Virtual stress testing based on electronic patient data

Abstract

A virtual stress test can be performed on a patient by creating an electronic model of a region of interest of the patient's anatomy, such as one or more coronary arteries, determining a pressure drop through the region of interest based on computational fluid dynamics at two different activity levels of the patient, and determining a range of pressure drops for the range of activity levels based on flow rates associated with the two activity levels. Based on the range of pressure drops, it can be determined whether the patient has a clinically significant pressure drop indicative of an obstructive stenosis at an activity level associated with the metabolic demands of the patient.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims the benefit of and priority to U.S. Provisional Application No. 62 / 788,911, filed on January 6, 2019, the entire contents of which are incorporated herein by reference. Technical Field

[0003] The present disclosure is generally directed to the determination and display of patient hemodynamic information based on non-invasive imaging and computational fluid dynamics, including the determination and display of blood pressure within the coronary arteries over a range of simulated patient activity levels. Background Art

[0004] Coronary heart disease (CHD) is the most common cause of death in the United States, with an estimated direct and indirect cost of hundreds of billions of dollars each year. CHD is caused by atherosclerosis, which progresses and leads to ischemia, angina, myocardial infarction, and death. Depending on the severity and complexity of the patient's lesions and clinical status, a variety of treatment options can be offered to the patient, including medical therapy, endovascular stents, and coronary artery bypass grafting (CABG). The 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 while receiving medical therapy or a significant defect is found in myocardial perfusion, the caregiver will perform an invasive coronary angiography on the patient. In such patients, the decision to revascularize or not to use coronary stents or CABG surgery is made based on the angiographic anatomic findings, and increasingly using hemodynamic information (such as invasively measured blood flow reserve fraction (FFR)). Measuring FFR in the catheterization laboratory requires insertion of a pressure wire into the patient's coronary arteries, and FFR values ​​less than 0.8 are generally considered clinically significant obstructive lesions that require revascularization in the appropriate clinical context. Summary of the invention

[0005] Systems and methods for virtual stress testing based on electronic patient data are disclosed. An example embodiment of a computer-implemented method for performing a virtual stress test for a patient may include receiving medical image data of the patient, the medical image data including an arterial segment; and generating a geometric representation of the arterial segment from the medical image data. The method may also include receiving metabolic demand information of the patient, and determining a threshold value based on the metabolic demand information. The method may also include determining a first flow field for the arterial segment using the geometric representation, the first flow field corresponding to a first activity level of the patient; determining a first pressure drop in the arterial segment based on the first flow field; determining a second flow field for the arterial segment using the geometric representation, the second flow field corresponding to a second activity level of the patient different from the first activity level; and determining a second pressure drop in the arterial segment based on the second flow field. The method may also include calculating a range of pressure drops for a range of activity levels based on the first pressure drop and the second pressure drop; comparing the range of pressure drops to the threshold value; and displaying the result of the comparison.

[0006] In an embodiment, the first activity level is associated with a first flow rate and the second activity level is associated with a second flow rate different from the first flow rate, and calculating the range of pressure drops for the range of activity levels includes calculating the range of pressure drops for the range of flow rates.

[0007] In an embodiment, the flow rate ranges between a first flow rate and a second flow rate.

[0008] In an embodiment, calculating the range of pressure drops for the range of activity levels includes calculating the range of pressure drops based on a quadratic relationship between pressure drop and flow rate.

[0009] In an embodiment, displaying the result of the comparison includes displaying a graph of the range and the threshold of the voltage drop.

[0010] In an embodiment, the first activity level is a resting state.

[0011] In an embodiment, the second activity level is a hyperemic state.

[0012] In an embodiment, the method further comprises determining an activity level at which the voltage drop exceeds a threshold.

[0013] In an embodiment, the first and second flow fields comprise first and second flow velocity fields, respectively.

[0014] In an embodiment, determining a first flow field for the arterial segment using the geometric representation may include obtaining a first inflow rate corresponding to the arterial segment; calculating a first outflow rate corresponding to the arterial segment based on the first inflow rate; and calculating the first flow field in the arterial segment based on the geometric representation, the first inflow rate, and the first outflow rate. In an embodiment, determining a second flow field for the arterial segment using the geometric representation may include obtaining a second inflow rate corresponding to the arterial segment; calculating a second outflow rate corresponding to the arterial segment based on the second inflow rate; and calculating the second flow field in the arterial segment based on the geometric representation, the second inflow rate, and the second outflow rate.

[0015] An example embodiment of a computer-implemented method for performing a virtual stress test on a patient includes obtaining a geometric representation of an arterial segment of the patient, the arterial segment including an inflow boundary and two or more outflow boundaries; receiving metabolic demand information of the patient; and determining a threshold value based on the metabolic demand information. The method may also include obtaining a first inflow rate corresponding to the inflow boundary; calculating a first outflow rate at two or more outflow boundaries based on the first inflow rate; calculating a first flow field in the arterial segment based on the geometric representation, the first inflow rate, and the first outflow rate; and determining a first pressure drop in the arterial segment based on the first flow field. The method may also include obtaining a second inflow rate corresponding to the inflow boundary, the second inflow rate being different from the first inflow rate; calculating a second outflow rate at two or more outflow boundaries based on the second inflow rate; calculating a second flow field in the arterial segment based on the geometric representation, the second inflow rate, and the second outflow rate; and determining a second pressure drop in the arterial segment based on the second flow field. The method may also include calculating a range of pressure drops for a range of inflow rates based on the first pressure drop and the second pressure drop; comparing the range of pressure drops to the threshold value; and displaying the result of the comparison.

[0016] In an embodiment, the first inflow rate is associated with a first activity level and the second inflow rate is associated with a second activity level different from the first activity level, and calculating the range of pressure drops for the range of flow rates includes calculating the range of pressure drops for the range of activity levels.

[0017] In an embodiment, the flow rate ranges between a first flow rate and a second flow rate.

[0018] In an embodiment, calculating the range of the pressure drop includes calculating the range of the pressure drop based on a quadratic relationship between the pressure drop and the inflow rate.

[0019] In an embodiment, displaying the result of the comparison includes displaying a graph of the range and the threshold of the voltage drop.

[0020] In an embodiment, the method further comprises determining an activity level at which the voltage drop exceeds a threshold.

[0021] In an embodiment, the first inflow rate corresponds to a resting state of the patient.

[0022] In an embodiment, the second inflow rate corresponds to a hyperemic state of the patient.

[0023] In an embodiment, the first and second flow fields comprise first and second flow velocity fields, respectively.

[0024] In an embodiment, the first and second flow fields are calculated according to three-dimensional, time-independent equations. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 is a schematic diagram of an example embodiment of an electronic system for performing virtual stress testing.

[0026] Figure 2 is a flow chart illustrating an example embodiment of a method for performing virtual stress testing based on electronic patient data.

[0027] Figure 3 is a flow chart illustrating an example embodiment of a method for performing virtual stress testing based on electronic patient data.

[0028] Figure 4 Illustrated are example geometric models of a patient's anatomical region that may be determined and used with the methods of the present disclosure.

[0029] Figure 5 is a graph illustrating a range of example pressure drops for a range of example activity levels for a set of patients.

