Patent Publication Number: US-11645754-B2

Title: System and methods for determining modified fractional flow reserve values

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application is a continuation of prior U.S. application Ser. No. 16/375,076, filed on Apr. 4, 2019, the disclosure of which id incorporated by reference herein in its entirety. 
    
    
     FIELD OF THE INVENTION 
     The present invention relates to systems and methods for determining a modified Fractional Flow Reserve value. More particularly, the present invention relates to a systems and methods for modifying Fractional Flow Reserve values based on lesion induced flow rate reductions. 
     BACKGROUND OF THE INVENTION 
     The severity of a stenosis or lesion in a blood vessel may be assessed by obtaining proximal and distal pressure measurements relative to the given stenosis and using those measurements for calculating a value of a Fractional Flow Reserve (FFR). FFR is defined as the ratio of a distal pressure P d  measured on a distal side of a stenosis to a proximal pressure P a  measured on a proximal side of the stenosis, typically within the aorta (FFR=P d /P a ). Conventionally, a sensor is placed on a distal portion of a guidewire (FFR wire) to obtain/measure the distal pressure P d , while an external pressure transducer is fluidly connected via tubing to a guide catheter for obtaining the proximal, or aortic (AO) pressure P a . Once the guide catheter is positioned in situ, and the pressure of the blood filling the lumen of the guide catheter is equal to the pressure of the blood at the distal tip of the guide catheter, tubing that fluidly connects the proximal end of the guide catheter to the external pressure transducer also fills with blood such that the external pressure transducer measures the pressure of the blood at the distal tip of the guide catheter, on the proximal side of the lesion. The FFR wire is advanced through the guide catheter and through the lesion to a distal side of the lesion. The sensor on the FFR wire measures the distal pressure. 
     Calculation of the FFR value provides a stenosis specific index of the functional severity of the stenosis in order to determine whether the blockage limits blood flow within the vessel to an extent that treatment is needed. An optimal or normal value of FFR in a healthy vessel is approximately 1.00, while values less than about 0.80 are generally deemed significant and in need of an interventional treatment. Common interventional treatment options include balloon angioplasty and/or stent implantation. 
     Conventional methods of FFR measurement, however, do not take into account disruptions and modifications to blood flow rates caused by the presence of the stenosis or lesion, potentially leading to false negative results. Accordingly, there is a need for systems and methods to determine modified FFR values that take into account changes in blood flow caused by the presence of lesions. 
     BRIEF SUMMARY OF THE INVENTION 
     Embodiments described herein relate to systems and methods for determining modified FFR values according to blood flow changes due to the presence of blood vessel lesions. The system is configured to model the structure and blood flow of the coronary vasculature of a patient according to obtained physiological data. Lesions of interest that may be clinically significant but have FFR values indicating clinical nonsignificance are identified within the coronary vasculature. A modified model of the patient&#39;s vasculature is then created to estimate blood flow conditions in the absence of a lesion of interest and to determine a modified FFR value based on the estimated blood flow conditions. 
     In an embodiment, a system for determining modified fractional flow reserve value is provided. The system comprises an angiographic system configured to receive angiographic images of a coronary vascular system; a vascular measurement system configured to receive blood flow measurements of the coronary vascular system; and a computer system including at least one processor configured to execute computer instructions. The computer instructions program the processor to generate a coronary vascular model according to the angiographic images and the blood flow measurements, the coronary vascular model inclining an arterial tree and a blood flow field describing blood flow and blood pressure, to identify at least one lesion of interest within the coronary vascular model, to generate a modified coronary vascular model according to a removal of the lesion of interest, and to determine a modified fractional flow reserve value for the lesion of interest according to the coronary vascular model and the modified coronary vascular model. 
     In another embodiment, a computer-implemented method for determining modified fractional flow reserve values is provided and configured to be carried out by at least one processor executing computer instructions. The method comprises receiving, by an angiographic measurement system, angiographic images of a coronary vascular system; receiving, by a vascular measurement system, blood flow measurements of the coronary vascular system; and generating, by the processor, a coronary vascular model according to the angiographic images and the blood flow measurements, the coronary vascular model including an arterial tree and a blood flow field describing blood flow and blood pressure. The method further comprises identifying, by the processor, at least one lesion of interest within the coronary vascular model; generating, by the processor, a modified coronary vascular model according to a removal of the lesion of interest; and determining, by the processor, a modified fractional flow reserve value for the lesion of interest according to the coronary vascular model and the modified coronary vascular model. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG.  1    is a side illustration of a guidewire for measuring an FFR value in accordance with an embodiment hereof. 
         FIGS.  2 A- 2 D  illustrate anatomy of a vascular lesion or stenosis and associated blood flows. 
         FIG.  3    illustrates a system for the determination of modified FFR values in accordance with embodiments hereof. 
         FIG.  4    is a flowchart depicting a process for determining a modified FFR value. 
         FIG.  5    is a flowchart depicting a process of obtaining patient physiologic data. 
         FIG.  6    is a diagram illustrating a blood flow network model. 
         FIGS.  7 A- 7 D  illustrate aspects of a blood flow computational model. 
         FIGS.  8 A- 8 C  illustrate aspects of a computational model modification process. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     Specific embodiments of the present invention are now described with reference to the figures, wherein like reference numbers indicate identical or functionally similar elements. The terms “distal” and “proximal”, when used in the following description to refer to a catheter, guidewire, or delivery system are with respect to a position or direction relative to the treating clinician. Thus, “distal” and “distally” refer to positions distant from, or in a direction away from the treating clinician, and the terms “proximal” and “proximally” refer to positions near, or in a direction toward the clinician. The terms “distal” and “proximal”, when used in the following description to refer to a vessel or a stenosis are used with reference to the direction of blood flow. Thus, “distal” and “distally” refer to positions in a downstream direction with respect to the direction of blood flow, and the terms “proximal” and “proximally” refer to positions in an upstream direction with respect to the direction of blood flow. 
     The term FFR is used throughout to refer to Fractional Flow Reserve measurements. As used herein, FFR values are defined by a ratio of a distal blood pressure to a proximal blood pressure. FFR values as used herein may be obtained by measurements that are accomplished in situ, i.e., through direct measurement of blood pressures. FFR values as used herein may also refer to FFR values computed or estimated from one or more estimated pressure values, where pressure values are estimated according to modeling techniques discussed herein. FFR values may be computed based on proximal and distal pressure values at any location. For example, an arterial tree specific FFR value may refer to an FFR value taken between a proximal inlet to the arterial tree and a distal vascular location. A vessel branch specific FFR value refers to an FFR value taken at proximal and distal ends of a vessel branch, as defined by locations at which the vessel branch meets a vascular junction. A lesion specific FFR value refers to an FFR value taken between locations close to the proximal and distal ends of a specific lesion. 
