WO2026006585A1 - Full vessel hemodynamic index value based on single measurement - Google Patents

Abstract

A method for providing patient hemodynamic information includes obtaining a three-dimensional electronic model of the patient's coronary artery, obtaining first and second boundary condition model value sets, and performing first and second three-dimensional computational fluid dynamics (CFD) simulations of the model based on the first and second boundary condition model value sets. For each CFD simulation, a first pressure drop between a first location and a second location of the coronary artery is calculated. The method further includes determining, based on the first and second pressure drops, patient-specific parameters, determining an inflow rate boundary condition based on the patient-specific parameters, performing a third three-dimensional CFD simulation of the coronary artery model based on the inflow rate boundary condition, and calculating, according to the third CFD simulation, a third pressure drop for any location in the coronary artery model.

Description

FULL VESSEL HEMODYNAMIC INDEX VALUE BASED ON SINGLE MEASUREMENT

CROSS-REFERENCE TO RELATED APPLICATION

[0001] This application claims the benefit of priority to US provisional application no. 63/665.633, filed on June 28, 2024, which is hereby incorporated by reference in its entirety.

FIELD

[0002] This disclosure is generally directed to determination and display of patient hemodynamic information based on noninvasive imaging and computational fluid dynamics, including determination and display of hemodynamic index values over a vessel's entire geometry.

BACKGROUND

[0003] Coronary heart disease (CHD) is the most common cause of death in the U.S., with estimated direct and indirect annual costs of hundreds of billions of dollars. CHD results from atherosclerosis, which can progress and lead to ischemia, angina, myocardial infarction and death. Various treatment options, including medical therapy, intravascular stents, and coronary artery bypass graft (CABG) surgery, can be provided to a patient depending upon the severity and complexity of the patient’s lesions and clinical status. A typical diagnostic and treatment plan includes clinical evaluation, non-invasive stress testing and, for appropriate patients, invasive coronary angiography and subsequent medical therapy and/or coronary revascularization. Typically, if the patient remains symptomatic on medical therapy or a significant defect is found in myocardial perfusion, the care provider will perform an invasive coronary angiography on the patient. In such patients, the decision to revascularize or not using coronary stents or CABG surgery is made based on angiographic anatomical findings and. increasingly, with use of hemodynamic information, such as invasively- measured fractional flow reserve (FFR). Measurement of FFR in the catheterization laboratory requires inserting a pressure wire into the patient’s coronary arteries, and an FFR value of less than 0.8 is generally considered to be indicative of a clinically-significant obstructive lesion warranting revascularization in the appropriate clinical context.

BRIEF DESCRIPTION OF THE DRAWINGS

[0004] FIG. 1 is a diagrammatic view of an example embodiment of an electronic system for determining hemodynamic index values for a vessel.

[0005] FIG. 2 is a flow chart illustrating an example embodiment of a method for determining hemodynamic index values for a vessel.

[0006] FIG. 3 illustrates example geometric models of a patient anatomical region that may be determined and find use with the methods of the present disclosure.

[0007] FIG. 4 is a diagrammatic view of an example embodiment of a user computing environment.

DETAILED DESCRIPTION

[0008] Screening for arterial lesions by non-invasive clinical diagnosis and traditional stress testing can be inaccurate, leading to unnecessary coronary angiography procedures for patients who have false positive tests, and can also result in false negatives in patients who have significant lesions. In a recent study, 55.3% of patients who had traditional noninvasive testing and went to invasive coronary angiography (ICA) had no obstructive CHD. In the U.S. alone, an estimated 1,115,000 inpatient cardiac catheterizations were performed in 2006, so a highly accurate noninvasive test that prevents unnecessary ICA procedures has the potential to save billions of dollars annually. Further, although ICA procedures are generally very safe for patients, negative outcomes do occur on rare occasions, and patient anxiety can be substantial, so reducing the number of unnecessary ICA procedures can improve outcomes and the patient experience. Further, studies demonstrate that deferring revascularization based on non-ischemic FFR values results in favorable outcomes relative to stenting, and FFR-based stenting results in approximately 30% reduction in the number of stents, death. heart attacks, and need for repeat stenting relative to standard angiographic guided care, as well as reducing costs.

[0009] Originally, FFR was defined as the ratio of maximum blood flow distal to a stenotic lesion to normal maximum flow in the same vessel. In clinical practice, however, FFR is usually defined as the pressure distal to a stenosis (Pd) relative to the aortic pressure (Pa) (i.e.. FFR = Pd/Pa) under hyperemic flow conditions. Because of the high percentage of unnecessary invasive angiograms, there has been great interest in developing a method to assess FFR noninvasively. From the underlying principles of physics, FFR is actually a variable that is dependent on the detailed hemodynamic flow field, which is a function of arterial geometry, pressures, and flow conditions. Thus, it is possible to calculate FFR from the fluid dynamic equations of motion, and this can be termed virtual FFR, or FFRv. This requires numerical procedures that are part of the field of computational fluid dynamics (CFD). CFD includes using detailed vessel geometry⁷ of the region of interest, as well as blood flow (or, alternatively, the imposed pressure) at the boundaries of the computational domain, to solve for the entire flow field and pressures within the region of interest (ROI). [0010] Coronary artery⁷ flow requirements vary among individuals and depend upon several physiological factors, including age, gender, body mass index (BMI), and level of physical activity. To assess whether coronary flow is adequate in the patient, and hence that there are no significant coronary⁷ stenoses, exercise stress tests may be performed. This involves increasing levels of exercise which, in turn, results in increased heart rate, systolic blood pressure, and myocardial contractility⁷. To sustain the exercise, coronary⁷ blood flow increases sufficiently to supply the myocardium to meet its increasing demand. If the patient has a significant epicardial stenosis, the autoregulatory reserve is outstripped and the corresponding myocardial bed becomes ischemic. This will manifest as symptoms of chest pain or dyspnea, electrocardiographic ST depressions, abnormal wall motion if adjunctive echocardiography is used, or abnormal myocardial perfusion if adjunctive myocardial perfusion imaging is performed. Although an indirect assessment of the presence of coronary stenosis, this approach provides a patient-specific estimate of the ischemic burden which informs the decision to perform further testing (e.g. angiography) and/or to revascularize the patient or not.

