Peripheral vascular pressure gradient determination

Abstract

A system (100) for assessing a blood vessel (260) of a patient includes a memory (151) storing instructions and a processor (153) executing the instructions. The instructions, when executed by the processor (153), 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) of the pressure measurements based on the pressure measurements of the blood vessel (260) of the patient during the cardiac cycle; and calculate an index based on pressure measurements within the diagnostic time window (1006).

Description

Background Technology

[0001] Over the past decade, physiology-based decision-making has become standard care for coronary interventions. A common outcome of this physiology-based decision-making is the combined use of pressure-sensing guidewires with the instantaneous flow-free ratio (iFR) index. The iFR index is a pressure-based ratio for a specific phase of the cardiac cycle, optimized for coronary pressure and based on wave intensity analysis. The iFR was developed to guide stent implantation decisions for coronary lesions. Specific portions of the cardiac cycle used as the basis for the iFR index are identified as the optimal portions for determining the ratio of distal to proximal pressure in the coronary lesion. To generate the iFR index, blood pressure changes over time at the distal and proximal ends of the lesion are measured. The period of the cardiac cycle during which no pressure or flow waves are generated is called the flow-free period, and this flow-free period is used to generate the iFR index. The flow-free period can be a segment of the cardiac cycle during which coronary pressure, reflecting wave intensity, does not change significantly, at least relative to the changes caused by various antegrade and retrograde compression and expansion waves in the cardiac cycle.

[0002] Currently, there is no index specifically designed for decision-making regarding peripheral vascular interventional procedures, and in particular, the waveless period and the iFR index are generally not applicable to physiologically based decisions regarding peripheral vascular interventions. In fact, the hemodynamics of peripheral arteries differ significantly from those of coronary arteries because they are not embedded in constricting myocardium, and therefore iFR cannot be applied to peripheral arteries. Summary of the Invention

[0003] According to a representative embodiment, a system for assessing a patient's non-coronary artery vessels is disclosed. The system includes: a memory storing instructions and a processor executing the instructions. When executed by the processor, the instructions cause the system to: receive pressure measurements obtained by an intravascular pressure sensing instrument at a first location and a second location in the non-coronary artery vessels during the patient's cardiac cycle; identify a diagnostic time window for the pressure measurements based on the pressure measurements from the non-coronary artery vessels during the patient's cardiac cycle; and calculate an index based on the pressure measurements within the diagnostic time window.

[0004] According to another representative embodiment, a method for assessing a patient's non-coronary artery vessels is disclosed. The method includes: receiving pressure measurements obtained by an intravascular pressure sensing instrument at a first location and a second location of the non-coronary artery vessels during the patient's cardiac cycle; identifying a diagnostic time window for the pressure measurements from the non-coronary artery vessels based on the pressure measurements during the patient's cardiac cycle; and calculating an index based on the pressure measurements within the diagnostic time window.

[0005] According to another representative embodiment, a tangible, non-transient computer-readable medium stores instructions that, when executed by a processor, cause the processor to: receive pressure measurements obtained by an intravascular pressure sensing instrument at a first and a second location in a non-coronary artery during a patient's cardiac cycle; identify a diagnostic time window for the pressure measurements based on the pressure measurements from the non-coronary artery during the patient's cardiac cycle; and calculate an index based on the pressure measurements within the diagnostic time window. Attached Figure Description

[0006] The exemplary embodiments can be best understood by reading in conjunction with the accompanying drawings, based on the following detailed description. It should be emphasized that the various features are not necessarily drawn to scale. In fact, dimensions may be increased or decreased arbitrarily for clarity of discussion. Where applicable and feasible, the same reference numerals refer to the same elements.

[0007] Figure 1 The illustration shows a system for determining peripheral vascular pressure gradients according to a representative embodiment.

[0008] Figure 2 The illustration shows a hybrid system and process for determining peripheral vascular pressure gradients according to a representative embodiment.

[0009] Figure 3 The illustration shows a method for determining the peripheral vascular pressure gradient (PV-iFR) index according to a representative embodiment.

[0010] Figure 4 The diagram illustrates the changes in pressure, velocity, and wave intensity over time.

[0011] Figure 5 The diagram illustrates the changes in pressure, velocity, and wave intensity over time.

[0012] Figure 6 The illustration shows a graph illustrating the pressure change over time within a symmetrical diagnostic time window for determining the peripheral vascular pressure gradient according to a representative embodiment.

[0013] Figure 7 The figure illustrates a graph of pressure versus time in another symmetrical time window for determining the peripheral vascular pressure gradient according to a representative embodiment.

[0014] Figure 8 The figure illustrates a graph showing the pressure change over time in an asymmetric time window for determining the peripheral vascular pressure gradient according to a representative embodiment.

[0015] Figure 9The figure illustrates a graph showing the pressure change over time in another asymmetric time window for determining the peripheral vascular pressure gradient according to a representative embodiment.

[0016] Figure 10 The figure illustrates a graph showing the pressure change over time in another time window for determining the peripheral vascular pressure gradient according to a representative embodiment.

[0017] Figure 11 This is a flowchart of a method for determining the PV-iFR index based on a representative embodiment.

[0018] Figure 12 The flowchart below shows a method for training a neural network according to a representative embodiment, which can be used to determine the diagnostic time window required to calculate the PV-iFR index.

[0019] Figure 13 The illustration shows a method for determining the peripheral vascular pressure gradient by deploying a trained neural network to determine the PV-iFR index, according to a representative embodiment.

[0020] Figure 14 A user interface according to a representative embodiment is shown. Detailed Implementation

[0021] In the detailed description below, representative embodiments with specific details disclosed are set forth for purposes of explanation and not for limitation, in order to provide a thorough understanding of embodiments according to this teaching. However, other embodiments consistent with this disclosure but departing from the specific details disclosed herein remain within the scope of the appended claims. Descriptions of known systems, apparatuses, materials, methods of operation, and methods of manufacture may be omitted to avoid obscuring the description of representative embodiments. Nevertheless, systems, apparatuses, materials, and methods within the capabilities of those skilled in the art are within the scope of this teaching and can be used according to representative embodiments. It should be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting. The definitions and interpretations of terminology herein supplement the technical and scientific meanings of terms commonly understood and accepted in the art field of this teaching.

[0022] It should 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 used only to distinguish one element or component from another. Therefore, without departing from the teachings of the inventive concept, the first element or component discussed below may also be referred to as the second element or component.

[0023] As used in the specification and claims, the singular forms of the terms “a,” “an,” and “the” are intended to include both the singular and plural forms, unless the context clearly specifies otherwise. Additionally, when used herein, the terms “comprising” and / or “including” and / or similar terms specify the presence of the recited features, elements, and / or components, but do not exclude 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 stated, 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 that there may be intermediate elements or components. That is, these and similar terms include cases where one or more intermediate elements or components may be used to connect two elements or components. However, when an element or component is referred to as being “directly connected to” or “adjacent to” another element or component, this only includes cases where two elements or components are connected to each other or arranged adjacent to each other without any intermediate or intervening elements or components.

[0025] As described herein, indices can be developed for peripheral vascular (PV) interventional decisions based on specific phases of the cardiac cycle optimized for PV intervention. The pressure-based indices described herein can be used as diagnostic tools to improve guidance during PV intervention. It is worth noting that while the vessels described herein are typically peripheral vessels, it should be pointed out that this teaching is not limited to peripheral blood vessels. More generally, this teaching also applies to non-coronary blood vessels. For example, this teaching can be applied to cerebral or abdominal vessels.

[0026] Figure 1 The illustration shows a system 100 for determining a peripheral vascular pressure gradient according to a representative embodiment.

[0027] Figure 1System 100 is a system for determining peripheral vascular pressure gradients and includes components provided together. 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, this teaching also contemplates the use of a single intravascular pressure sensing instrument that can perform measurements at, for example, two locations. Specifically, as described in more detail below, a single intravascular pressure sensing instrument can first be placed at a proximal location of a diseased (e.g., lesion) portion of an adjacent vessel and a first pressure measurement can be performed. Then, the single intravascular pressure sensing instrument can be placed at a distal location of the diseased portion of an adjacent vessel and a second measurement can be performed. Notably, the two pressure signals achieve beneficial synchronization based on the cardiac cycle.

