Computer system for computer-aided design (CAD) modeling of the mitral valve

Abstract

This invention provides a system and method for generating novel patient-specific CAD models of the mitral valve. [Solution] A system and method for generating a patient-specific CAD model of a mitral valve includes receiving a digital image of the patient's mitral valve and segmenting the digital image to identify the structure of the mitral valve. A CAD model of the mitral valve is generated, which includes a modeled structure representing the identified structure. The modeled structure in the CAD model is connected at multiple locations. A first hemodynamic model is used to determine a first load condition for the modeled structure. Based on the first load condition, the movement of the modeled structure is simulated. A specified region is determined based on the CAD model. A second load condition is determined using a second hemodynamic model that receives the specified region as input. The CAD model is calibrated by modifying the configuration of the modeled structure in the CAD model based on the load condition and the movement of the modeled structure.

Description

Technical Field

[0001] This description generally relates to generating a CAD model of a mitral valve.

Background Art

[0002] Generally, a patient-specific model includes an anatomical or physiological model of a patient or a part of a patient that will interact with a medical device that can be used in the treatment of the patient's physical condition. For example, a patient-specific model can be used to evaluate the suitability of a device or the design of a device to improve the effectiveness of treatment.

Summary of the Invention

[0003] Mitral regurgitation (MR) is a form of heart disease in which the mitral valve does not close properly when the patient's heart pumps blood. MR causes blood to flow backward from the left ventricle through the mitral valve into the left atrium when the left ventricle contracts. One way to treat MR involves a transcatheter edge-to-edge treatment in which clips are attached to each leaflet of the mitral valve to hold the leaflets together. In areas where clips are not attached, the mitral valve can continue to open and close.

[0004] This disclosure provides a method for generating a patient-specific model of the mitral valve that can be used to determine the effectiveness of transcatheter edge-to-edge repair. The patient-specific model may be a multiphysics model that includes both the structure of the patient's mitral valve represented by a computer-aided design (CAD) model of the mitral valve and the blood flow through the mitral valve represented by a concentrated parameter hemodynamic model. The patient-specific model may be calibrated to match physiological and anatomical parameters measured from the patient in a preoperative state. To simulate the dynamics of the patient's mitral valve in a postoperative state, a CAD model of the clip may be coupled to a CAD model of the mitral valve. The choice of clip type, number of clips, and clip positioning may be adjusted to improve postoperative outcomes. The patient may be treated by inserting the clip into the patient's heart (e.g., through a catheter) and attaching the clip to the patient's mitral valve based on the simulated postoperative state of the patient-specific model.

[0005] In an exemplary embodiment, a data processing system (e.g., a computer system having one or more processors) generates a patient-specific model of the mitral valve. The data processing system segments a digital image of the patient's mitral valve (e.g., a computed tomography (CT) scan) to isolate the mitral valve's structures (e.g., leaflets, chordae, annulus, papillary muscles). The data processing system generates a CAD model of the mitral valve based on the isolated structures. The CAD model includes data representing the isolated structures. The data processing system connects the modeled chordae to the modeled leaflets at multiple locations. The data processing system extends the modeled annulus in the CAD model to represent the fibrous tissue surrounding the mitral valve. The data processing system uses a standalone centralized parameter hemodynamic model that simulates blood flow through the patient's heart to determine the initial load conditions on the modeled structures in the CAD model. The data processing system performs co-simulation of the mitral valve using a CAD model, an anatomical valve orifice area module that determines the anatomical valve orifice area of ​​the CAD model, and a coupled centralized parameter hemodynamic model that takes the anatomical valve orifice area as input and outputs the valve leaflet pressure for application to the valve leaflets in the CAD model. The data processing system calibrates the CAD model by modifying the configuration of the modeled chordae tendineae and modeled valve leaflets based on the valve leaflet pressure determined from the coupled centralized parameter hemodynamic module and the movement of the modeled structures in the CAD model compared to the isolated structures from the segmentation.

[0006] The embodiments described herein can offer various technical advantages. For example, the embodiments described herein enable a computer system to represent the specific shape and performance of a particular patient's mitral valve, thereby enabling customized treatment of a patient's mitral regurgitation (MR). The method of this disclosure generates a patient-specific model that more accurately represents the actual physiological and anatomical state of the patient's mitral valve compared to conventional models. The patient-specific model of this disclosure can be used to assess whether a particular patient would benefit from receiving treatment. The computer system can output clinically relevant indicators for the evaluation and treatment of MR (e.g., MR grade, regurgitant blood flow, etc.). As another example, the embodiments described herein can be used to conduct in silico clinical trials to evaluate the effectiveness of a proposed treatment by applying the treatment to multiple patient-specific models.

[0007] In one embodiment, a method for generating a patient-specific CAD model of a mitral valve comprises: receiving one or more digital images of the patient's mitral valve by a data processing system; segmenting one or more digital images to identify the structure of the mitral valve by the data processing system; generating a CAD model of the mitral valve, the CAD model including data for modeled structures representing the identified structure of the mitral valve; connecting one or more first modeled structures from among the modeled structures to one or more second modeled structures and third modeled structures from among the modeled structures, the one or more first modeled structures being connected to one or more second modeled structures and third modeled structures at multiple locations; and using a first hemodynamic model, the data processing system models the CAD model The process involves determining one or more first load conditions for a modeled structure, simulating the movement of the modeled structure in a CAD model based on the first load conditions applied to the modeled structure using a data processing system, determining a designated region based on the CAD model using a data processing system, determining one or more second load conditions using a second hemodynamic model that receives data representing the designated region as input using a data processing system, and calibrating the CAD model by modifying the configuration of one or more first modeled structures and one or more second modeled structures among the modeled structures in the CAD model, wherein the modification includes calibration based on (i) one or more second load conditions determined using the second hemodynamic model, and (ii) the position of the modeled structure in the CAD model based on the movement of the modeled structure in the CAD model compared to the position of the identified structure.

[0008] Embodiments of this design may include one or more of the following features:

[0009] In some embodiments, segmenting one or more digital images includes rotating one or more digital images to align them with the annular surface of the mitral valve.

[0010] In some embodiments, the identified structures include the valve annulus, valve leaflets, papillary muscles, and chordae tendineae.

[0011] In some embodiments, segmenting one or more digital images includes verifying the shape of the valve leaflets across the identified leaflets within the multiple digital images.

[0012] In some embodiments, the modeled structure includes a modeled annulus, modeled leaflets, modeled papillary muscles, and modeled chordae tendineae, where one or more first modeled structures include modeled chordae tendineae, one or more second modeled structures include modeled leaflets, one or more third modeled structures include modeled papillary muscles, and a fourth modeled structure includes a modeled annulus.

[0013] In some embodiments, connecting one or more first modeled structures to one or more second modeled structures and one or more third modeled structures involves clustering the modeled chordae tendineae into circular regions on each modeled valve leaflet, where each circular region corresponds to a location among a plurality of locations on the modeled papillary muscle.

[0014] In some embodiments, calibrating a CAD model includes simulating the movement of a modeled structure in a CAD model by determining the initial chordal length for a modeled chordae tendineae based on a thermal analysis of the CAD model in systolic and diastolic conditions, and iteratively determining the position of a modeled leaflet, the orifice area relative to the position of the modeled leaflet, and the pressure applied to the modeled leaflet, wherein the pressure is determined using a second hemodynamic model and the orifice area; determining the distance error between the simulated movement and the position of the mitral valve in one or more digital images; and correcting the distance error by adjusting the shape of the modeled leaflet, the number of modeled chordae tendineae, the chordal attachment points of the modeled chordae tendineae, or the length of the modeled chordae tendineae, depending on whether the distance error is determined to exceed a threshold distance error.

[0015] Some embodiments include extending a modeled annulus in a CAD model to represent the tissue associated with the mitral valve by a data processing system.

[0016] In some embodiments, the first and second hemodynamic models include the first and second concentrated parameter hemodynamic models.

[0017] In some embodiments, generating a CAD model includes generating a modeled leaflet shape for the diastolic state of the mitral valve and generating a modeled leaflet shape for the systolic state of the mitral valve.

[0018] Some embodiments involve treating patients based on performing preoperative and postoperative simulations using a CAD model of the mitral valve.

[0019] In some embodiments, performing postoperative simulations using a CAD model involves positioning a model of a medical device that comes into contact with one or more of the modeled structures within the CAD model of the mitral valve.

[0020] In some embodiments, the medical device model includes a model of a clip that contacts a modeled structure of a mitral valve in the CAD model.

[0021] In some embodiments, the designated region includes defining two or more parallel cutting planes in a CAD model, forming one or more connected segments, determining the joint points and joint lengths for each of the two or more parallel cutting planes based on the one or more connected segments, and determining the designated region based on the joint lengths and the distance between the two or more parallel cutting planes.

[0022] In some embodiments, the first and second hemodynamic models include parameters determined by minimizing the difference between the outputs of the first and second hemodynamic models and corresponding measurements from patient data and literature data.

[0023] In some embodiments, the method includes generating clinical indices for use in evaluating mitral regurgitation in the preoperative and postoperative conditions of treated patients, the clinical indices including left atrial pressure, transmitral pressure gradient, and mitral regurgitation.