[0011] An alternative diagnostic approach is to perform a direct anatomic assessment of the coronary arteries using either invasive coronary angiography or non-invasive CT angiography. This provides information on the presence and extent of coronary arterydisease, and is particularly helpful to exclude disease or diagnose severe obstruction. However, in the frequent scenario of intermediate coronary lesions (40-80% diameter stenosis), anatomic assessment cannot accurately assess whether a narrowing is flow limiting or ischemia provoking. To overcome this limitation of the anatomic assessment, invasive FFR and noninvasive virtual FFR have been developed to complement the anatomic information. Invasive FFR has become the standard for functional assessment of coronary lesions and does incorporate some patient-specific physiologic data. For instance, the response of a myocardial bed to hyperemic agents administered during invasive FFR measurement reflects the individual's coronary microvascular function. In contrast, for virtual FFR an assumed flow rate is typically prescribed to a proximal vessel supplying a myocardial bed. However, the true blood flow rate for a given stenosis is dependent on the patient’s microvascular function and cardiopulmonary capacity (e.g., an active 30 year old patient will have a much higher flow rate than an 85 year old wheelchair-bound patient, even if the two patients have similar anatomy). Accordingly, current computational methods for assessing patient coronary stenosis do not fully account for patient-specific coronary flow rates to inform calculations and decisions. [0012] In order to provide a more patient-specific computational hemodynamic assessment, one or more blood flow physiological indices that describe pressure losses, such as FFR or instantaneous wave-free ratio (IWFR), may be calculated for an entirety of a vessel of interest, instead of at a single point in the vessel, as disclosed herein. Furthermore, the entire-vessel characterization may be performed by a computing system local to the patient, instead of requiring the greater processing power of a backend computing system. The instant disclosure provides a more computationally -efficient approach to computational patient diagnosis by: (1) operating on 3D, rather than 4D, modelling in CFD simulation; (2) using a computationally simple, geometry-based flow-splitting model for determining boundary conditions; and (3) determining whole-vessel pressure drop information based on only two CFD simulations.

[0013] The Navier-Stokes equations can be employed to describe the flow field in a coronary artery, e.g.. intravascular pressures and velocities in a region of interest (RO1) as functions of time and three-dimensional (3D) space, in some forms, and solely of 3D space in other forms. From these flow fields, quantities of clinical interest can be computed such as, for example, pressure drop, FFR, instantaneous wave-free ratio (IWFR), forces on artery' walls caused by intravascular pressure variations and viscous shearing stresses (wall shear stress, WSS), etc. In order to solve the Navier-Stokes equations, CFD is employed, and the solution includes imposing boundary conditions for the ROL The subject’s vessel lumen geometry (obtained from CT or other vascular imaging) is also included in CFD, along with some combination of inflow rate (e.g., at a selected inflow boundary) and flow distribution among vessel branches (e.g., one or more outflows). The pressure field in the RO1 may be computed as a deviation from a reference pressure, and thus the absolute level of pressure, e.g., Pa, in the RO1 may not be needed for CFD at the time of calculation of the pressure field. Once the deviations from reference are computed, the absolute pressures in the field can be determined when the reference pressure (e.g., Pa) is determined. It should be noted, however, that not all pressure-related determinations require a reference pressure, and can instead be made based on the pressure field without a baseline reference.

[0014] For many clinical applications in coronary artery flows, such as pressure drop. FFR and IWFR. the Navier-Stokes equations may be treated as independent of time (z.e., in three spatial dimensions, but independent of the time dimension). For example, the time average of the pressure ratio, Pd/Pa, is representative of the average of the instantaneous pressure ratio, where Pd is the pressure in the ROI and Pa is the reference pressure. This means that a three-dimensional CFD model is appropriate for computing these pressure indices, thus enabling a faster computation than a four-dimensional model (z.e., a model that incorporates three spatial dimensions and time).

[0015] A pressure field within a ROI may be determined based on, e.g., flow rates in and around the ROI. For coronary artery' flow and pressure, the relationship between flow rate and pressure gradient (AP) between a proximal location and a distal location in the region of interest can be approximated well by a quadratic equation, shown as equation (1) below:

AP = aQ + bQ² (Eq. 1) where a and b are constants that depend upon the vessel geometry’ and blood viscosity of an individual patient, and which may be calculated for a given patient in the manner described below. Equation (1) has both a physical and mathematical basis. Physically, the aQ term is related to pressure losses directly due to blood viscosity while the bQ² term is related to pressure losses arising from flow separation and, if present, turbulence. The bQ² term may be significant when a stenosis is sufficiently great to cause flow separation. Mathematically, the equation can be viewed as the first two terms in a polynomial series expansion for AP = fcn(Q). Equation (1) solves for the pressure drop across a region of a subject patient’s vasculature for which a flow field is determined via computational fluid dynamics. [0016] In order to compute a and b, the three-dimensional Navier-Stokes equations may be solved for two different values of Q. For example, a first value, Qi, may represent a flow rate typical of resting conditions and a second value, Q2, may represent a flow rate typical of an exercise (or hyperemic) state. These computations will give two values for AP so that equations (2) and (3), below, may be solved for a and b once Qi, Q2, APi and AP2 are known:

APi = aQi + bQi² (Eq. 2)

AP2 = aQ2 + bQ2² (Eq. 3)

[0017] Once coefficients a and b are known for a given patient, it is possible to compute AP over a range of flow conditions (e.g., a range of physiologically relevant flow conditions) without further CFD for each flow condition in the range. Similarly, it is possible to compute an inflow' rate QN associated with a given AP.

[0018] In addition to geometric and flow split uncertainties, there are tw o sources of uncertainty in noninvasively computing FFR: (1) Pa, the intravascular aortic pressure under hyperemia, is not known; and (2) Q, the inflow' under hyperemia, is not known.

[0019] CFD provides AP directly, independently of Pa. On the other hand, FFR (computed as the ratio Pd/Pa) depends directly on the patient-specific value of Pa. As a result, for computed FFR, a value of Pa must be assumed once AP is computed. The assumed value of Pa may be derived from a cuff blood pressure measurement, for example, or may be assumed to be a value representative of Pa across all subjects, such as Pa = 90 or 100 mmHg.

[0020] There are clinical data to suggest that AP itself can be used as a diagnostic index for obstructive CAD. To demonstrate this plausibility, consider a population average of Pa = 100 mmHg. For such an average Pa value: (a) FFR = 0.8 (generally considered to indicate an obstructive lesion) may correspond to AP = 20 mmHg; and (b) IWFR = 0.89 (also generally considered to indicate an obstructive lesion) may correspond to AP = 11 mmHg. [0021] Referring to the drawings, wherein like reference numerals refer to the same or similar features in the various views, FIG. 1 is a diagrammatic view of an example embodiment of an electronic system 10 for determining hemodynamic index values for a vessel. The example system 10 may include a patient image source 12. a user input device 14, a hemodynamic index computing system 16, and a display 18. As will be described in greater detail below, the system 10 may find use in determining hemodynamic index values over an entire region of interest based on electronic patient data e.g.. images of a region of interest of the patient and other data), and/or to make a recommendation regarding further testing, interventional evaluation, and/or interventional therapy for the patient.

