Atherosclerotic tissue characterization based on endovascular treatment dynamics

Abstract

A system to provide endovascular support for patients. The system includes a processor configured to receive, from a medical imaging device, a pre-treatment vessel roadmap and medical images acquired in response to an endovascular therapy performed on a patient, and obtain endovascular treatment parameters, including image-based endovascular treatment parameters based on features of the medical images. Such endovascular treatment parameters include a geometry and location of an endovascular therapy device within a vessel of the patient. The processor is further configured to predict tissue characteristics of a first lesion based on the endovascular treatment parameters and generate a map of the first lesion based on the pre-treatment vessel roadmap and the predicted tissue characteristics of the first lesion.

Description

ATHEROSCLEROTIC TISSUE CHARACTERIZATION BASED ON ENDOVASCULAR TREATMENT DYNAMICSFIELD

[0001] The following relates generally to medical tissue characterization. More particularly, embodiments herein relate to atherosclerotic tissue characterization based on endovascular treatment dynamics.BACKGROUND

[0002] In peripheral artery disease (PAD) and coronary artery disease (CAD), narrowing of blood vessels due to atherosclerotic plaque forming in the wall of the artery impedes blood flow and perfusion distal to the plaque. Lack of tissue perfusion can lead to many complications including loss of tissue function, non-healing wounds, amputation, or heart failure. There are numerous types of atherosclerotic plaque, which can include varying amounts of fatty, fibrotic, calcified, or necrotic tissues.

[0003] There are numerous endovascular methods to treat atherosclerosis, including balloon angioplasty (plain, drug-coated, scoring balloons, etc.) or atherectomy (rotational, laser, directional, orbital, etc.). Balloon angioplasty is a common treatment for peripheral and coronary artery disease. Balloon angioplasty may be used in combination with other treatments depending on the characteristics of a lesion in a patient. Such balloon angioplasty involves inflating a balloon inside the diseased vessel for a short time to try to increase the flow through the vessel by expanding the lumen. Additionally, or alternatively, atherectomy involves passing a device through the lesion that mechanically removes some of the plaque.

[0004] Deciding on the right therapy or set of therapies to treat a particular lesion is complex.Multiple treatments may be applied to a vessel sequentially to try to optimally restore blood flow. Deciding on which devices to use is largely dependent on the interventionalist’s perception of the tissue types that make up the plaque being treated; however, plaque composition is not easily quantified using standard Xray-based vascular imaging methods.

[0005] The following discloses certain improvements to overcome problems that exist in such treatments.SUMMARY

[0006] As discussed above, it is currently challenging to decide which devices to use as such decisions are largely dependent on the interventionalist’s perception of the tissue types that make up the plaque being treated, while plaque composition is not easily quantified using existing x-ray based vascular imaging methods.

[0007] For example, in PAD, it can be difficult to determine at what point a lesion has been treated enough to restore blood flow sufficiently. Treatment may require multiple passes of different types of therapy devices to open the vessel sufficiently to restore blood flow. Assessing the effect of the treatment often requires a digital subtraction angiography (DSA) run. Disadvantageously, such a digital subtraction angiography (DSA) run delivers both contrast and radiation to the patient.

[0008] Advantageously, in some implementations herein, by dynamically tracking therapy delivery parameters, information about the composition of the tissue or plaque are inferred, without additional digital subtraction angiography (DSA) runs. Such information about the tissue composition are used to determine if sufficient treatment has already been delivered, or to help determine the optimal devices to use if subsequent treatment is necessary.

[0009] As will be described in greater detail below, the degree of balloon inflation is influenced by tissue response due to the characteristics of the lesion, such as the amount of fatty, fibrotic, or calcific tissue present. In some implementations discussed herein, by tracking the borders of the balloon in fluoroscopy, the inflation dynamics can be assessed and used to infer information about the tissue stiffness and composition. Information about the tissue characteristics can be used to determine if additional therapy is required after balloon angioplasty deployment.

[0010] For example, in some implementations discussed herein, 2D fluoroscopy sequences are received from an imaging system (e.g., an interventional X-ray imaging system or the like) during endovascular treatment (e.g., balloon angioplasty, atherectomy procedures, or the like) and performs the following actions: tracking the geometry and location of the therapy delivery as function of time, inferring tissue characteristics based on therapy device dynamics, and / or mapping the inferred tissue characteristics to a vessel roadmap.

[0011] In one aspect, an endovascular support system includes a processor and a memory communicatively coupled to the processor. The memory stores instructions which, when executed by the processor, cause the processor to receive, from a medical imaging device, a pre-treatmentvessel roadmap and medical images acquired in response to an endovascular therapy performed on a patient; and obtain endovascular treatment parameters, including image-based endovascular treatment parameters based on features of the medical images. The endovascular treatment parameters include a geometry and location of an endovascular therapy device within a vessel of the patient. The instructions which, when executed by the processor, further cause the processor to predict tissue characteristics of a first lesion based on the endovascular treatment parameters and generate a map of the first lesion based on the pre-treatment vessel roadmap and the predicted tissue characteristics of the first lesion.

[0012] In yet another aspect, a method for endovascular consultation support, includes receiving, from a medical imaging device, a pre-treatment vessel roadmap and medical images acquired in response to an endovascular therapy performed on a patient; and obtaining endovascular treatment parameters, including image-based endovascular treatment parameters based on features of the medical images. The endovascular treatment parameters include a geometry and location of an endovascular therapy device within a vessel of the patient. The method further includes predicting tissue characteristics of a first lesion based on the endovascular treatment parameters and generating a map of the first lesion based on the pre-treatment vessel roadmap and the predicted tissue characteristics of the first lesion.

[0013] In still another aspect, a non-transitory machine-readable storage medium has stored a computer program including instructions which, when executed by a processor, cause the processor to receive, from a medical imaging device, a pre-treatment vessel roadmap and medical images acquired in response to an endovascular therapy performed on a patient; and obtain endovascular treatment parameters, including image-based endovascular treatment parameters based on features of the medical images. The endovascular treatment parameters include a geometry and location of an endovascular therapy device within a vessel of the patient. The instructions, when executed by the processor, further cause the processor to predict tissue characteristics of a first lesion based on the endovascular treatment parameters and generate a map of the first lesion based on the pre-treatment vessel roadmap and the predicted tissue characteristics of the first lesion.

[0014] It should be appreciated that all combinations of the foregoing concepts and additional concepts discussed in greater detail below (provided such concepts are not mutually inconsistent) are contemplated as being part of the subject matter disclosed herein. In particular, allcombinations of claimed subject matter appearing at the end of this disclosure are contemplated as being part of the subject matter disclosed herein. It should also be appreciated that terminology explicitly employed herein that also may appear in any disclosure incorporated by reference should be accorded a meaning most consistent with the particular concepts disclosed herein.

