Treatment outcome confirmation by simulating patency scenarios

Abstract

The outcome of the treatment applied to the treatment of the blood vessel is evaluated by retrieving three-dimensional data including the blood vessel anatomy and the patient vasculature. A post-treatment hemodynamic model is then generated based on the treatment of the vascular anatomy applied to the three-dimensional data. At least one lesion is introduced and expanded 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 predicted physiological state of the patient vasculature is generated based on the long-term hemodynamic model.

Description

Technical Field

[0001] This invention generally relates to systems and methods for evaluating the outcomes of treatments applied to the management of blood vessels. In particular, this invention relates to evaluating the management of stenotic lesions. Background Technology

[0002] Determining when an interventional physician has done enough during percutaneous treatment of stenotic lesions in patients with underlying conditions is challenging. Therefore, the physician must define the point at which treatment should cease. The goal is to treat what is necessary, but not to over-treat the patient. Over-treatment is generally undesirable, and there is evidence that over-treated lesions carry a higher risk of restenosis.

[0003] Therefore, when treating stenotic lesions, interventional physicians target the restoration of blood flow by opening the affected blood vessel, which thus confirms a successful treatment outcome. However, interventional physicians must avoid over-treating the patient and ideally leave a result that may result in long-term patency or unobstructed flow.

[0004] Typically, during intervention, little to no predictive information about the patency of the current interventional status is available. Therefore, it is difficult to determine whether the interventional physician has done enough at any given time, or whether further action is needed to ensure a robust and patency-proven (i.e., future-proven) outcome. In a patency scenario, future plaque growth does not immediately have a substantial impact on the blood supply to critical downstream structures. Therefore, it is necessary to test the relevant future scenarios in terms of robustness and vulnerability. During intervention, this information is highly actionable because the interventional physician can adjust the intervention accordingly.

[0005] Therefore, the expectation of identification and confirmation not only constitutes a technological success but also a potentially smooth long-term treatment outcome. Summary of the Invention

[0006] When treating stenotic lesions, interventional physicians target the restoration of blood flow by opening the affected blood vessel, but they must avoid over-treating the patient. Therefore, identification and confirmation are expected to contribute not only to technical success but also to potentially long-term patency.

[0007] Therefore, systems and methods are provided for utilizing three-dimensional descriptions of patient-specific anatomy after treatment and investigating the patency of treatment outcomes. An interactive interface can be provided to clinicians to test for potential future lesion growth at relevant locations. This can be achieved by utilizing modules that model different types of rationally synthesized lesions and insert them into the post-treatment anatomy. Flow models can then be applied to patient-specific anatomy with modeled synthetic lesions to estimate the functional consequences of these future scenarios, thereby predicting patency.

[0008] In some embodiments, prior knowledge about the possible future location of lesion growth can be used to identify vulnerable scenarios, where small morphological changes may lead to a significant reduction in patency.

[0009] In some embodiments, similar systems and methods can be utilized during treatment planning that uses hypothetical treatment outcomes, such as stent placement. Such treatment outcomes may also include longer-term risk scenarios.

[0010] In some embodiments, a method for evaluating the patency of treatment is provided. The method includes retrieving three-dimensional data including vascular anatomy and the patient's vascular system. The method then proceeds to generate a post-treatment hemodynamic model based on the treatment applied to the vascular anatomy. The method then introduces or expands at least one lesion into the patient's vascular system 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 predicted physiological state of the patient's vascular system based on the long-term hemodynamic model.

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

[0012] In some embodiments, the three-dimensional data includes three-dimensional imaging, including computed tomography angiography (CTA) or magnetic resonance angiography (MRA). In some such embodiments, the method further includes applying a segmentation process to the three-dimensional imaging. A post-treatment hemodynamic model is then generated based on the vascular lumen extracted from the three-dimensional imaging through the segmentation process.

[0013] In some embodiments, three-dimensional data are reconstructed based on two-dimensional interventional data after the treatment has been applied to the vascular anatomy.

[0014] In some embodiments, the post-treatment hemodynamic model is generated by: generating an initial hemodynamic model of the patient's vascular system based on three-dimensional data, identifying the current treatment state based on information from the applied treatment, and modeling the current treatment state in the context of the initial hemodynamic model to generate the post-treatment hemodynamic model.

[0015] In some embodiments, the method further includes retrieving parameters from the user for the lesion to be generated. At least one lesion is then introduced or expanded, and the lesion is introduced based on the retrieved parameters.

[0016] In some embodiments, the lesion to be introduced or expanded is at least one of the following: focal lesion, diffuse lesion, bifurcation lesion, and complete occlusion.