[0022] One or more aspects of the system 10 may be deployed in a clinical environment, in an embodiment. For example, in some embodiments, the patient image source 12, user input device 14, hemodynamic index computing system 16, and the display 18 may all be provided in a common clinical setting, such as a hospital. In some embodiments, the components of the system 10 may be embodied in a laptop or desktop computer or workstation that is local to the patient, at the point of care. In an embodiment, one or more components of the system, such as the hemodynamic index computing system 16, may be provided remotely from the clinical setting, such as in a cloud computing service deployment. [0023] The patient image source 12 may include a medical image acquisition device configured to acquire one or more medical images of a vascular system of a subject patient. For example, the patient image source 12 may be a noninvasive image acquisition device. In some embodiments, the patient image source 12 may include but is not limited to a computed tomography (CT) acquisition device, intravascular ultrasound (IVUS), biplane angiography, optical coherence tomography (OCT), magnetic resonance imaging (MRI), among others, or a combination thereof. [0024] Additionally or alternatively, the patient image source 12 may include a store of existing image data. In some embodiments, patient image source 12 may include a medical image storage device, such as a database or other local electronic data storage, or a remote storage (e.g., cloud-based storage) configured to store medical images.

[0025] The user input device 14 may be or may include one or more devices for input to a computing system, such as a mouse, touchpad, touchscreen, keyboard, microphone, camera, or other input device.

[0026] The hemodynamic index computing system 16 may include a processor 20 and a non-transitory, computer-readable memory 22 configured to store data and instructions. In an embodiment, the memory 22 may store images from a subject patient, and thus may serve as the patient image source 12, or an aspect thereof. The processor 20 may be configured to execute instructions stored in the memory 22 to perform one or more of the steps, methods, algorithms, etc. of this disclosure. In particular, the memory' 22 may be configured to store various functional modules in the form of instructions, including a geometry determination module 24, a boundary condition determination module 26, a flow field determination module 28, a pressure determination module, and a hemodynamic index calculation module 32.

[0027] The various modules 24, 26, 28, 30, 32 in the memory 22 will be described separately, but it should be understood that such separation is for ease of discussion only. The instructions in which the various modules are embodied may be in common files, storage devices, etc. and, similarly, one or more of the modules described herein may be separated into multiple separate files, storage devices, etc.

[0028] The geometry determination module 24 may be configured to generate an electronic geometrical representation (e.g., model) of an anatomical region of interest (ROI) from images obtained from the patient image source. In some embodiments, the ROI may be a portion of the subject patient's cardiovascular system, such as one or more arterial segments. The one or more arterial segments may include a portion of one or more arteries and one or more branches that extend therefrom.

[0029] In some embodiments, the one or more arterial segments may include one or more coronary arterial segments. The one or more coronary arterial segments may include a portion of one or more coronary arteries emanating from an aorta of a subject and one or more branches that extend therefrom. The one or more coronary arterial segments may include but is not limited to one or more portions of the left coronary artery (LCA) and/or the right coronary artery (RCA). The one or more coronary arterial segments for the left coronary artery (LCA) may include but is not limited the left main coronary artery (LM), the left anterior descending (LAD), the left circumflex artery (also referred to as the “Circumflex"’), among others, or a combination thereof.

[0030] The disclosure will make reference to coronary arterial segments. However, it will be understood that the one or more arterial segments are not limited to the coronary arterial segments discussed and may include other coronary arterial segments, other types of arterial segments, among others, or a combination thereof. For example, the one or more arterial segments may include cerebral arterial segment(s), femoral arterial segment(s), iliac arterial segment(s), popliteal arterial segment(s), carotid arterial segment(s), renal artery segments, and the like.

[0031] In some embodiments, the geometrical representation produced by the geometry' determination module 24 may be a three-dimensional (3D) electronic model of the spatial volume of one or more arterial segments. For example, the geometrical representation of one or more arterial segments may be discretized into a three-dimensional volumetric mesh, for example, polyhedrons (e.g., tetrahedrons). In some embodiments, the geometrical representation may include a surface mesh representing the boundary of the lumens of each arterial segment.

[0032] In some embodiments, the boundary condition determination module 26 may be configured to determine boundaries for each arterial segment. "‘Boundaries'’ may refer to cross-sections of the representation of the arterial segment and may include but are not limited to: inflow boundary corresponding to the cross-section through which the blood flows; one or more outflow boundaries corresponding to the cross-section disposed downstream or distal from the inflow boundary through which blood flow is directed outward; one or more vessel wall boundaries corresponding to an interface between the inner surface of the arterial wall and the flowing blood; among others; or combination thereof.

[0033] In some embodiments, the one or more outflow' boundaries may include an outflow boundary disposed at or adjacent to a junction point (e.g., bifurcation, trifurcation, and the like, and combinations thereof). In some embodiments, the one or more outflow boundaries may include an outflow' boundary' from the left coronary' artery. In some embodiments, the one or more outflow boundaries may include a first outflow boundary' and a second outflow' boundary' that is disposed betw een the inflow boundary' and the first outflow boundary⁷. In some embodiments, the first outflow boundary' may correspond to a distal boundary' of the segment (i.e., the cross-section disposed downstream or distal from the inflow' boundary). In some embodiments, for example, when the geometrical representation includes the left coronary' artery⁷, the second outflow' boundary' may correspond to the circumflex. In some embodiments, the first outflow⁷ boundary and the second outflow boundary' may be separated by one or more additional outflow⁷ boundaries, for example, at least a third outflow⁷ boundary. The third outflow boundary may correspond to or be adjacent to a junction point, such as a branch or bifurcation. [0034] In some embodiments, the boundary condition determination module 26 may be configured to determine geometrical data for each boundary using the geometric representation generated by the geometry determination module 24. In some embodiments, the geometrical data may include but is not limited to vessel radius, diameter, circumference, area, epicardial coronary volume, length, among others, or a combination thereof.

