Systems and methods for planning coronary interventions

Abstract

A method and system for planning a medical intervention, such as a coronary intervention, is provided. At least one image is retrieved, the image including at least a portion of a coronary artery. Based on the at least one image, the location and composition of plaque in the coronary artery is determined. A mechanical model of the portion of the coronary artery and the plaque within the coronary artery is generated, and multiple potential interventions are simulated in conjunction with the mechanical model. Following such simulation, an intervention for implementation is selected from the multiple potential interventions.

Description

[Technical Field]

[0001] The present disclosure relates generally to systems and methods for planning coronary interventions, such as coronary stenting, to improve the quality, speed, and outcomes of the procedures. In particular, the present disclosure relates to the use of computed tomography (CT) plaque characterization generated by non-invasive coronary CT angiography in procedural simulations to plan the intervention. [Background technology]

[0002] Coronary interventions, such as stent placement, are often guided by intraprocedural imaging. Such imaging may be via invasive coronary angiography using x-rays and contrast, or for more complex procedures, such angiography may be supplemented by intravascular measurements, such as pressure or flow measurements, or intravascular imaging, such as intravascular ultrasound (IVUS) or optical coherence tomography (OCT).

[0003] Plaque location and composition can be utilized to estimate the risk associated with a particular intervention and to plan the intervention with regard to the appropriate device type, size, or sequence. Similarly, information about coronary plaque can be used to determine whether a particular coronary procedure is likely to be successful or whether a different treatment strategy, such as coronary artery bypass grafting (CABG), should be selected. However, only limited information about plaque location or composition is available during the intervention. Therefore, plaque location and composition are typically not used today to plan or guide such interventions. Summary of the Invention [Problem to be solved by the invention]

[0004] Therefore, there is a need for systems and pre-operative methods for utilizing plaque analysis to determine appropriate coronary interventions, as well as for planning specific interventions. There is a further need for such systems and methods that can continue to guide the intervention once selected. [Means for solving the problem]

[0005] To improve the quality, speed, and outcomes of such procedures, methods and systems for planning coronary interventions, such as coronary stent placement, are provided. The embodiments described herein use CT plaque characterization generated by imaging, such as noninvasive coronary CT angiography, in procedural simulations to plan the intervention.

[0006] Noninvasive imaging using computed tomography can be used to derive information about the location, type, and composition of plaque, as well as physical parameters for a coronary artery segment or tree. This information is then utilized for treatment guidance and device selection. In many embodiments, such device selection relates specifically to stenting coronary arteries. The systems and methods described herein can help determine the length, diameter, placement, and type of stent when treating a vessel segment.

[0007] Thus, the systems and methods described herein use imaging-based plaque analysis prior to performing an intervention to determine the amount, type, shape, and / or location of plaque within a segment. Such imaging may be CT-based, and the analysis may include Hounsfield Unit (HU) imaging, mono-energy imaging (MonoE), Zeff imaging, calcium imaging, and iodine content imaging.

[0008] Such imaging can then be utilized for plaque analysis, which can be used to optimize device selection and treatment parameters for the intervention.

[0009] In some embodiments, a method for planning a medical intervention, such as a coronary intervention, is provided, the method including initially retrieving at least one image, the image including at least a portion of a coronary artery, and proceeding to determine a location and composition of plaque within the coronary artery based on the at least one image.

[0010] The method then proceeds to generate a mechanical model of a portion of the coronary artery and plaque within the coronary artery, and simulate a plurality of potential interventions in conjunction with the mechanical model. Following such simulation, the method selects an intervention for implementation from the plurality of potential interventions.

[0011] In some embodiments, the plurality of simulated potential interventions includes a first intervention in which a first device is implanted and a second intervention in which a second device different from the first device is implanted. For example, the first device and the second device may each be a stent selected from the group consisting of a bare metal stent, a drug-eluting stent, a polymeric stent, and a bioabsorbable stent. The first device and the second device may each have a different length or a different diameter.

[0012] In some embodiments, the multiple simulated potential interventions may include a first intervention in which a stent is implanted and a second intervention that includes a coronary artery bypass graft (CABG) procedure.

[0013] In some embodiments, the method includes identifying, in the image, a plurality of lesions to be addressed in a coronary intervention, and the plurality of simulated potential interventions then includes a first intervention including a first sequence of lesion treatments and a second intervention including a second sequence of lesion treatments different from the first.

