Treatment planning by simulating patency scenarios

Abstract

Potential treatment is planned by retrieving three-dimensional data including vascular anatomy and patient vasculature. Generating a post-treatment hemodynamic model based on a patient vasculature, the patient vasculature being modified based on a proposed treatment applicable to the patient vasculature; at least one lesion of the patient vasculature is introduced or enlarged in the post-treatment hemodynamic model to create a long-term hemodynamic model. Blood flow is simulated in a long-term hemodynamic model. A predicted physiological impact of the proposed treatment is generated based on the long-term hemodynamic model.

Description

TECHNICAL FIELD

[0001] The present invention relates generally to systems and methods for planning a treatment for treating a blood vessel. In particular, the present invention relates to evaluating and planning proposed treatment of a stenotic lesion. BACKGROUND

[0002] In percutaneous treatment of stenotic lesions in diseased patients, there is a challenge in determining when the interventionalist has done enough. Therefore, the interventionalist has to define the point at which to stop the treatment. It is desirable to treat what is necessary to restore blood flow by opening the diseased blood vessel, but not to over-treat the patient. Over-treatment is generally undesirable and there is evidence that over-treated lesions carry a higher risk of restenosis.

[0003] Therefore, when treating a stenotic lesion, the interventionalist targets restoration of blood flow by opening the diseased blood vessel, which constitutes technical success. However, the interventionalist must not over-treat the patient and ideally leave a result that is likely to be long-term patent or obstruction-free.

[0004] In a patent scenario, future plaque growth does not immediately have a substantial impact on the blood supply to critical downstream structures. Therefore, it is desirable to test future scenarios associated with different potential or prospective treatment plans in terms of robustness and vulnerability. This information can be used during interventional planning to make a decision between multiple potential treatment plans.

[0005] Therefore, it is desirable to identify and confirm prospective treatments and treatment plans that not only result in technical success, but are also likely to be long-term patent. SUMMARY

[0006] When treating a stenotic lesion, the interventionalist targets restoration of blood flow by opening the diseased blood vessel, but also must not over-treat the patient. Therefore, it is desirable to identify treatment plans that not only result in technical success, but are also likely to be long-term patent.

[0007] Therefore, systems and methods are provided for simulating hypothetical treatment plans with a three-dimensional description of a patient-specific anatomy and investigating the patency of such hypothetical treatments. An interactive interface can be provided for the clinician to test possible future lesion growth at relevant locations after a hypothetical treatment. This can be done by utilizing a module that models and inserts different types of plausible synthetic lesions into the anatomy. A flow model can then be applied to the patient-specific anatomy with the modeled synthetic lesions to estimate the functional consequences of these future scenarios, thereby predicting patency.

[0008] In some embodiments, prior knowledge about possible future lesion growth locations can be utilized to identify vulnerable scenarios in which small morphological changes can lead to a significant reduction in patency.

[0009] In some embodiments, a similar system and method can be utilized after an initial treatment using actual treatment results, such as stent placement. Such treatment results can then be used to evaluate whether further intervention is appropriate in order to improve patency.

[0010] In some embodiments, a method for evaluating patency of a treatment is provided. The method includes retrieving three-dimensional data including a blood vessel anatomy and a patient vasculature. The method then generates a post-treatment hemodynamic model based on the patient vasculature modified based on a proposed treatment applicable to the patient vasculature. The method then introduces or enlarges at least one lesion to the patient vasculature in the post-treatment hemodynamic model to create a long-term hemodynamic model, simulates blood flow in the long-term hemodynamic model, and generates a projected physiological impact of the proposed treatment based on the long-term hemodynamic model.

[0011] In some embodiments, generating the post-treatment hemodynamic model includes generating an initial hemodynamic model of the patient vasculature based on the three-dimensional data, and applying proposed modifications to the initial hemodynamic model based on the proposed treatment applicable to the patient vasculature to create the post-treatment hemodynamic model.

[0012] In some such embodiments, the proposed treatment is selected based on a rules-based model of at least one detected detail applied to the patient vasculature.

[0013] In some embodiments, the proposed treatment is a stent implantation to the at least one lesion.

[0014] In some embodiments, the three-dimensional data includes three-dimensional imaging including computed tomography angiography (CTA) or magnetic resonance angiography (MRA). In some such embodiments, the method includes applying a segmentation process to the three-dimensional imaging, and generating the initial hemodynamic model based on vessel lumens extracted from the three-dimensional imaging by the segmentation process.

[0015] In some embodiments, the method includes retrieving parameters for at least the lesion from a user, and introducing or enlarging the at least one lesion based on the retrieved parameters.

[0016] In some embodiments, the at least one lesion includes at least one of: a focal lesion, a diffuse lesion, a bifurcation lesion, and a total occlusion.

[0017] In some embodiments, the method includes evaluating at least one characteristic of the patient vasculature prior to introducing or enlarging the at least one lesion, and introducing or enlarging the at least one lesion at a location selected based on the evaluation of the patient vasculature.