[0017] In some embodiments, the method includes evaluating at least one characteristic of the patient's vascular system. Then, at least one lesion is introduced or expanded at a location selected based on the evaluation of the patient's vascular system.

[0018] In some such embodiments, the evaluation of the patient's vascular system identifies potential plaque formation sites. Introducing or enlarging at least one lesion involves introducing at least one lesion at a potential plaque formation site.

[0019] In some such embodiments, the method includes a first simulation of blood flow in a post-treatment hemodynamic model prior to the introduction of at least one lesion. Potential plaque formation locations are then identified based on the first simulation of blood flow in the post-treatment hemodynamic model.

[0020] In some embodiments, each flow model is a 0D or 1D model based on three-dimensional data.

[0021] In some embodiments, the expected physiological state is based on perfusion or flow relative to the downstream location of the applied treatment. In some such embodiments, the expected physiological state is at least one of the following: a measure of absolute perfusion value and a measure of relative perfusion change comparing a long-term hemodynamic model with a post-treatment 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 continues to generate at least one modified predicted physiological state of the patient's vascular system and the at least one lesion based on the corresponding at least one modified long-term hemodynamic model. The method then generates a patency measure based on a comparison of the predicted physiological state with at least one modified predicted physiological state.

[0023] In some such embodiments, the patency measure is then one of the following: a measure of relative perfusion change by comparing iterative long-term hemodynamic models in the context of parameter changes in at least one lesion, and a measure of relative sensitivity in the context of parameter changes in at least one lesion.

[0024] In some embodiments, the method includes applying modifications to the post-treatment hemodynamic model based on potential treatments applicable to the patient's vascular system to create an alternative post-treatment hemodynamic model. The method then includes introducing or expanding at least one lesion into the patient's vascular system within the alternative post-treatment hemodynamic model to create an alternative long-term hemodynamic model.

[0025] The method then continues to simulate blood flow in the alternative long-term hemodynamic model and, based on the alternative long-term hemodynamic model, generates the expected physiological state of the patient's vascular system and the at least one lesion.

[0026] The method then outputs a comparison of the predicted physiological state of the patient's vascular system associated with the long-term hemodynamic model with the predicted physiological state of the patient's vascular system associated with an 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 the predicted physiological state associated with each of the potential treatments and generates a recommendation based on a comparison of a patency metric associated with each of the plurality of potential treatments and an equivalent patency metric associated with the long-term hemodynamic model. In some such embodiments, the treatment location is different for each of the plurality of potential treatments.

[0028] In some embodiments, the method of introducing modifications based on potential treatment further includes modifying at least one parameter of at least one lesion in a post-treatment hemodynamic model to create at least one modified long-term hemodynamic model.

[0029] The method then includes: generating at least one modified predicted physiological state of the patient's vascular system and at least one lesion based on a corresponding at least one modified long-term hemodynamic model; and generating a patency measure based on a comparison between the predicted physiological state associated with the long-term hemodynamic model and at least one modified predicted physiological state associated with at least one modified long-term hemodynamic model.

[0030] The method then proceeds to modify at least one parameter of at least one lesion in the post-treatment hemodynamic model to create at least one modified alternative long-term hemodynamic model. The method then generates at least one modified predicted physiological state of the patient's vascular system and 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 between the predicted physiological state associated with the alternative long-term hemodynamic model and at least one modified predicted physiological state associated with the at least one modified alternative long-term hemodynamic model.

[0031] This method ultimately outputs a comparison between the fluency metric and alternative fluency metrics.

[0032] According to one embodiment of the present invention, an apparatus for evaluating the patency of treatment includes: a memory storing a plurality of instructions; and a processor coupled to the memory and configured to execute the plurality of instructions to perform the following operations: retrieving three-dimensional data including vascular anatomy and a patient's vascular system; generating a post-treatment hemodynamic model based on treatment applied to the vascular anatomy; introducing or amplifying at least one lesion into the patient's vascular system 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 predicted physiological state of the patient's vascular system based on the long-term hemodynamic model. Attached Figure Description

[0033] Figure 1 This is a schematic diagram of a system according to an embodiment of the present invention.

[0034] Figure 2 The illustration shows a method for evaluating accessibility according to an embodiment of the present invention.

[0035] Figure 3 The implementation is illustrated. Figure 2 A system block diagram of the method.

[0036] Figure 4 The illustration depicts a modeling description of a patient's vascular system according to an embodiment of the present invention.

[0037] Figure 5 The illustration shows the introduction of the lesion into Figure 4 The patient's vascular system.