     Although the description and discussion of embodiments herein relate to the determination of a modified FFR value, it is understood that the systems, techniques, and methods described herein may apply to any blood flow or pressure measurement made in an artery having an obstruction. In particular, the systems and methods described herein for FFR values may apply equally to instant wave-free ratio (iFR) values in coronary arteries. Accordingly, all description herein that refers to FFR values may be understood to apply to iFR values and computations as well. 
     The following detailed description is exemplary in nature and is not intended to limit the invention or the application and uses of the invention. Although the description of the invention is in the context of treatment of blood vessels such as the coronary arteries, the invention may also be used in any other body passageways where it is deemed useful, such as but not limited to peripheral arteries, carotid arteries, renal arteries and/or venous applications. Furthermore, there is no intention to be bound by any expressed or implied theory presented in the preceding technical field, background, brief summary, or the following detailed description. 
       FIG.  1    illustrates a pressure measurement system  100  for calculating an FFR value according to an embodiment of the present disclosure. The system  100  includes a guidewire  101  and a guide catheter  126 . The guidewire  101  includes a distal portion  108 , a proximal portion  102 , and at least one pressure sensor  118 . The guidewire  101  may further include a hub or handle coupled to a proximal end of the proximal portion  102  for convenient handling of the guidewire  100 . In embodiments, the guidewire  101  may be provided without any type of hub or handle. The guidewire  101  is configured to be disposed in a vessel  184  with a proximal end of the proximal portion  102  extending outside of a patient, and the distal  108  positioned in situ within a lumen of the vessel  184  having a lesion or stenosis  183 . The guidewire  101  is configured to measure a distal pressure P d  on a distal side  185  of the stenosis  183 . Various features of the components of the guidewire system  100  reflected in  FIG.  1    and described below may be modified or replaced with different structures and/or mechanisms. 
     The pressure sensor  118 , located on the distal portion  108  of the guidewire  101  measures the distal pressure P d . While an external pressure transducer  125  is fluidly connected via a lumen of the guide catheter  126  for obtaining the proximal, or aortic (AO) pressure P a . Once the guide catheter  126  is positioned in situ, and the pressure of the blood filling the lumen of the guide catheter  126  is equal to the pressure of the blood at the distal tip  128  of the guide catheter  126 , tubing  127  that fluidly connects a proximal end  124  of the guide catheter  126  to the external pressure transducer  125  also fills with blood such that the external pressure transducer  125  measures the pressure of the blood at the distal tip  128  of the guide catheter  126 . The guidewire  101  is advanced through the guide catheter  126  and through the lesion  183  to a distal side  185  of the lesion  183 . The sensor  118  on the guidewire  101  measures the distal pressure P d . The distal pressure P d  and the aortic or proximal pressure P a  are communicated to a computer system, such as computer system  300  described below. 
     Although  FIG.  1    illustrates a system  100  including the pressure sensing guidewire  101  and a guide catheter  126 , the systems and methods disclosed herein are compatible with any device capable of measuring or estimating FFR, including systems involving no guide catheter, systems involving FFR catheters, systems that include only a single pressure sensor, and various imaging technologies. 
       FIGS.  2 A- 2 D  illustrate anatomy of a vascular lesion or stenosis and associated blood flows through vessel branches. As used herein, the term “vessel branch” refers to a portion of a blood vessel between vessel bifurcations. A vessel branch is free from bifurcations. An arterial tree is formed of multiple vessel branches having bifurcations between them. In the following discussion of  FIGS.  2 A- 2 D , for the purposes of illustration, pressures and flow rates referred to by the same abbreviation are considered to be equal between the different figures. The illustrations of  FIGS.  2 A- 2 D  are intended to illustrate the effect on blood flow rates and pressures as caused by the presence of lesions. It is understood that other factors, such as vessel diameter, vessel length, vessel wall roughness, etc., may cause changes in pressure and flow rate. For the purposes of example, these factors are not considered in the following discussion. 
       FIG.  2 A  illustrates a lesion free vessel branch  212 . The vessel branch  212  has a proximal pressure P_a upstream and a distal pressure P_d 1  downstream. Due to the relatively unrestricted flow through the vessel branch, there is a minimal pressure drop between P_a and P_d 1 . The vessel branch  212  also has a proximal flow rate Q_d 1  upstream at an inlet of the vessel branch  212  and a distal flow rate Q_d 1  downstream at an outlet of the vessel branch  212 . The upstream and downstream flow rates are equal, due to the continuity principle, as all blood flowing into the vessel branch  212  must flow out. 
       FIG.  2 B  illustrates a vessel branch  200  having a lesion  201 . The vessel branch has a proximal pressure P_a upstream of the lesion  201  and a distal pressure P_d 2  downstream of the lesion  201 . The vessel branch  200  also has a proximal flow rate Q_d 1  upstream of the lesion and a distal flow rate Q_d 1  downstream of the lesion. The upstream and downstream flow rates are equal, due to the continuity principle, as all blood flowing into the vessel branch must flow out. The narrowing of the vessel branch  200  at the lesion  201  results in P_a being higher than P_d 2 . This pressure difference can be measured or determined by an FFR system and used in the determination of treatment plans. The lesion specific FFR value of the lesion  201  is computed as P_d 2 /P_a. In conventional uses of FFR, which is calculated by dividing the downstream pressure P_d 2  by the upstream pressure P_a, a score of 0.8 is considered to be the treatment threshold. Lesions scoring less than 0.8 are assessed as lesions to be treated. The 0.8 score indicates a 20% drop in pressure across the lesion. 
     The pressure drop between any two points in fluid flow is directly related to the volumetric fluid flow rate. An increase in volumetric fluid flow rate results in or requires an increase in pressure drop, while a decrease in volumetric fluid flow rate results in or requires a decrease in pressure drop. That is, larger pressure changes are required to drive larger flows. The exact relationship between volumetric fluid flow rate and pressure drop depends on several factors, such as the flow state of the fluid (e.g. smooth, turbulent, etc.), the diameter of the vessel, the shape of the vessel, the roughness of the vessel walls, and other factors. 