[0018] In some such embodiments, the evaluation of the patient's vasculature identifies a likely plaque formation location, and the at least one lesion is introduced or enlarged at the likely plaque formation location. In some such embodiments, the method includes performing a first simulation of blood flow in a post-procedural hemodynamic model prior to introducing the at least one lesion, and identifying the likely plaque formation location based on the first simulation of blood flow in the post-procedural hemodynamic model.

[0019] In some embodiments, each flow model is a 0D or ID model based on three-dimensional data.

[0020] In some embodiments, the projected physiological impact is based on perfusion relative to a downstream location of the applied treatment. In some such embodiments, the projected physiological impact is at least one of: a measure of relative perfusion change comparing the long-term hemodynamic model to an initial hemodynamic model of the patient's vasculature based on three-dimensional data, and a measure of relative perfusion change comparing the long-term hemodynamic model to the post-procedural hemodynamic model.

[0021] In some embodiments, the method further includes modifying at least one parameter of the at least one lesion to create at least one modified long-term hemodynamic model. The method then proceeds to generate at least one modified projected physiological impact of the proposed procedure based on the corresponding at least one modified long-term hemodynamic model, and generate the patency metric based on a comparison of the projected physiological impact to the at least one modified projected physiological impact.

[0022] In some such embodiments, the patency metric is at least one of: a measure of relative perfusion change comparing iterations of the long-term hemodynamic model in the context of parameter changes in the at least one lesion, and a measure of relative sensitivity in the context of parameter changes in the at least one lesion.

[0023] In some embodiments, the method includes applying a secondary modification to the initial hemodynamic model based on an alternative procedure applicable to the patient's vasculature to create an alternative post-procedural hemodynamic model. The method then introduces or enlarges the at least one lesion to the patient's vasculature in the alternative post-procedural hemodynamic model to create an alternative long-term hemodynamic model. The method then simulates blood flow in the alternative long-term hemodynamic model, and generates a projected physiological impact of the alternative procedure and the at least one lesion based on the alternative long-term hemodynamic model. The method then outputs a comparison of the projected physiological impact of the proposed procedure to the projected physiological impact of the alternative procedure.

[0024] In some such embodiments, the treatment location is different for the proposed procedure and the alternative procedure.

[0025] In some embodiments that consider alternative treatments, the method continues to modify at least one parameter of at least one lesion in the post-treatment hemodynamic model to create at least one modified long-term hemodynamic model. The method then generates at least one modified predicted physiological state of the patient's vasculature based on the corresponding at least one modified long-term hemodynamic model. Then, the method generates a patency metric based on a comparison of the predicted physiological state associated with the long-term hemodynamic model and the at least one modified predicted physiological state associated with the at least one modified long-term hemodynamic model.

[0026] The method then continues to modify at least one parameter of at least one lesion in the alternative post-treatment hemodynamic model to create at least one modified alternative long-term hemodynamic model. The method then continues to generate at least one modified predicted physiological state of the patient's vasculature based on the at least one modified alternative long-term hemodynamic model. Then, the method generates an alternative patency metric based on a comparison of the predicted physiological state associated with the alternative long-term hemodynamic model and the at least one modified predicted physiological state associated with the at least one modified alternative long-term hemodynamic model, and outputs a comparison of the patency metric and the alternative patency metric. BRIEF DESCRIPTION OF DRAWINGS

[0027] Figure 1 is a schematic diagram of a system according to one embodiment of the present application.

[0028] Figure 2 illustrates a method for evaluating patency according to one embodiment of the present application.

[0029] Figure 3 illustrates a block diagram of a system implementing the method of Figure 2

[0030] Figure 4 shows a modeled description of a patient's vasculature according to one embodiment of the present application.

[0031] Figure 5 illustrates the introduction of a synthetic lesion into the patient's vasculature of Figure 4

[0032] Figure 6 illustrates a simulation of blood flow in the modeled patient's vasculature of Figure 5 DETAILED DESCRIPTION

[0033] ​​​The description of illustrative embodiments according to the principles of the present disclosure is intended to be read in connection with the accompanying drawings, which are to be considered part of the entire written description. Any reference to direction or orientation in the description of embodiments of the disclosure disclosed herein is merely intended for clarity and, unless specifically stated, is not to be construed as limiting of the scope of the disclosure. Relative terms such as "lower," "upper," "horizontal," "vertical," "above," "below," "up," "down," "top" and "bottom" as well as derivative thereof (e.g., "horizontally," "downwardly," "upwardly," etc.) should be construed to refer to the orientation as then described or as shown in the drawing under discussion. These relative terms are for convenience of description only and do not require that the apparatus be constructed or operated in a particular orientation unless explicitly indicated otherwise. Terms such as "attached," "affixed," "connected," "coupled," "interconnected," and similar refer to a relationship wherein structures are secured or attached to one another either directly or indirectly, through intervening structures, as well as both movable or rigid attachments or relationships, unless expressly described otherwise. Moreover, the features and benefits of the disclosure are illustrated by reference to the exemplified embodiments. Accordingly, the disclosure expressly should not be limited to such exemplary embodiments illustrating some possible non-limiting combination of features that can exist alone or in other combinations of features; the scope of the disclosure being defined by the claims appended hereto.

