Peripheral vascular pressure gradient determination

Abstract

A system (100) for evaluating a blood vessel (260) of a patient includes a memory (151) that stores instructions; and a processor (153) that executes the instructions. When executed by the processor (153), the instructions cause the system (100) to: receive pressure measurements obtained by a first intravascular pressure-sensing instrument (160) and a second intravascular pressure-sensing instrument (170) during a cardiac cycle of the patient; identify a diagnostic time window (1006) for the pressure measurements based on the pressure measurements from the blood vessel (260) during the cardiac cycle of the patient; and calculate an index based on the pressure measurements in the diagnostic time window (1006).

Description

PERIPHERAL VASCULAR PRESSURE GRADIENT DETERMINATIONBACKGROUND

[0001] Physiology-based decision making has become the standard of care for coronary interventions in the last decade. One widespread result of such physiology-based decision making has been the use of pressure sensing guide wires in combination with the instantaneous wave-free ratio (iFR) index. The iFR index is a pressure-based ratio in a specific part of the cardiac cycle optimized for coronary pressure and based on wave intensity analysis. iFR was developed to guide stenting decisions for coronary artery lesions. The specific part of the cardiac cycle used as the basis for the iFR index was identified as optimal for determining the ratio of pressure distal from and pressure proximate to the coronary artery lesions. To generate the iFR index, blood pressure distal and proximal to a lesion are measured over time. A time period in the cardiac cycle in which no pressure and flow waves are generated is known as the wave-free period, and this wave-free period is used for generating the iFR index. The wave-free period may be a period in the cardiac cycle during which coronary pressure which reflects wave intensity does not substantially vary, at least relative to variances that occur with the various forward and backward compression and expansion waves in the cardiac cycle.

[0002] No such index exists for peripheral vascular intervention decision making, and the wave- free period specifically and the iFR index generally are not applicable to physiology-based decision making for peripheral vascular interventions. Indeed, in peripheral arteries hemodynamics is markedly different than in coronary arteries as they are not embedded in the contracting cardiac muscle, and therefore iFR cannot be applied to peripheral arteries.SUMMARY

[0003] In accordance with a representative embodiment, a system for evaluating a non-coronary blood vessel of a patient is disclosed. The system comprises: a memory that stores instructions; and a processor that executes the instructions. When executed by the processor, the instructions cause the system to: receive pressure measurements obtained by an intravascular pressuresensing instrument at a first location of the non-coronary blood vessel and a second location of the non-coronary blood vessel during a cardiac cycle of the patient; identify a diagnostic timewindow for the pressure measurements based on the pressure measurements from the noncoronary blood vessel during the cardiac cycle of the patient; and calculate an index based on the pressure measurements in the diagnostic time window.

[0004] In accordance with another representative embodiment, a method of evaluating a noncoronary blood vessel of a patient is disclosed. The method comprises: receiving pressure measurements obtained by an intravascular pressure-sensing instrument at a first location of the non-coronary blood vessel and a second location of the non-coronary blood vessel during a cardiac cycle of the patient; identifying a diagnostic time window for the pressure measurements based on the pressure measurements from the non-coronary blood vessel during the cardiac cycle of the patient; and calculating an index based on the pressure measurements in the diagnostic time window.

[0005] In accordance with another representative embodiment, a tangible, non-transitory computer readable medium stores instructions, which when executed by a processor, causes the processor to: receive pressure measurements obtained by an intravascular pressure-sensing instrument at a first location of a non-coronary blood vessel and a second location of the non- coronary blood vessel during a cardiac cycle of a patient; identify a diagnostic time window for the pressure measurements based on the pressure measurements from the non-coronary blood vessel during the cardiac cycle of the patient; and calculate an index based on the pressure measurements in the diagnostic time window.BRIEF DESCRIPTION OF THE DRAWINGS

[0006] The example embodiments are best understood from the following detailed description when read with the accompanying drawing figures. It is emphasized that the various features are not necessarily drawn to scale. In fact, the dimensions may be arbitrarily increased or decreased for clarity of discussion. Wherever applicable and practical, like reference numerals refer to like elements.

[0007] Fig. 1 illustrates a system for peripheral vascular pressure gradient determination, in accordance with a representative embodiment.

[0008] Fig. 2 illustrates a hybrid system and flow for peripheral vascular pressure gradient determination, in accordance with a representative embodiment.

[0009] Fig. 3 illustrates a method for determining a peripheral vascular pressure gradient (PV- iFR) index, in accordance with a representative embodiment.

[0010] Fig. 4 illustrates pressure, velocity and wave intensity versus time.

[0011] Fig. 5 illustrates pressure, velocity and wave intensity versus time.

[0012] Fig. 6 illustrates a graph of pressure versus time showing a symmetrical diagnostic time window for peripheral vascular pressure gradient determination, in accordance with a representative embodiment.

[0013] Fig. 7 illustrates a graph of pressure versus time in another symmetrical time window for peripheral vascular pressure gradient determination, in accordance with a representative embodiment.

[0014] Fig. 8 illustrates a graph of pressure versus time an asymmetrical time window for peripheral vascular pressure gradient determination, in accordance with a representative embodiment.

[0015] Fig. 9 illustrates a graph of pressure versus time in another asymmetrical time window for peripheral vascular pressure gradient determination, in accordance with a representative embodiment.

[0016] Fig. 10 illustrates a graph showing pressure versus time in another time window for peripheral vascular pressure gradient determination, in accordance with a representative embodiment.

[0017] Fig. 11 is a flow chart of a method of determining a PV-iFR index in accordance with a representative embodiment.

[0018] Fig. 12 a flow-chart of a method for training a neural network, which may be used to determine the diagnostic time window required to compute a PV-iFR index, in accordance with a representative embodiment.

[0019] Fig. 13 illustrates a method for peripheral vascular pressure gradient determination by deploying the trained neural network to determine a PV-iFR index, in accordance with a representative embodiment.

[0020] Fig. 14 shows a user interface in accordance with a representative embodiment.DETAILED DESCRIPTION

[0021] In the following detailed description, for the purposes of explanation and not limitation, representative embodiments disclosing specific details are set forth in order to provide athorough understanding of embodiments according to the present teachings. However, other embodiments consistent with the present disclosure that depart from specific details disclosed herein remain within the scope of the appended claims. Descriptions of known systems, devices, materials, methods of operation and methods of manufacture may be omitted so as to avoid obscuring the description of the representative embodiments. Nonetheless, systems, devices, materials and methods that are within the purview of one of ordinary skill in the art are within the scope of the present teachings and may be used in accordance with the representative embodiments. It is to be understood that the terminology used herein is for purposes of describing particular embodiments only and is not intended to be limiting. Definitions and explanations for terms herein are in addition to the technical and scientific meanings of the terms as commonly understood and accepted in the technical field of the present teachings.

[0022] It will be understood that, although the terms first, second, third etc. may be used herein to describe various elements or components, these elements or components should not be limited by these terms. These terms are only used to distinguish one element or component from another element or component. Thus, a first element or component discussed below could be termed a second element or component without departing from the teachings of the inventive concept.

[0023] As used in the specification and appended claims, the singular forms of terms ‘a’, ‘an’ and ‘the’ are intended to include both singular and plural forms, unless the context clearly dictates otherwise. Additionally, the terms "comprises", and / or "comprising," and / or similar terms when used in this specification, specify the presence of stated features, elements, and / or components, but do not preclude the presence or addition of one or more other features, elements, components, and / or groups thereof. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0024] Unless otherwise noted, when an element or component is said to be “connected to”, “coupled to”, or “adjacent to” another element or component, it will be understood that the element or component can be directly connected or coupled to the other element or component, or intervening elements or components may be present. That is, these and similar terms encompass cases where one or more intermediate elements or components may be employed to connect two elements or components. However, when an element or component is said to be “directly connected” or “immediately adjacent” to another element or component, this encompasses only cases where the two elements or components are connected or disposedimmediately adjacent to each other without any intermediate or intervening elements or components.

[0025] As described herein, an index for decision- making in peripheral vascular (PV) interventions may be developed based on a specific phase in the cardiac cycle optimized for peripheral vascular interventions. The pressure-based index described herein may be used as a diagnostic tool to improve guidance during peripheral vascular interventions. Notably, while the blood vessels described herein are generally peripheral blood vessels, it is noted that the present teachings are not limited in application to peripheral vessels. More generally, the present teachings are applicable to non-coronary blood vessels. For example, the present teachings may be applied to cerebral or abdominal blood vessels.

[0026] Fig. 1 illustrates a system 100 for peripheral vascular pressure gradient determination, in accordance with a representative embodiment.

[0027] The system 100 in Fig. 1 is a system for peripheral vascular pressure gradient determination and includes components that are provided together. The system 100 includes a first intravascular pressure-sensing instrument 160, a second intravascular pressure-sensing instrument 170, a controller 150, and a display 180. Notably, the present teachings also contemplate use of a single intravascular pressure-sensing instrument adapted to perform measurements on, for example, two locations. Specifically, and as described more fully below, a single intravascular pressure-sensing instrument can be disposed first at a proximal location adjacent to a diseased (e.g., lesion) portion of the blood vessel, and a first pressure measurement is taken. Then, the single intravascular pressure-sensing instrument can be located on at a distal location adjacent to the diseased portion to the blood vessel, and second measurement taken. Notably, the two pressure signals are beneficially synchronized based on the cardiac cycle.

