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WO2025129125A1 - Systèmes d'élimination de thrombus et procédés associés - Google Patents

Systèmes d'élimination de thrombus et procédés associés Download PDF

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Publication number
WO2025129125A1
WO2025129125A1 PCT/US2024/060213 US2024060213W WO2025129125A1 WO 2025129125 A1 WO2025129125 A1 WO 2025129125A1 US 2024060213 W US2024060213 W US 2024060213W WO 2025129125 A1 WO2025129125 A1 WO 2025129125A1
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Prior art keywords
clots
imaging data
model
assessing
characterizing
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Inventor
Praveen Krishna DALA
Uday Illindala
Svyatoslav KORNEEV
Daniel Joseph RACHLIN
Amr Salahieh
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Shifamed Holdings LLC
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Shifamed Holdings LLC
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Definitions

  • 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.
  • 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.
  • another part of the body e.g., a vein in the pelvis or leg
  • 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.
  • DVDTT deep vein thrombus
  • 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.
  • FIGS. 1 A-1B illustrate a medical device such as a thrombectomy catheter.
  • FIG. 2 is a schematic view of the pulmonary vasculature.
  • FIGS. 3A-3B show a system for generating a 3D anatomical model of a target anatomy.
  • 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.
  • FIGS. 5-7 are flowcharts for providing an assessment or recommendation or navigation before or during an interventional procedure.
  • FIGS. 8A-8K illustrate views of a 3D anatomical model including a graphical representation of the medical device in the 3D model.
  • 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).
  • FIGS. 10A-10F is an example of a navigation system that provides display of 3D pulmonary artery architecture and clot location.
  • FIGS. 11 A-l ID illustrate a pseudo fluoroscopy feature of a 3D pulmonary artery architecture.
  • FIGS. 12A-12F are examples of a 3D pulmonary artery architecture with segmented clots overlaid within the model or within medical imaging.
  • a thrombus removal 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.
  • 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
  • characterizing and assessing the one or more clots includes determining a clot type of the one or more clots.
  • characterizing and assessing the one or more clots includes determining a clot density of the one or more clots.
  • 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.
  • 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.
  • characterizing and assessing the one or more clots includes identifying clots that should not be accessed with a thrombectomy catheter.
  • characterizing and assessing the one or more clots includes identifying clots that can be accessed with a thrombectomy catheter.
  • 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.
  • 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.
  • 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.
  • the 2D or 3D imaging data includes X-ray imaging data.
  • the 2D or 3D imaging data includes computed tomography (CT) imaging data.
  • the 2D or 3D imaging data includes magnetic resonance imaging (MRI) imaging data.
  • MRI magnetic resonance imaging
  • the 2D or 3D imaging data includes positron emission tomography (PET) imaging data.
  • PET positron emission tomography
  • the 2D or 3D imaging data includes a fusion of one or more sources of imaging data.
  • the 2D or 3D imaging data includes ultrasound imaging data.
  • 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
  • characterizing and assessing the one or more clots includes determining a clot type of the one or more clots.
  • characterizing and assessing the one or more clots includes determining a clot density of the one or more clots.
  • 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.
  • 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.
  • characterizing and assessing the one or more clots includes identifying clots that can be accessed with a thrombectomy catheter.
  • 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.
  • 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.
  • 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.
  • the 2D or 3D imaging data includes X-ray imaging data.
  • the 2D or 3D imaging data includes computed tomography (CT) imaging data.
  • CT computed tomography
  • the 2D or 3D imaging data includes positron emission tomography (PET) imaging data.
  • PET positron emission tomography
  • the 2D or 3D imaging data includes a fusion of one or more sources of imaging data.
  • the 2D or 3D imaging data includes ultrasound imaging data.
  • 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.
  • characterizing and assessing the one or more clots includes determining a clot type of the one or more clots.
  • 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.
  • 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.
  • characterizing and assessing the one or more clots includes identifying clots that should not be accessed with a thrombectomy catheter.
  • characterizing and assessing the one or more clots includes identifying clots that can be accessed with a thrombectomy catheter.
  • characterizing and assessing the one or more clots includes determining a pre-operative Miller Score based on the one or more clots.
  • 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.
  • the 2D or 3D imaging data includes X-ray imaging data.
  • the 2D or 3D imaging data includes computed tomography (CT) imaging data.
  • CT computed tomography
  • the 2D or 3D imaging data includes magnetic resonance imaging (MRI) imaging data.
  • MRI magnetic resonance imaging
  • the 2D or 3D imaging data includes positron emission tomography (PET) imaging data.
  • PET positron emission tomography
  • the 2D or 3D imaging data includes a fusion of one or more sources of imaging data.
  • the 2D or 3D imaging data includes ultrasound imaging data.
  • 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.
  • 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
  • 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.
  • characterizing and assessing the one or more clots includes determining a clot type of the one or more clots.
  • characterizing and assessing the one or more clots includes determining a clot density of the one or more clots.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • the 2D or 3D imaging data includes X-ray imaging data.
  • the 2D or 3D imaging data includes computed tomography (CT) imaging data.
  • CT computed tomography
  • the 2D or 3D imaging data includes positron emission tomography (PET) imaging data.
  • PET positron emission tomography
  • the 2D or 3D imaging data includes a fusion of one or more sources of imaging data.
  • the 2D or 3D imaging data includes ultrasound imaging data.
  • 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.
  • thrombus removal 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).
  • pulmonary embolectomy e.g., pulmonary embolectomy
  • thrombus thrombus with a fluid
  • 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.).
  • 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.
  • 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
  • one or more lumens extending at least partially from the proximal portion to the distal portion.
  • 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).
  • 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.
  • 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.
  • the sensor 105 can comprise a pressure sensor disposed near the distal end 103.
  • 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).
  • FIG. IB shows the funnel 108 of the medical device 108 axially disposed out of a distal end 103 of the introducer catheter 102.
  • 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.
  • jets or fluid streams can also be delivered into the funnel or aspiration lumen to interact with and/or macerate the thrombus material.