[0024] Other embodiments of this specification include corresponding computer systems, devices, and computer programs recorded on one or more computer storage devices, each configured to perform the actions or operations described herein. One or more computer systems may be configured to perform specific actions by installing software, firmware, hardware, or a combination thereof on the system that causes an action to occur or causes the system to perform an action when it is running. One or more computer programs may be configured to perform specific actions by including instructions that cause the device to perform an action when executed by a data processing device.

[0025] Details of one or more embodiments of the subject matter of this specification are set forth in the accompanying drawings and the following description. Other features, aspects, and advantages of the subject matter will become apparent from the description, the drawings, and the claims.

Brief Description of the Drawings

[0026] [Figure 1] FIG. 8 is a block diagram of an exemplary system for mitral valve CAD modeling. [Figure 2] FIG. 11 is a flowchart of an exemplary process for generating a patient-specific model of a mitral valve. [Figure 3] FIG. 14 is a diagram illustrating an exemplary segmentation of digital images of a patient's mitral valve at multiple points in the cardiac cycle. [Figure 4] FIG. 17 is a block diagram of an exemplary lumped parameter hemodynamic model. [Figure 5A-5B] FIGS. 20A and 20B are a top view and a perspective view of an exemplary patient-specific CAD model of a mitral valve in diastole. [Figure 5C] FIG. 23 is a diagram illustrating the patient-specific CAD model of FIGS. 5A-5B in systole. [Figure 6] FIG. 26 is a composite plot illustrating an exemplary process for determining the anatomical valve area at multiple conditions of a patient-specific mitral valve. [Figure 7] FIG. 29 is a diagram illustrating an exemplary modeled attachment location of modeled chordae on a modeled leaflet of a mitral valve. [Figure 8] FIG. 32 is a flowchart of an exemplary process for calibrating a patient-specific CAD model of a mitral valve. [Figure 9A] FIG. 35 is a diagram depicting an exemplary CAD model of a clip for a mitral valve. [Figure 9B] FIG. 38 is a diagram depicting an exemplary CAD model of a clip for a mitral valve. [Figure 9C] FIG. 41 is a diagram depicting the clip of FIGS. 9A-9B attached to a CAD model of a mitral valve. [Figure 10A]This figure illustrates an exemplary CAD model of the mitral valve in both diastolic and systolic states, both pre- and post-operatively. [Figure 10B] This figure illustrates an exemplary CAD model of the mitral valve in both diastolic and systolic states, both pre- and post-operatively. [Figure 10C] This figure illustrates an exemplary CAD model of the mitral valve in both diastolic and systolic states, both pre- and post-operatively. [Figure 10D] This figure illustrates an exemplary CAD model of the mitral valve in both diastolic and systolic states, both pre- and post-operatively. [Figure 11] This is a flowchart illustrating an exemplary process for generating a patient-specific model of the mitral valve. [Figure 12] This is a diagram illustrating an exemplary computer system. [Modes for carrying out the invention]

[0027] Similar reference numbers and designations in various drawings refer to the same elements.

[0028] Figure 1 is a block diagram of an exemplary system 100 for CAD modeling of a mitral valve or mitral valve apparatus (MVA). The MVA includes the mitral valve annulus, mitral valve leaflets, papillary muscles, and chordae tendineae. System 100 in this embodiment is based on a client-server or cloud-based architecture and includes a server system 102 implemented as a computer system 104 (standalone or cloud-based) and client devices 106 connected via a network 108. The server system 102 includes memory 110, a bus system 112, interfaces 114 (e.g., user interface / network interface / display or monitor interface), and a processing unit 116. Memory 110 contains an image segmentation engine 118, a CAD model 120, a mesh preparation engine 122, a valve orifice identification engine 124, a hemodynamic model 126, and a calibration engine 128. System 100 also includes a data repository 130 containing digital images 132, models 134, patient data 136, and literature data 138.

[0029] The image segmentation engine 118 receives a digital image 132 from the data repository 130. The image segmentation engine 118 identifies the structure of the MVA in the digital image 132. For example, the image segmentation engine 118 determines the pixels in the digital image that correspond to the structure of the MVA (e.g., annulus, leaflets, chordae tendineae, papillary muscles) and pixels that do not correspond to the structure (e.g., blood pixels).

[0030] The CAD model 120, the valve orifice identification engine 124, and the hemodynamic model 126 form a patient-specific multiphysics model that includes data representing the physical structure of the MVA and the blood flow through the MVA. The CAD model 120, the valve orifice identification engine 124, and the hemodynamic model 126 are coupled together to perform a multiphysics simulation (e.g., co-simulation) of the MVA. For example, the CAD model 120 may be used in a structural simulation to determine the position of the mitral valve leaflets depending on applied boundary conditions such as the pressure applied to the leaflets and position data applied to the annular region. The structural simulation can determine the position of the modeled leaflets using an incremental method. For example, the structural simulation can determine the next position of the modeled leaflets by advancing the solution using small time steps without iterations within each increment. The leaflet positions are sent to the valve orifice identification engine 124, which identifies the mitral valve leaflets. The valve orifice identification engine 124 can determine the orifice area of ​​the mitral valve (e.g., the area through which blood flows) based on the position of the valve leaflets in the CAD model 120. The valve orifice identification engine 124 transmits the identified orifice (e.g., orifice area) to the hemodynamic model 126, which can use the identified orifice as input to determine load conditions (e.g., valve leaflet pressure, transmitral pressure difference) for the CAD model 120 based on blood flow through the heart. The load conditions are transmitted to the CAD model 120 to be applied as boundary conditions in subsequent iterations of the simulation.

[0031] The CAD model 120 includes data representing the structure and shape of the patient's MVA. For example, the CAD model 120 includes data representing the location and orientation of MVA structures such as the leaflets, papillary muscles and chordae tendineae attached to the leaflets, and the annular region. The CAD model 120 also includes structural properties (e.g., shape, thickness, leaflet fibers, chordae tendineae composition and cross-sectional area) as well as material properties (e.g., anisotropy, heterogeneity, hyperelasticity, density). The shape of the CAD model 120 may be specified for one or more states during the cardiac cycle. The cardiac cycle includes a complete heartbeat (e.g., from the start of one heartbeat to the start of the next). The cardiac cycle includes the diastolic phase (e.g., ventricular filling) and the systolic phase (e.g., ventricular contraction / ejection).

[0032] The CAD model 120 is incorporated into a structural simulation (e.g., a finite element model) to determine the movement of the elements of the CAD model 120 according to the applied boundary conditions (e.g., pressure acting on the valve leaflets, displacement of the annulus and papillary muscles coinciding with the observed positions of the patient's annulus and papillary muscles). For example, the structural simulation can solve three-dimensional time-resolved equations derived from the principle of virtual work. This method provides a balance between internal and external forces with respect to virtual displacements, enabling accurate simulation of the solid mechanics of the MVA.

[0033] The mesh preparation engine 122 can determine a mesh to be applied to the CAD model 120 in a structural simulation. The mesh may be a discretized representation of the CAD model 120 having volumetric elements (e.g., cells, voxels, tetrahedral elements, or hexahedral elements) that represent the modeled structure within the CAD model 120. Figure 1 shows the mesh preparation engine 122 in memory 110, but the mesh preparation engine may be a third-party application running on a system different from the server system 102. Regardless of whether the mesh preparation engine 122 runs in memory 110 or on a system different from the server 102, the mesh preparation engine 122 receives a user-supplied mesh definition based on the CAD model 120, then prepares the mesh, and transmits (and / or stores) the prepared mesh for use in a structural simulation (e.g., finite element simulation) of the CAD model 120.

[0034] In some embodiments, the CAD model 120 includes models of both the mitral valve leaflets and idealized chordae tendineae. The papillary muscles may not be modeled, but the coordinates of the papillary-chordae attachment points are defined in the CAD model 120. The valve leaflets may be modeled with a uniform thickness (e.g., about 2 mm). The valve leaflets may be discretized using hexahedral brick elements of linear incompatible modes (e.g., by the mesh preparation engine 122). The valve leaflet tissue may be simulated using the anisotropic Holzapfel-Ogden constitutive law. Material constants for the CAD model 120 may be determined using data from the literature (e.g., biaxial human valve leaflet tensile data). The chordae tendineae may be modeled with a uniform cross-sectional area and may be discretized using truss elements. The material response of the chordae tendineae may be modeled using the Ogden hyperelastic constitutive law. Each chordae tendineae may be conceived as a single line of elements (e.g., unbranched) between the origin of the modeled papillary muscle and the insertion point of the modeled valve leaflet.

[0035] The papillary-chordae attachment sites and the edges of the valve leaflets on the annulus can be defined as boundary conditions (time-varying or fixed boundary conditions). In some embodiments, general surface contact may be frictionless to account for collisions between opposing leaflets and between elements within the same leaflet.

