Systems and methods for planning coronary intervention

Abstract

A method and system for planning a medical intervention, such as a coronary artery intervention, is provided. At least one image is retrieved, where the image comprises at least a portion of a coronary artery. A position and composition of plaques in the coronary artery is determined based on the at least one image. A mechanical model of the portion of the coronary artery and the plaque in the coronary artery is generated, and a plurality of potential interventions are simulated in the context of the mechanical model. After such simulation, an intervention is selected from the plurality of potential interventions for implementation.

Description

Technical Field

[0001] The present disclosure generally relates to systems and methods for planning coronary interventions (e.g., coronary stent implantation) to improve the quality, speed, and outcomes of such procedures. In particular, the present disclosure relates to planning interventions using CT plaque characterizations generated by noninvasive coronary computed tomography (CT) angiography in surgical simulations. Background Art

[0002] Coronary interventions (e.g., stent implantation) are often guided by intraoperative imaging. This imaging can be performed via invasive coronary angiography using X-rays and contrast agents, or in the case of more complex procedures, this angiography can be supplemented by intravascular measurements (e.g., pressure or flow measurements, or intravascular imaging, such as intravascular ultrasound (IVUS) or optical coherence tomography (OCT)).

[0003] The location and composition of plaque can be used to estimate the risk associated with a particular intervention and to plan the intervention according to the appropriate device type, size, or sequence. Similarly, information about coronary plaque can be used to decide whether a particular coronary surgery is likely to be successful, or whether a different treatment strategy (e.g., coronary artery bypass graft surgery (CABG)) should be selected. However, during an intervention, the information available about the location or composition of the plaque is limited. Therefore, the location and composition of the plaque is not commonly used today to plan or guide such interventions.

[0004] As such, there is a need for a system and pre-procedural method for utilizing plaque analysis to determine appropriate coronary intervention and for planning a specific intervention. There is also a need for such a system and method that, once selected, can continue to guide an intervention. Summary of the invention

[0005] Methods and systems for planning coronary interventions (e.g., coronary stent implantation) are provided to improve the quality, speed, and outcomes of such procedures. Embodiments described herein use CT plaque representations generated by imaging (e.g., noninvasive coronary CT angiography) in surgical simulations to plan interventions.

[0006] Non-invasive imaging using computed tomography can be used to derive information about the location, type, and composition of plaque, and to derive physical parameters of a coronary segment or coronary tree. This information is then used for treatment guidance and device selection. In many embodiments, such device selection specifically relates to coronary stents. The systems and methods described herein can help decide on the length, diameter, positioning, and type of stent when treating a vessel segment.

[0007] Therefore, the systems and methods described herein use imaging-based plaque analysis prior to performing an intervention to determine the number, type, shape, and / or location of plaque in the segment. Such imaging can be CT-based, and the analysis can include Hounsfield Unit (HU) imaging, monoenergetic imaging (MonoE), Zeff imaging, calcium imaging, and iodine content imaging.

[0008] This imaging can then be used for plaque analysis, and this analysis 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 comprising first retrieving at least one image, wherein the image comprises at least a portion of a coronary artery. The method proceeds to determine the location and composition of plaque in the coronary artery based on the at least one image.

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

[0011] In some embodiments, the plurality of potential interventions simulated include: 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 can each be selected from the group consisting of a bare metal stent, a drug eluting stent, a polymer stent, and a bioresorbable stent. The first device and the second device can each have a different length or a different diameter.

[0012] In some embodiments, the plurality of potential interventions simulated may include a first intervention that implants a stent, and a second intervention that includes a coronary artery bypass graft surgery (CABG).

[0013] In some embodiments, the method includes identifying in the image a plurality of lesions to be addressed in the coronary intervention.Then, the simulated plurality of potential interventions 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 sequence.

[0014] In some embodiments, the plurality of potential interventions simulated include a first intervention in which a stent is placed using a first balloon pressure, and a second intervention in which the stent is placed using a second balloon pressure different from the first balloon pressure.

[0015] In some embodiments, the method further comprises implementing the selected intervention. During such implementation, the method may further comprise displaying the mechanical model on a display during implementation of the selected intervention.

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

[0017] In some embodiments where the model is displayed during performance of the intervention, the selected intervention utilizes a device, and the method includes monitoring the position of the device during performance of the selected intervention and displaying a risk metric on the display. 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 the location and the composition of the plaque 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. The mechanical model of the portion of the coronary artery and the plaque in 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.