[0034] The present disclosure describes one or more best modes presently contemplated for practicing the present disclosure. This description is not to be taken in a restrictive sense but is presented as a single example to provide an enabling description for the examples of the disclosure presented herein for the purpose of informing those skilled in the art of the advantages and instrumentality of the present disclosure. In the various views of the drawings, like reference numerals represent similar or analogous parts.

[0035] 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 the application do not necessarily limit any of the various claimed inventions. Moreover, some statements can apply to some inventive features but not to others. In general, unless otherwise indicated herein, singular elements can be in the plural and vice versa, without loss of generality.

[0036] Systems and methods are provided for utilizing a three-dimensional description of patient-specific anatomy prior to treatment and investigating the patency of a proposed treatment's assumed treatment outcome. An interactive interface can be provided for a clinician to test for possible future lesion growth at relevant locations after the assumed treatment. This can be done by utilizing a module that models and inserts different types of plausible synthetic lesions into the post-treatment anatomy. A flow model can then be applied to the patient-specific anatomy with the modeled synthetic lesions to estimate the functional consequences of these future scenarios, thereby predicting patency.

[0037] The flow model can be reconstructed in the context of multiple proposed treatments. Thus, such treatments can be compared to the results of simulating future lesion growth. In a patency scenario, future plaque growth would not immediately have a substantial impact on blood supply to critical downstream structures. Thus, the relevant future scenarios need to be tested in terms of robustness and vulnerability.

[0038] Generally, if a patient is scheduled for treatment, imaging has already occurred. Thus, while the description herein includes acquisition of imaging, it should be understood that pre-existing imaging can be leveraged and modified in accordance with any proposed treatment.

[0039] Figure 1 is a schematic diagram of a system 100 according to one embodiment of the present disclosure. As shown, the system 100 generally includes a processing device 110 and an imaging device 120.

[0040] The processing device 110 can apply processing routines to images or measurement data, such as projection data, received from the imaging device 120. The processing device 110 can include a memory 113 and a processor circuit 111. The memory 113 can store a plurality of instructions. The processor circuit 111 can be coupled to the memory 113 and can be configured to execute the instructions. The instructions stored in the memory 113 can 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 the memory 113, it should be understood that in some embodiments, some data can be stored in a database, which itself can be stored in memory or in a discrete, separate system.

[0041] The processing device 110 can also include an input 115 and an output 117. The input 115 can receive information, such as images or measurement data, from the imaging device 120. The output 117 can output information, such as processed images or models generated therefrom, to a user or user interface device. The output 117 similarly can be providing data to a user interface, where a user can manipulate inputs for a hemodynamic model to be generated. The output 117 can similarly output determinations generated by the methods described below, such as recommendations, projected physiological states for a patient, and patency metrics and projections. The output can include a monitor or display, which can display additional information or models updated in real-time or near real-time based on user inputs.

[0042] In some embodiments, the processing device 110 can be directly related to the imaging device 120. In alternative embodiments, the processing device 110 can be distinct from the imaging device 120, such that it receives images or measurement data for processing over a network or other interface at the input 115.

[0043] In some embodiments, the imaging device 120 can include an image data processing device and a spectral or conventional CT scanning unit for generating CT projection data when scanning a subject (e.g., a patient). Further, the imaging device 120 can be set up for invasive or non-invasive coronary CT angiography. Thus, imaging can be performed with contrast and image timing can be set so as to track fluid flow in the vessels.

[0044] In addition to conventional and spectral CT images, the method can rely on multiple spectral image results, photon counting CT images, and / or dark field CT images. In this way, the method can rely on three-dimensional data retrieved directly from the imaging device 120. Alternatively, the imaging device 120 can acquire two-dimensional images that can be used to derive three-dimensional data. For example, multiple two-dimensional images can be considered together so as to infer three-dimensional characteristics.

[0045] Alternatively, two-dimensional imaging can be used with auxiliary information sufficient to infer three-dimensional characteristics. For example, two-dimensional projections can be paired with luminal characteristics of the patient vasculature to derive data required for a flow model. Similarly, two-dimensional imaging can be fused with flow information, such as flow visible in x-ray fluoroscopy or x-ray angiography. For purposes of this disclosure, three-dimensional data includes such instances of two-dimensional imaging infused with flow information to better describe the vascular anatomy.

[0046] While a system is shown that includes an imaging device 120 and a processing device 110, it should be understood that the method can be implemented directly on the processing device, as in the context of images received over a network at the input 115 or imaging. The method described herein involves processing data as part of evaluating the patency of a proposed treatment, such as a stent implant. As described above, typically, imaging is performed prior to such a procedure. Thus, previously generated imaging can be retrieved through the input 115 and a post-treatment model can be generated based on the previously retrieved data in combination with information based on the proposed treatment to be performed.

[0047] Figure 2 A method for evaluating patency according to the present disclosure is illustrated. As described above, the method for evaluating patency involves the patency of a proposed or expected treatment to be applied to a patient's blood vessels. For example, the proposed treatment can be a therapy applicable to a stenotic lesion. Figure 3 A block diagram of a system 400 implementing the method of Figure 2 is illustrated.