[0038] Figure 6 The diagram shows... Figure 5 Modeling and simulating blood flow in the patient's vascular system. Detailed Implementation

[0039] The description of illustrative embodiments according to the principles of the invention is intended to be read in conjunction with the accompanying drawings, which are considered an integral part of the entire written description. Any references to directions or orientations in the description of the embodiments disclosed herein are intended only for ease of description and not to limit the scope of the invention in any way. Relative terms such as “lower,” “upper,” “horizontal,” “vertical,” “above,” “below,” “upward,” “downward,” “top,” and “bottom,” and their derivatives (e.g., “horizontally,” “downward,” “upward,” etc.) should be interpreted as referring to the orientation as described thereafter or as shown in the drawings discussed. These relative terms are for ease of description only and do not require the device to be constructed or operated in a particular orientation unless expressly indicated otherwise. Unless expressly stated otherwise, terms such as “attached,” “joined,” “connected,” “coupled,” “interconnected,” etc., refer to a relationship in which structures are directly or indirectly fixed or attached to each other, either directly or indirectly through intermediate structures, and to movable or rigid attachments or relationships. Furthermore, the features and benefits of the invention are illustrated by reference to exemplary embodiments. Therefore, the invention should not be explicitly limited to such exemplary embodiments illustrating some possible non-limiting combinations of features that may exist alone or in other combinations of features; the scope of the invention is defined by the appended claims.

[0040] This disclosure describes one or more preferred modes of practicing the invention as currently contemplated. This description is not intended to be construed in a limiting sense, but rather to provide examples of the invention presented for illustrative purposes only, with reference to the accompanying drawings, to inform those skilled in the art of the advantages and construction of the invention. In the various views of the drawings, similar reference numerals denote similar or analogous portions.

[0041] It is important to note that the disclosed embodiments are merely examples of the many advantageous uses of the inventive teachings herein. Generally, the statements made in the specification of this application do not necessarily limit any of the various claimed embodiments. Furthermore, some statements may apply to some inventive features but not to others. Generally, unless otherwise stated, singular elements may be plural and vice versa, without loss of generality.

[0042] When treating stenotic lesions, interventional physicians target the restoration of blood flow by opening the affected blood vessel, but they must avoid over-treating the patient. Therefore, it is desirable to identify and confirm the treatment outcome, which not only constitutes technical success but may also lead to long-term patency.

[0043] Therefore, systems and methods are provided for utilizing three-dimensional descriptions of patient-specific anatomy after treatment and investigating the patency of treatment outcomes. An interactive interface can be provided to clinicians to test for potential future lesion growth at relevant locations. This can be achieved by utilizing modules that model different types of rationally synthesized lesions and insert them into the post-treatment anatomy. Flow models can then be applied to patient-specific anatomy with modeled synthetic lesions to estimate the functional consequences of these future scenarios, thereby predicting patency.

[0044] In unobstructed scenarios, future plaque growth will not immediately have a substantial impact on the blood supply to critical downstream structures. Therefore, it is necessary to test relevant future scenarios in terms of robustness and vulnerability.

[0045] Typically, imaging has already occurred if a patient is scheduled for treatment. Therefore, while the description herein includes the acquisition of images, it should be understood that pre-existing images can be utilized and modified depending on any treatment applied.

[0046] The method described in this paper can be used to provide feedback during intervention to offer actionable insights into the future accessibility of the current state of care. Therefore, it can be, for example, part of an intervention suite. Intervention applications will benefit from cloud access to pre-intervention data and cloud access to potential cloud assets surrounding complex flow models that require expanded computing resources.

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

[0048] Processing device 110 can apply processing routines to images or measurement data, such as projection data, received from imaging device 120. Processing device 110 may include memory 113 and processor circuitry 111. Memory 113 may store multiple instructions. Processor circuitry 111 may be coupled to memory 113 and may be configured to execute instructions. Instructions stored in memory 113 may include processing routines and data associated with those routines, such as machine learning algorithms and various filters for processing images. While all data is described as being stored in memory 113, it should be understood that in some embodiments, some data may be stored in a database, which itself may be stored in memory or in a discrete, separate system.

[0049] The processing device 110 may further include an input unit 115 and an output unit 117. The input unit 115 may receive information, such as images or measurement data, from the imaging device 120. The output unit 117 may output information, such as processed images or models generated therefrom, to a user or user interface device. Similarly, the output unit 117 may provide data to a user interface, where the user can manipulate inputs for a hemodynamic model to be generated. The output unit 117 may similarly output determinations generated by the methods described herein, such as recommendations, expected physiological states for the patient, and patency measurements and projections. The output unit may include a monitor or display that can show additional information or models updated in real-time or near real-time based on user input.