[0028] The controller 150 includes at least a first interface 156, a second interface 158, a memory 151 that stores instructions and a processor 153 that executes the instructions. The controller may be implemented by a computer that includes more elements than the controller 150 in Fig. 1. The controller 150 may include additional such as a third interface and a fourth interface. The first interface 156 connects the controller 150 to the first intravascular pressuresensing instrument 160, and the second interface 158 connects the controller 150 to the second intravascular pressure-sensing instrument 170. The first interface 156 and the second interface 158 may each comprise a patient interface module (PIM) or another form of standardizedinterfaced module used in medical contexts. One or more of the interfaces may include ports, disk drives, wireless antennas, or other types of receiver circuitry that connect the controller 150 to other electronic elements. One or more of the interfaces may also include user interfaces such as buttons, keys, a mouse, a microphone, a speaker, a display separate from the display 180, or other elements that users can use to interact with the controller 150 such as to enter instructions and receive output.

[0029] The memory 151 may store a set of software instructions that can be executed to cause the system 100 to perform any some or all aspects of the methods or computer-based functions disclosed herein. The controller 150 may operate as a standalone device or may be connected, for example, using a network to other computer systems or peripheral devices. In embodiments, the system 100 performs logical processing based on digital signals received via an analog-to-digital converter. The controller 150 can also be implemented as or incorporated into various devices, such as a workstation that includes a controller, a stationary computer, a mobile computer, a personal computer (PC), a laptop computer, a tablet computer, or any other machine capable of executing a set of software instructions (sequential or otherwise) that specify actions to be taken by that machine. The controller 150 can be incorporated as or in a device that in turn is in an integrated system that includes additional devices. In an embodiment, the controller 150 can be implemented in a device that also provides video or data communication.

[0030] The processor 153 may be considered a representative example of a processor of the controller 150 and executes instructions to implement some or all aspects of methods and processes described herein. The processor 153 is tangible and non-transitory. As used herein, the term “non-transitory” is to be interpreted not as an eternal characteristic of a state, but as a characteristic of a state that will last for a period. The term “non-transitory” specifically disavows fleeting characteristics such as characteristics of a carrier wave or signal or other forms that exist only transitorily in any place at any time. The processor 153 is an article of manufacture and / or a machine component. The processor 153 is configured to execute software instructions to perform functions as described in the various embodiments herein. The processor 153 may be a general-purpose processor or may be part of an application specific integrated circuit (ASIC). The processor 153 may also be a microprocessor, a microcomputer, a processor chip, a controller, a microcontroller, a digital signal processor (DSP), a state machine, or a programmable logic device. The processor 153 may also be a logical circuit, including aprogrammable gate array (PGA), such as a field programmable gate array (FPGA), or another type of circuit that includes discrete gate and / or transistor logic. The processor 153 may be a central processing unit (CPU), a graphics processing unit (GPU), or both. Additionally, any processor described herein may include multiple processors, parallel processors, or both. Multiple processors may be included in, or coupled to, a single device or multiple devices.

[0031] The term “processor” as used herein encompasses an electronic component able to execute a program or machine executable instruction. References to a processor should be interpreted to include more than one processor or processing core, as in a multi-core processor. A processor may also refer to a collection of processors within a single computer system or distributed among multiple computer systems.

[0032] The memory 151 may include a main memory and / or a static memory, where memories in the system 100 communicate with each other and the processor 153 via a bus. The memory 151 may be considered a representative example of a memory of the controller 150, and store instructions used to implement some or all aspects of methods and processes described herein. Memories described herein are tangible storage mediums for storing data and executable software instructions and are non-transitory during the time software instructions are stored therein. As used herein, the term “non-transitory” is to be interpreted not as an eternal characteristic of a state, but as a characteristic of a state that will last for a period. The term “non- transitory” specifically disavows fleeting characteristics such as characteristics of a carrier wave or signal or other forms that exist only transitorily in any place at any time. The memory 151 is an article of manufacture and / or machine components. The memory 151 is a computer-readable medium from which data and executable software instructions can be read by a computer (e.g., by the processor 153 of the controller 150). The memory 151 may be implemented as one or more of random access memory (RAM), read only memory (ROM), flash memory, electrically programmable read only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, a hard disk, a removable disk, tape, compact disk read only memory (CD-ROM), digital versatile disk (DVD), floppy disk, blu-ray disk, or any other form of storage medium known in the art. The memory may be volatile or non-volatile, secure and / or encrypted, unsecure and / or unencrypted.

[0033] Notably, and as described more fully below, various aspects of the present teachings are directed to determining a window in which to take pressure measurements to gamer the pressuresignal for determination of the PV-iFR. As described more fully below, two algorithms of approximating a diagnostic time window for determining a PV-iFR index (sometimes referred to below as “index”) based on the pressure measurements in the diagnostic time window are contemplated by the present teachings. Further details of the iFR index are described in commonly owned U.S. Patent 9,339,348 the disclosure of which is specifically incorporated herein by reference.

[0034] One algorithm used to approximate the diagnostic time window for determining the PV- iFR is an analytical processing sequence that is heuristic, and another algorithm is a computational model that is a deep learning algorithm trained on ground truth data. As will be appreciated, and as described more fully below, both algorithms are contemplated to be stored as instructions in memory 151, for example. When executed by the processor, these algorithms ultimately are used to calculate the index in a diagnostic time window. As such, and as described more fully below, software algorithms used approximate the diagnostic time window for determining the index by analytical processing, as well as to train and execute the deep learning model serve as instructions, which when executed by a processor (e.g., processor 153) cause the processor to perform various steps and methods according to the present teachings. The trained model may be referred to as a computational model, which is an artificial intelligence (Al) model, may be stored in the memory 152. As described more fully below, the computational models of various representative embodiments may be derived from a known Al model architecture including recurring neural networks (RNNs), transformer networks and other neural network-based models, and computer programs, all of which are executable by the processor 153 of controller 150.

[0035] Additionally, the memory 151 is an example of a computer-readable storage medium. Computer memory is any memory which is directly accessible to a processor. Examples of computer memory include, but are not limited to RAM memory, registers, and register files. References to “memory” should be interpreted as possibly being multiple memories. The memory may for instance be multiple memories within the same computer system. The memory may also be multiple memories distributed amongst multiple computer systems or computing devices. Software instructions, when executed by the processor 153, perform one or more steps of the methods and processes as described herein. In an embodiment, the software instructions may reside all or in part within the memory 151 and / or the processor 153 during execution by thecontroller 150.

[0036] In an embodiment, dedicated hardware implementations, such as application-specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), programmable logic arrays and other hardware components, are constructed to implement one or more of the methods described herein. One or more embodiments described herein may implement functions using two or more specific interconnected hardware modules or devices with related control and data signals that can be communicated between and through the modules. Accordingly, the present disclosure encompasses software, firmware, and hardware implementations. Nothing in the present application should be interpreted as being implemented or implementable solely with software and not hardware such as a tangible non-transitory processor and / or memory.

[0037] In accordance with various embodiments of the present disclosure, the methods described herein may be implemented using a hardware computer system that executes software programs. Further, in an exemplary, non-limited embodiment, implementations can include distributed processing, component / object distributed processing, and parallel processing. Virtual computer system processing may implement one or more of the methods or functionalities as described herein, and a processor described herein may be used to support a virtual processing environment.

[0038] The display 180 is local to the controller 150. The display 180 may be connected to the controller 150 via a local wired interface such as an Ethernet cable or via a local wireless interface such as a Wi-Fi connection. The display 180 may be interfaced with other user input devices by which users can input instructions, including mouses, keyboards, thumbwheels and so on. The display 180 may be a monitor such as a computer monitor, a display on a mobile device, an augmented reality display, a television, an electronic whiteboard, or another screen configured to display electronic imagery. The display 180 may also include one or more input interface(s) such as those noted above that may connect to other elements or components, as well as an interactive touch screen configured to display prompts to users and collect touch input from users.

[0039] The first intravascular pressure-sensing instrument 160 and the second intravascular pressure-sensing instrument 170 may each be used as interventional instruments in peripheral intravascular interventions. The first intravascular pressure-sensing instrument 160 and the second intravascular pressure-sensing instrument 170 may each include elements such as a guidewire with a distal portion with a distal tip, a housing adjacent to the distal portion, a shaft extending from the housing away from the distal tip, and cable connectors to connect to the first interface 156 and the second interface 158. The housing may include one or more sensors, transducers, and / or other monitoring elements configured to obtain raw diagnostic information regarding a peripheral blood vessel. The sensor(s) in the housing may include a pressure sensor configured to monitor a pressure within the peripheral blood vessel into which the first intravascular pressure-sensing instrument 160 and the second intravascular pressure-sensing instrument 170 are inserted. Pressure data input to the controller 150 may be generated by a pressure sensor such as a pressure wire in each of the first intravascular pressure-sensing instrument 160 and the second intravascular pressure-sensing instrument 170. For purposes of illustration and not limitation, the first intravascular pressure-sensing instrument 160 and the second intravascular pressure-sensing instrument 170 may be commercially available devices, such as Omniwire® or ComboWire® from Koninklijke Philips N.V.

[0040] The controller 150 may perform some of the operations described herein directly and may implement other operations described herein indirectly. For example, the controller 150 may indirectly control operations such as by generating and transmitting content to be displayed on the display 180. The controller 150 may directly control other operations such as logical operations performed by the processor 153 executing instructions from the memory 151 based on input received from electronic elements and / or users via the interfaces. Accordingly, the processes implemented by the controller 150 when the processor 153 executes instructions from the memory 151 may include steps not directly performed by the controller 150.