[0015] These and other aspects of the various embodiments will be apparent from and elucidated with reference to the embodiment(s) described hereinafter.BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The various advantages of the embodiments will become apparent to one skilled in the art by reading the following specification and appended claims, and by referencing the following drawings, in which:

[0017] FIG. 1 is an illustration of a block diagram of an example endovascular support system according to an embodiment;

[0018] FIG. 2 is an illustration of a block diagram of another example endovascular support system according to an embodiment;

[0019] FIG. 3 is an illustration of balloon angioplasty according to an embodiment;

[0020] FIG. 4 is an illustration of rotational atherectomy according to an embodiment;

[0021] FIG. 5 is an illustration of balloon segmentation detection performed on a balloon angioplasty image according to an embodiment;

[0022] FIG. 6 is an illustration of a schematic diagram of balloon inflation according to an embodiment;

[0023] FIG. 7 is an illustration of a series of balloon inflation graphs according to an embodiment;

[0024] FIG. 8 is an illustration of a mapping of tissue type according to an embodiment;

[0025] FIG. 9 is an illustration of a schematic diagram of balloon inflation with variations in contrast according to an embodiment;

[0026] FIG. 10 is an illustration of a flowchart of a method for managing endovascular therapy to individual patients according to an embodiment;

[0027] FIG. 11 is an illustration of a flowchart of a further method for managing endovascular therapy according to an embodiment;

[0028] FIG. 12 is an illustration of a block diagram of a computer program product according to an embodiment;

[0029] FIG. 13 is a further illustration of a EMR management system according to an embodiment; and

[0030] FIG. 14 is an illustration of a hardware apparatus including a semiconductor package according to an embodiment.DETAILED DESCRIPTION

[0031] FIG. 1 is an illustration of a block diagram of an example endovascular support system 100 according to an embodiment. In the illustrated implementation, the endovascular support system 100 may include a main platform 102 (e.g., also referred to as a care platform herein). In some implementations, the main platform 102 may be embodied as a server computer or a plurality of server computers (e.g., interconnected to form a server cluster, cloud computing resource, the like, and / or combinations thereof).

[0032] In some implementations, the main platform 102 is a care platform designed for one or more aspects of patient care. In such an example, the main platform 102 includes one or more of the following care platforms Performance Management Data Platform, Medical Asset Track & Trace System, Virtualized Imaging Solution, Centralized Care Management System, Interoperability Solution, Electronic Medical Record (EMR), radiology information systems (RIS), picture archiving and communication system (PACS), etc. In a patient care setting, there may be many care platforms connected to various clinical and operational data sources (e.g., Health Level Seven (HL7), Fast Healthcare Interoperability Resources (FHIR), machine logs, Real-Time Location System (RTLS), sensors, etc.).

[0033] In some implementations, a first application 104 though an Nth application 106, which may include an endovascular support application 108, may be associated with the main platform 102. As will be described in greater detail below, the operations of the endovascular support application 108 provide for receiving 2D fluoroscopy sequences (or the like) from an imaging system (e.g., an interventional X-ray imaging system or the like) during endovascular treatment (e.g., balloon angioplasty, atherectomy procedures, or the like). The endovascular support application 108 then performs the following actions: tracking the geometry and location of the therapy delivery as function of time, inferring tissue characteristics based on therapy device dynamics, and / or mapping the inferred tissue characteristics to a vessel roadmap.

[0034] Additionally, or alternatively, the endovascular support system 100 may include a patient monitor 110, a sensor 112 (e.g., one or more sensors 112 that may be associated with a care facility, sensors 112 that may be associated with a patient 114, the like, and / or combinations thereof), a therapeutic device 116, a medical management device 118, medical imager 119, a database 120, a user interface 122 (e.g., one or more user interfaces 122 may be associated with a user 124), an information system 126, the like, and / or combinations thereof. For example, the main platform 102, the patient monitor 110, the sensor 112, the therapeutic device 116, the medical management device 118, medical imager 119, the database 120, the user interface 122, and / or the information system 126, may be in communication with one another via Internet based communicating, cloud based communication, wired communication, wireless communication, the like, and / or combinations thereof.

[0035] In an example, the patient monitor 110 may be utilized to access patient data from and / or enter patient data to the database 120. For example, the patient monitor 110 may determine measured patient data (e.g., via one of more of the sensors 112). In such an example, the patient monitor 110 may be configured to monitor a patient for vital signs and the like, and the patient monitor 110 may communicate such measured patient data to the database 120. For example, the sensors 112 may determine dynamic patient condition data including patient vitals (e.g., blood pressure, pulse, temperature, respiration, and / or the like) and / or patient test results (e.g., creatine level, urine flow, potassium level, oxygen saturation, blood glucose level, carbon dioxide level, and / or the like).

[0036] In some implementations, the patient monitor 110 may include a bedside-type monitor, a transport-type monitor, a central station-type monitor, the like, and / or combinations thereof.

[0037] In some implementations, the sensors 112 may be minimally invasive-style sensors (e.g., by puncturing the skin, sensing through the skin, the like, and / or combinations thereof). Sensors 112 may be wired or wireless.

[0038] In another example, the therapeutic device 116 may be utilized to access patient data from and / or enter patient data to the database 120. For example, the therapeutic device 116 may determine measured patient data. In such an example, the therapeutic device 116 may be configured to monitor the delivery of a particular therapy (e.g., a non-medi cation treatment) to a patient and may communicate such measured patient data to database 120.

[0039] In some implementations, the therapeutic device 116 may supply and / or monitor the administration of one or more patient procedures (e.g., endovascular therapy).

[0040] As used herein, the term “endovascular therapy” refers to balloon angioplasty therapy, atherectomy therapy, the like, and / or combinations thereof.

[0041] In a further example, the medical management device 118 may be utilized to access patient data from and / or enter patient data to the database 120. For example, the medical management device 118 may determine measured patient data. In such an example, the medical management device 118 may be configured to monitor medication delivery to a patient and may communicate such measured patient data to the database 120. In some implementations, the medical management device 118 may supply and / or monitor the administration of one or more patient medications (e.g., blood pressure medications, diuretic medications, anti -anemia medications, cholesterol lowering medications, vitamin supplements, and / or the like).

[0042] In some examples, the medical imager 119 include one or more medical imaging devices, medical imaging systems, the like, and / or combinations thereof. For example, the medical imager 119 include a magnetic resonance imaging (MRI) device, an ultrasound device, an x-ray device, a computerized tomography (CT) device, a radiology information systems (RIS), a picture archiving and communication system (PACS), the like, and / or combinations thereof. In such an implementation, the medical imager 119 is associated with standardized format medical imaging information to transmit, store, retrieve, print, process, and display medical imaging information (e.g., Digital Imaging and Communications in Medicine (DICOM) data).

[0043] Additionally, or alternatively, in a still further example, the user interface 122 may be utilized to access patient data from and / or enter patient data to the database 120. In some implementations, user interface 122 may be implemented via one or more formfactor devices (e.g., a smart phone, a tablet, a laptop, a workstation, and / or the like), an interface associated with the main platform, and / or an interface associated with the patient monitor 110. Additionally, or alternatively, a care provider (e.g., user 124) may access patient data and / or enter patient data through an analog device, a non-networked patient monitor, a non-networked therapeutic device, a non-networked medical management device, the like, and / or combinations thereof.

[0044] In the illustrated implementation, the database 120 may include one or more types of patient data. For example, the database 120 may include patient data including medical images, laboratory result data, microbiology data, medication data, vital sign data, care order data,admission discharge and transfer data, and / or the like. As used herein, the term "database" refers to a collection of data and information organized in such a way as to allow the data and information to be stored, retrieved, updated, and / or manipulated. The term "database" as used herein may also refer to databases that may reside locally or that may be accessed from a remote location (e.g., via remote network servers).

[0045] As used herein, the term "patient data" refers to clinical data, imaging, or information related to an individual patient. Patient data may include measured patient data from a medical imaging device, an analog medical device, a sensor, a patient monitor, a therapeutic device, a medical management device, the like, and / or combinations thereof.