[0030] Figure 6 is a schematic diagram of an example embodiment of a user computing environment. DETAILED DESCRIPTION

[0031] Screening for arterial lesions by non-invasive clinical diagnosis and traditional stress testing can be inaccurate, leading to unnecessary coronary angiography procedures in false-positive patients, and can also result in false negatives in patients with significant lesions. In a recent study, 55.3% of patients who underwent traditional non-invasive testing and underwent invasive coronary angiography (ICA) did not have obstructive CHD. In the United States alone, approximately 1,115,000 inpatient cardiac catheterizations were performed in 2006, so highly accurate non-invasive testing that prevents unnecessary ICA procedures has the potential to save billions of dollars each year. In addition, while ICA procedures are generally very safe for patients, negative results do occur in rare cases and patient anxiety can be significant, so reducing the number of unnecessary ICA procedures can improve outcomes and patient experience. In addition, studies have shown that deferring revascularization based on non-ischemic FFR values ​​produces favorable outcomes relative to stenting, and that FFR-based stenting reduces the number of stents, deaths, heart attacks, and repeat stenting by approximately 30% relative to standard angiography-guided care, as well as reducing costs.

[0032] Initially, FFR was defined as the ratio of the maximum blood flow distal to the stenotic lesion to the normal maximum blood flow in the same vessel. However, in clinical practice, FFR is usually defined as the pressure distal to the stenosis (Pd) relative to the aortic pressure (Pa) under hyperemic flow conditions (i.e., FFR=Pd / Pa). Due to the high percentage of unnecessary invasive angiography, there is great interest in developing non-invasive methods to assess FFR. From the underlying principles of physics, FFR is actually a variable that depends on the detailed hemodynamic flow field, which is a function of arterial geometry, pressure, and flow conditions. Therefore, it is possible to calculate FFR from the fluid dynamics equations of motion, and this can be called virtual FFR, or FFRv. This requires numerical procedures that belong to the field of computational fluid dynamics (CFD). CFD involves solving the entire flow field and pressure within the region of interest (ROI) using the detailed vascular geometry of the region of interest, as well as the blood flow (or, alternatively, the applied pressure) at the boundaries of the computational domain.

[0033] Coronary blood flow requirements vary from person to person and depend on several physiological factors, including age, sex, body mass index (BMI), and physical activity level. To assess whether a patient's coronary blood flow is adequate and therefore does not have significant coronary artery stenosis, an exercise stress test may be performed. This involves increasing the level of exercise, which in turn results in an increase in heart rate, systolic blood pressure, and myocardial contractility. To maintain the exercise, coronary blood flow increases enough to supply the myocardium to meet its increased demands. If the patient has significant epicardial stenosis, then the autoregulatory reserve is insufficient and the corresponding myocardial bed becomes ischemic. This will manifest as chest pain or dyspnea, ST-segment depression on an electrocardiogram, abnormal wall motion if an adjunct echocardiogram is used, or abnormal myocardial perfusion if an adjunct myocardial perfusion imaging is performed. Although an indirect assessment of the presence of coronary artery stenosis is performed, this approach provides a patient-specific estimate of ischemic burden, which informs the decision to perform further testing (e.g., angiography) and / or whether to revascularize the patient.

[0034] An alternative diagnostic approach is to perform a direct anatomical assessment of the coronary arteries using either invasive coronary angiography or non-invasive CT angiography. This provides information about the presence and extent of coronary artery disease and is particularly helpful in ruling out disease or diagnosing severe obstruction. However, in the common case of intermediate coronary lesions (40-80% stenosis in diameter), anatomical assessment cannot accurately assess whether the stenosis is flow limiting or causing ischemia. To overcome this limitation of anatomical assessment, invasive FFR and non-invasive virtual FFR have been developed to supplement the anatomical information. Invasive FFR has become the standard for functional assessment of coronary artery lesions and does incorporate some patient-specific physiological data. For example, during invasive FFR measurements, the response of the myocardial bed to an administered hyperemic agent reflects the individual's coronary microvascular function. In contrast, for virtual FFR, an assumed flow rate is usually prescribed for the proximal vessels supplying the myocardial bed. However, the true blood flow rate for a given stenosis depends on the patient's microvascular function and cardiopulmonary capacity (e.g., an active 30-year-old patient will have a much higher blood flow rate than an 85-year-old patient in a wheelchair, even if the two patients have similar anatomy). Thus, current calculation methods used to assess a patient's coronary artery stenosis do not fully consider patient-specific coronary artery flow rates to inform calculations and decisions.

[0035] In order to provide a more patient-specific calculated hemodynamic assessment, one or more physiological blood flow indicators describing pressure loss, such as FFR or instantaneous wave-free ratio (IWFR), can be calculated within a range of simulated activity levels or metabolic demands. Information about the estimated or calculated level of a patient's physical and metabolic activity can be used to inform a patient-specific calculated FFR or IWFR for a given coronary artery lesion. For example, a patient's specific physical activity or metabolic data can be obtained from a validated activity or lifestyle questionnaire. For example, such a questionnaire can ask what activities the patient is engaged in, and the metabolic demands associated with those activities can be determined. Sidney et al. describe an example method for associating metabolic demands with activities in Metabolic Equivalents (METS) in Exercise Testing, Exercise Prescription, and Evaluation of Functional Capacity, 13 Clin. Cardiol. 555-565 (1990), which is incorporated herein by reference in its entirety. In short, the metabolic demand associated with a given activity can be defined as a multiple of a metabolic equivalent (MET), which is the amount of oxygen consumed by a patient at rest. For certain vigorous aerobic activities, metabolic demand can range from one (1) MET (at rest) to over fifteen (15) METS. The value of a single MET can be patient-specific and can depend on the patient's weight. The number of METS for a given activity can depend on the intensity and difficulty of the particular activity. In an embodiment, a single MET can be defined as a volume of oxygen per minute. Because the volume of oxygen per minute can be linearly proportional to the patient's flow rate, a direct relationship can be established between the patient's blood flow rate at a given point in the vasculature and the metabolic demand (i.e., the number of METS) provided by that flow.

[0036] In addition to or as an alternative to determining metabolic demand based on activity questionnaires, a patient's metabolic demand can also be determined by standardized exercise testing, such as the Bruce protocol, which many patients with suspected coronary heart disease undergo as part of their preoperative diagnosis. An example protocol for such exercise testing is described in Lear et al., Exercise Stress Testing: An Overview of Current Guidelines, 27(5) Sports Med. 285-312 (May 1999), which is incorporated herein by reference in its entirety. In short, based on the exercise test, a patient's specific maximum MET value can be determined, that is, the MET value associated with the maximum activity level in which the patient engages.

[0037] This patient-specific metabolic workload can be used to inform the coronary flow rates specified in hemodynamic calculations (e.g., one or more boundary conditions) and improve the diagnostic accuracy of the calculated FFR value or other (one or more) hemodynamic values ​​relative to known methods.

[0038] Navier-Stokes equations can be used to describe the flow field in the coronary artery, for example, the intravascular pressure and velocity in the region of interest (ROI) as a function of time and three-dimensional (3D) space in some forms, and only as a function of 3D space in other forms. From these flow fields, clinically interesting quantities such as pressure drop, FFR, instantaneous wave-free ratio (IWFR), forces on the arterial wall caused by intravascular pressure changes and viscous shear stress (wall shear stress, WSS), etc. can be calculated. In order to solve the Navier-Stokes equations, CFD is used, and the solution includes applying boundary conditions to the ROI. The subject's vascular cavity geometry (obtained from CT or other vascular imaging) is also included in the CFD, as well as a certain combination of inflow rate (e.g., at the selected inflow boundary) and flow distribution between vascular branches (e.g., one or more output flows). The pressure field in the ROI can be calculated as a deviation from a reference pressure, so CFD may not require an absolute pressure level in the ROI, such as Pa, when calculating the pressure field. Once the deviation from the reference is calculated, the absolute pressure in the field can be determined when a reference pressure (e.g., Pa) is determined. However, it should be noted that not all pressure-related determinations—such as some embodiments of virtual stress testing disclosed herein—require a reference pressure, and determinations may instead be made based on a pressure field without a baseline reference.