[0035] In some embodiments, the boundary condition determination module 26 may be configured to determine boundary conditions for each boundary’ for each arterial segment. By way of example, the boundary conditions for each segment may include inflow boundary conditions, outflow boundary conditions, one or more vessel wall boundary conditions, among others, or a combination thereof. The inflow boundary' condition may be a value or a range of values for velocity', flow rate, pressure or other characteristics. Each outflow boundary condition may be a value or a range of values for velocity, flow rate, pressure, a percentage of inflow boundary, or other characteristic. Each vessel wall boundary condition may be a value or a range of values for velocity, flow rate, pressure, a combination thereof, or other characteristic. In some embodiments, vessel walls may be treated as rigid with a “noslip” boundary’ condition, which states that the fluid velocity' at the wall surface is equal to zero.

[0036] In some embodiments, the determination of the inflow boundary' condition and/or outflow boundary' conditions may be determined based on patient information, an applicable physiological state (e.g., resting state, hyperemic state), the ty pe of segment (e.g., LCA or RCA), among others, or a combination thereof. In some embodiments, an inflow boundary condition may be determined according to an expected patient activity level (e.g., based on information provided by' the patient). In some embodiments, the inflow boundary condition may be a stored value and/or specified by the user. [0037] In some embodiments, an inflow boundary condition may be determined according to the geometry of the anatomical ROI, such as the radius, diameter, length, or volume of a vessel portion. For example, the flow rate may be calculated according to a model based on the lumen volume of the region of interest, as shown in equation 4 below:

Qin = aVP (Eq. 4) where Qin is a flow rate at an inlet of the anatomical model. V is the lumen volume of the region of interest, a is a coefficient that depends on the physiologic state of the patient, and (3 is a coefficient that depends on the vessel tree structure and, in some embodiments, resolution of the images used to generate the 3D model of the anatomical region.

[0038] In embodiments in which the region of interest is the coronary artery tree, V may be the lumen volume of the LCA or RCA defined from the proximal origin to a location where the segmented vessel diameter is a particular diameter, which diameter may depend on the resolution of the images used to create the model of the patient anatomical portion. For example, the location may be defined as the diameter of three or four voxels in the image data set, in some embodiments. In a particular example, the location may be where the lumen has a diameter of 1 mm or 1.5 mm.

[0039] In some embodiments, parameters a and p may be constants across all patients and may be determined from an example data set having both noninvasive and invasive data from which the values of a and P may be validated. In other embodiments, parameters a and p may be specific to an individual and/or to an imaging device.

[0040] In some embodiments, the outflow boundary conditions may be determined using an outflow distribution model. The outflow distribution model may be determined using geometrical data and/or stored hemodynamic data. The stored hemodynamic data may define or be used to define an empirical relationship between geometry (e.g, radii, diameters, lengths, volumes, or other geometric characteristics) of outflow boundaries and respective flow rates. For example, the boundary condition generation module can determine the outflow distribution model using stored hemodynamic data and the radii, diameters, lengths, volumes, and/or other geometric features of the first and second (or more) outflow boundaries of the segment. In another example, the boundary condition generation module can determine the outflow distribution model using only geometrical data, for example, the radius, diameter, length, volume, and/or other geometric features of the first outflow boundary (the distal boundary) of the segment. The outflow distribution model can be used to determine outflow (e.g., velocity, flow rate, percentage of inflow) for each outflow boundary, thereby determining each outflow boundary condition.

[0041] By way of example, the boundary conditions determined by the boundary condition determination module 26 can be used with steady and/or unsteady flow computations to determine flow field (e.g., pressure, blood flow, wall shear stress, etc.) and hemodynamic information (e.g., FFR, IWFR, etc.). The boundary condition determination module can also use an optimization approach based on physiological principles to define the artery segment flow splitting. Therefore, the boundary⁷ condition generation module 26 can provide flexibility⁷, accuracy, and efficiency in determining the boundary⁷ conditions.

[0042] The flow field determination module 28 may be configured to determine a flow field for each arterial segment using the geometrical representation determined by the geometry⁷ determination module 24, the one or more boundary conditions determined by the boundary⁷ condition determination module 26, and pressure data respective of the patient. The pressure data may be, for example, a cuff pressure of the patient at a state of rest. In some embodiments, the flow field may include but is not limited to pressure field, velocity⁷ field, wall shear stress field, axial plaque stress, among others, or a combination thereof.

[0043] In some embodiments, a flow field parameter (e.g., pressure field, velocity , etc.) may be based on only the geometrical data and boundary conditions. This way, the flow field determination module may be configured to determine the flow field based only spatial location (i.e.. independent of time).

[0044] The pressure determination module 30 may be configured to determine blood pressure at one or more points in a patient anatomy using the flow field determined by the flow field determination module 28. In some embodiments, the pressure data can be determined from a computed flow/pressure field, a non-invasive determination of a mean blood pressure of the patient, for example, determined by a blood pressure cuff, among others, or a combination thereof.

[0045] The pressure determination module 30 may be configured to determine a specific pressure at a specific location in the patient's anatomy responsive to a user (e.g., physician) selection of the specific location. The user may enter that selection with the user input device 14 relative to a display of the geometric model of the patient region of interest (e.g. , arteries) on the display 18. In embodiments, the pressure determination module may be configured to determine pressures upstream and downstream from the user-selected location, so as to determine a pressure drop at the user-selected location.

[0046] The hemodynamic index module 32 may be configured to calculate a plurality⁷ of values for one or more hemodynamic indices at different locations in a patient ROI. The indices may be representative of different physiologic states, such as hyperemia or rest, and/or specific portions of the cardiac cycle, such as diastole. For example, the hemodynamic index module 32 may calculate IWFR, FFR, dPR, etc., at one or more locations in a patient coronary artery⁷.

[0047] In some embodiments, the hemodynamic index module 32 may calculate a first and a second index value at a desired location within the ROI based on flow fields for two prescribed input flow rates. The hemodynamic index value may also, in some embodiments, receive a third index value respective of the same desired location in the ROI. The third index value may be a result of an invasive measurement, for example, or from a separate approach to calculating the index (e.g., a machine learning model analysis of the ROI). As noted above, once coefficients a and b of Equations (2) and (3) are known for a given patient, an inflow rate QN associated with a given AP can be calculated. Thus, equations (2) and (3) may be used to calculate an inflow rate that correspond to the third index value.

[0048] The hemodynamic index module 32 may then cause the flow field determination module 28 to perform a further CFD simulation using the inflow rate corresponding to the third index value. Based on the further CFD simulation, the hemodynamic index module 32 may determine a value for the same hemodynamic index at any arbitrary location in the vessel that was included in the simulation. Based on the determined level of activity, it can be determined by the system 16 or by a clinician whether further diagnostic procedures and/or interventional procedures should be performed.