[0046] In the illustrated implementation, the information system 126 may have or have access to one or more types of patient data that are the same or in addition to the patient data of the database 120. For example, the information system 126 may be a Hospital Information System (HIS). Such a Hospital Information System (HIS) has patient data including Health Level Seven (HL7) data, Fast Healthcare Interoperability Resources (FHIR) data, the like, and / or combinations thereof. Additionally, or alternatively, in some implementations, the information system 126 has or has access to one or more of the following information sources: an Electronic Medical Record (EMR), a radiology information systems (RIS), a picture archiving and communication system (PACS), a Digital Imaging and Communications in Medicine (DICOM), medical imaging devices, a Real-Time Location System (RTLS), sensors, machine logs, a Performance Management Data Platform, a Medical Asset Track & Trace System, a Virtualized Imaging Solution, a Centralized Care Management System, an Interoperability Solution, etc.

[0047] Additionally, or alternatively, in some implementations, the database 120 and / or the information system 126 may include or be associated with a simulated database. In such an example, such a simulated database may generate estimated patient data. For example, the simulated database may utilize some measured patient data from the patient monitor 110, the sensor 112, the therapeutic device 116, the medical management device 118, the medical imager 119, and / or the user interface 122 to generate some other estimated patient data. Such a simulated database may utilize digital twin technology to perform the estimation, for example. In such an example, such estimated patient data may be marked to indicate its estimated nature (rather than measured patient data). Additionally, or alternatively, a weight factor may be applied to theestimated patient data so that the estimated patient data may have a lower weight than corresponding measured patient data.

[0048] In some implementations, the endovascular support system 100 may be utilized as an element of an Integrated Clinical Environment (ICE). As used herein, the “Integrated Clinical Environment (ICE)” refers to a platform to create a medical Internet of Things (loT) associated with the care of a patient. In such an implementation, the endovascular support system 100 may support many real-time clinical decision support algorithms. Additionally, or alternatively, the endovascular support system 100 may support closed loop control algorithms of medical devices in the ICE.

[0049] For example, in some implementations discussed herein, 2D fluoroscopy sequences are received from an imaging system (e.g., an interventional X-ray imaging system or the like) during endovascular treatment (e.g., balloon angioplasty, atherectomy procedures, or the like) and performs the following actions: tracking the geometry and / or location of the therapy delivery as function of time, inferring tissue characteristics based on therapy device dynamics, and / or mapping the inferred tissue characteristics to a vessel roadmap.

[0050] An interventional X-ray imaging system including an X-ray tube is adapted to generate X-rays and an X-ray detector configured to acquire X-ray images. Examples of such systems are a fixed monoplane and biplane C-arm X-ray system, a mobile C-arm X-ray system, the like, and / or combinations thereof. The X-ray imaging system can generate both regular fluoroscopy (X-ray) images as well as contrast enhanced fluoroscopy images like digital subtraction angiography (DSA) images.

[0051] Artificial intelligence and / or image processing techniques can be used to segment and track an endovascular treatment device (e.g., an angioplasty balloon, atherectomy catheter, or the like) during live fluoroscopic imaging. Tissue response to the therapy can be assessed by analyzing the endovascular treatment device dynamics. For example, such endovascular treatment device dynamics include the balloon inflation spatiotemporal dynamics, the atherectomy burr rotation speed, or the atherectomy catheter’s distal shaft motion, or the like. In some implementations, such endovascular treatment device dynamics are correlated with tissue stiffness in the therapy delivery region. An estimated relative tissue stiffness can then be visualized on a vessel roadmap. This visualized estimated relative tissue stiffness on the vessel roadmap is used to determine if furthertreatment is required in a given location and, if so, help identify which treatment method may be most appropriate.

[0052] As will be described in greater detail below, the endovascular support system 100 and / or the endovascular support application 108 may include several operable components.

[0053] FIG. 2 is an illustration of a block diagram of another example endovascular support system 200 according to an embodiment. As illustrated, the endovascular support system 200 includes a control unit 201 with an endovascular support logic 208, an endovascular therapeutic device 216, a medical imager 219, the like, and / or combinations thereof. For example, the control unit 202, the endovascular therapeutic device 216, and / or the medical imager 219, may be in communication with one another via Internet based communication, cloud based communication, wired communication, wireless communication, the like, and / or combinations thereof.

[0054] In the illustrated example, the control unit 201 may include a processor 202 and a memory 204 communicatively coupled to the processor 202. The memory 204 may include endovascular support logic 208 as a set of instructions. In some implementations the endovascular support logic 208 may be implemented as software (e.g., as illustrated by endovascular support logic 108 in FIG. 1) . In an embodiment, the endovascular support logic 208, when executed by the processor 202, implements one or more aspects of the method 1000 (FIG. 10), the method 1100 (FIG. 11), as will be discussed in greater detail below.

[0055] In some implementations, the processor 202 may include a general purpose controller, a special purpose controller, a storage controller, a storage manager, a memory controller, a microcontroller, a general purpose processor, a special purpose processor, a central processor unit (CPU), the like, and / or combinations thereof. Further, implementations may include distributed processing, component / object distributed processing, parallel processing, the like, and / or combinations thereof. For example, virtual computer system processing may implement one or more of the methods or functionalities as described herein, and the processor 202 described herein may be used to support such virtual processing.

[0056] In some examples, the memory 204 is an example of a computer-readable storage medium. For example, memory 204 may be any memory which is accessible to the processor 202, including, but not limited to RAM memory, registers, and register files, the like, and / or combinations thereof. References to “computer memory” or “memory” should be interpreted as possibly being multiple memories. The memory may for instance be multiple memories within thesame computer system. The memory may also be multiple memories distributed amongst multiple computer systems or computing devices.

[0057] In some implementations, the endovascular therapeutic device 116 may supply and / or monitor the administration of one or more patient procedures (e.g., endovascular therapy). For example, such an endovascular therapeutic device 116 is implementable to perform balloon angioplasty therapy, atherectomy therapy, the like, and / or combinations thereof.

[0058] In some examples, the medical imager 219 include one or more medical imaging devices, medical imaging systems, the like, and / or combinations thereof. For example, the medical imager 219 includes X-ray imaging system. Such an X-ray imaging system typically will include an X-ray tube adapted to generate X-rays and an X-ray detector configured to acquire X- ray images. For example, such an X-ray imaging system is implementable by a fixed monoplane X-ray system, a biplane C-arm X-ray system, a mobile C-arm X-ray system, the like, and / or combinations thereof.

[0059] In operation, the memory 204 stores logic (e.g., endovascular support logic 208) that includes a set of instructions executable by the processor 202, which when executed by the processor 202, cause the processor 202 to receive a pre-treatment vessel roadmap from a medical imaging device (e.g., medical imager 219). Endovascular treatment parameters are received or obtained, where the endovascular treatment parameters include image-based endovascular treatment parameters based on features of medical images from the medical imaging device (e.g., medical imager 219) in response to an endovascular therapy performed on a patient. The endovascular treatment parameters include a geometry and / or location of the endovascular therapy device 216 within a vessel of the patient. Tissue characteristics of a first lesion are predicted based on the endovascular treatment parameters. A map of the first lesion is generated based on the pretreatment vessel roadmap and the predicted tissue characteristics of the first lesion.