[0048] The method initially retrieves three-dimensional data describing the vascular anatomy of a patient to be treated (200). The three-dimensional data includes a description corresponding to the patient vasculature of the patient to be treated. The three-dimensional data can take various forms. Portions of the method can be performed by the data processing and manipulation module 410 (Figure 3 The data is then implemented and can be used for lesion and flow modeling, as described below.

[0049] Three-dimensional data can be three-dimensional imaging, such as computed tomography (CT) scans or coronary angiography (CTA). Alternatively, three-dimensional imaging can be magnetic resonance imaging (MRI) or coronary angiography (MRA).

[0050] In a typical embodiment, after retrieving the 3D data (at 200), the lumen and / or centerline of the vascular system to be modeled can be identified (210). If the 3D data is a 3D image, the method can continue by applying a segmentation process to the image. In such a scenario, the various hemodynamic models discussed below can be generated based on the vascular lumen extracted and identified (at 210) through the segmentation process.

[0051] In some embodiments, the three-dimensional data includes composite information from multiple two-dimensional images. In such embodiments, instead of retrieving a three-dimensional image, the system can retrieve multiple two-dimensional images (such as conventional X-rays) and infer three-dimensional characteristics by consistently considering the images.

[0052] In some embodiments, two-dimensional data may be provided, but representations of the lumen and / or centerline may be provided as supplementary data. Such data combinations can thus be integrated to form three-dimensional data retrieved (in 200) by this method. In such scenarios, the data may never actually be processed as volumetric data in three dimensions, but can be directly applied to modeling the vascular tree. Therefore, it should be understood that three-dimensional data can take many forms and represent three-dimensional descriptions of patient-specific anatomy.

[0053] The method can then proceed to generate an initial hemodynamic model (220) of the patient's vascular system based on the retrieved (at 200) 3D data. The initial hemodynamic model can be based in part on the patient's profile, such as a user-defined profile by the system implementing the method. Such a user could be a clinician. The initial model can similarly be based on patient data independent of imaging, which can be retrieved at any time. The method can then proceed to apply proposed modifications to the initial hemodynamic model based on a proposed treatment applicable to the patient's vascular system (230). The proposed treatment can be an intervention based on a hypothetical treatment plan and can include, for example, angioplasty at the proposed location and / or stenting at the proposed location (such as a location corresponding to an existing lesion). Before applying the modifications to the initial hemodynamic model, details associated with the proposed treatment can be retrieved from the user (at 230).

[0054] In some embodiments, a proposed treatment may be selected based on a rule-based model of at least one detected detail applied to the patient’s vascular system.

[0055] Following modifications to the initial hemodynamic model (generated at 220) (at 230), the method continues to generate a post-treatment hemodynamic model (240) based on the patient's vascular system, which is based on three-dimensional data (retrieved at 200) modified from a proposed treatment (at 230) applicable to the patient's vascular system. As discussed in more detail herein, the proposed treatment (at 230) can be one of several potential proposed treatments. Therefore, in iterative versions of the method described herein, the method can repeat the modeling steps described herein several times to generate such comparisons between iterations.

[0056] While the initial hemodynamic model (generated at 220) is discussed as a preparatory step for creating a post-treatment hemodynamic model (generated at 240) representing the patient's vascular system after some hypothetical treatment, it should be understood that the initial hemodynamic model is not necessary in all cases. Therefore, in some embodiments, the method can proceed by directly generating a post-treatment hemodynamic model of the patient's vascular system based on three-dimensional data derived from proposed treatment modifications (at 200).

[0057] In some embodiments, the method may use a local displacement field in three-dimensional data to model the expansion of the blood vessel at the proposed treatment site based on fluorescence fluoroscopy or angiography of the stent to be placed in the hypothetical treatment plan. The stent can then be manually inserted into the three-dimensional image.

[0058] In practice, the segmentation of the vascular lumen can be extracted from 3D data as a bitmask or surface mesh and used as input for the lesion model. Manipulation based on the proposed treatment plan can then be applied to the extracted lumen instead of the original image voxel space.

[0059] Figure 4 The illustration depicts a modeling description of a patient's vascular system according to this disclosure. Figure 4The model shown is an example of a post-treatment hemodynamic model of a patient's vascular system (such as the model generated at 240) based on a successful execution of a proposed treatment. Users (such as clinicians) can then leverage this method to test the robustness of certain future scenarios after a specific proposed treatment of a patient simulated in the model. For example, a clinician could simulate stent implantation for multiple ablation lesions and then test the patency of the resulting state. The method then attempts to determine how fragile the hypothetical outcome would be and what the risks of re-intervention would be for the patient. In some embodiments, the proposed treatment scenarios are automatically generated by the method, and therefore, the method can be automatic rather than interactive.

[0060] The post-treatment hemodynamic model (generated at 240) is designed to reflect the flow status immediately following the hypothetical treatment. However, such a model does not account for the expected changes in the vascular system over time. Therefore, to anticipate the long-term patency of the patient's vascular system, the method continues by introducing or expanding (250) at least one synthetic lesion into the patient's vascular system in the post-treatment hemodynamic model. This can be implemented at the synthetic lesion modeling and insertion module 420 in system 400. Such introduction or expansion allows the method to create a long-term hemodynamic model 430 ( Figure 2 (of 260).