[0014] In some embodiments, the plurality of simulated potential interventions includes a first intervention in which a first balloon pressure is used to place a stent and a second intervention in which a second balloon pressure different from the first balloon pressure is used to place the stent.

[0015] In some embodiments, the method further includes performing the selected intervention. During such performance, the method may further include displaying the mechanical model on a display during performance of the selected intervention.

[0016] In some such embodiments, the method may further include determining that the location or composition of the plaque has changed during performance of the selected intervention. The method may then perform a real-time assessment of potential complications based on the updated location or composition of the plaque.

[0017] In some embodiments in which the model is displayed during the performance of an intervention, the intervention utilizes a device, and the method includes monitoring the position of the device and displaying a risk metric on the display during the performance of the selected intervention. The risk metric may be based at least in part on the precise position of the device during the selected intervention.

[0018] In some embodiments, the determination of plaque location and composition is based on an artificial intelligence (AI) model. In some such embodiments, the composition of the plaque defines the stiffness or deformability of the plaque. A mechanical model of a coronary artery segment and plaque within the coronary artery simulates the deformation of the coronary artery segment in response to mechanical forces based on the composition of the plaque.

[0019] In some such embodiments, the mechanical model may further simulate stresses and strains as isometric forces resulting from the use of a stent or balloon.

[0020] In some embodiments, the simulation of multiple potential interventions is based on an AI model that is trained based on the results of previous interventions.

[0021] In some embodiments, determining the composition of plaque in the coronary artery comprises quantifying calcium content, density, or fibrous component, hi some such embodiments, the method further comprises classifying the plaque as positive modeling plaque, lumen-narrowing plaque, or thrombus.

[0022] A system for performing a coronary intervention is also provided, including a plurality of potential devices available for implantation, a memory for storing a plurality of instructions, and a processor circuit coupled to the memory.

[0023] The processor circuit is configured to initially execute instructions to retrieve at least one image, the image including at least a portion of a coronary artery, and instructions to determine, based on the image, the location and composition of plaque within the coronary artery.

[0024] The processor then generates a mechanical model of the portion of the coronary artery and plaque within the coronary artery, and simulates a plurality of potential interventions in conjunction with the mechanical model, each of the potential interventions utilizing at least one of the plurality of potential devices.

[0025] The processor then proceeds to select an intervention for implementation from the plurality of potential interventions.

[0026] In some embodiments, each of the plurality of potential devices is a stent selected from the group consisting of a bare metal stent, a drug-eluting stent, a polymeric stent, and a bioabsorbable stent, and at least two of the plurality of potential devices have different lengths or diameters from each other.

[0027] In some embodiments, the plurality of simulated potential interventions includes a first intervention in which a first balloon pressure is used to place a stent and a second intervention in which a second balloon pressure different from the first balloon pressure is used to place the stent.

[0028] In some embodiments, the determination of plaque location and composition is based on an AI model. In some such embodiments, the composition of the plaque defines the stiffness or deformability of the plaque. A mechanical model of the coronary artery segment and the plaque within the coronary artery then simulates the deformation of the coronary artery segment in response to mechanical forces based on the plaque composition. [Brief explanation of the drawings]

[0029] [Figure 1] FIG. 1 is a schematic diagram of a system according to one embodiment of the present disclosure. [Figure 2] 1 illustrates a method for processing an image according to the present disclosure. [Figure 3] 3 illustrates the use of a plaque model during intervention according to the method of FIG. 2. DETAILED DESCRIPTION OF THE INVENTION

[0030] The description of illustrative embodiments according to the principles of the present invention is intended to be read in connection with the accompanying drawings, which are to be considered part of the entire written description. In describing the embodiments of the present invention disclosed herein, any reference to direction or orientation is intended for convenience of description only and is in no way intended to limit the scope of the invention. Relative terms such as "lower," "upper," "horizontal," "vertical," "above," "lower," "up," "below," "top," and "bottom," and their derivatives (e.g., "horizontally," "below," "upward," etc.), should be interpreted as referring to the orientation described in the description or shown in the drawings. These relative terms are for convenience of description only and do not require that a device be constructed or operated in a particular orientation, unless expressly so indicated. Terms such as "mounted," "fixed," "connected," "coupled," "interconnected," and similar terms refer to a relationship in which structures are fixed or attached to one another, both directly or indirectly through intervening structures, and through movable or rigid attachments or relationships, unless expressly stated otherwise. Furthermore, the features and advantages of the present invention are illustrated by reference to the illustrated embodiments. Accordingly, the present invention should not be expressly limited to such exemplary embodiments, which illustrate some possible non-limiting combinations of features that may exist alone or in other combinations of features, and the scope of the present invention is defined by the claims appended hereto.