[0060] As used herein the term “lesion” refers to an area of tissue with one or more tissue characteristics, including the amount of fatty, fibrotic, calcific tissue present, the like, and / or combinations thereof, identifiable within an individual lesion.

[0061] FIG. 3 is an illustration of balloon angioplasty 300 according to an embodiment. For example, there are numerous endovascular methods to treat atherosclerosis, including balloon angioplasty 300 (e.g., plain, drug-coated, scoring balloons, etc.). As illustrated balloonangioplasty 300 may include a balloon 310 located along a guidewire 314 and catheter 312 within an artery 302 having a plaque 304.

[0062] Balloon angioplasty 300 is a common treatment for peripheral and coronary artery disease. Balloon angioplasty may be used in combination with other treatments depending on the characteristics of a lesion in a patient. Such balloon angioplasty involves inflating a balloon inside the diseased vessel for a short time to try to increase the flow through the vessel by expanding the lumen.

[0063] Additionally, or alternatively, as will be described below, atherectomy involves passing a device through the lesion that mechanically removes some of the plaque.

[0064] FIG. 4 is an illustration of rotational atherectomy 400 according to an embodiment. For example, FIG. 4 provides a visualization of rotational atherectomy 400 to treat atherosclerosis.

[0065] There are numerous endovascular methods to treat atherosclerosis, including atherectomy (rotational, laser, directional, orbital, etc.). Atherectomy involves passing a device through the lesion that mechanically removes some of the plaque.

[0066] Deciding on the right therapy (or set of therapies) to treat a particular lesion is complex. Multiple treatments may be applied to a vessel sequentially to try to optimally restore blood flow. Deciding on which devices to use is largely dependent on the interventionalist’s perception of the tissue types that make up the plaque being treated; however, plaque composition is not easily quantified using standard Xray-based vascular imaging methods.

[0067] As discussed above, it is currently challenging to decide which devices to use as such decisions are largely dependent on the interventionalist’s perception of the tissue types that make up the plaque being treated, while plaque composition is not easily quantified using existing vascular imaging methods.

[0068] For example, in PAD, it can be difficult to determine at what point a lesion has been treated enough to restore blood flow sufficiently. Treatment may require multiple passes of different types of therapy devices to open the vessel sufficiently to restore blood flow. Assessing the effect of the treatment often requires a digital subtraction angiography (DSA) run. Disadvantageous^, such a digital subtraction angiography (DSA) run delivers both contrast and radiation to the patient.

[0069] Advantageously, in some implementations herein, by dynamically tracking therapy delivery parameters, information about the composition of the tissue or plaque are inferred,without additional digital subtraction angiography (DSA) runs. Such information about the tissue composition are used to determine if sufficient treatment has already been delivered, or to help determine the optimal devices to use if subsequent treatment is necessary.

[0070] As will be described in greater detail below, the characteristics of balloon inflation are influenced by tissue response due to the characteristics of the lesion, such as the amount of fatty, fibrotic, or calcific tissue present. In some implementations discussed herein, by tracking the borders of the balloon in fluoroscopy, the inflation dynamics can be assessed and used to infer information about the tissue stiffness and composition. Information about the tissue characteristics can be used to determine if additional therapy is required after balloon angioplasty deployment.

[0071] For example, in some implementations discussed herein, 2D fluoroscopy sequences are received from an imaging system (e.g., an interventional X-ray imaging system or the like) during endovascular treatment (e.g., balloon angioplasty, atherectomy procedures, or the like) and performs the following actions: tracking the geometry and / or location of the therapy delivery as function of time, inferring tissue characteristics based on therapy device dynamics, and / or mapping the inferred tissue characteristics to a vessel roadmap.

[0072] Image-based balloon segmentation & inflation tracking

[0073] FIG. 5 is an illustration of balloon segmentation performed on a balloon angioplasty image for balloon inflation tracking 500 according to an embodiment. For example, balloon inflation tracking 500 shows an example of an angioplasty balloon segmented in a fluoroscopic image. As illustrated, an angioplasty balloon inflated with x-ray contrast are shown in black and the borders of the balloon are shown with a white border 502, which is automatically detected using image processing methods.

[0074] During most PAD procedures, an angioplasty balloon is delivered over a guidewire to a lesion location. The lesion location is often determined based on the DSA roadmap, or other imaging modalities such as intravascular ultrasound (IVUS), acquired prior to advancing the balloon. Once the balloon is aligned with the lesion location, it is inflated by filling the balloon with a mixture of an x-ray contrast agent and saline. This makes the balloon visible in fluoroscopic imaging as it is being inflated. The balloon may be inflated continuously or incrementally up to the nominal pressure or to the burst pressure (nominal and burst pressure are specified by balloon manufacturer). The balloon then remains inflated in the vessel for, typically, up to a few minutes.

[0075] As will be described in greater detail below, in some implementations discussed herein, various methods can be used to identify and segment the balloon in fluoroscopic images during inflation. Image processing methods including thresholding, filtering, and edge detection can be used to identify the balloon borders. For example, setting a threshold on pixel intensity can eliminate objects in the image that are not radiopaque enough to be the contrast-agent-filled balloon. Edge detection filtering can then be applied to identify the borders of candidate objects and the balloon can be selected from the candidate objects based on parameters such as expected size or shape of the balloon.

[0076] Additionally, or alternatively, deep neural networks, such as convolutional neural network, can be trained to segment the balloon in the image. As will be described in greater detail below, in some implementations discussed herein, the model is trained on a set of ground truth training images with binary masks indicating the pixels representing the balloon. The model is then optimized to identify the balloon in fluoroscopic images.

[0077] Assessment of inflation dynamics

[0078] FIG. 6 is an illustration of a schematic diagram 600 of balloon inflation according to an embodiment. For example, FIG. 6 shows an example of a balloon inflating in a vessel with fibrotic and calcified plaque.

[0079] As illustrated, schematic 602 shows balloon inflation inside of a vessel with both fibrotic plaque 610 and calcified plaque 612. The sequential scale 614 of the balloon indicates the balloon contours at increasing pressure.

[0080] As illustrated, schematic 604 shows arrows 616 to indicate balloon inflation vectors, which represent the distance from the guidewire to the balloon border at a particular time point.

[0081] As the balloon inflates, it typically expands symmetrically in the radial direction in the absence of external forces from the vessel. When inflated inside the vessel, the shape of the balloon may be deformed based on forces exerted on the balloon by the vessel and plaque resisting expansion. The amount of deformation of the balloon is related to the stiffness of the vessel or plaque in a given region.

[0082] Various methods can be used to automatically identify regions of under-expansion of the balloon. The contours of the balloon can be quantified based on the spatial derivative of the balloon borders at one or more points throughout inflation and / or when the balloon is fully inflated. Additionally, or alternatively, vectors perpendicular to the guidewire can be defined at discretelocations along the wire between the radiopaque markers at the ends of the balloon. These balloon inflation vectors indicate the distance between the guidewire and balloon border at each location (e.g., at one or more points throughout inflation and / or when the balloon is fully inflated), and the maximum vector length relative to neighboring vectors at the end of inflation is indicative of regions of full expansion. Shorter vectors between the guidewire and balloon border are indicative of under-expansion, and the degree of under-expansion is indicative of the stiffness of the tissue.

[0083] FIG. 7 is an illustration of a series of balloon inflation graphs 700 according to an embodiment. For example, balloon inflation graphs 700 show a representation of balloon expansion versus inflation pressure in the presence of various vascular plaque tissues.

