WO2024200348A1

WO2024200348A1 - Therapy outcome confirmation by simulating patency scenarios

Info

Publication number: WO2024200348A1
Application number: PCT/EP2024/057945
Authority: WO
Inventors: Tobias WISSEL; Marco Baragona; Christian Buerger; Erik BRESCH; George KOLOKOLNIKOV; Andrei Poliakov
Original assignee: Koninklijke Philips NV
Current assignee: Koninklijke Philips NV
Priority date: 2023-03-31
Filing date: 2024-03-25
Publication date: 2024-10-03
Anticipated expiration: 2025-09-30
Also published as: CN120883284A

Abstract

An outcome of therapy applied to treat a blood vessel is evaluated by retrieving a three-dimensional data comprising a blood vessel anatomy and a patient vasculature. Then, a post-treatment hemodynamic model is generated based on a therapy applied to the blood vessel anatomy of the three-dimensional data. At least one lesion is introduced and enlarged to the patient vasculature in the post-treatment hemodynamic model to create a long-term hemodynamic model, and blood flow is simulated in the long-term hemodynamic model. A projected physiological status of the patient vasculature is generated based on the long-term hemodynamic model.

Description

THERAPY OUTCOME CONFIRMATION BY SIMULATING PATENCY SCENARIOS

FIELD

[0001] The present invention generally relates to systems and methods for evaluating an outcome of therapy applied to treat a blood vessel. In particular, the present invention relates to evaluating treatments of stenotic lesions.

BACKGROUND

[0002] In percutaneous treatment of stenotic lesions in diseased patients, there are challenges to determining when an interventionalist has done enough. Accordingly, the interventionalist must define a point at which to stop treatment. It is desirable to treat what is necessary, but not to overtreat the patient. Excessive treatment is generally undesirable, and there is evidence that an overtreated lesion imposes a higher risk for re-stenosis.

[0003] Accordingly, when treating stenotic lesions, the interventionalist targets the restoration of blood flow by opening the diseased vessel, which would therefore confirm a therapy result which constitutes a technical success. However, the interventionalist must not over-treat the patient and ideally leaves a result that is likely to be patent, or unobstructed, over the long-term.

[0004] Typically, during treatment, no or little predictive information about the patency of the current treatment status is available. Accordingly, it is difficult to determine whether the interventionalist has done enough at any given time, or if further actions are required to ensure a robust and patent, i.e. future -proof, result. In a patent scenario, future plaque growth would not immediately have a substantial impact on blood supply to the critical downstream structures. Therefore, there is a need to test relevant future scenarios with respect to robustness and fragility. During the intervention, this information is highly actionable, since the interventionalist can adjust the treatment accordingly.

[0005] Therefore, it is desirable to identify and confirm a therapy result which not only constitutes technical success, but also is likely to be patent over long-term.

SUMMARY

[0006] When treating stenotic lesions, an interventionalist targets the restoration of blood flow by opening the diseased vessel but also must not over-treat the patient. Therefore, it is desirable to identify and confirm a therapy result which not only constitutes technical success, but also is likely to be patent over long-term.

[0007] Systems and methods are, therefore, provided for utilizing a three-dimensional description of patient-specific anatomy after treatment and investigating the patency of the treatment result. An interactive interface may be provided for the clinician to test possible future lesion growth at relevant locations. This may be by utilizing a module which models and inserts different types of plausible synthetic lesions into the post-treatment anatomy. A flow model may 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 likely future lesion growth locations may be utilized to identify fragile scenarios, where small morphological changes may result in substantial reduction of patency.

[0009] In some embodiments, similar systems and methods may be utilized during treatment planning using hypothetical therapy results, such as stent placements. Such therapy results may also include longer term risk scenarios.

[0010] In some embodiments, a method is provided for evaluating patency of a therapy. The method includes retrieving three-dimensional data comprising a blood vessel anatomy and a patient vasculature. The method then proceeds to generate a post-treatment hemodynamic model based on a therapy applied to the blood vessel anatomy. 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 and simulate blood flow in the long-term hemodynamic model. The method then generates a projected physiological status of the patient vasculature based on the long-term hemodynamic model.

[0011] In some embodiments, the three-dimensional data includes composite information from a plurality of two-dimensional images.

[0012] In some embodiments, the three-dimensional data comprises three-dimensional imaging comprising either computed tomography angiography (CTA) or magnetic resonance angiography (MRA). In some such embodiments, the method further comprises applying a segmentation process to the three-dimensional imaging. The post-treatment hemodynamic model is then generated based on a vessel lumen extracted from the three-dimensional imaging by way of the segmentation process.

[0013] In some embodiments, the three-dimensional data is reconstructed from two-dimensional interventional data following an application of the therapy applied to the blood vessel anatomy. [0014] In some embodiments, the post-treatment hemodynamic model is generated by generating an initial hemodynamic model of the patient vasculature based on the three- dimensional data, identifying a current treatment status based on information from the therapy applied, and modeling the current treatment status in the context of the initial hemodynamic model in order to generate the post-treatment hemodynamic model.

