provide consistent or controlled injection of fluid or contrast agent near the distal end of the introducer catheter. In some embodiments, the fluid or contrast source can be configured to inject a predetermined or pre-selected bolus or volume of fluid or contrast agent into the patient at the target location within the vasculature. For example, the fluid or contrast source may be configured to deliver a bolus of contrast agent (e.g., a 5ml bolus or “shot” of contrast) at a pre-determined time interval (e.g., every 3-5 seconds).

[0107] While the embodiments herein have been described as being intended to remove thrombi from a patient’s vasculature, other applications of this technology are provided. For example, the devices described herein can be used for breaking up and removing hardened stool from the digestive tract of a patient, such as from the intestines or colon of a patient. In one embodiment, the device can be inserted into a colon or intestine of the patient (such as through the anus) and advanced to the site of hardened stool. Next, the aspiration system can be activated to engage the hardened stool with an engagement member (e.g., funnel) of the device. Finally, the jets or irrigation can be activated to break off pieces of the hardened stool and aspirate them into the system. Any of the techniques described above with respect to controlling the system or removing clots can be applied to the removal of hardened stool. [0108] As background, FIG. 2 is a diagram of the pulmonary vasculature. Clots or pulmonary embolisms are typically found within the pulmonary vasculature at locations 1 (left and right pulmonary arteries), 2 (left and right interlobar pulmonary arteries), and 3 (left and right segmental branches including the anterior, superior, and lateral branches). Accessing clots in each of these locations with a thrombectomy catheter or device has challenges. For example, while the left and right pulmonary arteries are relatively large and easy to access, the size of the pulmonary arteries can make it difficult to position a thrombectomy catheter near or adjacent to a target clot, even with fluoroscopy imaging. Interfacing or accessing clots at locations 2 and 3 can be even more challenging, as the tortuous pathways and branching into smaller lumens can be difficult or impossible to navigate with current imaging techniques like fluoroscopy.

[0109] To overcome the limitations described above with respect to navigating the pulmonary vasculature with a medical device such as a thrombectomy catheter, the present disclosure provides systems and methods for providing real-time 3D navigation of the pulmonary vasculature, including 3D models of the pulmonary vasculature with overlays representing the medical device (e.g., thrombectomy catheter) within the pulmonary vasculature for improved navigation, steering, and positioning during medical procedures. [0110] In one embodiment shown in FIGS. 3A-3B, a system 300 is shown configured to generate a 3D model of an anatomical structure 301 of a subject (e.g., the pulmonary arteries or pulmonary vasculature, the heart, coronary vasculature, peripheral vasculature, etc.). The system 300 can include, for example, an imaging system 318 such as a fluoroscopy imaging system, a computed tomography (CT) imaging system, a cone-beam CT imaging system (CBCT), a magnetic resonance imaging (MRI) imaging system, an ultrasound imaging system, any other high resolution medical or diagnostic imaging system, contrast-enhanced imaging (e.g., angiography), or any combination of these imaging systems. In the illustrated example, the imaging system 318 can include a C-arm 316 that supports an X-ray irradiation unit (not shown) and an X-ray detector 318. A patient table 320 and support base 322 can be positioned within the C-arm to image a patient.

[OHl] The system can further include a console 328 having one or more optional displays 326. The console can include one or more input devices such as a graphical user interface (GUI), a keyboard, mousejoystick, etc. to allow a user to control the system including the imaging system and to view, manipulate, or interact with 3D models of the anatomical structure produced by the system.

[0112] The imaging system 318 can be operatively connected to the console 328.

Processing of the data collected by the imaging system may be accomplished via electronics and software in the console. The console may include, for example, various processors, power supplies, memory, firmware, and software configured to receive, store, and process imaging data collected by the imaging system 318.

[0113] The 3D anatomical models can be presented on a display of the system, such as the one or more displays of FIG. 3 A. The user or clinician can interact with one or more inputs or icons 830, such as with a GUI or with other input devices such as a keyboard or mouse. The inputs or icons 830 allow the user to select various features or functionality of the 3D model, including “clot reveal”, “target vessel highlight”, “contrast”, “volumize target vessel”, “clot highlight”, “colorize target vessel”, “filter non-targef ’, and “view rotate and zoom”.

[0114] An external device 352 for interacting/interfacing with the 3D sensors described in FIG. IB is also shown in FIG. 3 A. The external device can include a locator pad with three separate low-level magnetic field emitting coils being arranged as a triangle under the patient, configured to work with the medical device (e.g., thrombectomy catheter) of FIG. IB with embedded 3D magnetic location sensors. The external device can include, for example, a data processing unit and a graphic display unit to provide visualization of the electroanatomical model being created. The field strength of the three electromagnets can be measured by a sensor element of the catheter tip and used for position determination via a triangulation algorithm which allows an exact calculation of the distance from each magnetic coil.

[0115] In various embodiments, the system uses scan, imaging and/or patient data to generate information for a clinician or user of the system. For example, the system may construct a 3D anatomical model based on medical scan or image data taken from multiple locations (e.g., multiple C-arm positions of the imaging system). In another example, the system generates image data based on composite data from multiple locations, and/or from multiple imaging modalities. For example, the system may generate the 3D model from any combination of imaging data including CT, X-ray, fluoroscopy, MRI, ultrasound, etc.

[0116] The system may further use patient or physiological data or information in constructing the 3D anatomical model, including but not limited to patient age, sex, medical history, disease state of the patient (e.g., whether the patient is diagnosed with any known diseases relevant to the target anatomy), prior surgical or medical treatment history, physical exam results, or vital signs including but not limited to blood pressure, ECG, respiration, etc. [0117] In some embodiments, the position and location of a medical device (e.g., a thrombectomy catheter) can be overlaid or tracked in real-time in the 3D anatomical model. For example, a graphical overlay or representation of the medical device can be displayed on the 3D anatomical model. As a physician or clinician navigates or advances the medical device within the anatomy (e.g., within the pulmonary vasculature) while referencing the 3D anatomical model on the display(s), the graphical overlay or representation of the medical device on the 3D anatomical model can track the precise location of the medical device within the patient.

[0118] Several embodiments are provided for tracking or determining the actual position and orientation of the medical device within the patient so that the system can provide the graphical overlay of the medical device on the 3D model. Alternatively, the system can generate a 3D model of the device, and incorporate the 3D model of the device into the 3D model of the pulmonary vasculature. In one example, the length of the catheter inserted into the patient past some known reference point (e.g., the femoral access point) can be monitored, such as with optical sensors or rotational encoders. The amount and/or degree of steering of the catheter can also be tracked or determined with similar sensors that monitor the steering mechanism of the catheter (e.g., pull wire displacement). The system can then model the orientation of the catheter based on the insertion length and steerage, or from information such as 3D sensors on the device, and use that information to provide the graphical overlay or 3D model of the device in the 3D model of the anatomy. In another embodiment, radiopaque markers or other trackable sensors can be placed at known positions along the length of the medical device, including along the catheter shaft and at the tip or any other deflection points of the device. The markers can be periodically imaged (e.g., with fluoroscopy) and registered to the 3D model using known landmarks in the anatomy. Other known techniques for tracking the position and orientation of a medical device such as a catheter within the body can also be implemented.