[0041] A method of evaluating a blood vessel of a patient using the system of Fig. 1 may include introducing the first intravascular pressure-sensing instrument 160 into the blood vessel of the patient on a first side of a location and introducing the second intravascular pressure-sensing instrument 170 into the blood vessel of the patient on a second side of a location. The controller 150 may be some or all of the components of a processing system. The controller 150 may then be used to implement a process for the system 100 that includes receiving, at the controller 150 in communication with the first intravascular pressure-sensing instrument 160 and the second intravascular pressure-sensing instrument 170, pressure measurements obtained by the first intravascular pressure-sensing instrument 160 and the second intravascular pressure-sensing instrument 170 during a cardiac cycle of the patient; identifying a diagnostic time window for thepressure measurements based on the pressure measurements from the blood vessel during the cardiac cycle of the patient; and calculating an index based on the pressure measurements in the diagnostic time window. As described more fully below, the diagnostic time window may be determined based on a wave intensity analysis (WIA). The characteristics of the diagnostic time window and the index calculated by the controller 150 may vary in different embodiments explained herein. Pressure data from the first intravascular pressure-sensing instrument 160 and the second intravascular pressure-sensing instrument 170 may be taken specifically from the diagnostic time window, and used to calculate the index. The index may then be output on the display 180 to visualize the index as a number or other graphical representation of the calculated index. The index may be considered a peripheral vascular pressure gradient index, insofar as the index is calculated based on the pressure data from both of the first intravascular pressuresensing instrument 160 and the second intravascular pressure-sensing instrument 170.

[0042] Fig. 2 illustrates a hybrid system 200 and flow for peripheral vascular pressure gradient determination, in accordance with a representative embodiment. Various aspects and details of the hybrid system 200 are common to the various representative embodiments described above in connection with Fig. 1. These common aspects and details may not be repeated to avoid obscuring the presently described representative embodiments.

[0043] The hybrid system 200 in Fig. 2 includes an index determination module 250, which as described below, is used to determine a diagnostic time window in which to determine an index. The index determination module 250 comprises a diagnostic time window module 252 and an index calculation module 253. The diagnostic time window module 252 and the index calculation module 253 may be stored in memory 151 and comprises instructions, which when executed by processor 153, determines the index as described more fully herein.

[0044] The hybrid system 200 further comprises a first pressure sensor 256 and a second pressure sensor 258. The first pressure sensor 256 and a second pressure sensor 258 may correspond to the first interface 156 and the second interface 158 in Fig. 1, and may be connected via a sheath / catheter to pressure sensors (e.g., pressure sensor 262) and guidewires (e.g., guidewire 264) of a first intravascular pressure-sensing instrument and a second intravascular pressure-sensing instrument. Notably, the second pressure sensor 258 may be located outside of the body. As the sheath / catheter lumen is rigid the pressure at the tip of the sheath / catheter (disposed inside blood vessel 260) is similar as the pressure at the end of thesheath / catheter (that is outside the body). Still alternatively, and as described more fully below, a single pressure sensor may be used to measure both the first pressure and the second pressure.

[0045] A pressure sensor 262 distal to the index determination module 250 (and distal to a diseased portion 266) may measure a first pressure signal Pl . A second pressure sensor 263 proximate to the index determination module 250 may measure a second pressure signal P2. As shown in Fig. 2, the first pressure signal Pl is taken at a location upstream from the diseased portion 266 of the blood vessel causing a narrowing 268 of the blood vessel 260 results. For example, this diseased portion 266 may be a lesion or similar obstruction to the flow of blood. The second pressure signal P2 may be taken at a location proximal to the index determination module 250 (and proximal to the diseased portion 266 of the blood vessel 260).

[0046] The input pressure Pl(t) and the input pressure P2(t) may be provided as time-series of pressure data for to the diagnostic time window module 252 (e.g., stored in memory 151 and executed by the controller 150) to determine a diagnostic time window. The diagnostic time window (between a first time ti and a second time t2) may be used to define which measurements from the input pressure Pl(t) and the input pressure P2(t) to use to generate a PV iFR index, which, as described more fully below may be a ratio of the distal peak pressure (PD) and the proximal pressure (Pp) during the determined diagnostic time window (i.e., PD / PPin the determined diagnostic time window). The diagnostic time window, in which the distal and proximal pressures are beneficially taken, is then provided to the index calculation module 253 (e.g., stored in memory 151 and executed by the controller 150) to determine the PV-iFR index. The resultant index may then be displayed as a value on a screen of the display 280.

[0047] More generally, the present teachings contemplate determination of an index that quantifies the functional significance of the lesion. With reference to the already existing iFR index for coronaries, and as described herein an illustrative index is the peripheral vascular iFR index (PV-iFR). The method of determining such an index from pressure measurements in one or two diagnostic time windows is to summarize the pressure in each time window (e.g., average it between tl and t2) and then compute the ratio. Notably, however, the present teachings are not intended to be restricted to this particular way of computing the index, but rather other operations that take the pressure values in the windows and determines a suitable index are contemplated. For example, and just by way of illustration the index may be equal to an average of (Pd(t)) / avg(Pp(t)) or equal to an average of (Pd(t) / Pp(t)). Still alternatively, or instead thedetermination of the average of the noted quantities the median can be taken, or the maximum or 95th percentile of these quantities can be taken within the diagnostic time window.

[0048] As described more fully below, in accordance with certain illustrative embodiments, the diagnostic time window may be determined based on an analytical processing step applied to the pressure data by the diagnostic time window module 252. The design of this processing step to determine the diagnostic window is based on wave intensity analysis (WIA), where the optimal period for determining the index may be the wave-free period between two forward moving waves. As WIA can only be carried out with available flow and pressure signals, while only pressure signals are available in the interventional setting, the analytic processing step carried out in diagnostic time window module 252 aims at approximating the optimal wave-free period as will be further described below. The index is calculated in a different diagnostic time window than the iFR index for coronary vessels, and the window may be dynamically determined during an intravascular intervention as described herein.

[0049] Alternatively, and as also described herein, wave intensity analysis (WIA) may be used to train a neural network so that only pressure and / or ECG signals are provided as the input to the Al model to determine the desired index.

[0050] Fig. 3 illustrates a method 300 for determining a peripheral vascular pressure gradient (PV-iFR) index, in accordance with a representative embodiment. Various aspects and details of the method 300 are common to those described in connection with representative embodiments in Figs. 1 and 2. These common aspects and details may not be repeated in order to avoid obscuring the description of the presently described representative embodiments.

[0051] At 302, the method 300 begins with introducing an IV pressure-sensing instrument. Again, it is noted that one or two IV pressure-sensing instruments may be used to determine blood pressures proximal and distal pressures, which may be taken upstream and downstream from a diseased portion of a blood vessel.

[0052] At 304 the method 300 comprises receiving IV pressure measurements at two locations of the blood vessel (e.g., upstream and downstream from a diseased portion of a blood vessel). In accordance with a representative embodiment, a clinician may identify a potential treatment site. The at least two pressure measurements must come from one location proximal and one location distal to this site such as described above in connection with the representative embodiments of Fig. 2. There may be more than one measurement on each site or each of the measurements maycover several cardiac cycles and therefore contain multiple valid diagnostic time windows to detect.

[0053] At 306, an algorithm for determining a diagnostic time window is loaded. As alluded to above, and as described more fully below, the present teachings contemplate the use of an analytical calculation algorithm or a neural network (Al) algorithm.

[0054] At 308, the method 300 comprises applying the selected algorithm to predict a diagnostic time window in which to take pressure measurements to determine the PV-iFR index. Notably a plurality of diagnostic time windows can be determined. These are determined at minimum one window when synchronously measuring pressure at two sensors (one measuring pressure at each location), so the window detected on one sensor signal can be applied to the other sensor signal, or two windows when using only one sensor that needs to change location to do both measurements and therefore there are no time-wise corresponding pressure signals. In still further embodiments, a plurality of diagnostic time windows may be used to determine a diagnostic time window in which to measure pressures to determine the PV-iFR index. For example, the determined diagnostic time window may be found by averaging a plurality of diagnostic time windows determined over a plurality of cardiac cycles. In this case the diagnostic time window is not defined by its absolute interval border in time domain, but relative to the repeating cardiac cycle. Alternatively, the pressure data from the plurality of diagnostic windows for one location may be averaged instead of the window interval to determine the PV-iFR index in a next step. At 310, the method 300 continues with the calculation of the PV-iFR index in the determined diagnostic time window (e.g., PD / PPin the determined diagnostic time window). In accordance with various representative embodiments, pressure data are taken in the diagnostic time window for both measurement locations and related to receive the PV-iFR index. This can be, but is not limited to: averaging the pressure in the window and computing the ratio of timeaverages between the two locations; taking the maximum pressure in the window and compute the ratio between distal & proximal location pressure data; compute the ratio between distal and proximal pressure at each time step first and then average the resulting indices. As alluded to above, and in the interest of simplicity using one device that measures at the first location and then at a second location, but the below would also apply for the other case. For example, one diagnostic time window can be determined at a first location, and the pressure is averaged at that location in that diagnostic time window. Next, a diagnostic time window can be determined at asecond location, and the pressure is averaged at the second location. Next, using both average pressure values (each representative of all pressure values in the diagnostic time window the first location and the second location) the index is determined by determining the ratio. Alternatively, this measurement sequence may be modified to determine several diagnostic time windows for each location and compute several indicative pressure values for each location. These are then averaged to provide a single overall pressure value per location and the index is computed. Moreover, several ratios may first be determined and the indices averaged. Still alternatively, this measurement sequence may be modified by determining several diagnostic time windows (again for several cardiac cycles as there is always only one window per cycle). Instead of interval borders tl and t2, which are absolute, the intervals are converted to cl and c2 borders expressed relative to the beginning (=0) and end (=1) of the cardiac cycle. The average of these window borders is determined to provide an overall window and apply it to one or more cardiac cycles to get indicative pressure values. Accordingly, the present teachings contemplate determining more than one diagnostic time window for each location of a diseased portion of a blood vessel. The average may be determined average diagnostic time duration across various diagnostic time windows can be taken before computing the ratio (or vice versa). This index may be displayed on a user interface, such as described in connection with representative embodiments of Fig. 14 below.

[0055] At 312, the method 300 continues with identifying a treatment based on the calculated PV-iFR index. While in no way intended to be limiting, illustrative treatments contemplated include angioplasty, stenting and atherectomy, to name only a few.

[0056] At 314, the method concludes with treatment of the patient based on the identified treatment from 312.