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

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

[0021] In some embodiments, the determination of the composition of the plaque in the coronary artery comprises quantifying calcium content, density, or fibrous composition. In some such embodiments, the method further comprises classifying the plaque as a positively remodeling plaque, a luminal stenotic plaque, or a thrombus.

[0022] A system for performing a coronary intervention is also provided. The system includes a plurality of potential devices that can be implanted, a memory for storing a plurality of instructions, and a processor circuit coupled to the memory.

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

[0024] The processor then proceeds to generate a mechanical model of the portion of the coronary artery and the plaque in the coronary artery and simulate a plurality of potential interventions in the context of the mechanical model. Each of the potential interventions utilizes at least one potential device from the plurality of potential devices.

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

[0026] In some embodiments, the plurality of potential devices are stents, each of the plurality of potential devices being selected from the group consisting of a bare metal stent, a drug eluting stent, a polymer stent, and a bioresorbable stent. At least two of the plurality of potential devices have different lengths or diameters from each other.

[0027] In some embodiments, the plurality of potential interventions simulated include a first intervention in which a stent is placed using a first balloon pressure, and a second intervention in which the stent is placed using a second balloon pressure different from the first balloon pressure.

[0028] In some embodiments, the determination of the location and the composition of the plaque is based on an AI model. In some such embodiments, the composition of the plaque defines the hardness or deformability of the plaque. The mechanical model of the portion of the coronary artery and the plaque in 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 is a schematic diagram of a system according to one embodiment of the present disclosure.

[0030] Figure 2 A method for processing an image according to the present disclosure is illustrated.

[0031] Figure 3 The diagram shows Figure 2 Methods for the use of plaque models during intervention. DETAILED DESCRIPTION

[0032] The description of the illustrative embodiments according to the principles of the present invention is intended to be read in conjunction with the accompanying drawings, which are considered part of the entire written description. In the description of the embodiments of the invention disclosed herein, any reference to direction or orientation is only for the convenience of description and is not intended to limit the scope of the invention in any way. Relevant terms such as "below", "above", "horizontally", "vertically", "above", "below", "above", "below", "top" and "bottom" and their derivatives (e.g., "horizontally", "downward", "upward", etc.) should be interpreted as referring to the orientation described later or shown in the drawings discussed. These relevant terms are only for the convenience of description, and unless explicitly stated, it is not required to construct or operate the device in a specific orientation. Terms such as "attach", "attach", "connect", "couple", "interconnect" refer to the relationship in which structures are fastened or attached to each other directly or indirectly through intermediate structures and the attachment or relationship of removable or rigid, unless otherwise explicitly described. In addition, the features and benefits of the present invention are explained by reference to exemplary embodiments. Therefore, the present invention should not be explicitly limited to exemplary embodiments that illustrate certain possible non-limiting feature combinations, which may exist alone or in the form of other feature combinations; the scope of the present invention is defined by the appended claims.

[0033] The present disclosure describes the best mode of practicing the present invention currently contemplated. The description is not intended to be understood in a limiting sense, but rather provides examples of the present invention, which are presented for illustrative purposes only by reference to the accompanying drawings to inform those of ordinary skill in the art of the advantages and configurations of the present invention. In the various views of the accompanying drawings, the same reference numerals indicate the same or similar parts.

[0034] Importantly, it should be noted that the disclosed embodiments are merely examples of many advantageous uses of the innovative teachings herein. In general, the statements in the specification of the present application do not necessarily limit any of the various claimed disclosures. In addition, some statements may apply to some inventive features, but not to other features. In general, unless otherwise stated, without loss of generality, singular elements may be plural, and vice versa.

[0035] A pre-operative method for enhancing / improving the quality, speed and outcome of coronary interventions based on plaque characterization prior to coronary interventions is described. The plaque characterization is based on images performed prior to the procedure (eg, coronary CT angiography).

[0036] Typically, if a patient is scheduled for a coronary intervention, the methods described herein may be applied to identify the ideal form of the intervention. This may include selecting an intervention type from among several available types, and may include selecting a specific implantable device to be used in the intervention. For example, the methods described herein may be used to determine which of several available stents should be implanted, and to determine the ideal balloon pressure to be used during the implantation process.

[0037] Thus, once it is determined that a type of intervention is appropriate, the method involves selecting an appropriate implant and, in some embodiments, directing the actual implantation of the appropriate device during the intervention.

[0038] The method involves generating a mechanical model of a portion of a coronary artery requiring intervention and the plaque residing therein. The method then involves simulating potential interventions in the context of the mechanical model in order to evaluate potential risks associated with a plurality of potential interventions.