       FIG.  2 C  illustrates a small arterial tree  210  including a bifurcation  211  and two lesion free vessel branches  212  and  213 . In the arterial tree  210 , the proximal flow Q_p upstream of the bifurcation  211  splits into two distal flows Q_d 1  and Q_d 2  through lesion free vessel branches  212 ,  213  downstream of the bifurcation  211 . Due to the continuity principle, the blood flow volume prior to the bifurcation must equal the total blood flow through both branches after the bifurcation. Further, because there are no significant obstructions in either vessel branch  212 ,  213 , the downstream pressure in each P_d 3 , is approximately equal. The sum of Q_d 1  and Q_d 2  equals Q_p. Specific values of Q_d 1  and Q_d 2  are related to various characteristics of the downstream vessel branches  212 ,  213  that affect the flow, including size, shape, wall roughness, the presence and nature of lesions, the size and structure of downstream blood vessels, and others. In this structure, with no lesions to cause significant pressure drops, there is minimal pressure drop from the upstream pressure P_a to the downstream pressure P_d 1 . Although there may be a slight pressure differential between P_a and P_d 1  as a result of impeded blood flow caused by the bifurcation  211 , this differential is insignificant for the illustrative purposes of these examples. 
       FIG.  2 D  illustrates an arterial tree  220  having the vessel branch  200  as one of its downstream branches and the lesion free vessel branch  213  as another downstream branch. The lesion free vessel branch  212  of the arterial tee  210  of  FIG.  2 C  is replaced by the vessel branch  200  having lesion  201 . For the purposes of illustration, vessel branch  200  is understood to be identical, e.g., in length and diameter, to lesion free vessel branch  212 , with the exception of the lesion  201 . As discussed above, the lesion  201  restricts blood flow through vessel branch  200 , causing a pressure drop from P_a to P_d 3  across the lesion  201 . The pressure P_d 1  in the lesion free vessel branch  213  is approximately the same as the upstream, or aortic pressure P_a in the vessel branch  200 . As discussed above, the continuity principle requires that the total blood flow prior to the bifurcation  215  be the same as the total blood flow downstream of the bifurcation  215 . Accordingly, the upstream, or proximal flow rate Q_p must equal the sum of the downstream flow rates Q_d 3  and Q_d 4  in vessel branch  200  and lesion free vessel branch  213 , respectively. 
     Due to the partial flow blockage in vessel branch  200 , the flow rate Q_d 3  through the vessel branch  200  is reduced as compared to the flow rate Q_d 1  through vessel branch  212  in  FIG.  2 C . This reduction, in turn, requires that the flow rate Q_d 4  through vessel branch  213  is increased as compared to the flow rate Q_d 2  through vessel branch  213  in  FIG.  2 C . The total flow rate (Q_d 3 +Q_d 4 ) must remain equal to the upstream flow rate Q_p, but the division of flow between the downstream vessel branches  200 ,  213  changes when the lesion  201  is introduced to the system. 
     Comparing the vasculature arrangements of  FIGS.  2 A to  2 D , it can be shown that the effects of the bifurcation  215  and lesion  201  combine to create a reduced FFR score for the lesion  201  in the arterial tree  220  as compared to the vessel branch  200  with no bifurcation  215 . As discussed above, the addition of the lesion  201  to the arterial tree  220  including the bifurcation  215  causes blood flow to shift to the lesion free vessel  213 . The pressure drop across lesion  201  is directly affected by the blood flow rate through the lesion. Thus the pressure drop across lesion  201  in the arterial tree  220 , where the blood flow rate Q_d 3  is reduced in comparison to the blood flow rate Q_d 1 , is reduced compared to the pressure drop across lesion  201  in the structure of  FIG.  2 B  containing no bifurcation. The shift in blood flow from the partially obstructed vessel branch  200  to the lesion free vessel branch  213  moderates the pressure drop across the lesion  201  and serves to increase the FFR value of the lesion  201  inside the arterial tree  220 . This moderation in the pressure drop and increase in the FFR value may serve to obscure the clinical dangers that a particular lesion presents to a patient. 
       FIG.  3    illustrates a schematic of a system for determining modified FFR values. The system  300  includes one or more of a computer system  310 , a vascular measurement system  320 , and an angiographic measurement system  330 . The computer system  310  includes one or more processing modules, including a diagnostic module  311 , a modeling module  312 , a lesion identification module  313 , and a user interface module  314 . 
     Although illustrated in  FIG.  3    as including several components, the system  300  may include more or fewer components than those described, connected and/or communicating in ways different than those described. For example, the computer system  310  is configured to receive physiologic data, as discussed further below, from the vascular measurement system  320  and the angiographic measurement system  330 . In embodiments, the computer system  310  may receive equivalent physiologic data from any system or device capable of obtaining the required data. In embodiments, the computer system  310  may receive the physiologic data directly from the vascular measurement system  320  and angiographic measurement system  330 . In further embodiments, the computer system  310  may access data obtained by such systems and stored on a computer memory. In further embodiments, all or a portion of the capabilities and functionality of any of the components of the system  300  may be carried out by other components of the system  300 . 
     The computer system  310  may be configured as a server (e.g., having one or more server blades, processors, etc.), a personal computer (e.g., a desktop computer, a laptop computer, etc.), a smartphone, a tablet computing device, and/or other device that can be programmed to receive and output data and to interact with an operator. In an embodiment, any or all of the functionality of the computer system may be performed as part of a cloud computing platform. 
     The computer system  310  includes one or more processors  301  (also interchangeably referred to herein as processors  301 , processor(s)  301 , or processor  301  for convenience), one or more storage device(s)  302 , and any input and output components such as displays, speakers, mice, keyboards, Wi-Fi antennas, communications ports, etc., required for operation. The processor  301  is programmed by one or more computer program instructions stored on the storage device  302 . For example, the processor  301  is programmed by a diagnostic module  311 , a modeling module  312 , a lesion identification module  313 , and a user interface module  314 , the software instructions for which are stored on the storage device  302 . As used herein, for convenience, the various instruction modules and systems will be described as performing an operation, when, in fact, the modules and systems program the processor  301  (and therefore the computer system  310 ) to perform the operation. 
     The vascular measurement system  320  includes one or more vascular measurement devices and any associated or required software, hardware, and/or other components required to obtain, receive, or measure physiologic data of a patient as described herein. The vascular measurement system  320  is configured to obtain blood flow measurements, e.g., physiologic data about one or both of blood flow velocity and vascular blood pressure. To make such measurements, the vascular measurement system  320  may include pressure wires or catheters as well as devices for determining blood flow velocities. In embodiments, the vascular measurement system  320  may include an FFR measurement system such as the system  100  as described with respect to  FIG.  1   . 