[0015] In some embodiments, the method further includes retrieving, from a user, parameters for a lesion to be generated. The introduction or enlarging of the at least one lesion is then the introduction of a lesion based on the retrieved parameters.

[0016] In some embodiments, the lesion to be introduced or enlarged is 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. The introduction or enlarging of the at least one lesion is then at a location selected based on the evaluation of the patient vasculature.

[0018] In some such embodiments, the evaluation of the patient vasculature identifies a likely plaque formation location. The introduction or enlarging of the at least one lesion is the introduction of at least one lesion at the likely plaque formation location.

[0019] In some such embodiments, the method includes a first simulation of blood flow in the post-treatment hemodynamic model prior to introducing the at least one lesion. The identification of the likely plaque formation location is then based on the first simulation of blood flow in the post-treatment hemodynamic model.

[0020] In some embodiments, each of the flow models is a 0D or a ID model based on the three- dimensional data.

[0021] In some embodiments, the projected physiological status is based on the perfusion or flow of a downstream location relative to the therapy applied. In some such embodiments, the projected physiological status is at least one of a measure of absolute perfusion value and a measure of relative perfusion change comparing the long term hemodynamic model and the posttreatment hemodynamic model.

[0022] 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 status of the patient vasculature and the at least one lesion based on the corresponding at least one modified long-term hemodynamic model. The method then generates a patency metric based on a comparison of the projected physiological status to the at least one modified projected physiological status.

[0023] In some such embodiments, the patency metric is then 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.

[0024] In some embodiments, the method includes applying a modification to the post-treatment hemodynamic model based on a potential treatment applicable to the patient vasculature to create an alternative post-treatment hemodynamic model. The method then includes introducing or enlarging at least one lesion to the patient vasculature in the alternative post-treatment hemodynamic model to create an alternative long-term hemodynamic model.

[0025] The method then proceeds to simulate blood flow in the alternative long-term hemodynamic model and generates a projected physiological status of the patient vasculature and the at least one lesion based on the alternative long-term hemodynamic model.

[0026] The method then outputs a comparison of the projected physiological status of the patient vasculature associated with the long-term hemodynamic model and the projected physiological status of the patient vasculature associated with the alternative long-term hemodynamic model. [0027] In some such embodiments, the potential treatment is one of a plurality of potential treatments. The method then compares a projected physiological status associated with each of the potential treatments and generates a recommendation based on a patency metric associated with each of the plurality of potential treatments as compared to the equivalent patency metric associated with the long-term hemodynamic model. In some such embodiments, a treatment location differs for each of the plurality of potential treatments. [0028] In some embodiments, the method in which a modification is introduced based on a potential treatment further includes modifying at least one parameter of the at least one lesion in the post-treatment hemodynamic model to create at least one modified long-term hemodynamic model.

[0029] The method then includes generating at least one modified projected physiological status of the patient vasculature and the at least one lesion based on the corresponding at least one modified long-term hemodynamic model and generating a patency metric based on a comparison of the projected physiological status associated with the long-term hemodynamic model to the at least one modified projected physiological status associated with the at least one modified longterm hemodynamic model.

[0030] The method then proceeds to modify at least one parameter of the 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 generates at least one modified projected physiological status of the patient vasculature and the at least one lesion based on the at least one modified alternative long-term hemodynamic model and generates an alternative patency metric based on a comparison of the projected physiological status associated with the alternative longterm hemodynamic model to the at least one modified projected physiological status associated with the at least one modified alternative long-term hemodynamic model.

[0031] The method finally outputs a comparison of the patency metric to the alternative patency metric.

[0032] According to one embodiment of the present invention, an apparatus for evaluating patency of a therapy comprises a memory that stores a plurality of instructions and a processor that couples to the memory and is configured to execute the plurality of instructions to retrieve three-dimensional data comprising a blood vessel anatomy and a patient vasculature; generate a post-treatment hemodynamic model based on a therapy applied to the blood vessel anatomy; introduce or enlarging at least one lesion to the patient vasculature in the post-treatment hemodynamic model to create a long-term hemodynamic model; simulate blood flow in the longterm hemodynamic model; and generate a projected physiological status of the patient vasculature based on the long-term hemodynamic model. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] FIG. 1 is a schematic diagram of a system according to one embodiment of the present invention.

[0034] FIG. 2 illustrates a method for evaluating patency in accordance with one embodiment of the present invention.

[0035] FIG. 3 illustrates a block diagram for a system implementing the method of FIG. 2.

[0036] FIG. 4 illustrates a modeled description of a patient vasculature in accordance with one embodiment of the present invention.

[0037] FIG. 5 illustrates the introduction of a lesion to the patient vasculature of FIG. 4.