[0084] As the balloon is inflated to incrementally higher pressures, different tissue types will have unique responses to the expansion of a given balloon, as shown. For example, in a primarily fatty plaque region 702 the balloon will experience little resistance and will expand nearly linearly until the maximum balloon diameter is reached. Ina primarily fibrotic region 704, the balloon may experience more resistance with increasing pressure and may or may not reach its full diameter. In a calcified region, 706 there may be an abrupt stop to increasing balloon diameter when the balloon interacts with the hard calcification and the balloon may remain substantially underinflated in that region.

[0085] By assessing the inflation dynamics as a function of pressure or time at discrete locations along the balloon, the tissue types that the balloon interacts with may be determined. For example, a calcified region 706 may be detected by identifying underinflated regions of the balloon, where neighboring balloon inflation vectors expand at substantially different rates throughout inflation. Alternatively, a calcified region 706 may be identified where there is a change in the sign of the spatial derivative of the balloon border indicative of a local maximum or minimum where the balloon is underinflated.

[0086] Further refinement of tissue type identification may be achieved by training a neural network to classify the tissue types in the lesion based on balloon inflation dynamics. Supervised learning may be employed using a convolutional neural network, for example, to classify tissue types. The model would be trained using ground truth tissue types (e.g., from spectral ultrasound, virtual histology, spectral CT, and / or the like) mapped along the vessel and the input data would include the balloon inflation vectors along the length of the balloon as a function of time or pressure, the native vessel diameter, and the balloon characteristics (e.g., size, type, and / or thelike). Additionally, or alternatively, unsupervised learning may be employed by training an autoencoder, for example, to extract key features in the balloon inflation dynamics related to lesion tissue types. Additionally, or alternatively, this can also be achieved with more complex networks that learn to implicitly model the tissue distension along with balloon surface deformation.

[0087] If the balloon is undersized, it may not interact substantially with the plaque, which may also be determined by dynamically tracking inflation pattern. In case of an asymmetric plaque, multiple imaging angles may be used to fully appreciate the characteristics of the plaque.

[0088] Mapping of tissue parameters to vessel roadmap

[0089] FIG. 8 is an illustration of a map 800 of tissue type according to an embodiment. For example, map 800 shows a schematic representation of tissue type probability map overlaid on vessel roadmap. In the illustrated example, the color bar 801 at the right is used to indicate probability of calcification (e.g., where a denser color or different color scheme indicates high probability). Here, calcification 802 has a higher probability of calcification than calcification 804. Additionally, or alternatively, text percentages superimposed on map 800 may be utilized to indicate probability of calcification.

[0090] For example, the relative stiffness of the plaque based on balloon inflation dynamics can be overlaid as a heatmap (and / or numerically) on the DSA vessel roadmap, and / or overlaid on other co-registered imaging modalities such as intravascular ultrasound (IVUS), computed tomography angiography (CTA), the like, and / or combinations thereof. Based on the relative stiffness assessment, a probability map that indicates the likelihood of each tissue type at each location along the plaque can also be overlaid on the vessel roadmap, as shown.

[0091] This information about lesion characteristics can be utilized to determine if additional treatment is needed, such as deployment of additional atherectomy and / or an additional angioplasty balloon expansion. An updated lesion characteristic map can be created after each subsequent therapy is delivered within a given treatment. This information can also be stored in a database to help understand the effectiveness of various therapies on given tissue types across a large group of patients and procedures.

[0092] FIG. 9 is an illustration of a schematic diagram of balloon inflation with variations in contrast 900 according to an embodiment. For example, in-plane balloon deformation may cause variation in volume of contrast being imaged in a particular region of the balloon, causing a lighter appearance in the compressed, calcified region.

[0093] In some implementations, the tracking of the geometry of the therapy delivery location as function of time can be complemented with the readout parameters of the therapy device. An example is where the angioplasty balloon shape and corresponding vessel shape can be combined with information about balloon inflation pressure. This combined information is then used to infer the tissue characteristics.

[0094] In some examples, the balloon can also be inflated and deflated with a certain frequency allowing tissue response measurements as function of this repeating inflation / deflation actions allowing even better tissue characterization. Alternatively, specific inflation patterns may be applied to the balloon inflation such as, for instance, releasing the pressure at once in the balloon and determine the relaxation of the vessel shape as function of time.

[0095] In some aspects, balloon inflation pattern may also be determined by assessing the gray level gradient of the contrast in the balloon. If the plaque is in the plane of the imaging detector, there may not be a visible deformation in the balloon border, but the calcified regions may appear lighter than non-calcified regions due to less contrast in the compressed region of the balloon. During the inflation, this information may indicate to the user to change the C-arm angle to the achieve the plane which best visualizes the plaque. It could also be inputted to a neural network which is trained based on the images of various balloons with different gray levels due to known plaque composition at known locations out of the view plane. In case more than one image plane is used to determine the shape of the balloon also asymmetries in the balloon shape can be detected. From this for instance it can be inferred whether for instance calcification are not evenly distributed around the vessel.

[0096] In some implementations, a deep learning approach using synthetic data can be utilized for modelling various plaque compositions and expected balloon inflation patterns. A neural network can be trained utilizing a combination of synthetic data and clinical data with known lesion characteristics and balloon size / compliance / inflation pressures / contours to determine expected balloon contour dynamics for each tissue type. The network confidence may depend on the quality of the fluoroscopy images and the new devices used during the procedure which were not included in the training process.

[0097] In some examples, in presence of the biplane imaging systems, the images from 2 views may be used to track the balloon dynamics and increase the estimation accuracy as well as determining tissue properties in three dimensions.

[0098] In some aspects, in case of any time-resolved 3D fluoroscopic imaging system all the previously explained approaches may be applied to the volumetric images to provide the plaque characteristics at all angles around the vessel simultaneously.

[0099] In some implementations, to increase the signal to noise and hence the accuracy of determining the shape of the balloon, image processing methods, can be applied.

[0100] In some examples, tissue characteristics may be determined while delivering rotational atherectomy therapy. During rotational atherectomy therapy, the rotational speed of the atherectomy burr can be indicative of the stiffness of the tissue with which it is interacting. It will rotate at the full speed at which it is set in the absence of resistive forces, but as it interacts with stiff tissues the rotational speed may decrease. The rotational speed of the burr can be detected, for example, based on a sensor integrated with the device itself, or by acoustic tracking during atherectomy treatment. A microphone in the procedure room can detect when the rotational atherectomy system is turned on and a frequency analysis of the acoustic data will be indicative of the rotational speed of the burr, and thus the stiffness of the tissue it is treating. This information can be synchronized with fluoroscopic image-based tracking of the rotational atherectomy tip to generate a tissue characteristic map overlaid on the vessel roadmap, as described above.

[0101] In some aspects, movement of the distal section of the mechanical atherectomy device is often limited to the tip rotation around the center of shaft in the non-calcified regions. When entering a region with calcified plaque, the device shaft may be deflected as the burr comes in contact with less compliant tissue. Accordingly, in some implementations, this irregular motion may be picked up by a neural network or through any other image / video processing methods and the location of the calcifications may be identified.

[0102] Additionally, or alternatively, some implementations described herein can be utilized across various clinical spaces and interventional technologies. For example, such clinical spaces include peripheral artery disease, coronary artery disease, the like, and / or combinations thereof. Further, such interventional technologies include balloon angioplasty (e.g., plain balloons, drug coated balloons, scoring balloons, compliant / non-compliant balloons), mechanical atherectomy, the like, and / or combinations thereof.