[0050] In some embodiments, the processing device 110 may be directly associated with the imaging device 120. In alternative embodiments, the processing device 110 may be different from the imaging device 120, such that it receives image or measurement data for processing via a network or other interface at the input unit 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 CT projection data while scanning a subject (e.g., a patient). Furthermore, the imaging device 120 may be configured for invasive or non-invasive coronary CT angiography. Therefore, imaging can be performed using contrast agents, and image timing can be set to track fluid flow within the blood vessels.

[0052] In addition to conventional and / or energy-dispersive CT images, this method may rely on multiple energy-dispersive image results, photon-counting CT images, and / or dark-field CT images. In this way, 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 that can be used to derive three-dimensional data. For example, multiple two-dimensional images may be considered together to infer three-dimensional characteristics.

[0053] Alternatively, two-dimensional imaging can be used with auxiliary information sufficient to infer three-dimensional characteristics. For example, a two-dimensional projection can be paired with the luminal characteristics of a patient's vascular system to derive the data needed for a flow model. Similarly, two-dimensional imaging can be fused with flow information, such as flow visible in X-ray fluoroscopy or X-ray angiography. For the purposes of this disclosure, three-dimensional data including such instances of two-dimensional imaging infused with flow information are used to better characterize vascular anatomy.

[0054] Although Figure 1The system shown includes an imaging device 120 and a processing device 110; however, it should be understood that the method can be implemented directly on the processing device, such as in the context of an image received via a network at the input unit 115 or an imaging setup. The method described herein relates to processing data as a component of evaluating the patency of a treatment (such as stent implantation). As mentioned above, imaging is typically performed prior to such a procedure. Therefore, previously generated images can be retrieved via the input unit 115, and a post-treatment model can be generated based on the previously retrieved data combined with information based on the performed treatment.

[0055] Figure 2 The illustration depicts a method for evaluating patency according to the present disclosure. As described above, the method for evaluating patency relates to the patency of such treatment when it is applied to a patient's blood vessels. For example, the treatment could be for a stenotic lesion. Figure 3 The implementation is illustrated. Figure 2 The system block diagram of the method 400.

[0056] Typically, this method is performed by an interventional physician during a procedure (200). The procedure may involve, for example, inserting a stent at at least one lesion site (stent implantation).

[0057] The method then proceeds to retrieve three-dimensional data (210) describing the vascular anatomy of the treated patient. The three-dimensional data includes the patient's vascular system corresponding to the treated patient, and it can be viewed in system 400 ( Figure 4 The data is received at the data processing and manipulation module 410. The three-dimensional data can take various forms and may have been generated before or after the interventional physician performs the procedure (at 200). Part of the method can be implemented by the data processing and manipulation module 410, and the data can then be used for lesion and flow modeling, as described below.

[0058] Three-dimensional data can be three-dimensional imaging, such as computed tomography (CT) scans or coronary angiography (CTA). Alternatively, three-dimensional imaging can be magnetic resonance imaging (MRI) or coronary angiography (MRA).

[0059] In a typical embodiment, after retrieving the 3D data (at 210), the lumen and / or centerline of the vascular system to be modeled can be identified (220). If the 3D data is a 3D imaging, the method can continue by applying a segmentation process to the imaging. In such a scenario, the post-treatment hemodynamic model discussed herein can be generated based on the vascular lumen extracted and identified (at 220) through the segmentation process.

[0060] In some embodiments, the three-dimensional data includes composite information from multiple two-dimensional images. In such embodiments, instead of retrieving three-dimensional images, the system can retrieve multiple two-dimensional images (such as conventional X-rays) and infer three-dimensional characteristics by considering the images together. In some embodiments, three-dimensional data can be reconstructed from two-dimensional interventional data after treatment has been applied to vascular anatomy.

[0061] In some embodiments, two-dimensional data may be provided, but representations of the lumen and / or centerline may be provided as supplementary data. Such data combinations can thus be integrated to form three-dimensional data retrieved (at 210) by the method. In such a scenario, the data may never actually be processed as volumetric data into three dimensions, but can be directly applied to modeling the vascular tree. Therefore, it should be understood that three-dimensional data can take many forms and represent a three-dimensional description of patient-specific anatomy after treatment.

[0062] At some point after the treatment, the method can then continue to generate a post-treatment hemodynamic model (230) based on the treatment applied to the described vascular anatomy. Thus, such a model can be generated (230) at the data processing and manipulation module 410 based on three-dimensional data acquired or generated prior to the treatment, and then modified to reflect the outcome of the applied treatment. Therefore, three-dimensional information about the patient's vascular system, such as preoperative CTA data, can be retrieved, and process information, such as the automatic recording and quantification of the process, can be retrieved separately, which may include, for example, stent placement.