[0039] For many clinical applications in coronary blood flow, such as pressure drop, FFR, and IWFR, the Navier-Stokes equations can be considered independent of time (i.e., in three spatial dimensions, but independent of the time dimension). For example, the time average of the pressure ratio Pd / Pa represents 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 suitable for calculating these pressure indicators, thereby achieving faster calculations than a four-dimensional model (i.e., a model that combines three spatial dimensions and time).

[0040] The pressure field within the ROI can be determined based on, for example, the flow rate within and around the ROI. For coronary flow and pressure, the relationship between the flow rate and the pressure gradient (ΔP) between the proximal and distal locations in the region of interest can be well approximated by a quadratic equation, as shown in equation (1):

[0041] △P=aQ+bQ 2(Equation 1) where a and b are constants that depend on the individual patient's vascular geometry and blood viscosity and can be calculated for a given patient in the following manner. Equation (1) has a physical and mathematical basis. Physically, the aQ term is related to the pressure loss directly caused by blood viscosity, while bQ 2 The term is related to the pressure loss due to flow separation and turbulence (if present). When the stenosis is large enough to cause flow separation, bQ 2 The term may be significant. Mathematically, this equation can be viewed as the first two terms in a polynomial series expansion of ΔP = fcn(Q). Equation (1) solves for the pressure drop across a region of the vasculature of a subject patient for which the flow field is determined via computational fluid dynamics.

[0042] To calculate a and b, we can solve the three-dimensional Navier-Stokes equation for two different values ​​of Q. For example, the first value Q 1 represents the flow rate typical for resting conditions, while the second value Q 2 These calculations will give two values ​​of ΔP so that once Q 1 , Q 2 , △P 1 and △P 2 Knowing that, we can solve the following equations (2) and (3) for a and b:

[0043] △P 1 =aQ 1 +bQ 1 2 (Formula 2)

[0044] △P 2 =aQ 2 +bQ 2 2 (Formula 3)

[0045] Once the coefficients a and b are known for a given patient, it is possible to calculate ΔP over a range of flow conditions (eg, a range of physiologically relevant flow conditions) without having to perform further CFD for every flow condition within the range.

[0046] In addition to the uncertainties in geometry and flow splitting, there are two other sources of uncertainty in the noninvasive calculation of FFR: (1) Pa, the intravascular aortic pressure under hyperemia, which is unknown; and (2) Q, the inflow under hyperemia, which is unknown.

[0047] CFD provides ΔP directly, independent of Pa. On the other hand, FFR (calculated as the ratio Pd / Pa) depends directly on the patient-specific value of Pa. Therefore, for the calculated FFR, once ΔPa is calculated, an assumption must be made about the value of Pa. For example, the assumed value of Pa can be derived from cuff blood pressure measurements, or Pa = 100 mmHg can be assumed for all subjects.

[0048] There are clinical data suggesting that ΔP can be used as a diagnostic marker for obstructive CAD. To demonstrate this rationale, consider a population mean of Pa = 100 mmHg. For this mean Pa value: (a) FFR = 0.8 (generally considered indicative of obstructive lesions) corresponds to ΔP = 20 mmHg; and (b) IWFR = 0.9 (also generally considered indicative of obstructive lesions) corresponds to ΔP = 10 mmHg.

[0049] Therefore, it is reasonable to use a computational approach as a virtual stress test in the following scheme: (1) calculate ΔP at two different hypothetical flow rates based on images of the patient's ROI, such as CT images or other non-invasively obtained images; (2) determine the coefficients a and b for the subject using the quadratic relationship set forth in equations (1) and (2) above; (3) present a curve of ΔP versus Q over a range of flow rates; (4) determine the flow rate corresponding to a critical value of ΔP (e.g., 20 mmHg for FFR=0.8; 10 mmHg for IWFR=0.9); and (5) determine whether the resulting ΔP value is indicative of obstructive CAD based on the patient's expected flow requirements at rest and under expected lifestyle activities. This approach will be discussed in more detail below.

[0050] Referring to the drawings, wherein like reference numerals refer to the same or similar features throughout the various views, Figure 1 is a schematic diagram of an example embodiment of an electronic system 10 for performing virtual stress testing. The example system 10 may include a patient image source 12, a user input device 14, a virtual stress test computing system 16, and a display 18. As will be described in more detail below, the system 10 may be used to perform a virtual stress test based on electronic patient data (e.g., images of a region of interest of the patient and other data) to determine various hemodynamic information about the patient, and / or to make recommendations for further testing, interventional evaluation, and / or interventional treatment of the patient.

[0051] In embodiments, one or more aspects of the system 10 may be deployed in a clinical setting. For example, in some embodiments, the patient image source 12, the user input device 14, the virtual stress test 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 implemented in a laptop or desktop computer or workstation. In embodiments, one or more components of the system (such as the virtual stress test computing system 16) may be provided remotely from a clinical setting, such as in a cloud computing service deployment.

[0052] 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 non-invasive 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), etc., or a combination thereof.

[0053] Additionally or alternatively, the patient image source 12 may include a repository of existing image data. In some embodiments, the patient image source 12 may include a medical image storage device, such as a database or other local electronic data storage device, or a remote storage device (e.g., a cloud-based storage device) configured to store medical images.

[0054] User input device 14 may be or include one or more devices for inputting into a computing system, such as a mouse, touchpad, touch screen, keyboard, microphone, camera, or other input device.

[0055] The virtual stress test 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 be used as a patient image source 12, or aspects 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 the present 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 virtual stress test module 32.

[0056] 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 only for the convenience of discussion. The instructions for implementing the various modules can be in a common file, storage device, etc., and similarly, one or more of the modules described herein can be separated into multiple separate files, storage devices, etc.

[0057] The geometry determination module 24 may be configured to generate an electronic geometric representation (e.g., a model) of an anatomical region of interest (ROI) based on an image obtained from a patient image source. In some embodiments, the ROI may be a portion of the cardiovascular system of the subject patient, 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 extending therefrom.

[0058] In some embodiments, the one or more arterial segments may include one or more coronary artery segments. The one or more coronary artery segments may include a portion of one or more coronary arteries emanating from the subject's aorta and one or more branches extending therefrom. The one or more coronary artery segments may include, but are 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 artery segments of the left coronary artery (LCA) may include, but are not limited to, the left main coronary artery (LM), the left anterior descending artery (LAD), the left circumflex artery (also known as "Circumflex"), etc., or a combination thereof.

[0059] The present disclosure will refer to coronary artery segments. However, it will be understood that the one or more arterial segments are not limited to the coronary artery segments discussed and may include other coronary artery segments, other types of arterial segments, etc., or combinations thereof. For example, the one or more arterial segments may include (one or more) cerebral artery segments, (one or more) femoral artery segments, (one or more) iliac artery segments, (one or more) popliteal artery segments, (one or more) carotid artery segments, (one or more) renal artery segments, etc.