[0049] To calculate an absolute pressure or an absolute pressure-based hemodynamic index, one or more Pa values, such as a mean diastolic aortic pressure, may be calculated. In an embodiment, brachial cuff pressure measurements can be used to estimate the mean diastolic pressure. Cuff pressures provide peak sy stolic pressure (SP) and minimum diastolic pressure (DP). The resting mean aortic diastolic pressure (Padmean, or dPa) — which may be used for an IWFR calculation — during the wave free period may be estimated from cuff values of SP and DP according to the transfer function set forth in the following equation 5, in some embodiments:

P mean ~ (SP + 3DP)/4 (Eq. 5)

In other embodiments, other transfer functions may be used to relate a cuff pressure values to a reference pressure.

[0050] In another embodiment, the resting mean aortic diastolic pressure Pa (Pad mean, OI dPa) may be calculated from a cuff pressure as shown in equation 6 below: dPa = Pc + offset (Eq. 6) where Pc is the resting brachial cuff pressure given by equation 7 below:

Pc = dPc + FF*PP (Eq. 7) where dPc is the diastolic cuff pressure, FF is a scalar form factor, and PP is the cuff pulse pressure (e.g., the difference between the systolic (SP) and diastolic (DP) cuff pressures of the subj ect patient at rest). The scalar form factor FF may have a value of between 0. 15 and 0.45, and may depend patient-specific characteristics, including heart rate, age, height, systolic pressure, and/or augmentation index, for example. In some embodiments, FF may be approximately 0.2, 0.25, or 0.33. In some embodiments, the offset value of equation 6 may be between about zero (0) and -10 mmHg. In an embodiment, the offset value of equation 6 may be about -7 mmHg. The value of the offset may depend on the value of the form factor FF, the desired hemodynamic index (and, therefore, the portion of the cardiac cycle under examination), and the physiological state of the patient, in some embodiments.

[0051] In other embodiments, the mean aortic diastolic pressure may be estimated using a transfer function that relates cuff pressures to aortic pressures during diastole. For example, the diastolic Pa value may be determined from a cuff pressure of the subject patient by finding a value of form factor FF and/or an offset value that fits a data set including invasively measured cuff pressures and diastolic resting central pressures of a patient population. Once this function is known, it can be used to obtain an estimate of the diastolic resting pressures from non-invasively measured cuff pressures.

[0052] In another embodiment, the mean aortic diastolic pressure may be estimated from a combination of an optical finger device or other wearable pressure measurement device (e.g, a radial tonometry device) and a brachial cuff pressure device. For example, a Fourier analysis of the optical finger device output may be performed and mathematically combined with the brachial cuff pressure to determine a value of Pa. [0053] FIG. 2 is a flow chart illustrating an example embodiment of a method 40 for determining hemodynamic index values for a vessel. The method 40, or one or more aspects of the method 40, may be performed by the hemodynamic index computing system 16 of FIG. 1. in embodiments.

[0054] The method 40 may include, at block 42, receiving patient data. The patient data may include, for example, basic information about the patient, such as the patient’s age, gender, brachial cuff blood pressure, a description of user symptoms, etc. The patient data may further include, in embodiments, metabolic data of the patient, such as a user's typical activity level (e.g., sedentary or active, amount of exercise per week, amount of specific activities per week, such as walking and running, etc.). The patient data may further include, in an embodiment, patient data from one or more diagnostic tests, such as echocardiography. [0055] The method 40 may further include, at block 44, receiving images of the patient anatomy. The received patient images may be CT images, MRI images, or other noninvasively-obtained images, in embodiments, or invasively -obtained images, such as angiography images. The images may include an anatomical region of interest of the patient. In an embodiment, for example, the received images may include one or more coronary arteries or other vasculature of interest. The images may be received from a patient image source, such as an imaging device or a database or other computer memory.

[0056] The method 40 may further include, at block 46, creating an anatomical model respective of the patient region of interest based on the images received at block 44. In an embodiment, the anatomical model may be created by segmenting the anatomy of interest from the images received at block 44. The anatomy of interest may be, for example, one or more coronary arteries. FIG. 3 illustrates an example anatomical model 56 of coronary arteries. The anatomical model may be created by the geometry determination module 24 of FIG. 1, in an embodiment. In some embodiments, an anatomical model may be obtained by a computing system (e.g, the hemodynamic information computing system 16) by being created by the computing system, or by receiving an existing model of the subject patient. The model may be a 3D model.

[0057] The method 40 may further include, at block 48, determining one or more boundary conditions model value sets. The one or more boundary conditions value sets may be respective sets of values for the same boundary conditions model, in an embodiment (e.g, respective sets of condition values for the same inflow boundaries, outflow boundaries, wall boundaries, etc.). Block 48 may include sub-parts 48a and 48b, in some embodiments. Accordingly, the method 40 may include, at block 48a, obtaining an inflow rate. The inflow rate may be a flow rate for an inlet of the anatomical ROI of the patient, in some embodiments. In other embodiments, the blood flow rate may be a flow rate for another portion of the ROI.

[0058] The inflow rate obtained at block 48a may be representative of a particular activity' level or physiologic state of the patient. For example, in some embodiments, the inflow rate obtained at block 48a may be representative of a resting state of the patient. In another embodiment, the inflow rate may be representative of a hyperemic state of the patient. In still other embodiments, the inflow rate obtained at block 48a may be representative of an arbitrary activity⁷ level of the patient (e.g, any feasible flow rate for the patient given the patient’s age, health, etc.).

[0059] The inflow rate obtained at block 48a may be representative of a desired point or portion of the cardiac cycle of the patient, in some embodiments. For example, in some embodiments, the inflow rate obtained at block 48a may be representative of an average flow rate over the entire cardiac cycle of the patient, or over the entire wave-free period of diastole of the patient, or over the entirety of diastole of the patient. In other embodiments, the inflow rate may be representative of the particular time point within the cardiac cycle, such as the midpoint of diastole.

[0060] In some embodiments, obtaining the inflow rate at block 48a may include calculating an inflow rate according to a geometry of the patient anatomical model as discussed with respect to equation (4) above. As discussed above with respect to equation (4). the inflow rate Qin includes an a term which depends on the physiological state of the patient. Accordingly, obtaining an inflow rate at block 48a may include determining or selecting a value of a that is appropriate for the desired activity level.

