WO2024200799A1

WO2024200799A1 - Therapy planning by simulating patency scenarios

Info

Publication number: WO2024200799A1
Application number: PCT/EP2024/058734
Authority: WO
Inventors: Tobias WISSEL; Marco Baragona; Georgii KOLOKOLNIKOV; Christian Buerger
Original assignee: Koninklijke Philips NV
Current assignee: Koninklijke Philips NV
Priority date: 2023-03-31
Filing date: 2024-03-29
Publication date: 2024-10-03
Anticipated expiration: 2025-09-30
Also published as: CN121039751A

Abstract

A potential therapy is planned by retrieving three-dimensional data comprising a blood vessel anatomy and a patient vasculature. A post-treatment hemodynamic model is generated based on the patient vasculature which is modified based on a proposed treatment applicable to the patient vasculature. At least one lesion to 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 the long-term hemodynamic model. A projected physiological impact of the proposed treatment is generated based on the long-term hemodynamic model.

Description

THERAPY PLANNING BY SIMULATING PATENCY SCENARIOS

FIELD

[0001] The present invention generally relates to systems and methods for planning a therapy for treating a blood vessel. In particular, the present invention relates to evaluating and planning proposed 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, thereby restoring blood flow by opening the diseased blood vessel, but not to over-treat 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 constitute 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] 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 future scenarios associated with different potential or prospective treatment plans with respect to robustness and fragility. During planning of an intervention, this information may be used to decide between multiple potential treatment plans.

[0005] Therefore, it is desirable to identify and confirm a prospective therapy and treatment plan which not only leads to 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 a treatment plan which not only leads to 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 to simulate a hypothetical treatment plan and investigate the patency of the result of such a hypothetical treatment. An interactive interface may be provided for the clinician to test possible future lesion growth at relevant locations following the hypothetical treatment. This may be by utilizing a module, which models and inserts different types of plausible synthetic lesions into the anatomy. A flow model may then be applied to the patientspecific 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 following an initial treatment using actual therapy results, such as stent placements. Such therapy results may then be used to evaluate whether further intervention is appropriate in order to improve patency.

[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 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 of the post treatment hemodynamic model comprises generating an initial hemodynamic model of the patient vasculature based on the three- dimensional data and applying a proposed modification 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 rule-based model applied to at least one detected detail of the patient vasculature.

[0013] In some embodiments, the proposed treatment is the stenting of the at least one lesion. [0014] 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 includes applying a segmentation process to the three-dimensional imaging, and the initial hemodynamic model is generated based on a vessel lumen extracted from the three-dimensional imaging by way of the segmentation process.

[0015] In some embodiments, the method includes retrieving, from a user, parameters for the at least lesion and introducing or enlarging the at least one lesion based on retrieved parameters. [0016] In some embodiments, the at least one lesion comprises 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 vasculature identifies a likely plaque formation location, and introducing or enlarging the at least one lesion at the likely plaque formation location. In some such embodiments, the method comprises implementing a first simulation of blood flow in the post-treatment 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-treatment hemodynamic model.

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

[0020] In some embodiments, the projected physiological impact is based on the perfusion of a downstream location relative to the therapy applied. In some such embodiments, the projected physiological impact is as least one of a measure of relative perfusion change comparing the long-term hemodynamic model to an initial hemodynamic model of the patient vasculature based on the three-dimensional data and a measure of relative perfusion change comparing the longterm hemodynamic model to the post-treatment 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 with generating at least one modified projected physiological impact of the proposed treatment based on the corresponding at least one modified long-term hemodynamic model, and generates a 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 treatment applicable to the patient vasculature to create an alternative post-treatment hemodynamic model. The method then introduces or enlarges at least one lesion to the patient vasculature in the alternative post-treatment 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 treatment 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 treatment to that of the alternative treatment. [0024] In some such embodiments, a treatment location differs for the proposed treatment and for the alternative treatment.

[0025] In some embodiments in which an alternative treatment is considered, the method proceeds to modify 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. The method then generates at least one modified projected physiological status of the patient vasculature 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 associated with the long-term hemodynamic model to the at least one modified projected physiological status associated with the at least one modified long-term hemodynamic model. [0026] 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 proceeds to generate at least one modified projected physiological status of the patient vasculature based on the at least one modified alternative long-term hemodynamic model. The method then generates an alternative patency metric 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 and outputs a comparison of the patency metric to the alternative patency metric.