[0036] The valve orifice identification engine 124 receives a mesh of MVAs from the CAD model 120. The valve orifice identification engine 124 identifies valve orifices within the received mesh. The valve orifice identification engine can determine the valve orifice area through which blood can flow, such that it is represented by the location of the modeled structure in the CAD model. The valve orifice area changes throughout the entire cardiac cycle. Therefore, the valve orifice area can be determined at multiple points in the cardiac cycle to reflect the size of the area through which blood can flow at each point in the cardiac cycle. The valve orifice identification engine 124 can be invoked at multiple time steps of the structural simulation of the CAD model 120 (e.g., every time step, every other time step, every five time steps, every ten time steps, a time determined based on the phase of the cardiac cycle, etc.). Exemplary processes that can be performed by the valve orifice identification engine 124 to determine the valve orifice area are discussed in more detail with reference to Figure 6.

[0037] The hemodynamic model 126 simulates blood flow through the patient's heart. The hemodynamic model 126 may be a closed-loop concentrated parameter model (LPM) that predicts pressure boundary conditions for the MVA (e.g., transmitral pressure difference). The hemodynamic model 126 simulates the bulk effects of blood flow into and from the circulatory system within the heart. Flow between the ventricles and atria through the mitral and tricuspid valves can be modeled using Bernoulli relations, which can capture the effects of blood inertia and blood resistance. The hemodynamic model 126 can receive valve orifices identified by the valve orifice identification engine 124 as input. The orifice area of ​​the identified valve orifice relates to the pressure applied to the valve leaflets in the CAD model 120. For example, a relatively large orifice area (compared to the total mitral valve area) corresponds to a lower pressure difference across the valve leaflets by blood flow during diastole. However, a larger orifice area corresponds to greater regurgitation during systole. The hemodynamic model 126 can generate as output the load conditions (e.g., valve leaflet pressure) applied to the CAD model 120 for subsequent time steps of the structural simulation. An exemplary centralized parameterized hemodynamic model is discussed in more detail with reference to Figure 4.

[0038] The calibration engine 128 is used to fit the CAD model 120 and hemodynamic model 126 to match specific clinical indicators from the patient with the specific shape of the patient's MVA. The calibration engine 128 can adjust the configuration of the modeled structure in the CAD model 120 based on the position of the modeled structure in the CAD model 120 during simulated motion, compared with identified structures from the image segmentation engine 118. The calibration engine 128 can adjust the parameters of the hemodynamic model to match clinically measured patient data 136 with literature data 138. After calibration, the CAD model 120 and hemodynamic model 126 represent the patient's preoperative state. The system 100 can store the CAD model 120 and hemodynamic model 126 in a data repository for later retrieval and use. For example, the computer system 104 can access model 134 for use in device processing simulations of the MVA.

[0039] System 100 can generate clinically relevant output parameters that can be used to evaluate the feasibility and effectiveness of treatment. Exemplary clinical outputs include regurgitation, regurgitation rate, effective valve orifice area, left atrial pressure, mitral valve orifice area, diastolic mitral pressure gradient, diastolic left ventricular volume, and left ventricular ejection fraction.

[0040] System 100 can simulate blood flow through the mitral valve in the patient's preoperative state over the entire cardiac cycle using a CAD model 120, a valve orifice identification engine 124, and a hemodynamic model 126. For example, the simulation can be started at 70% of diastole. Initial chamber pressures are specified (e.g., based on literature data 138, patient data 136, and flow equations). Nodes in the CAD model 120 at the modeled annular region and modeled papillary muscle attachment points are fixed (e.g., based on image segmentation data). The length of the modeled chordae tendineae is adjusted in diastole so that it reaches approximately the systolic state where the modeled chordae tendineae are straight. The strain introduced in the modeled chordae tendineae and modeled leaflets is set to zero (e.g., approximately nearly unstrained at 70% of diastole). After adjustment of the modeled chordae tendineae and modeled leaflets, a co-simulation using the CAD model 120, the valve orifice identification engine 124, and the hemodynamic model 126 can be initiated. The hemodynamic model 126 uses the valve orifice area determined by the valve orifice identification engine 124 to determine the load conditions (e.g., pressure) to be applied to the modeled valve leaflets in the CAD model 120. During co-simulation, the locations of the annular and attachment nodes in the pre-fixed CAD model 120 are specified to change over time. The system 100 simulates multiple heart cycles until a steady-state response is reached.

[0041] Exemplary inputs to the structural simulation of CAD model 120 include leaflet material parameters, chordae tendineae material parameters, chordal length, leaflet size and shape, annulus size, shape, and motion, as well as chordae tendineae-papilla attachment location and motion. Exemplary outputs to the structural simulation of CAD model 120 include motion and shape of geometric components, joint behavior (e.g., height, tenting area, posterior leaflet angle), stress and strain of leaflets and chordae tendineae, mitral valve orifice area, and regurgitant valve orifice area and location. Other inputs and outputs to the structural simulation of CAD model 120 are possible.

[0042] Exemplary inputs to hemodynamic model 126 include initial chamber pressure and volume, passive chamber compliance, active chamber elastance, blood flow model parameters through the mitral and tricuspid valves (e.g., resistance, inertance, blood density), blood flow resistance through the pulmonary and aortic valves, and blood flow resistance through the chambers (excluding valves). Exemplary outputs to hemodynamic model 126 include traces of chamber pressure and volume, left ventricular ejection fraction, left ventricular systolic and diastolic volumes, systolic and diastolic transmitral pressure gradients, regurgitation volume and rate, pulmonary venous waveform, and mean atrial pressure. Other inputs and outputs to hemodynamic model 126 are possible.

[0043] Figure 2 is a flowchart of an exemplary process 200 for generating a patient-specific model of the MVA. Process 200 may be performed on a data processing system (e.g., the computer system in Figure 12). Process 200 includes generating a CAD model of the patient's MVA (steps 202-208), generating a standalone concentrated parameter hemodynamic model to determine initial loading conditions for structural simulation of the CAD model (steps 210-214), and calibrating the CAD model to match the comparator through co-simulation with a second concentrated parameter hemodynamic model (steps 216-226). Process 200 is completed when the patient-specific model represents the patient's preoperative condition (228).

[0044] A CAD model of the patient's MVA is generated based on isolated structures identified in three-dimensional (3D) digital images (e.g., ultrasound scans including CT, MRI, transesophageal echocardiography, and transthoracic echocardiography). The data processing system segments the MVA within the image, including the mitral leaflets, annulus, and papillary muscles, throughout the entire cardiac cycle (step 202). To segment the image, the data processing system adjusts the orientation of the leaflets and rotates the image to clearly display the location of the commissure (e.g., where the anterior and posterior valves meet as they are inserted into the annulus). The image may be rotated to align the image plane with the annular plane of the mitral valve. The data processing system can segment the annulus, anterior and posterior valves, papillary muscles, and chordae tendineae to isolate these structures. The data processing system can perform segmentation at defined intervals during the cardiac cycle.

[0045] The data processing system creates the shapes of the modeled valve leaflets, modeled papillary muscles, and modeled chordae tendineae within the CAD model in the diastolic state (step 204). For example, the data processing system determines the 3D positions of the valve leaflets, papillary muscles, and chordae tendineae based on the isolated structures from the segmentation. Based on the determined 3D positions of the isolated structures in the diastolic state, the data processing system creates the ideal shapes of the modeled structures in the diastolic state.

[0046] The data processing system creates the shapes of the modeled valve leaflets, modeled papillary muscles, and modeled chordae tendineae within the CAD model in the systolic state (step 206). For example, the data processing system determines the 3D positions of the valve leaflets, papillary muscles, and chordae tendineae based on the isolated structures from the segmentation. Based on the determined 3D positions of the isolated structures in the systolic state, the data processing system creates the ideal shape of the modeled structures in the systolic state.

[0047] The data processing system compares the CAD model in the expansion phase and the CAD model in the contraction phase with the image segmentation to determine whether the generated CAD model has an acceptable level of consistency with the image (step 208). If there is no acceptable consistency, process 200 returns to MVA segmentation (step 202).

[0048] Turning to Figure 3, which shows a series of frames 300 visualizing the segmented structure of the MVA throughout the entire heart cycle. The eighth frame 302 shows the outline of the annulus 304. The outline of the annulus 304 is superimposed on the other frames 300 to evaluate the consistency between the segmentation and the modeled shape. The data processing system identifies discrepancies 306, 307, and 308 where there is a mismatch between the outline of the annulus 304 and the modeled shape. The modeled shape and the outline of the annulus 304 are aligned using a line 310 extending between two intersection points. After identifying discrepancies 306, 307, and 308, the data processing system modifies the modeled shape to resolve the discrepancies.

[0049] Returning to Figure 2, the data processing system defines an LPM representing the hemodynamics of blood flow through the patient's heart (step 210). The data processing system utilizes the LPM to determine the initial load conditions to apply to the CAD model during the calibration of the CAD model. The LPM defined in step 210 is a standalone LPM that uses anatomical valve orifice area based on an approximation. For example, valve hemodynamics may be determined using the Mynard model (the entire model is incorporated herein by reference: Mynard, JP, Davidson, MR, Penny, DJ, Smolich, JJ, “A simple, versatile valve model for use in lumped parameter and one-dimensional cardiovascular models,” International Journal of Numerical Methods in Biomedical Engineering (2012)). A second LPM is generated in step 218 for use in co-simulations where anatomical valve orifice area is used instead based on the CAD model.