[0061] In other embodiments, instead of calculating an inflow rate according to equation 4 above, obtaining a blood flow rate at block 48a may include receiving a user input of an inflow rate. The inflow rate may be received via user manual entry (e.g., with the user input device 14 of the system 10). In an embodiment, the inflow rate at block 48a may be determined (e.g., by a clinician or by an electronic system) based on the metabolic demands and condition of the patient.

[0062] Block 48 may further include, at block 48b, calculating outlet flow rates according to the inflow⁷ rate obtained at block 48a and according to a flow splitting model (which may also be referred to herein as an outflow distribution model). The flow splitting model may be, or may have been, calculated or otherwise determined according to a geometry of the three-dimensional electronic model of the patient anatomical region. The flow splitting model may be calculated according to the relative radii, diameters, circumferences, lengths, volumes, mass, and/or surface areas of the vessels in the electronic model, in some embodiments.

[0063] In conjunction with the flow rate obtained in block 48a, the outlet flow rates calculated at block 48b may be comprise a boundary condition model value set respective of the patient anatomical region. The boundary condition model value set may be representative of a particular physiologic state or activity level of the patient (e.g.. a resting state, a hyperemic state, or other state) and a particular portion or point in the cardiac cycle of the patient (e.g, the entire cardiac cycle, diastole, a time point in the cardiac cycle, etc.).

[0064] In embodiments in which the anatomical region is a coronary artery of the patient, the inflow rate obtained at block 48a may be an inlet flow rate for the coronary artery’, and the flow splitting model may be calculated according to the geometry of the coronary’ artery portions downstream of the inlet in the electronic model. The flow splitting model may be calculated according to the relative radii, diameters, circumferences, lengths, surface areas, or volumes of the coronary artery portions downstream of the inlet. In some embodiments, the flow splitting model may be calculated according to the epicardial volume of the electronic model.

[0065] The method 40 may further include, at block 50, computing fluid dynamics of the blood flow through the patient anatomy based on the anatomical model (e.g., model 56), the one or more boundary conditions model value sets, and. in some embodiments, the patient data. Block 50 may include determining the flow field for each arterial segment using the anatomical model, boundary’ conditions, and pressure data respective of the patient (e.g., aortic pressure data). In some embodiments, the pressure data may be obtained for the patient, for example, cuff pressure, and/or may be a stored value. In some embodiments, the flow field may include but is not limited to a pressure field, velocity’ field, among others, or a combination thereof. The fluid dynamics may be computed by the flow field determination module 28 of FIG. 1, in an embodiment.

[0066] In some embodiments, the velocity field and/or pressure field may’ be determined based only on the boundaries and the boundary conditions, ’ithout regard to time (that is, the CFD simulation may⁷ be 3D, rather than 4D). For example, the velocity⁷ field and/or pressure field may be determined using a steady flow⁷ Navier-Stokes equation in ’hich the velocity and pressure variables are functions of only spatial location (z.e., time is not considered).

This way, pressure and velocity can be accurately and efficiently determined in near real-time so as to enable point of care analysis by the clinician.

[0067] The method 40 may further include, at block 52, calculating a plurality of values for one or more hemodynamic indices. Block 52 may include, for example, calculating patient-specific parameters based on pressure drops at a particular location in the patient ROI (e.g., a distal location) based on CFD simulations at block 50 respective of two separate inflow rates, Qi and Q2, by solving equations (2) and (3) above. The two separate inflow rates, Qi and Q2, and the associated boundary condition value sets, may be associated with respective physiological states. For example, Qi may be associated with a resting state, and Q2 may be associated with ahyperemic state. In another example, Qi and Q2, and the associated boundary condition value sets, may be associated with different portions of the cardiac cycle. Based on those patient-specific parameters and a prescribed pressure drop, an inflow rate Q3 may be derived, and that inflow rate Q3 may be used as an inflow boundary condition for a CFD simulation (e.g., as described with respect to block 50). From the CFD simulation, a pressure drop may be determined at any point in the patient ROI according to equation (2).

[0068] At block 52, the prescribed pressure drop may be, for example, a pressure drop determined via an invasive measurement, from an artificial intelligence or other machine learning determination, or from some other source. For example, the invasive measurement may be an invasive FFR determination via a pressure wire pull. In another example, the prescribed pressure drop may be an output from a machine learning model, such as a classifier model, that is trained to output a pressure drop value (or a hemodynamic index value that incorporates a pressure drop) based on, for example, a three-dimensional of fourdimensional patient image or 3D or 4D model of the patient anatomy. [0069] The additional processing described herein enables a single-location measurement to be extrapolated to pressure drop values throughout a region of interest in a short period of time (e.g., a matter of minutes, on-site during a patient evaluation). This additional processing may reveal, for example, a location in the patient’s vasculature where intervention is needed, when an initial measurement respective of a different location did not indicate a need for intervention.

[0070] The method 40 may further include, at block 54, determining that intervention is needed and recommending a further procedure for the patient based on the plurality’ of hemodynamic index values. For example, if the calculated values indicate a clinically- significant pressure drop at a location in the patient's vasculature, then a further diagnostic procedure may be recommended for the patient, such as an invasive angiography and pressure measurement at the site of the suspected stenosis. Additionally or alternatively, a corrective procedure may be recommended to address the stenosis, such as placement of a stent at the location of the suspected stenosis, for example.