  • 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.
  • the funnel can disperse the contrast agent as it’ s delivered past the funnel from the introducer catheter.
  • a dilator device or other medical device can be inserted into the introducer catheter, as will be described below.
  • 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.
  • 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.
  • 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.
  • 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.
  • the fluid or contrast source 112 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.
  • the injector can be configured to automatically and/or continuously deliver contrast at the selected volumes and frequency.
  • 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.
  • 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.
  • 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.
  • 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).
  • 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.
  • 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.
  • a medical device such as a thrombectomy catheter
  • a system 300 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.
  • CT computed tomography
  • CBCT cone-beam CT imaging system
  • MRI magnetic resonance imaging
  • ultrasound imaging system any other high resolution medical or diagnostic imaging system
  • contrast-enhanced imaging e.g., angiography
  • 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.
  • 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.
  • GUI graphical user interface
  • 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.
  • 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”.
  • the 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.
  • the system uses scan, imaging and/or patient data to generate information for a clinician or user of the system.
  • 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).
  • the system generates image data based on composite data from multiple locations, and/or from multiple imaging modalities.
  • the system may generate the 3D model from any combination of imaging data including CT, X-ray, fluoroscopy, MRI, ultrasound, etc.
  • 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.
  • a medical device e.g., a thrombectomy catheter
  • a graphical overlay or representation of the medical device can be displayed on the 3D anatomical model.
  • 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.
  • the system can provide the graphical overlay of the medical device on the 3D model.
  • 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.
  • the length of the catheter inserted into the patient past some known reference point e.g., the femoral access point
  • some known reference point e.g., the femoral access point
  • 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).
  • 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.
  • a user control such as a tab, button, etc.
  • 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.
  • 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.
  • the engines described herein, or the engines through which the systems and devices described herein can be implemented, may be cloud-based engines.
  • 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.
  • 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.
  • 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.
  • the input engine 401 may receive training images, including supervised training images.
  • 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.
  • 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.
  • the anatomical segmentation engine 402 may also process (e.g., convert, transform) 2D or 3D imaging or scan data into a 3D model.
  • 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.
  • the target tissue includes coronary vasculature and/or peripheral vasculature.
  • 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).
  • 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.
  • 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.
  • the clot segmentation engine 402 may also process (e.g., convert, transform) 2D or 3D imaging or scan data into a 3D model.
  • 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 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.
  • 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.
  • MRI magnetic resonance imaging
  • segmentation of occlusive material may refer to ‘clot’ or ‘embolism,’ it should be appreciated that other occlusive blockages within the anatomy are contemplated.
  • the segmentation engine 404 can alternatively or additionally segment vascular lesions.
  • the lesions can be calcified lesions.
  • calcified lesions can correspond to locations within the coronary and/or peripheral vasculature of the patient.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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).
  • 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.
  • GUI graphical user interface
  • 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.
  • 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.
  • 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.
  • 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.
  • the user-input information from may be used by system 450 for further segmentation training by training engine 465.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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).
  • 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.
  • 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.
  • the supervised training data can be labeled to identify clot locations, clot sizes or volumes, and/or clot types or densities.
  • 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.
  • the training of a neural network may be associated with treatment planning or a treatment planning system.
  • 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.
  • 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.
  • 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.
  • 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.
  • CT computed tomography
  • MRI magnetic resonance imaging
  • PET positron emission tomography
  • 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.
  • the 3D model of the pulmonary vasculature is separate from 3D models of each clot or lesion identified from the imaging or scan data.
  • the anatomy and clots are located within the same 3D model.
  • the system 400 segments the 3D model(s).
  • the system 400 can segment the 3D model into individual vessels, arteries, branches, clots, and/or lesions.
  • 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.
  • 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.
  • 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.
  • 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.).
  • 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).
  • 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.
  • color coding e.g., green for can be accessed, red for cannot be accessed
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 10D-10E show additional views of algorithms used to calculate and display the catheter or tool position in real-time within the model.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • the motion modeling and 3D anatomical model can be be trained and build partially using synthetic data.
  • 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.
  • 2D or 3D imaging data with clots present can be segmented by the system to identify clots within the imaging data or within the 3D model of the pulmonary vasculature.
  • the 3D anatomical model can be used to identify clots and their locations within the anatomy.
  • 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.
  • the system may output a score indicative of the impact of removing various clots during a procedure.
  • 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.
  • 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.
  • a blood vessel such as fat, tissue, or a foreign substance.
  • 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).
  • 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.