     The angiographic system  330  is configured to obtain or receive angiographic imaging data, in the form of angiographic images, of a patient and includes systems and devices for collecting such angiographic data of the patient, along with any associated hardware and software. Suitable systems may include those capable of angiographic imaging via fluoroscopy, Computed Tomography (CT), and Magnetic Resonance Imaging (MRI). The angiographic system  330  is configured to inject the patient with radio-opaque contrast fluid and use imaging techniques to image the coronary vasculature and the rate of contrast fluid clearance. In embodiments, multiple images or slices may be obtained to generate a three-dimensional image of the coronary vasculature. In additional embodiments, other coronary imaging systems, such as radionuclide angiography systems may be used in place of the angiographic system  330 . 
     The diagnostic module  311  is a software module in operation on the computer system  310 . The diagnostic module  311  is configured to collect, obtain, or otherwise receive patient physiologic data describing the coronary vascular system, including at least blood flow measurements and angiographic images. The diagnostic module  311  may be configured to receive and interpret patient physiologic data directly from the vascular measurement system  320  and the angiographic system  330 . The diagnostic module  311  may further be configured to obtain patient physiologic data from a storage location. The diagnostic module  311  may further be configured to receive patient physiologic data at any level of processing, from direct raw data such as angiographic images and pressure measurements, to processed data that has previously been refined and analyzed. The diagnostic module  311  is further configured to communicate with the modeling module  312  to provide the modeling module  312  with the required data. In embodiments, the diagnostic module  311  is configured to operate as the vascular measurement system to receive coronary vascular blood flow measurements. In embodiments, the diagnostic module  311  is configured to operate as the angiographic system to receive angiographic images of the coronary vascular system. Further features and operations of the diagnostic module  311  are discussed below with respect to the operational processes of  FIGS.  4  and  5   . 
     The modeling module  312  is a software module in operation on the computer system  310 . The modeling module  312  is configured to construct and modify patient coronary vascular models. The modeling module  312  is configured to generate a coronary vascular model according to the angiographic images and the blood flow measurements. The coronary vascular model generated by the modeling module  312  includes information describing coronary vascular structure and coronary vascular blood flow of the patient coronary vascular system. The modeling module  312  is further configured to generate a modified coronary vascular model and, based on the original coronary vascular model and the modified coronary vascular model, determine modified FFR values of lesions of interest. Further features and operations of the modeling module  312  are discussed below with respect to the operational processes of  FIGS.  4  and  5   . 
     The lesion identification module  313  is a software module in operation on the computer system  310 . The lesion identification module  314  is configured to identify lesions within the patient coronary vasculature according to the patient physiologic data, including both the angiographic images and the blood flow measurements. The lesion identification module  314  is further configured to identify lesions of interest within the patient coronary vasculature according to the patient physiologic data, including both the angiographic images and the blood flow measurements. A lesion of interest is a lesion that is not identified as clinically significant under standard FFR guidelines (i.e., because it has an FFR value greater than 0.8) but appearing to a physician, other analyst, or the system  300  as requiring further analysis. Further features and operations of the lesion identification module  313  are discussed below with respect to the operational processes of  FIGS.  4  and  5   . 
     The user interface module  314  is a software module in operation on the computer system  310 . The user interface module  314  is configured to receive user input, provide user output, and otherwise provide all necessary components and features to facilitate user interaction with the computer system  310 . The user interface module  314  may be configured to receive and output information to and from any number of user input/output devices, including mice, keyboards, touchscreens, displays, speakers, and others. Further features and operations of the user interface module  314  are discussed below with respect to the operational processes carried out. 
       FIG.  4    illustrates a modified FFR value determination process  409  according to embodiments hereof. As discussed above with respect to  FIG.  2   , shifts in blood flow caused by the presence of a lesion in a blood vessel that is part of a larger arterial tree may cause an increased measured FFR value with respect to the same blood vessel and lesion in an isolated scenario. The modified FFR value determination process  400  serves to determine a modified FFR value that accounts for this increase in measured FFR when a measured blood vessel is part of a larger arterial tree or vascular network. The modified FFR value determination process  400  includes a series of operations for anatomical modeling based on measured blood flows and velocities, identification of potentially treatable lesions, and model modification to determine modified FFR values of the treated lesions. The operations of the process  400  may be implemented by components of the system  300 , including the computer system  310 , the vascular measurement system  320 , and the angiographic measurement system  330 . Other hardware or components may be used to carry out the various operations of the process  400  as discussed below, and the description of hardware and components herein is intended as an example and not a limitation. 
     In an operation  402 , physiologic data of a patient is collected. The collected physiologic data includes at least patient vascular data and may further include any data relevant to a patient&#39;s treatment. Patient vascular data is data describing a patient&#39;s vascular system. In particular, patient vascular data includes data describing a patient&#39;s coronary vascular network. Patient vascular data includes at least information about blood pressures, blood flow rates and velocities, and vascular structure including blood vessel geometry, locations, and branches. In embodiments, collection of physiologic data may be assisted by the diagnostic module  311 . 
     The diagnostic module  311  is configured to interface with the angiographic measurement system  330  and/or the vascular measurement system  320 . The diagnostic module  311  may communicate with, send instructions to, and/or receive information from the angiographic measurement system  330  and/or the vascular measurement system  320 . In embodiments, physiologic data collection performed by the angiographic measurement system  330  and/or the vascular measurement system  320  may be performed under the control of the diagnostic module  311 . In additional embodiments, an operator may operate the angiographic measurement system  330  and the vascular measurement system  320  independently and transfer recorded data to the computer system  310  under control of the diagnostic module  311 . The physiologic data collection operation  402  is described in greater detail with respect to  FIG.  5   . 
       FIG.  5    is a flow diagram illustrating a physiologic data collection process  500  consistent with embodiments hereof. Physiologic data collection process  500  may be implemented to collect patient vascular data. The process  500  gathers required data to describe the patient baseline vascular anatomy as used for subsequent modeling. As used herein, the baseline vascular anatomy refers to the measured patient anatomy and blood flow measurements prior to any clinical intervention. Physiologic data collection processes consistent with embodiments hereof may include any combination of the operations of the process  500  as described with respect to  FIG.  5   , including combinations that do not include all of the described operations and including combinations that include the described operations conducted in any order. 