[0103] FIG. 10 shows an example method 1000 for endovascular consultation support to manage endovascular therapy according to an embodiment. The method 1000 may generally beimplemented in the endovascular support system 100 (FIG. 1) and / or the endovascular support system 200 (FIG. 2), already discussed.

[0104] In an embodiment, the method 1000 (as well as method 1100 (FIG. 11) may be implemented in logic instructions (e.g., software), configurable logic (e.g., firmware), fixed- functionality hardware logic (e.g., hardware), etc., or any combination thereof.

[0105] In some examples, the methods described herein (e.g., method 1000 and / or method 1100) may be performed at least in part by cloud processing.

[0106] It will be appreciated that some or all of the operations described herein (e.g., method 1000 and / or method 1100) that have been described using a “pull” architecture (e.g., polling for new information followed by a corresponding response) may instead be implemented using a “push” architecture (e.g., sending such information when there is new information to report), and vice versa.

[0107] Illustrated processing block 1002 provides for receiving a pre-treatment vessel roadmap. For example, a pre-treatment vessel roadmap may be received from a medical imaging device.

[0108] Illustrated processing block 1004 provides for receiving or obtaining endovascular treatment parameters. For example, endovascular treatment parameters include image-based endovascular treatment parameters that may be received based on features of medical images from the medical imaging device in response to an endovascular therapy performed on a patient. In some examples, the endovascular treatment parameters include a geometry and location of an endovascular therapy device within a vessel of the patient.

[0109] As used herein the term “image-based endovascular treatment parameters” includes either image-based parameters provided directly to a model (e.g., balloon inflation vectors), image features that the model interprets itself when provided with a series of images, the like, and / or combinations thereof. Similarly, the terms “image-based endovascular treatment parameters” and / or “geometry and location” refer to endovascular treatment parameters explicitly provided to the model (e.g., via balloon inflation vectors or the like), provided by the model from features in the medical images (e.g., via providing the image series to the model and allowing the model itself to infer the most relevant geometric characteristics for predicting tissue characteristics), the like, and / or combinations thereof.

[0110] Illustrated processing block 1006 provides for predicting tissue characteristics. For example, tissue characteristics of a first lesion may be predicted based on the endovascular treatment parameters.

[0111] Illustrated processing block 1008 provides for generating a map. For example, a map may be generated of the first lesion based on the pre-treatment vessel roadmap and the predicted tissue characteristics of the first lesion.

[0112] Illustrated processing block 1010 provides for presenting the map to a user. For example, the map may be presented to a user via a display.

[0113] Additional and / or alternative operations for method 1000 are described in greater detail below in the description of FIG. 11.

[0114] FIG. 11 is a flowchart of an example of another method 1100 for endovascular consultation support to manage endovascular therapy according to an embodiment. The method 1100 may generally be implemented in the endovascular support system 100 (FIG. 1) and / or the endovascular support system 200 (FIG. 2), already discussed.

[0115] As illustrated, various processing blocks are illustrated as being performed by an endovascular support application 1108, an endovascular therapeutic device 1116, a medical imager 1119, and / or a display 1120 in conjunction with one another (e.g., as discussed above in FIG. 2).

[0116] Illustrated processing blocks 1122 provide for training the endovascular support application.

[0117] Illustrated processing blocks 1124 provide for utilizing the endovascular support application once the endovascular support application is trained.

[0118] Illustrated processing block 1130 provides for receiving a tissue characteristic map. For example, a pre-treatment tissue characteristic map may be received from a medical imaging device.

[0119] In some implementations, processing block 1130 includes acquiring pre-treatment tissue characteristic map (e.g., from spectral CT, US, etc.) as ground truth.

[0120] Illustrated processing block 1132 provides for receiving endovascular treatment parameters. For example, endovascular treatment parameters include image-based endovascular treatment parameters that may be received based on features of medical images from the medical imaging device. In some examples, the endovascular treatment parameters include a geometry and location of an endovascular therapy device within a vessel of the patient.

[0121] In some examples, the endovascular treatment parameters include information about the therapy device being used for treatment (e.g., type, size, or the like), balloon inflation pressure, atherectomy rotational speed, etc.

[0122] In some implementations, processing block 1132 includes acquiring fluoroscopic images or the like during angioplasty.

[0123] In some implementations, where the endovascular therapy device includes an angioplasty balloon, the endovascular treatment parameters include balloon inflation geometry measurements taken at a plurality of discrete locations along the angioplasty balloon. Additionally, or alternatively, the endovascular treatment parameters include balloon inflation geometry measurements taken at a plurality of degrees of inflation.

[0124] In some examples, where the endovascular therapy device includes an atherectomy catheter, the endovascular treatment parameters include a dynamic geometry of an endovascular therapy device within a vessel of the patient.

[0125] For example, such dynamic geometry may include a deflection derived from imaging.

[0126] Illustrated processing block 1134 provides for segmenting angioplasty balloon borders on each fluoroscopic image.

[0127] Illustrated processing block 1136 provides for calculating a guidewire to balloon border distance at discrete locations along wire in each image.

[0128] Illustrated processing block 1138 provides for providing data to the model for training routine. For example, image-based balloon inflation parameters (input), balloon characteristics (input), inflation parameters from the pump (optional input) tissue characteristics (ground truth), and / or the like may be provided to a deep learning model for a training routine.

[0129] Illustrated processing block 1140 provides for training the model. For example, the model is trained to optimize the model to predict tissue characteristics based on balloon inflation parameters.

[0130] Illustrated processing block 1150 provides for receiving a pre-treatment vessel roadmap. For example, a pre-treatment vessel roadmap may be received from a medical imaging device.

[0131] In some implementations, processing block 1150 includes acquiring a pre-treatment angiographic vessel roadmap (or the like).

[0132] Illustrated processing block 1152 provides for receiving endovascular treatment parameters. For example, endovascular treatment parameters include image-based endovascular treatment parameters that may be received based on features of medical images from the medical imaging device in response to an endovascular therapy performed on a patient. In some examples, the endovascular treatment parameters include a geometry and location of an endovascular therapy device within a vessel of the patient.

[0133] In some implementations, where the endovascular therapy device includes an angioplasty balloon, the endovascular treatment parameters include balloon inflation geometry measurements taken at a plurality of discrete locations along the angioplasty balloon. Additionally, or alternatively, the endovascular treatment parameters include balloon inflation geometry measurements taken at a plurality of degrees of inflation.

[0134] In some examples, the endovascular treatment parameters include information about the therapy device being used for treatment (e.g., type, size, or the like), balloon inflation pressure, atherectomy rotational speed, etc.

[0135] In some examples, where the endovascular therapy device includes an atherectomy catheter, the endovascular treatment parameters include a dynamic geometry of an endovascular therapy device within a vessel of the patient.

[0136] For example, such dynamic geometry may include a deflection derived from imaging. Additionally, or alternatively, such dynamic geometry may include rotational speed provided from the endovascular therapy device itself or another sensor.

[0137] In some implementations, processing block 1152 includes acquiring fluoroscopic images during angioplasty (or the like).

[0138] Illustrated processing block 1154 provides for segmenting angioplasty balloon borders on each fluoroscopic image.

[0139] Illustrated processing block 1156 provides for calculating guidewire to balloon border distance at discrete locations along wire in each image.