[0031] This disclosure describes the best mode or modes of carrying out the present invention presently contemplated. This description is not intended to be understood in a limiting sense, but provides examples of the invention presented solely for illustrative purposes, with reference to the accompanying drawings, to advise those skilled in the art of the advantages and configuration of the present invention. In the various views of the drawings, like reference numerals indicate like or similar parts.

[0032] It is important to note that the disclosed embodiments are merely examples of the many advantageous uses of the innovative teachings herein. In general, statements made in the specification of this application do not necessarily limit any of the various claimed disclosures. Moreover, some statements may apply to some inventive features but not to others. In general, unless otherwise specified, singular elements may be in the plural and vice versa without loss of generality.

[0033] A pre-operative method is described for improving the quality, speed, and outcome of coronary interventions based on plaque characterization prior to such intervention, where the plaque characterization is based on imaging, such as a coronary CT angiogram, performed prior to the procedure.

[0034] Typically, when a patient is scheduled for a coronary intervention, the methods described herein can be applied to identify the ideal form of the intervention. This may include selecting the type of intervention from several available types, and may also include selecting a particular implantable device for use in the intervention. For example, the methods described herein can be used to determine which of several available stents should be implanted, as well as the ideal balloon pressure to be used during implantation.

[0035] Thus, once the category of intervention is determined to be appropriate, the method includes selecting an appropriate implant and, in some embodiments, guiding the actual implantation of the appropriate device during the intervention.

[0036] The method includes generating a mechanical model of a portion of a coronary artery requiring intervention and the plaque present therein, and then simulating potential interventions in relation to the mechanical model to assess potential risks associated with multiple potential interventions.

[0037] Thus, the method typically includes capturing an image of at least a portion of a coronary artery requiring intervention. Such an image is typically a non-invasive coronary CT angiogram and may include spectral CT imaging. In many embodiments, such a CT angiogram is performed prior to the intervention and is therefore captured by a processor implementing the method, while in other embodiments, the system implementing the method includes an imaging system for generating the image. The CT angiogram is then analyzed to assess plaque content; such analysis may include Hounsfield Unit (HU) imaging, mono-energy imaging (MonoE), Zeff imaging, calcium imaging, and iodine content imaging.

[0038] Plaque content can be used to generate mechanical models used to plan and evaluate interventions. Thus, coronary artery properties, such as stiffness, can be informed by plaque location and composition.

[0039] Additionally, in some embodiments, pre-interventional imaging may take forms other than CT. In medical imaging other than CT, such as magnetic resonance imaging (MRI) or positron emission tomography (PET), different methods may be used to process images, and the resulting images may take different forms. In this disclosure, embodiments are described with respect to CT imaging. However, it will be understood that the methods and systems described herein may also be used in connection with other imaging modalities.

[0040] 1 is a schematic diagram of a system 100 according to one embodiment of the present disclosure. As shown, the system 100 typically includes a processing unit 110 and an imaging unit 120.

[0041] Processing device 110 may apply processing routines to image or measurement data, such as projection data received from imaging device 120. Processing device 110 may include memory 113 and processor circuitry 111. Memory 113 may store a plurality of instructions. Processor circuitry 111 may be coupled to memory 113 and configured to execute the instructions. The instructions stored in memory 113 may include processing routines, as well as data associated with the processing routines, such as machine learning algorithms, and various filters for processing images. While all data is described as being stored in memory 113, it will be understood that in some embodiments, some data may be stored in a database that may itself be stored in memory or that may be stored remotely (e.g., cloud-based) in a separate system.

[0042] Processing device 110 may further include input 115 and output 117. Input 115 may receive information, such as images or measurement data, from imaging device 120. Output 117 may output information, such as processed images, to a user or a user interface device. Output 117 may also output decisions generated by the methods described below, such as recommendations and risk decisions. The output may include a monitor or display that may display updated models or additional information in real time.