[0119] FIG. 4A is a diagram showing an example of a system 400; the system 400 may be incorporated into a portion of another system (e.g., a general treatment planning system, as described below) and may therefore also be referred to as a sub-system. Alternatively the methods and apparatuses for performing them described herein may be included as part of a different system. In any of the methods and apparatuses described herein, the system 400 may be invoked by a user control, such as a tab, button, etc., as part of treatment planning system, as part of a navigation system, or may be separately invoked.

[0120] In FIG. 4A, the system 400 may include a plurality of engines and datastores. A computer system can be implemented as an engine, as part of an engine or through multiple engines. As used herein, an engine includes one or more processors, such as a GPU, CPU, ASIC, FPGA, or a portion thereof. A portion of one or more processors can include some portion of hardware, less than all of the hardware comprising any given one or more processors, such as a subset of registers, the portion of the processor dedicated to one or more threads of a multi-threaded processor, a time slice during which the processor is wholly or partially dedicated to carrying out part of the engine’s functionality, or the like. As such, a first engine and a second engine can have one or more dedicated processors, or a first engine and a second engine can share one or more processors with one another or other engines. Depending upon implementation-specific or other considerations, an engine can be centralized, or its functionality distributed. An engine can include hardware, firmware, or software embodied in a computer-readable medium for execution by the processor. The processor transforms data into new data using implemented data structures and methods, such as is described with reference to the figures herein.

[0121] The engines described herein, or the engines through which the systems and devices described herein can be implemented, may be cloud-based engines. As used herein, a cloud-based engine is an engine that can run applications and/or functionalities using a cloudbased computing system. All or portions of the applications and/or functionalities can be distributed across multiple computing devices, and need not be restricted to only one computing device. In some embodiments, the cloud-based engines can execute functionalities and/or modules that end users access through a web browser or container application without having the functionalities and/or modules installed locally on the end-users’ computing devices.

[0122] As used herein, datastores are intended to include repositories having any applicable organization of data, including tables, comma-separated values (CSV) files, traditional databases (e.g., SQL), or other applicable known or convenient organizational formats. Datastores can be implemented, for example, as software embodied in a physical computer-readable medium on a specific-purpose machine, in firmware, in hardware, in a combination thereof, or in an applicable known or convenient device or system. Datastore- associated components, such as database interfaces, can be considered “part of’ a datastore, part of some other system component, or a combination thereof, though the physical location and other characteristics of datastore-associated components is not critical for an understanding of the techniques described herein.

[0123] Datastores can include data structures. As used herein, a data structure is associated with a particular way of storing and organizing data in a computer so that it can be used efficiently, within a given context. Data structures are generally based on the ability of a computer to fetch and store data at any place in its memory, specified by an address, a bit string that can be itself stored in memory and manipulated by a program. Thus, some data structures are based on computing the addresses of data items with arithmetic operations; while other data structures are based on storing addresses of data items within the structure itself. Many data structures use both principles, sometimes combined in non-trivial ways. The implementation of a data structure usually entails writing a set of procedures that create and manipulate instances of that structure. The datastores described herein can be cloud-based datastores. A cloud-based datastore is a datastore that is compatible with cloud-based computing systems and engines.

[0124] The system 400 may include or be part of a computer-readable medium, and may include an input engine 401 (e.g., providing and/or allowing access to the patient’s scan or imaging data, patient medical history, and/or patient characteristic(s)). The scan or imaging data 403 may include two-dimensional (2D) or three-dimensional (3D) scan or imaging data provided by a medical imaging device, including but not limited to ultrasound images, X-ray images, computed tomography (CT) images, angiogram images (pulmonary, coronary, or peripheral), real-time fluoroscopy images, magnetic resonance imaging (MRI) images, positron emission tomography (PET) images, or the like. In some embodiments, the input engine 401 may receive training images, including supervised training images. In some embodiments, the input engine 401 may receive synthetic training images generated from other patient imaging or scan data. Additionally, the input images may be run through an inference engine. As will be described herein, the training images may be used to train one or more neural networks.

[0125] The system 400 may include an anatomical segmentation engine 402 that may segment 3D models into different objects, sections, parts, or the like. Segmentation may be performed in any feasible manner. In some variations, the anatomical segmentation engine 402 may also process (e.g., convert, transform) 2D or 3D imaging or scan data into a 3D model. For example, the 2D or 3D imaging or scan data can include medical imaging data (e.g., X-ray, MRI, CT, ultrasound) of a target tissue, such as the pulmonary vasculature. In some embodiments, the target tissue includes coronary vasculature and/or peripheral vasculature. For example, the anatomical segmentation engine 402 can receive 2D or 3D scan or imaging data of the patient’s pulmonary vasculature from the input engine 401, generate 3D models of the patient’s pulmonary vasculature and then optionally segment the 3D models into separate objects, sections, parts, or the like (e.g., into the left and right pulmonary arteries, or any of the lobes, branches or segments described above in FIG. 2). For example, the anatomical segmentation engine 402 can receive 2D or 3D scan or imaging data of the patient’s coronary or peripheral vasculature and then optionally segment the 3D models into separate objects, sections, parts, or the like representative of the imaged anatomy. In some examples the system 400 may include a memory, register or datastore storing all or some of the patient’s scan or imaging data, patient medical history, and/or patient character! stic(s). Patient characteristics may include patient demographical info such as age, gender, vital signs, and the like.

[0126] The system 400 may also include a clot segmentation engine 404. The clot segmentation engine may segment 3D models into different objects, sections, parts, surfaces, or the like. Segmentation may be performed in any feasible manner. In some variations, the clot segmentation engine 402 may also process (e.g., convert, transform) 2D or 3D imaging or scan data into a 3D model. For example, the 2D or 3D imaging or scan data can include medical imaging data (e.g., X-ray, fluoroscopy, MRI, CT, ultrasound, or fusion of any of the preceding imaging modalities) of a target tissue, such as the pulmonary vasculature. The imaging data can include not only the patient’s pulmonary vasculature, but also imaging data of clots or thrombus within the pulmonary vasculature. For example, the clot segmentation engine 402 can receive 2D or 3D scan or imaging data of the patient’s pulmonary vasculature from the input engine 401, generate 3D models of clots within the patient’s pulmonary vasculature, and then optionally segment the 3D models into separate objects, sections, parts, or the like. Alternatively, the clot segmentation engine 402 can receive the 3D model of the patient’s pulmonary vasculature from the anatomical segmentation engine 402, generate 3D models of clots within the patient’s pulmonary vasculature, and then optionally segment the 3D models into separate objects, sections, parts, or the like. The 3D model of the clots can be integrated with, or combined with the 3D model of the patient’s pulmonary vasculature. The clot segmentation engine may be configured to not only segment and generate 3D models of the clots, but also to identify the location of the clots within the anatomy. In some embodiments, the clot segmentation engine is configured to determine the volume or size of the clots compared to or relative to the vasculature. Additionally, the clot segmentation engine can determine the type or age of the clot, or alternatively, the density of the clot. The density/toughness of clot may be estimated based on assessed imaging parameters for a given imaging modality. For example, for X-ray imaging modalities signal attenuation (e.g,. in Hounsfeld units) and/or contrast uptake can be assessed. For ultrasound imaging modalities, ultrasonic attenuation or ultrasonic backscatter can be assessed. For magnetic resonance imaging (MRI), signal intensity parameters can be assessed. In some embodiments, the engine(s) may characterize the effect of the clots or lesions on blood flow.