[0028] The controller 150 includes at least a first interface 156, a second interface 158, a memory 151 for storing instructions, and a processor 153 for executing instructions. The controller can be implemented by a computer, which contains more components than... Figure 1 The controller 150 is further subdivided. The controller 150 may additionally include, for example, a third and a fourth interface. A first interface 156 connects the controller 150 to a first intravascular pressure sensing instrument 160, and a second interface 158 connects the controller 150 to a second intravascular pressure sensing instrument 170. The first interface 156 and the second interface 158 may each include a patient interface module (PIM) or other form of standardized interface module used in a medical environment. One or more interfaces may include ports, disk drives, wireless antennas, or other types of receiver circuitry that connect the controller 150 to other electronic components. One or more 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 a user can use to interact with the controller 150 (e.g., input commands and receive outputs).

[0029] Memory 151 may store a set of software instructions that can be executed to cause system 100 to perform any part or all of the methods or computer-based functions disclosed herein. Controller 150 may operate as a standalone device or be connected to other computer systems or peripheral devices via a network or similar means. In embodiments, system 100 performs logic processing based on digital signals received via an analog-to-digital converter. Controller 150 may also be implemented as or incorporated into various devices, such as workstations, fixed computers, mobile computers, personal computers (PCs), laptops, tablets, or any other machine capable of executing a set of software instructions (sequential or otherwise) specifying the operations to be performed by the machine. Controller 150 may be incorporated as a device or incorporated into a device that is subsequently included in an integrated system containing additional devices. In embodiments, controller 150 may be implemented in a device that also provides video or data communication.

[0030] Processor 153 can be considered a representative example of the processor of controller 150 and executes instructions to implement some or all aspects of the methods and processes described herein. Processor 153 is tangible and non-transient. As used herein, the term "non-transient" should not be interpreted as a permanent characteristic of a state, but rather as a characteristic of a state that will last for a period of time. The term "non-transient" specifically negates fleeting characteristics, such as the characteristics of a carrier wave or signal, or other forms that exist only transiently at any time and place. Processor 153 is an article of manufacture and / or a machine part. Processor 153 is configured to execute software instructions to perform the functions described in the various embodiments herein. Processor 153 may be a general-purpose processor or may be part of an application-specific integrated circuit (ASIC). Processor 153 may also be a microprocessor, microcomputer, processor chip, controller, microcontroller, digital signal processor (DSP), state machine, or programmable logic device. Processor 153 may also be logic circuitry, including a programmable gate array (PGA) such as a field-programmable gate array (FPGA), or another type of circuitry including discrete gate and / or transistor logic. Processor 153 may be a central processing unit (CPU), a graphics processing unit (GPU), or both. Furthermore, 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] As used herein, the term "processor" encompasses any electronic component capable of executing programs or machine-executable instructions. The term "processor" should be interpreted as including more than one processor or processing core, as in a multi-core processor. A processor can also refer to a collection of processors within a single computer system or distributed across multiple computer systems.

[0032] Memory 151 may include main memory and / or static memory, wherein the memories in system 100 communicate with each other and with processor 153 via a bus. Memory 151 can be considered a representative example of the memory of controller 150 and stores instructions for implementing some or all aspects of the methods and processes described herein. The memory described herein is a tangible storage medium for storing data and executable software instructions, and is non-transient during the time during which software instructions are stored therein. As used herein, the term "non-transient" should not be construed as a permanent characteristic of a state, but rather as a characteristic of a state that will last for a period of time. The term "non-transient" specifically negates fleeting characteristics, such as the characteristics of a carrier wave or signal, or other forms that exist only transiently at any time and place. Memory 151 is an article of manufacture and / or machine component. Memory 151 is a computer-readable medium from which a computer (e.g., processor 153 of controller 150) can read data and executable software instructions. The memory 151 may be implemented as one or more of the following: random access memory (RAM), read-only memory (ROM), flash memory, electrically programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), register, hard disk, removable disk, magnetic tape, optical disc read-only memory (CD-ROM), digital versatile disk (DVD), floppy disk, Blu-ray disc, or any other form of storage medium known in the art. The memory may be volatile or non-volatile, secure and / or encrypted, insecure and / or unencrypted.

[0033] It is worth noting that, as described in more detail below, all aspects of this teaching aim to define a window within which pressure measurements are performed to acquire pressure signals, thereby determining the PV-iFR. As described in more detail below, this teaching considers two algorithms for approximating a diagnostic time window to determine the PV-iFR index (hereinafter sometimes referred to as the “index”) based on pressure measurements within the diagnostic time window. Further details of the iFR index are described in commonly owned U.S. Patent US 9,339,348, the disclosure of which is expressly incorporated herein by reference.

[0034] One algorithm used to approximate the diagnostic time window for PV-iFR is a heuristic analytical processing sequence, while another is a computational model, i.e., a deep learning algorithm trained on a benchmark ground truth. As will be understood, and as described in more detail below, both algorithms are intended to be stored as instructions in memory 151. When executed by a processor, these algorithms are ultimately used to compute the exponent in the diagnostic time window. Thus, and as described in more detail below, the software algorithm for approximating the diagnostic time window for determining the exponent through analytical processing, and the software algorithm for training and executing the deep learning model, serve as instructions that, when executed by a processor (e.g., processor 153), cause the processor to perform various steps and methods according to this teaching. The trained model, which may be referred to as a computational model, is an artificial intelligence (AI) model and may be stored in memory 152. As described in more detail below, the computational models of various representative embodiments may be derived from known AI model architectures (including recurrent neural networks (RNNs), Transformer networks, and other neural network-based models) and computer programs, all of which may be executed by processor 153 of controller 150.

[0035] Furthermore, memory 151 is an example of a computer-readable storage medium. Computer memory is any memory that a processor can directly access. Examples of computer memory include, but are not limited to, RAM memory, registers, and register files. The reference to “memory” should be interpreted as potentially referring to multiple memories. Memory can be, for example, multiple memories within the same computer system. Memory can also be multiple memories distributed across multiple computer systems or computing devices. When executed by processor 153, software instructions perform one or more steps of the methods and processes described herein. In embodiments, software instructions may reside wholly or partially in memory 151 and / or processor 153 during execution by controller 150.

[0036] In embodiments, 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 methods described herein. One or more embodiments described herein may use two or more specific interconnected hardware modules or devices to implement functionality, said hardware modules or devices having associated control and data signals that can communicate between and through modules. Therefore, this disclosure includes software, firmware, and hardware implementations. Nothing in this application should be construed as being implemented or achievable solely in software and not in hardware such as tangible non-transient processors and / or memory.

[0037] According to various embodiments of this disclosure, the methods described herein can be implemented using a hardware computer system that executes software programs. Furthermore, in exemplary non-limiting embodiments, implementations may include distributed processing, component / object distributed processing, and parallel processing. Virtual computer system processing can implement one or more methods or functions as described herein, and the processors described herein can be used to support virtual processing environments.

[0038] Display 180 is located locally on controller 150. Display 180 can be connected to controller 150 via a local wired interface (e.g., Ethernet cable) or via a local wireless interface (e.g., Wi-Fi connection). Display 180 can be connected to other user input devices (including a mouse, keyboard, scroll wheel, etc.) through which a user can input commands. Display 180 can 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 images. Display 180 may also include: one or more input interfaces (e.g., those mentioned above) that can be connected to other elements or components; and an interactive touchscreen configured to display prompts to the user and collect the user's touch input.