[0043] In some embodiments, processing device 110 may be directly associated with image capture device 120. In alternative embodiments, processing device 110 may be separate from image capture device 120, receiving image or measurement data for processing at input 115 via a network or other interface.

[0044] In some embodiments, the imaging device 120 may include an image data processor and a spectral, photon counting, or conventional CT scanning unit for generating CT projection data when scanning a subject (e.g., a patient). Additionally, the imaging device 120 may be set up for either invasive or non-invasive coronary CT angiography. Thus, imaging may be performed using a contrast agent, and image timing may be set to track fluid flow within the blood vessels.

[0045] In addition to conventional and spectral CT images, the method may rely on multiple spectral imaging results, photon counting CT images, or dark-field CT images.

[0046] While a system including imaging device 120 and processing device 110 is shown, it will be understood that the method may be performed directly on the processing device, such as in the context of images received over a network at input 115. The methods described herein involve processing images as a component of evaluating and planning a potential intervention, generally in the context of a procedure such as stent placement. As noted above, imaging is typically performed prior to such a procedure. Thus, previously generated imaging may be retrieved by input 115 and evaluated before or instead of acquiring new images.

[0047] 2 illustrates a method for processing an image according to the present disclosure. As shown, in performing the method, the system 100 may first retrieve (200) at least one image at the input 115. The image includes at least a portion of a patient's coronary artery. The retrieved image is typically a CT scan, such as a non-invasive coronary CT angiogram, that may be used to assess plaque within the coronary artery.

[0048] In some embodiments, before assessing the plaque content of the coronary artery, the method may identify (205) one or more lesions in the image, the identified lesions requiring potential intervention.

[0049] The method may then determine (210) the location and composition of plaque within the coronary artery based on at least one image. Plaque may be defined in terms of location, distribution, and composition. Such determination may be by manual or automatic detection, classification, and segmentation. Thus, the determination of plaque location and composition may be based on an AI model, such as a convolutional neural network (CNN).

[0050] In embodiments where multiple lesions have been identified (at 205), plaque may then be located for each lesion or for the coronary artery imaged as a whole as part of the determination (210).

[0051] Thus, the determination (at 210) may include defining the location 220 of the plaque along the coronary artery, the shape and volume 230 of the plaque at that location, and the composition 240 of the plaque. The composition may be determined based on various strategies, such as Hounsfield Unit (HU) imaging, mono-energy imaging (MonoE), Zeff imaging, calcium imaging, or iodine content imaging. This may be used to classify the composition of the plaque using averages, histograms, or by dividing the plaque into sub-volumes of different plaque components.

[0052] The determination may also classify the type of plaque, for example, by identifying positive remodeling plaque, lumen narrowing plaque, or thrombus.

[0053] To aid in such determination, different types of plaque may be evaluated based on images, and determining the composition of the plaque (at 210) may include quantifying calcium content 212, density 214, or fiber component 216.

[0054] In some embodiments, quantification of calcium content may be in terms of CAN=non-calcified / CAL=low or mixed level calcification / CAH=high calcification.

[0055] High-risk plaque (HRP) features can also be identified. These high-risk features are: LA = low-attenuation plaque <30 HU / NR = napkin ring sign (i.e., a low-attenuation core surrounded by a rim-like high-attenuation and <130 HU) / PR = positive remodeling (remodeling idx >1.1 = 10% increase in CSA) / SC = spot calcification (calcified lesions between 1-3 mm and >130 HU).

[0056] Other patterns may be distinguishable as well. For example, plaques may be classified as DC = dense calcification (massive calcification, negligible other plaque components) / FM = mixed fibrocalcification (mixture of calcified lesions and fibrous components) / FP = fibrous plaque (exclusively fibrous, negligible other plaque components).

[0057] Other features of interest may include thrombus / pericoronary inflammation (eg, due to pFAI).

[0058] Additional parameters such as the distribution of plaque along the vessel centerline, the angular coverage of plaque in a cross-section of the coronary artery, the portion of the lumen occupied by plaque as seen in the cross-section, the distance to the next branch of the vessel, and the stiffness or deformability of the plaque can then be derived (250) from the plaque data.

[0059] The method then proceeds to generate (260) a mechanical model of the coronary artery segments and plaque within the coronary arteries. Such a mechanical model may then rely on the plaque characteristics or classification generated as part of the determination (at 210) as well as additional parameters (derived at 250) to accurately model response to various interventions. The mechanical model may be a numerical model (lumped element or finite element model or finite volume or finite difference or meshfree numerical method or coupled computational fluid dynamics model) of the plaque and vessel segments.