[0039] As such, the method typically involves retrieving an image of at least a portion of the coronary artery requiring intervention. Such an image is typically non-invasive coronary CT angiography, and may include spectral CT imaging. In many embodiments, such CT angiography is performed prior to intervention and is therefore retrieved 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 angiography is then analyzed to assess plaque content, and such analysis may include Hounsfield Unit (HU) imaging, mono-energy imaging (MonoE), Zeff imaging, calcium imaging, and iodine content imaging.

[0040] Plaque content can be used to generate mechanistic models for planning and evaluating interventions. As such, the properties of the coronary arteries (eg, stiffness) can be informed by the location and composition of the plaque.

[0041] In addition, in some embodiments, pre-intervention imaging can take forms other than CT. In medical imaging other than CT (e.g., magnetic resonance imaging (MRI) or positron emission tomography (PET)), different methods can be used to process images, and the resulting images can take different forms. In the present disclosure, embodiments are discussed based on CT imaging. However, it should be understood that the methods and systems described herein can also be used in the context of other imaging modalities.

[0042] Figure 1 1 is a schematic diagram of a system 100 according to an embodiment of the present disclosure. As shown in the figure, the system 100 generally includes a processing device 110 and an imaging device 120.

[0043] The processing device 110 may apply a processing routine to images or measurement data (e.g., projection data) received from the imaging device 120. The processing device 110 may include a memory 113 and a processor circuit 111. The memory 113 may store a plurality of instructions. The processor circuit 111 may be coupled to the memory 113 and may be configured to run the instructions. The instructions stored in the memory 113 may include a processing routine and data associated with the processing routine (e.g., a machine learning algorithm and various filters for processing images). Although all data is described as being stored in the memory 113, it should be understood that in some embodiments, some data may be stored in a database, which itself may be stored in the memory, or may be stored remotely (e.g., cloud-based) in a discrete system.

[0044] The processing device 110 may also include an input 115 and an output 117. The input 115 may receive information (e.g., images or measurement data) from the imaging device 120. The output 117 may output information (e.g., processed images) to a user or user interface device. Similarly, the output 117 may output determinations (e.g., recommendations and risk determinations) generated by the methods described below. The output may include a monitor or display that may display additional information or a model updated in real time.

[0045] In some embodiments, processing device 110 may be directly associated with imaging device 120. In alternative embodiments, processing device 110 may be distinct from imaging device 120 such that it receives image or measurement data at input 115 through a network or other interface for processing.

[0046] In some embodiments, the imaging device 120 may include an image data processing device, and a spectral, photon counting, or conventional CT scanning unit for generating CT projection data when scanning an object (e.g., a patient). In addition, the imaging device 120 may be configured for invasive or non-invasive coronary CT angiography. As such, imaging may be performed using contrast agents, and image timing may be configured to track fluid flow in a blood vessel.

[0047] In addition to conventional CT images and spectral CT images, the method may also rely on multiple spectral image results, photon counting CT images or dark field CT images.

[0048] Although the system is shown as including an imaging device 120 and a processing device 110, it should be understood that the method can also be implemented directly on the processing device (such as in the context of receiving images over a network at an input 115). The methods described herein involve processing images as part of evaluating and planning potential interventions, typically in the context of a procedure such as stent implantation. As described above, imaging is typically performed prior to such a procedure. As such, previously generated images can be retrieved via the input 115 and evaluated prior to or in lieu of obtaining new images.

[0049] Figure 2 A method for processing images according to the present disclosure is illustrated. As shown, in implementing the method, the system 100 can first retrieve (200) at least one image at the input 115. The image includes at least a portion of the patient's coronary artery. The retrieved image is typically a CT image (e.g., non-invasive coronary CT angiography), which can be used to evaluate plaque in the coronary artery.

[0050] In some embodiments, prior to evaluating the plaque content of the coronary arteries, the method may identify (205) one or more lesions in the image, wherein the identified lesions require potential intervention.

[0051] The method may then determine (210) the location and composition of plaque in the coronary artery based on at least one of the images. Plaque may be defined based on location, distribution, and composition. This determination may be performed by manual or automatic detection, classification, and segmentation. Thus, the determination of the location and composition of plaque may be based on an AI model (e.g., a convolutional neural network (CNN)).

[0052] In embodiments where multiple lesions have been identified (at 205), plaque may then be located relative to each lesion or relative to the coronary artery as a whole as part of the determination (210).