[0060] In some embodiments, the geometric representation generated by geometry determination module 24 can be a three-dimensional (3-D) electronic model of the spatial volume of one or more arterial segments. For example, the geometric representation of one or more arterial segments can be discretized into a three-dimensional volume mesh, such as a polyhedron (e.g., a tetrahedron). In some embodiments, the geometric representation can include a surface mesh representing the boundary of the lumen of each arterial segment.

[0061] In some embodiments, the boundary condition determination module 26 can be configured to determine the boundaries of each arterial segment. A "boundary" can refer to a cross-section of a representation of an arterial segment and can include, but is not limited to: an inflow boundary corresponding to a cross-section through which blood flows; one or more outflow boundaries corresponding to the cross-section disposed downstream or distal to the inflow boundary through which blood flow is directed outward; one or more vessel wall boundaries corresponding to the interface between the inner surface of the arterial wall and the flowing blood; etc.; or a combination thereof.

[0062] In some embodiments, one or more outflow boundaries may include an outflow boundary deployed at or adjacent to a junction (e.g., a bifurcation, a trifurcation, etc., and combinations thereof). In some embodiments, one or more outflow boundaries may include an outflow boundary deployed at or adjacent to the left circumflex artery. In some embodiments, one or more outflow boundaries may include a first outflow boundary and a second outflow boundary deployed between the inflow boundary and the first outflow boundary. In some embodiments, the first outflow boundary may correspond to a distal boundary of the segment (i.e., a cross-section deployed downstream or distal to the inflow boundary). In some embodiments, for example, when the geometric representation includes a left coronary artery, the second outflow boundary may correspond to the circumflex branch. In some embodiments, the first outflow boundary and the second outflow boundary may be separated by one or more additional outflow boundaries (e.g., at least a third outflow boundary). The third outflow boundary may correspond to or be adjacent to a junction such as a branch or a bifurcation.

[0063] In some embodiments, boundary condition determination module 26 may be configured to determine geometric data for each boundary using the geometric representation generated by geometry determination module 24. In some embodiments, the geometric data may include, but is not limited to, vessel radius, diameter, circumference, area, epicardial coronary volume, length, etc., or a combination thereof.

[0064] In some embodiments, the boundary condition determination module 26 can be configured to determine the boundary conditions of each boundary of each arterial segment. For example, the boundary conditions of each segment can include an inflow boundary condition, an outflow boundary condition, one or more vessel wall boundary conditions, etc., or a combination thereof. The inflow boundary condition can be a value or value range of a velocity, flow rate, pressure, or other characteristic. Each outflow boundary condition can be a value or value range of a velocity, flow rate, pressure, percentage of an inflow boundary, or other characteristic point. Each vessel wall boundary condition can be a value or value range of a velocity, flow rate, pressure, a combination thereof, or other characteristic.

[0065] In some embodiments, the determination of the inflow boundary condition and / or the outflow boundary condition can be determined based on patient information, applicable physiological state (e.g., resting state, hyperemic state), type of segment (e.g., LCA or RCA), etc. or a combination thereof. In some embodiments, the inflow boundary condition can be determined based on the expected patient activity level (e.g., based on information provided by the patient). In some embodiments, the inflow boundary condition can be a stored value and / or specified by the user.

[0066] In some embodiments, the inflow boundary condition can be determined based on the geometry of the anatomical ROI (such as the radius, diameter, length or volume of the vessel portion). For example, the flow rate can be calculated based on a model based on the lumen volume of the region of interest, as shown in the following formula 4:

[0067] Q in =αV β (Formula 4)

[0068] Where Q in is the flow rate at the inlet of the anatomical model, V is the luminal volume of the region of interest, α is a coefficient that depends on the physiological state of the patient, and β is a coefficient that depends on the vascular tree structure and, in some embodiments, the resolution of the image used to generate the 3D model of the anatomical region.

[0069] In embodiments where the region of interest is a coronary artery tree, V may be the luminal volume of the LCA or RCA defined from the proximal origin to a location where the segmented vessel diameter is a particular diameter, which may depend on the resolution of the images used to create the model of the patient's anatomy. For example, in some embodiments, a location may be defined as the diameter of three or four voxels in the image data set. In a specific example, a location may be where the luminal diameter is 1 mm or 1.5 mm.

[0070] In some embodiments, the parameters α and β may be constant for all patients and may be determined from an example data set having non-invasive and invasive data, from which the values ​​of α and β may be validated.

[0071] In some embodiments, an outflow distribution model can be used to determine the outflow boundary conditions. The outflow distribution model can be determined using geometric data and / or stored hemodynamic data. The stored hemodynamic data can define or be used to define an empirical relationship between the geometry of the outflow boundary (e.g., radius, diameter, length, volume, or other geometric features) and the corresponding flow rate. For example, the boundary condition generation module can use the stored hemodynamic data and the radius, diameter, length, volume, and / or other geometric features of the first and second outflow boundaries of the segment to determine the outflow distribution model. In another example, the boundary condition generation module can determine the outflow distribution model using only geometric data (e.g., the radius, diameter, length, volume, and / or other geometric features of the first outflow boundary (distal boundary) of the segment). The outflow distribution model can be used to determine the outflow (e.g., velocity, flow rate, percentage of inflow) of each outflow boundary, thereby determining each outflow boundary condition.

[0072] For example, the boundary conditions determined by the boundary condition determination module 26 can be used with steady-state and / or unsteady-state flow calculations to determine flow fields (e.g., blood flow, wall shear stress, etc.) and hemodynamic information (e.g., FFR, IWFR, etc.). The boundary condition determination module also uses optimization methods to define arterial segment flow splitting. Therefore, the boundary condition generation module 26 can provide flexibility, accuracy, and efficiency in determining boundary conditions.

[0073] The flow field determination module 28 can be configured to determine the flow field of each arterial segment using the geometric representation determined by the geometry determination module 24, one or more boundary conditions determined by the boundary condition determination module 26, and pressure data corresponding to the patient. The pressure data can be, for example, a cuff pressure of the patient at rest. In some embodiments, the flow field can include, but is not limited to, a pressure field, a velocity field, a wall shear stress field, an axial plaque stress, etc., or a combination thereof.

[0074] In some embodiments, flow field parameters (eg, pressure field, velocity, etc.) may be based only on geometric data and boundary conditions. Thus, the flow field determination module may be configured to determine the flow field based only on spatial position (ie, independent of time).

[0075] Pressure determination module 30 may be configured to determine the blood pressure at one or more points in the patient's anatomy using the flow field determined by flow field determination module 28. In some embodiments, pressure data may be determined from a calculated flow / pressure field, a non-invasive determination of the patient's average blood pressure (e.g., determined by a blood pressure cuff), the like, or a combination thereof.

[0076] The pressure determination module 30 may be configured to determine a specific pressure at a specific location in the patient's anatomy in response to a user (e.g., a physician) selecting a specific location. The user may input the selection using the user input device 14 relative to a display of a geometric model of a patient's region of interest (e.g., an artery) on the display 18. In an embodiment, the pressure determination module may be configured to determine the pressure upstream and downstream of the user-selected location in order to determine a pressure drop at the user-selected location.