[0038] FIG. 6 illustrates the simulation of blood flow in the modeled patient vasculature of FIG. 5.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0039] The description of illustrative embodiments according to principles of the present invention is intended to be read in connection with the accompanying drawings, which are to be considered part of the entire written description. In the description of embodiments disclosed herein, any reference to direction or orientation is merely intended for convenience of description and is not intended in any way to limit the scope of the present invention. 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 as such. 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 invention are illustrated by reference to the exemplified embodiments. Accordingly, the invention expressly should not be limited to such exemplary embodiments illustrating some possible nonlimiting combination of features that may exist alone or in other combinations of features; the scope of the invention being defined by the claims appended hereto. [0040] This disclosure describes the best mode or modes of practicing the invention as presently contemplated. This description is not intended to be understood in a limiting sense, but provides an example of the disclosure presented solely for illustrative purposes by reference to the accompanying drawings to advise one of ordinary skill in the art of the advantages and construction of the invention. In the various views of the drawings, like reference characters designate like or similar parts.

[0041] It is important to note that the embodiments disclosed are only examples of the many advantageous uses of the innovative teachings herein. In general, statements made in the specification of the present application do not necessarily limit any of the various claimed embodiments. Moreover, some statements may apply to some inventive features but not to others. In general, unless otherwise indicated, singular elements may be in plural and vice versa with no loss of generality.

[0042] When treating stenotic lesions, an interventionalist targets the restoration of blood flow by opening the diseased vessel but also must not over-treat the patient. Therefore, it is desirable to identify and confirm a therapy result, which not only constitutes technical success, but also is likely to be patent over the long-term.

[0043] Systems and methods are, therefore, provided for utilizing a three-dimensional description of patient-specific anatomy after treatment and investigating the patency of the treatment result. An interactive interface may be provided for the clinician to test possible future lesion growth at relevant locations. This may be by utilizing a module, which models and inserts different types of plausible synthetic lesions into the post-treatment anatomy. A flow model may 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.

[0044] In a patent scenario, future plaque growth would not immediately have a substantial impact on blood supply to critical downstream structures. Therefore, there is a need to test relevant future scenarios with respect to robustness and fragility.

[0045] Typically, if a patient is scheduled for a therapy, imaging has already taken place. Accordingly, while the description herein includes the acquisition of imaging, it is understood that preexisting imaging may be utilized and modified in accordance with whatever therapy is being applied. [0046] The approach described herein may be used to provide feedback during an intervention in order to provide actionable insights about future patency of the current treatment status. It can therefore be part of the interventional suite, for example. The interventional application would benefit from cloud access to pre-interventional data, as well as to potential cloud assets around complex flow models that require extended computing resources.

[0047] FIG. 1 is a schematic diagram of a system 100 according to one embodiment of the present disclosure. As shown, the system 100 typically includes a processing device 110 and an imaging device 120.

[0048] The processing device 110 may apply processing routines to images or measured data, such as projection data, received from the imaging device 120. The processing device 110 may include a memory 113 and processor circuitry 111. The memory 113 may store a plurality of instructions. The processor circuitry 111 may couple to the memory 113 and may be configured to execute the instructions. The instructions stored in the memory 113 may comprise processing routines, as well as data associated with 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 will be understood that in some embodiments, some data may be stored in a database, which may itself either be stored in the memory or a discrete separate system.

[0049] The processing device 110 may further include an input 115 and an output 117. The input 115 may receive information, such as images or measured data, from the imaging device 120. The output 117 may output information, such as processed images or models generated therefrom, to a user or a user interface device. The output 117 similarly may be provision of data to a user interface in which the user can manipulate the inputs for hemodynamic models to be generated. The output 117 may similarly output determinations generated by the method described herein, such as recommendations, projected physiological status for a patient, and patency metrics and projections. The output may include a monitor or display, which may display additional information or a model updated in real-time or near real-time based on user inputs.

[0050] In some embodiments, the processing device 110 may relate to the imaging device 120 directly. In alternate embodiments, the processing device 110 may be distinct from the imaging device 120, such that it receives images or measured data for processing by way of a network or other interface at the input 115.

[0051] In some embodiments, the imaging device 120 may include an image data processing device and a spectral or conventional CT scanning unit for generating the CT projection data when scanning an object (e.g., a patient). Further, the imaging device 120 may be set up for either invasive or non-invasive coronary CT angiography. As such, the imaging may be performed with a contrast agent, and the image timing may be set up in order to track fluid flow in blood vessels.

[0052] In addition to conventional and/or spectral CT images, the method may rely on multiple spectral image results, photon counting CT images, and/or dark field CT images. In this manner, the method may rely on retrieving three-dimensional data stored in the imaging device 120. Alternatively, the imaging device 120 may acquire two-dimensional images which may be utilized to derive three-dimensional data. For example, multiple two-dimensional images may be considered together in order to infer three-dimensional characteristics.

[0053] Alternatively, two-dimensional imaging may be used in concert with secondary information sufficient to infer three-dimensional characteristics. For example, two-dimensional projections may be paired with lumen characteristics of patient vasculature to derive data needed for flow models. Similarly, two-dimensional imaging may be fused with flow information, such as a flow visible in X-ray fluoroscopy or X-ray angiography. For the purposes of this disclosure, three-dimensional data includes such instances of two-dimensional imaging infused with flow information to better describe blood vessel anatomy.