[0127] While herein segmentation of occlusive material may refer to ‘clot’ or ‘embolism,’ it should be appreciated that other occlusive blockages within the anatomy are contemplated. For example, the segmentation engine 404 can alternatively or additionally segment vascular lesions. The lesions can be calcified lesions. In some embodiments calcified lesions can correspond to locations within the coronary and/or peripheral vasculature of the patient.

[0128] Segmentation performed by the anatomical segmentation model and the clot segmentation model may classify pixels from imaging data, or from a 3D model into structures in another 3D structural model, such as pulmonary arteries, artery volumes, artery surfaces, or structures in an artery.

[0129] Both the anatomical segmentation engine 402 and the clot segmentation engine 404 may employ machine learning models, including neural networks. The machine learning models or neural networks for anatomical segmentation engine 402 and clot segmentation engine 404 may be trained to detect and identify the pulmonary vasculature and clots, thrombus, or other abnormalities within the pulmonary vasculature. While the present disclosure describes the anatomical segmentation engine 402 and the clot segmentation engine 404 as being separate components or engines, it should be understood that a single segmentation engine could segment both the patient anatomy and any lesions/clots within the anatomy, and provide 3D models of the anatomy and the clots. The clot’s position and/or orientation within the pulmonary vasculature, or within the 3D model of the pulmonary vasculature may be used, at least in part, to determine a patient’s treatment plan or provide a treatment recommendation or assessment. In some variations, the system 400 may store a library of training images or supervised training images 404. These images may be used to train the machine learning model or neural network for anatomical segmentation engine 402 or clot segmentation engine 404.

[0130] The system 400 may include a treatment plan engine 406. The treatment plan engine 406 may process patient imaging or scan data, 3D model or segmentation data from the anatomical segmentation engine 402 and/or the clot segmentation engine 404, patient characteristics, clinician input and the like to determine a patient’s treatment plan or provide a treatment assessment or recommendation. In some variations, a patient’s treatment plan, assessment, or recommendation may include identifying target clots or thrombi for removal. The treatment plan engine 406 may also provide an assessment or recommendation of clots or thrombi not to target (e.g., clots that are too deep within the vasculature, or would not result in significant patient improvement if removed). In some variations, the treatment plan engine 406 can generate a pre-operative assessment of the patient’s condition and/or a post-operative assessment of the patient’s condition if specific action is taken. For example, the treatment plan engine may calculate or determine pre and post-operative Miller Scores, Modified Miller Scores (MMS), or some other quantifiable measurement of the patient’s condition and/or outcome. In yet another embodiment, the treatment plan engine 406 may identify clots or thrombi to target for removal (e.g., with a thrombectomy system), and may provide or calculate an estimated Miller Score or Modified Miller Score if the targeted clots or thrombi are removed.

[0131] A Miller Score, for example, provides an assessment of flow in the pulmonary vasculature, and considers the nine major branches of the right pulmonary artery and the seven major branches of the left pulmonary artery, and an embolus in any of these branches is given a score of one point. Each lung is further considered to have an upper, middle, and lower zone, and in each of these three zones, the absence of a pulmonary artery flow confers a score of three points, severely reduced flow gives two points, mildly reduced flow gives one point, and normal flow scores 0 points. The Miller Score can therefore range from 0 to 38.

[0132] Any of these apparatuses or systems may include an output engine 410 for outputting the treatment plan from treatment planning engine 406. The system may also include a display or graphical user interface (GUI) 412 configured for displaying the 3D models and data discussed above. In some embodiments, the display or GUI 412 presents the 3D model of the anatomy, such as the pulmonary vasculature, and also presents the segmented clots and/or 3D model of the clots, including their location and orientation within the 3D model of the pulmonary vasculature. The display can further present or highlight clots to be targeted, or recommend clots for removal. In some examples, the display or GUI can present a Miller Score, Modified Miller Score, or some other quantitative assessment of patient outcomes if targeted clots are removed. Additionally, the display or GUI can provide real-time tracking or navigation of the 3D model of the anatomy. In some examples, the display or GUI can include a real-time graphical overlay or model of an interventional device such as a thrombectomy catheter, within the 3D model of the anatomy. Additionally, the display or GUI can provide or present a procedural plan for tracking the device to one or more target clots or lesions, including optionally providing real-time navigation instructions or directions for the device and any other aspects of the system including introducer sheaths, etc.

[0133] In some embodiments, an image system according to the present invention is configured to comprise a training engine or user-generated feedback for anatomy and/or occlusive material segmentation. As shown in FIG. 4B a system 450 includes many similar features to those described of system 400 in FIG. 4A: input engine 451; patient imaging data 453; anatomical segmentation engine 452; clot segmentation engine 454; library of training images 458; display or GUI 462; treatment planning engine 466; and output engine 460. FIG. 4B further includes an inference engine 455, an optional training engine 465, and optional user-generated segmentation engine 470. The inference engine 455 can comprise a trained model for segmentation. The inference engine 455 can be implemented locally (e.g., “on premises”) with respect to system 450, or the inference engine 455 can be implemented in the cloud and communicate with system 450 via a network connection.

[0134] In some embodiments of the system 450, anatomical segmentation engine 452 and clot segmentation engine 454 are configured to output to, and receive inputs from, usergenerated segmentation engine 470 to provide the system 450 with user-identified improvements in segmentation. For example, a user may be able to view the 3D models generated by the segmentation engines 452/454, and mark-up or further identify features to segment, including additional branches or features of vasculature, and/or identifying additional clots/lesions, or marking features segmented as obstructions (e.g., clots) as not being obstructions. In some embodiments, the user-input information from may be used by system 450 for further segmentation training by training engine 465. In some embodiments the training engine 465 is locally instantiated (e.g., “on premises”). In some embodiments the training engine 465 is instantiated in the cloud. In some embodiments, the inference engine 455 can be updated based on inputs from the training engine 465 and/or user-generated segmentation engine 470. In some embodiments the updates can be in real-time.

[0135] One or more of the engines of the systems 400/450 may be coupled to one another (e.g., through the example couplings shown in FIGS. 4A-4B) or to modules/engines not explicitly shown in FIGS. 4A-4B. The computer-readable medium may include any computer-readable medium, including without limitation a bus, a wired network, a wireless network, or some combination thereof.