[0077] The virtual stress test module 32 may be configured to simulate a stress test, i.e., perform a virtual stress test on the patient's anatomy. In an embodiment, the virtual stress test may include calculating a range of pressure drops at one or more points in the patient's anatomy for a range of activity levels as disclosed herein. Based on the results of the virtual stress test, the virtual stress test computing system may determine the patient's activity level at which the pressure drop indicates a clinically significant stenosis in the patient's anatomy. Based on the determined activity level, it may be determined by the system 16 or by a clinician whether further diagnostic procedures and / or interventional procedures should be performed.

[0078] In some embodiments, the virtual stress test performed by the virtual stress test module 32 may be performed based on a pressure gradient within the anatomical region of interest without determining an absolute pressure, a reference pressure, or a hemodynamic index based on absolute pressure. In other embodiments, the virtual stress test may include determining a reference pressure and calculating one or more hemodynamic index values ​​(such as IWFR, FFR, dPR, etc.).

[0079] To calculate absolute pressure or a hemodynamic index based on absolute pressure, one or more Pa values ​​(such as mean diastolic aortic pressure) may be calculated. In embodiments, mean diastolic pressure may be estimated using brachial cuff pressure measurements. The cuff pressure provides peak systolic pressure (SP) and minimum diastolic pressure (DP). In some embodiments, resting mean aortic diastolic pressure (Pa dmean or dPa)—which can be used for IWFR calculations—can be estimated from the cuff values ​​of SP and DP during waveless periods according to the transfer function set forth in Equation 5 below:

[0080] Pa dmean =(SP+3DP) / 4 (Formula 5)

[0081] In other embodiments, other transfer functions may be used to relate the cuff pressure value to the reference pressure.

[0082] In another embodiment, the resting mean aortic diastolic pressure Pa (Pa dmean , or dPa) can be calculated from the cuff pressure as shown in Equation 6 below:

[0083] dPa=Pc+offset (Formula 6)

[0084] where Pc is the resting brachial cuff pressure given by Equation 7:

[0085] Pc=dPc+FF*PP (Formula 7)

[0086] Where dPc is the diastolic cuff pressure, FF is a scalar shape factor, and PP is the cuff pulse pressure (e.g., the difference between the systolic (SP) and diastolic (DP) cuff pressures of the subject patient at rest). The scalar shape factor FF can have a value between 0.15 and 0.45 and can depend on patient-specific characteristics, including, for example, heart rate, age, height, systolic blood pressure, and / or augmentation index. In some embodiments, FF can be approximately 0.2, 0.25, or 0.33. In some embodiments, the offset value of Equation 6 can be between approximately zero (0) and -10 mmHg. In an embodiment, the offset value of Equation 6 can be approximately -7 mmHg. In some embodiments, the value of the offset can depend on the value of the shape factor FF, the desired hemodynamic index (and therefore the portion of the cardiac cycle being examined), and the patient's physiological state.

[0087] In other embodiments, the mean aortic diastolic pressure can be estimated using a transfer function that relates the cuff pressure to the aortic pressure during diastole. For example, the diastolic Pa value can be determined from the cuff pressure of the subject patient by finding the value of the shape factor FF and / or the offset value of the fitted data set, which includes the invasively measured cuff pressure and the diastolic resting core pressure of the patient population. Once this function is known, it can be used to obtain an estimate of the diastolic resting pressure from the non-invasively measured cuff pressure.

[0088] In another embodiment, mean aortic diastolic pressure can be estimated from a combination of an optical finger device or other wearable pressure measurement device (e.g., radial tonometry device) and a brachial cuff pressure device. For example, a Fourier analysis of the output of the optical finger device can be performed and mathematically combined with the brachial cuff pressure to determine the value of Pa.

[0089] Figure 2 is a flow chart illustrating an example embodiment of a method 40 for performing a virtual stress test. In an embodiment, the method 40 or one or more aspects of the method 40 may be performed by Figure 1 The virtual stress test computing system 16 is executed.

[0090] 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, description of the user's symptoms, etc. In embodiments, the patient data may also include metabolic data of the patient, such as the user's typical activity level (e.g., sedentary or active, amount of exercise per week, amount of specific activities (such as walking and running) per week, etc.). In embodiments, the patient data may also include patient data from one or more diagnostic tests, such as an echocardiogram.

[0091] The method 40 may also include, at block 44, receiving an image of the patient's anatomical structure. In embodiments, the received patient image may be a CT image, an MRI image, or other non-invasively obtained image. The image may include an anatomical region of interest to the patient. In embodiments, for example, the received image may include one or more coronary arteries or other vasculature of interest. The image may be received from a patient image source, such as an imaging device or a database or other computer memory.

[0092] The method 40 may also include, at block 46, creating an anatomical model corresponding to the region of interest of the patient based on the image received at block 44. In an embodiment, the anatomical model may be created by segmenting the anatomical structure of interest from the image received at block 44. The anatomical structure of interest may be, for example, one or more coronary arteries. Figure 4 An example anatomical model 56 of a coronary artery is shown. In an embodiment, the anatomical model may be composed of Figure 1 The geometry determination module 24 creates the anatomical model. In some embodiments, the anatomical model can be obtained by a computing system (eg, the hemodynamic information computing system 16) by creating it by the computing system or by receiving an existing model of the subject patient.

[0093] Method 40 may also include, at block 48, determining one or more boundary condition model value sets. In embodiments, one or more boundary condition value sets may be corresponding value sets of the same boundary condition model (e.g., corresponding condition value sets of the same inflow boundary, outflow boundary, wall boundary, etc.). In some embodiments, block 48 may include subsections 48a and 48b. Thus, method 40 may include obtaining an inflow rate at block 48a. In some embodiments, the inflow rate may be a flow rate at an inlet of an anatomical ROI of the patient. In other embodiments, the blood flow rate may be a flow rate of another portion of the ROI.

[0094] The inflow rate obtained at box 48a can represent a particular activity level or physiological state of the patient. For example, in some embodiments, the inflow rate obtained at box 48a can represent the patient's resting state. In another embodiment, the inflow rate can represent the patient's congestion state. In still other embodiments, the inflow rate obtained at box 48a can represent any activity level of the patient (e.g., any feasible flow rate for a patient given the patient's age, health status, etc.).

[0095] In some embodiments, the inflow rate obtained at block 48a may represent a desired point or portion of the patient's cardiac cycle. For example, in some embodiments, the inflow rate obtained at block 48a may represent an average flow rate over the patient's entire cardiac cycle, or over the entire waveless period of diastole of the patient, or over the entire diastole of the patient. In other embodiments, the inflow rate may represent a specific time point within the cardiac cycle, such as the midpoint of diastole.

[0096] In some embodiments, obtaining the inflow rate at block 48a may include calculating the inflow rate based on the geometry of the patient anatomical model, as discussed above with respect to equation (4). As discussed above with respect to equation (4), the inflow rate Q in An alpha term is included that depends on the patient's physiological state. Thus, obtaining the inflow rate at block 48a may include determining or selecting an alpha value that is appropriate for the expected activity level.

[0097] In other embodiments, instead of calculating the inflow rate according to Equation 4 above, obtaining the blood flow rate at block 48a may include receiving a user input of the inflow rate. The inflow rate may be received via manual user input (e.g., using a user input device 14 of the system 10). In an embodiment, the inflow rate at block 48a may be determined based on the patient's metabolic needs and condition (e.g., by a clinician or by an electronic system).