[0063] Therefore, in some embodiments, an initial hemodynamic model of the patient's vascular system can be created (223) based on three-dimensional data before generating the post-treatment hemodynamic model (230). The method can then identify the current treatment state (226) based on process information from the applied treatment and model the current treatment state in the context of the initial hemodynamic model to generate the post-treatment hemodynamic model (230).

[0064] If three-dimensional data is generated prior to treatment (200), the method can use the local displacement field in the three-dimensional data to model the expansion of the blood vessel at the treatment site based on fluorescence fluoroscopy or angiography imaging of the placed stent. The stent can then be manually inserted into the three-dimensional image.

[0065] In practice, the segmentation of the vascular lumen can be extracted from 3D data as a bitmask or surface mesh and used as input to the lesion model. Manipulation based on the current treatment state can then be applied to the extracted lumen instead of the original image voxel space.

[0066] Alternatively, a post-treatment model (230) can be generated based on three-dimensional data generated or partially generated after the treatment is performed.

[0067] In cases where 3D data depends on interventional data following treatment steps and the 3D scene is reconstructed, such a scene can be reconstructed based on modern data-driven inverse graph inference from one or more 2D images, or it can be reconstructed based on 3D images from a set of projections. This method can be analogous to cone-beam computed tomography (CBCT). This type of reconstruction can be directly targeted at the lumen mask without considering the voxelized image space, thus reducing the required processing and data volume.

[0068] Figure 4 The illustration depicts a modeling description of a patient's vascular system according to this disclosure. Figure 4 The model shown is an example of a post-treatment hemodynamic model of a patient's vascular system based on a previously treated procedure (such as the model generated at 230). Users, such as clinicians, can then leverage this method to test robustness to certain future scenarios following a specific treatment of a patient. For example, a clinician could initially perform a stent placement on one or more lesions and then stop testing the patency of the current state. The method then attempts to determine how fragile the current outcome is and what the risk of re-intervention for the patient is.

[0069] The post-treatment hemodynamic model (generated at 230) was designed to reflect the flow immediately following treatment. However, such a model does not account for the expected changes in the vascular system over time. Therefore, to predict the long-term patency of the patient's vascular system, the method ( Figure 2 ) Continue to introduce or expand (240) at least one synthetic lesion into the patient's vascular system in the post-treatment hemodynamic model. This can be done in system 400 ( Figure 3 The synthetic lesion modeling and insertion module 420 is implemented in the method. Such introduction or expansion allows the method to create long-term hemodynamic models 430. Figure 2 (250 in the middle).

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

[0071] Figure 5 The illustration shows the introduction of a synthetic lesion (at position 240). Figure 4 The patient's vascular system. Model 430, which includes the introduced synthetic lesions, corresponds to the long-term hemodynamic model (250) described and generated herein.

[0072] In some embodiments, a user of a system implementing the described method can provide some input parameters for the synthetic lesion to be generated or expanded at the interactive user interface 440. Therefore, the method can retrieve parameters for the synthetic lesion from the user (233) before introducing the synthetic lesion (240). In such embodiments, the introduction or expansion of the synthetic lesion (240) can be based on parameters input by the user. This can be achieved through the synthetic lesion modeling and insertion module 420 (… Figure 3 Such a module can be used in a given user interface 440(( Figure 3 Synthetic lesions are generated based on the expected parameterization or other inputs provided at the location, and integrated into the patient-specific vascular system upon request.

[0073] In such an embodiment, an interactive user interface 440 can be presented to the user, where clinicians may be able to use the lesion modeling and insertion module 420 to test potential future lesion growth at relevant locations. The lesion modeling and insertion module 420 models different types of reasonable synthetic lesions and inserts them into the post-treatment anatomy. As described herein, multiple scenarios can ultimately be modeled and compared.

[0074] For example, a clinician might instruct the system implementing this method to achieve a certain degree of in-stent restenosis or to increase the significance or spread of another lesion. The method would then evaluate the physiological consequences in the following steps. For instance, if no further intervention was attempted, the method might attempt to determine whether a slight restenosis at a specified location would have a significant impact on volumetric flow rate.

[0075] Alternatively, in some embodiments, the method may further identify at least one characteristic of the patient's vascular system (238). The identified characteristic may then have other characteristics for determining the location or extent of the synthetic lesion to be introduced or expanded (240). For example, the method may include identifying possible plaque-forming locations in the patient's vascular system. The introduction or expansion of the lesion may then be the introduction of the synthetic lesion (240) at the possible plaque-forming location.