[0136] FIG. 5 schematically illustrates processes and/or steps associated with generating a 3D model of the pulmonary vasculature and any clots or lesions, and providing a recommendation or assessment to a user regarding clots to target or not target for removal. In general, the assessment or recommendation of clots to target may be determined based on a treatment plan. Clots to target may be determined from patient imaging or scan data, such as 2D or 3D imaging data of the patient’s pulmonary vasculature. The imaging or scan data can be converted into a model (e.g., a 3D model) that can include the pulmonary vasculature including the left and right pulmonary arteries and the various branches of the pulmonary arteries, and any clots, thrombi, or other lesions disposed within the pulmonary vasculature. In some examples, a trained machine learning model or neural network (also referred to as a trained machine learning agent) may be executed to identify clots within the 3D model and provide a pre and or post treatment assessment or quantifiable analysis of the patient’s health or condition if one or more of the clots, thrombi, or lesions are removed (e.g., via a thrombectomy procedure).

[0137] Some clots or lesions may be more difficult to detect or locate, particularly when the clots are of a particular age, location within the anatomy, or size. A machine learning model or neural network, trained with models that include these types of clots or lesions, may more accurately locate and/or identify clots or lesions, clot types, or provide assessments or recommendations on clots to target or remove.

[0138] Patient imaging or scan data 502 is converted to a 3D model 506 at block 508, which can include extracting image data comprising special information associated specifically with pulmonary vasculature features. The 3D model can include a point cloud data representation of the patient’s pulmonary vasculature and the size and location of any clots, thrombi, or lesions. Segmented clots, thrombi, lesions, or other data from the 3D model can be provided to a machine learning model or neural network at block 508 for determining additional information or parameters about the clots or lesions, including the clot type, or providing an assessment or recommendation of what clots to target, the expected patient outcome or patient improvement if selected clots are removed, and/or navigation guidelines or instructions for navigating to the targeted clot(s).

[0139] FIG. 6 is a flowchart showing an example method 600 for training a machine learning model or neural network to provide an assessment or recommendation on clots to target or treat. Some examples may perform the operations described herein with additional operations, fewer operations, operations in a different order, operations in parallel, and some operations differently. Patients with pulmonary embolisms may have numerous clots, emboli, thrombi, or lesions within the pulmonary vasculature. Some clots or thrombi may be a higher priority for removal, or lead to improved patient outcomes, relative to other clots or thrombi which may not lead to patient improvement, or may be too difficult to reach or remove. Thus, various factors on clot location, size, and type may be used to determine the location of the clots or lesions to be targeted. Any of the machine learning models or neural networks described herein may be used to identify or recommend one or more clots or lesions for removal or treatment. The method 600 is described below with respect to the system 400 of FIG. 4A, however, the method 600 may be performed by any other suitable system or device. [0140] In block 610, the system 400 receives supervised training data. Supervised training data can include images or scans of the pulmonary vasculature that have been manually labeled by skilled personnel. The labeled images include any and all clots or lesions or other characteristics that the machine learning model or neural network will be trained to recognize. In some variations, the supervised training data can be labeled to identify clot locations, clot sizes or volumes, and/or clot types or densities.

[0141] In some variations, the supervised training data is “balanced.” That is, the supervised training data should include cases with regular anatomy and unusual anatomy in approximately equal proportions. In some cases, some of the training data can be manipulated or synthesized to transform regular pulmonary anatomies into unusual pulmonary anatomies. Alternatively, clots or lesions can be added to pulmonary anatomies without clots or lesions. In some examples, the supervised training data can include up to 10,000 different cases, up to 50,000 different cases, or up to 100,000 different cases.

[0142] In block 620, the system 400 trains the machine learning model or neural network with the supervised training data received in block 610. As described above, the system 400 can train the machine learning model or neural network (machine learning agent) to respond with two channels. A first channel can identify all clots or lesions within the pulmonary vasculature and a second channel can identify clots or lesions to be targeted, and optionally provide an assessment or score of an expected patient outcome if the targeted clots or lesions are removed. The neural network can be trained to respond with the first and second channels to identify the classes described herein within the point cloud files.

[0143] The training of a neural network to identify or locate clots or lesions to be treated may be associated with various aspects of a computing environment that is used to determine pulmonary embolism treatment. By way of example and not limitation, the training of a neural network may be associated with treatment planning or a treatment planning system. For example, a treatment planning system may include one or more modules configured to receive or obtain supervised training data and determine or train a neural network to identify or locate clots or lesions to be treated based on the supervised training data.

[0144] FIG. 7 is a flowchart showing an example method 700 for providing pre-operative planning for pulmonary embolism. The method 700 is described below with respect to the system 400 of FIG. 4 A, however, the method 700 may be performed by any other suitable system or device.

[0145] The method 700 begins in block 702 as the system 400 converts 2D or 3D imaging or scan data into one or more 3D models. In some variations, the 2D or 3D scan data can be imaging data provided by a medical imaging device, including but not limited to ultrasound images, X-ray images, computed tomography (CT) images, magnetic resonance imaging (MRI) images, positron emission tomography (PET) images, or the like. The system 400 can transform the imaging or scan data into a 3D model that includes a 3D model of the patient’s pulmonary vasculature and any clots or lesions within the anatomy. In some examples, the 3D model of the pulmonary vasculature is separate from 3D models of each clot or lesion identified from the imaging or scan data. In other embodiments, the anatomy and clots are located within the same 3D model.

[0146] Next, in block 708, the system 400 segments the 3D model(s). In some examples, the system 400 can segment the 3D model into individual vessels, arteries, branches, clots, and/or lesions. In some cases, the system 400 can execute a machine learning model or neural network, as described above, to segment the 3D model into various portions of the anatomy and the clots/lesions.

[0147] Next, in block 706 the system 400 selects or identifies one or more clots or lesions in the 3D model. The selected clot(s) can be any or all clots in the 3D model. In block 708, the system 400 determines if the selected clot(s) can be accessed via the thrombectomy removal device (e.g., the thrombectomy catheter). Numerous factors can be used to determine whether or not clots can be accessed. For example, clots within lumens or branches of the pulmonary vasculature smaller than a minimum diameter may be flagged as inaccessible. Alternatively, any clots more distal (e.g., further within the anatomy) to a specific portion of the pulmonary vasculature may be deemed inaccessible (e.g., clots located beyond the left or right pulmonary artery, clots within the upper, middle, or lower lobes, etc.). Additionally, tortuosity of the pulmonary vasculature (or the 3D model of the pulmonary vasculature) may be used to determine if a specified clot can be accessed with the medical device(s).

[0148] If the selected clot cannot be accessed, then in block 710 the system 400 can provide a recommendation or assessment that the clot should not be treated. If the selected clot can be accessed, then in block 712 the system 400 can mark or flag the selected clot as being accessible with the thrombectomy system. In some examples, the clots that are recommended and not recommended for treatment can be identified or flagged on the display or GUI, such as with color coding (e.g., green for can be accessed, red for cannot be accessed) or can be accompanied with indication or text to the user alongside the 3D model that the clot can or cannot be accessed.