[0054] While a system, as shown in FIG. 1, includes an imaging device 120 and a processing device 110, it will be understood that the method may be implemented directly on a processing device, as in the context of an image, or imaging, received by way of a network at the input 115. The methods described herein involve processing data as a component of evaluating patency of a therapy, such as stenting. As noted above, typically, prior to such a procedure, imaging is performed. As such, previously generated imaging may be retrieved by way of the input 115, and post-treatment models may be generated based on the previously retrieved data combined with information based on the therapy performed. [0055] FIG. 2 illustrates a method for evaluating patency in accordance with this disclosure. As noted above, the method for evaluating patency relates to patency of a therapy upon the application of such a therapy to a blood vessel of a patient. For example, the therapy may be a treatment for stenotic lesions. FIG. 3 illustrates a block diagram for a system 400 implementing the method of FIG. 2.

[0056] Typically, the method proceeds upon the performance of the treatment (200) by the interventionalist. The performance of the treatment may involve, for example, inserting a stent (stenting) at least one lesion.

[0057] The method then proceeds with retrieving three-dimensional data (210) describing a blood vessel anatomy of the patient that has been treated. The three-dimensional data includes a patient vasculature corresponding to the patient being treated, and it may be received at a data handling and manipulation module 410 of the system 400 (FIG. 4). The three-dimensional data may take a variety of forms, and it may have been generated either before or after the performance of the treatment by the interventionalist (at 200). Portions of the method may be implemented by the data handling and manipulation module 410, and the data may then be used for lesion and flow modeling as described below.

[0058] The three-dimensional data may be three-dimensional imaging, such as computed tomography scans (CT) or angiography (CTA). Alternatively, the three-dimensional imaging may be magnetic resonance imaging (MRI) or angiography (MR A).

[0059] In typical embodiments, following retrieval of the three-dimensional data (at 210), a lumen and/or centerline for the vasculature to be modeled may be identified (220). Where the three-dimensional data is three-dimensional imaging, the method may proceed by applying a segmentation process to the imaging. In such a scenario, the post-treatment hemodynamic model discussed herein may be generated based on a vessel lumen extracted and identified (at 220) by way of the segmentation process.

[0060] In some embodiments, the three-dimensional data comprises composite information from a plurality of two-dimensional images. In such embodiments, instead of retrieving three- dimensional imaging, the system may retrieve multiple two-dimensional images, such as conventional X-rays, and infer three-dimensional characteristics by considering the images in concert. In some embodiments, the three-dimensional data may be reconstructed from two- dimensional interventional data following an application of the therapy applied to the blood vessel anatomy.

[0061] In some embodiments, two-dimensional data may be provided, but a lumen and/or centerline representation is provided as secondary data. Such a combination of data may thereby be integrated in order to form the three-dimensional data retrieved (at 210) by the method. In such a scenario, the data may never actually be processed in three-dimensions as volumetric data, and may instead be applied directly to the modeling of a vascular tree. Accordingly, it will be understood that the three-dimensional data can take a wide variety of forms and represents a three-dimensional description of the patient-specific anatomy after treatment.

[0062] At some point following treatment, the method may then proceed to generate a posttreatment hemodynamic model (230) based on a therapy applied to the blood vessel anatomy described. Accordingly, such a model may be generated (230) at the data handling and manipulation module 410 based on three-dimensional data acquired or generated prior to the performance of the therapy, and then modified to reflect the outcome of the treatment applied. As such, three-dimensional information on the patient vasculature, such as pre-operational CT A data, may be retrieved, and separately, procedural information, such as auto-documentation and quantification of the procedure, which may include, e.g., stent placement, may be retrieved.

[0063] Accordingly, in some embodiments, prior to generating the post-treatment hemodynamic model (230), an initial hemodynamic model of the patient vasculature may be created (223) based on the three-dimensional data. The method may then identify a current treatment status (226) based on procedural information from the therapy applied, and model the current treatment status in the context of the initial hemodynamic model in order to generate the post-treatment hemodynamic model (230).

[0064] Where the three-dimensional data was generated prior to the treatment (200), the method may use local displacement fields in the three-dimensional data to model ballooning of the vessel at the treatment site based on fluoroscopic or angiographic imaging of a placed stent. The stent can then be artificially inserted into the three-dimensional image.

[0065] In practice, a segmentation of the vessel lumen may be extracted from the three- dimensional data as a bitmask or surface mesh and used as an input to a lesion model. Manipulations based on current treatment status may then be applied to the extracted lumen instead of the original image voxel space.

[0066] Alternatively, the post-treatment model may be generated (230) based on three- dimensional data generated, or generated in part, following the performance of the therapy. [0067] Where the three-dimensional data relies on interventional data after the treatment step and reconstructs a three-dimensional scene, such a scene may be reconstructed based on a modern, data-driven inverse graphics inference from one or more two-dimensional images, or it may be based on a three-dimensional image reconstruction from a set of projections. This approach may be similar to a cone-beam CT scan (CBCT). This type of reconstruction may directly target the lumen mask without considering voxelized image space, thereby reducing the amount of processing and data necessary.