[0039] Both the first intravascular pressure sensing instrument 160 and the second intravascular pressure sensing instrument 170 can be used as interventional instruments in peripheral endovascular interventions. The first intravascular pressure sensing instrument 160 and the second intravascular pressure sensing instrument 170 may each include: for example, a guidewire having a distal portion, a housing adjacent to the distal portion, a shaft extending from the housing away from the distal portion, and cable connectors for connection 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 about peripheral blood vessels. The sensors(s) in the housing may include pressure sensors configured to monitor pressure within the peripheral blood vessels in 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 pressure sensors, such as pressure lines in each of the first intravascular pressure sensing instrument 160 and the second intravascular pressure sensing instrument 170. For illustrative purposes and not for limitation, the first intravascular pressure sensing instrument 160 and the second intravascular pressure sensing instrument 170 may be commercially available devices, such as the Omniwire from Royal Philips Ltd. ® Or ComboWire ®

[0040] Controller 150 may directly perform some of the operations described herein, and may indirectly perform other operations described herein. For example, controller 150 may indirectly control operations such as generating and transmitting content to be displayed on display 180. Controller 150 may directly control other operations, such as logical operations performed by processor 153 based on input received from electronic components and / or users via an interface, from instructions received from memory 151. Therefore, when processor 153 executes instructions from memory 151, the process implemented by controller 150 may include steps that controller 150 does not directly execute.

[0041] use Figure 1 A systematic method for assessing a patient's blood vessels may include: introducing a first intravascular pressure sensor 160 into a location on one side of the patient's blood vessel, and introducing a second intravascular pressure sensor 170 into a location on another side of the patient's blood vessel. A controller 150 may be a component of the processing system. The controller 150 may then be used to implement a process of system 100 comprising: receiving pressure measurements obtained by the first intravascular pressure sensor 160 and the second intravascular pressure sensor 170 during the patient's cardiac cycle at a controller 150 in communication with the first intravascular pressure sensor 160 and the second intravascular pressure sensor 170; identifying a diagnostic time window for the pressure measurements based on the pressure measurements from the blood vessel during the patient's cardiac cycle; and calculating an index based on the pressure measurements within the diagnostic time window. As described in more detail below, the diagnostic time window may be determined based on wave intensity analysis (WIA). The characteristics of the diagnostic time window and the index calculated by the controller 150 may differ in the various embodiments explained herein. Pressure data from the first intravascular pressure sensor 160 and the second intravascular pressure sensor 170 can be specifically acquired within a diagnostic time window and used to calculate the index. The index can then be output to a display 180, thereby visualizing the calculated index as a numerical or other graphical representation of the index. This index can be considered a peripheral vascular pressure gradient index because it is calculated based on pressure data from both the first intravascular pressure sensor 160 and the second intravascular pressure sensor 170.

[0042] Figure 2 The illustration shows a hybrid system 200 and process for determining a peripheral vascular pressure gradient according to a representative embodiment. Various aspects and details of the hybrid system 200 are combined with the above description. Figure 1 The various representative embodiments described are identical. These common aspects and details may not be repeated to avoid obscuring the representative embodiments currently described.

[0043] Figure 2The hybrid system 200 includes an index determination module 250, as described below, for determining a diagnostic time window in which the index is to be determined. The index determination module 250 includes 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 a memory 151 and include instructions that, when executed by a processor 153, determine the index, as described in more detail herein.

[0044] The hybrid system 200 also includes a first pressure sensor 256 and a second pressure sensor 258. The first pressure sensor 256 and the second pressure sensor 258 can correspond to Figure 1 The first interface 156 and the second interface 158 are provided, and can be connected via a sheath / catheter to the pressure sensors (e.g., pressure sensor 262) and guidewires (e.g., guidewire 264) of the first and second intravascular pressure sensing instruments. Notably, the second pressure sensor 258 can be located externally. Because the sheath / catheter lumen is rigid, the pressure at the sheath / catheter tip (located within the vessel 260) is similar to the pressure at the sheath / catheter distal end (located externally). Alternatively, and as described in more detail below, a single pressure sensor can be used to measure both the first and second pressures.

[0045] Pressure sensor 262, located distal to index determination module 250 (and distal to the affected portion 266), can measure a first pressure signal P1. A second pressure sensor 263, located closer to index determination module 250, can measure a second pressure signal P2. For example... Figure 2 As shown, the first pressure signal P1 is taken from an upstream location of the diseased portion 266 of the blood vessel 260 that causes the narrowing 268 of the blood vessel 260. For example, the diseased portion 266 may be a lesion or a similar obstruction to blood flow. The second pressure signal P2 may be taken from a location close to the index determination module 250 (and proximal to the diseased portion 266 of the blood vessel 260).

[0046] Input pressures P1(t) and P2(t) can be provided as a time series of pressure data to the diagnostic time window module 252 (e.g., stored in memory 151 and executed by controller 150) to determine the diagnostic time window. The diagnostic time window (between a first time t1 and a second time t2) can be used to define which measurement values ​​from input pressures P1(t) and P2(t) are used to generate the PV iFR index, as described in more detail below, whereby the PV iFR index can be the distal peak pressure (PiFR) during the determined diagnostic time window. D ) and proximal pressure (P p The ratio of P within a defined diagnostic time window (i.e., P within a defined diagnostic time window)D / P p The diagnostic time window (during which distal and proximal pressures can be effectively acquired) is then provided to the index calculation module 253 (e.g., stored in memory 151 and executed by controller 150) to determine the PV-iFR index. The resulting index can then be displayed as a value on the screen of display 280.

[0047] More generally, this teaching considers determining an index to quantify the functional significance of lesions. Referring to existing iFR indices for coronary arteries, and as described herein, the peripheral vascular iFR index (PV-iFR) can serve as an illustrative index. One method for determining such an index from pressure measurements within one or two diagnostic time windows is to aggregate the pressures within each time window (e.g., averaging the pressures between t1 and t2) and then calculate the ratio. However, it is important to note that this teaching is not intended to limit itself to this particular method of calculating the index, but rather to consider other operations for taking pressure values ​​within a window and determining an appropriate index. For example, and only illustratively, the index could be equal to the average of (Pd(t)) / avg(Pp(t)), or equal to the average of (Pd(t) / Pp(t)). Alternatively, or instead of determining the average of the mentioned quantities, the median, or the maximum or 95th percentile of these quantities could be taken within the diagnostic time window.

[0048] As described in more detail below, according to a particular exemplary embodiment, the diagnostic time window can be determined based on the analytical processing steps applied to pressure data by the diagnostic time window module 252. This processing step for determining the diagnostic window is designed based on wave intensity analysis (WIA), where the optimal period for determining the index is likely a waveless period between two forward-moving waves. Since WIA can only be performed using available flow and pressure signals, and only pressure signals are available in the interventional setting, the analytical processing steps performed in the diagnostic time window module 252 are intended to approximate the optimal waveless period, as will be further described below. The index is calculated within a diagnostic time window different from the iFR index for coronary arteries, and this window can be dynamically determined during endovascular intervention, as described herein.

[0049] Alternatively, and as also described herein, wave intensity analysis (WIA) can be used to train a neural network such that only pressure and / or ECG signals are used as input to the AI ​​model to determine the desired exponent.

[0050] Figure 3 The illustration depicts a method 300 for determining a peripheral vascular pressure gradient (PV-iFR) index according to a representative embodiment. Various aspects and details of method 300 are illustrated. Figure 1 and Figure 2The representative embodiments described herein share the same aspects and details. To avoid obscuring the description of the representative embodiments, these same aspects and details may not be repeated.

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

[0052] At 304, method 300 includes receiving IV pressure measurements at two locations on the blood vessel (e.g., upstream and downstream of the diseased portion of the vessel). According to a representative embodiment, clinicians can identify potential treatment sites. At least two pressure measurements must be received, one from a location proximal to the site and the other from a location distal to the site, as described above. Figure 2 The representative embodiments are described. Each site may have more than one measurement result, or each measurement result may cover multiple cardiac cycles, and therefore include multiple effective diagnostic time windows for detection.

[0053] At position 306, an algorithm for determining the diagnostic time window is loaded. As described above, and in more detail below, this teaching considers the use of analytical computation algorithms or neural network (AI) algorithms.