[0050] The data processing system uses LPM to simulate blood flow through the heart and compares the LPM output to a comparison set (step 212). The LPM output includes pressures and volumes of the cardiac chambers, pressure differences across the cardiac valves, MR volume, etc. The comparison set includes patient data that can be measured in correspondence with the LPM output. In some embodiments, patient data is unavailable, and the comparison set can be based on data reported in the literature.

[0051] The data processing system determines whether the LPM has an acceptable match with the comparison target (step 214). If the LPM is acceptable, process 200 proceeds to the cosimulation definition. If the match is not acceptable, process 200 returns to step 210, and the LPM input parameters are adjusted to better match the comparison target. For example, the data processing system may adjust the LPM model parameters using an optimization algorithm that seeks to minimize the mismatch between the LPM output and the comparison target. Constraints on optimization may include the physiological range of the input parameters. The data processing system repeats steps 210-214 until an acceptable match is achieved.

[0052] Exemplary LPM outputs that the data processing system can attempt to match with patient measurements include left ventricular ejection fraction (LV EF), left ventricular end-diastolic volume (LV ED vol), left ventricular end-systolic volume (LV ES vol), left atrial end-diastolic volume (LA ED vol), left atrial end-systolic volume (LV ES vol), left atrial mean pressure, left atrial V-wave pressure, mitral stenosis, and MR severity.

[0053] Figure 4 is a block diagram of an exemplary LPM400. The LPM400 represents bulk blood flow within the heart and bulk blood flow to and from the circulatory system. The LPM400 models the active chambers 402-408 of the heart (right atrium 402, right ventricle 404, left atrium 406, left ventricle 408) as active capacitors with time-varying capacitance. The time-varying capacitance simulates the elasticity of the ventricular and atrium chambers of the heart. The LPM400 includes passive capacitors representing arteries 410, veins 412, pulmonary arteries 414, and pulmonary veins 416. The LPM400 includes several flow nodes 418-424 (body 418, veins to the right ventricle 420, pulmonary capillaries 422, and pulmonary veins to the left atrium 424). The blood flow nodes 418–424 are modeled as resistors that linearly correlate pressure gradients with blood flow. The heart valves 426–432 (tricuspid valve 426, pulmonary valve 428, mitral valve 430, and aortic valve 432) are modeled as resistors that provide resistance to blood flow within the heart. The tricuspid valve 426 and mitral valve 430 may be modeled based on the Mynard model, and the pulmonary valve 428 and aortic valve 432 may be modeled as unidirectional blood flow nodes (for example, allowing flow in only one direction).

[0054] LPM400 includes a timer 434 that represents the time step of the model. In LPM400, blood flow is initiated according to the initial pressure difference assigned to the active chambers 402-408. Blood flow between the ventricles and atria through the tricuspid valve 426 and mitral valve 430 can be modeled using Bernoulli equations that capture the effects of blood inertia, blood resistance, and time-varying valve orifice area, with the valve orifice area determined based on the Mynard model.

[0055] The LPM400 can output pressure and volume histories for the active chambers 402-408 and passive chambers 410-416 during the duration of the simulation. The LPM400 can output flow rates and flow-time integrals (e.g., blood flow volume) for valves 426-432.

[0056] Returning to Figure 2, in step 216, the data processing system determines the boundary conditions for the annulus and papillary muscles. The boundary conditions for the annulus and papillary muscles may be determined based on the shapes of the leaflets and papillary muscles created in steps 204 and 206.

[0057] The data processing system extends the modeled annular region as a stiffer material to represent the fibrous tissue around the mitral valve. The data processing system applies boundary conditions to the extended annular structure rather than directly to the modeled valve leaflets. For example, the data processing system can extend the modeled annular region by creating lines tangent to a curve positioned 3 mm away from the modeled annular region for each point at the end of the annular band. The points can be connected to splines, and the data processing system can close the splines to form volumes. The data processing system can connect nodes in the extended annular region (e.g., mesh nodes) to ground nodes using elements representing springs. The data processing system can apply displacement boundary conditions to the ground nodes through the elements representing springs, which transmit displacement to the nodes in the extended annular region. Applying displacement boundary conditions to the extended annular region through the ground nodes and spring elements removes kinematic over-constraints on the nodes in the extended annular region. The data processing system can adjust the spring stiffness to achieve accurate motion of the modeled annular region.

[0058] In short, Figures 5A and 5B show a top view and perspective view of an exemplary patient-specific CAD model 500 of the MVA in a diastolic state 502. Figure 5C illustrates the patient-specific CAD model 500 in a systolic state 504. The CAD model includes an extended annular region 506 representing the fibrous tissue around the MVA. In structural simulations using the CAD model 500, boundary conditions may be applied to the extended annular region 506 rather than directly to the valve leaflet 508.

[0059] Returning to Figure 2, the data processing system defines a co-simulation model based on the CAD model, LPM, and anatomical valve orifice area module. The LPM used in the co-simulation model is substantially the same as the standalone LPM, except that the LPM in the co-simulation model receives the valve orifice area from the anatomical valve orifice area module rather than approximating the valve orifice area for the mitral valve. Other input parameters of the LPM, adjusted to match the comparison target, remain the same.

[0060] The anatomical valve orifice area module determines the valve orifice area based on the CAD model. The module defines parallel cross-sections within the CAD model. Each cross-section intersects with a valve leaflet on one or more connected segments. A connected segment is a continuous curve on the valve leaflet. The anatomical valve orifice area module determines one or more potential junctions on each connected segment. A potential junction can only junction with another potential junction on a different connected segment. When a junction pair (e.g., junctions for each of two segments of a cross-section) is identified, the dominant point is considered the junction. The closest distance to the opposite connected segment defines the junction length for that cross-section. The anatomical valve orifice area is determined based on the sum of the junction lengths and the distances between adjacent cross-sections.

[0061] Figure 6 is a composite plot 600 illustrating the junction lines and junction points used to determine the anatomical valve orifice area under multiple conditions of a patient-specific MVA model. The top row 602 shows a top view of the mesh of the CAD model of the MVA. The middle row 604 shows a top view of the junction lines along the cross-section. The bottom row 606 shows a side view of the junction lines along the cross-section. The first column 608 shows the open valve in the preoperative state. The second column 610 shows the closed valve in the preoperative state. The third column 612 shows the open valve in the postoperative state. The fourth column 614 shows the closed valve in the postoperative state. The anatomical valve orifice area module defines parallel cross-sections in the CAD model. The anatomical valve orifice area module defines the junction lines and junction points for each cross-section.

[0062] Returning to Figure 2, the data processing system defines the modeled chordae tendineae within the CAD model (step 220). The modeled chordae tendineae connect the locations of the modeled papillary muscles to the modeled valve leaflets, taking into account the anatomical structure of the papillary muscles based on image segmentation. During segmentation, the data processing system segments the chordae tendineae in the diastolic frame and the papillary muscles in each frame being processed. Not all chordae tendineae are visualized in the digital image, but the modeled chordae tendineae are positioned in the CAD model based on the location and orientation of the visualized chordae tendineae. The data processing system defines additional chordae tendineae (e.g., chordae tendineae not visualized in the digital image) within the CAD model according to the physiological range of the chordae tendineae.

[0063] The data processing system places the chordae tendineae in the CAD model according to the anatomical structure of the mitral valve and papillary muscles. There are two groups of papillary muscles: the anterior papillary muscles (APM) and the posterior papillary muscles (PPM). Each group of papillary muscles contains 13 origin sites. For each half of the valve leaflet (one half connected to the APM and the other half connected to the PPM), the insertion points of the chordae tendineae are clustered into circular regions. Each region contains several chordae tendineae insertion sites (e.g., 5-6 insertion sites). The data processing system connects the modeled chordae tendineae in each circular region to the origin sites of specific papillary muscles. The data processing system explicitly defines the connections between the circular regions and the origin sites of the papillary muscles. For example, the data processing system identifies the origin sites of the papillary muscles relative to the modeled chordae tendineae based on their proximity to the insertion sites of the modeled valve leaflets, and / or their shape identified in segmentation and digital imaging.

[0064] Figure 7 illustrates an exemplary hemi-leaflet 700 with modeled chordae tendineae insertion sites. The hemi-leaflet 700 is flattened, and the curved free end 700 of the leaflet 700 faces upward. Several circular regions 704 are defined along the leaflet 700, and the modeled chordae tendineae insertion sites 706 within each region are connected to the origins of specific papillary muscles. Some of the chordae tendineae insertion sites 706 are defined on the curved end 702. The insertion sites 706 are unevenly distributed across the leaflet 700. Defining the chordae tendineae insertion sites 706 in this manner can result in a larger mitral valve orifice area in the CAD model during diastole, which better represents the clinical behavior observed when a patient is treated with a device that clips the mitral valve.

[0065] Returning to Figure 2, the data processing system fine-tunes the chordae tendineae structure and valve leaflet shape (step 222). The data processing system can modify the length and tension of the modeled chordae tendineae. The data processing system can also modify the length of the modeled valve leaflets. Modifying the modeled chordae tendineae and valve leaflet structure can result in a better match between the systolic shape and the regurgitation of blood passing through the mitral valve.