[0068] FIG. 4 illustrates a modeled description of a patient vasculature in accordance with this disclosure. The model shown in FIG. 4 is an example of a post-treatment hemodynamic model (such as that generated at 230) based on the patient vasculature that has been treated. A user, such as a clinician, may then utilize the method to test certain future scenarios for robustness following a specific treatment of the patient. For example, the clinician may initially stent one or more lesions, and then stop to test the patency of the current status. The method then attempts to determine how fragile the current result is, and what the risk of patient reinterventions is.

[0069] The post-treatment hemodynamic model (generated at 230) is designed to reflect flow conditions immediately following the therapy. However, such a model does not take into account expected changes in the vasculature over time. Accordingly, in order to project a longer-term patency expectation for the patient vasculature, the method (FIG. 2) proceeds with introducing or enlarging (240) at least one synthetic lesion to the patient vasculature in the post-treatment hemodynamic model. This may be implemented at a synthetic lesion modeling and insertion module 420 in the system 400 (FIG. 3). Such an introduction or enlargement allows the method to create a long-term hemodynamic model 430 (250 in FIG. 2).

[0070] In some embodiments, patient data may be made available to the method throughout the process described herein or at specific times during the process. Accordingly, as shown, the patient data may be utilized in order to better inform the long-term model (generated at 250). For example, patient characteristics, such as patient condition or drugs utilized by the patient may be used to improve the accuracy of the model.

[0071] Fig. 5 illustrates the introduction of a synthetic lesion (at 240) to the patient vasculature of FIG. 4. The model 430 incorporating the introduced synthetic lesion corresponds to the longterm hemodynamic model described and generated herein (250).

[0072] In some embodiments, a user of a system implementing the method described may provide some input parameters for a synthetic lesion to be generated or enlarged at an interactive user interface 440. Accordingly, prior to the introduction of the synthetic lesion (240), the method may retrieve parameters for a synthetic lesion from a user (233). In such an embodiment, the introduction or enlargement of a synthetic lesion (240) may be an introduction of a synthetic lesion based on the user-entered parameters. This may be by the synthetic lesion modeling and insertion module 420 (FIG. 3). Such a module may generate synthetic lesions given a desired parameterization or other inputs provided at the user interface 440 (FIG. 3) and integrate them on request into the patient specific vasculature.

[0073] In such embodiments, the user may be presented with an interactive user interface 440, in which a clinician may be able to test possible future lesion growth at relevant locations using the lesion modeling and insertion module 420 which models and inserts different types of plausible synthetic lesions into the post-treatment anatomy. As noted herein, multiple scenarios may ultimately be modeled and compared.

[0074] For example, the clinician may instruct the system implementing the method to assume an in-stent restenosis of a certain degree or to increase the significance or diffusivity of another lesion. The method would then evaluate the physiological consequences in the following steps. For example, the method may attempt to determine if a slight restenosis in a specified location has a dramatic effect on volume flow if no further treatment is attempted.

[0075] Alternatively, in some embodiments, the method may proceed to identify at least one characteristic of the patient vasculature (238). The identified characteristic may then be usable to determine a location or other characteristic for a synthetic lesion to be introduced or enlarged (240). For example, the method may include identifying a likely plaque formation location in the patient vasculature. The introduction or enlargement of a lesion may then be the introduction of a synthetic lesion (240) at the likely plaque formation location. [0076] In some embodiments, additional processing may be applied in order to identify the characteristic (238). For example, in some embodiments, the method may implement a first simulation of blood flow (235) in the post-treatment hemodynamic model prior to introducing the at least one synthetic lesion (240). The identification of the likely plaque formation location (238) is then based on the first simulation of blood flow (235). This may be, for example, by identifying a region of low wall shear stress in the patient vasculature or a bifurcation within existing stents.

[0077] In some embodiments, the introduction or enlargement of the at least one synthetic lesion (240) may be based on a combination of user-entered parameters (233) and some evaluation of vasculature characteristics identified by the method (238). Accordingly, the evaluation of the patient vasculature may be at least partially based on, e.g., a patient profile defined by the user (233). For example, a user may be allowed to select the most appropriate patient profile among available or customizable (diseased) presets. Such a selection may then influence the plaque growth model through knowledge based categorical priors. As noted herein, patient data, as shown in FIG. 2, may be generally made available to the model at various times prior to creating the long-term model (250).

[0078] Further, in addition to choosing, for example, an appropriate location for a synthetic lesion to be introduced, characteristics and parameters of synthetic lesions introduced or enlarged may be similarly selectable, either by a user (233) or by the system itself (238). Accordingly, the synthetic lesion to be introduced or enlarged may be a focal lesion, a diffuse lesion, a bifurcation lesion, and/or a total occlusion. Further, the synthetic lesion to be introduced may have parameters that are score-driven (such as lesion significance, a Medina score, or parts of a Syntax II score) or geometry driven (such as length, longitudinal profile, symmetry, or concentricity, among others).