[0054] At 308, method 300 includes applying a selected algorithm to predict the diagnostic time window for obtaining pressure measurements to determine the PV-iFR index. It is noteworthy that multiple diagnostic time windows can be determined. When pressure is measured simultaneously from two sensors (one sensor measuring pressure at each location), these are determined as at least one window, so a window detected on one sensor signal can be applied to the other sensor signal; or, when using only one sensor that requires a change of location to make two measurements, there are two windows, and therefore no time-corresponding pressure signal. In further embodiments, multiple diagnostic time windows can be used to determine the measured pressure to determine the diagnostic time window for the PV-iFR index. For example, the diagnostic time window can be determined by averaging multiple diagnostic time windows determined over multiple cardiac cycles. In this case, the diagnostic time window is not defined by its absolute interval boundaries in the time domain, but rather relative to repeated cardiac cycles. Alternatively, pressure data from multiple diagnostic windows from one location can be averaged, rather than averaging window intervals, to determine the PV-iFR index in the next step. At 310, method 300 then calculates the PV-iFR index within the determined diagnostic time window (e.g., the P index within the determined diagnostic time window). D / P pAccording to various representative embodiments, pressure data are acquired for two measurement locations within a diagnostic time window and correlated to obtain the PV-iFR index. This can be, but is not limited to: averaging the pressure within the window and calculating the ratio of the time averages between the two locations; taking the maximum pressure within the window and calculating the ratio of pressure data at the distal and proximal locations; first calculating the ratio of distal to proximal pressure within each time step, and then averaging the resulting index. As described above, and for simplicity, a single device is used to measure at the first location and then at the second location, but the following will also apply to other cases. For example, a diagnostic time window can be determined at the first location, and the pressure at that location within that diagnostic time window can be averaged. Next, a diagnostic time window can be determined at the second location, and the pressure can be averaged at the second location. Next, the index is determined by determining the ratio using the two average pressure values ​​(each representing all pressure values ​​at the first and second locations within the diagnostic time window). Alternatively, this measurement sequence can be varied to determine several diagnostic time windows for each location and calculate several indicative pressure values ​​for each location. These pressure values ​​are then averaged to provide a single overall pressure value for each location, and the index is calculated. Alternatively, several ratios can be determined first, and then these indices averaged. Or, alternatively, this measurement sequence can be altered by determining several diagnostic time windows (again, for several cardiac cycles, since there is always only one window per cycle). Instead of using absolute interval boundaries t1 and t2, the intervals are transformed into c1 and c2 boundaries represented relative to the start (=0) and end (=1) of the cardiac cycle. The average of these window boundaries is determined to provide an overall window, and this is applied to one or more cardiac cycles to obtain an indicative pressure value. Therefore, this teaching anticipates determining multiple diagnostic time windows for each location of the vascular diseased portion. The average can be determined by taking the average diagnostic time duration of each diagnostic time window before calculating the ratio (or vice versa). The index can be displayed on the user interface, for example, in the following combination. Figure 14 The representative embodiments are described.

[0055] In step 312, method 300 then identifies a treatment based on the calculated PV-iFR index. While not intended to limit the choice of treatment options, exemplary treatments considered include angioplasty, stent implantation, and plaque resection, among others.

[0056] At 314, the method ends with the patient being treated based on the treatment identified from 312.

[0057] Figure 4 The diagram illustrates the changes in pressure, velocity, and wave intensity over time. (Combined with...) Figure 4The various aspects and details of the representative embodiments described are combined Figure 1-3 The representative embodiments described are identical in all aspects and details. To avoid obscuring the description of the representative embodiments currently described, these identical aspects and details may not be repeated.

[0058] As in Figure 4 As shown, graph 400 illustrates the pressure, velocity (flow rate), and wave intensity at the proximal (e.g., upstream) and distal (e.g., downstream) ends of a short segment of the femoral artery occlusion or a similar diseased portion of the vessel. Various pressure measurements can be performed, for example, using the systems 100 and 200 described above.

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

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

[0061] Graph 410 shows the change in wave intensity of blood upstream (proximal) of the diseased portion of the blood vessel (e.g., diseased portion 266) over time, and graph 412 shows the change in wave intensity of blood downstream (distal) of the diseased portion of the blood vessel over time. 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 a forward-moving wave can be called WI+, and can be determined as (1 / 4pc)(dP / dt–pc(dU / dt)). 2 The wave intensity is a function of , where p is the blood density, c is the wave velocity, dU is the change in flow velocity, and dP is the change in pressure. Similarly, the intensity of a backward-moving wave can be expressed as WI-, and can be determined as -(1 / 4pc)(dP / dt–pc(dU / dt)). 2 The net wave intensity can be expressed as WInet, and can be determined as a function of WI++WI-, which is then equal to (dP / dt)(dU / dt).

[0063] Figure 5The diagram illustrates the changes in pressure, velocity, and wave intensity over time. (Combined with...) Figure 5 The various aspects and details of the representative embodiments described are combined Figure 1-4 The representative embodiments described are identical in all aspects and details. To avoid obscuring the description of the representative embodiments currently described, these identical aspects and details may not be repeated.

[0064] As in Figure 5 As shown, graph 500 illustrates the pressure, velocity (flow rate), and wave intensity at the proximal (e.g., upstream) and distal (e.g., downstream) ends of a short segment of the femoral artery occlusion or a similar diseased portion of the vessel. Various pressure measurements can be performed, for example, using the systems 100 and 200 described above.

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

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

[0067] Graph 510 shows the change in wave intensity of blood upstream (proximal) of the diseased portion of the blood vessel (e.g., diseased portion 266) over time, and graph 512 shows the change in wave intensity of blood downstream (distal) of the diseased portion of the blood vessel over time. 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 in graph 510, a first waveless period 522 exists between peaks 524 and 526 of curve 514, which shows the wave intensity of the forward-moving wave at the upstream (proximal) position; and a second waveless period 528 exists between peaks 530 and 532 of curve 516, which shows the wave intensity of the forward-moving wave at the downstream (distal) position. As shown, the first and second waveless periods 522 and 528 are positioned between two dashed lines. As will become clearer as the description continues, the peak pressure 540 of the proximal wave and the peak pressure 544 of the distal wave, as well as the peak velocity 542 and the peak velocity 546 of the distal wave, lie between the dashed lines. Therefore, these peaks are located within the first and second waveless periods 522 and 528, respectively. During this waveless period of the cardiac cycle, the pressure gradient between the proximal and distal pressures of the forward-moving wave relative to the lesion or other affected parts of the blood vessel is the largest and constant. Determining the PV-iFR index during the waveless period helps clinicians identify treatment options, such as those described above. According to various representative embodiments, specific features in the pressure data are used to identify the correct diagnostic window (i.e., a waveless period that can be determined by WIA, which requires measuring both pressure and flow data). Therefore, this teaching considers determining waveless periods (the so-called optimal diagnostic time window) based on pressure and / or ECG measurements using analytical processing (heuristic models) or trained artificial intelligence models as described herein.

[0068] The diagnostic time window for calculating the PV-iFR index is based on the current (real-time) pressure reading. As shown in the figure, the diagnostic time window is roughly taken between the two peaks shown in the bottom wave intensity map, and specifically, between the two peaks where the wave intensity is approximately zero.

[0069] For example, and as described in more detail below, the peak value of the pressure curve can be determined, and a diagnostic time window can be defined around the peak value, which lies within the waveless period between the peak values. Similarly, ECG features (e.g., T-wave data) can also be used, or alternatively, to define the diagnostic time window. Furthermore, a ground truth containing pressure and velocity data, or containing ECG data, can be used to train a neural network, which is then used to predict the correct diagnostic time window to calculate the PV-iFR index based on the current (real-time) pressure reading. It is noteworthy that, according to this teaching, the neural network can use ECG data along with pressure data as input data to determine the PV-iFR index for treatment identification and subsequent treatment, rather than using paired pressure and velocity data as required by WIA.