[0071] FIG. 4 is a diagrammatic view of an example embodiment of a user computing environment that includes a general purpose computing system environment 190, such as a desktop computer, laptop, smartphone, tablet, or any other such device having the ability to execute instructions, such as those stored within anon-transient, computer-readable medium. Furthermore, while described and illustrated in the context of a single computing system 190, those skilled in the art will also appreciate that the various tasks described hereinafter may be practiced in a distributed environment having multiple computing systems 190 linked via a local or wide-area network in which the executable instructions may be associated with and/or executed by one or more of multiple computing systems 190. The computing environment 190, or portions thereof, may comprise the system 10 of FIG. 1, in embodiments. [0072] In its most basic configuration, computing system environment 190 typically includes at least one processing unit 192 and at least one memory 194, which may be linked via a bus 196. Depending on the exact configuration and type of computing system environment, memory 194 may be volatile (such as RAM 200). non-volatile (such as ROM 198, flash memory, etc.) or some combination of the two. Computing system environment 190 may have additional features and/or functionality. For example, computing system environment 190 may also include additional storage (removable and/or non-removable) including, but not limited to. magnetic or optical disks, tape drives and/or flash drives. Such additional memory devices may be made accessible to the computing system environment 190 by means of, for example, a hard disk drive interface 202, a magnetic disk drive interface 204, and/or an optical disk drive interface 206. As will be understood, these devices, which would be linked to the system bus 196, respectively, allow for reading from and writing to a hard disk 208, reading from or writing to a removable magnetic disk 210, and/or for reading from or writing to a removable optical disk 212, such as a CD/DVD ROM or other optical media. The drive interfaces and their associated computer-readable media allow for the nonvolatile storage of computer readable instructions, data structures, program modules and other data for the computing system environment 190. Those skilled in the art will further appreciate that other types of computer readable media that can store data may be used for this same purpose. Examples of such media devices include, but are not limited to, magnetic cassettes, flash memory cards, digital videodisks, Bernoulli cartridges, random access memories, nano-drives, memory sticks, other read/write and/or read-only memories and/or any other method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Any such computer storage media may be part of computing system environment 190. [0073] A number of program modules may be stored in one or more of the memory/media devices. For example, a basic input/output system (BIOS) 214. containing the basic routines that help to transfer information between elements within the computing system environment 190, such as during start-up, may be stored in ROM 198. Similarly, RAM 200, hard drive 208, and/or peripheral memory devices may be used to store computer executable instructions comprising an operating system 216, one or more applications programs 218 (such as the modules 24, 26, 28, 30, 32 of FIG. 1), other program modules 220, and/or program data 222. Still further, computer-executable instructions may be downloaded to the computing environment 190 as needed, for example, via a network connection.

[0074] An end-user, e.g., a clinician, may enter commands and information into the computing system environment 190 through input devices such as a keyboard 224 and/or a pointing device 226. While not illustrated, other input devices may include a microphone, a joystick, a game pad, a scanner, etc. These and other input devices would typically be connected to the processing unit 192 by means of a peripheral interface 228 which, in turn, would be coupled to bus 196. Input devices may be directly or indirectly connected to processor 192 via interfaces such as, for example, a parallel port, game port, firewire, or a universal serial bus (USB). To view information from the computing system environment 190, a monitor 230 or other type of display device may also be connected to bus 196 via an interface, such as via video adapter 232. In addition to the monitor 230, the computing system environment 190 may also include other peripheral output devices, not shown, such as speakers and printers.

[0075] The computing system environment 190 may also utilize logical connections to one or more computing system environments. Communications between the computing system environment 190 and the remote computing system environment may be exchanged via a further processing device, such a network router 242, that is responsible for network routing. Communications with the network router 242 may be performed via a network interface component 244. Thus, within such a networked environment, e.g., the Internet, World Wide Web, LAN. or other like type of wired or wireless network, it will be appreciated that program modules depicted relative to the computing system environment 190, or portions thereof, may be stored in the memory storage device(s) of the computing system environment 190.

[0076] The computing system environment 190 may also include localization hardware 186 for determining a location of the computing system environment 190. In embodiments, the localization hardware 246 may include, for example only, a GPS antenna, an RFID chip or reader, a WiFi antenna, or other computing hardware that may be used to capture or transmit signals that may be used to determine the location of the computing system environment 190.

[0077] In a first aspect of the present disclosure, a method for providing hemodynamic information respective of a patient is provided. The method includes obtaining a three- dimensional electronic model of a coronary artery of the patient, obtaining a first boundarycondition model value set, performing a first three-dimensional computational fluid dynamics (CFD) simulation of the coronary artery- model based on the first boundary- condition model value set, calculating, according to the first CFD simulation, a first pressure drop between a first location of the coronary- artery' and a second location of the coronary- artery', obtaining a second boundary- condition model value set, performing a second three-dimensional CFD simulation of the coronary' artery- model based on the second boundary condition model value set, calculating, according to the second CFD simulation, a second pressure drop between the first location of the coronary- artery and the second location of the coronary- artery, determining, based on the first and second pressure drops, patient-specific parameters, determining a third boundary condition model value set including an inflow rate, the inflow rate based on the patient-specific parameters, performing a third three-dimensional CFD simulation of the coronary artery model based on the third boundary condition model value set. and calculating, according to the third CFD simulation, a third pressure drop for a location in the coronary artery that is not the first location or the second location.

[0078] In an embodiment of the first aspect, calculating the third pressure drop comprises calculating a plurality of third pressure drops, each at a respective location of the coronary artery that is not the first location or the second location. In a further embodiment of the first aspect, the method further includes determining that a particular one of the plurality of third pressure drops is indicative of a need for medical intervention.

[0079] In an embodiment of the first aspect, the method further includes calculating a hemodynamic index value based on the third pressure drop.

[0080] In an embodiment of the first aspect, the patient-specific parameters comprise a plurality of coefficients that relate flow rates to pressure drops.

[0081] In an embodiment of the first aspect, the inflow rate is further based on a prescribed pressure drop within the coronary' artery. In a further embodiment of the first aspect, the prescribed pressure drop is based on an invasive measurement of the patient. [0082] In an embodiment of the first aspect, the first and second boundary condition model value sets correspond to two different physiologic states of the patient.

[0083] In an embodiment of the first aspect, the first and second boundary condition model value sets correspond to two different portions of the cardiac cycle of the patient. [0084] In a second aspect of the present disclosure, a computer system is provided that includes a processor and a non-transitory, computer-readable memory storing instructions that, when executed by the processor, cause the computer system to perform the method of the first aspect and any of its embodiments.

[0085] In an embodiment of the second aspect, the computer system is local to the patient. [0086] In a third aspect of the present disclosure, a non-transitory. computer-readable medium is provided that stores instructions that, when executed by a processor, cause the processor to perform the method of the first aspect and any of its embodiments.

[0087] While this disclosure has described certain embodiments, it will be understood that the claims are not intended to be limited to these embodiments except as explicitly recited in the claims. On the contrary, the instant disclosure is intended to cover alternatives, modifications and equivalents, which may be included within the spirit and scope of the disclosure. Furthermore, in the detailed description of the present disclosure, numerous specific details are set forth in order to provide a thorough understanding of the disclosed embodiments. However, it will be obvious to one of ordinary skill in the art that systems and methods consistent with this disclosure may be practiced without these specific details. In other instances, well known methods, procedures, components, and circuits have not been described in detail as not to unnecessarily obscure various aspects of the present disclosure. [0088] Some portions of the detailed descriptions of this disclosure have been presented in terms of procedures, logic blocks, processing, and other symbolic representations of operations on data bits within a computer or digital system memory'. These descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. A procedure, logic block, process, etc., is herein, and generally, conceived to be a self-consistent sequence of steps or instructions leading to a desired result. The steps are those requiring physical manipulations of physical quantities. Usually, though not necessarily, these physical manipulations take the form of electrical or magnetic data capable of being stored, transferred, combined, compared, and otherwise manipulated in a computer system or similar electronic computing device. For reasons of convenience, and with reference to common usage, such data is referred to as bits, values, elements, symbols, characters, terms, numbers. or the like, with reference to various presently disclosed embodiments.