[0079] In some embodiments, implementation may be made available in semi-analytical platforms. Such an implementation is then semi analytical in nature, and may therefore be fast and efficient. Fast modeling may be useful in enabling the interactive approaches described herein.

[0080] Ultimately, a synthetic lesion is introduced or enlarged (240), and the results are then used to generate the long term hemodynamic model (250). [0081] The method then proceeds to simulate blood flow (260) in the long-term hemodynamic model (250). The outcome of such simulation is then used to generate a projected physiological status (270a) of the patient vasculature and the at least one synthetic lesion introduced or enlarged based on the long-term hemodynamic model.

[0082] FIG. 6 illustrates the simulation of blood flow (260 in FIG. 2) in the modeled patient vasculature of FIG. 5. As shown, physiological impact is evaluated by simulating blood flow based on the anatomy and potentially other boundary conditions. For example, aortic pressure or other patient characteristics may be considered.

[0083] It is noted that while the renderings of the models shown in FIGS. 4 - 6 are shown in three dimensions, and the model is often utilized in three dimensions, such a model could also be a simplified 0D or ID model based on the three-dimensional data. Similarly, a computational fluid dynamics (CFD) simulation may be used. As such, different complexities may be used, as well as combinations (i.e., three dimensional CFD calculations within a 0D model framework). [0084] The impact of the inserted synthetic lesion may be evaluated based on a target measure, such as the perfusion of a downstream location in the vasculature and/or supplied tissue (e.g., in the context of a myocardial perfusion assessment). Such measures may correspond to, be a portion of, or be based on the projected physiological status (270a) determined. Patency may be quantified in the context of the projected physiological status based on either the absolute perfusion value achieved at the target or by the change in perfusion relative to the morphological change introduced (i.e., fragility). The latter can be quantified, for example, by a change in a lesion significance parameter.

[0085] In some embodiments, the projected physiological status (generated at 270a) may be based on the perfusion of a downstream location relative to the therapy applied. For example, the downstream location may be a downstream location in the vasculature of the patient modeled. The projected physiological status may then be a measure of absolute perfusion value or a measure of relative perfusion change comparing the long-term hemodynamic model to the posttreatment hemodynamic model.

[0086] In some embodiments, following the simulation of blood flow (260) in the modeled patient vasculature, or in parallel with the implementation of the modeling process described herein, the method proceeds to apply a modification (280) to the post-treatment hemodynamic model of FIG. 5. Such a modification may be based on varying parameters of the lesion introduced or enlarged (at 240), as well as based on introducing or varying a potential treatment applicable to the patient vasculature, which may then be used to create an alternative post treatment hemodynamic model (at 230).

[0087] Accordingly, in some embodiments, the method includes applying a modification (at 280) to at least one parameter of the at least one lesion introduced or enlarged (at 240) by way of the synthetic lesion modelling and insertion module 420. This may be by directly modifying a parameter of the lesion itself or by modifying the parameters provided (at 233) or the characteristics identified (at 238) that led to the parameters of the lesion generated. Such a modification may then be used to generate at least one modified long-term hemodynamic model (at 250) and to simulate blood flow (at 260) in such a modified model in order to generate a modified projected physiological status (at 270b) of the patient vasculature and the at least one lesion based on the corresponding modified long-term hemodynamic model.

[0088] Once the projected physiological status and the modified projected physiological status are generated (at 270a, 270b), the statuses may be used to generate a patency metric 450 based on a comparison of the statuses (at 290). It is understood that while the method is discussed in the context of two iterations of the projected physiological status (at 270a, 270b), additional iterations may be provided (such as at 270c) so as to provide additional data for generation of a patency metric 450. Such a patency metric 450 may then provide 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. Alternatively, or in addition, the patency metric 450 may provide a measure of relative sensitivity in the context of parameter changes in the at least one lesion.

[0089] Accordingly, several long-term models may be generated based on small variations around the chosen lesion scenario, resulting in variations of the projected physiological status. The patency metric 450 may then relate to relative sensitivity of, e.g., the perfusion, based on several similar long-term models. For example, modifying lesion significance from 0-50% in different steps may yield non-linear behavior of perfusion with respect to such a lesion parameter. The patency metric 450 may then describe fragility in a complex manner. [0090] As noted above, in addition to variations to the lesion introduced or enlarged (at 240), the method may introduce modifications (at 280) to the post-treatment model (at 230) based on hypothetical treatments applicable to the patient vasculature.

[0091] The method may then repeat the process described above in order to introduce or enlarge at least one synthetic lesion to the patient vasculature (at 240) in the alternative post-treatment hemodynamic model (at 230) to create an alternative long-term hemodynamic model (at 250). The method then simulates blood flow (at 260) in the alternative long-term hemodynamic model and generates a projected physiological status (at 270b) of the modification (introduced at 280) and the at least one synthetic lesion (introduced at 240). The modification (at 280) may be a potential treatment, as discussed above, and the projected physiological status (at 270b) allows for an evaluation of the impact of the proposed treatment.