[0060] For example, additional parameters (at 250) may rely on the composition of the plaque to define its stiffness or deformability. The plaque data may then be combined with anatomical parameters describing the vessel segment. For example, an AHA model of the vessel may be relied upon, if available. The mechanical model may then consider the location of the plaque along the centerline, the distance to the coronary ostium and / or the next bifurcation, the local curvature at the plaque location, and the curvature in the vicinity (i.e., tortuosity).

[0061] The mechanical model can then simulate the deformation of the coronary artery in response to mechanical forces based on the composition of the plaque. For example, the mechanical model may be used to simulate the mechanical forces resulting from the use of a stent or a balloon. The balloon may then be simulated at different pressure levels.

[0062] Once the mechanical model is generated (at 260), the method proceeds to simulating (at 270a, 270b, 270c) multiple potential interventions in conjunction with the mechanical model. Such simulations may be performed based on an AI model, such as a CNN. Such an AI model may be trained on training data, which may include the results of previous interventions in conjunction with similar plaque determinations.

[0063] Once such interventions are simulated, the method selects one such intervention for implementation (280).

[0064] The simulated potential interventions may include multiple different types of interventions or interventions that involve separate devices for implantation. Alternatively, the potential interventions may be similar procedures that utilize different devices. For example, the potential interventions may include the implantation of different sizes or types of stents, or implantations that utilize different balloon pressures.

[0065] For example, in some embodiments, a first intervention 270a of the plurality of potential interventions may be the implantation of a first device, and a second intervention 270b may be the implantation of a second device that is different from the first device.

[0066] The first and second devices may each be a stent selected from the group consisting of a bare metal stent, a drug-eluting stent, a polymeric stent, and a bioabsorbable stent. Furthermore, the first and second devices may have different lengths or diameters. For example, the lengths may be selected to remove the stenosis and cover the adjacent plaque.

[0067] Similarly, the first intervention 270a may be the implantation of a stent in which a first balloon pressure is used to deploy the stent, and the second intervention 270b relies on a second balloon pressure different from the first balloon pressure to deploy the stent.

[0068] Alternatively, in some embodiments, the first intervention 270a may be the implantation of a stent and the second intervention 270b may be a coronary artery bypass graft procedure.

[0069] In embodiments where multiple lesions have been identified (at 205) and multiple lesions will be addressed in the coronary intervention being evaluated, the multiple potential interventions 270a, 270b, 270c being simulated may include a first intervention 270a having a first lesion treatment sequence and a second intervention 270b having a second lesion treatment sequence that is different from the first lesion treatment sequence.

[0070] Figure 3 illustrates the use of a plaque model 400 during an intervention according to the method of Figure 2. In some embodiments, following the selection of an intervention for performance (at 280), the method proceeds with the actual performance (290) of the selected intervention 270a. In such embodiments, the method may then display (300) the mechanical model on a display during the performance of the intervention 270a. Thus, as shown in Figure 3, the mechanical model 400 may be shown in the context of a coronary artery 410 during the intervention.

[0071] For example, the mechanical model 400 may be an overlay on a coronary angiography image. The display may include different levels of information, such as image-derived parameters of the plaque or the results of a model generated based on the image-derived plaque parameters. The information may also be displayed on top of the intravascular image. Additionally, the derived information may be presented to the user as a type of task list.

[0072] The method may continue to monitor (310) the risks associated with the intervention during its performance and may alert the user to any changes in the risk model. For example, in some embodiments, the method may monitor ongoing imaging or otherwise track plaque within the coronary arteries. The method may then determine (320) that the location or composition of the plaque has changed during the performance of the selected intervention. The method may then proceed to perform a real-time assessment (330) of potential complications based on the updated location or composition of the plaque. In such embodiments, the method may continue to guide the intervention based on the mechanical model updated to reflect any changes and alert the user to any new or revised risks associated with the intervention.

[0073] In some embodiments, plaque information or plaque biomarkers derived from the plaque data can be used to predict the risk of a particular intervention. Thus, the method may make recommendations regarding the steps involved in a particular intervention, or the user may provide an indication as to which steps should be performed for a particular patient. The method may then provide an indication of the risk level.