[0061] In some embodiments, patient data may be available to the method throughout the process described herein or at specific times during the process. Thus, as shown in the figure, patient data can be utilized to better inform the long-term model (generated at 260). For example, patient characteristics such as patient condition or medications used by the patient can be used to improve the accuracy of the model.

[0062] Figure 5 The illustration shows the introduction of a synthetic lesion (at position 250). Figure 4 The patient's vascular system. Model 430, which includes the introduced synthetic lesions, corresponds to the long-term hemodynamic model described and generated at 260.

[0063] In some embodiments, a user of the system implementing this method can provide several input parameters for the synthetic lesion to be generated or expanded at the interactive user interface 440. Therefore, the method can retrieve multiple parameters (at 243) of the synthetic lesion to be generated from the user before introducing the synthetic lesion (at 250). In such an embodiment, the introduction or expansion of the synthetic lesion (at 250) can be based on the retrieved parameters. This can be achieved through the lesion modeling and insertion module 420. Such a module can generate synthetic lesions given desired parameterization or other inputs provided at the user interface 440 and integrate them into a patient-specific vascular system upon request.

[0064] In such an embodiment, an interactive user interface 440 can be presented to the user, where a clinician may be able to use the lesion modeling and insertion module 420 to test for possible future lesion growth at relevant locations. The lesion modeling and insertion module 420 models different types of plausible synthetic lesions and inserts them into hypothetical post-treatment anatomy. As described herein, multiple scenarios can ultimately be modeled and compared within the context of a single hypothetical treatment scenario.

[0065] For example, a clinician might instruct the system implementing this method to achieve a certain degree of in-stent restenosis or to increase the significance or spread of another lesion. The method would then evaluate the physiological consequences in the following steps. For instance, if no further intervention was attempted, the method might attempt to determine whether restenosis at a specified location has a significant impact on volumetric flow rate.

[0066] Alternatively, in some embodiments, the method may further identify at least one characteristic (248) of the patient's vascular system. The identified characteristic may then include other characteristics for determining the location or extent of the synthetic lesion to be introduced or expanded (at 250). For example, the method may include identifying possible plaque-forming locations in the patient's vascular system. The introduction or expansion of the lesion may then be the introduction of a synthetic lesion (at 250) at the possible plaque-forming location.

[0067] In some embodiments, additional processing may be applied to identify characteristics (at 248). For example, in some embodiments, the method may perform a first simulation of blood flow in a post-treatment hemodynamic model (at 245) before introducing at least one lesion (at 250). Possible plaque formation locations are then identified (at 248) based on the first blood flow simulation (at 245). This could be, for example, by identifying areas of low wall shear stress in the patient's vascular system or bifurcations within an existing stent.

[0068] In some embodiments, the introduction or expansion of at least one synthetic lesion (250) can be based on a combination of user input of parameters (such as at 243) and an evaluation of vascular system characteristics identified by the method (such as at 248). Thus, the evaluation of the patient's vascular system can be based at least in part on, for example, a user-defined patient profile (at 243 or earlier). For example, the user can be allowed to select the most suitable patient profile from available or customizable (disease-related) presets. Such a selection can then influence the plaque growth model through knowledge-based classification priors. As mentioned above, patient data can typically be made available to the model at various times prior to the creation of a long-term model (at 260).

[0069] Furthermore, in addition to selecting, for example, the appropriate location of the synthetic lesion to be introduced, the characteristics and parameters of the introduced or enlarged synthetic lesion can be similarly selected by the user (at 243) or by the system itself (at 248). Therefore, the synthetic lesion to be introduced or enlarged can be a focal lesion, a diffuse lesion, a bifurcation lesion, or a complete occlusion. Furthermore, the synthetic lesion to be introduced can have score-driven parameters (such as lesion significance, Medina score, or a portion of the Syntax II score) or geometry-driven parameters (such as length, longitudinal profile, symmetry, or concentricity).

[0070] In some embodiments, the implementation may be available in a semi-analytical platform. Such an implementation is inherently semi-analytical and can therefore be fast and efficient. Rapid modeling can be used to implement the interactive methods described herein.

[0071] Ultimately, synthetic lesions are introduced or amplified (at 250), and the results are then used to generate a long-term hemodynamic model (at 260).

[0072] The method then proceeds to simulate blood flow in a long-term hemodynamic model (generated at 260) (270). The results of such simulation are then used to generate the expected physiological effects of proposed treatments (and at least one synthetic lesion introduced or amplified) based on the long-term hemodynamic model (280a).

[0073] Figure 6 The diagram shows... Figure 5 The model simulates blood flow in the patient's vascular system (at 270). Such a flow model 430... Figure 3 The system 400 is shown in the figure. As illustrated, physiological effects are evaluated by simulating blood flow based on anatomical structures and potential other boundary conditions. For example, aortic pressure or other patient characteristics may be considered.

[0074] Note that, although Figures 4-6 The model shown is rendered in three dimensions, and this model is typically used in three dimensions; however, such a model can also be a simplified 0D or 1D model based on 3D data. Similarly, computational fluid dynamics (CFD) simulations can be used. Therefore, different levels of complexity and combinations can be used (i.e., 3D CFD calculations within the framework of a 0D model).