[0149] The method 700 proceeds to block 718 where the system 400 can provide or generate a quantitative assessment of the accessible clots. This can include, for example, generating a treatment plan or identifying or recommending specific clots to target for removal. In some embodiments, the system can generate a Miller Score, a Modified Miller Score, or some other quantitative assessment of the accessible clots. The assessment can include a recommendation of clots to remove, and can further include information for the user or physician on the expected outcomes or patient recovery/improvement if the targeted clots are removed. The assessment can be based on, but not limited to, clot location, clot size or volume, and clot type or density. Additionally, the system can provide a Miller Score if the targeted clots are removed. The assessment or recommendation may be output at block 716, such as on a display or GUI of the system.

[0150] FIGS. 8A-8J illustrate examples of one or more 3D anatomical models produced by a system as discussed above as disclosed herein, such as the system 300 of FIGS. 3A-3B or system 400 of FIG. 4 A. The 3D models can include, for example, 3D models of the pulmonary vasculature 828, 3D models of medical devices including introducer sheath 801 and thrombectomy catheter 808, and 3D models of clots 832. In addition to tracking the position and orientation of clots and/or one or more medical devices (e.g., introducer sheath, thrombectomy catheter) in the anatomical 3D model and displaying a graphical representation of the medical device(s) and clots in the 3D model, the 3D anatomical model can include additional features as will be described below. In some embodiments, specific features of the introducer sheath or the thrombectomy catheter can be highlighted or identified in the 3D model, including but not limited to the distal ends of the introducer sheath or thrombectomy catheter, or other features of either device including, for example, an expandable funnel or working end of the thrombectomy catheter.

[0151] FIG. 8 A shows a standard view of a 3D anatomical model 828. In the illustrated example, the 3D anatomical model is a 3D model of the pulmonary vasculature. However it should be understood that the systems and methods of the present disclosure can be used to generate 3D anatomical models of other locations in a patient (e.g., heart, other vasculature systems, etc.). The standard view shown in FIG. 8A includes a 3D model, graphical overlay, or digital representation of an introducer sheath 801 and medical device 808 within the 3D model. As shown, the medical device can comprise a thrombectomy catheter, in this example, positioned within a pulmonary artery of the 3D anatomical model. The 3D models of the introducer sheath and the medical device or thrombectomy catheter can be integrated with or overlaid within the 3D model of the pulmonary vasculature.

[0152] FIG. 8B illustrates a “clot reveal” feature of the 3D anatomical model 828 which, when selected, can reveal or highlight one or more target clots 832 in the 3D model. As shown, the 3D model can include the 3D model of the medical device 808.

[0153] In some embodiments, referring to FIG. 8C, the 3D model of the medical device and/or the pulmonary vasculature can provide step by step navigation or steering instructions to a user of the device to navigate the medical device towards a targeted clot, such as one or more of the clots revealed when selecting the “clot reveal” feature. For example, the system can use the position and orientation of the medical device within the anatomy and the location of the targeted clot, and calculate or determine how to navigate the medical device within the vasculature to reach the targeted clot. In some examples, these navigation instructions can also be overlaid onto the 3D model, as shown by reference number 838. The instructions can be customized depending on the steerability of the medical device. For example, a catheter with bi-directional steering can be navigated through vasculature by rotation of the device (about a longitudinal axis), steering of the tip (e.g., with pull wires), and advancing or withdrawing the catheter.

[0154] FIG. 8D illustrates a “target vessel highlight” feature of the 3D anatomical model 828 which, when selected, can add simulated contrast 836 or emphasis to a targeted vessel (e.g., the vessel which includes a targeted clot). Alternatively, the transparency of the 3D models may be adjusted to improve visibility of the model. For example, the 3D view of the model may be converted to a wire-frame only view as away of highlighting structures of interested. In the illustrated example, the pulmonary artery is the targeted vessel, and the “target vessel highlight” feature highlights or enhances the structure of the pulmonary artery, such as by changing the color of the targeted vessel to stand out from the other vasculature in the 3D model. This feature can improve visibility and usability for the user and allow the user to focus on the targeted vessel.

[0155] Similarly, FIG. 8E illustrates a “colorize target vessel” feature of the 3D anatomical model 828 which, when selected, can color 838 (e.g., anatomically correct coloring) to a targeted vessel. In the illustrated example, the pulmonary artery is the targeted vessel, and the “colorize target vessel” feature colorizes the targeted vessel to stand out from the other vasculature in the 3D model. This feature can improve visibility and usability for the user and allow the user to focus on the targeted vessel.

[0156] FIGS. 8F-8I illustrate a “view rotate and zoom” feature of the 3D anatomical model 828. When this feature is selected, the user can manipulate the view of the 3D model, which allows for 3D rotation about the targeted vessel, the medical device, or in free space. This feature also allows for adjusting the zoom of the 3D anatomical model.

[0157] FIGS. 8G-8H show rotation of the 3D model. This feature can be useful when the medical device is out of plane from a targeted clot. Rotating the 3D model can provide the user or clinician with additional information relating to the anatomy, medical device, and any targeted clots to allow for proper and precise positioning. As stated above, precise positioning can result in less blood being aspirated from the patient before engaging with a clot.

[0158] FIG. 81 shows the 3D model zoomed in further on the medical device. The user or clinician can adjust the zoom level to increase precision in positioning and assist with positioning the medical device against clots or navigating tortuous or smaller vasculature as the device is advanced further into the anatomy.

[0159] FIGS. 8J-8K illustrate the “filter non-target” feature of the 3D anatomical model. In FIG. 8J, non-targeted vessels 840 are shown, and in FIG. 8K, the non-targeted vessels are removed from the 3D model.

[0160] FIGS. 9A-9B illustrate a scenario in which the 3D anatomical model 928 of the present disclosure can be used by a clinician to guide one or more medical devices such as an introducer catheter 901 or a thrombectomy catheter 908 to being in close proximity to a targeted clot within the anatomy, such as within the pulmonary vasculature. FIG. 9A shows the medical devices in an imaging plane that appears to convey that the medical device is adjacent to a targeted clot. However, a clinician may find that when aspiration is turned on, the device does not engage with the clot and only blood is aspirated. This illustrates a scenario in which the clot may be out of plane with the medical device. Using the 3D anatomical model of the present disclosure, the clinician can rotate the view of the 3D model, as shown in FIG. 9B, to identify the navigation that must be enacted upon the device to bring it in proximity with the clot. In some examples, the 3D anatomical model can provide step by step or detail navigation instructions to the clinician on how to manipulate the device to get to the intended target.

[0161] In one embodiment, shown in FIG. 9B, the 3D model of the medical device(s) may further provide a visual indicator or 3D model 929 that conveys the position, orientation, or direction that the distal end of the device is pointing or facing. This can be conveyed, for example, as an arrow, a ray, a line, a cone, a cylinder, etc. extending from the distal end of either device. In some embodiments, this 3D model 929 can include visual indicators or queues to a user to indicate if the device is interfacing with or engaged with clot, For example, if the distal end of the thrombectomy catheter is not engaged with or interfacing with clot, as shown in FIG. 9B, then the 3D model 929 may be dashed, or color coded to indicate that the clot is not engaged or near the device. If/when the device interfaces with the clot, the 3D model can visually change on the GUI to indicate to the user that the clot is proximate, interfaced, or engaged with the device. For example, the 3D model may transition from a dashed line to a solid line, or from the color red to color yellow to color green as the device gets closer to, and eventually interfaces with, the clot.