     In an operation  502 , the physiologic data collection process  500  includes measuring patient anatomy. The coronary vascular structure of the patient is measured to generate a coronary map of the patient, including a map of the coronary vascular network and the size of the blood vessels at each point in the vascular network. The coronary map may include a three dimensional map generated through the use of multi-image angiography. Any suitable angiographic technique, including those using flouroscopy, CT, and MRI, may be applied to capture the patient coronary map. In further embodiments, a CT scan may be used to generate the patient coronary map. 
     In an operation  504 , the physiologic data collection process  500  includes measuring blood velocity and flow throughout the patient coronary vascular network. A plurality of blood flow measurements at different locations may be made to determine blood flow velocity at each of the different locations. Repeated measurements throughout the vasculature are used to generate a blood flow field describing the blood flow and blood pressure. In an embodiment, a flow wire that measures velocity at the distal tip of the wire is used to make blood flow velocity measurement. In additional embodiments, the blood flow velocity throughout the vasculature may be estimated based on the rate at which contrast fluid is cleared. Both the vascular measurement system  320  and the angiographic measurement system  330  may be used in operation  504 . 
     In an operation  506 , the physiologic data collection process  500  includes measuring pressure throughout the patient coronary vascular network. Pressure measurements may be performed through the use an FFR pressure wire or catheter as described herein and/or by any other suitable device for measuring intravascular blood pressure. Pressure measurements conducted at operation  506  provide information about pressure at measured locations of the vascular network. Measurements taken by the FFR system can be used to generate a patient coronary vascular network pressure field. The FFR system may also be used to determine FFR scores of known lesions within the patient&#39;s vascular network. 
     Returning now to  FIG.  4   , in an operation  404  a physiologic model is generated according to the measured physiologic data. The measured physiologic data, as received and processed by the diagnostic module  311 , is transmitted to the modeling module  312  for model generation at operation  404 . The modeling module  312  is configured to construct a patient coronary vascular model according to the received physiologic data. 
     In embodiments, the modeling module  312  is configured to generate a blood flow network model according to the received physiologic data.  FIG.  6    is a diagram illustrating a blood flow network model  600 . The network model  600  is a model describing the coronary vascular structure and the blood flows according to a network graph. The network model  600  stores information about an arterial tree  605 , including each vessel branch  601  in the network and the vessel branch nodes  602 . The network model  600  also stores a blood flow field, including information about the blood pressures and blood flow rates at one or more locations within the vessel branches. As illustrated in  FIG.  6   , the network model  600  includes a plurality of vessel branches  601  and nodes  602 . Each vessel branch  601  includes two nodes  602 . The pressures and blood flow rates for each of the plurality of vessel branches  601  are stored in the network model  600 . In embodiments, the network model  600  includes proximal and distal measurements or estimates of blood pressure and blood flow rate. In embodiments, the network model  600  includes three or more blood pressure and blood flow rate values. For vessel branches  601  where blood pressure and flow were measured during operation  402 , the measurements are stored in the network model  600 . 
     For vessel branches  601  where blood pressure and flow were not measured, modeling module  312  is configured to estimate blood flow and blood pressure values according to existing measurements based on principles that govern the network model  600 . For example, the network model  600  requires that the continuity principle be maintained. Blood flows in to and out of any vessel branch node  602  must be equal. In another example, differences in blood pressures at adjacent nodes  602  may be determined according to pressure change causing features (including, for example, vessel diameter changes, lesion presence, vessel wall friction, vessel length, vessel geometry, and vessel junction geometry) of a vassal branch  601  extending between them. Accordingly, each vessel branch  601  of the network model  600  is characterized by proximal and distal blood pressure and blood flow measurements, and by two vessel branch nodes  602 . 
     In embodiments, the modeling module  312  is configured to generate the network model  600  according to the patient vascular data, including angiographic imaging data describing the coronary structure and measured blood flow rates, velocities, and blood pressures. Although the network graph of network model  600  is described with specific requirements, variations in the network graph, including additional blood flow and pressure measurements and/or additional or different linking requirements between vessel branches  601  and vessel branch nodes  602  may be used. 
     In embodiments, the modeling module  312  is configured to generate a computational model according to the received physiologic data.  FIGS.  7 A- 7 C  illustrate aspects of a computational model  700 . The computational model  700  is a blood flow model including an arterial tree  710  containing information about the coronary vascular structure according to an anatomical model and a blood flow field including information about blood flow rates and blood pressures and blood flows throughout the coronary vascular structure. The computational model  700  is a model suitable for use with finite element analysis (FEA) techniques, software, and methods. 
       FIG.  7 A  illustrates the arterial tree  710 , the branching network of coronary blood vessels that define the coronary vasculature. As discussed above, images of the coronary vasculature are captured during the physiologic measurement operation  504 . The measured physiologic data, e.g., the angiographic images, are converted, if necessary, into digital form. Image capture and interpretation is employed to digitally define the arterial lumens of the coronary vascular network. The arterial lumens of the blood vessels are each defined by a series of three-dimensional coordinates defining the arterial wall.  FIG.  7 B  illustrates a wall element  702  on the interior arterial wall  704  of a coronary blood vessel  701 . The wall element  702  is defined by three-dimensional coordinates. In embodiments using a rectilinear coordinate scheme, each wall element  702  is defined by x, y, z coordinates. Each wall element  702  borders a neighboring wall element  702 , with the size of the wall elements  702  being based on a desired resolution of the computational model  700 . The blood vessel  701  is further defined by a plurality of wall elements  702  spread across the entirety of the arterial wall  704 . The rest of the arterial tree  710  is similarly defined by a plurality of wall locations  702  spread throughout the entirety of the vascular network. 
       FIG.  7 B  illustrates an interior element  703  of the blood vessel  701 . Each interior element  703  is defined by three-dimensional coordinates, and the entire interior of the blood vessel  701  is defined by a plurality of interior elements  703 . In embodiments using a rectilinear coordinate scheme, each interior element  703  is defined by x, y, z coordinates. Each vessel  701  of the arterial tree  710  may be defined by a plurality of interior elements  703  such that the entirety of the arterial tree  710  is defined. 
     The blood flow velocity or flow rate data gathered at operation  504  may then be used to construct a velocity field for the arterial tree  710 . According to measurements obtained at operation  504 , the blood flow rate at various locations within the blood vessels  701  is known. Based on the modeled assumptions regarding the flow rate in the blood vessels, such as a parabolic local velocity field in the case of laminar flow, the blood flow velocity can be determined and assigned to the interior elements  703  as an additional variable (x, y, z, v). The modeling module  312  is configured to determine the blood flow velocities according to the measured physiologic data. Blood flow velocities may be determined for all or some of the interior elements  703 . 