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Abstract

La présente technologie concerne des systèmes et des procédés pour éliminer un thrombus d'un vaisseau sanguin d'un patient. Dans certains modes de réalisation, la présente technologie concerne des systèmes comprenant un cathéter allongé ayant une partie distale conçue pour être positionnée à l'intérieur du vaisseau sanguin du patient, une partie proximale conçue pour être externe au patient, et une lumière s'étendant entre celles-ci. Les systèmes et les procédés décrits ici fournissent des modèles 3D du système vasculaire pulmonaire, des caillots à l'intérieur du système vasculaire pulmonaire, et/ou de dispositifs médicaux à l'intérieur du patient, pour une planification et une navigation pré et périopératoires.
PCT/US2024/060213 2023-12-13 2024-12-13 Systèmes d'élimination de thrombus et procédés associés Pending WO2025129125A1 (fr)

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US202463699003P 2024-09-25 2024-09-25
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Citations (5)

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US20150230810A1 (en) * 2012-05-15 2015-08-20 Pulse Therapeutics, Inc. Magnetic-based systems and methods for manipulation of magnetic particles
US20160022371A1 (en) * 2014-07-22 2016-01-28 Siemens Aktiengesellschaft Method and System for Automated Therapy Planning for Arterial Stenosis
US20200253670A1 (en) * 2019-02-08 2020-08-13 Auris Health, Inc. Robotically controlled clot manipulation and removal
US20220202506A1 (en) * 2020-06-19 2022-06-30 Remedy Robotics, Inc. Systems and methods for guidance of intraluminal devices within the vasculature
US20230285081A1 (en) * 2020-08-11 2023-09-14 Intuitive Surgical Operations, Inc. Systems for planning and performing biopsy procedures and associated methods

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150230810A1 (en) * 2012-05-15 2015-08-20 Pulse Therapeutics, Inc. Magnetic-based systems and methods for manipulation of magnetic particles
US20160022371A1 (en) * 2014-07-22 2016-01-28 Siemens Aktiengesellschaft Method and System for Automated Therapy Planning for Arterial Stenosis
US20200253670A1 (en) * 2019-02-08 2020-08-13 Auris Health, Inc. Robotically controlled clot manipulation and removal
US20220202506A1 (en) * 2020-06-19 2022-06-30 Remedy Robotics, Inc. Systems and methods for guidance of intraluminal devices within the vasculature
US20230285081A1 (en) * 2020-08-11 2023-09-14 Intuitive Surgical Operations, Inc. Systems for planning and performing biopsy procedures and associated methods

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