[0057] Fig. 4 illustrates pressure, velocity and wave intensity versus time. Various aspects and details of the representative embodiments described in connection with Fig. 4 are common to those described in connection with representative embodiments in Figs. 1-3. These common aspects and details may not be repeated in order to avoid obscuring the description of the presently described representative embodiments.

[0058] As shown in Fig. 4, a graph 400 shows pressure and velocity (flow) and wave intensity proximal to (e.g., upstream) and distal from (e.g., downstream) a short femoral artery occlusion or similarly diseased portion of a blood vessel. The various pressure measurements may be madefor example using the systems 100, 200 described above.

[0059] Graph 402 shows the pressure versus time upstream (proximal) of the diseased portion of the blood vessel (e.g., diseased portion 266), and graph 404 shows the pressure versus time downstream (distal) of the diseased portion of the blood vessel.

[0060] Graph 406 shows the velocity (flow) of blood versus time upstream (proximal) of the diseased portion of the blood vessel (e.g., diseased portion 266), and graph 408 shows the velocity (related to volume flow) versus time downstream (distal) of the diseased portion of the blood vessel.

[0061] Graph 410 shows the wave intensity of blood versus time upstream (proximal) of the diseased portion of the blood vessel (e.g., diseased portion 266), and graph 412 shows the wave intensity versus time downstream (distal) of the diseased portion of the blood vessel. In graph 410, curve 414 shows the wave intensity of the forward moving wave at the upstream (proximal) location, and curve 416 shows the wave intensity of the backward moving wave at the proximal location. Similarly, in graph 412, curve 418 shows the wave intensity of the forward moving wave at the downstream (distal) location, and curve 420 shows the wave intensity of the backward moving wave at the distal location.

[0062] The wave intensity of the forward moving wave may be referred to as WI+, and may be determined as a function of (l / 4pc)(dP / dt - pc(dU / dt))2, wherein p is density of blood, c is the speed of the wave, dU is the change in flow velocity, and dP the change in pressure. Similarly, the intensity of the backward moving wave may be referenced as WI-, and may be determined as a function of -(l / 4pc)(dP / dt - pc(dU / dt))2. As described more fully below, the net wave intensity may be referenced as WInet, and may be detennined as a function of WI+ + WI- which, in turn, is equal to (dP / dt)(dU / dt).

[0063] Fig. 5 illustrates pressure, velocity and wave intensity versus time. Various aspects and details of the representative embodiments described in connection with Fig. 5 are common to those described in connection with representative embodiments in Figs. 1-4. These common aspects and details may not be repeated in order to avoid obscuring the description of the presently described representative embodiments.

[0064] As shown in Fig. 5, a graph 500 shows pressure and velocity (flow) and wave intensity proximal to (e.g., upstream) and distal from (e.g., downstream) a short femoral artery occlusion or similarly diseased portion of a blood vessel. The various pressure measurements may bemade for example using the systems 100, 200 described above.

[0065] Graph 502 shows the pressure versus time upstream (proximal) of the diseased portion of the blood vessel (e.g., diseased portion 266), and graph 504 shows the pressure versus time downstream (distal) of the diseased portion of the blood vessel.

[0066] Graph 506 shows the velocity (flow) of blood versus time upstream (proximal) of the diseased portion of the blood vessel (e.g., diseased portion 266), and graph 508 shows the velocity (flow) versus time downstream (distal) of the diseased portion of the blood vessel.

[0067] Graph 510 shows the wave intensity of blood versus time upstream (proximal) of the diseased portion of the blood vessel (e.g., diseased portion 266), and graph 512 shows the wave intensity versus time downstream (distal) of the diseased portion of the blood vessel. In graph 510, curve 514 shows the wave intensity of the forward moving wave at the upstream (proximal) location, and curve 516 shows the wave intensity of the backward moving wave at the proximal location. Similarly, in graph 512, curve 518 shows the wave intensity of the forward moving wave at the downstream (distal) location, and curve 520 shows the wave intensity of the backward moving wave at the distal location. As shown at graph 510, a first wave free period 522 exists between the peaks 524 and 526 of the curve 514 showing the wave intensity of the forward moving wave at the upstream (proximal) location, and a second free period 528 exists between the peaks 530 and 532 of the curve 516 showing the wave intensity of the forward moving wave at the downstream (distal) location. As shown as well, the first and second wave free periods 522, 528 are disposed between two dashed lines. As will become clearer as the present description continues, a peak pressure 540 in the proximal wave and a peak pressure 544 in the distal wave, as well as a peak velocity 542 and a peak velocity 546 in the distal wave lie between the dashed lines. As such, these peaks lie within the respective first and second wave free periods 522, 528. In this wave free period of a cardiac cycle, the pressure gradient between the proximal pressure and the distal pressure of the forward moving wave relative to a lesion or other diseased portion of the blood vessel will be greatest and constant. Determination of the PV- iFR index in the wave free period is useful in identification of treatment options by a clinician, such as those noted above. In accordance with various representative embodiments, specific features in the pressure data are used to identify a correct diagnostic window (i.e., the wave-free period that could be determined by WIA, which requires measurement of both pressure and flow data). As such, the present teachings contemplate the determination of the wave-free period (theso-called optimal diagnostic time window) based one pressure and / or ECG measurements using analytical processing (the heuristic models) or the trained Al models described herein.

[0068] diagnostic time window in which to calculate the PV-iFR index based on current (realtime) pressure readings. As shown, the diagnostic time window is taken from approximately between the two peaks shown on the wave intensity graphs on the bottom, and specifically where the wave intensity is approximately zero between the two peaks.

[0069] For example, and as described more fully below, the peak of the pressure curve can be determined and a diagnostic time window around the peak that fits in the wave-free period between the peaks can be defined. Similarly, electrocardiogram characteristics such as T-wave data can also or alternatively be used to define the diagnostic time window. Still further, ground truth data comprising pressure data and velocity data, or comprising ECG data can be used to train a neural, which in turn is used to predict a correct diagnostic time window in which to calculate the PV-iFR index based on current (real-time) pressure readings. Notably, a neural network may use ECG data as input data along with the pressure data rather than paired pressure and velocity data as required for WIA to determine the PV-iFR index used for treatment identification and subsequent treatment in accordance with the present teachings /

[0070] Fig. 6 illustrates a graph 600 comprising a pressure curve 602 of pressure versus time showing a symmetrical time window for peripheral vascular pressure gradient determination, in accordance with a representative embodiment. Notably, the graph 600 may be provided to a user in display 180 of the system 100. Various aspects and details of the method for determining a diagnostic time window and PV-iFR index described in connection with Fig. 6 are common to those described in connection with representative embodiments in Figs. 1-5. These common aspects and details may not be repeated in order to avoid obscuring the description of the presently described representative embodiments. The pressure curve 602 has a peak 604 as shown. In this representative embodiment, the pressure curve 602 is substantially symmetric about the peak 604. The diagnostic time window 606 is determined by selecting a time duration that is also substantially symmetric. Specifically, according to the data of pressure curve, the diagnostic time window 606 is estimated by selection of the peak 604 and the selection of a symmetric time duration centered on the peak 604. This selection is made to approximate the optimal diagnostic time window (the interval for a wave-free period) as based on wave intensity analysis including both the peak pressure, the peak velocity and the wave intensity such asdescribed in connection with Fig. 5. While the peak velocity is not known in the determination of the diagnostic time window 606, prior knowledge about the velocity and its relationship to the pressure signal can be used to provide a method for determining the diagnostic time window based on the peak 604 by taking a symmetric time frame centered on the peak pressure considering certain factors that impact the duration and location of the diagnostic time window. As such, based on a theoretical WIA, the pressure and flow signals are temporally related in this part of the body (e.g., extremities). The diagnostic time window is then selected on pressure only measurements assuming that the flow signal (not measured) follows its typical behavior. Just by way of illustration, based on wave intensity analysis, it is known around the peak, the selected diagnostic time window should be within the diagnostic time window determined using both pressure and velocity data. Based on such data, it can be estimated that the diagnostic time window 606 has a duration of approximately 100 ms in many patients. So, in accordance with a representative embodiment, the diagnostic time window can be approximated by selecting a duration of 100 ms centered on the peak 604. Once the diagnostic time window is determined, the PV-iFR index can be determined. So, in accordance with a representative embodiment, a pressure measurement can be provided to the processor 153, and the instructions stored in memory 151 would cause the processor 153 then calculate the diagnostic time window as being 100 ms symmetrically disposed about the peak 604. It is noted that the selection of 100 ms for the duration of the estimated diagnostic time window is merely illustrative, and other durations based on wave intensity analysis can be used to determine the diagnostic time window. For example, the duration of the diagnostic time window may be 50 ms or 75 ms. Moreover, in selecting the duration to estimate the width of the diagnostic time window, care must be taken to not exceed the diagnostic time window determined with both the pressure and flow versus time data. For example, selection of comparatively small time windows provide a safer estimation since the flow data are not measured. By contrast, selection of a time window that is too large, the selected time window may be partially outside the wave-free period, which could corrupt the computed index by, for example, changing its interpretation. Taken to the limit, the selection of a very narrow range (i.e., less than 100 ms) around the peak 604, or even just the peak 604 will result in an estimated diagnostic time window that will be within the diagnostic time window determined with both the pressure and flow versus time data.

[0071] After the diagnostic time window is determined, the instructions cause the processor tocalculate the PV-iFR that can be used to determine an appropriate treatment.

[0072] Among other benefits, the method of determining the diagnostic time window and the PV-iFR index results in a practical application allowing the estimation of the diagnostic time window and the PV-iFR without the need to measure the velocity and calculate the wave intensity. As will be appreciated, this affords less complexity in gathering data to determine the diagnostic time window through complete wave intensity analysis. This reduction in complexity reduces not only the time and equipment required to determine the diagnostic time window or the PV-iFR, but also the cost of making these determinations.