[0098] Block 48 may also include, at block 48b, calculating an outlet flow rate based on the inflow rate obtained at block 48a and based on 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 based on the geometry of the three-dimensional electronic model of the patient's anatomical region. In some embodiments, the flow splitting model may be calculated based on the relative radius, diameter, circumference, length, volume, and / or surface area of ​​the blood vessels in the electronic model.

[0099] In conjunction with the flow rate obtained in block 48a, the outlet flow rate calculated at block 48b may include a set of boundary condition model values ​​corresponding to an anatomical region of the patient. The set of boundary condition model values ​​may represent a particular physiological 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 patient's cardiac cycle (e.g., the entire cardiac cycle, diastole, a time point in the cardiac cycle, etc.).

[0100] In embodiments where the anatomical region is a coronary artery of a patient, the inflow rate obtained at block 48a may be an inlet flow rate of the coronary artery, and the flow splitting model may be calculated based on the geometry of the portion of the coronary artery downstream of the inlet in the electronic model. The flow splitting model may be calculated based on a relative radius, diameter, circumference, length, surface area, or volume of the portion of the coronary artery downstream of the inlet. In some embodiments, the flow splitting model may be calculated based on the epicardial volume of the electronic model.

[0101] Method 40 may also include, at block 50, calculating fluid dynamics of blood flow through the patient's anatomical structure based on the anatomical model (e.g., model 56), one or more boundary condition model value sets, and, in some embodiments, patient data. Block 50 may include determining a flow field for each arterial segment using the anatomical model, boundary conditions, and pressure data corresponding to the patient (e.g., aortic pressure data). In some embodiments, the patient's pressure data may be obtained, such as a 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, a velocity field, the like, or a combination thereof. In embodiments, the fluid dynamics may be determined by Figure 1 The flow field determination module 28 calculates.

[0102] In some embodiments, the velocity field and / or pressure field can be determined based only on boundaries and boundary conditions, without regard to time. For example, the velocity field and / or pressure field can be determined using a steady-state flow Navier-Stokes formula, where the velocity and pressure variables are functions of spatial position only (i.e., without regard to time). In this way, pressure and velocity can be accurately and efficiently determined in near real time, enabling clinicians to perform point-of-care analysis.

[0103] The method 40 may also include, at block 60, performing a virtual stress test based on the patient data and the calculated fluid dynamics. Performing the virtual stress test may include calculating a set of pressure drops at one or more locations in the patient's vasculature that span a range of activity levels of the patient, and thus a range of blood flow levels. The set of pressure drops may be analyzed to determine whether the patient has a clinically significant pressure drop indicative of stenosis at an activity level relevant to the patient.

[0104] In some embodiments, block 60 may additionally or alternatively include calculating a reference pressure, an absolute pressure, and / or one or more hemodynamic index values ​​across a range of activity levels. The absolute pressure and / or hemodynamic index values ​​may be analyzed (e.g., compared to a threshold) to determine whether the patient has a clinically significant pressure value or hemodynamic index value indicative of stenosis at a clinically significant activity level.

[0105] The method 40 may also include, at block 54, recommending further procedures for the patient based on the virtual stress test. For example, if the virtual stress test indicates a clinically significant pressure drop at a location in the patient's vasculature for an activity level associated with the patient, then further diagnostic procedures may be recommended for the patient, such as invasive angiography and pressure measurement at the suspected stenosis site. Additionally or alternatively, for example, a corrective procedure may be recommended to address the stenosis, such as placement of a stent at the suspected stenosis site.

[0106] Figure 3 is a flow chart illustrating an embodiment of a method 60 for performing a virtual stress test. In an embodiment, the method 60 or one or more aspects of the method 60 may be performed by Figure 1 The method 60 may be considered as an embodiment of blocks 48 , 50 , and 52 of the method 40 .

[0107] The method 60 may include, at box 62, obtaining a first inflow rate at a first activity level of the patient. The first inflow rate may be an expected flow rate of the patient under a first physiological condition, for example, which may be associated with a first activity level. The first activity level may be any activity level within a range of relevant activities of the patient. For example, the first blood flow rate may be a resting flow rate expected by the patient, corresponding to a low level of physical activity (i.e., a resting state). The flow rate at box 62 may be obtained via manual input of the flow rate by a user (e.g., using a user input device 14 of the system 10). In an embodiment, the flow rate at box 62 may be determined based on the metabolic needs and conditions of the patient (e.g., according to equation (4) herein, determining an α value suitable for the first activity level) (e.g., by a clinician or by an electronic system). In an embodiment, the first inflow rate obtained at box 62 may be any flow rate within a range of relevant flow rates of the patient (i.e., a flow rate that the patient is expected to experience during normal activity of the patient).

[0108] The method 60 may also include, at block 64, calculating a flow rate and a pressure in the region of interest at the first activity level of the patient. In some embodiments, the velocity field and / or the pressure field may be determined based on defined boundaries and boundary conditions corresponding to the region of interest (which in turn may be based on the flow rate received at block 62). For example, the velocity field and / or the pressure field may be determined using a steady-state flow Navier-Stokes formula, where the velocity and pressure variables are functions of only spatial position (i.e., time is not considered). The flow rate and pressure may be, for example, determined by Figure 1 The flow field determination module 28 of the system 10 determines.

[0109] The method 60 may also include, at block 66, calculating a first pressure drop (e.g., at one or more user-specified points and / or other points) for a first activity level (i.e., for a first inflow rate). The pressure drop may be a pressure drop from a vessel inlet to a point of interest (e.g., user-specified or otherwise). The pressure drop may be calculated based on the flow field calculated for the first activity level.

[0110] The method 60 may include, at box 68, obtaining a second inflow rate at a second activity level of the patient. For example, the second inflow rate may be an expected flow rate of the patient under a second physiological condition, which may be associated with a second activity level, and the second inflow rate is different from the first inflow rate. The second activity level may be any activity level within the patient's relevant activity range. For example, the second blood flow rate may be an expected hyperemic flow rate of the patient, corresponding to a high level of physical activity (i.e., a maximum exercise state). The flow rate at box 68 may be obtained via manual input of the flow rate by a user (e.g., using a user input device 14 of the system 10). In an embodiment, the flow rate at box 68 may be determined (e.g., by a clinician or by an electronic system) based on the patient's metabolic needs and conditions (e.g., determining an α value suitable for the second activity level according to equation (4) herein). In an embodiment, the second inflow rate obtained at box 68 may be any flow rate within the patient's relevant flow rate range (i.e., a flow rate that the patient is expected to experience during the patient's normal activity).

[0111] Boxes 62, 68 are described above with reference to an example in which the first flow rate represents a resting flow rate and the second flow rate represents a hyperemic flow rate. However, it should be noted that the first and second flow rates obtained at boxes 62, 68 may be any two different flow rates within a range relevant to the patient's metabolic needs.

[0112] As discussed herein, boundary condition flow rates may be specific to a portion or time point in the cardiac cycle. For example, the inflow rate may represent an average flow rate for the entire cardiac cycle of the patient, or the entire wave-free period of diastole of the patient, or the entire diastole of the patient, or may represent a specific time point within the cardiac cycle (such as the midpoint of diastole). Thus, the first and second inflow rates obtained at blocks 62, 68 may both represent the same cardiac cycle portion or point, and the associated boundary condition model value sets and subsequently calculated flow fields for the first and second inflow rates may similarly represent the same cardiac cycle portion or point.