[0076] In some embodiments, additional processing may be applied to identify characteristics (238). For example, in some embodiments, the method may perform a first simulation of blood flow (235) in a post-treatment hemodynamic model prior to the introduction of at least one synthetic lesion (240). Possible plaque formation locations (238) are then identified based on the first simulation of blood flow (235). This could be, for example, by identifying areas of low wall shear stress in the patient's vascular system or bifurcations within an existing stent.

[0077] In some embodiments, the introduction or expansion of at least one synthetic lesion (240) can be based on a combination of user-input parameters (233) and some evaluation of vascular system characteristics identified by the method (238). Thus, the evaluation of the patient's vascular system can be based at least in part on, for example, a patient profile defined by the user (233). For example, the user can be allowed to select the most suitable patient profile from available or customizable (disease) presets. Such a selection can then influence plaque growth models through knowledge-based classification priors. As described herein, Figure 2 The patient data shown can typically be available for the model at different times before the creation of the long-term model (250).

[0078] Furthermore, in addition to selecting, for example, the appropriate location of the synthetic lesion to be introduced, the characteristics and parameters of the introduced or expanded synthetic lesion can be similarly selected by the user (233) or by the system itself (238). Thus, the synthetic lesion to be introduced or expanded can be a focal lesion, a diffuse lesion, a bifurcation lesion, and / or a complete occlusion. Furthermore, the synthetic lesion to be introduced can have score-driven parameters (such as lesion significance, Medina score, or a portion of Syntax II score) or geometry-driven parameters (such as length, longitudinal profile, symmetry, or concentricity).

[0079] In some embodiments, the implementation may be available in a semi-analytical platform. Such an implementation is inherently semi-analytical and can therefore be fast and efficient. Rapid modeling can be used to implement the interactive methods described herein.

[0080] Ultimately, synthetic lesions are introduced or amplified (240), and the results are then used to generate long-term hemodynamic models (250).

[0081] The method then proceeds to simulate blood flow (260) in a long-term hemodynamic model (250). The results of such simulations are then used to generate the expected physiological state of the patient's vascular system and at least one synthetic lesion introduced or amplified based on the long-term hemodynamic model (270a).

[0082] Figure 6 The diagram shows ( Figure 2 (260 in the middle) Figure 5 This model simulates blood flow in the patient's vascular system. As shown in the figure, physiological effects are evaluated by simulating blood flow based on anatomical structures and potential other boundary conditions. For example, aortic pressure or other patient characteristics can be considered.

[0083] Note that, although Figure 4-6The model shown is rendered in three dimensions, and this model is typically used in three dimensions; however, such a model can also be a simplified 0D or 1D model based on 3D data. Similarly, computational fluid dynamics (CFD) simulations can be used. Therefore, different levels of complexity and combinations can be used (i.e., 3D CFD calculations within the framework of a 0D model).

[0084] The impact of the inserted synthetic lesion can be evaluated based on target metrics, such as perfusion at downstream locations in the vascular system and / or supplied tissues (e.g., in the context of myocardial perfusion evaluation). Such metrics can correspond to, be part of, or be based on the determined expected physiological state (270a). Patency can be quantified in the context of the expected physiological state based on the absolute perfusion value achieved at the target site or by changes in perfusion relative to the introduced morphological changes (i.e., vulnerability). The latter can be quantified, for example, by changes in lesion significance parameters.

[0085] In some embodiments, the expected physiological state (generated at 270a) may be based on perfusion relative to a downstream location of the applied treatment. For example, the downstream location may be a downstream location in the patient's vascular system being modeled. The expected physiological state may then be a measure of absolute perfusion value or a measure of relative perfusion change comparing a long-term hemodynamic model with a post-treatment hemodynamic model.

[0086] In some embodiments, after simulating blood flow in the modeling patient's vascular system (260), or in parallel with performing the modeling process described herein, the method continues to apply modifications (280). Figure 5 Post-treatment hemodynamic models. Such modifications can be based on the introduction or expansion of lesion variation parameters (at 240), and on the introduction or alteration of potential treatments applicable to the patient's vascular system, which can then be used to create alternative post-treatment hemodynamic models (at 230).

[0087] Therefore, in some embodiments, the method includes applying modification (at 280) to at least one parameter of at least one lesion introduced or expanded (at 240) by the synthetic lesion modeling and insertion module 420. This can be done by directly modifying the parameters of the lesion itself or by modifying the provided parameters (at 233) or the characteristics of the parameters identified that lead to the generated lesion (at 238). Such modification can then be used to generate at least one modified long-term hemodynamic model (at 250) and to simulate blood flow in such a modified model (at 260) to generate a modified predicted physiological state of the patient's vascular system and at least one lesion based on the corresponding modified long-term hemodynamic model (at 270b).