BRIEF DESCRIPTION OF THE DRAWINGS

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

[0028] FIG. 2 illustrates a method for evaluating patency in accordance with one embodiment of the present invention.

[0029] FIG. 3 illustrates a block diagram for a system implementing the method of FIG. 2.

[0030] FIG. 4 illustrates a modeled description of a patient vasculature in accordance with one embodiment of the present invention.

[0031] FIG. 5 illustrates the introduction of a synthetic lesion to the patient vasculature of FIG.

4.

[0032] FIG. 6 illustrates the simulation of blood flow in the modeled patient vasculature of FIG.

5.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0033] The description of illustrative embodiments according to 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. In the description of embodiments of the disclosure 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 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 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 atachments 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 may exist alone or in other combinations of features; the scope of the disclosure being defined by the claims appended hereto.

[0034] This disclosure describes the best mode or modes of practicing the disclosure 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 disclosure. In the various views of the drawings, like reference characters designate like or similar parts.

[0035] 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 disclosures. 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.

[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 hypothetical treatment result for a proposed treatment. An interactive interface may be provided for the clinician to test possible future lesion growth at relevant locations following the hypothetical treatment. 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.

[0037] The flow model may be rebuilt in the context of multiple hypothetical proposed treatments. As such, the results of such treatments with simulated future lesion growth can be compared. 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.

[0038] 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 proposed.

[0039] 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.

[0040] 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 stored in a discrete separate system. [0041] 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 below, 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.

[0042] 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.

[0043] 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 contrast, and the image timing may be set up in order to track fluid flow in blood vessels.

[0044] In addition to conventional and 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 directly from 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.

[0045] 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.

[0046] While a system is shown including 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 proposed 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 proposed therapy to be performed. [0047] 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 proposed or prospective treatment to be applied to a blood vessel of a patient. For example, the proposed treatment may be a therapy applicable to stenotic lesions. FIG. 3 illustrates a block diagram for a system 400 implementing the method of FIG. 2.

[0048] The method initially retrieves three-dimensional data (200) describing a blood vessel anatomy of a patient to be treated. The three-dimensional data includes a description of a patient vasculature corresponding to the patient to be treated. The three-dimensional data may take a variety of forms. Portions of the method may be implemented by a data handling and manipulation module 410 (FIG. 3), and the data may then be used for lesion and flow modeling as described below.

[0049] 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 (MRA).

[0050] In typical embodiments, following retrieval of the three-dimensional data (at 200) a lumen and/or centerline for the vasculature to be modeled may be identified (210). 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 various hemodynamic models discussed below may be generated based on a vessel lumen extracted and identified (at 210) by way of the segmentation process.

[0051] 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.

[0052] 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 200) 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.

[0053] The method may then proceed to generate an initial hemodynamic model of the patient vasculature (220) based on the three-dimensional data retrieved (at 200). The initial hemodynamic model may be partially based on a patient profile, such as a profile defined by a user of the system implementing the method. Such a user may be a clinician. The initial model may similarly be based on patient data independent of the imaging, which may be retrieved at any time. The method may then proceed to apply a proposed modification to the initial hemodynamic model (230) based on a proposed treatment applicable to the patient vasculature. This proposed treatment may be an intervention based on a hypothetical treatment plan, and may include, for example, an angioplasty at a proposed location and/or a stent at a proposed location, such as a location corresponding to an existing lesion. Details associated with the proposed treatment may be retrieved from a user prior to applying the modification to the initial hemodynamic model (at 230).

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

[0055] Following the modification (at 230) of the initial hemodynamic model (generated at 220), the method proceeds to generate a post-treatment hemodynamic model (240) based on the patient vasculature based on the three-dimensional data (retrieved at 200) modified based on the proposed treatment (at 230) applicable to the patient vasculature. As is discussed in more detail herein, the proposed treatment (at 230) may be one of several potential proposed treatments. As such, in an iterative version of the method described herein, the method may repeat the modeling steps described herein several times in order to generate a comparison between such iterations. [0056] While an initial hemodynamic model (generated at 220) is discussed as a preparatory step for creating the post-treatment hemodynamic model (generated at 240) representing the patient vasculature following some hypothetical therapy, it will be understood that the initial hemodynamic model is not necessary in all cases. Accordingly, in some embodiments, the method may proceed by directly generating the post-treatment hemodynamic model based directly on the patient vasculature derived from the three-dimensional data (at 200) modified based on the proposed treatment. [0057] In some embodiments, the method may use local displacement fields in the three- dimensional data to model ballooning of the vessel at a proposed treatment site based on fluoroscopic or angiographic imaging of a stent to be placed in the hypothetical treatment plan. The stent can then be artificially inserted into the three-dimensional image.