[0075] The effects of the inserted synthetic lesion (at 250) can be evaluated based on target metrics, such as perfusion at downstream locations in the vascular system and / or supplied tissues (e.g., in the context of myocardial perfusion evaluation). Such metrics can correspond to or be part of the determined expected physiological state (at 280a). Patency at 450 can be quantified in the context of the expected physiological effects based on the absolute perfusion value achieved at the target site or by changes in perfusion relative to the introduced morphological changes (i.e., vulnerability). The latter can be quantified, for example, by changes in lesion significance parameters.

[0076] In some embodiments, the expected physiological effect (generated at 280a) may be based on perfusion relative to a downstream location of the applied treatment. For example, the downstream location may be a downstream location in the patient's vascular system modeled. The expected physiological effect may then be a measure of relative perfusion change by comparing a long-term hemodynamic model with an initial hemodynamic model of the patient's vascular system based on three-dimensional data. Alternatively, the expected physiological effect may be a measure of relative perfusion change by comparing a long-term hemodynamic model with a post-treatment hemodynamic model.

[0077] In some embodiments, following the simulation of blood flow in the patient's vascular system (at 270), or in parallel with the implementation of the modeling process described herein, the method continues to modify one or more parameters of the lower-level model (275) to Figure 5 The post-treatment hemodynamic model can be modified based on the introduced or expanded lesion variation parameters (at 240) and the proposed treatment (at 230) used to modify the initial model. This method can then be used to create alternative long-term models (at 260) or post-treatment hemodynamic models (at 240) to which the lesion modifications are applied.

[0078] Therefore, in some embodiments, the method includes applying modification (at 275) to at least one parameter of at least one lesion introduced or expanded (at 250) by the synthetic lesion modeling and insertion module 420. This can be done by directly modifying the parameters of the lesion itself or by modifying the provided parameters (at 243) or the characteristics of the parameters identified that lead to the generated (at 250) lesion (at 248). Such modification can then be used to generate at least one modified long-term hemodynamic model (at 260) and to simulate blood flow in such a modified model (at 270) in order to generate a proposed treatment and a modified predicted physiological effect of at least one lesion based on the corresponding modified long-term hemodynamic model (at 270b).

[0079] Once the expected physiological effects and the modified expected physiological effects are generated (at 280a, 280b), the states can be used to generate a patency metric 450 or an estimate based on a comparison of states (at 290). It should be understood that while the method is discussed in the context of two iterations of the expected physiological effects (at 280a, 280b), additional iterations (such as at 280c) can be provided to provide additional data for generating the patency metric 450. Such a patency metric 450 can then provide a measure of relative perfusion changes compared to iterations of a long-term hemodynamic model in the context of parameter changes in at least one lesion. Alternatively or additionally, the patency metric 450 can provide a measure of relative sensitivity in the context of parameter changes in at least one lesion.

[0080] Therefore, several long-term models can be generated based on small changes around the selected lesion scenario, leading to changes in the expected physiological effects. The patency measure 450 can then be correlated with, for example, the relative sensitivity of perfusion based on several similar long-term models. For example, modifying the lesion significance from 0-50% at different steps can produce nonlinear perfusion behavior with respect to such lesion parameters. The patency measure 450 can then describe vulnerability in sophisticated ways.

[0081] In some embodiments, following the simulation of blood flow in the patient's vascular system (at 270), or in parallel with the implementation of the modeling process described herein, the method continues to apply secondary or alternative modifications (at 230). Figure 4 The post-treatment hemodynamic model can be generated automatically as part of the implementation of the procedure, or it can be based on manual modifications of parameters by the clinician (at 275). Such secondary modifications can replace the initially applied modifications (at 230) and can be based on alternative potential treatments suitable for the patient's vascular system. This can then be used to create a post-treatment hemodynamic model (at 240).

[0082] The method can then repeat the above process to introduce or expand at least one synthetic lesion into the patient's vascular system in a post-treatment hemodynamic model (at 240) (at 250) to create an alternative long-term hemodynamic model (at 260). The method then simulates blood flow in the alternative long-term hemodynamic model (at 270) and generates the expected physiological effects (at 280b) of the secondary modification (introduced at 230) and at least one synthetic lesion (introduced at 250). As described above, the modification (at 230) is a potential treatment, and the expected physiological effects (at 280b) allow for the evaluation of the proposed treatment's impact. The physiological effects (280a) can be considered in the context of the patency metric 450 generated for each potential treatment scenario and can be considered together with the corresponding physiological effects (280b, 280c) for the alternative potential treatment scenarios.

[0083] Therefore, the long-term hemodynamic model associated with each potential treatment scenario can itself be modified based on the synthetic lesion parameters that have changed as described above (at 275). A patency metric 450 can then be generated for each scenario. Thus, this method allows the user to modify the parameters of the lesion to be introduced or amplified (at 250). Therefore, as the described method is iteratively repeated, modifications can be introduced along different dimensions, allowing different lesion scenarios to be considered for each potential treatment.