     Where the measured physiologic data obtained at operation  504  includes pressure data, then a pressure field throughout the arterial tree  710  is constructed as well. In the case of laminar flow in a tube, pressure only varies along the length of the tube, so all points in the same cross-section have the same pressure value. Pressure in a given cross-section is determined and each point in that cross-section is assigned an additional variable p, so that each interior element  703  is defined by the variables: (x, y, z, v, p). The modeling module  312  is configured to determine blood pressures throughout the arterial tree  710  according to the measured physiologic data. Blood pressures may be determined for all or some of the interior elements  703 . 
     Physiologic data determined at operation  402  may not be sufficient to determine blood pressure and velocity at the location of every single interior element  703  within the computational model  700 . In further embodiments, FEA techniques may be employed to generate pressure and velocity values of any interior element  703  for which it is not measured directly. FEA is a computational approach that finds an approximate solution to the problem under study according to governing rules and boundary conditions. The accuracy of the final result is improved by looking at the problem in finer and finer scale, i.e., by making the elements such as interior elements  703  and wall elements  702  smaller. Such accuracy comes at the expense of additional computational time or resources required. 
     The FEA computational solution for generating computational model  700  requires several inputs. The first required input is the domain. The domain is defined by the arterial tree  710  and the interior elements  703  and wall elements  702  that define it. Accordingly, the modeling module  312  may generate the computation model  700  domain according to physiologic data describing the arterial tree  710 , or coronary vascular network. 
     Another required input is boundary conditions. The modeling module  312  may be configured to generate the computational model  700  according to boundary conditions. The boundary conditions constrain the FEA computations by providing restricted results at specific interior elements  703 . For example, a no-slip boundary condition might be applied to the vessel wall, requiring that, at any point on the wall, the blood flow velocity is set to zero. For any interior element  703  located adjacent to the lumen wall, the variables become (x, y, z, 0, p), with the velocity being set at zero. This boundary condition is illustrated at  FIG.  7 D , which illustrates a velocity profile  708  through blood vessel  701  having a velocity of 0 adjacent to the vessel wall. Another example boundary condition includes the addition of known data. Thus, the pressures and flow rates obtained during operation  402  are included for all interior elements  703  to which they apply. In another example boundary condition, the measured volumetric flow rate throughout the arterial tree  710  may be used to establish a velocity profile spanning several interior elements  703  at the inlet to the domain. In this case the inlet to the domain, or the arterial tree  710 , might be the cross-section of the arterial tree at the opening of the left main artery. For example, a parabolic velocity profile would be chosen that when integrated across the vessel area yields the measured flow-rate. By maintaining the inlet velocity profile boundary condition as a constant throughout the computational process, all calculations are constrained to the correct volume flow rate throughout the entirety of the arterial tree  710  domain. Further boundary conditions may include boundary conditions establishing a datum or baseline for the pressure field. For example, the pressure value at a single point at the exit from the arterial tree  710  domain in one of the distal vessels  701  of the arterial tree  710  may be constrained to 0. 
     Another required input is a set of governing equations. The modeling module  312  is configured to generate the computational model  700  according to governing equations. In the simplest case of a Newtonian fluid, there are two variables (velocity and pressure). Accordingly, two governing equations are needed to avoid over constraint or under definition of the FEA problem-space. For example, governing equations that describe conservation of mass and conservation of momentum may be used. In tensor form these may be written as
 
Conservation of mass: ∇· v= 0
 
Conservation of momentum μ∇ 2   v+∇p= 0
 
where v is the velocity vector, p is pressure and μ is a material property of the fluid called viscosity.
 
     The modeling module  312  is configured to generate, using FEA techniques, the computational model  700  of the patient coronary vascular using a domain, boundary conditions, and governing equations consistent with the above discussion. For the FEA computation, the arterial tree  710  domain is divided into interior elements  703 , and, within each element, the variables of interest are approximated with simple polynomial functions. For example, the velocity may be approximated within each element using a quadratic polynomial function and the pressure with a linear polynomial function. The polynomial functions are constrained to be continuous across element boundaries. There can be no discontinuities or step changes in the function. The goal of FEA is to establish the size and shape of each of the local polynomial functions by determining the individual polynomial parameters for every local polynomial function. The FEA output is a set of polynomial parameters for every local polynomial function. These output parameter values are calculated to be the best fit to the requirements established across the entire domain. That is, the FEA solution honors the boundary conditions and meets the requirements of the governing equations in a best fit manner. 
     The modeling module  312  is configured to perform the FEA computations throughout the arterial tree  710  domain to establish blood flow and pressure throughout every location within the coronary vasculature. As discussed above, increasing the precision and resolution of the FEA computations may create a strain on computational resources. Accordingly, the modeling module  312  may be configured, in embodiments, to generate the computational model  700  to define only a portion of the patient vasculature. The FEA computations and inputs may be adjusted accordingly to achieve a computational model  700  defining a portion of the patient vasculature. 
     Returning now to  FIG.  4   , in an operation  406  of process  400 , vascular lesions are identified. A portion of the identified vascular lesions are further identified as lesions of interest. The lesion identification module  413  is configured to identify vascular lesions and lesions of interest, as described below. Vascular lesions include all lesions, or any size or shape, within the patient coronary vasculature. Lesions of interest include any vascular lesions for which a physician or other operator wishes to obtain more information and/or any vascular lesion that meets specific predetermined criteria. For example, a lesion of interest is a lesion that is not identified as clinically significant under standard FFR guidelines (i.e., because it has an FFR value greater than 0.8) but appearing to a physician, other analyst, or the system  300  as requiring further analysis. Lesions and lesions of interest may be identified via several analysis techniques using the data and models obtained from earlier steps. The following analysis techniques may be combined in any manner without departing from the scope of the invention. For example, multiple identification techniques may be used in identifying lesions and lesions of interest to create redundancy. In another example, one or more techniques may be used to identify lesions while one or more techniques that may differ are used to identify lesions of interest. 