[0058] 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 a proposed treatment plan may then be applied to the extracted lumen instead of the original image voxel space.

[0059] 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 240) based on the patient vasculature following successful performance of the proposed treatment. A user, such as a clinician, may then utilize the method to test certain future scenarios for robustness following the specific proposed treatment of the patient simulated in the model. For example, the clinician may simulate the stenting of a number of lesions, and then test the patency of the resulting status. The method then attempts to determine how fragile the hypothetical result would be, and what the resulting risk of reintervention would be for the patient. In some embodiments, the proposed treatment scenario is automatically generated by the method, and as such, the method may be automatic instead of interactive.

[0060] The post-treatment hemodynamic model (generated at 240) is designed to reflect flow conditions immediately following the hypothetical 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 proceeds with introducing or enlarging (250) 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. Such an introduction or enlargement allows the method to create a long-term hemodynamic model 430 (260 of Fig. 2).

[0061] 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 260). For example, patient characteristics, such as patient condition or drugs utilized by the patient may be used to improve the accuracy of the model.

[0062] FIG. 5 illustrates the introduction of a synthetic lesion (at 250) to the patient vasculature of FIG. 4. The model 430 incorporating the introduced synthetic lesion corresponds to the longterm hemodynamic model described and generated at 260.

[0063] In some embodiments, a user of a system implementing the present method 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 (at 250), the method may retrieve from a user a number of parameters for a synthetic lesion to be generated (at 243). In such an embodiment, the introduction or enlargement of a synthetic lesion (at 250) may be an introduction of a synthetic lesion based on the retrieved parameters. This may be by a lesion modeling and insertion module 420. Such a module may generate synthetic lesions given a desired parameterization or other inputs provided at the user interface 440 and integrate them on request into the patient specific vasculature.

[0064] 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 hypothetical post-treatment anatomy. As noted herein, multiple scenarios may ultimately be modeled and compared within the context of a single hypothetical treatment scenario.

[0065] 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 restenosis in a specified location has a dramatic effect on volume flow if no further treatment is attempted.

[0066] Alternatively, in some embodiments, the method may proceed to identify at least one characteristic of the patient vasculature (248). The identified characteristic may then be usable to determine a location or other characteristic for a synthetic lesion to be introduced or enlarged (at 250). 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 (at 250) at the likely plaque formation location.

[0067] In some embodiments, additional processing may be applied in order to identify the characteristic (at 248). For example, in some embodiments, the method may implement a first simulation of blood flow (at 245) in the post-treatment hemodynamic model prior to introducing the at least one lesion (at 250). The identification of the likely plaque formation location (at 248) is then based on the first simulation of blood flow (at 245). This may be, for example, by identifying a region of low wall shear stress in the patient vasculature or a bifurcation within existing stents.

[0068] In some embodiments, the introduction or enlargement of the at least one synthetic lesion (250) may be based on a combination of user input of parameters (such as at 243) and some evaluation of vasculature characteristics identified by the method (such as at 248). Accordingly, the evaluation of the patient vasculature may be at least partially based on, e.g., a patient profile defined by the user (at 243 or earlier). 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 above, patient data may be generally made available to the model at various times prior to creating the long-term model (at 260)

[0069] 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 (at 243) or by the system itself (at 248). Accordingly, the synthetic lesion to be introduced or enlarged may be a focal lesion, a diffuse lesion, a bifurcation lesion, 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).

[0070] 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. [0071] Ultimately, a synthetic lesion is introduced or enlarged (at 250), and the results are then used to generate the long-term hemodynamic model (at 260).

[0072] The method then proceeds to simulate blood flow (270) in the long-term hemodynamic model (generated at 260). The outcome of such simulation is then used to generate a projected physiological impact (280a) of the proposed treatment (and the at least one synthetic lesion introduced or enlarged) based on the long-term hemodynamic model.

[0073] FIG. 6 illustrates the simulation of blood flow (at 270) in the modeled patient vasculature of FIG. 5. Such a flow model 430 is shown in the system 400 in FIG. 3. 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.

[0074] 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). [0075] The impact of the inserted synthetic lesion (at 250) 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 or be a portion of the projected physiological status (at 280a) determined. Patency may be quantified 450 in the context of the projected physiological impact 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.