[0053] Thus, the determination (at 210) may include defining the location of the plaque along the coronary artery 220, the shape and volume of the plaque at the location 230, and the composition of the plaque 240. The composition may be determined based on various strategies (e.g., using Hounsfield Unit (HU) imaging, monoenergetic imaging (MonoE), Zeff imaging, calcium imaging, or iodine content imaging). This may be used to classify the composition of the plaque using an average, a histogram, or by dividing the plaque into sub-volumes of different plaque components.

[0054] The determination may also classify the type of plaque by identifying, for example, positively remodeling plaque, luminal narrowing plaque, or thrombus.

[0055] To support such a determination, different types of plaque may be evaluated based on the images, and the determination of the composition of the plaque (at 210 ) may include quantifying calcium content 212 , density 214 , or fiber content 216 .

[0056] In some embodiments, calcium content quantification may be expressed as CAN = uncalcified / CAL = low or mixed calcification level / CAH = highly calcified.

[0057] High-risk plaque (HRP) features can also be identified. Such high-risk features may include LA = low attenuation plaque <30HU / NR = napkin ring sign, i.e., (low attenuation core surrounded by a rim-like higher attenuation and <130HU) / PR = positive remodeling (remodeling idx >1.1 = CSA increase of 10%) / SC = spotty calcifications (calcified lesions are between 1-3mm and >130HU)

[0058] Other patterns can also be identified. For example, plaque can be classified as DC = dense calcification (large amount of calcification, other plaque components are negligible) / FM = mixed fibrocalcification (mixture of calcified lesions and fibrous components) / FP = fibrous plaque (only fibers, other plaque components are negligible).

[0059] Other features of interest may include thrombosis / pericoronary inflammation (eg, via pFAI).

[0060] Additional parameters (e.g., the distribution of the plaque along the centerline of the vessel, the angular coverage of the plaque in a cross-section of the coronary artery, the portion of the lumen occupied by the plaque when viewed in cross-section, the distance to the next bifurcation of the vessel, and the hardness or deformability of the plaque) can then be derived (250) from the plaque data.

[0061] The method then proceeds to generate (260) a mechanical model of a portion of a coronary artery and plaque in the coronary artery. Such a mechanical model may then rely on the characteristics or classification of the plaque generated as part of the determination (at 210) and additional parameters (derived at 250) in order to accurately model the 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 meshless numerical method or coupled computational fluid dynamics model) of the plaque and vessel segment.

[0062] For example, additional parameters (at 250) may define the hardness or deformability of the plaque depending on the composition of the plaque. 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 take into account 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 (i.e., tortuosity) in the neighborhood.

[0063] 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 can be used to simulate the mechanical forces generated by the use of a stent or a balloon. The balloon can then be simulated using different pressure levels.

[0064] Once the mechanistic model is generated (at 260), the method proceeds to simulate (270a, 270b, 270c) a plurality of potential interventions in the context of the mechanistic model. Such simulations may be performed based on an AI model (e.g., a CNN). Such an AI model may be trained based on training data, which may include results of previous interventions performed in the context of similar plaque determination results.

[0065] Once the interventions are simulated, the method selects (280) one such intervention for implementation.

[0066] The potential intervention simulated may include multiple different types of interventions or interventions including different implanted devices. Alternatively, the potential intervention may be a similar operation using different devices. For example, the potential intervention may include the implantation of stents of different sizes or types or the implantation using different balloon pressures.

[0067] For example, in some embodiments, a first intervention 270a of the plurality of potential interventions may be an implantation of a first device, while a second intervention 270b may be an implantation of a second device different from the first device.

[0068] The first device and the second device can each be a stent selected from the group consisting of a bare metal stent, a drug eluting stent, a polymer stent, and a bioresorbable stent. Additionally, the first device and the second device can have different lengths or diameters. For example, a length can be selected to remove stenosis and cover adjacent plaque.

[0069] Similarly, the first intervention 270a may be a stent implantation using a first balloon pressure to place the stent, while the second intervention 270b relies on a second balloon pressure different from the first balloon pressure to place the stent.

[0070] Alternatively, in some embodiments, the first intervention 270a may be a stent implantation and the second intervention 270b may be a coronary artery bypass graft surgery.

[0071] In an embodiment where multiple lesions have been identified (at 205) and are to be addressed in the coronary intervention being evaluated, the multiple potential interventions 270a, 270b, 270c to be simulated may include: a first intervention 270a including a first sequence of lesion treatments, and a second intervention 270b including a second sequence of lesion treatments different from the first sequence.