[0088] Once the predicted physiological state and the modified predicted physiological state (at 270a, 270b) are generated, the states can be used to generate a patency measure 450 (at 290) based on a comparison of the states. It should be understood that although the method is discussed in the context of two iterations of the predicted physiological state (at 270a, 270b), additional iterations (such as at 270c) can be provided to provide additional data for generating the patency measure 450. Such a patency measure 450 can then provide a measure of relative perfusion changes compared to iterations of a long-term hemodynamic model in the context of parameter changes in at least one lesion. Alternatively or additionally, the patency measure 450 can provide a measure of relative sensitivity in the context of parameter changes in at least one lesion.

[0089] Therefore, several long-term models can be generated based on small changes around the selected lesion scenario, leading to predicted changes in physiological state. The patency measure 450 can then be correlated with, for example, the relative sensitivity of perfusion based on several similar long-term models. For example, modifying the lesion significance from 0-50% at different steps can produce nonlinear perfusion behavior with respect to such lesion parameters. The patency measure 450 can then describe vulnerability in sophisticated ways.

[0090] As described above, in addition to introducing or amplifying changes in the lesion (at 240), the method can also introduce modifications (at 280) to the post-treatment model (at 230) based on hypothetical treatment applicable to the patient's vascular system.

[0091] The method can then repeat the above process to introduce or expand at least one synthetic lesion into the patient's vascular system (at 240) in a post-treatment hemodynamic model (at 230) to create an alternative long-term hemodynamic model (at 250). The method then simulates blood flow in the alternative long-term hemodynamic model (at 260) and generates a projected physiological state (at 270b) for the modification (introduced at 280) and at least one synthetic lesion (introduced at 240). As described above, the modification (at 280) can be a potential treatment, and the projected physiological state (at 270b) allows for evaluation of the impact of the proposed treatment.

[0092] In some embodiments, this portion of the process can be repeated multiple times to simulate, for example, different potential treatments available to the patient based on their vascular system. Thus, additional anticipated physiological states can be generated (at 270c) to evaluate additional scenarios. Such repetition can be iterative, allowing a user, such as a physician, to propose a treatment (at 280) to be introduced into the model while reviewing the anticipated physiological states of the initial scenario (at 270a). The physician can then review the anticipated physiological states of the proposed treatment (at 270b) and propose different or modified treatments.

[0093] After generating multiple predicted physiological states (270a, b, c), the method can continue to generate and output a comparison of predicted physiological states (at 290) to evaluate the impact of the proposed treatment. Therefore, the method can compare predicted physiological states associated with each potential treatment to generate a recommendation based on the physiological state or patency metric 450 associated with each potential treatment.

[0094] As described above, this method allows users to modify the parameters of the introduced or expanded lesion (at 240). Therefore, when the described method is repeated iteratively, modifications can be introduced along different dimensions, allowing different lesion scenarios to be considered for each proposed treatment.

[0095] Therefore, in some embodiments, after the initial iteration of the method leading to the predicted physiological state, the method may modify (introduced or amplified at 240) at least one parameter of at least one lesion to create at least one modified long-term hemodynamic model (at 250). The method may then generate at least one modified predicted physiological state (at 270b) corresponding to at least one modified long-term hemodynamic model. The method may then generate a patency metric 450 based on a comparison between the predicted physiological state associated with the long-term hemodynamic model (at 270a) and the at least one modified predicted physiological state associated with at least one modified long-term hemodynamic model (at 270b).

[0096] The method can then similarly modify at least one parameter of at least one lesion (introduced or amplified at 240) in the post-alternative hemodynamic model to create at least one modified alternative long-term hemodynamic model (at 250). The method can then generate at least one modified predicted physiological state of the patient's vascular system and at least one lesion based on the at least one modified alternative long-term hemodynamic model. The method can then employ the same approach as with the long-term hemodynamic model and generate an alternative patency measure 450 based on a comparison of the predicted physiological state associated with the alternative long-term hemodynamic model and at least one modified predicted physiological state associated with the at least one modified alternative long-term hemodynamic model.

[0097] The method can then output a comparison between the fluency metric 450 and alternative fluency metrics to account for the fragility of fluency in each proposed scenario.

[0098] In some embodiments, such as the potential treatment introduced therein (at 280) is a description of one of a plurality of potential treatments, the method comparing the expected physiological state associated with each of the potential treatments, and generating (at 300) a recommendation based on a measure of patency vulnerability in each of the plurality of potential treatments (e.g., patency metric 450) compared to a measure of patency vulnerability in a long-term hemodynamic model.