[0070] Figure 6 The illustrated graph 600 according to a representative embodiment includes a pressure-time curve 602, which shows a symmetrical time window for determining the peripheral vascular pressure gradient. Notably, graph 600 can be provided to a user on the display 180 of system 100. Figure 6 The various aspects and details of the methods described for determining the diagnostic time window and the PV-iFR index are combined with Figure 1-5 The aspects and details described in the representative embodiment are the same. To avoid obscuring the description of the representative embodiment currently described, these same aspects and details may not be repeated. As shown, pressure curve 602 has a peak 604. In this representative embodiment, pressure curve 602 is substantially symmetrical about peak 604. The diagnostic time window 606 is determined by selecting a substantially symmetrical duration. Specifically, based on the pressure curve data, the diagnostic time window 606 is estimated by selecting peak 604 and selecting a symmetrical time period centered on peak 604. This selection is made to approximate the optimal diagnostic time window (the time interval of the waveless period) as based on wave intensity analysis (including peak pressure, peak velocity, and wave intensity), for example, by combining... Figure 5As described. Although the peak velocity is unknown when determining 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 symmetrical time frame centered on the peak pressure and considering specific factors affecting the duration and location of the diagnostic time window. Therefore, based on the theoretical WIA, the pressure and flow signals at this part of the body (e.g., the limbs) are temporally correlated. The diagnostic time window is then selected solely based on the pressure measurement results, assuming the flow signal (not measured) follows its typical behavior. It is known, only illustratively, based on wave intensity analysis that the selected diagnostic time window should be within the diagnostic time window determined using pressure and velocity data near the peak. Based on these data, it can be estimated that the duration of the diagnostic time window 606 is approximately 100 milliseconds in many patients. Therefore, according to a representative embodiment, the diagnostic time window can be approximated by selecting a duration of 100 milliseconds centered on the peak 604. Once the diagnostic time window is determined, the PV-iFR index can be determined. Therefore, according to a representative embodiment, pressure measurement results can be provided to processor 153, and instructions stored in memory 151 will cause processor 153 to calculate a diagnostic time window of 100 milliseconds, symmetrically distributed around peak 604. It is worth noting that choosing 100 milliseconds as the estimated duration of the diagnostic time window is merely illustrative, and other durations based on wave intensity analysis can also be used to determine the diagnostic time window. For example, the duration of the diagnostic time window could be 50 milliseconds or 75 milliseconds. Furthermore, when selecting the duration to estimate the width of the diagnostic time window, care must be taken not to exceed the diagnostic time window determined using pressure and flow rate versus time data. For example, choosing a relatively small time window can provide a safer estimate because no flow rate data is measured. In contrast, choosing an excessively large time window may result in the selected time window falling partially outside of waveless periods, which could corrupt the calculated index by, for example, altering its interpretation. In extreme cases, choosing a very narrow range around peak 604 (i.e., less than 100 milliseconds), or even choosing only peak 604, will result in the estimated diagnostic time window falling within the diagnostic time window determined using pressure and flow rate versus time data.

[0071] After determining the diagnostic time window, the instruction causes the processor to calculate the PV-iFR, which can be used to determine the appropriate treatment.

[0072] Among other advantages, the method for determining the diagnostic time window and the PV-iFR index offers a practical application that allows estimation of the diagnostic time window and PV-iFR without measuring velocity and calculating wave intensity. As will be understood, this provides a reduction in the complexity of collecting data through a complete wave intensity analysis to determine the diagnostic time window. This reduction in complexity not only reduces the time and equipment required to determine the diagnostic time window or PV-iFR, but also reduces the cost of making these determinations.

[0073] Figure 7 The illustrated graph 700 according to a representative embodiment includes a pressure-time curve 702, which shows a symmetrical time window for determining the peripheral vascular pressure gradient. Notably, graph 700 can be provided to a user on the display 180 of system 100. Figure 7 The various aspects and details of the methods described for determining the diagnostic time window and the PV-iFR index are combined with Figure 1-6 The aspects and details described in the representative embodiments are the same. In order to avoid obscuring the description of the representative embodiments described herein, these same aspects and details may not be repeated.

[0074] As shown in the figure, pressure curve 702 has a peak 704. In this representative embodiment, pressure curve 702 is substantially symmetrical about peak 704. The diagnostic time window 706 is determined by selecting a substantially symmetrical duration. Specifically, based on the pressure curve data, the diagnostic time window 706 is estimated by selecting peak 704 and a symmetrical time period centered on peak 704. This selection is made to approximate the diagnostic time window based on wave intensity analysis, including peak pressure, peak velocity, and wave intensity. Although peak velocity is unknown when determining diagnostic time window 706, it can be predicted based on peak 704 using a symmetrical time frame centered on peak pressure, taking into account specific factors affecting the duration and location of the diagnostic time window. By way of illustration only, based on wave intensity analysis, it is known that near the peak, the selected diagnostic time window should be within the diagnostic time window determined using pressure and velocity data. Based on these data, it can be estimated that diagnostic time window 706 corresponds to only a small fraction of a heartbeat. For example, the width of diagnostic time window 706 is approximately 1 / 6 of the heartbeat duration. Therefore, according to a representative embodiment, the diagnostic time window can be approximated by selecting a duration that is 1 / 6 of the heartbeat duration centered on peak 704. Furthermore, when selecting the duration to estimate the width of the diagnostic time window, care must be taken not to exceed the diagnostic time window determined using pressure and flow rate versus time data. In extreme cases, selecting a very narrow range around peak 704, or even selecting only peak 704, will result in the estimated diagnostic time window falling within the diagnostic time window determined using pressure and flow rate versus time data.

[0075] It is worth noting that, with, for example Figure 6 Compared to a fixed duration, selecting the duration of a diagnostic time window as a portion of the heartbeat allows for a more accurate determination of the diagnostic time window 706, since heart rate may vary between patients. Furthermore, it should be emphasized that the indicated portion of the heartbeat duration is merely illustrative, and other portions are also contemplated. As will be understood, those skilled in the art, having benefited from this teaching, can determine other portions of the heartbeat duration based on data from wave intensity analysis.

[0076] Once the diagnostic time window is determined, the PV-iFR index can be determined. Therefore, according to a representative embodiment, pressure measurement results can be provided to processor 153, and instructions stored in memory 151 will cause processor 153 to calculate the diagnostic time window, which is a selected fraction (in this example, 1 / 6 of the heartbeat duration symmetrically distributed around peak 704). It is worth noting that selecting 1 / 6 of the heartbeat duration as the estimated duration of the diagnostic time window is merely illustrative, and other durations based on wave intensity analysis can also be used to determine the diagnostic time window. For example, the duration of the diagnostic time window could be 1 / 7 or 1 / 8 of the heartbeat duration.

[0077] After determining the diagnostic time window, the instruction causes the processor to calculate the PV-iFR, which can be used to determine the appropriate treatment.

[0078] Among other advantages, the method for determining the diagnostic time window and PV-iFR offers a practical application that allows estimation of the diagnostic time window and PV-iFR without measuring velocity and calculating wave intensity. As will be understood, this provides a reduction in the complexity of collecting data through a complete wave intensity analysis to determine the diagnostic time window. This reduction in complexity not only reduces the time and equipment required to determine the diagnostic time window or PV-iFR, but also reduces the cost of making these determinations.

[0079] Figure 8 The illustrated graph 800 according to a representative embodiment includes a pressure-time curve 802, showing an asymmetric time window for determining the peripheral vascular pressure gradient. Notably, graph 800 can be provided to a user on the display 180 of system 100. (In conjunction with...) Figure 8 The various aspects and details of the methods described for determining the diagnostic time window and the PV-iFR index are combined with Figure 1-7 The aspects and details described in the representative embodiments are the same. In order to avoid obscuring the description of the representative embodiments described herein, these same aspects and details may not be repeated.

[0080] As shown in the figure, pressure curve 802 has a peak value 804. In this representative embodiment, pressure curve 802 is substantially asymmetrical about the peak value 804. It is worth noting, and as can be seen from the view... Figure 5 It will be understood that pressure and velocity curves may be asymmetrical or skewed. Therefore, the diagnostic time window determined based on pressure and velocity data will be skewed relative to the peak of the pressure curve (i.e., an offset relative to the peak of the pressure curve). Combined with... Figure 8 and Figure 9The techniques of the representative embodiments described take into account this asymmetry and provide a method for estimating the asymmetric diagnostic time window for such pressure curves.