[0066] The data processing system uses a co-simulation model to simulate the movement of the MVA and blood flow through the mitral valve. The data processing system evaluates the modeled chordae tendineae and modeled valve leaflets to determine the degree of agreement with a reference (e.g., systolic shape) (step 224). The data processing system determines the acceptableness of the agreement with the reference (step 226). If the determined degree of agreement is not acceptable, the data processing system modifies the configuration of the modeled chordae tendineae and modeled valve leaflets to achieve a better degree of agreement with the reference (e.g., agreement between the systolic state from the structural simulation of the CAD model and the image segmentation). The data processing system may repeat steps 222-226 until an acceptable degree of agreement is achieved.

[0067] Process 200 is completed when the CAD model has an acceptable degree of agreement with the comparison object (228). When process 200 is completed (228), the data processing system stores the patient-specific model in a hardware storage device for use in subsequent (e.g., later occurring) simulations and / or rendering visualizations of the patient-specific model for display on a display device. The visualizations include, for example, animations of the MVA in one or more phases of the heart cycle, such as those determined by the movement of the modeled structures in the CAD model, and clinical indicators (e.g., blood flow, blood pressure, etc.) determined from the simulations.

[0068] Figure 8 is a flowchart of an exemplary process 800 for calibrating a patient-specific CAD model of MVA. Process 800 may be performed by a data processing system to modify the configuration of the modeled chordae tendineae and modeled leaflets in the CAD model to better match with image segmentation, patient data, and / or literature data. For example, the data processing system may perform process 800 to modify the modeled chordae tendineae and modeled leaflets while performing steps 220-226 of process 200.

[0069] Process 800 begins with the data processing system calculating the initial chordal length for the modeled chordae tendineae based on a thermal analysis of the CAD model of the MVA in systolic and diastolic conditions (step 802). After setting the initial chordal length in the CAD model, the data processing system performs a structural simulation of the motion of the modeled MVA and determines the distance error between the simulated motion and the position of the MVA determined from image segmentation (step 804). If the distance error is below a threshold (e.g., the average distance error is less than 2 mm), process 800 is completed. Otherwise, the data processing system determines whether the distance error can be corrected by adjusting the chordae tendineae (step 806). For example, distance errors caused by tent-like and fold-like reactions in the modeled leaflets can be corrected by adjusting the chordae tendineae. Distance errors caused by the modeled leaflets being too long or too short can be corrected by adjusting the length of the modeled leaflets.

[0070] If the distance error exceeds a threshold and cannot be corrected using chordal adjustment, the data processing system modifies the shape of the modeled valve leaflet (step 808). For example, the data processing system may adjust the length of the modeled valve leaflet. After adjusting the modeled valve leaflet, the data processing system returns to step 804 to rerun the structural simulation and determine the distance error.

[0071] If the distance error exceeds a threshold and can be corrected using chordal adjustment, the data processing system determines whether the distance error can be corrected using the current chordal configuration (step 810).

[0072] If the distance error can be corrected using the current chordal configuration, the data processing system adjusts the chordal length to resolve the distance error (step 814). For example, the data processing system may adjust the initial chordal length of the modeled chordae tendineae based on the determined distance error. Additionally or alternatively, the data processing system may adjust the pretension of the modeled chordae tendineae attached to the modeled valve leaflets. The data processing system may adjust the pretension of the modeled chordae tendineae based on the discrepancy between the structural simulation and image segmentation in the systolic situation. For example, the data processing system may adjust the pretension of the commissure chordae tendineae to reduce the valve opening at the commissure. After adjusting the length of the modeled chordae tendineae, the data processing system returns to step 804 to rerun the structural simulation and determine the distance error.

[0073] If the distance error cannot be corrected using the current chordae tendineae configuration, the data processing system modifies the chordae tendineae configuration (step 812). For example, the data processing system can add modeled chordae tendineae to the CAD model or remove modeled chordae tendineae from the CAD model. Additionally or alternatively, the data processing system can move the insertion / attachment locations of the chordae tendineae on the modeled valve leaflets. After adjusting the modeled chordae tendineae configuration, the data processing system returns to step 804 to rerun the structural simulation and determine the distance error.

[0074] The data processing system can repeat steps 804-812 until the distance error falls below a threshold distance error. In some embodiments, the data processing system stops iterating once a set number of iterations is reached, and the distance error still exceeds the threshold distance error. In such embodiments, the data processing system can generate an error message indicating that the upper limit of the number of iterations has been reached and the distance error has exceeded the threshold.

[0075] Processes 200 and 800 generate a patient-specific MVA model in the preoperative state. For example, the patient-specific MVA model reflects the current state of the patient's MVA before any intervention is performed. The patient-specific model can then be used to determine the effectiveness of performing an intervention to resolve mitral regurgitation (MR). For example, to alleviate the symptoms of MR, a clip may be inserted into the patient's heart and attached to the mitral valve leaflets. To simulate the effectiveness of such treatment, a CAD model of the clip may be deployed within the patient-specific MVA model. The data processing system can then perform a co-simulation using a composite structural CAD model, a centralized parameter hemodynamic model, and an anatomical valve orifice area module to determine clinical indicators useful to healthcare professionals in determining the potential effectiveness of the treatment. This co-simulation can be performed iteratively, adjusting the choice of clip type, number of clips, and / or clip placement on the mitral valve leaflets between iterations to find the position that best reduces regurgitation through the mitral valve while maintaining an acceptable transmitral pressure gradient.

[0076] Based on the results of treatment simulations using patient-specific models, the patient may be treated by inserting a clip into the patient's heart and attaching the clip to the patient's mitral valve. The clip can be inserted using a catheter without requiring open-heart surgery.

[0077] Figures 9A and 9B depict exemplary CAD models of a clip 900 for a mitral valve. Figure 9A shows the clip 900 in the open position. Figure 9B shows the clip 900 in the closed position. The clip 900 includes two arms 902, 904 connected to a clip base 906. The arms 902, 904 have rounded ends 902a, 904a. A stopper plate 908 is positioned near the clip base 906 to prevent the valve leaflets from slipping through the base of the clip 900. The clip 900 also includes two grippers 910, 912. When fitted to the mitral valve, the valve leaflets are inserted between the arms 902, 904 and their respective grippers 912, 910. After the valve leaflets are inserted, the clip 900 is closed to attach to the valve leaflets. During the simulation, no sliding boundary conditions may be enforced between the arms 902, 904, grippers 912, 910, and the modeled valve leaflet after the valve leaflet has been fully inserted into the arms, in order to prevent relative movement between the clip 900 and the mitral valve leaflet (effectively adhering the valve leaflet to the arms and grippers). When closed, the clip 900 may be able to move with the mitral valve during the simulation (e.g., be able to move with 6 degrees of freedom).

[0078] Figure 9C depicts a combined CAD model 918 that includes a CAD model of clip 900 attached to a CAD model of MVA920. The combined CAD model 918 can be used to simulate clip deployment and postoperative MR. Clip 900 reduces MR by holding the valve leaflet 922 of CAD model 918 in a closed position. The combined CAD model (including clip 900 and MVA920) can be included as a CAD model in the co-simulation of the MVA. Exemplary additional inputs specific to the combined CAD model include translation and rotation of clip deployment, and rotation of the clip's gripper and arm. Exemplary additional outputs specific to the combined CAD model include clipping force and moment before clip release, clipping length, grip pressure and pattern, and regurgitation valve orifice area, shape, and location after clipping.

[0079] Figures 10A–10D illustrate exemplary CAD models of the MVA1000 with modeled chordae tendineae 1002 in diastolic and systolic states for both preoperative and postoperative conditions. Figures 10A–10B show the preoperative conditions in diastolic and systolic states, respectively. In the systolic state, the mitral valve orifice 1004 is still present, allowing regurgitation through the mitral valve 1000. Figures 10C–10D show the postoperative conditions in diastolic and systolic states, respectively. The clip position 1006 is seen in the postoperative diastolic state (Figure 10C), creating a clinically observable double orifice that allows ventricular filling. In the systolic state (Figure 10D), the orifice is opened, and therefore mitral valve regurgitation is significantly reduced.

[0080] Using physical or synthetic data, multiple patient-specific models (virtual patient cohorts) of MVA can be generated. The results of treatment simulations can be used to predict efficacy for the cohort. During the design and testing of medical devices and therapies, in silico clinical trials conducted on virtual patient cohorts can be useful in predicting patient outcomes and reducing the costs and patient risks associated with new treatments. In silico clinical trials can also be used to determine enrollment criteria for physical clinical trials to proactively exclude patients who are unlikely to respond well to intervention.

[0081] Exemplary process Figure 11 shows an exemplary process 1100 for generating a CAD model of a mitral valve. In some embodiments, the process 1100 may be carried out by the system 100 described herein.

[0082] In process 1100, the system receives one or more digital images of the patient's mitral valve (1102). The system segments one or more digital images to identify the structure of the mitral valve (1104). The system generates a CAD model of the mitral valve (1106). The CAD model contains data for modeled structures that represent the identified structure of the mitral valve. Within the CAD model, the system connects one or more first modeled structures to one or more second and third modeled structures (1108). One or more first modeled structures connect to one or more second and third modeled structures at multiple locations. The system uses a first hemodynamic model to determine one or more first loading conditions for the modeled structures in the CAD model (1110). The system simulates the movement of the modeled structures in the CAD model based on the first loading conditions applied to the modeled structures (1112). The system determines a specified region based on the CAD model (1114). The system determines one or more second load conditions using a second hemodynamic model that receives data representing the specified region as input (1116). The system calibrates the CAD model by modifying the configuration of one or more first modeled structures and one or more second modeled structures among the modeled structures in the CAD model (1118), the modification being based on (i) one or more second load conditions determined using the second hemodynamic model and (ii) the position of the modeled structures in the CAD model based on the movement of the modeled structures in the CAD model compared to the position of the identified structures.