[0113] The method 60 may also include calculating flow rate and pressure in the region of interest at a second activity level of the patient at block 70. The flow rate and pressure may be calculated substantially as described above with respect to block 64, but with boundary conditions determined based on the second inflow rate.

[0114] The method 60 may also include calculating a second pressure drop at one or more points of the second activity level at block 72. The pressure drop may be calculated based on the flow field calculated for the second activity level at one or more user-specified points.

[0115] The method 60 may also include, at block 74, calculating a range of pressure drops at associated (eg, user-specified) points for a range of activity levels based on the first and second pressure drops. 1 To Q 2 The pressure drop range is calculated based on the activity level range, where Q 1 is the first inflow rate and Q 2 is the second inflow rate. The pressure drop for a given flow rate can be calculated according to the quadratic equation, shown in this disclosure as equation (1) and repeated below:

[0116] ΔP=aQ+bQ 2

[0117] Where Q is the flow rate at a given activity level, and a and b are patient-specific constants. As described above, to calculate a and b, the three-dimensional Navier-Stokes equation can be solved for two values ​​of Q (e.g., the first and second inflow rates), as discussed herein with respect to equations (2) and (3). Additionally or alternatively, other computational methods can be used to solve for a and b, such as the Lattice-Boltzmann method or the one-dimensional Navier-Stokes equation. Once the coefficients a and b are known, ΔP can be calculated over a range of inflow rates (and therefore, over a range of activity levels) without requiring CFD for each activity level.

[0118] Figure 5 is a graph 100 illustrating an example range of pressure drops, for example, for an example range of activity levels for a set of patients. Figure 5 The graph 100 includes a pressure drop on the vertical axis (in mmHg / 100) and a flow rate on the horizontal axis (in ml / sec), showing six curve lines 102, 104, 106, 108, 110, 112 corresponding to six patients, and a horizontal threshold line 114 (shown as 20 mmHg) that can indicate a clinically significant pressure drop caused by obstructive disease. In the graph 100, the pressure drop curves corresponding to three patients - curve lines 102, 104 and 106 - exceed the threshold at different flow rates and therefore at different physical activity levels.

[0119] It should be noted that Figure 5 The pressure drop curves and thresholds shown in are examples only. In some embodiments, the illustrated threshold (20 mmHg), which corresponds approximately to an FFR of 0.8. Under hyperemic conditions, may be considered clinically relevant to a particular patient and may therefore be applied. In other embodiments, values ​​below or above 20 mmHg may be considered clinically relevant and may therefore be used as thresholds.

[0120] As described above, the range of pressure drops for each patient that exceed the threshold level of physical activity may or may not actually be related to the lifestyle of that particular patient. For example, the patient represented by graph line 104 may lead a very active lifestyle and therefore may often experience high blood flow rates, so a pressure drop exceeding the threshold at very high flow rates may be clinically significant to the patient. Therefore, further diagnostic or interventional procedures may be recommended for the patient. Conversely, the patient represented by graph line 102 may lead a sedentary lifestyle and therefore may rarely, if ever, experience high blood flow rates, so a pressure drop exceeding the threshold at a somewhat high flow rate may not be clinically significant to the patient.

[0121] The method 60 may also include, at box 76, determining an activity level at which the pressure drop exceeds a threshold based on the range of the pressure drop. In an embodiment, the threshold may be set at a clinically relevant pressure drop value and may be manually selected based on clinical judgment. For example, in some embodiments, the threshold may be selected based on a maximum MET value associated with the patient's lifestyle. The threshold may also be selected based on the portion of the cardiac cycle calculated at boxes 64, 66, 70, 72. Thus, in an embodiment, a threshold may be selected that is appropriate for both the patient's expected or estimated metabolic needs and the portion of the cardiac cycle for which analysis is performed in method 60. The user's activity level at which the pressure drop exceeds the threshold may then be compared to the patient's lifestyle and general activity level to determine whether further diagnostic or interventional procedures are needed.

[0122] The method 60 may also include, at block 78, displaying the flow rate and / or pressure drop at the first and / or second activity levels, and / or the flow thresholds applied in block 76 or the activity level at which the patient's flow rate exceeds the threshold. The display may include, for example, one or more indicators of flow rate and / or pressure at one or more locations in the patient's vasculature, overlaid on or presented adjacent to the geometric model of the patient's anatomy.

[0123] As a result of method 60, flow fields and / or pressure fields may be calculated (and, in embodiments, displayed or otherwise output) for one or more locations in the patient's vasculature for one or more cardiac cycle states of the patient. For example, in embodiments, flow fields and pressure fields may be calculated and output for the start and end of diastole. Such flow fields may be used to determine hemodynamic information corresponding to the patient, such as, for example, an IWFR value or other diastole-based hemodynamic values.

[0124] Figure 6 1 is a schematic diagram of an example embodiment of a user computing environment that includes a general purpose computing system environment 190, such as a desktop computer, laptop computer, smart phone, tablet computer, or any other such device having the ability to execute instructions, such as instructions stored in a non-transitory computer-readable medium. Furthermore, while described and illustrated in the context of a single computing system 190, those skilled in the art will also recognize that the various tasks described below may be practiced in a distributed environment having multiple computing systems 190 linked via a local or wide area network in which executable instructions may be associated with and / or executed by one or more of the multiple computing systems 190. In an embodiment, the computing environment 190 or portions thereof may include Figure 1 System 10.

[0125] In its most basic configuration, the 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 the computing system environment, the memory 194 may be volatile (such as RAM 200), non-volatile (such as ROM 198, flash memory, etc.), or some combination of the two. The computing system environment 190 may have additional features and / or functionality. For example, the computing system environment 190 may also include additional storage devices (removable and / or non-removable), including, but not limited to, magnetic or optical disks, tape drives, and / or flash drives. Such additional storage devices may be made accessible to the computing system environment 190 by means of, for example, a hard drive interface 202, a magnetic disk drive interface 204, and / or an optical disk drive interface 206. As will be appreciated, these devices, which will be linked to the system bus 196 respectively, allow reading from and writing to the hard disk 208, reading from or writing to the removable disk 210, and / or for reading from or writing to the removable optical disk 212 (such as a CD / DVD ROM or other optical media). The drive interface and its associated computer-readable media allow non-volatile 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 recognize that other types of computer-readable media that can store data can be used for this same purpose. Examples of such media devices include, but are not limited to, magnetic tapes, flash memory cards, digital video disks, Bernoulli cassettes, random access memories, nano drives, memory sticks, other read / write and / or read-only memories, and / or any other methods or technologies for storing information (such as computer-readable instructions, data structures, program modules, or other data). Any such computer storage media can be part of the computing system environment 190.

[0126] A number of program modules may be stored in one or more memory / media devices. For example, a basic input / output system (BIOS) 214, containing the basic routines that help transfer information between elements within the computing system environment 190 (such as during startup), 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, including an operating system 216, one or more application programs 218 (such as an operating system), and a plurality of application programs. Figure 1 Modules 24, 26, 28, 30, 32), other program modules 220 and / or program data 222. Further, computer executable instructions can be downloaded to the computing environment 190 as needed, for example via a network connection.

[0127] 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. Although not shown, other input devices may include a microphone, a joystick, a game controller, a scanner, and the like. These and other input devices will typically be connected to the processing unit 192 by means of a peripheral interface 228, which in turn will be coupled to the bus 196. The input devices may be connected directly or indirectly to the processor 192 via an interface such as, for example, a parallel port, a 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 the bus 196 via an interface, such as via a 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.