[0081] Based on pressure curve data, a diagnostic time window 806 is estimated by selecting a peak value 804 and an asymmetric duration relative to that peak value 804. This selection is made to approximate the diagnostic time window based on wave intensity analysis, including peak pressure, peak velocity, and wave intensity. Although the peak velocity is unknown when determining the diagnostic time window 806, it can be predicted based on peak value 804 by taking an asymmetric time frame relative to peak pressure, considering specific factors influencing the duration and location of the diagnostic time window. By way of illustration only, based on wave intensity analysis, it is known that near the peak, the selected diagnostic time window should fall within the diagnostic time window determined using pressure and velocity data. Based on these data, it can be estimated that for many patients, the duration of the diagnostic time window 806 is only a fraction of a heartbeat (e.g., 150 milliseconds). However, due to the skewness of the pressure curve 802, the diagnostic time window 806 has a relatively long duration in the first portion 808 and a relatively short duration in the second portion 810. For example, using an exemplary duration of a 150-millisecond diagnostic time window, the estimated duration of the first portion 808 is approximately 50 milliseconds of the heartbeat duration before peak 904, while the estimated duration of the second portion 810 is approximately 100 milliseconds of the heartbeat duration before peak 904. Therefore, according to a representative embodiment, the diagnostic time window can be approximated by selecting durations of 50 milliseconds and 100 milliseconds. Once the diagnostic time window is determined, the PV-iFR index can be determined. Thus, according to a representative embodiment, stress measurement results can be provided to processor 153, and instructions stored in memory 151 will cause processor 153 to calculate the diagnostic time window, which is 1 / 9 of the heartbeat duration before peak 904 and 1 / 18 of the heartbeat duration after peak 804. It is worth noting that selecting 150 milliseconds as the estimated diagnostic time window, and estimating the first portion 808 and the second portion 810 as 50 milliseconds and 100 milliseconds respectively, is merely illustrative; other durations based on wave intensity analysis can also be used to determine the diagnostic time window. For example, as described above, the expected duration of the diagnostic time window may be less than 150 milliseconds of the heartbeat duration. Furthermore, and as mentioned above, when selecting the duration to estimate the width of the diagnostic time window, care must be taken not to exceed the diagnostic time window determined using pressure and flow rate versus time data. In extreme cases, selecting a very narrow range (i.e., less than 150 milliseconds) that is asymmetrically set relative to peak value 804, or even selecting only peak value 804, will result in the estimated diagnostic time window falling within the diagnostic time window determined using pressure and flow rate versus time data.

[0082] After determining the diagnostic time window, the instruction causes the processor to calculate the PV-iFR, which can be used to determine the appropriate treatment.

[0083] Among other advantages, the method for determining the diagnostic time window and PV-iFR offers a practical application that allows estimation of the diagnostic time window and PV-iFR without measuring velocity and calculating wave intensity. As will be understood, this provides a reduction in the complexity of collecting data through a complete wave intensity analysis to determine the diagnostic time window. This reduction in complexity not only reduces the time and equipment required to determine the diagnostic time window or PV-iFR, but also reduces the cost of making these determinations.

[0084] Figure 9 The illustrated graph 900 according to a representative embodiment includes a pressure curve 902 over time, showing an asymmetric time window for determining the peripheral vascular pressure gradient. Notably, graph 900 can be provided to a user on the display 180 of system 100. (In conjunction with...) Figure 9 The various aspects and details of the methods described for determining the diagnostic time window and the PV-iFR index are combined with Figure 1-8 The aspects and details described in the representative embodiments are the same. In order to avoid obscuring the description of the representative embodiments described herein, these same aspects and details may not be repeated.

[0085] As shown in the figure, pressure curve 902 has a peak at 904. In this representative embodiment, pressure curve 902 is substantially asymmetrical about the peak at 904. It is worth noting, and as can be seen from the view... Figure 5 It will be understood that pressure and velocity curves may be asymmetrical or skewed. Therefore, the diagnostic time window determined based on pressure and velocity data will be skewed relative to the peak of the pressure curve (i.e., an offset relative to the peak of the pressure curve). Combined with... Figure 8 and Figure 9 The techniques of the representative embodiments described take into account this asymmetry and provide a method for estimating the asymmetric diagnostic time window for such pressure curves.

[0086] Based on pressure curve data, a diagnostic time window 906 is estimated by selecting peak value 904 and the asymmetric duration relative to peak value 904. This selection is made to approximate the diagnostic time window based on wave intensity analysis, including peak pressure, peak velocity, and wave intensity, for example, by combining... Figure 5As described. Although the peak velocity is unknown when the diagnostic time window 906 is determined, it can be predicted based on the peak value 904 by taking an asymmetric time frame relative to the peak pressure, considering specific factors affecting the duration and location of the diagnostic time window. By way of illustration only, based on wave intensity analysis, it is known that near the peak, the selected diagnostic time window should be within the diagnostic time window determined using pressure and velocity data. Based on these data, it can be estimated that the duration of the diagnostic time window 906 is approximately 1 / 6 of the heartbeat duration in many patients. However, due to the skewness of the pressure curve 902, the diagnostic time window 906 has a relatively long duration in the first portion 908 and a relatively short duration in the second portion. For example, following the exemplary duration of a diagnostic time window of 1 / 6 of the heartbeat duration, the duration of the first portion 908 is estimated to be approximately 1 / 9 of the heartbeat duration (ms), while the duration of the second portion 910 is estimated to be 1 / 18 of the heartbeat duration. Therefore, according to a representative embodiment, the diagnostic time window can be approximated by selecting 1 / 9 of the heartbeat duration before peak 904 and 1 / 9 of the heartbeat duration after peak 904. Once the diagnostic time window is determined, the PV-iFR index can be determined. Thus, according to a representative embodiment, pressure measurement results can be provided to processor 153, and instructions stored in memory 151 will cause processor 153 to calculate the diagnostic time window as 1 / 6 of the heartbeat duration, asymmetrically set relative to peak 904. It is worth noting that selecting 1 / 6 of the heartbeat duration as the estimated duration of the diagnostic time window, and estimating the durations of the first portion 908 and the second portion 910 as 1 / 9 and 1 / 18 of the heartbeat duration, respectively, is merely illustrative, and other durations based on wave intensity analysis can also be used to determine the diagnostic time window. For example, the duration of the diagnostic time window may be asymmetrically set relative to peak 904. Furthermore, when selecting the duration to estimate the width of the diagnostic time window, care must be taken not to exceed the diagnostic time window determined using pressure and flow rate versus time data. In extreme cases, choosing a very narrow range that is asymmetrically set relative to the peak value of 904 (e.g., less than 1 / 6 of the heartbeat duration, as mentioned above), or even choosing only the peak value of 904, will result in the estimated diagnostic time window falling within the diagnostic time window determined using pressure and flow versus time data.

[0087] After determining the diagnostic time window, the instruction causes the processor to calculate the PV-iFR, which can be used to determine the appropriate treatment.

[0088] Among other advantages, the method for determining the diagnostic time window and PV-iFR offers a practical application that allows estimation of the diagnostic time window and PV-iFR without measuring velocity and calculating wave intensity. As will be understood, this provides a reduction in the complexity of collecting data through a complete wave intensity analysis to determine the diagnostic time window. This reduction in complexity not only reduces the time required to determine the diagnostic time window or PV-iFR but also reduces the cost of making these determinations.

[0089] Figure 10 The illustrated graph 1000 according to a representative embodiment includes a pressure curve 1002 and a first derivative curve 1003 of pressure versus time, which shows a symmetrical time window for determining the peripheral vascular pressure gradient. It is noteworthy that this graph can be provided to a user on the display 180 of the system 100. It is noteworthy that the graph 1000 can be provided to a user on the display 180 of the system 100. (In conjunction with...) Figure 10 The various aspects and details of the methods described for determining the diagnostic time window and the PV-iFR index are combined with Figure 1-9 The aspects and details described in the representative embodiments are the same. In order to avoid obscuring the description of the representative embodiments described herein, these same aspects and details may not be repeated.