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

[0100] In some embodiments, the modification to be introduced (280) is not a difference in treatment at all, but rather a difference in the expected location of the synthetic lesion to be introduced or amplified (at 240). Such a difference can be introduced by a user (such as a clinician) using an interactive user interface, as described above. In some embodiments, the method itself can test for a variety of potential synthetic lesions and can leverage knowledge of previous scenarios to identify vulnerable scenarios where small morphological changes may lead to a significant decrease or increase in patency. Thus, the patency metric 450 can take into account the outcomes associated with the various synthetic lesions introduced.

[0101] In some embodiments, an optimization algorithm may be provided, wherein the user may be informed of the ideal treatment location, such as for stent placement, or the worst-case scenario for post-intervention risk assessment may be identified given a set of user-selected constraints.

[0102] The methods according to this disclosure can be implemented on a computer as computer-implemented methods, or in dedicated hardware, or in a combination of both. Executable code for the methods according to this disclosure can be stored on a computer program product. Examples of computer program products include memory devices, optical storage devices, integrated circuits, servers, online software, etc. Preferably, the computer program product may include non-transient program code stored on a computer-readable medium for performing the methods according to this disclosure when the program product is executed on a computer. In embodiments, the computer program may include computer program code adapted to perform all steps of the methods according to this disclosure when the computer program is run on a computer. The computer program may be embodied on a computer-readable medium.

[0103] While this disclosure has been described with respect to several described embodiments at considerable length and with certain specificity, it is not intended to limit it to any such details or embodiments or any particular embodiment, but rather to be interpreted with reference to the appended claims so as to provide the broadest possible interpretation of such claims in light of the prior art, and thus effectively cover the intended scope of this disclosure.

[0104] All examples and conditional language described herein are intended for pedagogical purposes to aid the reader in understanding the principles of this disclosure and the concepts contributed by the inventors to advance the art, and should be construed as not being limited to these specifically described examples and conditions. Furthermore, all statements herein describing the principles, aspects, and embodiments of this disclosure and their specific examples are intended to cover both their structural and functional equivalents.

Claims

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

2. The method according to claim 1, wherein, The three-dimensional data includes composite information from multiple two-dimensional images.

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

4. The method according to claim 1, wherein, The three-dimensional data is reconstructed based on two-dimensional interventional data after the treatment is applied to the vascular anatomy.

5. The method according to claim 1, wherein, The post-treatment hemodynamic model is generated by generating an initial hemodynamic model of the patient's vascular system based on the three-dimensional data, and identifying the current treatment status based on information from the applied treatment.

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

7. The method according to claim 1, wherein, The lesion to be introduced or expanded is at least one of the following: focal lesion, diffuse lesion, bifurcation lesion, and complete occlusion.

8. The method of claim 1, further comprising evaluating at least one characteristic of the patient's vascular system, wherein, Introduce or enlarge the at least one lesion at a location selected based on the patient's vascular system.

9. The method of claim 1, further comprising identifying the location of plaque formation, wherein, The at least one lesion was introduced or expanded at the location of the plaque formation.

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

11. The method according to claim 1, wherein, The expected physiological state is at least one of the following: Measurement of absolute perfusion value, and A measure of relative perfusion change by comparing the long-term hemodynamic model with the post-treatment hemodynamic model.

12. The method according to claim 1, further comprising: Modify at least one parameter of the at least one lesion to create at least one modified long-term hemodynamic model, generate at least one modified predicted physiological state of the patient's vascular system and the at least one lesion based on the corresponding at least one modified long-term hemodynamic model, and generate a patency measure based on a comparison of the predicted physiological state with the at least one modified predicted physiological state.

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

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

15. The method according to claim 14, wherein, The potential treatment is one of a plurality of potential treatments, and the method further includes: comparing the expected physiological state associated with each of the potential treatments, and generating a recommendation based on a comparison of a patency metric associated with each of the plurality of potential treatments and an equivalent patency metric associated with the long-term hemodynamic model.

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

17. The apparatus according to claim 16, wherein, The three-dimensional data includes composite information from multiple two-dimensional images.

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

19. The apparatus according to claim 16, wherein, The three-dimensional data is reconstructed based on two-dimensional interventional data after the treatment is applied to the vascular anatomy.

20. The apparatus according to claim 16, wherein, The post-treatment hemodynamic model is generated by generating an initial hemodynamic model of the patient's vascular system based on the three-dimensional data, and identifying the current treatment status based on information from the applied treatment.

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