[0128] 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 remote computing system environments may be exchanged via additional processing devices responsible for network routing, such as a network router 242. Communications with the network router 242 may be performed via a network interface component 244. Thus, in such a networking environment, such as the Internet, the World Wide Web, a LAN, or other similar 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 (one or more) memory storage devices of the computing system environment 190.

[0129] The computing system environment 190 may also include positioning hardware 186 for determining the location of the computing system environment 190. In an embodiment, by way of example only, the positioning hardware 246 may include 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.

[0130] Although the present disclosure has described certain embodiments, it will be understood that the claims are not intended to be limited to these embodiments unless explicitly stated in the claims. On the contrary, the present disclosure is intended to cover substitutions, modifications, and equivalents that may be included within the spirit and scope of the present disclosure. In addition, in the detailed description of the present disclosure, many specific details are set forth in order to provide a thorough understanding of the disclosed embodiments. However, it is clear to those of ordinary skill in the art that systems and methods consistent with the present disclosure can be practiced without these specific details. In other cases, well-known methods, processes, components, and circuits are not described in detail to avoid unnecessary confusion of various aspects of the present disclosure.

[0131] Some parts of the detailed description of the present disclosure have been presented in a computer or digital system memory according to processes, logic blocks, processing and other symbolic representations of the operation of data bits. These descriptions and representations are the means by which the technical personnel in the field of data processing use to most effectively convey the essence of their work to other technical personnel in the field. Processes, logic blocks, processing, etc. are generally considered to be the self-consistent sequence of steps or instructions that lead to a desired result in this article. These steps are steps that require physical manipulation of physical quantities. Usually, although not required, these physical manipulations take the form of electrical or magnetic data that can be stored, transmitted, combined, compared and otherwise manipulated in a computer system or similar electronic computing device. For convenience, and with reference to common usage, with reference to various embodiments currently disclosed, such data are referred to as bits, values, elements, symbols, characters, items, numbers, etc.

[0132] It should be remembered, however, that these terms are to be interpreted as referring to physical manipulations and quantities, and are merely convenient labels that should be further interpreted according to terminology commonly used in the art. Unless otherwise specifically noted, as is clear from the discussion herein, it should be understood that throughout the discussion of the present embodiments, discussions using terms such as "determining" or "outputting" or "transmitting" or "recording" or "locating" or "storing" or "displaying" or "receiving" or "identifying" or "utilizing" or "generating" or "providing" or "accessing" or "examining" or "notifying" or "delivering" refer to the actions and processes of a computer system or similar electronic computing device that manipulates and transforms data. Data is represented as physical (electronic) quantities within the registers and memories of a computer system, 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 by one of ordinary skill in the art.

Claims

1. A computer-implemented method of performing a virtual stress test on a patient, the method comprising: receiving medical image data of a patient, the medical image data comprising an artery segment; generating geometric representations of arterial segments from medical image data; Receive information about the patient's metabolic needs; Determine thresholds based on metabolic demand information; determining a first flow field for the arterial segment using the geometric representation, the first flow field corresponding to a first activity level of the patient; determining a first pressure drop in the arterial segment based on the first flow field; determining a second flow field for the arterial segment using the geometric representation, the second flow field corresponding to a second activity level of the patient different from the first activity level; determining a second pressure drop in the arterial segment based on the second flow field; calculating a range of pressure drops for a range of activity levels based on the first pressure drop and the second pressure drop; comparing the range of the voltage drop to a threshold value; as well as Displays the results of the comparison.

2. The method of claim 1 wherein a first activity level is associated with a first flow rate and a second activity level is associated with a second flow rate different from the first flow rate, wherein calculating a range of pressure drops for a range of activity levels comprises calculating a range of pressure drops for a range of flow rates. The method of claim 2 , wherein the flow rate ranges between a first flow rate and a second flow rate.

4. The method of claim 2, wherein calculating the range of pressure drops for the range of activity levels comprises calculating the range of pressure drops based on a quadratic relationship between pressure drop and flow rate. The method of claim 1 , wherein displaying the result of the comparison comprises displaying a graph of a range and a threshold value of the voltage drop. The method of claim 1 , wherein the first activity level is a resting state.

7. The method of claim 1, wherein the second activity level is a hyperemic state.

8. The method of claim 1 further comprising determining an activity level at which a voltage drop exceeds a threshold.

9. The method of claim 1, wherein the first flow field and the second flow field comprise a first flow velocity field and a second flow velocity field, respectively.

10. The method of claim 1, wherein: Determination of the first flow field of the arterial segment using the geometric representation includes: obtaining a first inflow rate corresponding to the arterial segment; calculating a first outflow rate corresponding to the arterial segment based on the first inflow rate; and calculating a first flow field in the arterial segment based on the geometric representation, the first inflow rate, and the first outflow rate; and Determination of the second flow field of the arterial segment using the geometric representation includes: obtaining a second inflow rate corresponding to the arterial segment; calculating a second outflow rate corresponding to the arterial segment based on the second inflow rate; and A second flow field in the arterial segment is calculated based on the geometric representation, the second inflow rate, and the second outflow rate.

11. A computer-implemented method of performing a virtual stress test on a patient, the method comprising: obtaining a geometric representation of an artery segment of a patient, the artery segment comprising an inflow boundary and two or more outflow boundaries; Receive information about the patient's metabolic needs; Determine thresholds based on metabolic demand information; obtaining a first inflow rate corresponding to an inflow boundary; calculating a first outflow rate at the two or more outflow boundaries based on the first inflow rate; calculating a first flow field in the arterial segment based on the geometric representation, the first inflow rate, and the first outflow rate; determining a first pressure drop in the arterial segment based on the first flow field; obtaining a second inflow rate corresponding to the inflow boundary, the second inflow rate being different from the first inflow rate; calculating a second outflow rate at the two or more outflow boundaries based on the second inflow rate; calculating a second flow field in the arterial segment based on the geometric representation, the second inflow rate, and the second outflow rate; determining a second pressure drop in the arterial segment based on the second flow field; calculating a range of pressure drops for a range of inflow rates based on the first pressure drop and the second pressure drop; comparing the range of the voltage drop to a threshold value; as well as Displays the results of the comparison.

12. The method of claim 11, wherein a first inflow rate is associated with a first activity level and a second inflow rate is associated with a second activity level different from the first activity level, wherein calculating a range of pressure drops for a range of flow rates comprises calculating a range of pressure drops for a range of activity levels.

13. The method of claim 11, wherein the flow rate ranges between a first flow rate and a second flow rate.

14. The method of claim 11, wherein calculating the range of pressure drop comprises calculating the range of pressure drop based on a quadratic relationship between pressure drop and inflow rate.

15. The method of claim 11, wherein displaying the result of the comparison comprises displaying a graph of a range and a threshold value of the voltage drop.

16. The method of claim 11 further comprising determining an activity level at which a voltage drop exceeds a threshold.

17. The method of claim 11, wherein the first inflow rate corresponds to a resting state of the patient.

18. The method of claim 11, wherein the second inflow rate corresponds to a hyperemic state of the patient.

19. The method of claim 11, wherein the first flow field and the second flow field comprise a first flow velocity field and a second flow velocity field, respectively.

20. The method of claim 11, wherein the first flow field and the second flow field are calculated according to a three-dimensional, time-independent formula.

Images