[0090] Figure 10 The diagnostic time window is shown, its width determined based on the maximum and minimum values ​​of the derivative of the pressure measurement over time. Alternatively, or to further improve the robustness of extracting the diagnostic time window, the roots of the second derivative of the pressure curve (i.e., the points where the second derivative crosses zero and where the pressure curve has inflection points) can be used to determine the diagnostic time window. It is worth noting that, referring to... Figure 5 The peak value of the wave intensity changing over time roughly coincides with the peak value of the derivative. Therefore, according to a representative embodiment, the diagnostic time window is determined based on the duration between the peak values ​​in the derivative.

[0091] refer to Figure 10 The pressure curve 1002 has a peak value of 1004, and the derivative curve 1003 has a maximum value of 1005 (which appears at T). ax ) and the minimum value 1011 (the maximum negative value of the derivative of the derivative curve 1003) (which appears at T min In this representative embodiment, the pressure curve 1002 is substantially symmetrical about the peak value 1004. The diagnostic time window 1006 is determined by selecting a substantially symmetrical duration. Specifically, based on data from the derivative curve 1003, the diagnostic time window 1006 is estimated by selecting a duration based on the width 1012 of the duration between the maximum value 1005 and the minimum value 1011. Figure 10 In the representative embodiment shown, the duration of the diagnostic time window 1006 is determined by ΔT = K(T ax -T min The following is provided. Based on a specific representative embodiment, the applicant found that selecting K=0.5 can reasonably approximate the duration of the diagnostic time window.

[0092] Although not explicitly shown, this teaching considers the use of a method based on determining the derivative of an asymmetric pressure curve, in which, similar to the embodiment based on an asymmetric pressure curve / diagnostic time window, ΔT will shift along the direction of curve skew and have a change from T ax The time difference offset from the peak value to 1004 is greater than the time difference offset from the peak value to T. min The time difference. In addition, this teaching considers taking the second derivative with respect to time and determining the duration of the diagnostic time based on the inflection point where the second derivative is zero.

[0093] Once the diagnostic time window is determined, the PV-iFR index can be determined. Therefore, according to a representative embodiment, the pressure measurement results can be provided to processor 153, and instructions stored in memory 151 will cause processor 153 to calculate the diagnostic time window as a selected fraction (in this example, ΔT = 0.5(T)). ax -T min It is worth noting that K=0.5 was chosen for estimating the time window ΔT=K(T). ax -T min This is merely illustrative, and other durations based on wave intensity analysis can be used to determine the diagnostic time window. For example, K could be 0.4, 0.45, or 0.55.

[0094] After determining the diagnostic time window, the instruction causes the processor to calculate the PV-iFR, which can be used to determine the appropriate treatment.

[0095] Among other advantages, the method for determining the diagnostic time window and PV-iFR offers a practical application that allows estimation of the diagnostic time window and PV-iFR without measuring velocity and calculating wave intensity. As will be understood, this provides a reduction in the complexity of collecting data through a complete wave intensity analysis to determine the diagnostic time window. This reduction in complexity not only reduces the time and equipment required to determine the diagnostic time window or PV-iFR, but also reduces the cost of making these determinations.

[0096] Figure 11 This is a flowchart of a method 1100 for determining the PV-iFR index according to a representative embodiment. Various aspects and details of method 1100 are combined with the above. Figure 1-10The same aspects and details are described. These aspects and details may not be repeated to avoid obscuring representative embodiments currently described. It is worth noting that method 1100 relates to heuristic or analytical processing methods (e.g., such as...) associated with the various embodiments described above. Figure 3 (As shown). As will be understood, method 1100 is stored in memory 151 as instructions, which are executed by processor 153 to determine the PV-iFR index or a similar index as described above.

[0097] At 1102, the method begins with measuring blood pressure. As described above, in a particular representative embodiment, pressure is measured upstream and downstream of the diseased portion (e.g., lesion) of the blood vessel.

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

[0099] At 1106, the method includes determining the PV-iFR index (or other index mentioned above) within the estimated diagnostic time window.

[0100] Figure 12 This is a flowchart of a method 1200 for training a neural network according to a representative embodiment, the neural network being used to determine the diagnostic time window required to calculate peripheral vascular indices. (In conjunction with...) Figure 12 The representative embodiments described herein describe various aspects and details of the method for determining the diagnostic time window and the PV-iFR index, and their combination. Figure 1-11 The aspects and details of the method described in the representative embodiments are the same. To avoid obscuring the description of the representative embodiments currently described, these same aspects and details may not be repeated. Again, according to the representative embodiments, method 1200 is contemplated as being instantiated as instructions stored in memory 151 and executed by a processor.

[0101] Training process of computational model ( Figure 12The training loop is performed using one of many machine learning techniques known to those skilled in the art of AI and mathematical modeling. In machine learning, the algorithmic model “learns” how to transform its input data into meaningful outputs. As described above, during the learning process, the computational model is shown known input examples and their correct or desired outputs. These examples include benchmark truths and, according to a representative embodiment, an optimal diagnostic time window at the target time interval S1270. During the learning process, given the input parameters of the examples, the computational model adjusts its internal parameters and produces a corresponding meaningful output, or so-called target. The adjustment process is guided by instructions on how to measure the distance between the current output and the desired output. These instructions are referred to as the objective function or optimization loss. As will be understood, this adjustment process is performed by executing instructions stored in memory 151, which is executed by processor 153.

[0102] In deep learning (a subfield of machine learning), the parameters inside a computational model are organized into successively connected layers, where each layer produces increasingly meaningful outputs as inputs to the next layer, until the final layer produces the final output.

[0103] Deep learning layers are typically implemented as so-called neural networks, meaning a layer consists of a set of nodes, each representing an output value and a rule for calculating that output value based on the set of output values ​​from the previous layer's nodes. For example, the rule might be a weighted sum of the transformed output values ​​of the previous layer's nodes, with each node only needing to store the weights. The transformation function is the same for all nodes in the same layer and is also called the activation function. Currently, the number of activation functions used is limited. A special method for setting which nodes in the previous layer provide input to the nodes in the next layer is convolution. Networks based on this method are called convolutional networks. An alternative method for setting which outputs from the previous layer provide input to the next layer's output is attention. Networks based on self-attention or cross-attention are called Transformer networks.

[0104] This teaching considers the use of a 1D convolutional network that processes measured pressure (and optionally ECG) data over fixed intervals, containing at least one cardiac cycle. Therefore, the input is a 2xN matrix, where N is the number of time samples, and the query must be the same for each new input (as well as the time intervals between points along the N-dimensional plane). Notably, the two columns of the matrix are based on the pressure data and the ECG data, respectively. If only pressure data is available, the matrix will have a 1xN shape.

[0105] This teaching also considers the use of recurrent (e.g., Long Short-Term Memory (LSTM) architectures) or attention-based (e.g., Transformer) architectures that can handle data intervals of varying sizes for measuring stress (and optionally ECG), each interval containing at least one cardiac cycle, such that the input is a 2×N' matrix, where N is the number of time samples, and the query can be different for each new input (this does not apply to time intervals between points along the N' dimension, which must remain consistent). The model will produce an output sequence with indicator tags t1 and t2 defining the intervals, where there may be more than one pair. In the latter case, the iFR exponent can be calculated as an average of all found intervals.

[0106] Refer again Figure 12 At 1220, the method begins by measuring blood pressure at two locations. This measurement will be combined with... Figure 1 and Figure 2 A representative embodiment will be described in more detail. It is worth noting that, as an alternative, ECG data can be collected at S1210 instead of measuring blood pressure.

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

[0108] As mentioned above, by deriving the wave intensity curve data at S1240 using blood pressure and blood flow data, the diagnostic time window can be determined relatively accurately. Combined with... Figure 13 The teaching described herein uses a trained neural network to predict or estimate a diagnostic time window using only measured blood pressure data or measured ECG data. Therefore, collecting baseline ground truth is solely for training the neural network, thereby providing a trained deep learning model (referred to herein as the trained computational model). Thus, S1220, S1230, and S1240 are performed on a relatively large population to provide baseline ground truth for training the computational model.