[0073] Fig. 7 illustrates a graph 700 comprising a pressure curve 702 of pressure versus time showing a symmetrical time window for peripheral vascular pressure gradient determination, in accordance with a representative embodiment. Notably, the graph 700 may be provided to a user in display 180 of the system 100. Various aspects and details of the method for determining a diagnostic time window and PV-iFR index described in connection with representative embodiments of Fig. 7 are common to those described in connection with representative embodiments in Figs. 1-6. These common aspects and details may not be repeated in order to avoid obscuring the description of the presently described representative embodiments.

[0074] The pressure curve 702 has a peak 704 as shown. In this representative embodiment, the pressure curve 702 is substantially symmetric about the peak 704. The diagnostic time window 706 is determined by selecting of a time duration that is also substantially symmetric.Specifically, according to the data of pressure curve, the diagnostic time window 706 is estimated by selection of the peak 704 and the selection of a symmetric time duration centered on the peak 704. This selection is made to approximate the diagnostic time window based on wave intensity analysis including the peak pressure, the peak velocity and the wave intensity. While the peak velocity is not known in the determination of the diagnostic time window 706, it be predicted based on the peak 704 by taking a symmetric time frame centered on the peak pressure considering certain factors that impact the duration and location of the diagnostic time window. Just by way of illustration, based on wave intensity analysis, it is known around the peak, the selected diagnostic time window should be within the diagnostic time window determined using both pressure and velocity data. Based on such data, it can be estimated that the diagnostic time window 706 has a fraction of a heartbeat. Illustratively, the diagnostic time window 706 has a width of approximately 1 / 6 of the duration of a heartbeat. So, in accordancewith a representative embodiment, the diagnostic time window can be approximated by selecting a duration of 1 / 6 of the duration of a heart beat centered on the peak 704. Moreover, in selecting the duration to estimate the width of the diagnostic time window, care must be taken to not exceed the diagnostic time window determined with both the pressure and flow versus time data. Taken to the limit, the selection of a very narrow range around the peak 704, or even just the peak 704 will result in an estimated diagnostic time window that will be within the diagnostic time window determined with both the pressure and flow versus time data.

[0075] Notably, selection of the duration of the diagnostic time window as a fraction of a heartbeat may provide a more accurate determination of the diagnostic time window 706 compared to a fixed time duration such as in Fig. 6, because heart rates can vary among patients. Furthermore, it is emphasized that the noted fraction of the duration of a heartbeat is merely illustrative, and other fractions are contemplated. As will be appreciated, one of ordinary skill in the art having had the benefit of the present teachings could determine from data from wave intensity analyses other fractions of the duration of a heartbeat.

[0076] Once the diagnostic time window is determined, the PV-iFR index can be determined. So, in accordance with a representative embodiment, a pressure measurement can be provided to the processor 153, and the instructions stored in memory 151 would cause the processor 153 then calculate the diagnostic time window as being the selected fraction (in this example 1 / 6 of the duration of a heart beat symmetrically disposed about the peak 704. It is noted that the selection of 1 / 6 of the duration of a heart beat for the duration of the estimated diagnostic time window is merely illustrative, and other durations based on wave intensity analysis can be used to determine the diagnostic time window. For example, the duration of the diagnostic time window may be, for example 1 / 7 or 1 / 8 of the duration of a heartbeat.

[0077] After the diagnostic time window is determined, the instructions cause the processor to calculate the PV-iFR that can be used to determine an appropriate treatment.

[0078] Among other benefits, the method of determining the diagnostic time window and the PV-iFR results in a practical application allowing the estimation of the diagnostic time window and the PV-iFR without the need to measure the velocity and calculate the wave intensity. As will be appreciated, this affords less complexity in gathering data to determine the diagnostic time window through complete wave intensity analysis. This reduction in complexity reduces not only the time and equipment required to determine the diagnostic time window or the PV-iFR, but also the cost of making these determinations.

[0079] Fig. 8 illustrates a graph 800 comprising a pressure curve 802 of pressure versus time showing an asymmetrical time window for peripheral vascular pressure gradient determination, in accordance with a representative embodiment. Notably, the graph 800 may be provided to a user in display 180 of the system 100. Various aspects and details of the method for determining a diagnostic time window and PV-iFR index described in connection with representative embodiments of Fig. 8 are common to those described in connection with representative embodiments in Figs. 1-7. These common aspects and details may not be repeated in order to avoid obscuring the description of the presently described representative embodiments.

[0080] The pressure curve 802 has a peak 804 as shown. In this representative embodiment, the pressure curve 802 is substantially asymmetric about the peak 804. Notably, and as will be appreciated from a review of Fig. 5, the pressure and velocity curves may be asymmetric or skewed. As such, the diagnostic time window determined from both the pressure and velocity data is skewed relative to the peak of the pressure curve (i.e., offset relative to the peak of the pressure curve). The techniques of the representative embodiments described in connection with Figs. 8 and 9 account for this asymmetry and provide a method of estimating an asymmetric diagnostic time window for such pressure curves.

[0081] According to the data of pressure curve, the diagnostic time window 806 is estimated by selection of the peak 804 and the selection of an asymmetric time duration relative to the peak 804. This selection is made to approximate the diagnostic time window based on wave intensity analysis including the peak pressure, the peak velocity and the wave intensity. While the peak velocity is not known in the determination of the diagnostic time window 806, it can be predicted based on the peak 804 by taking an asymmetric time frame relative to the peak pressure considering certain factors that impact the duration and location of the diagnostic time window. Just by way of illustration, based on wave intensity analysis, it is known around the peak, the selected diagnostic time window should be within the diagnostic time window determined using both pressure and velocity data. Based on such data, it can be estimated that the diagnostic time window 806 has a duration that is a fraction of a heartbeat (e.g., 150 ms) in many patients. However, because of the skew in the pressure curve 802, the diagnostic time window 806 has a comparatively longer duration in a first portion 808 and a comparatively shorter duration in a second portion 810. For example, keeping with the illustrative duration of the diagnostic timewindow of 150 ms, the first portion 808 is estimated to have a duration of approximately 50 ms of the duration of a heartbeat before the peak 904, whereas the second portion 810 is estimated to have a duration of approximately 100 ms of the duration of a heartbeat before the peak 904. So, in accordance with a representative embodiment, the diagnostic time window can be approximated by selecting a duration of 50 ms and 100 ms. Once the diagnostic time window is determined, the PV-iFR index can be determined. So, in accordance with a representative embodiment, a pressure measurement can be provided to the processor 153, and the instructions stored in memory 151 would cause the processor 153 then calculate the diagnostic time window as being 1 / 9 of the duration of a heartbeat before the peak 904 and 1 / 18 the duration of a heartbeat after the peak 804. It is noted that the selection of 150 ms for the estimated diagnostic time window, and the estimation of the first and second portions 808, 810 have a 50 ms and 100 ms, respectively, is merely illustrative, and other durations based on wave intensity analysis can be used to determine the diagnostic time window. For example, as noted above, the duration of the diagnostic time window may be less than 150 ms of the duration of a heartbeat are contemplated. Moreover, and as noted above, in selecting the duration to estimate the width of the diagnostic time window, care must be taken to not exceed the diagnostic time window determined with both the pressure and flow versus time data. Taken to the limit, the selection of a very narrow range (i.e., less than 150 ms) asymmetrically disposed relative to the peak 804, or even just the peak 804 will result in an estimated diagnostic time window that will be within the diagnostic time window determined with both the pressure and flow versus time data.

[0082] After the diagnostic time window is determined, the instructions cause the processor to calculate the PV-iFR that can be used to determine an appropriate treatment.

[0083] Among other benefits, the method of determining the diagnostic time window and the PV-iFR results in a practical application allowing the estimation of the diagnostic time window and the PV-iFR without the need to measure the velocity and calculate the wave intensity. As will be appreciated, this affords less complexity in gathering data to determine the diagnostic time window through complete wave intensity analysis. This reduction in complexity reduces not only the time and equipment required to determine the diagnostic time window or the PV- iFR, but also the cost of making these determinations.

[0084] Fig. 9 illustrates a graph 900 comprising a pressure curve 902 of pressure versus time showing an asymmetrical time window for peripheral vascular pressure gradient determination,in accordance with a representative embodiment. Notably, the graph 900 may be provided to a user in display 180 of the system 100. Various aspects and details of the method for determining a diagnostic time window and PV-iFR index described in connection with representative embodiments of Fig. 9 are common to those described in connection with representative embodiments in Figs. 1-8. These common aspects and details may not be repeated in order to avoid obscuring the description of the presently described representative embodiments.

[0085] The pressure curve 902 has a peak 904 as shown. In this representative embodiment, the pressure curve 902 is substantially asymmetric about the peak 904. Notably, and as will be appreciated from a review of Fig. 5, the pressure and velocity curves may be asymmetric or skewed. As such, the diagnostic time window determined from both the pressure and velocity data is skewed relative to the peak of the pressure curve (i.e., offset relative to the peak of the pressure curve). The techniques of the representative embodiments described in connection with Figs. 8 and 9 account for this asymmetry and provide a method of estimating an asymmetric diagnostic time window for such pressure curves.