[0072] Figure 3 The diagram shows Figure 2 The method uses the plaque model 400 during an intervention. In some embodiments, after selecting an intervention for implementation (at 280), the method proceeds to actually implement (290) the selected intervention 270a. In such embodiments, the method may then display (300) the mechanical model on a display during implementation of the intervention 270a. Thus, as Figure 3 As shown, the mechanical model 400 may be shown in the context of a coronary artery 410 during an intervention.

[0073] For example, the mechanical model 400 can be an overlay for a coronary angiography image. The display can include different levels of information, such as image-derived plaque parameters or results of a model generated based on image-derived plaque parameters. This information can be similarly displayed on top of the intravascular image. Additionally, the derived information can be presented to the user as a kind of work list.

[0074] The method may continue to monitor (310) the risks associated with the intervention during implementation, and may alert the user to any changes in the risk model. For example, in some embodiments, the method may monitor continuous imaging or may track plaque within a coronary artery. The method may then determine (320) that the location or composition of the plaque has changed during implementation of the selected intervention. The method may then continue to run a real-time assessment of potential complications (330) based on the updated location or updated composition of the plaque. In such embodiments, the method may continue to guide the intervention based on the mechanical model that is updated to reflect any changes, and alert the user to any new or changed risks associated with the intervention.

[0075] In some embodiments, plaque information or plaque biomarkers derived from plaque data can be used to predict the risk of a particular intervention. Thus, the method can make recommendations for the steps involved in a particular intervention, or the user can provide instructions on what steps to perform for a particular patient. The method can then provide an indication of the level of risk.

[0076] In some embodiments, where the selected intervention utilizes a device, the precise location of the device during the intervention may affect risk. As such, 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 a risk metric (350), wherein the risk metric is based at least in part on the precise location of the device during the selected intervention 270a.

[0077] Similarly, when a device, guidewire or similar positioning element reaches a specific location in the vascular tree, the risk level can be displayed, from which it can be inferred where the next step of the procedure will be performed. The method can then update the risk model accordingly.

[0078] In this context, risk prediction can include the risk of surgical complications or cardiac events (ie, MACE) at 1, 2, or 5 years after the intervention. Risk prediction can also take into account quality of life after the intervention.

[0079] The combined system described herein (i.e., combining CT imaging of plaque characterization and interventional procedures) can be used to train an AI-based system that predicts surgical steps and parameters to treat specific lesions in future implementations. After plaque characterization, the equipment or other parameters (such as balloon pressure) used to treat lesions with plaque can be stored in a database. Based on this data, the system can then be trained to predict which equipment should be used and what risk is associated with the procedure given a specific plaque configuration in a vessel.

[0080] Similarly, a system for implementing the method is provided. Such a system may include a number of potential devices that can be used for implementation, depending on the intervention to be utilized. In addition, as Figure 1 As shown, such a system may include a memory 113 for storing a plurality of instructions and a processor circuit 111 coupled to the memory, wherein the processor circuit 111 is configured to execute the above-mentioned Figure 2 Instructions describing the method.

[0081] When executing the method, it is noted that when simulating a plurality of potential interventions 270a, 270b, 270c in the context of the mechanical model (generated at 260), each of these potential interventions utilizes at least one potential device of a plurality of potential devices that can be used for implementation.

[0082] Although the system and method are described herein with respect to coronary arteries, similar methods may also be applied to other vascular structures (e.g., peripheral or pulmonary vessels, carotid arteries, etc.). Similarly, as described above, the system implementing the method may include various forms of computing. As such, the processing may be performed on-site or on a cloud computing platform. The predictions and interventional plans may be displayed in a CT console, advanced workstation, or interventional laboratory before or during the selected surgery.

[0083] The method can be used in CT systems and imaging workstations dedicated to coronary artery analysis using CCTA scans and PACS viewers, and in interventional C-arm systems when the information is used intraoperatively.

[0084] In some cases, the retrieved imaging (at 200) may not be adequate for the type of planning required. For example, the retrieved non-invasive imaging may be low resolution or suboptimal, but may be adequate to provide an indication of the presence of plaque-related risk. The method may then use and guide additional intravascular imaging prior to treatment.

[0085] Similarly, if additional information is available, such information can 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 about plaque dynamics can be included in the analysis.