[0083] In some embodiments, segmenting one or more digital images includes rotating one or more digital images to align them with the annular surface of the mitral valve.

[0084] In some embodiments, the identified structures include the valve annulus, valve leaflets, papillary muscles, and chordae tendineae.

[0085] In some embodiments, segmenting one or more digital images includes verifying the shape of the valve leaflets across the identified leaflets in the multiple digital images.

[0086] In some embodiments, the modeled structure includes a modeled annulus, modeled leaflets, modeled papillary muscles, and modeled chordae tendineae, where one or more first modeled structures include modeled chordae tendineae, one or more second modeled structures include modeled leaflets, one or more third modeled structures include modeled papillary muscles, and a fourth modeled structure includes a modeled annulus.

[0087] In some embodiments, connecting one or more first modeled structures to one or more second modeled structures and one or more third modeled structures involves clustering the modeled chordae tendineae into circular regions on each modeled valve leaflet, where each circular region corresponds to a location among a plurality of locations on the modeled papillary muscle.

[0088] In some embodiments, calibrating a CAD model includes simulating the movement of a modeled structure in a CAD model by determining the initial chordal length for a modeled chordae tendineae based on a thermal analysis of the CAD model in systolic and diastolic conditions, and iteratively determining the position of a modeled leaflet, the orifice area relative to the position of the modeled leaflet, and the pressure applied to the modeled leaflet, wherein the pressure is determined using a second hemodynamic model and the orifice area; determining the distance error between the simulated movement and the position of the mitral valve in one or more digital images; and correcting the distance error by adjusting the shape of the modeled leaflet, the number of modeled chordae tendineae, the chordal attachment points of the modeled chordae tendineae, or the length of the modeled chordae tendineae, depending on whether the distance error is determined to exceed a threshold distance error.

[0089] In some embodiments, the system extends the modeled annulus in the CAD model to represent the tissue associated with the mitral valve.

[0090] In some embodiments, the first and second hemodynamic models include the first and second concentrated parameter hemodynamic models.

[0091] In some embodiments, generating a CAD model includes generating a modeled leaflet shape for the diastolic state of the mitral valve and generating a modeled leaflet shape for the systolic state of the mitral valve.

[0092] Some embodiments involve treating patients based on performing preoperative and postoperative simulations using a CAD model of the mitral valve.

[0093] In some embodiments, performing postoperative simulations using a CAD model involves positioning a model of a medical device that comes into contact with one or more of the modeled structures within the CAD model of the mitral valve.

[0094] In some embodiments, the medical device model includes a model of a clip that contacts a modeled structure of a mitral valve in the CAD model.

[0095] In some embodiments, the designated region includes defining two or more parallel cutting planes in a CAD model, forming one or more connected segments, determining the joint points and joint lengths for each of the two or more parallel cutting planes based on the one or more connected segments, and determining the designated region based on the joint lengths and the distance between two of the two or more parallel cutting planes.

[0096] In some embodiments, the first and second hemodynamic models include parameters determined by minimizing the difference between the outputs of the first and second hemodynamic models and corresponding measurements from patient data and literature data.

[0097] In some embodiments, the system generates clinical indicators for use in evaluating mitral regurgitation in the preoperative and postoperative conditions of treated patients, the clinical indicators including left atrial pressure, transmitral pressure gradient, and mitral regurgitation.

[0098] Exemplary computer system Figure 12 depicts an exemplary computing system in an implementation of the present disclosure. System 1200 may be used for any of the operations described in relation to the various implementations discussed herein. System 1200 may include one or more processors 1210, memory 1220, one or more hardware storage devices 1230, and one or more input / output devices 1260 controllable through one or more input / output (I / O) interfaces 1240. Various components 1210, 1220, 1230, 1240, or 1260 may be interconnected through at least one system bus 1250, which may enable the transfer of data between various modules and components of System 1200.

[0099] The processor 1210 may be configured to process instructions for execution within the system 1200. The processor 1210 may include a single-threaded processor, a multi-threaded processor, or both. The processor 1210 may be configured to process instructions stored in memory 1220 or on storage device 1230. The processor 1210 may include a hardware-based processor, each containing one or more cores. The processor 1210 may include a general-purpose processor, a dedicated processor, or both.

[0100] Memory 1220 may store information within the system 1200. In some embodiments, memory 1220 includes one or more computer-readable media. Memory 1220 may include any number of volatile memory units, any number of non-volatile memory units, or both volatile and non-volatile memory units. Memory 1220 may include read-only memory, random-access memory, or both. In some examples, memory 1220 may be used as active memory or physical memory by one or more executable software modules.

[0101] The storage device 1230 may be configured to provide the system 1200 with (e.g., persistent) large-capacity storage. In some embodiments, the storage device 1230 may include one or more computer-readable media. For example, the storage device 1230 may include a floppy disk device, a hard disk device, an optical disk device, or a tape device. The storage device 1230 may include read-only memory, random-access memory, or both. The storage device 1230 may include one or more internal hard drives, external hard drives, or removable drives.

[0102] One or both of the memory 1220 or the storage device 1230 may include one or more computer-readable storage media (CRSMs). The CRSMs may include one or more electronic storage media, magnetic storage media, optical storage media, magneto-optical storage media, quantum storage media, and mechanical computer storage media. The CRSMs may enable the storage of computer-readable instructions that describe data structures, processes, applications, programs, other modules, or other data for the operation of the system 1200. In some embodiments, the CRSMs may include a data store that enables the storage of computer-readable instructions or other information in a non-temporary format. The CRSMs may be integrated into the system 1200 or external to the system 1200. The CRSMs may include read-only memory, random-access memory, or both. One or more CRSMs suitable for tangibly embodying computer program instructions and data include, but are not limited to, any type of non-volatile memory, such as semiconductor memory devices including EPROMs, EEPROMs, and flash memory; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROMs and DVD-ROM disks. In some examples, the processor 1210 and memory 1220 may be complemented by or incorporated into one or more application-specific integrated circuits (ASICs).

[0103] System 1200 may include one or more I / O devices 1260. I / O devices 1260 may include one or more input devices such as a keyboard, mouse, pen, game controller, touch input device, audio input device (e.g., microphone), gesture input device, haptic input device, image or video capture device (e.g., camera), or other devices. In some examples, I / O devices 1260 may also include one or more output devices such as a display, LED, audio output device (e.g., speaker), printer, and haptic output device. I / O devices 1260 may be physically integrated into one or more computing devices of System 1200, or they may be external to one or more computing devices of System 1200.

[0104] System 1200 may include one or more I / O interfaces 1240 to enable components or modules of System 1200 to control, interface with, or otherwise communicate with I / O device 1260. The I / O interfaces 1240 may enable the transfer of information within or outside System 1200, or between components of System 1200, via serial, parallel, or other types of communication. For example, the I / O interface 1240 may conform to a version of the RS-232 standard for serial ports, or to a version of the IEEE 1284 standard for parallel ports. Alternatively, the I / O interface 1240 may be configured to provide connectivity via Universal Serial Bus (USB) or Ethernet. In some examples, the I / O interface 1240 may be configured to provide serial connectivity conforming to a version of the IEEE 1394 standard.

[0105] The I / O interface 1240 may also include one or more network interfaces that enable communication between computing devices within system 1200 or between system 1200 and other networked computing systems. The network interfaces may include one or more network interface controllers (NICs) or other types of transceiver devices configured to send and receive communications over one or more networks using any network protocol.

[0106] The computing devices of System 1200 may communicate with each other or with other computing devices using one or more networks. Such networks may include public networks such as the Internet, private networks such as an intranet of an organization or a person, or any combination of private and public networks. The networks may include, but are not limited to, any type of wired or wireless network, including local area networks (LANs), wide area networks (WANs), wireless WANs (WWANs), and wireless LANs (WLANs), and mobile communication networks (e.g., 3G, 4G, edge, etc.). In some embodiments, communication between computing devices may be encrypted or otherwise secured. For example, communication may use one or more public or private cryptographic keys, cryptographs, digital certificates, or other credentials supported by a security protocol such as any version of the Secure Sockets Layer (SSL) or Transport Layer Security (TLS) protocol.

[0107] System 1200 may include any number of computing devices of any kind. Computing devices may include, but are not limited to, personal computers, smartphones, tablet computers, wearable computers, embedded computers, mobile gaming devices, e-readers, in-vehicle computers, desktop computers, laptop computers, notebook computers, game consoles, home entertainment devices, network computers, server computers, mainframe computers, distributed computing devices (e.g., cloud computing devices), microcomputers, systems on a chip (SoC), and systems in a package (SiP). The examples herein may describe computing devices as physical devices, but embodiments are not limited thereto. In some examples, computing devices may include one or more virtual computing environments, hypervisors, emulations, or virtual machines running on one or more physical computing devices. In some examples, two or more computing devices may include a cluster, cloud, farm, or other group of multiple devices that coordinate their operation to provide load balancing, failover support, parallel processing capabilities, shared storage resources, shared networking capabilities, or other aspects.