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

[0077] In some embodiments, following the simulation of blood flow (at 270) in the modeled patient vasculature, or in parallel with the implementation of the modeling process described herein, the method proceeds to modify one or more parameter of the underlying models (275) 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 the proposed treatment utilized for modifying the initial model (at 230). This approach may then be used to create an alternative long-term model (at 260) or post-treatment hemodynamic model (at 240) to which the lesion modifications are applied.

[0078] Accordingly, in some embodiments, the method includes applying a modification (at 275) to at least one parameter of the at least one lesion introduced or enlarged (at 250) 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 243) or the characteristics identified (at 248) that led to the parameters of the lesion generated (at 250). Such a modification may then be used to generate at least one modified long-term hemodynamic model (at 260) and to simulate blood flow (at 270) in such a modified model in order to generate a modified projected physiological impact (at 270b) of the proposed treatment and the at least one lesion based on the corresponding modified long-term hemodynamic model.

[0079] Once the projected physiological impact and the modified projected physiological impact are generated (at 280a, 280b), the statuses may be used to generate a patency metric 450 or an estimate 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 impact (at 280a, 280b), additional iterations may be provided (such as at 280c) 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. [0080] Accordingly, several long-term models may be generated based on small variations around the chosen lesion scenario, resulting in variations of the projected physiological impact. 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.

[0081] In some embodiments, following the simulation of blood flow (at 270) in the modeled patient vasculature, or in parallel with the implementation of the modeling process described herein, the method proceeds to apply a secondary, or alternative, modification (at 230) to the post-treatment hemodynamic model of FIG. 4. This may occur automatically as part of the implementation of the process, or it may be based on a manual modification of a parameter (at 275) by a clinician. Such a secondary modification may be in place of the originally applied modification (at 230) and may be based on an alternative potential treatment applicable to the patient vasculature. This may then be used to create an alternative post-treatment hemodynamic model (at 240).

[0082] 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 250) in the alternative post-treatment hemodynamic model (at 240) to create an alternative long-term hemodynamic model (at 260). The method then simulates blood flow (at 270) in the alternative long-term hemodynamic model and generates a projected physiological impact (at 280b) of the secondary modification (introduced at 230) and the at least one synthetic lesion (introduced at 250). The modification (at 230) is a potential treatment, as discussed above, and the projected physiological impact (at 280b) allows for an evaluation of the impact of the proposed treatment. The physiological impact (280a) may be considered in the context of a patency metric 450 generated for each potential treatment scenario, and may be considered alongside corresponding physiological impacts (280b, 280c) for alternative potential treatment scenarios.

[0083] Accordingly, the long-term hemodynamic model associated with each potential treatment scenario may itself be modified (at 275) on the basis of varied synthetic lesion parameters as discussed above. A patency metric 450 may then be generated for each scenario. As such, the method may allow users to modify parameters of lesions to be introduced or enlarged (at 250). Accordingly, when iteratively repeating the method described, modifications may be introduced along different dimensions, such that different lesion scenarios may be considered with respect to each potential treatment.

[0084] Accordingly, in some embodiments, following the initial iteration of the method, resulting in a projected physiological impact, the method may modify at least one parameter of the at least one lesion (introduced or enlarged at 250) in order to create at least one modified long-term hemodynamic model (at 260). The method may then generate at least one modified projected physiological impact (at 280b) corresponding to the at least one modified long-term hemodynamic model. The method may then generate a patency metric based on a comparison of the projected physiological status associated with the long-term hemodynamic model (at 280a) to the at least one modified projected physiological status associated with the at least one modified long-term hemodynamic model (at 270b).

[0085] The method may then similarly modify at least one parameter of the at least one lesion (introduced or enlarged at 250) in the alternative post-treatment hemodynamic model to create at least one modified alternative long-term hemodynamic model (at 260). The method may then generate at least one modified projected physiological impact 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 impact associated with the alternative long-term hemodynamic model to the at least one modified projected physiological impact associated with the at least one modified alternative long-term hemodynamic model.

[0086] 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. [0087] 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 impacts may be generated (at 280c) in order to evaluate additional scenarios. Such repetitions may be iterative, such that, upon reviewing the projected physiological impact of the initial proposed modification (at 280a), a user, such as a doctor, may propose an alternative treatment to introduce to the model as a secondary modification (at 230). The doctor may then review the projected physiological impact (at 280b) of the proposed treatment and propose a different or modified treatment at the interactive interface 440.