[0084] Therefore, in some embodiments, after the initial iteration of the method leading to the expected physiological effect, the method may modify (introduced or amplified at 250) at least one parameter of at least one lesion to create at least one modified long-term hemodynamic model (at 260). The method may then generate at least one modified expected physiological effect (at 280b) corresponding to at least one modified long-term hemodynamic model. The method may then generate a patency metric based on a comparison between the expected physiological state associated with the long-term hemodynamic model (at 280a) and at least one modified expected physiological state associated with at least one modified long-term hemodynamic model (at 270b).

[0085] The method can then similarly modify at least one parameter of at least one lesion (introduced or amplified at 250) in the post-alternative hemodynamic model to create at least one modified alternative long-term hemodynamic model (at 260). The method can then generate at least one modified predicted physiological effect on the patient's vascular system and at least one lesion based on the at least one modified alternative long-term hemodynamic model. The method can then employ the same approach as with the long-term hemodynamic model and generate an alternative patency measure 450 based on a comparison of the predicted physiological effects associated with the alternative long-term hemodynamic model and at least one modified predicted physiological effect associated with the at least one modified alternative long-term hemodynamic model.

[0086] The method can then output a comparison of the accessibility metric 450 with alternative accessibility metrics to take into account the vulnerability of accessibility in each of the proposed scenarios.

[0087] In some embodiments, this part of the process can be repeated multiple times to simulate, for example, different potential treatments available to the patient based on their vascular system. Thus, additional anticipated physiological effects can be generated (at 280c) to evaluate additional scenarios. Such repetition can be iterative, allowing a user, such as a physician, to propose alternative treatments as secondary modifications to the model while reviewing the anticipated physiological effects of modifications to the initially proposed treatment (at 280a) (at 230). The physician can then review the anticipated physiological effects of the proposed treatments (at 280b) and propose different or modified treatments at the interactive interface 440.

[0088] After generating multiple predicted physiological effects (280a, 280b, 280c), the method can continue to generate and output a comparison of predicted physiological states (at 290) to evaluate the impact of the proposed treatment. Therefore, the method can compare the predicted physiological effects associated with each of the potential treatments to generate a recommendation based on the physiological state or patency metric 450 associated with each of the potential treatments.

[0089] In some embodiments, such as the one described where the modification initially introduced (at 230) is only one of a plurality of potential treatments, the method compares the expected physiological effects associated with each of the potential treatments and generates (at 300) a recommendation based on a measure of the vulnerability to the accessibility of each of the plurality of potential treatments.

[0090] In some embodiments, the different treatments considered and ultimately modeled (at 260) vary in the extent of treatment, such as how many lesions are to be treated. In other embodiments, the differences may be based on the different treatment locations for each potential treatment. In other embodiments, the differences may be based on the material of the treatment to be applied, such as the type of stent to be applied.

[0091] In some embodiments, in addition to changing the hypothetical treatment to be applied (at 230), the method also models different synthetic lesions within the context of a single hypothetical treatment. Thus, the method can introduce differences at the expected location of the synthetic lesion to be introduced or expanded (at 250). Such differences can be introduced by a user (such as a clinician) using an interactive user interface 440, as described above. In some embodiments, the method itself can test for a variety of potential synthetic lesions and can leverage knowledge of previous scenarios to identify vulnerable scenarios where small morphological changes may lead to a significant decrease or increase in patency. Therefore, the patency metric 450 can take into account the outcomes associated with the various synthetic lesions introduced.

[0092] In some embodiments, an optimization algorithm may be provided, wherein the user may be informed of the ideal treatment location, such as for stent placement, or the worst-case scenario for post-intervention risk assessment may be identified given a set of user-selected constraints.

[0093] The methods according to this disclosure can be implemented on a computer as computer-implemented methods, or in dedicated hardware, or in a combination of both. Executable code for the methods according to this disclosure can be stored on 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-transient program code stored on a computer-readable medium for performing the methods according to this disclosure when the program product is executed on a computer. In embodiments, the computer program may include computer program code adapted to perform all steps of the methods according to this disclosure when the computer program is run on a computer. The computer program may be embodied on a computer-readable medium.

[0094] Although the invention has been described with respect to several described embodiments at considerable length and with certain specificity, it is not intended to limit it to any such details or embodiments or any particular embodiment, but rather to 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, and thus effectively cover the intended scope of this disclosure.

[0095] All examples and conditional language described herein are intended for pedagogical purposes to aid the reader in understanding the principles of this disclosure and the concepts contributed by the inventors to advance the art, and should be construed as not being limited to these specifically described examples and conditions. Furthermore, all statements herein describing the principles, aspects, and embodiments of this disclosure and their specific examples are intended to cover both their structural and functional equivalents. Additionally, such equivalents are intended to include both currently known equivalents and those developed in the future, i.e., any element developed that performs the same function, regardless of its structure.

Claims

1. A method for evaluating the patency of a potential treatment, comprising: Retrieve three-dimensional data including vascular anatomy and the patient's vascular system; A post-treatment hemodynamic model is generated based on the patient's vascular system, which is modified based on a proposed treatment suitable for the patient's vascular system. In the post-treatment hemodynamic model, at least one lesion is introduced or expanded into the patient's vascular system to create a long-term hemodynamic model; Blood flow is simulated in the long-term hemodynamic model; as well as The expected physiological effects of the proposed treatment are generated based on the long-term hemodynamic model.