[0092] In some embodiments, this portion of the process may be repeated multiple times in order to simulate, for example, distinct potential treatments available to the patient based on their vasculature. Accordingly, additional projected physiological statuses may be generated (at 270c) in order to evaluate additional scenarios. Such repetitions may be iterative, such that, upon reviewing the projected physiological status of the initial scenario (at 270a), a user, such as a doctor, may propose a treatment (at 280) to introduce to the model. The doctor may then review the projected physiological status (at 270b) of the proposed treatment and propose a different or modified treatment.

[0093] Following the generation of multiple projected physiological statuses (270a, b, c), the method may proceed to generate and output a comparison (at 290) of the projected physiological statuses, so as to evaluate the impact of proposed treatments. Accordingly, the method may compare a projected physiological status associated with each of the potential treatments in order to generate a recommendation based on a physiological status or a patency metric 450 associated with each of the potential treatments.

[0094] As discussed above, the method may allow users to modify parameters of the introduced or enlarged lesions (at 240). Accordingly, when iteratively repeating the described method, modifications may be introduced along different dimensions, such that different lesion scenarios may be considered with respect to each proposed treatment. [0095] Accordingly, in some embodiments, following the initial iteration of the method resulting in a projected physiological status, the method may modify at least one parameter of the at least one lesion (introduced or enlarged at 240) in order to create at least one modified long-term hemodynamic model (at 250). The method may then generate at least one modified projected physiological status (at 270b) corresponding to the at least one modified long-term hemodynamic model. The method may then generate a patency metric 450 based on a comparison of the projected physiological status associated with the long-term hemodynamic model (at 270a) to the at least one modified projected physiological status associated with the at least one modified long-term hemodynamic model (at 270b).

[0096] The method may then similarly modify at least one parameter of the at least one lesion (introduced or enlarged at 240) in the alternative post-treatment hemodynamic model to create at least one modified alternative long-term hemodynamic model (at 250). The method may then generate at least one modified projected physiological status of the patient vasculature and the at least one lesion based on the at least one modified alternative long-term hemodynamic model. The method may then take the same approach as with the long-term hemodynamic model and generate an alternative patency metric 450 based on a comparison of the projected physiological status associated with the alternative long-term hemodynamic model to the at least one modified projected physiological status associated with the at least one modified alternative long-term hemodynamic model.

[0097] The method may then output a comparison of the patency metric 450 to the alternative patency metric in order to consider the fragility of patency in each of the proposed scenarios. [0098] In some embodiments, such as that described in which the potential treatment introduced (at 280) is one of a plurality of potential treatments, the method compares the projected physiological status associated with each of the potential treatments and generates (at 300) a recommendation based on a measure of fragility of patency, e.g., the patency metric 450, in each of the plurality of potential treatments as compared to the measure of fragility of patency in the long-term hemodynamic model.

[0099] In some embodiments, the different treatments considered and ultimately modeled (at 250) vary in terms of a degree of treatment, such as how many lesions are to be treated. In other embodiments, the difference may be in terms of different treatment locations for each potential treatment. In other embodiments, the difference may be in terms of the substance of the treatment to be applied, such as a type of stent to be applied.

[00100] In some embodiments, the modification to be introduced (280) is not a difference in treatment at all, but is instead a difference in the projected location of the synthetic lesion to be introduced or enlarged (at 240). Such differences may be introduced by a user, such as a clinician, using an interactive user interface, as discussed above. In some embodiments, the method itself may test a variety of potential synthetic lesions, and may leverage knowledge of prior scenarios so as to identify fragile scenarios where small morphological changes may result in substantial reduction or increase of patency. Accordingly, the patency metric 450 may consider outcomes associated with a wide variety of synthetic lesions introduced.

[00101] In some embodiments, an optimization algorithm may be provided in which users may be informed of ideal treatment locations, such as for stent placement, or may identify the worst possible scenarios for post-interventional risk assessment, given a set of user selected constraints. [00102] The methods according to the present disclosure may be implemented on a computer as a computer implemented method, or in dedicated hardware, or in a combination of both.

Executable code for a method according to the present disclosure may 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-transitory program code stored on a computer readable medium for performing a method according to the present disclosure when said program product is executed on a computer. In an embodiment, the computer program may include computer program code adapted to perform all the steps of a method according to the present disclosure when the computer program is run on a computer. The computer program may be embodied on a computer readable medium.

[00103] While the present disclosure has been described at some length and with some particularity with respect to the several described embodiments, it is not intended that it should be limited to any such particulars or embodiments or any particular embodiment, but it is to be construed with references to the appended claims so as to provide the broadest possible interpretation of such claims in view of the prior art and, therefore, to effectively encompass the intended scope of the disclosure. [00104] All examples and conditional language recited herein are intended for pedagogical purposes to aid the reader in understanding the principles of the disclosure and the concepts contributed by the inventor to furthering the art, and are to be construed as being without limitation to such specifically recited examples and conditions. Moreover, all statements herein reciting principles, aspects, and embodiments of the disclosure, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof.