[0088] Following the generation of multiple projected physiological impacts (280a, 280b, 280c), 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 impact 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.

[0089] In some embodiments, such as that described in which the modification initially introduced (at 230) is just one of a plurality of potential treatments, the method compares the projected physiological impacts associated with each of the potential treatments and generates (at 300) a recommendation based on a measure of fragility of patency in each of the plurality of potential treatments.

[0090] In some embodiments, the different treatments considered and ultimately modeled (at 260) 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.

[0091] In some embodiments, in addition to varying the hypothetical treatment to be applied (at 230), the method further models different synthetic legions in the context of a single hypothetical treatment. Accordingly, the method may introduce a difference in the projected location of the synthetic lesion to be introduced or enlarged (at 250). Such differences may be introduced by a user, such as a clinician, using an interactive user interface 440, 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. [0092] 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. [0093] 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.

[0094] While the present invention 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.

[0095] 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.

Additionally, it is intended that such equivalents include both currently known equivalents as well as equivalents developed in the future, i.e., any elements developed that perform the same function, regardless of structure.

Claims

What is claimed is:

1. A method for evaluating patency of a potential therapy, comprising: retrieving three-dimensional data comprising a blood vessel anatomy and a patient vasculature; generating a post-treatment hemodynamic model based on the patient vasculature which is modified based on a proposed treatment applicable to the patient vasculature; 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 impact of the proposed treatment based on the longterm hemodynamic model.

2. The method of claim 1, wherein generating the post treatment hemodynamic model comprises: generating an initial hemodynamic model of the patient vasculature based on the three- dimensional data; and applying a proposed modification to the initial hemodynamic model based on the proposed treatment applicable to the patient vasculature.

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

4. The method of claim 1, wherein the proposed treatment is the stenting of at least one existing lesion.

5. 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, and wherein the initial hemodynamic model is generated based on a vessel lumen extracted from the three-dimensional imaging using the segmentation process.

6. The method of claim 1, further comprising retrieving from a user a number of parameters for the at least one lesion, and introducing or enlarging the at least one lesion based on the retrieved parameters.

7. The method of claim 1, further comprising evaluating at least one characteristic of the patient vasculature, and introducing or enlarging the at least one lesion at a location selected based on the evaluation.

8. The method of claim 7, further comprising identifying a likely plaque formation location, and introducing or enlarging the at least one lesion at the likely plaque formation location.

9. The method of claim 8, further comprising simulating blood flow in the post-treatment hemodynamic model prior to introducing the at least one lesion, and identifying the likely plaque formation location based on the simulation of blood flow in the post-treatment hemodynamic model.

10. The method of claim 1, wherein the projected physiological impact is as least one of: a measure of relative perfusion change comparing the long-term hemodynamic model to an initial hemodynamic model of the patient vasculature based on the three-dimensional data; and a measure of relative perfusion change comparing the long-term hemodynamic model to the post-treatment hemodynamic model.

11. 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 impact of the proposed treatment 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 impact to the at least one modified projected physiological impact.

12. The method of claim 1, 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.

13. The method of claim 1, further comprising: applying a secondary modification to the initial hemodynamic model based on an alternative 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; and generating a projected physiological impact of the alternative treatment and the at least one lesion based on the alternative long-term hemodynamic model; outputting a comparison of the projected physiological impact of the proposed treatment to that of the alternative treatment.

14. The method of claim 13, wherein a treatment location differs for the proposed treatment and for the alternative treatment.

15. An apparatus for evaluating patency of a potential 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 the patient vasculature which is modified based on a proposed treatment applicable to the patient vasculature; introduce or enlarge 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 impact of the proposed treatment based on the long-term hemodynamic model.

16. The apparatus of claim 15, wherein the processor is further configured to: generate an initial hemodynamic model of the patient vasculature based on the three- dimensional data; and apply a proposed modification to the initial hemodynamic model based on the proposed treatment applicable to the patient vasculature to generate the post treatment hemodynamic model.

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

18. The apparatus of claim 15, wherein the proposed treatment is the stenting of at least one existing lesion.

19. The apparatus of claim 15, wherein the three-dimensional data comprises three-dimensional imaging, wherein a segmentation process is applied to the three-dimensional imaging, and wherein the initial hemodynamic model is generated based on a vessel lumen extracted from the three-dimensional imaging using the segmentation process.

20. The apparatus of claim 15, wherein a number of parameters for the at least one lesion is retrieved from a user, and wherein the at least one lesion is introduced or enlarged based on the retrieved parameters.