[0089] It should be bome in mind, however, that these terms are to be interpreted as referencing physical manipulations and quantities and are merely convenient labels that should be interpreted further in view of terms commonly used in the art. Unless specifically stated otherwise, as apparent from the discussion herein, it is understood that throughout discussions of the present embodiment, discussions utilizing terms such as “determining'’ or “outputting” or “transmitting” or “recording” or “locating” or “storing” or “displaying” or “receiving” or “recognizing” or “utilizing” or “generating” or “providing” or “accessing” or “checking” or “notifying” or “delivering” or the like, refer to the action and processes of a computer system, or similar electronic computing device, that manipulates and transforms data. The data is represented as physical (electronic) quantities within the computer system's registers and memories and is transformed into other data similarly represented as physical quantities within the computer system memories or registers, or other such information storage, transmission, or display devices as described herein or otherwise understood to one of ordinary' skill in the art.

Claims

CLAIMS What is claimed is:

1. A method for providing hemodynamic information respective of a patient, the method comprising: obtaining a three-dimensional electronic model of a coronary artery of the patient; obtaining a first boundary condition model value set; performing a first three-dimensional computational fluid dynamics (CFD) simulation of the coronary artery model based on the first boundary condition model value set; calculating, according to the first CFD simulation, a first pressure drop between a first location of the coronary' artery and a second location of the coronary' artery; obtaining a second boundary condition model value set; performing a second three-dimensional CFD simulation of the coronary artery' model based on the second boundary' condition model value set; calculating, according to the second CFD simulation, a second pressure drop between the first location of the coronary artery' and the second location of the coronary artery'; determining, based on the first and second pressure drops, patient-specific parameters; determining a third boundary condition model value set including an inflow rate, the inflow rate based on the patient-specific parameters; performing a third three-dimensional CFD simulation of the coronary' artery model based on the third boundary condition model value set; and calculating, according to the third CFD simulation, a third pressure drop for a location in the coronary artery’ that is not the first location or the second location.

2. The method of claim 1, wherein calculating the third pressure drop comprises calculating a plurality of third pressure drops, each at a respective location of the coronary artery that is not the first location or the second location.

3. The method of claim 2, further comprising: determining that a particular one of the plurality of third pressure drops is indicative of a need for medical intervention.

4. The method of claim 1, further comprising calculating a hemodynamic index value based on the third pressure drop.

5. The method of claim 1, wherein the patient-specific parameters comprise a plurality⁷ of coefficients that relate flow rates to pressure drops.

6. The method of claim 1, wherein the inflow rate is further based on a prescribed pressure drop within the coronary⁷ artery'.

7. The method of claim 6, wherein the prescribed pressure drop is based on an invasive measurement of the patient.

8. The method of claim 1, wherein the first and second boundary condition model value sets correspond to two different physiologic states of the patient.

9. The method of claim 1, wherein the first and second boundary condition model value sets correspond to two different portions of the cardiac cycle of the patient.

10. A computer system comprising: a processor; and a non-transitory, computer-readable memory storing instructions that, when executed by the processor, cause the computer system to perform a method comprising: obtaining a three-dimensional electronic model of a coronary artery of a patient; obtaining a first boundary condition model value set; performing a first three-dimensional computational fluid dynamics (CFD) simulation of the coronary artery model based on the first boundary condition model value set; calculating, according to the first CFD simulation, a first pressure drop between a first location of the coronary artery and a second location of the coronary' artery; obtaining a second boundary condition model value set; performing a second three-dimensional CFD simulation of the coronary artery' model based on the second boundary condition model value set; calculating, according to the second CFD simulation, a second pressure drop between the first location of the coronary artery' and the second location of the coronary artery; determining, based on the first and second pressure drops, patient-specific parameters; determining a third boundary' condition model value set including an inflow rate, the inflow rate based on the patient-specific parameters; performing a third three-dimensional CFD simulation of the coronary^ artery' model based on the third boundary^ condition model value set; and calculating, according to the third CFD simulation, a third pressure drop for a location in the coronary- artery that is not the first location or the second location.

11. The computer system of claim 10, wherein calculating the third pressure drop comprises calculating a plurality of third pressure drops, each at a respective location of the coronary artery that is not the first location or the second location.

12. The computer system of claim 11, further comprising: determining that a particular one of the plurality of third pressure drops is indicative of a need for medical intervention.

13. The computer system of claim 10, further comprising calculating a hemodynamic index value based on the third pressure drop.

14. The computer system of claim 10, wherein the patient-specific parameters comprise a plurality of coefficients that relate flow rates to pressure drops.

15. The computer system of claim 10, wherein the inflow rate is further based on a prescribed pressure drop within the coronary artery.

16. The computer system of claim 15, wherein the prescribed pressure drop is based on an invasive measurement of the patient.

17. The computer system of claim 10. wherein the first and second boundary condition model value sets correspond to two different physiologic states of the patient.

18. The computer system of claim 10. wherein the first and second boundary condition model value sets correspond to two different portions of the cardiac cycle of the patient.

19. The computer system of claim 10, wherein the computer system is local to the patient.

20. A non-transitory. computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform a method comprising: obtaining a three-dimensional electronic model of a coronary artery of a patient; obtaining a first boundary' condition model value set; performing a first three-dimensional computational fluid dynamics (CFD) simulation of the coronary artery' model based on the first boundary condition model value set; calculating, according to the first CFD simulation, a first pressure drop between a first location of the coronary' artery' and a second location of the coronary' artery⁷; obtaining a second boundary' condition model value set; performing a second three-dimensional CFD simulation of the coronary artery' model based on the second boundary' condition model value set; calculating, according to the second CFD simulation, a second pressure drop between the first location of the coronary artery' and the second location of the coronary artery'; determining, based on the first and second pressure drops, patient-specific parameters; determining a third boundary condition model value set including an inflow rate, the inflow rate based on the patient-specific parameters; performing a third three-dimensional CFD simulation of the coronary' artery model based on the third boundary condition model value set; and calculating, according to the third CFD simulation, a third pressure drop for a location in the coronary artery that is not the first location or the second location.