[0086] According to the data of pressure curve, the diagnostic time window 906 is estimated by selection of the peak 904 and the selection of an asymmetric time duration relative to the peak 904. This selection is made to approximate the diagnostic time window based on wave intensity analysis including the peak pressure, the peak velocity and the wave intensity such as described in connection with Fig. 5. While the peak velocity is not known in the determination of the diagnostic time window 906, it can be predicted based on the peak 904 by taking an asymmetric time frame relative to the peak pressure considering certain factors that impact the duration and location of the diagnostic time window. Just by way of illustration, based on wave intensity analysis, it is known around the peak, the selected diagnostic time window should be within the diagnostic time window determined using both pressure and velocity data. Based on such data, it can be estimated that the diagnostic time window 906 has a duration of approximately 1 / 6 of the duration of a heartbeat in many patients. However, because of the skew in the pressure curve 902, the diagnostic time window 906 has a comparatively longer duration in a first portion 908 and a comparatively shorter duration in a second portion. For example, keeping with the illustrative duration of the diagnostic time window of 1 / 6 of the duration of a heartbeat, the first portion 908 is estimated to have a duration of approximately 1 / 9 of the duration of a heartbeat ms, whereas the second portion 910 is estimated to have a duration of 1 / 18 of the duration of aheartbeat. So, in accordance with a representative embodiment, the diagnostic time window can be approximated by selecting a duration of 1 / 9 of the duration of a heartbeat before the peak 904 and 1 / 9 of the duration of a heartbeat after the peak 904. Once the diagnostic time window is determined, the PV-iFR index can be determined. So, in accordance with a representative embodiment, a pressure measurement can be provided to the processor 153, and the instructions stored in memory 151 would cause the processor 153 then calculate the diagnostic time window as being 1 / 6 of the duration of a heartbeat asymmetrically disposed relative to the peak 904. It is noted that the selection of 1 / 6 of the duration of a heartbeat for the duration of the estimated diagnostic time window, and the estimation of the first and second portions 908, 910 have durations of 1 / 9 and 1 / 18 of the duration of a heartbeat, respectively, is merely illustrative, and other durations based on wave intensity analysis can be used to determine the diagnostic time window. For example, the duration of the diagnostic time window may be asymmetrically disposed relative to the peak 904. Moreover, in selecting the duration to estimate the width of the diagnostic time window, care must be taken to not exceed the diagnostic time window determined with both the pressure and flow versus time data. Taken to the limit, the selection of a very narrow range (e.g., as noted above, less than 1 / 6 of the duration of a heartbeat) asymmetrically disposed relative to the peak 904, or even just the peak 904 will result in an estimated diagnostic time window that will be within the diagnostic time window determined with both the pressure and flow versus time data.

[0087] After the diagnostic time window is determined, the instructions cause the processor to calculate the PV-iFR that can be used to determine an appropriate treatment.

[0088] Among other benefits, the method of determining the diagnostic time window and the PV-iFR results in a practical application allowing the estimation of the diagnostic time window and the PV-iFR without the need to measure the velocity and calculate the wave intensity. As will be appreciated, this affords less complexity in gathering data to determine the diagnostic time window through complete wave intensity analysis. This reduction in complexity reduces not only the time required to determine the diagnostic time window or the PV-iFR, but also the cost of making these determinations.

[0089] Fig. 10 illustrates a graph 1000 comprising a pressure curve 1002 of pressure versus time and a derivative curve 1003 of the first derivative of the pressure versus time of the showing a symmetrical time window for peripheral vascular pressure gradient determination, in accordancewith a representative embodiment. Notably, this graph may be provided to a user in display 180 of the system 100. Notably, the graph 1000 may be provided to a user in display 180 of the system 100. Various aspects and details of the method for determining a diagnostic time window and PV-iFR index described in connection with representative embodiments of Fig. 10 are common to those described in connection with representative embodiments in Figs. 1-9. These common aspects and details may not be repeated in order to avoid obscuring the description of the presently described representative embodiments.

[0090] Fig. 10 shows a diagnostic time window with a width determined based on a maximum of a derivative of the pressure measurements over time and a minimum of the pressure measurements over time. Alternatively, or in addition to increase robustness of extracting the diagnostic time window, the roots of the second derivate of the pressure curve (i.e., where it crosses zero and the pressure curve has inflection points) can be used to determine the diagnostic time window. Notably, with reference to Fig. 5, the peaks of the wave intensity in time coincide approximately with the peaks in the derivative. As such, according to a representative embodiment, the diagnostic time window is determined based on the duration between the peaks in the derivative.

[0091] With reference to Fig. 10, the pressure curve 1002 has a peak 1004, and the derivative curve 1003 has a maximum at 1005, which occurs at Taxand a minimum 1011, (maximum negative value of the derivative of derivative curve 1003), which occurs atIn this representative embodiment, the pressure curve 1002 is substantially symmetric about the peak 1004. The diagnostic time window 1006 is determined by selecting of a time duration that is also substantially symmetric. Specifically, according to the data of derivative curve 1003, the diagnostic time window 1006 is estimated by selection of a duration based on a width 1012 of the duration between the maximum 1005 and the minimum 1011. In the representative embodiment shown in Fig. 10, the duration of the diagnostic time window 1006 is given by AT=K(Tax-Twin). In accordance with certain representative embodiments, Applicants have found, selection of K=0.5 provides a reasonable approximation for the duration of the diagnostic time window.

[0092] While not shown explicitly, the present teachings contemplate the use of this method based on determination of derivatives in an asymmetric pressure curve in this case, like embodiments based on asymmetric pressure curves / diagnostic time window, AT would beshifted in the direction of the skew of the curves, with an offset of the time difference from Taxto the peak 1004 being greater the time difference from the peak to Tmin. Furthermore, the present teachings contemplate taking the second derivative with respect to time, and basing the determination of the duration of the diagnostic time based on inflection points where the second derivatives are zero.

[0093] Once the diagnostic time window is determined, the PV-iFR index can be determined. So, in accordance with a representative embodiment, a pressure measurement can be provided to the processor 153, and the instructions stored in memory 151 would cause the processor 153 then calculate the diagnostic time window as being the selected fraction (in this example AT=0.5 (TaxIt is noted that the selection of K = 0.5 in AT=K (Tax-Tmin) for the estimate time window is merely illustrative, and other durations based on wave intensity analysis can be used to determine the diagnostic time window. For example, K may be 0.4, 0.45 or 0.55.

[0094] After the diagnostic time window is determined, the instructions cause the processor to calculate the PV-iFR that can be used to determine an appropriate treatment.

[0095] Among other benefits, the method of determining the diagnostic time window and the PV-iFR results in a practical application allowing the estimation of the diagnostic time window and the PV-iFR without the need to measure the velocity and calculate the wave intensity. As will be appreciated, this affords less complexity in gathering data to determine the diagnostic time window through complete wave intensity analysis. This reduction in complexity reduces not only the time and equipment required to determine the diagnostic time window or the PV- iFR, but also the cost of making these determinations.

[0096] Fig. 11 is a flow chart of a method 1100 of determining a PV-iFR index in accordance with a representative embodiment. Various aspects and details of the method 1100 are common to those described above in connection with Figs. 1-10. These aspects and details may not be repeated to avoid obscuring the presently described representative embodiments. Notably, the method 1100 relates to the heuristic or analytical processing methods (e.g., as noted in Fig. 3) described above in connection with various embodiments. As will be appreciated, the method 1100 is stored as instructions in memory 151, which are executed by the processor 153 to determine the PV-iFR index or similar indices alluded to above.

[0097] At 1102, the method begins with the measurement of blood pressure. As noted above, in certain representative embodiments, the pressure is measured upstream and downstream for adiseased portion (e.g., a lesion) of a blood vessel.

[0098] At 1104, the method continues by estimating the diagnostic time window. As described above, in the so-called heuristic method, the pressure data from 1100 are used to estimate the location of the diagnostic time window.

[0099] At 1106, the method comprises determining the PV-iFR index (or other indices alluded to above) in the estimated diagnostic time window.

[0100] Fig. 12 is a flow-chart of a method 1200 for training a neural network, which may be used to determine the diagnostic time window required to compute a peripheral vascular index, in accordance with a representative embodiment. Various aspects and details of the method for determining a diagnostic time window and PV-iFR index described in connection with representative embodiments of Figs. 12 are common to those described in connection with representative embodiments in Figs. 1-11. These common aspects and details may not be repeated in order to avoid obscuring the description of the presently described representative embodiments. It is again noted that according to a representative embodiment, the method 1200 is contemplated to be instantiated in instructions store in memory 151 and executed by the processor.

[0101] The training procedure of the computational model (the training loop of Fig.12) is carried out using one of a number of known machine-learning techniques known to those of ordinary skill in the art of Al and mathematical models. In machine-learning, an algorithmic model “learns” how to transform its input data into meaningful output. As noted above, during the learning procedure, the computational model is shown known examples of inputs and their correct or desired output. These examples comprise the ground truth, and in accordance with a representative embodiment provide the optimal diagnostic time windows at target time interval SI 270. During the learning sequence, the computational model adjusts its inner parameters given the input parameters of the examples, and produces the corresponding meaningful output or so- called target. The adjustment process is guided by instructions on how to measure the distance between the currently produced output and the desired output. These instructions are called the objective function or optimization loss. As will be appreciated, this adjustment process is carried out by execution of instructions stored in memory 151, executed by the processor 153.

[0102] In deep learning, which is a subfield of machine-learning, the inner parameters inside the computational model are organized into successively connected layers, where eachlayer produces increasingly meaningful output as input to the next layer, until the last layer which produces the final output.

[0103] Deep learning layers are typically implemented as so-called neural networks, that is, layers are comprised of a set of nodes, each representing an output value and a prescription on how to compute this output value from the set of output values of the previous layer’s nodes. The prescription being, for instance, a weighted sum of transformed output values of the previous layer’s nodes, each node only needs to store the weights. The transformation function is the same for all nodes in a layer and is also called activation function. There are a limited number of activation functions that are used today. A particular way to set which previous layer’s nodes provide input to a next layer’s node is convolution. Networks based on this way are called convolutional networks. An alternative way to set which previous layer’s outputs provide input for computing a next layer’s output is attention. Networks based on selfattention or cross-attention are called transformer networks.