[0162] FIG. 10A is an example of a 3D anatomical model 1028 that shows a 3D display of pulmonary artery architecture and clot location. This provides anatomical visualization to potentially reduce procedural risk. In FIGS. 10B-10C, the model can be manipulated (e.g., by a user) to show different 3D views of the anatomy. Additionally, a device such as a thrombectomy catheter 1000 can be overlaid onto the 3D model to assist with positioning and navigation of the device. The device can be a simulated device, or actual imaging of the device overlaid onto the model. Alternatively, sensors, such as the magnetic sensors described above, can be positioned on the device to provide real-time position, orientation, and/or curvature information of the device. FIGS. 10D-10E show additional views of algorithms used to calculate and display the catheter or tool position in real-time within the model. In FIG. 10F, the visualization allows the physician or user to manipulate the view of the anatomy in 3D (e.g., by rotating or adjusting the viewing angle of the anatomy and the tool/catheter within the 3D model.

[0163] FIGS. 11 A-l ID illustrate a pseudo fluoroscopy feature of a 3D pulmonary artery architecture and model. The pseudo fluoroscopy view can be visualized with the pulmonary artery 3D model to provide clot highlights within the model from a simulated thrombectomy device 1000. In some examples, the pseudo fluoroscopy is synced with C-arm positioning. This provides anatomical visualization to further reduce risk and potentially reduce procedure time. FIG. 11 A shows a fluoroscopy or X-ray image of a patient including the pulmonary vasculature. FIG. 1 IB shows a pseudo fluoroscopy view of the pulmonary artery along with a thrombectomy catheter 1100 positioned within the anatomy. The pseudo fluoroscopy view can be a simulated puff or bolus of fluro delivered from a simulated or modeled thrombectomy catheter into a 3D model of the patient’s pulmonary vasculature. As described above, the 3D model can include segmented clots or lesions within the 3D model. Therefore, the simulated or pseudo fluoroscopy can operate within the 3D model like it would in a patient under normal fluoroscopy imaging. When the pseudo or simulated fluoroscopy is activated by a user, a display of the system can present the pseudo or simulated fluoroscopy puff or bolus within the 3D model of the pulmonary vasculature to highlight or identify clots within the 3D model. FIGS. 11C-1 ID show additional pseudo fluoroscopy views of the catheter 1100 being advanced further into the pulmonary artery and various branches.

[0164] In some embodiments, the 3D anatomical models described herein can incorporate breathing motion modeling to apply breathing motion to the model. The breathing modeling can be synced to actual patient breathing to update the model in real-time to account for motion caused by breathing. In some examples, the motion modeling and 3D anatomical model can be be trained and build partially using synthetic data. For example, 3D anatomical scans of patient’s pulmonary architecture with and without clots present can be input into a training or modeling system. The scans without clot data or imaging can have clots artificially or manually added to the scans. Those modified scans can then be input into the model to generate the pulmonary architecture model and also to provide additional inputs for the breathing motion modeling.

[0165] As discussed above, 2D or 3D imaging data with clots present (or with simulated/ synthetic clots added) can be segmented by the system to identify clots within the imaging data or within the 3D model of the pulmonary vasculature. In some embodiments, the 3D anatomical model can be used to identify clots and their locations within the anatomy. Additionally, image processing can be done to apply a treatment score to various clots as a way for physicians to prioritize which clots to target during a thrombectomy procedure. For example, the system may output a score indicative of the impact of removing various clots during a procedure. The system may recommend clots to remove that will have the highest treatment score, or have the most impact on patient health. In some embodiments, the system will only segment and evaluate clots within specific portions of the anatomy. For example, clots deeper in the anatomy (e.g., at the 8^th or 5^th branch) may be too difficult to access with a thrombectomy device. Therefore, the system may ignore any clots in these deep locations and only evaluate the potential impact of clots treated within the first 3 branches or within the main pulmonary arteries.

[0166] In some aspects, referring to FIGS. 12A-12B, the 3D model 1228 can further include segmented clots C overlaid onto or within the 3D model 1228. The view of the 3D model can be rotated by a user, which can also provide additional views and rotation of the segmented clots and their location within the anatomy. FIG. 12C shows a 3D model 1228 of the anatomy with the segmented clots C overlaid within the model. FIG. 12D shows only the segmented clots C, and not the 3D model of the pulmonary vasculature. As can be seen in FIG. 12D, the ability to hide the model 1228 of the vasculature allows for a user to view the full extent of the clots, which otherwise may not be visible with the vasculature in place. [0167] FIGS. 12E-12F show additional examples of segmented clot being overlaid onto pre or peri-operative imaging of the patient. In FIG. 8E, only the imaging is shown, and the location of the clots within the anatomy are not readily apparent. In FIG. 8F, the segmented clots are shown overlaid onto the imaging.

[0168] In any of the embodiments described herein, including any embodiment that shows the catheter within a 3D model or within medical imaging of the pulmonary arteries (e.g., CT, MRI, fluoro, etc.), the location, position, and orientation of the catheter can be determined or located within the model or imaging using the system described above, particularly the catheter location system including the 3D sensors described in FIG. IB and the external device shown in FIG. 3 A. [0169] As one of skill in the art will appreciate from the disclosure herein, various components of the thrombus removal systems described above can be omitted without deviating from the scope of the present technology. As discussed previously, for example, the present technology can be used and/or modified to remove other types of emboli that may occlude a blood vessel, such as fat, tissue, or a foreign substance. Further, although some embodiments herein are described in the context of thrombus removal from a pulmonary artery, the disclosed technology may be applied to removal of thrombi and/or emboli from other portions of the vasculature (e.g., in neurovascular, coronary, or peripheral applications). Likewise, additional components not explicitly described above may be added to the thrombus removal systems without deviating from the scope of the present technology. Accordingly, the systems described herein are not limited to those configurations expressly identified, but rather encompasses variations and alterations of the described systems. Conclusion

[0170] The above detailed description of embodiments of the technology are not intended to be exhaustive or to limit the technology to the precise forms disclosed above. Although specific embodiments of, and examples for, the technology are described above for illustrative purposes, various equivalent modifications are possible within the scope of the technology as those skilled in the relevant art will recognize. For example, although steps are presented in a given order, alternative embodiments may perform steps in a different order. The various embodiments described herein may also be combined to provide further embodiments.

[0171] From the foregoing, it will be appreciated that specific embodiments of the technology have been described herein for purposes of illustration, but well-known structures and functions have not been shown or described in detail to avoid unnecessarily obscuring the description of the embodiments of the technology. Where the context permits, singular or plural terms may also include the plural or singular term, respectively.