[0074] In some embodiments where the selected intervention utilizes a device, the precise location of the device during the intervention may affect risk. Thus, the monitoring performed by the method (at 310) may be paired with monitoring (340) the location of the device during the intervention. The method may then track and / or display (350) a risk metric, the risk metric based at least in part on the precise location of the device during the selected intervention 270a.

[0075] Similarly, a risk level may be displayed when a device, guidewire, or similar placement element reaches a particular location within the vascular tree, from which it can be concluded where the next step of the procedure will be performed. The method may then update the risk model accordingly.

[0076] In this context, risk prediction may include the risk of procedural complications or the risk of cardiac events (i.e., MACE) in the next 1, 2, or 5 years after the intervention. Risk prediction may further take into account quality of life after the intervention.

[0077] The joint system described herein, i.e., CT imaging combined with plaque characterization and interventional procedures, can be used to train an AI-based system that predicts treatment steps and parameters for treating a particular lesion in future implementations. After plaque characterization, the device or other parameters, such as balloon pressure, used to treat the plaque-bearing lesion can then be stored in a database. Based on such data, the system can then be trained to predict which device should be used and what risks are associated with the procedure, given a particular plaque configuration in a vessel.

[0078] Systems are also provided for implementing the described methods. Such systems may include multiple potential devices made available for implementation depending on the intervention to be utilized. Further, as shown in FIG. 1, such systems may include a memory 113 for storing multiple instructions and a processor circuit 111 coupled to the memory and configured to execute instructions for implementing the methods described above with respect to FIG.

[0079] It should be noted that when the method is performed, multiple potential interventions 270a, 270b, 270c are simulated in conjunction with the mechanical model (generated in 260), each of the potential interventions utilizing at least one of multiple potential devices made available for implementation.

[0080] Although the systems and methods are described herein with respect to coronary arteries, similar methods may be applied to other vascular structures, such as peripheral or pulmonary vessels, carotid arteries, etc. Similarly, as noted above, systems implementing the methods may include various forms of computing. Thus, processing may be performed on-premise or on a cloud computing platform. Predictions and intervention plans may be displayed on a CT console in an advanced workstation or in an interventional lab before or during a selected procedure.

[0081] The method can be used in CT systems and imaging workstations and PACS viewers dedicated to coronary artery analysis using CCTA scans, as well as interventional C-arm systems where the information is used during the procedure.

[0082] In some cases, the imaging obtained (at 200) may be insufficient for the type of planning required. For example, the noninvasive imaging obtained may be low-resolution or otherwise suboptimal, but may be sufficient to provide an indication that plaque-related risk is present. The method may then use additional intravascular imaging to guide treatment prior to treatment.

[0083] Similarly, if additional information is available, such information may be incorporated into the various analyses discussed herein. For example, if noninvasive imaging of more than one cardiac phase is available, thereby providing temporal information, such information regarding plaque dynamics may be included in the analysis.

[0084] The method according to the present disclosure may be implemented on a computer as a computer-implemented method, or on dedicated hardware, or a combination of both. Executable code of the method according to the present disclosure may be stored in a computer program product. Examples of computer program products include memory devices, optical storage devices, integrated circuits, servers, online software, etc. Preferably, the computer program product may include non-transitory program code stored on a computer-readable medium for performing the method according to the present disclosure when the program product is run on a computer. In one embodiment, the computer program may include computer program code configured to perform all steps of the method according to the present disclosure when the computer program is run on a computer. The computer program may be embodied on a computer-readable medium.

[0085] While the present disclosure has been described at some length and with some detail with respect to certain illustrated embodiments, it is not intended to be limited to any such details or embodiments or to any particular embodiment, but should be construed with reference to the appended claims so as to provide the broadest possible interpretation of such claims in view of the prior art, and therefore so as to effectively encompass the intended scope of the present disclosure.

[0086] All examples and conditional language recited herein are intended for educational purposes to aid the reader in understanding the principles of the present disclosure and concepts contributed by the inventors to further the art, and should not be construed as being limited to such specifically recited examples and conditions. Moreover, all statements herein reciting principles, aspects, and embodiments of the present specification, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Additionally, such equivalents are intended to include both currently known equivalents and future-developed equivalents, i.e., any elements developed that perform the same function, regardless of structure.