     The following lesion identification techniques may be performed by an operator, such as a physician, automatically through computation, or a combination of both. For example, the lesion identification module  313  may identify all vascular lesions and display these to the operator via the user interface module  314 . The operator may then select lesions of interest from among the identified vascular lesions. Display of the vascular lesions may include display of any analysis information obtained from the analysis techniques discussed below, such as imaging data and/or FFR data. The analysis information may be used by the operator to select lesions of interest. In another example, the lesion identification module  313  may identify lesions of interest through computation alone based on predetermined criteria. In yet another example, the lesion identification module  313  may identify potential lesions of interest according to predetermined criteria and then request operator verification and approval of each potential lesion of interest. 
     The techniques and models discussed below for identifying lesions and lesions of interest represent an array of tools available to an operator of the computer system  310  and to the computational capabilities of the computer system  310 . The tools and techniques discussed may be used in any combination with or without operator intervention. The discussion of specific combinations is intended for example purposes only and is not intended as limiting with respect to specific combinations that are not discussed. 
     In an embodiment, lesions are identified by the lesion identification module  313  according to imaging analysis. Based on the imaging obtained during the physiologic data collection steps, an operator or image analysis software may identify lesions within the coronary vasculature, image analysis may further be used to select one or more lesions of interest for further study. In further embodiments, image analysis software may be used to identify lesions of interest within the imaging data. 
     In a further embodiment lesions are identified according to FFR values determined from pressure measurements taken during physiologic data collection operation  504 . FFR values used to identify lesions and lesions of interest may be arterial tree specific, vessel branch specific, and or lesion specific. FFR values from the physiologic data collection may include any comparison of measured proximal and distal pressures, whether the measurements were made concurrently or not, with an FFR catheter or not, or otherwise. FFR values of less than 1 i.e., any FFR value indicating an obstruction of flow, may be used to identify a vascular lesion. Lesions of interest are also identified according to FFR values obtained according to data collected during the physiologic data collection operation  402 . Lesions of interest are identified according to FFR value criteria. Accordingly an FFR range may be used as the FFR value criteria to identify lesions in some embodiments. For example, an FFR range between 0.95 and 0.8 may be used to identify lesions of interest. Lesions with measurements greater than 0.95 may not be significant enough to warrant further investigation while lesions with measurements below 0.8 may already be established as clinically significant based on standard guidelines. Different FFR ranges may be used as required. 
     In embodiments, lesions and lesions of interest may be identified according to the vascular network model  600 . The vascular network model  600  includes proximal and distal pressure data points throughout the vasculature. As discussed above, the data points may be a mix of measured and estimated pressures. The proximal and distal pressure data points may be used to compute FFR values between any two points within the network mode  600 . FFR values used to identify lesions and lesions of interest may be arterial tree specific, vessel branch specific, and or lesion specific, depending on the data available within the network model  600 . The network model  600  FFR values may be used to identify lesions through FFR values less than one. Lesions of interest are identified according to FFR value criteria. Accordingly, an FFR range, e.g., between 0.95 and 0.8, may be used as the FFR value criteria to identify lesions in some embodiments. Other FFR ranges may be used as necessary. In embodiments, lesions and lesions of interest identified through use of the network model  600  may be confirmed via imaging or other technique. In embodiments, the presence of lesions may be identified based on arterial tree specific FFR values and the identification of lesions of interest may be performed based on branch specific or lesion specific FFR values. 
     In embodiments, lesions and lesions of interest may be identified according to the computational model  700 . The computational model  700  includes interior elements  703  storing pressure data throughout the vasculature. Any two interior elements  703  within the arterial tree  710  may be used to compute an FFR based on a pressure drop between them. The pressure data of the interior elements  703  may be used both to identify lesions and identify lesions of interest according to FFR computations. In embodiments, FFR may be computed with respect to the pressure at the inlet to the arterial tree. Thus, the downstream pressure at any location in the tree may be compared to the inlet pressure in an arterial tree FFR value. The arterial tree specific FFR value may be used to identify lesions based on downstream pressure measurements that show a significant drop in pressure. In further embodiments, FFR values may be computed with respect to specific blood vessels or lesions. Because the pressure drop within an unobstructed vessel branch is relatively low, a branch specific FFR computed based on pressure measurements at the proximal and distal ends of the branch in the case that the branch includes one lesion will be approximately the same as an FFR computed based on pressure measurements immediately upstream and immediately downstream of a lesion. If the branch includes more than one lesion, the branch specific FFR will differ from the lesion specific FFRs or each lesion. 
     Lesions of interest are identified from among the lesions according to FFR value criteria. Accordingly, an FFR range may be used as the FFR value criteria to identify lesions in some embodiments. The appropriate FFR range may be adjusted according to the specific FFR value used (e.g., arterial tree specific, branch specific, lesion specific). In embodiments, the FFR range may be a range between 0.95 and 0.8 or other range found to be appropriate. In embodiments, lesions and lesions of interest identified through use of the computational model  700  may be confirmed via imaging or other technique. In embodiments, the presence of lesions may be identified based on arterial tree specific FFR values and the identification of lesions of interest may be performed based on branch specific or lesion specific FFR values. 
     In an operation  408  of process  400 , the modeling module  312  is employed to modify the vascular model according to the lesion of interest selection. Any of the exemplary models, including the network model  600  and the computational model  700  may be modified by the modeling module  312  according to the following. 
     The modeling module  312  is configured to modify the vascular model by reconstructing the model without the presence of one or more lesions of interest. For example, the modeling module may remove a single lesion of interest from the vascular model and compute the modified vascular model based on the removal. The modeling module  312  is configured to estimate or compute modified blood pressures and flows in the modified vascular model based on the removal of the lesion of interest. 
     In the network model  600 , removal of a lesion of interest by the modeling module  312  may be performed by setting the proximal and distal pressures of the vessel branch  601  which contains the lesion of interest to be equal. Thus, instead of showing a pressure drop across the length of the vessel branch where the lesion of interest was, the modified network model  600  shows no pressure drop. Blood flows and pressures may then be redetermined throughout the modified network model  600  according to the adjusted pressure in the lesion of interest containing vessel according to the governing principles of the network model  600 . 
     In the computational model  700 , removal of a lesion of interest by the modeling module  312  may be performed by using FEA tools to reconstruct the lesion containing vessel without the lesion of interest.  FIGS.  8 A- 8 C  illustrate aspects of modifying the computational model  700 .  FIG.  8 A  illustrates blood flow through a vessel  901 A past lesion  902 . The walls and lesion of vessel  901 A are defined by wall elements  702  and the interior of vessel  901 A is defined by interior elements  703 .  FIG.  8 B  illustrates blood flow through a modified vessel  901 B. The modeling module  312  generates the modified vessel  901 B by eliminating the lesion of interest and regenerating the modified vessel  901 B as if the lesion of interest did not exist. The modeling module  312  selects points proximal and distal of the lesion of interest and determine a monotonic change in blood vessel diameter between the proximal point and the distal point, so as to model a smoothly changing diameter from the proximal point to the distal point without including the lesion of interest. After blood vessel remodeling, all of the points that were contained within the lesion are identified as interior elements  703  in the updated or modified computational model  700 . New wall elements  702  are introduced to the portions of the blood vessel  901 B where the newly defined vessel wall is located. 