Claims

What is claimed is:

1. A method for evaluating patency of a therapy, comprising: retrieving three-dimensional data comprising a blood vessel anatomy and a patient vasculature; generating a post-treatment hemodynamic model based on a therapy applied to the blood vessel anatomy; introducing or enlarging at least one lesion to the patient vasculature in the post-treatment hemodynamic model to create a long-term hemodynamic model; simulating blood flow in the long-term hemodynamic model; and generating a projected physiological status of the patient vasculature based on the longterm hemodynamic model.

2. The method of claim 1, wherein the three-dimensional data comprises composite information from a plurality of two-dimensional images.

3. The method of claim 1, wherein the three-dimensional data comprises three-dimensional imaging, and further comprising applying a segmentation process to the three-dimensional imaging, wherein the post-treatment hemodynamic model is generated based on a vessel lumen extracted from the three-dimensional imaging using the segmentation process.

4. The method of claim 1, wherein the three-dimensional data is reconstructed from two- dimensional interventional data following an application of the therapy applied to the blood vessel anatomy.

5. The method of claim 1, wherein the post-treatment hemodynamic model is generated by generating an initial hemodynamic model of the patient vasculature based on the three- dimensional data and identifying a current treatment status based on information from the applied therapy.

6. The method of claim 1, further comprising retrieving, from a user, parameters for a lesion to be generated, wherein the introduction or enlarging of the at least one lesion is based on the retrieved parameters.

7. The method of claim 1, wherein the lesion to be introduced or enlarged is at least one of a focal lesion, a diffuse lesion, a bifurcation lesion, and a total occlusion.

8. The method of claim 1, further comprising evaluating at least one characteristic of the patient vasculature, and wherein the introduction or enlarging of the at least one lesion is at a location selected based on the patient vasculature.

9. The method of claim 1, further comprising identifying a plaque formation location, and wherein the at least one lesion is introduced or enlarged at the plaque formation location.

10. The method of claim 9, further comprising simulating blood flow in the post-treatment hemodynamic model prior to introducing the at least one lesion, and wherein the plaque formation location is identified based on the simulation of blood flow in the post-treatment hemodynamic model.

11. The method of claim 1, wherein the projected physiological status is at least one of: a measure of absolute perfusion value, and a measure of relative perfusion change comparing the long-term hemodynamic model to the post-treatment hemodynamic model.

12. The method of claim 1, further comprising modifying at least one parameter of the at least one lesion to create at least one modified long-term hemodynamic model, generating at least one modified projected physiological status of the patient vasculature and the at least one lesion based on the corresponding at least one modified long-term hemodynamic model, and generating a patency metric based on a comparison of the projected physiological status to the at least one modified projected physiological status.

13. The method of claim 12, wherein 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.

14. The method of claim 1, further comprising: applying a modification to the post-treatment hemodynamic model based on a potential treatment applicable to the patient vasculature to create an alternative post-treatment hemodynamic model; introducing or enlarging at least one lesion to the patient vasculature in the alternative post-treatment hemodynamic model to create an alternative long-term hemodynamic model; simulating blood flow in the alternative long-term hemodynamic model; generating a projected physiological status of the patient vasculature and the at least one lesion based on the alternative long-term hemodynamic model; outputting a comparison of the projected physiological status of the patient vasculature associated with the long-term hemodynamic model and the projected physiological status of the patient vasculature associated with the alternative long-term hemodynamic model.

15. The method of claim 14, wherein the potential treatment is one of a plurality of potential treatments, and further comprising comparing a projected physiological status associated with each of the potential treatments and generating a recommendation based on a patency metric associated with each of the plurality of potential treatments as compared to the equivalent patency metric associated with the long-term hemodynamic model.

16. An apparatus for evaluating patency of a therapy, comprising: a memory that stores a plurality of instructions; and a processor that couples to the memory and is configured to execute the plurality of instructions to: retrieve three-dimensional data comprising a blood vessel anatomy and a patient vasculature; generate a post-treatment hemodynamic model based on a therapy applied to the blood vessel anatomy; introduce or enlarging at least one lesion to the patient vasculature in the posttreatment hemodynamic model to create a long-term hemodynamic model; simulate blood flow in the long-term hemodynamic model; and generate a projected physiological status of the patient vasculature based on the long-term hemodynamic model.

17. The apparatus of claim 16, wherein the three-dimensional data comprises composite information from a plurality of two-dimensional images.

18. The apparatus of claim 16, wherein the three-dimensional data comprises three-dimensional imaging, and further comprising applying a segmentation process to the three-dimensional imaging, wherein the post-treatment hemodynamic model is generated based on a vessel lumen extracted from the three-dimensional imaging using the segmentation process.

19. The apparatus of claim 16, wherein the three-dimensional data is reconstructed from two- dimensional interventional data following an application of the therapy applied to the blood vessel anatomy.

20. The apparatus of claim 16, wherein the post-treatment hemodynamic model is generated by generating an initial hemodynamic model of the patient vasculature based on the three- dimensional data and identifying a current treatment status based on information from the applied therapy.