[0140] Illustrated processing block 1158 provides for providing data to the model for inferencing. For example, image-based balloon inflation parameters, balloon characteristics, inflation parameters from the pump (optional), and / or the like to the deep learning model (e.g., as described at processing block 1140) may be provided to the model for inferencing.

[0141] Illustrated processing block 1160 provides for predicting tissue characteristics. For example, tissue characteristics of a first lesion may be predicted based on the endovascular treatment parameters.

[0142] In some implementations, processing block 1160 includes predicting tissue characteristics of a plurality of lesions (e.g., a second lesion or more). For example, such a second lesion may be predicted based on the endovascular treatment parameters.

[0143] In some examples, processing block 1160 includes predicting one or more confidence levels. For example, a first confidence level associated with the first lesion and a second confidence level associated with the second lesion may be predicted.

[0144] In some implementations, processing block 1160 includes predicting tissue characteristics based on balloon inflation parameters (or the like) and / or predicting confidence level for tissue type classifications,

[0145] Illustrated processing block 1162 provides for generating a map. For example, a map may be generated of the first lesion based on the pre-treatment vessel roadmap and the predicted tissue characteristics of the first lesion.

[0146] In implementations where multiple lesions are predicted, processing block 1162 includes generating the map to include both the first lesion and the second lesion, etc.

[0147] In examples where one or more confidence levels are predicted, processing block 1162 includes presenting the one or more confidence levels in the map. For example, the first confidence level and the second confidence level are presented on the map as a first region associated with the first lesion and a second region associated with the second lesion.

[0148] In some implementations, processing block 1162 includes overlaying tissue type classifications and confidence level on the vessel roadmap.

[0149] Illustrated processing block 1164 provides for generating guidance. For example, procedure guidance may be generated for a user based on the pre-treatment vessel roadmap and / or the predicted tissue characteristics of the first lesion.

[0150] Illustrated processing block 1166 provides for presenting the map to a user. For example, the map may be presented to a user via a display.

[0151] Illustrated processing block 1168 provides for providing procedure guidance to a user. For example, procedure guidance may be provided to a user based on the pre-treatment vessel roadmap and / or the predicted tissue characteristics of the first lesion.

[0152] Additionally, or alternatively, the procedures described herein may provide a framework for clinical deployment of decision support algorithms. These procedures can work together with many clinical decision support (CDS) algorithms (such as acute kidney injury (AKI), acute respiratory distress syndrome (ARDS), acute decompensated heart failure (ADHF), etc.). Clinical decision support (CDS) refers to computer-based support of clinical staff responsible for making decisions for the care of patients. Computer-based support for clinical decision-making staff may take many forms, from patient-specific visual / numeric health status indicators to patientspecific health status predictions and patient-specific health care recommendations. Further, the procedures described herein may be deployed on analytics platforms (such as Inference Engine, Critical Care Information System, Interoperability Solution, etc.) in conjunction with CDS algorithms.

[0153] FIG. 12 illustrates a block diagram of an example computer program product 1200. In some examples, as shown in FIG. 12, computer program product 1200 includes a machine- readable storage 1202 that may also include logic 1204. In some implementations, the machine- readable storage 1202 may be implemented as a non-transitory machine-readable storage. In some implementations the logic 1204 may be implemented as machine-readable instructions, such as software, for example. In an embodiment, the logic 1204, when executed, implements one or more aspects of the method 1000 (FIG. 10), the method 1100 (FIG. 11), and / or realize the system 100 (FIG. 1 and / or FIG. 2), already discussed.

[0154] FIG. 13 shows an illustrative example of a system 1300. In the illustrated example, the system 1300 may include a processor 1302 and a memory 1304 communicatively coupled to the processor 1302. The memory 1304 may include logic 1306 as a set of instructions. In some implementations the logic 1306 may be implemented as software. In an embodiment, the logic 1306, when executed by the processor 1302, implements one or more aspects of the method 1000 (FIG. 10), the method 1100 (FIG. 11), and / or realize the system 100 (FIG. 1 and / or FIG. 2), already discussed.

[0155] In some implementations, the processor 1302 may include a general purpose controller, a special purpose controller, a storage controller, a storage manager, a memory controller, a microcontroller, a general purpose processor, a special purpose processor, a central processor unit (CPU), the like, and / or combinations thereof.

[0156] Further, implementations may include distributed processing, component / object distributed processing, parallel processing, the like, and / or combinations thereof. For example, virtual computer system processing may implement one or more of the methods or functionalities as described herein, and the processor 1302 described herein may be used to support such virtual processing.

[0157] In some examples, the memory 1304 is an example of a computer-readable storage medium. For example, memory 1304 may be any memory which is accessible to the processor 1302, including, but not limited to RAM memory, registers, and register files, the like, and / or combinations thereof. References to “computer memory” or “memory” should be interpreted as possibly being multiple memories. The memory may for instance be multiple memories within the same computer system. The memory may also be multiple memories distributed amongst multiple computer systems or computing devices.

[0158] FIG. 14 shows an illustrative semiconductor apparatus 1400 (e.g., chip and / or package). The illustrated semiconductor apparatus 1400 includes one or more substrates 1402 (e.g., silicon, sapphire, or gallium arsenide) and logic 1404 (e.g., configurable logic and / or fixed-functionality hardware logic) coupled to the substrate(s) 1402. In an embodiment, the logic 1404 implements one or more aspects of the method 1000 (FIG. 10), the method 1100 (FIG. 11), and / or realize the system 100 (FIG. 1 and / or FIG. 2), already discussed.

[0159] In some implementations, logic 1404 may include transistor array and / or other integrated circuit / IC components. For example, configurable logic and / or fixed-functionality hardware logic implementations of the logic 1404 may include configurable logic such as, for example, programmable logic arrays (PLAs), field programmable gate arrays (FPGAs), complex programmable logic devices (CPLDs), or fixed-functionality logic hardware using circuit technology such as, for example, application specific integrated circuit (ASIC), complementary metal oxide semiconductor (CMOS) or transistor-transistor logic (TTL) technology, the like, and / or combinations thereof.

[0160] All definitions, as defined and used herein, should be understood to control over dictionary definitions, definitions in documents incorporated by reference, and / or ordinary meanings of the defined terms.

[0161] The herein described subject matter sometimes illustrates different components contained within, or connected with, different other components. It is to be understood that suchdepicted architectures are merely exemplary, and that in fact many other architectures can be implemented which achieve the same functionality. In a conceptual sense, any arrangement of components to achieve the same functionality is effectively "associated" such that the desired functionality is achieved. Hence, any two components herein combined to achieve a particular functionality can be seen as "associated with" each other such that the desired functionality is achieved, irrespective of architectures or intermedial components. The term “coupled” may be used herein to refer to any type of relationship, direct or indirect, between the components in question, and may apply to electrical, mechanical, fluid, optical, electromagnetic, electromechanical, or other connections. Likewise, any two components so associated can also be viewed as being "operably connected", or "operably coupled", to each other to achieve the desired functionality, and any two components capable of being so associated can also be viewed as being "operably couplable", to each other to achieve the desired functionality. Specific examples of operably couplable include but are not limited to physically mateable and / or physically interacting components.

[0162] In the claims, as well as in the specification above, the terms “first”, “second”, etc. may be used herein only to facilitate discussion, and carry no particular temporal or chronological significance unless otherwise indicated.