[0172] Unless the context clearly requires otherwise, throughout the description and the examples, the words "comprise," "comprising," and the like are to be construed in an inclusive sense, as opposed to an exclusive or exhaustive sense; that is to say, in the sense of "including, but not limited to." As used herein, the terms "connected," "coupled," or any variant thereof, means any connection or coupling, either direct or indirect, between two or more elements; the coupling of connection between the elements can be physical, logical, or a combination thereof. Additionally, the words "herein," "above," "below," and words of similar import, when used in this application, shall refer to this application as a whole and not to any particular portions of this application. Where the context permits, words in the above Detailed Description using the singular or plural number may also include the plural or singular number respectively. As used herein, the phrase "and/or" as in "A and/or B" refers to A alone, B alone, and A and B. Additionally, the term "comprising" is used throughout to mean including at least the recited feature(s) such that any greater number of the same feature and/or additional types of other features are not precluded. It will also be appreciated that specific embodiments have been described herein for purposes of illustration, but that various modifications may be made without deviating from the technology. Further, while advantages associated with some embodiments of the technology have been described in the context of those embodiments, other embodiments may also exhibit such advantages, and not all embodiments need necessarily exhibit such advantages to fall within the scope of the technology. Accordingly, the disclosure and associated technology can encompass other embodiments not expressly shown or described herein.

Claims

CLAIMS: What is claimed is:

1. A system comprising: one or more processors; memory coupled to the one or more processors, wherein the memory includes computer-program instructions that, when executed by the one or more processors, cause the device to perform operations comprising: acquiring two-dimensional (2D) and/or three-dimensional (3D) imaging data of at least a torso of a patient; segmenting the imaging data of the patient to identify 1) the pulmonary vasculature, and/or 2) one or more clots or lesions within the pulmonary vasculature; generating a 3D pulmonary vasculature model of the patient from the segmented imaging data; generating a 3D clot model of the patient from the segmented imaging data; characterizing and assessing the one or more clots or lesions within the 3D pulmonary vasculature model and the 3D clot model; and considering the characterizing and assessing, outputting a treatment recommendation of selected clots or lesions of the one or more clots or lesions to target for removal.

2. The system of claim 1, wherein characterizing and assessing the one or more clots comprises determining a clot type of the one or more clots

3. The system of claim 1, wherein characterizing and assessing the one or more clots comprises determining a clot density of the one or more clots.

4. The system of claim 1, wherein characterizing and assessing the one or more clots comprises determining volume of the clot relative to a vessel diameter at a location of the clot.

5. The system of claim 1, wherein characterizing and assessing the one or more clots comprises identifying clots that should be excluded from treatment based on their location within the 3D pulmonary vasculature model.

6. The system of claim 1, wherein characterizing and assessing the one or more clots comprises identifying clots that should not be accessed with a thrombectomy catheter.

7. The system of claim 1, wherein characterizing and assessing the one or more clots comprises identifying clots that can be accessed with a thrombectomy catheter.

8. The system of claim 1, wherein characterizing and assessing the one or more clots comprises determining a pre-operative Miller Score or an equivalent index or metric based on the one or more clots to indicate an extent of obstruction to blood flow caused by the one or more clots.

9. The system of claim 1, wherein characterizing and assessing the one or more clots comprises determining a post-operative Miller Score or an equivalent index or metric based if selected clots are removed to indicate an extent of obstruction to blood flow caused by the one or more clots.

10. The system of claim 1, wherein characterizing and assessing the one or more clots comprises determining a pre-operative Miller Score based on the one or more clots, identifying one or more selected clots to be removed, and determining a postoperative Miller Score if the selected clots are removed.

11. The system of claim 1, wherein the 2D or 3D imaging data comprises X-ray imaging data.

12. The system of claim 1, wherein the 2D or 3D imaging data comprises computed tomography (CT) imaging data.

13. The system of claim 1, wherein the 2D or 3D imaging data comprises magnetic resonance imaging (MRI) imaging data.

14. The system of claim 1, wherein the 2D or 3D imaging data comprises positron emission tomography (PET) imaging data.

15. The system of claim 1, wherein the 2D or 3D imaging data comprises a fusion of one or more sources of imaging data.

16. The system of claim 1, wherein the 2D or 3D imaging data comprises ultrasound imaging data.

17. A system comprising: one or more processors; memory coupled to the one or more processors, wherein the memory includes computer-program instructions that, when executed by the one or more processors, cause the device to perform operations comprising: acquiring two-dimensional (2D) and/or three-dimensional (3D) imaging data of at least a torso of a patient; segmenting the imaging data of the patient to identify 1) the pulmonary vasculature, and/or 2) one or more clots or lesions within the pulmonary vasculature; generating a 3D pulmonary vasculature model of the patient from the segmented imaging data; generating a 3D clot model of the patient from the segmented imaging data; characterizing and assessing the one or more clots or lesions within the 3D pulmonary vasculature model and the 3D clot model; determining if one or more clots or lesions within the 3D pulmonary vasculature model and the 3D clot model can be accessed with a thrombectomy device; and considering the characterizing and assessing, outputting a treatment recommendation of selected clots or lesions of the one or more clots or lesions to target for removal.

18. The system of claim 17, wherein characterizing and assessing the one or more clots comprises determining a clot type of the one or more clots

19. The system of claim 17, wherein characterizing and assessing the one or more clots comprises determining a clot density of the one or more clots.

20. The system of claim 17, wherein characterizing and assessing the one or more clots comprises determining volume of the clot relative to a vessel diameter at a location of the clot.

21. The system of claim 17, wherein characterizing and assessing the one or more clots comprises identifying clots that should be excluded from treatment based on their location within the 3D pulmonary vasculature model.

22. The system of claim 17, wherein characterizing and assessing the one or more clots comprises identifying clots that should not be accessed with a thrombectomy catheter.

23. The system of claim 17, wherein characterizing and assessing the one or more clots comprises identifying clots that can be accessed with a thrombectomy catheter.

24. The system of claim 17, wherein characterizing and assessing the one or more clots comprises determining a pre-operative Miller Score or an equivalent index or metric based on the one or more clots to indicate an extent of obstruction to blood flow caused by the one or more clots.

25. The system of claim 17, wherein characterizing and assessing the one or more clots comprises determining a post-operative Miller Score or an equivalent index or metric based if selected clots are removed to indicate an extent of obstruction to blood flow caused by the one or more clots.

26. The system of claim 17, wherein characterizing and assessing the one or more clots comprises determining a pre-operative Miller Score based on the one or more clots, identifying one or more selected clots to be removed, and determining a postoperative Miller Score if the selected clots are removed.