       FIG.  8 C  illustrates an example of removing a lesion of interest where a blood vessel is not straight in the region of the lesion. The vessel  901 C includes significant curvature in the region of lesion  902 B. In an embodiment, the modeling module  312  identifies the vessel centerline  903  and uses the centerline  903  as an aid in generating a modified vessel  901 D. After reconstruction of the modified vessel  901 D without the lesion  902 B, the modeling module  312  may prompt an operator for confirmation of the change. In embodiments, the modeling module  312  may proceed without requesting confirmation. In embodiments, the operator may perform a manual adjustment of the modified blood vessel  901 D. For example, in the case of highly asymmetric lesions, the centerline may be biased to one side or the other which may bias the result. 
     The modified blood vessel  901 B (or  901 D) is included in a modified arterial tree (not shown) representing a modified patient vascular model. The modified computational model  700  establishes a modified domain for FEA computations. FEA techniques, as described above, are then repeated by the modeling module  312  to determine a modified velocity and pressure field throughout the modified domain of the modified computational model  700 . 
     Model modification in each of the above cases, for the network model  600  and the computation model  700  results in modified velocity and pressure fields. Modified velocities permit the computation of modified flow rates. In particular, such modification results in increased flow across the region that the lesion of interest was located in prior to model modification due to the loss of flow resistance caused by the presence of the lesion. In embodiments, the local velocity field in the region of the removed lesion of interest may be integrated to determine a modified local flow rate. 
     Returning again to  FIG.  4   , in an operation  410  of process  400  a modified FFR value for the lesion of interest is determined by the modeling module  412  according to the modified flow rate in the region of the lesion of interest. As discussed above, removal of the lesion of interest from the vascular model removes resistance to flow, causing a modeled increase in flow rate in the region of the lesion of interest in the modified vascular model. The modeling module  412  is configured to determine the modified FFR values for lesions of interest based on the modified flow rates. 
     In the network model  600 , the modeling module  312  uses the unmodified values of proximal and distal pressures and the flow rate for the vessel branch  601  containing the lesion of interest to determine the resistance to flow provided by the lesion of interest. The resistance to flow of the lesion of interest is then used in conjunction with the modified flow rate by the modeling module  312  to determine a modified pressure drop across the lesion of interest. The modeling module  312  therefore computes a modified pressure drop across the lesion of interest based on the unmodified proximal pressure, the unmodified lesion resistance to flow, and the modified flow rate. In other words, the modeling module  312  determines how much pressure drop across the lesion is required to achieve the same flow rate in the vessel branch  601  as would occur if the lesion were not present. The modified pressure drop is used to determine the modified distal pressure. The modified distal pressure and unmodified proximal pressure are then used in a modified FFR value computation. 
     In the computational model  700 , the modeling module  312  uses the FEA techniques discussed above for computing FFR values employing the modified flow rate. The FEA calculation may be constrained to the region local to the lesion of interest to reduce computing demands. To perform this computation, the FEA analysis is performed according to the unmodified blood vessel structure, e.g., including the lesion of interest, the unmodified inlet pressure, and the modified flow rate. 
     In an operation  412 , process  400  includes identification of lesions for treatment based on modified FFR values. Modified FFR value computation may reveal that lesions of interest having a standard FFR value greater than 0.8, indicating non-treatment, may have a modified FFR value less than 0.8, indicating that treatment may be recommended. The increased flow rate of the modified model results in a greater pressure drop across the lesions of interest which in turn causes the FFR value to be lower than that measured (or calculated) for the patient at baseline. Thus, the modified FFR may identify lesions that should be treated from among a group of lesions determined for no treatment according to standard methods. 
     Operations  408 ,  410 , and  412  may be repeated for all lesions of interest to identify additional lesions for treatment. The computer system  310  may operate to determine lesions for treatment from among the lesions of interest in an automated fashion and/or with the assistance of an operator. For example, after having identified multiple lesions of interest at operation  408 , the computer system  310  may operate to automatically compute modified FFR values for each lesion of interest. In another example, the computer system  310  may receive additional input from the operator via the user interface module  314  to adjust the modified FFR computation. The computer system  310  may prompt the user for approval and verification at any stage of the modified FFR computation as a check on the computational process. 
     In additional embodiments, the computer system  310  may perform modified FFR computations based on modifications involving a plurality of lesions. The flow fields of the computational model  700  and the network model  600  may be modified according to the removal of more than one lesion. For example, an obstructive proximal lesion may restrict the distal blood flow to a downstream vessel branch including a distal lesion. Because the blood flow rate arriving at the distal lesion is reduced due to the proximal lesion, the standard FFR value of the distal lesion may be skewed. However, the above described method of computing the modified FFR of the distal lesion, based on the removal of a single lesion, may still not yield accurate results, because the flow rate restriction is caused by the proximal lesion. Accordingly, computing a modified FFR value based only on removal of the distal lesion may not provide enough accuracy. Accordingly, the modified FFR value across the distal lesion may be computed according to the removal of both the proximal and the distal lesion. When performing modified FFR computations for a lesion of interest, the modeling module  312  may assess the proximal blood vessel conditions to determine whether one or more additional lesions exist that may modify the blood flow rate that reaches the lesion of interest. Additional lesions may be lesions of interest, clinically significant lesions, and/of any other lesion that affects the flow rate. Additional lesions may be located upstream and/or downstream of the lesion of interest. 
     According to the above, systems and methods for determining modified FFR values are provided. While only some embodiments according to the present invention have been described herein, it should be understood that they have been presented by way of illustration and example only, and not limitation. Various changes in form and detail be made therein without departing from the spirit and scope of the invention. Further, each feature of each embodiment discussed herein, an of each reference cited herein, can be used in combination with the features of any other embodiment. For example, and not by way of limitation, any feature of embodiments describing use of the network model  600  may be combined, as appropriate, with any feature of embodiments describing the use of the computational model  700 . All patents and publications discussed herein ere incorporated by reference herein in their entirety.