[0108] This specification uses the term “configured” in relation to systems and computer program components. One or more computer systems are configured to perform a particular operation or action to mean that the systems have installed software, firmware, hardware, or a combination thereof that causes the operation or action to be performed when in operation. One or more computer programs are configured to perform a particular operation or action to mean that one or more programs, when executed by a data processing device, contain instructions that cause the device to perform the operation or action.

[0109] Embodiments and functional operations of the subject matter described herein may be implemented in digital electronic circuits, in tangibly embodied computer software or firmware, in computer hardware including structures disclosed herein and their structural equivalents, or in one or more combinations thereof. Embodiments of the subject matter described herein may be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a tangible non-temporary storage medium for execution by a data processing device or for controlling the operation of a data processing device. The computer storage medium may be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or one or more combinations thereof. Alternatively or additionally, the program instructions may be encoded into artificially generated propagating signals, e.g., machine-generated electrical, optical, or electromagnetic signals, which are generated to encode information for transmission to a suitable receiving device for execution by a data processing device.

[0110] The term "data processing device" refers to data processing hardware and encompasses all types of devices, machines, and equipment for processing data, including, for example, programmable processors, computers, or multiple processors or computers. A device may also be a dedicated logic circuit, such as an FPGA (Field-Programmable Gate Array) or ASIC (Application-Specific Integrated Circuit), or may include these further. In addition to hardware, a device may optionally include code that creates an execution environment for computer programs, such as processor firmware, protocol stacks, database management systems, operating systems, or code comprising one or more of these.

[0111] Computer programs may also be called, or described as, programs, software, software applications, apps, modules, software modules, scripts, or code, and may be written in any form of programming language, including compiled or interpreted languages, or declarative or procedural languages, and may be deployed as standalone programs or modules, or in any form, including components, subroutines, or other units suitable for use in a computing environment. A program may, but may not, correspond to a file in a file system. A program may be stored in part of a file that holds other programs or data, in one or more scripts stored in, for example, a markup language document, in a single file dedicated to the program, or in multiple collaborative files, in, for example, a file that stores one or more modules, subprograms, or parts of code. A computer program may be deployed to run on one computer, or on multiple computers located in one site, or distributed across multiple sites and interconnected by a data communication network.

[0112] In this specification, the term “database” is used broadly to refer to any collection of data, which does not need to be structured in any particular way, or does not need to be structured at all, and may be stored on one or more storage devices in one or more locations. Thus, for example, an index database may contain multiple collections of data, each of which may be configured and accessed differently.

[0113] Similarly, in this specification, the term “engine” is used broadly to refer to a software-based system, subsystem, or process programmed to perform one or more specific functions. Generally, an engine will be implemented as one or more software modules or components installed on one or more computers in one or more locations. In some cases, one or more computers are dedicated to a particular engine, while in other cases, multiple engines may be installed and run on the same one or more computers.

[0114] The processes and logic flows described herein may be implemented by one or more programmable computers executing one or more computer programs so that they perform functions by acting on input data and producing outputs. The processes and logic flows may also be implemented by dedicated logic circuits, such as FPGAs or ASICs, or by a combination of dedicated logic circuits and one or more programmed computers.

[0115] A computer suitable for running computer programs may be based on a general-purpose or dedicated microprocessor, or both, or any other type of central processing unit. Generally, the central processing unit will receive instructions and data from read-only memory or random-access memory, or both. Essential elements of a computer are a central processing unit for executing or running instructions, and one or more memory devices for storing instructions and data. The central processing unit and memory may be complemented by or integrated into dedicated logic circuits. Generally, a computer will also include one or more mass storage devices for storing data, such as magnetic disks, magneto-optical disks, or optical disks, or will be operably coupled to one or more mass storage devices to receive data, or transfer data, or both. However, a computer is not required to have such devices. Furthermore, a computer may be integrated into another device, for example, a mobile phone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a Global Positioning System (GPS) receiver, or a portable storage device, such as a Universal Serial Bus (USB) flash drive, to name just a few.

[0116] Computer-readable media suitable for storing computer program instructions and data include, for example, all forms of non-volatile memory, media, and memory devices, such as semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks.

[0117] To provide user interaction, embodiments of the subject matter described herein may be implemented on a computer having a display device for displaying information to the user, such as a CRT (cathode ray tube) or LCD (liquid crystal display) monitor, and a keyboard and pointing device for providing input to the computer, such as a mouse or trackball. Other types of devices may also be used to provide user interaction, for example, the feedback provided to the user may be any form of sensory feedback, such as visual feedback, auditory feedback, or tactile feedback, and input from the user may be received in any form, including acoustic, voice, or tactile input. The computer may also interact with the user by sending documents to and receiving documents from the user's device, for example, by sending a web page to a web browser on the user's device in response to a request received from a web browser. The computer may also interact with the user by sending text messages or other forms of messages to a personal device running a messaging application, such as a smartphone, and receiving response messages from the user accordingly.

[0118] Embodiments of the subject matter described herein may be implemented in a computing system that includes, for example, a data server as a backend component, or a middleware component, such as an application server, or a client computer having a graphical user interface, a web browser, or an application on which a user can interact with the embodiment of the subject matter described herein, or one or more such combinations of backend, middleware, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication, such as a communication network. Examples of communication networks include local area networks (LANs) and wide area networks (WANs), such as the Internet.

[0119] A computing system can include clients and servers. Clients and servers are generally geographically separated and typically interact through a communication network. The relationship between a client and a server arises from computer programs running on each computer and having a client-server relationship with each other. In some embodiments, the server sends data, such as an HTML page, to a user device for the purpose of displaying data to a user interacting with a device acting as a client and receiving user input from the user. Data generated on the user device, such as the results of user interaction, may be received by the server from the device.

[0120] While this specification includes details of many specific embodiments, these should not be construed as limiting the scope of any invention or claim, but rather as descriptions of features that may be specific to a particular embodiment of a particular invention. Certain features described herein in the context of separate embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may be implemented separately in multiple embodiments or in any suitable subcombination. Furthermore, features may be described above as functioning in a certain combination, and may even be initially claimed in that manner, but one or more features from a claimed combination may be removed from the combination in some cases, and the claimed combination may cover a subcombination or a variation of a subcombination.

[0121] Similarly, while the operations are depicted in the drawings and described in the claims in a specific order, this should not be understood as requiring that such operations be performed in a specific illustrated or sequential order, or that all described operations be performed, in order to achieve the desired results. In certain circumstances, multitasking and parallel processing may be advantageous. Furthermore, the separation of various system modules and components in the embodiments described above should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged in multiple software products.

[0122] Specific embodiments of the subject matter have been described. Other embodiments are included within the scope of the following claims. For example, the operations described in the claims may be carried out in a different order, and the desired results can still be achieved. As an example, the process depicted in the accompanying figures does not necessarily require the specific illustrated order or sequential order to achieve the desired results. In some cases, multitasking and parallel processing may be advantageous. [Explanation of Symbols]

[0123] 100 Systems 102 Server System 104 Computer Systems 106 Client Devices 108 Network 110 memory 112 Bus System 114 Interfaces 118 Image Segmentation Engine 120 CAD models 122 Mesh Preparation Engine 124 Valve Nose Identification Engine 126 Hemodynamic Models 128 Calibration Engine 130 Data Repositories 132 Digital Images 134 Models 136 Patient Data 138 Literature Data 200 processes Steps 202-228 300 frames 302 The 8th Frame 304 Valve ring 306, 307, 308 mismatch 400 LPM 402 Right atrium 404 Right ventricle 406 Left atrium 408 Left ventricle 410 Arteries 412 Veins 414 Pulmonary artery 416 Pulmonary Vein 418 body part 420 Veins to the right ventricle 422 Pulmonary capillaries 424 Pulmonary veins to the left atrium 426 Tricuspid valve 428 Pulmonary valve 430 Mitral valve 432 Aortic valve 500 CAD models 502 Expansion Phase Status 504 Systolic status 506 Circular Region 508 Valve Leaflet 600 composite plot 602 Upper section 604 Middle row 606 Side view of the joint line along the cross-section 608 Column 1 610 Second column 612 Third column 614 Fourth column 700 Half petal tip 702 Curved end 704 Circular Area 706 Insertion location 800 processes Steps 802-814 900 clips 902, 904 Arm 902a, 904a end 906 Clip Base 908 Stopper Plate 912, 910 Gripper 918 Combined CAD Models 922 Valve Leaflet 1000 MVA, mitral valve 1002 Modeled chordae tendineae 1004 Mitral valve opening 1006 Clip position 1100 processes Steps 1102-1118 1200 System 1210 Processor 1220 memory 1230 Storage Devices 1240 Input / Output (I / O) Interfaces 1250 System Bus 1260 Input / Output Devices

Claims

1. A method performed by a data processing system for generating a patient-specific computer-aided design (CAD) model of the mitral valve, The data processing system receives one or more digital images of the patient's mitral valve, The data processing system segments the one or more digital images in order to identify the structure of the mitral valve. The data processing system generates a CAD model of the mitral valve, wherein the CAD model includes data for a modeled structure representing the identified structure of the mitral valve. The data processing system connects one or more first modeled structures from the modeled structures within the CAD model to one or more second modeled structures and third modeled structures from the modeled structures, wherein the one or more first modeled structures connect to the one or more second modeled structures and third modeled structures at multiple locations. The data processing system determines one or more first load conditions for the modeled structure in the CAD model using a first hemodynamic model, The data processing system simulates the movement of the modeled structure in the CAD model based on the first load conditions applied to the modeled structure, The data processing system determines the specified area based on the CAD model, The data processing system determines one or more second load conditions using a second hemodynamic model that receives data representing the specified region as input. Calibrating the CAD model by modifying the configuration of one or more first modeled structures and one or more second modeled structures among the modeled structures in the CAD model using the data processing system, wherein the modification is based on (i) one or more second load conditions determined using the second hemodynamic model, and (ii) the position of the modeled structure in the CAD model based on the movement of the modeled structure in the CAD model compared to the position of the identified structure. Methods that include...