[0104] The present teachings contemplate use of ID convolutional networks that operate on a fixed interval of measured pressure (and optionally ECG) data that contains at least one cardiac cycle, so that the input is a 2 x N matrix, where N is the number of temporal samples and must be the same for each new input query (same goes for the temporal spacing between points along N dimension). Notably, the two columns of the matrices are based on the pressure data and ECG data. In case of only pressure data, the matrix would have shape IxN.

[0105] The present teachings also contemplate use of recurrent (such as long-short term memory (LSTM) architectures, or attention-based (such as transformers) architectures that can operate on differently sized intervals of measured pressure (and optionally ECG) data that each contain at least one cardiac cycle, so that the input is a 2 x N’ matrix, where N is the number of temporal samples and can be different for each new input query (this does not hold for the temporal spacing between points along N’ dimension, which must be consistent). The model will produce an output sequence with indicator tokens for tl and t2 defining the interval, of which there may be more than one pair. In the latter case the iFR index can be computed as an average across all found intervals.

[0106] Referring again to Fig. 12, at 1220 the method begins with measuring blood pressure at two locations. This measuring is described in further detail in connection with representative embodiments of Figs. 1 and 2. Notably, as an alternative, at S1210, rather thanmeasuring blood pressure, ECG data can be gathered.

[0107] At SI 230, blood flow data are measured. The blood flow data and the blood pressure data are then used to derive wave intensity curve data at S1240. The wave intensity curve data are used to determine the target diagnostic time window for each data set from S1220, S1230 and S1240.

[0108] As alluded to above, using both the blood pressure data and the blood flow data to derive the wave intensity curve data at S1240 provides a comparatively accurate determination of the diagnostic time window. The present teachings described in connection with Fig. 13 use a trained neural network to predict or estimate the diagnostic time window using only measured blood pressure data or measured ECG data. As such, ground truth data are only gathered to train the neural network to provide a trained deep learning model (referred to herein as a trained computational model). Accordingly, SI 220, SI 230 and SI 240 are carried out for a comparatively large population of people to provide the ground truth data for training the computational model.

[0109] Thus, in the learning phase or so-called training described in connection with a representative embodiment of Fig. 12, for each example, the output SI 260 of the final layer is computed. Outputs for all or a subset of all examples are compared with the desired outputs by way of the objective function S1280, where a known optimization algorithm. The output of the objective function at S1280, the so-called loss, is used as a feedback signal to adjust the weights such that the loss is reduced. The adjustment, i.e., which weights to change and by how much, is computed by the central algorithm of deep learning, so-called backpropagation, which is based on the fact that the weighted sums that connect the layer nodes are functions that have simple derivatives. The adjustment is iterated until the loss reaches a prescribed threshold or no longer changes significantly.

[0110] A deep-learning network thus can be stored (e.g., in memory 151) as a topology that describes the layers and activation functions and a (large) set of weights (simply values). A trained network is the same, only the weights are now fixed to particular values. Once the network is trained, it is put to use, that is, to predict output for new input for which the desired output is unknown.

[0111] In the present teaching, the computational model is stored as instructions to provide the machine-learning algorithm, such as a convolutional, recurrent or transformer neural-networkdeep learning algorithm as noted above. When executed by a processor, the machine-learning algorithm (the computational model) of the representative embodiments is used to infer the diagnostic time window from input data. The limited set of input parameters includes the measured pressure data and may also include, but is not limited to, ECG data measured from the patient.

[0112] The method of Fig. 12 shows a training process for a trained machine learning model. The trained machine learning model may be applied to assess the correct period in the cardiac cycle, i.e., the diagnostic time window, from pressure data alone. The machine learning model may be a deep learning neural network trained using pressure and flow data, and thus wave intensity data from a large patient data base as the ground truth data for the optimal diagnostic time window. Training data may consist of pairs of pressure data matched to the ground truth diagnostic time windows. The ground truth diagnostic time windows are computed as an interval between the two maximum peaks of the net wave intensity. The net wave intensity (WI) is computed from the derivatives of the flow / velocity data U and pressure data P as indicated in equation 3 below and as noted above.

[0113] In accordance with a representative embodiment, the wave intensity can be determined as follows:Proximal originating wave intensity:Distal originating wave-intensity:Net wave-intensity:Where p is the density of blood, c wave-speed, dU change in flow velocity and dP change in pressure.

[0114] During training, the model is optimized to predict the diagnostic time window from only the pressure signal by minimizing a loss function between its prediction and the ground truth optimal diagnostic time window. After training, during deployment of the model, no flow / velocity data U are available, and the model makes a prediction for the optimal diagnostictime window based on the optimized parameters garnered during training of the model based on pressure data P only.

[0115] As noted above, in Fig. 12, the method of Fig. 12 may receive an additional input at S1210 with measuring electrocardiogram data. Notably, this parameter is not essential, and the method can forego its inclusion. While the pressure signals are measured close to the treatment site in the vessel, the ECG signal is a more global proxy of the electromechanical behavior of the heart which in turn is the central driver for the measured downstream pressure signal. In cases where the pressure signal is low and does not exhibit clear features such as rising and falling slopes in the context of measurement noise, the ECG signal can still provide indications for the computational model about the current cardiac phase. Therefore, the prediction of the diagnostic time window will be more robust when made based on these two sources of input information as during training the model can learn correlations between the pressure and ECG signal and how to behave when one of them is of poor quality.

[0116] At S1220, the method of Fig. 12 includes measuring pressure curve data.At SI 230, the method of Fig. 12 includes measuring velocity or flow curve data. In accordance with a representative embodiment, the flow curve data are measured using a known flow sensor device. For example, the flow sensing device may be an intravascular flow sensing device intravascular such as ComboWire® or FloWire® Alternatively, the flow curve data may be measured extravascularly, such as via known Duplex ultrasound (US) technology. This is only done to receive ground truth when setting up the training database for model training. During model deployment no flow or velocity is measured.

[0117] The measured pressure curved data from S1220 and the measured flow curve data from SI 230 are used at SI 240 to derive wave intensity curve data used for computing the training ground truth for the optimal diagnostic time window in S1280.

[0118] The measured electrocardiogram data from S1210 and the measured pressure curve data from S1220 are input to an artificial neural network model at S1250. At S1260, the artificial neural network model outputs a predicted diagnostic time interval based on the measured pressure curve data and the measured electrocardiogram data.

[0119] At S1270, the derived wave intensity curve data from S1240 is used to determine a target time interval. At S1280, an optimization loss is computed and backpropagated through the artificial neural network model to compute updates for the model parameters in SI 250 based onthe predicted time interval from S1260 and the target time interval from S1270. The optimization loss from SI 280 is therefore used as the basis for training the artificial neural network model in a training loop. The process may repeat as new measurements are obtained at S1210, S1220 and S1230.

[0120] Fig. 13 illustrates a method for peripheral vascular pressure index determination by deploying the trained neural network to pressure data to predict a diagnostic time window, in accordance with a representative embodiment. Various aspects and details of the method for determining a diagnostic time window and PV-iFR index described in connection with representative embodiments of Figs. 13 are common to those described in connection with representative embodiments in Figs. 1-12. These common aspects and details may not be repeated in order to avoid obscuring the description of the presently described representative embodiments. It is again noted the trained neural network may be stored in memory 151. The stored instructions may then be executed the processor 153 described in connection with Fig. 1.

[0121] The method of Fig. 13 includes measuring pressure curve data proximal and distal to the potential treatment site at S1310, and optionally measuring electrocardiogram data at SI 320. The pressure curve data from S1310 and / or the electrocardiogram data from S1320 are input to an artificial neural network model at SI 330 to obtain a time window for the peripheral vascular index at SI 340. To perform the prediction the model executes a sequence and interplay of mathematical operations according to its architecture, while using the parameters optimized during training in these mathematical operations. Using the predicted diagnostic time windows for the proximal and distal pressure data, a peripheral vascular index is output as a result at S1350. In the method of Fig. 13, a diagnostic time window may be estimated for determining the peripheral vascular index based on the measured pressure curve alone, or based on the measured pressure curve data and electrocardiogram data. In other words, in Fig. 13, velocity (flow) data is neither available nor required as inputs for an artificial neural network data. In some embodiments, the artificial neural network also will not require two sets of pressure data from downstream (distal) and upstream sensors (proximal), and as noted above, instead may operate with two sets of pressure data from only one sensor that made two measurements at two locations in the vessel at different times. In one embodiment this is done by pulling the pressure sensor from a downstream location (distal to the potential treatment site) to an upstream location (proximal to the potential treatment site) while measuring one pressure signal at each of the twolocations for some given time T.

[0122] Fig. 14 shows a user interface 1400 in accordance with a representative embodiment. Various aspects and details described in connection with representative embodiments of Fig. 14 are common to those described in connection with representative embodiments in Figs. 1-13. These common aspects and details may not be repeated in order to avoid obscuring the description of the presently described representative embodiments.

[0123] It is noted that the user interface 1400 may include control elements (e.g., elements on a graphic user interface (GUI)) that enable a user to execute various methods described above in connection with representative embodiments. For example, the user interface 1400 may have elements that enable the estimation of diagnostic time windows based on input data (blood pressure and / or ECG data) using the heuristic methods or the Al methods of the various representative embodiments described above.

[0124] Fig. 14 shows a DSA (digital subtraction angiography) image 1402 of a portion of a body, including a number of peripheral blood vessels. Notably, a lesion 1404 exists in one of the blood vessels, and measurements are taken upstream (proximal) to the lesion and downstream (distal) to the lesion.