27. The system of claim 17, wherein the 2D or 3D imaging data comprises X-ray imaging data.

28. The system of claim 17, wherein the 2D or 3D imaging data comprises computed tomography (CT) imaging data.

29. The system of claim 17, wherein the 2D or 3D imaging data comprises magnetic resonance imaging (MRI) imaging data.

30. The system of claim 17, wherein the 2D or 3D imaging data comprises positron emission tomography (PET) imaging data.

31. The system of claim 17, wherein the 2D or 3D imaging data comprises a fusion of one or more sources of imaging data.

32. The system of claim 17, wherein the 2D or 3D imaging data comprises ultrasound imaging data.

33. A system comprising: an elongate medical device insertable into a human subject; one or more processors; memory coupled to the one or more processors, wherein the memory includes computer-program instructions that, when executed by the one or more processors, cause the device to perform operations comprising: acquiring two-dimensional (2D) and/or three-dimensional (3D) imaging data of at least a torso of a patient; segmenting the imaging data of the patient to identify 1) the pulmonary vasculature, and/or 2) one or more clots or lesions within the pulmonary vasculature; generating a 3D pulmonary vasculature model of the patient from the segmented imaging data; generating a 3D clot model of the patient from the segmented imaging data; generating a 3D model of the elongate medical device including a position and/or orientation within the subject; presenting the 3D pulmonary vasculature model, 3D clot model, and the 3D model of the medical device to a user.

34. The system of claim 33, further comprising outputting instructions to a user to navigate the 3D model of the medical device to a selected clot from the 3D clot model within the 3D pulmonary vasculature model.

35. The system of claim 33, wherein characterizing and assessing the one or more clots comprises determining a clot type of the one or more clots

36. The system of claim 33, wherein characterizing and assessing the one or more clots comprises determining a clot density of the one or more clots.

37. The system of claim 33, wherein characterizing and assessing the one or more clots comprises determining volume of the clot relative to a vessel diameter at a location of the clot.

38. The system of claim 33, wherein characterizing and assessing the one or more clots comprises identifying clots that should be excluded from treatment based on their location within the 3D pulmonary vasculature model.

39. The system of claim 33, wherein characterizing and assessing the one or more clots comprises identifying clots that should not be accessed with a thrombectomy catheter.

40. The system of claim 33, wherein characterizing and assessing the one or more clots comprises identifying clots that can be accessed with a thrombectomy catheter.

41. The system of claim 33, wherein characterizing and assessing the one or more clots comprises determining a pre-operative Miller Score based on the one or more clots.

42. The system of claim 33, wherein characterizing and assessing the one or more clots comprises determining a post-operative Miller Score if selected clots are removed.

43. The system of claim 33, wherein characterizing and assessing the one or more clots comprises determining a pre-operative Miller Score based on the one or more clots, identifying one or more selected clots to be removed, and determining a postoperative Miller Score if the selected clots are removed.

44. The system of claim 33, wherein the 2D or 3D imaging data comprises X-ray imaging data.

45. The system of claim 33, wherein the 2D or 3D imaging data comprises computed tomography (CT) imaging data.

46. The system of claim 33, wherein the 2D or 3D imaging data comprises magnetic resonance imaging (MRI) imaging data.

47. The system of claim 33, wherein the 2D or 3D imaging data comprises positron emission tomography (PET) imaging data.

48. The system of claim 33, wherein the 2D or 3D imaging data comprises a fusion of one or more sources of imaging data.

49. The system of claim 33, wherein the 2D or 3D imaging data comprises ultrasound imaging data.

50. A computer implemented method, comprising: acquiring two-dimensional (2D) and/or three-dimensional (3D) imaging data of at least a torso of a patient; segmenting the imaging data of the patient to identify 1) the pulmonary vasculature, and/or 2) one or more clots or lesions within the pulmonary vasculature; generating a 3D pulmonary vasculature model of the patient from the segmented imaging data; generating a 3D clot model of the patient from the segmented imaging data; characterizing and assessing the one or more clots or lesions within the 3D pulmonary vasculature model and the 3D clot model; and considering the characterizing and assessing, outputting a treatment recommendation of selected clots or lesions of the one or more clots or lesions to target for removal.

51. A computer implemented method, comprising: acquiring two-dimensional (2D) and/or three-dimensional (3D) imaging data of at least a torso of a patient; segmenting the imaging data of the patient to identify 1) the pulmonary vasculature, and/or 2) one or more clots or lesions within the pulmonary vasculature; generating a 3D pulmonary vasculature model of the patient from the segmented imaging data; generating a 3D clot model of the patient from the segmented imaging data; characterizing and assessing the one or more clots or lesions within the 3D pulmonary vasculature model and the 3D clot model; determining if one or more clots or lesions within the 3D pulmonary vasculature model and the 3D clot model can be accessed with a thrombectomy device; and considering the characterizing and assessing, outputting a treatment recommendation of selected clots or lesions of the one or more clots or lesions to target for removal.

52. A computer implemented method, comprising: acquiring two-dimensional (2D) and/or three-dimensional (3D) imaging data of at least a torso of a patient; segmenting the imaging data of the patient to identify 1) the pulmonary vasculature, and/or 2) one or more clots or lesions within the pulmonary vasculature; generating a 3D pulmonary vasculature model of the patient from the segmented imaging data; generating a 3D clot model of the patient from the segmented imaging data; generating a 3D model of a medical device including a position and/or orientation within the subject; presenting the 3D pulmonary vasculature model, 3D clot model, and the 3D model of the medical device to a user.

53. The method of any of claims 50-52, wherein characterizing and assessing the one or more clots comprises determining a clot type of the one or more clots

54. The method of any of claims 50-52, wherein characterizing and assessing the one or more clots comprises determining a clot density of the one or more clots.

55. The method of any of claims 50-52, wherein characterizing and assessing the one or more clots comprises determining volume of the clot relative to a vessel diameter at a location of the clot.

56. The method of any of claims 50-52, wherein characterizing and assessing the one or more clots comprises identifying clots that should be excluded from treatment based on their location within the 3D pulmonary vasculature model.

57. The method of any of claims 50-52, wherein characterizing and assessing the one or more clots comprises identifying clots that should not be accessed with a thrombectomy catheter.

58. The method of any of claims 50-52, wherein characterizing and assessing the one or more clots comprises identifying clots that can be accessed with a thrombectomy catheter.

59. The method of any of claims 50-52, wherein characterizing and assessing the one or more clots comprises determining a pre-operative Miller Score or an equivalent index or metric based on the one or more clots to indicate an extent of obstruction to blood flow caused by the one or more clots.

60. The method of any of claims 50-52, wherein characterizing and assessing the one or more clots comprises determining a post-operative Miller Score or an equivalent index or metric based if selected clots are removed to indicate an extent of obstruction to blood flow caused by the one or more clots.

61. The method of any of claims 50-52, wherein characterizing and assessing the one or more clots comprises determining a pre-operative Miller Score based on the one or more clots, identifying one or more selected clots to be removed, and determining a post-operative Miller Score if the selected clots are removed.

62. The method of any of claims 50-52, wherein the 2D or 3D imaging data comprises X- ray imaging data.

63. The method of any of claims 50-52, wherein the 2D or 3D imaging data comprises computed tomography (CT) imaging data.

64. The method of any of claims 50-52, wherein the 2D or 3D imaging data comprises magnetic resonance imaging (MRI) imaging data.

65. The method of any of claims 50-52, wherein the 2D or 3D imaging data comprises positron emission tomography (PET) imaging data.

66. The method of any of claims 50-52, wherein the 2D or 3D imaging data comprises a fusion of one or more sources of imaging data.

67. The method of any of claims 50-52, wherein the 2D or 3D imaging data comprises ultrasound imaging data.