2. The method according to claim 1, wherein, Generating the post-treatment hemodynamic model includes: Based on the aforementioned three-dimensional data, an initial hemodynamic model of the patient's vascular system is generated; and The proposed modifications are applied to the initial hemodynamic model based on the proposed treatment applicable to the patient's vascular system.

3. The method according to claim 1, wherein, The proposed treatment is selected based on a rule-based model that is applied to at least one detected detail of the patient's vascular system.

4. The method according to claim 1, wherein, The proposed treatment involves stent implantation in at least one existing lesion.

5. The method according to claim 1, wherein, The three-dimensional data includes three-dimensional imaging, and the method further includes applying a segmentation process to the three-dimensional imaging, wherein the initial hemodynamic model is generated based on vascular lumens extracted from the three-dimensional imaging using the segmentation process.

6. The method according to claim 1, further comprising: Retrieve multiple parameters from the user for the at least one lesion, and introduce or expand the at least one lesion based on the retrieved parameters.

7. The method according to claim 1, further comprising: Evaluate at least one characteristic of the patient's vascular system, and introduce or expand the at least one lesion at a location selected based on the evaluation.

8. The method according to claim 7, further comprising: Identify potential plaque formation sites, and introduce or expand the at least one lesion at the potential plaque formation sites.

9. The method according to claim 8, further comprising: Blood flow is simulated in the post-treatment hemodynamic model prior to the introduction of the at least one lesion, and the possible plaque formation location is identified based on the simulation of blood flow in the post-treatment hemodynamic model.

10. The method according to claim 1, wherein, The expected physiological effects are at least one of the following: A measure of relative perfusion change comparing the long-term hemodynamic model with an initial hemodynamic model of the patient's vascular system based on the three-dimensional data; and A measure of relative perfusion change by comparing the long-term hemodynamic model with the post-treatment hemodynamic model.

11. The method according to claim 1, further comprising: Modify at least one parameter of the at least one lesion to create at least one modified long-term hemodynamic model, generate the proposed treatment and at least one modified predicted physiological effect of the at least one lesion based on the corresponding at least one modified long-term hemodynamic model, and generate a patency measure based on a comparison of the predicted physiological effect with the at least one modified predicted physiological effect.

12. The method according to claim 1, wherein, The unobstructedness measure is at least one of the following: A measure of relative perfusion change by comparing iterative comparisons of the long-term hemodynamic model in the context of parameter variations in at least one lesion; as well as A measure of relative sensitivity in the context of parameter variations in the at least one lesion.

13. The method according to claim 1, further comprising: The initial hemodynamic model is modified based on alternative treatments applicable to the patient's vascular system to create a post-alternative hemodynamic model. In the alternative treatment post-hemodynamic model, at least one lesion is introduced or expanded into the patient's vascular system to create an alternative long-term hemodynamic model; Blood flow was simulated in the alternative long-term hemodynamic model; as well as The anticipated physiological effects of the alternative treatments and the at least one lesion are generated based on the alternative long-term hemodynamic model. Output a comparison of the expected physiological effects of the proposed treatment with the expected physiological effects of the alternative treatment.

14. The method according to claim 13, wherein, The disposal location differs for the proposed disposal and the alternative disposal.

15. An apparatus for evaluating the patency of a potential treatment, comprising: Memory, which stores multiple instructions; as well as A processor, coupled to the memory and configured to execute the plurality of instructions to perform the following operations: Retrieve three-dimensional data including vascular anatomy and the patient's vascular system; A post-treatment hemodynamic model is generated based on the patient's vascular system, which is modified based on a proposed treatment suitable for the patient's vascular system. In the post-treatment hemodynamic model, at least one lesion is introduced or expanded into the patient's vascular system to create a long-term hemodynamic model; Blood flow is simulated in the long-term hemodynamic model; as well as The expected physiological effects of the proposed treatment are generated based on the long-term hemodynamic model.

16. The apparatus according to claim 15, wherein, The processor is also configured to: Based on the aforementioned three-dimensional data, an initial hemodynamic model of the patient's vascular system is generated; and The proposed modifications are applied to the initial hemodynamic model based on the proposed treatment applicable to the patient's vascular system to generate a post-treatment hemodynamic model.

17. The apparatus according to claim 15, wherein, The proposed treatment is selected based on a rule-based model that is applied to at least one detected detail of the patient's vascular system.

18. The apparatus according to claim 15, wherein, The proposed treatment involves stent implantation in at least one existing lesion.

19. The apparatus according to claim 15, wherein, The three-dimensional data includes three-dimensional imaging, wherein a segmentation process is applied to the three-dimensional imaging, and wherein the initial hemodynamic model is generated based on vascular lumens extracted from the three-dimensional imaging using the segmentation process.

20. The apparatus according to claim 15, wherein, Multiple parameters for the at least one lesion are retrieved from the user, and the at least one lesion is introduced or amplified based on the retrieved parameters.

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