[0086] The method according to the present disclosure can be implemented on a computer as a computer-implemented method, or implemented in dedicated hardware, or implemented in a combination of the two. The executable code of the method according to the present disclosure can be stored on a computer program product. Examples of computer program products include storage devices, optical storage devices, integrated circuits, servers, online software, etc. Preferably, the computer program product may include a non-transient program code stored on a computer-readable medium for executing the method according to the present disclosure when the program product is running on a computer. In one embodiment, the computer program may include a computer program code, and when the computer program is running on a computer, the computer program code is suitable for executing all steps of the method according to the present disclosure. The computer program may be embodied on a computer-readable medium.

[0087] While the present disclosure has been described at some length and with a certain particularity with respect to several described embodiments, it is not intended that the present disclosure should be limited to any such details or embodiments or to any particular embodiments, but rather the present disclosure should be interpreted with reference to the appended claims so as to provide the broadest possible interpretation of such claims in light of the prior art, thereby effectively covering the intended scope of the present disclosure.

[0088] All examples and conditional language recorded herein are intended to be used for teaching purposes, to help readers understand the principles of the present disclosure and the conception contributed by the inventor to promote this area, and should be interpreted as not being limited to the examples and conditions of these specific records. In addition, all statements and specific examples thereof of the principles, aspects and embodiments of the present disclosure are recorded herein and are intended to cover their structural and functional equivalents. In addition, this equivalent is intended to include currently known equivalents and equivalents developed in the future (that is, any element of the performance of the same function developed, regardless of structure).

Claims

1. A method for planning a coronary intervention, include: retrieving at least one image, the 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 in the coronary artery; simulating multiple potential interventions in the context of the mechanistic model; and An intervention is selected for implementation from the plurality of potential interventions.

2. The method according to claim 1, in, The plurality of potential interventions simulated include 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. The method according to claim 2, in, The first device and the second device are stents, each of the first device and the second device is selected from the group consisting of a bare metal stent, a drug eluting stent, a polymer stent, and a bioresorbable stent, and wherein each of the first device and the second device has a different length or diameter.

4. The method according to claim 1, in, The multiple potential interventions simulated include a first intervention in which a stent is implanted, and a second intervention including a coronary artery bypass graft surgery (CABG).

5. The method according to claim 1, further comprising: include: A plurality of lesions to be addressed in the coronary intervention are identified in the image, and wherein the plurality of potential interventions simulated include 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 sequence.

6. The method according to claim 1, in, The plurality of potential interventions simulated include a first intervention using a first balloon pressure to place a stent and a second intervention using a second balloon pressure different from the first balloon pressure to place the stent.

7. The method according to claim 1, further comprising: include: The selected intervention is implemented, and the mechanical model is displayed on a display during implementation of the selected intervention.

8. The method according to claim 7, further comprising: include: A determination is made as to whether the location or the composition of the plaque has changed during performance of the selected intervention, and a real-time assessment of potential complications is performed based on the updated location or updated composition of the plaque.

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

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

11. The method according to claim 10, in, The composition of the plaque defines the stiffness or deformability of the plaque, and wherein the mechanical model of the portion of the coronary artery and the plaque in 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 according to claim 11, in, The mechanical model simulates stress and strain as isometric forces resulting from the use of a stent or balloon.

13. The method according to claim 1, in, The simulation of the plurality of potential interventions is based on an AI model that is trained based on results of previous intervention performances.

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

15. The method according to claim 14, further comprising: include: The plaques are classified as positively remodeling plaques, luminal stenosis plaques, or thrombi.

16. A system for performing a coronary intervention, include: A number of potential devices that could be implanted; A memory for storing a plurality of instructions; A processor circuit coupled to the memory and configured to execute the instructions to perform the following operations: retrieving at least one image, the at least one image including at least a portion of the 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 in the coronary artery; simulating a plurality of potential interventions in the context of the mechanistic model, each of the potential interventions utilizing at least one potential device of the plurality of potential devices; and An intervention is selected for implementation from the plurality of potential interventions.

17. The system according to claim 16, in, The plurality of potential devices are stents, each of the plurality of potential devices being selected from the group consisting of a bare metal stent, a drug eluting stent, a polymer stent, and a bioresorbable stent, and wherein at least two of the plurality of potential devices have different lengths or diameters from each other.

18. The system according to claim 16, in, The plurality of potential interventions simulated include a first intervention in which a stent is placed using a first balloon pressure, and a second intervention in which the stent is placed using a second balloon pressure different from the first balloon pressure.

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

20. The system according to claim 19, in, The composition of the plaque defines the stiffness or deformability of the plaque, and wherein the mechanical model of the portion of the coronary artery and the plaque in 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