[0125] Blood pressure measurements made using the system 100 are shown in a graph with a first curve 1406 of a pressure measurement made at the upstream location of the lesion 1404, and a second curve 1408 of a pressure measurement made downstream of the lesion. By implementing either a heuristic or Al methods described above, a diagnostic time window 1410 is determined and shown on the user interface 1400. Notably, outside of the diagnostic time window 1410, the pressure drop is considerably smaller than the pressure drops inside the window. Moreover, the PV-iFR index is determined, again, based one either a heuristic or Al methods described above. In accordance with a representative embodiment, an average pressure above and below the lesion 1404 in the diagnostic time window 1410 can be done by repeated measurements and the ratio of these pressures can be determined. This ratio (in this case 0.84) is then determined based on the average of the pressure above the lesion 1403 and before the lesion 1404. In certain representative embodiments, the ratio can be averaged over a plurality of cycles (e.g., 3 cycles shown in Fig. 14). Notably, in a representative embodiment, the ratio of the PV- iFR index is displayed, although other indices are contemplated. As will be appreciated from Fig. 14, several cardiac cycles of pressure data are acquired for each location and in each cardiaccycle a diagnostic time window is detected to mark an interval of relevant pressure data. For example, within each diagnostic time window an average pressure (or the maximum pressure) is taken, resulting in N pressure values upstream and M pressure values downstream of the lesion. Then, based on the above-described averaging methods all N pressure values upstream and all M pressure values downstream of the lesion are averaged, leaving two values, one upstream and one downstream of the lesion. From these two values the index as a ratio is determined.

[0126] As will be appreciated, first and second curves 1406, 1408 and diagnostic time window 1410 are determined continuously, and the PV-iFR index is continuously updated. As such, the methods of determining the diagnostic time window 1410 and the PV-iFR index are applied in a continuous real-time manner by the elements of the systems of Figs. 1 and 2, and are far too complex to be determined by human hand and presented in real time as is done using the systems and methods of the various representative embodiments.

[0127] It is further noted that in accordance with various representative embodiments, the distal pressure sensor can be tracked in an image (e.g., x-ray image) and the PV-iFR index can be displayed on the x-ray image. This is similar to iFR co-registration for Advanced Guidance by Philips Healthcare. This may be done by tracking the pressure sensor on the image determined PV-iFR, and display the PV-iFR value on the x-ray image at location of the sensor and the sensor is moved. This allows tracking of the PV-iFR index at multiple locations of the pressure sensor.

[0128] Although peripheral vascular pressure gradient determination has been described with reference to several exemplary embodiments, it is understood that the words that have been used are words of description and illustration, rather than words of limitation. Changes may be made within the purview of the appended claims, as presently stated and as amended, without departing from the scope and spirit of peripheral vascular pressure gradient determination in its aspects. Although peripheral vascular pressure gradient determination has been described with reference to particular means, materials and embodiments, peripheral vascular pressure gradient determination is not intended to be limited to the particulars disclosed; rather peripheral vascular pressure gradient determination extends to all functionally equivalent structures, methods, and uses such as are within the scope of the appended claims.

[0129] The illustrations of the embodiments described herein are intended to provide a general understanding of the structure of the various embodiments. The illustrations are not intended to serve as a complete description of all of the elements and features of the disclosuredescribed herein. Many other embodiments may be apparent to those of skill in the art upon reviewing the disclosure. Other embodiments may be utilized and derived from the disclosure, such that structural and logical substitutions and changes may be made without departing from the scope of the disclosure. Additionally, the illustrations are merely representational and may not be drawn to scale. Certain proportions within the illustrations may be exaggerated, while other proportions may be minimized. Accordingly, the disclosure and the figures are to be regarded as illustrative rather than restrictive.

[0130] One or more embodiments of the disclosure may be referred to herein, individually and / or collectively, by the term “invention” merely for convenience and without intending to voluntarily limit the scope of this application to any particular invention or inventive concept. Moreover, although specific embodiments have been illustrated and described herein, it should be appreciated that any subsequent arrangement designed to achieve the same or similar purpose may be substituted for the specific embodiments shown. This disclosure is intended to cover any and all subsequent adaptations or variations of various embodiments. Combinations of the above embodiments, and other embodiments not specifically described herein, will be apparent to those of skill in the art upon reviewing the description.

[0131] The Abstract of the Disclosure is provided to comply with 37 C.F.R. § 1.72(b) and is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. In addition, in the foregoing Detailed Description, various features may be grouped together or described in a single embodiment for the purpose of streamlining the disclosure. This disclosure is not to be interpreted as reflecting an intention that the claimed embodiments require more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive subject matter may be directed to less than all of the features of any of the disclosed embodiments. Thus, the following claims are incorporated into the Detailed Description, with each claim standing on its own as defining separately claimed subject matter.

[0132] The preceding description of the disclosed embodiments is provided to enable any person skilled in the art to practice the concepts described in the present disclosure. As such, the above disclosed subject matter is to be considered illustrative, and not restrictive, and the appended claims are intended to cover all such modifications, enhancements, and other embodiments which fall within the true spirit and scope of the present disclosure. Thus, to themaximum extent allowed by law, the scope of the present disclosure is to be determined by the broadest permissible interpretation of the following claims and their equivalents and shall not be restricted or limited by the foregoing detailed description.

Claims

CLAIMS:

1. A system (100) for evaluating a non-coronary blood vessel (260) of a patient, comprising: a memory (151) that stores instructions; and a processor (153) that executes the instructions, wherein, when executed by the processor (153), the instructions cause the system (100) to: receive pressure measurements obtained by an intravascular pressure-sensing instrument at a first location of the non-coronary blood vessel (260) and a second location of the non- coronary blood vessel (260) during a cardiac cycle of the patient; identify a diagnostic time window (1006) for the pressure measurements based on the pressure measurements from the non-coronary blood vessel (260) during a cardiac cycle of the patient; and calculate an index based on the pressure measurements in the diagnostic time window (1006).

2. The system (100) of claim 1, wherein a third location of the non-coronary blood vessel (260) is between the first location of the non-coronary blood vessel (260) and the second location of the non-coronary blood vessel (260), and comprises a diseased portion (266) of the non-coronary blood vessel (260).

3. The system (100) of claim 1, wherein the diagnostic time window (1006) for the pressure measurements comprises a symmetrical window around a peak pressure (540).

4. The system (100) of claim 1, wherein the diagnostic time window (1006) for the pressure comprises an asymmetrical time window around a peak pressure (540).

5. The system (100) of claim 1, wherein the diagnostic time window (1006) is approximated based on a maximum (1005) of a derivative of the pressure measurements and a minimum (1011) of the derivative of the pressure measurements.

6. The system (100) of claim 1, wherein the intravascular pressure-sensing instrument is a first intravascular pressure sensing instrument, and the system (100) further comprises a second intravascular pressure-sensing instrument (170).

7. The system (100) of claim 6, wherein the first intravascular pressure-sensing instrument (160) is disposed in the non-coronary blood vessel (260) of the patient at the first location and the second intravascular pressure-sensing instrument (170) is introduced into the non-coronary blood vessel (260) of the patient at the second location.

8. The system (100) of claim 1, wherein the index is based on a ratio of the pressure measurement at the first location and the pressure measurement obtained at the second location.

9. A method (1100) of evaluating a non-coronary blood vessel (260) of a patient, the method (1100) comprising: receiving pressure measurements obtained by an intravascular pressure-sensing instrument at a first location of the non-coronary blood vessel (260) and a second location of the non-coronary blood vessel (260) during a cardiac cycle of the patient; identifying a diagnostic time window (1006) for the pressure measurements based on the pressure measurements from the non-coronary blood vessel (260) during the cardiac cycle of the patient; and calculating an index based on the pressure measurements in the diagnostic time window (1006).

10. The method (1100) of claim 9, wherein a third location of the non-coronary blood vessel (260) is between the first location of the non-coronary blood vessel (260) and the second location of the non-coronary blood vessel (260), and comprises a diseased portion (266) of the non- coronary blood vessel (260).

11. The method (1100) of claim 9, wherein the diagnostic time window (1006) for the pressure measurements comprises a symmetrical window around a peak pressure (540).

12. The method (1100) of claim 9, wherein the diagnostic time window (1006) for the pressure comprises an asymmetrical time window around a peak pressure (540).

13. The method (1100) of claim 9, wherein the diagnostic time window (1006) is approximated based on a maximum (1005) of a derivative of the pressure measurements and a minimum (1011) of the derivative of the pressure measurements.

14. A tangible, non-transitory computer readable medium (14) that stores instructions, which when executed by a processor (153), causes the processor (153) to: receive pressure measurements obtained by an intravascular pressure-sensing instrument at a first location of a non-coronary blood vessel (260) and a second location of the non-coronary blood vessel (260) during a cardiac cycle of a patient; identify a diagnostic time window (1006) for the pressure measurements based on the pressure measurements from the non-coronary blood vessel (260) during the cardiac cycle of the patient; and calculate an index based on the pressure measurements in the diagnostic time window (1006).

15. The tangible, non-transitory computer readable medium 14, wherein a third location of the non-coronary blood vessel (260) is between the first location of the non-coronary blood vessel (260) and the second location of the non-coronary blood vessel (260), and comprises a diseased portion (266) of the non-coronary blood vessel (260).

16. The tangible, non-transitory computer readable medium 14, wherein the diagnostic time window (1006) for the pressure measurement comprises a symmetrical window around a peak pressure (540).

17. The tangible, non-transitory computer readable medium 14, wherein the diagnostic time window (1006) for the pressure comprises an asymmetrical time window around a peak pressure (540).

18. The tangible, non-transitory computer readable medium 14, wherein the diagnostic time window (1006) is approximated based on a maximum (1005) of a derivative of the pressure measurement and a minimum (1011) of the derivative of the pressure measurement.

19. The tangible, non-transitory computer readable medium 14, wherein the intravascular pressure-sensing instrument comprises a first intravascular pressure sensing instrument and a second intravascular pressure sensing instrument.

20. The tangible, non-transitory computer readable medium (14) of claim 15, wherein the first intravascular pressure-sensing instrument (160) is disposed in the non-coronary blood vessel (260) of a patient at the first location and the second intravascular pressure-sensing instrument (170) is introduced into the non-coronary blood vessel (260) of the patient at the second location.

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