2. The method according to claim 1, wherein segmenting the one or more digital images includes rotating the one or more digital images to align them with the annular surface of the mitral valve.

3. The method according to claim 1, wherein the identified structure includes an annulus, leaflets, papillary muscles, and chordae tendineae.

4. The method according to claim 3, wherein segmenting one or more digital images includes verifying the shape of the valve leaflet identified in the plurality of digital images.

5. The method according to claim 3, wherein the modeled structure includes a modeled annulus, a modeled leaflet, a modeled papillary muscle, and a modeled chordae tendineae, the one or more first modeled structures include a modeled chordae tendineae, the one or more second modeled structures include the modeled leaflet, the one or more third modeled structures include the modeled papillary muscle, and the fourth modeled structure includes the modeled annulus.

6. The method according to claim 5, wherein connecting one or more first modeled structures among the modeled structures to one or more second modeled structures and one or more third modeled structures includes clustering the modeled chordae tendineae into circular regions on each modeled valve leaflet, each circular region corresponding to a location among a plurality of locations on the modeled papillary muscle.

7. Calibrating the aforementioned CAD model is Based on the thermal analysis of the CAD model in the systolic and diastolic states, the initial chordal length for the modeled chordae tendineae is determined. The simulation involves simulating the movement of the modeled structure in the CAD model by iteratively determining the position of the modeled valve leaflets, the valve orifice area relative to the position of the modeled valve leaflets, and the pressure applied to the modeled valve leaflets, wherein the pressure is determined using the second hemodynamic model and the valve orifice area. Determining the distance error between the simulated movement and the position of the mitral valve in one or more digital images, In response to determining that the distance error exceeds a threshold distance error, the distance error is corrected by adjusting the shape of the modeled valve leaflets, the number of modeled chordae tendineae, the insertion locations of the modeled chordae tendineae, or the length of the modeled chordae tendineae. The method according to claim 5, including the method described in claim 5.

8. The method according to claim 5, further comprising using the data processing system to expand the modeled annulus in the CAD model to represent the tissue associated with the mitral valve.

9. The method according to claim 1, wherein the first and second hemodynamic models include first and second concentrated parameter hemodynamic models.

10. The method according to claim 1, wherein generating the CAD model includes generating the modeled shape of the valve leaflets in the diastolic state of the mitral valve and generating the modeled shape of the valve leaflets in the systolic state of the mitral valve.

11. The method according to claim 1, further comprising treating a patient based on performing preoperative and therapeutic simulations using the CAD model of the mitral valve.

12. The method according to claim 11, wherein performing a treatment simulation using the CAD model includes arranging a model of a medical device that contacts one or more of the modeled structures within the CAD model of the mitral valve.

13. The method according to claim 12, wherein the model of the medical device includes a model of a clip that contacts the modeled structure of the mitral valve in the CAD model.

14. The designated area is, Defining two or more parallel cross-sections within the aforementioned CAD model, To form a segment that is connected to one end, Based on the one or more connected segments, determine the joint point and joint length for each of the two or more parallel cross-sections, The designated area is determined based on the joining length and the distance between two of the two or more parallel cross-sections. The method according to claim 1, including the method described in claim 1.

15. The method according to claim 1, wherein the first and second hemodynamic models include parameters determined by minimizing the difference between the outputs of the first and second hemodynamic models and corresponding measurements from patient data and literature data.

16. The method according to claim 1, further comprising generating clinical indicators for use in evaluating mitral regurgitation in the preoperative and postoperative conditions of treated patients, wherein the clinical indicators include left atrial pressure, transmitral pressure gradient, and mitral regurgitation.

17. A computer system for generating patient-specific computer-aided design (CAD) models of the mitral valve, One or more processors, When executed by the at least one processor, a memory that stores instructions causing the at least one processor to perform an operation and The operation is provided, Receiving one or more digital images of the patient's mitral valve, In order to identify the structure of the mitral valve, the one-territory digital image is segmented, To generate a CAD model of the mitral valve, wherein the CAD model includes data for a modeled structure representing the identified structure of the mitral valve. In the CAD model, one or more first modeled structures from the modeled structures are connected to one or more second modeled structures from the modeled structures and a third modeled structure from the modeled structures, wherein the one or more first modeled structures are connected to the one or more second modeled structures and the third structure at multiple locations. Using a first hemodynamic model, determine one or more first load conditions for the modeled structure in the CAD model, Based on the first load conditions applied to the modeled structure, the motion of the modeled structure in the CAD model is simulated. The specified area is determined based on the aforementioned CAD model, Using a second hemodynamic model that receives data representing the specified region as input, one or more second load conditions are determined. Calibrating the CAD model by modifying the configuration of one or more first modeled structures and one or more second modeled structures among the modeled structures in the CAD model, wherein the modification is based on (i) one or more second load conditions determined using the second hemodynamic model, and (ii) the position of the modeled structures in the CAD model based on the movement of the modeled structures in the CAD model compared to the position of the identified structures. A computer system, including a computer system.

18. Calibrating the aforementioned CAD model is Based on the thermal analysis of the CAD model in the systolic and diastolic states, the initial chordal length for the modeled chordae tendineae is determined. The simulation involves simulating the movement of the modeled structure in the CAD model by iteratively determining the position of the modeled valve leaflets, the valve orifice area relative to the position of the modeled valve leaflets, and the pressure applied to the modeled valve leaflets, wherein the pressure is determined using the second hemodynamic model and the valve orifice area. Determining the distance error between the simulated movement and the position of the mitral valve in one or more digital images, In response to determining that the distance error exceeds a threshold distance error, the distance error is corrected by adjusting the shape of the modeled valve leaflets, the number of modeled chordae tendineae, the attachment locations of the modeled chordae tendineae, or the length of the modeled chordae tendineae. The computer system according to claim 17, including the computer system according to claim 17.

19. One or more non-temporary computer-readable storage media storing instructions for generating a patient-specific computer-aided design (CAD) model of a mitral valve, wherein, when the instructions are executed by at least one processor, the instructions cause the at least one processor to perform an action, and the action is Receiving one or more digital images of the patient's mitral valve, In order to identify the structure of the mitral valve, the one-territory digital image is segmented, To generate a CAD model of the mitral valve, wherein the CAD model includes data for a modeled structure representing the identified structure of the mitral valve. In the CAD model, one or more first modeled structures from the modeled structures are connected to one or more second modeled structures from the modeled structures and a third modeled structure from the modeled structures, wherein the one or more first modeled structures are connected to the one or more second modeled structures and the third modeled structures at multiple locations. Using a first hemodynamic model, determine one or more first load conditions for the modeled structure in the CAD model, Based on the first load conditions applied to the modeled structure, the motion of the modeled structure in the CAD model is simulated. The specified area is determined based on the aforementioned CAD model, Using a second hemodynamic model that receives data representing the specified region as input, one or more second load conditions are determined. Calibrating the CAD model by modifying the configuration of one or more first modeled structures and one or more second modeled structures among the modeled structures in the CAD model, wherein the modification is based on (i) one or more second load conditions determined using the second hemodynamic model, and (ii) the position of the modeled structures in the CAD model based on the movement of the modeled structures in the CAD model compared to the position of the identified structures. One or more non-temporary computer-readable storage media, including [the specified element].

20. Calibrating the aforementioned CAD model is Based on the thermal analysis of the CAD model in the systolic and diastolic states, the initial chordal length for the modeled chordae tendineae is determined. The simulation involves simulating the movement of the modeled structure in the CAD model by iteratively determining the position of the modeled valve leaflets, the valve orifice area relative to the position of the modeled valve leaflets, and the pressure applied to the modeled valve leaflets, wherein the pressure is determined using the second hemodynamic model and the valve orifice area. Determining the distance error between the simulated movement and the position of the mitral valve in one or more digital images, In response to determining that the distance error exceeds a threshold distance error, the distance error is corrected by adjusting the shape of the modeled valve leaflets, the number of modeled chordae tendineae, the attachment locations of the modeled chordae tendineae, or the length of the modeled chordae tendineae. One or more non-temporary computer-readable storage media according to claim 17, including the following:

Images