Claims

1. 1. A method for planning a coronary intervention, comprising: retrieving at least one image including at least a portion of a coronary artery; determining a location and composition of plaque in the coronary artery based on the at least one image; generating a mechanical model of the portion of the coronary artery and the plaque within the coronary artery; simulating a plurality of potential interventions in conjunction with the mechanical model; selecting an intervention for implementation from the plurality of potential interventions; A method having the following.

2. 10. The method of claim 1, wherein the plurality of simulated potential interventions includes a first intervention in which a first device is implanted and a second intervention in which a second device different from the first device is implanted.

3. 3. The method of claim 2, wherein the first device and the second device are each a stent selected from the group consisting of a bare metal stent, a drug-eluting stent, a polymeric stent, and a bioabsorbable stent, and the first device and the second device each have a different length or diameter.

4. 10. The method of claim 1, wherein the plurality of simulated potential interventions includes a first intervention in which a stent is implanted and a second intervention having a coronary artery bypass graft surgery (CABG).

5. 10. The method of claim 1, further comprising identifying, in the image, a plurality of lesions to be addressed in the coronary intervention, wherein the plurality of simulated potential interventions includes a first intervention having a first lesion treatment sequence and a second intervention having a second lesion treatment sequence different from the first sequence.

6. 2. The method of claim 1, wherein the plurality of simulated potential interventions includes a first intervention in which a first balloon pressure is used to deploy a stent and a second intervention in which a second balloon pressure different from the first balloon pressure is used to deploy a stent.

7. The method of claim 1 , further comprising the steps of: performing the selected intervention; and displaying the mechanical model on a display during the performance of the selected intervention.

8. 8. The method of claim 7, further comprising determining whether the location or composition of the plaque has changed during performance of the selected intervention, and performing a real-time assessment of potential complications based on the updated location or composition of the plaque.

9. 8. The method of claim 7, wherein the selected intervention utilizes a device, the method further comprising the steps of monitoring a position of the device and displaying a risk metric on the display during performance of the selected intervention, the risk metric being based at least in part on the precise position of the device during the selected intervention.

10. The method of claim 1 , wherein the determination of the location and composition of the plaque is based on an AI model.

11. 11. The method of claim 10, wherein the composition of the plaque defines a stiffness or deformability of the plaque, and the mechanical model of the portion of the coronary artery and the plaque within the coronary artery simulates deformation of the portion of the coronary artery in response to a mechanical force based on the composition of the plaque.

12. The method of claim 11 , wherein the mechanical model simulates stresses and strains as isometric forces resulting from the use of a stent or balloon.

13. 10. The method of claim 1, wherein the simulation of the plurality of potential interventions is based on an AI model trained based on the results of previous interventional performances.

14. The method of claim 1 , wherein determining the composition of plaque in the coronary artery comprises quantifying calcium content, density, or fibrous component.

15. 15. The method of claim 14, further comprising classifying the plaque as a positive remodeling plaque, a lumen narrowing plaque, or a thrombus.

16. 1. A system for performing a coronary intervention, comprising: Several potential devices available for implantation; a memory for storing a plurality of instructions; a processor circuit coupled to the memory, the processor circuit comprising: instructions to retrieve at least one image including at least a portion of a coronary artery; instructions for determining a location and composition of plaque in the coronary artery based on the at least one image; instructions for generating a mechanical model of the portion of the coronary artery and the plaque within the coronary artery; instructions for simulating a plurality of potential interventions in conjunction with the mechanical model, each of the potential interventions utilizing at least one of the plurality of potential devices; and instructions for selecting an intervention for implementation from said plurality of potential interventions; a processor circuit configured to execute A system having:

17. 17. The system of claim 16, wherein the plurality of potential devices are each a stent selected from the group consisting of a bare metal stent, a drug-eluting stent, a polymeric stent, and a bioabsorbable stent, and at least two of the plurality of potential devices have different lengths or diameters from one another.

18. 17. The system of claim 16, wherein the plurality of simulated potential interventions includes a first intervention in which a first balloon pressure is used to deploy a stent and a second intervention in which a second balloon pressure different from the first balloon pressure is used to deploy a stent.

19. 17. The system of claim 16, wherein the determination of the location and composition of the plaque is based on an AI model.

20. 20. The system of claim 19, wherein the composition of the plaque defines a stiffness or deformability of the plaque, and the mechanical model of the portion of the coronary artery and the plaque within the coronary artery simulates deformation of the portion of the coronary artery in response to a mechanical force based on the composition of the plaque.

Images