[0163] In the claims, as well as in the specification above, all transitional phrases such as “comprising,” “including,” “carrying,” “having,” “containing,” “involving,” “holding,” “composed of,” and the like are to be understood to be open-ended, i.e., to mean including but not limited to. Only the transitional phrases “consisting of’ and “consisting essentially of’ shall be closed or semi-closed transitional phrases, respectively.

[0164] The indefinite articles “a” and “an,” as used herein in the specification and in the claims, unless clearly indicated to the contrary, should be understood to mean “at least one.”

[0165] As used herein, the term “or” or “and / or” is inclusive and not exclusive, unless expressly indicated otherwise or indicated otherwise by context. Therefore, herein, “A or B” means “A, B, or both,” unless expressly indicated otherwise or indicated otherwise by context. Moreover, “and” is both joint and several, unless expressly indicated otherwise or indicated otherwise by context. Therefore, herein, “A and B” means “A and B, jointly or severally,” unless expressly indicated otherwise or indicated otherwise by context.

[0166] As used herein in the specification and in the claims, the phrase “at least one,” in reference to a list of one or more elements, should be understood to mean at least one element selected from any one or more of the elements in the list of elements, but not necessarily including at least one of each and every element specifically listed within the list of elements and not excluding any combinations of elements in the list of elements. This definition also allows that elements may optionally be present other than the elements specifically identified within the list of elements to which the phrase “at least one” refers, whether related or unrelated to those elements specifically identified.

[0167] As used in this application and in the claims, a list of items joined by the term “one or more of’ may mean any combination of the listed terms. For example, the phrases “one or more of A, B or C” may mean A; B; C; A and B; A and C; B and C; or A, B and C.

[0168] As is described above in greater detail, one or more processor, other unit, the like, and / or combinations thereof may fulfill the functions of several items recited in the claims.

[0169] As is described above in greater detail, a computer program may be stored / distributed on a suitable computer readable medium, such as an optical storage medium or a solid-state medium supplied together with or as part of other hardware, but may also be distributed in other forms, such as via the Internet or other wired or wireless telecommunication systems.

[0170] It should also be understood that, unless clearly indicated to the contrary, in any methods discussed herein that include more than one step or act, the order of the steps or acts of the method is not necessarily limited to the order in which the steps or acts of the method are recited. Further, such methods may include additional or alternative steps or acts.

[0171] As used in the claims, the mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage.

[0172] It is also noted that the claims may include reference signs / numerals in accordance with PCT Rule 6.2(b). However, the present claims should not be considered to be limited to the exemplary embodiments corresponding to the reference signs / numerals.

[0173] Those skilled in the art will appreciate from the foregoing description that the broad techniques of the embodiments of the present invention can be implemented in a variety of forms. Therefore, while the embodiments of this invention have been described in connection with particular examples thereof, the true scope of the embodiments of the invention should not be solimited since other modifications will become apparent to the skilled practitioner upon a study of the drawings, specification, and following claims.

Claims

CLAIMS:

1. An endovascular support system, comprising: a processor; and a memory communicatively coupled to the processor, the memory storing instructions which, when executed by a processor, cause the processor to: receive, from a medical imaging device, a pre-treatment vessel roadmap and medical images acquired in response to an endovascular therapy performed on a patient, obtain endovascular treatment parameters, including image-based endovascular treatment parameters based on features of the medical images, wherein the endovascular treatment parameters include a geometry and location of an endovascular therapy device within a vessel of the patient, predict tissue characteristics of a first lesion based on the endovascular treatment parameters, and generate a map of the first lesion based on the pre-treatment vessel roadmap and the predicted tissue characteristics of the first lesion.

2. The endovascular support system of claim 1, wherein the instructions, when executed by the processor, further to cause the processor to provide procedure guidance to a user based on the pre-treatment vessel roadmap and the predicted tissue characteristics of the first lesion.

3. The endovascular support system of claim 1, wherein the instructions, when executed by the processor, further to cause the processor to: predict tissue characteristics of a second lesion based on the endovascular treatment parameters, and generate the map to include both the first lesion and the second lesion.

4. The endovascular support system of claim 3, wherein the instructions, when executed by the processor, further to cause the processor to:predict a first confidence level associated with the first lesion and a second confidence level associated with the second lesion, and present the first confidence level and the second confidence level on the map as a first region associated with the first lesion and a second region associated with the second lesion.

5. The endovascular support system of claim 1, wherein the endovascular therapy device comprises an angioplasty balloon, wherein the endovascular treatment parameters include balloon inflation geometry measurements taken at a plurality of discrete locations along the angioplasty balloon and taken at a plurality of degrees of inflation, and wherein the endovascular treatment parameters include one or more of a balloon type, a balloon size, or inflation pressure.

6. The endovascular support system of claim 1 , wherein the endovascular treatment parameters include a dynamic geometry of an endovascular therapy device within a vessel of the patient, wherein the endovascular therapy device comprises an atherectomy catheter.

7. The endovascular support system of claim 1, further comprising: a display communicatively coupled to the processor, the display configured to present the map to a user.

8. A method for endovascular support, the method comprising: receiving, from a medical imaging device, a pre-treatment vessel roadmap and medical images acquired in response to an endovascular therapy performed on a patient, obtaining endovascular treatment parameters, including image-based endovascular treatment parameters based on features of the medical images, wherein the endovascular treatment parameters include a geometry and location of an endovascular therapy device within a vessel of the patient, predicting tissue characteristics of a first lesion based on the endovascular treatment parameters, and generating a map of the first lesion based on the pre-treatment vessel roadmap and the predicted tissue characteristics of the first lesion.

9. The method of claim 8, further comprising providing procedure guidance to a user based on the pre-treatment vessel roadmap and the predicted tissue characteristics of the first lesion.

10. The method of claim 8, further comprising: predicting tissue characteristics of a second lesion based on the endovascular treatment parameters, and generating the map to include both the first lesion and the second lesion.

11. The method of claim 8, further comprising: predicting a first confidence level associated with the first lesion and a second confidence level associated with the second lesion, and presenting the first confidence level and the second confidence level on the map as a first region associated with the first lesion and a second region associated with the second lesion.

12. The method of claim 8, wherein the endovascular therapy device comprises an angioplasty balloon, wherein the endovascular treatment parameters include balloon inflation geometry measurements taken at a plurality of discrete locations along the angioplasty balloon and taken at a plurality of degrees of inflation, and wherein the endovascular treatment parameters include one or more of a balloon type, a balloon size, or inflation pressure.

13. The method of claim 8, wherein the endovascular treatment parameters include a dynamic geometry of an endovascular therapy device within a vessel of the patient, wherein the endovascular therapy device comprises an atherectomy catheter.

14. The method of claim 8, further comprising presenting the map to a user via a display.

15. A non-transitory machine-readable storage medium storing a computer program comprising instructions which, when executed by a processor, cause the processor to: receive, from a medical imaging device, a pre-treatment vessel roadmap and medical images acquired in response to an endovascular therapy performed on a patient,obtain endovascular treatment parameters, including image-based endovascular treatment parameters based on features of the medical images, wherein the endovascular treatment parameters include a geometry and location of an endovascular therapy device within a vessel of the patient, predict tissue characteristics of a first lesion based on the endovascular treatment parameters, and generate a map of the first lesion based on the pre-treatment vessel roadmap and the predicted tissue characteristics of the first lesion.

Images