[0109] Therefore, in combination Figure 12In the learning phase or so-called training described in the representative embodiment, for each example, the output S1260 of the last layer is computed. The output for all examples or a subset of examples is compared with the desired output via an objective function S1280, where an optimization algorithm is known. The output of the objective function at S1280, the so-called loss, is used as a feedback signal to adjust the weights to reduce the loss. The adjustment, i.e., which weights are changed and by how much, is computed by the core algorithm of deep learning, the so-called backpropagation, which is based on the fact that the weighted sum of the connection layer nodes is a function with a simple derivative. The adjustment process iterates until the loss reaches a predetermined threshold or no longer changes significantly.

[0110] Therefore, a deep learning network can be stored (e.g., in memory 151) as a topology describing the layers and activation functions, along with a (large) set of weights (simple values). The trained network is identical, except the weights are now fixed to specific values. Once trained, the network is ready for use, i.e., to predict the output of new inputs with unknown expected outputs.

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

[0112] Figure 12 The method shown illustrates the training process of a trained machine learning model. A trained machine learning model can be applied to evaluate the correct time period in the cardiac cycle, i.e., the diagnostic time window, based solely on stress data. The machine learning model can be a deep learning neural network trained using stress and flow data from a large patient database, and the resulting wave intensity data, as a baseline ground truth for the optimal diagnostic time window. The training data can include pairs of stress data matched to the baseline ground truth diagnostic time window. The baseline ground truth diagnostic time window is calculated as the time interval between the two maximum peaks of the net wave intensity. The net wave intensity (WI) is calculated from the derivatives of the flow / velocity data U and the stress data P, as indicated in Equation 3 below and as described above.

[0113] According to a representative embodiment, the wave intensity can be determined as follows: Wave intensity originating from the near end: (1) Wave intensity originating from a distant location: (2) Net wave intensity: (3) Where ρ is the density of blood, c is the wave velocity, dU is the change, and dP is the pressure change.

[0114] During training, the model is optimized to predict the diagnostic time window based solely on the stress signal by minimizing the loss function between the predicted value and the baseline true value. After training, during model deployment, no flow / velocity data U is available, and the model predicts the optimal diagnostic time window based solely on the stress data P, using the optimized parameters obtained during training.

[0115] As mentioned above, in Figure 12 middle, Figure 12 The method can receive additional input at S1210 by measuring electrocardiogram (ECG) data. It is worth noting that this parameter is not required, and the method can omit including this parameter. Although the pressure signal is measured within the blood vessel near the treatment site, the ECG signal more comprehensively reflects the electromechanical behavior of the heart, which is then the core driver of the measured downstream pressure signal. Even when the pressure signal is low and does not exhibit significant rise and fall slopes against a background of measurement noise, the ECG signal can still provide the computational model with an indication of the current cardiac phase. Therefore, predicting the diagnostic time window based on these two sources of input information is more robust because, during training, the model can learn the correlation between pressure and ECG signals, and how to respond when one of the signals is of poor quality.

[0116] At S1220, Figure 12 The method includes measuring pressure curve data. At S1230, Figure 12 The method includes measuring velocity or flow rate profile data. According to a representative embodiment, flow rate profile data is measured using a known flow sensor device. For example, the flow sensing device could be an intravascular flow sensing device, such as ComboWire. ® or FloWire ®。 Alternatively, flow profile data can be measured extravascularly, such as via known dual-function ultrasound (US) techniques. This is done solely to obtain a baseline ground truth when building the training database for model training. Flow or velocity is not measured during model deployment.

[0117] In S1240, wave intensity curve data is derived using pressure curve data measured from S1220 and flow curve data measured from S1230, which is used to calculate the training baseline true value for the optimal diagnostic time window in S1280.

[0118] The electrocardiogram data measured in S1210 and the pressure curve data measured in S1220 are input into the artificial neural network model in S1250. In 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] In S1270, the wave intensity curve data derived from S1240 is used to determine the target time interval. In S1280, the optimization loss is calculated and backpropagated through an artificial neural network model to update the model parameters in S1250 based on the predicted time interval from S1260 and the target time interval from S1270. Therefore, the optimization loss from S1280 is used as the basis for training the artificial neural network model in the training loop. The process can be repeated as new measurements are obtained at S1210, S1220, and S1230.

[0120] Figure 13 The illustration depicts a method, according to a representative embodiment, for determining a peripheral vascular pressure index by applying a trained neural network to pressure data to predict a diagnostic time window. Combined with... Figure 13 The representative embodiments described herein describe various aspects and details of the method for determining the diagnostic time window and the PV-iFR index, and their combination. Figure 1-12 The aspects and details of the methods described in the representative embodiments are the same. To avoid obscuring the description of the representative embodiments currently described, these same aspects and details may not be repeated. Again, the trained neural network can be stored in memory 151. The stored instructions can then be combined with... Figure 1 The processor 153 described is executed.

[0121] Figure 13 The method includes measuring pressure curve data proximal and distal to the potential treatment site at S1310, and (optionally) measuring electrocardiogram (ECG) data at S1320. At S1330, the pressure curve data from S1310 and / or the ECG data from S1320 are input into an artificial neural network model to obtain a time window for the peripheral vascular index at S1340. To perform predictions, the model performs a series of mathematical operations and their interactions according to its architecture, using parameters optimized during training. Using the predictive diagnostic time window for the proximal and distal pressure data, the peripheral vascular index is output as a result at S1350. Figure 13 The method shown can estimate the diagnostic time window for determining peripheral vascular indices based solely on the measured pressure curve, or based on both measured pressure curve data and electrocardiogram data. In other words, in Figure 13In this approach, velocity (flow) data is neither available nor required as input to the artificial neural network. In some embodiments, the artificial neural network also does not require two sets of pressure data from downstream (distal) and upstream (proximal) sensors, and as described above, it can instead operate using two sets of pressure data from a single sensor that takes two measurements at two locations within the vessel at different times. In one embodiment, this is achieved by moving the pressure sensor from the downstream location (distal to the potential treatment site) to the upstream location (proximal to the potential treatment site), while simultaneously measuring a pressure signal at each of the two locations within a given time T.

[0122] Figure 14 A user interface 1400 according to a representative embodiment is shown. (In conjunction with...) Figure 14 The representative embodiments described in the description of various aspects and details are combined with Figure 1-13 The aspects and details described in the representative embodiments are the same. To avoid obscuring the description of the representative embodiments currently described, these same aspects and details may not be repeated.

[0123] It is worth noting that the user interface 1400 may include control elements (e.g., elements on a graphical user interface (GUI)) that enable the user to perform the various methods described above in relation to the representative embodiments. For example, the user interface 1400 may include elements that enable the estimation of a diagnostic time window based on input data (blood pressure and / or ECG data) using the heuristic or AI methods described above in the various representative embodiments.

[0124] Figure 14 A DSA (digital subtraction angiography) image 1402 of a portion of the human body is shown, including numerous peripheral blood vessels. Notably, a lesion 1404 is present in one of the vessels, and measurements were taken upstream (proximal) and downstream (distal) of the lesion.

[0125] Blood pressure measurements taken using system 100 are displayed in a graph, where the first curve 1406 represents a pressure measurement taken upstream of lesion 1404, and the second curve 1408 represents a pressure measurement taken downstream of lesion 1404. A diagnostic time window 1410 is determined by implementing the heuristic or AI method described above, and is displayed on the user interface 1400. Notably, outside the diagnostic time window 1410, the pressure drop is significantly smaller than within the window. Furthermore, the determination of the PV-iFR index is also based on the heuristic or AI method described above. According to a representative embodiment, the average pressure above and below lesion 1404 within the diagnostic time window 1410 can be calculated by repeated measurements, and the ratio of these pressures can be determined. This ratio is then determined based on the average pressure above lesion 1403 and before lesion 1404 (0.84 in this example). In a particular representative embodiment, the ratio can be averaged over multiple cycles (e.g., Figure 14 (The three periods shown). It is worth noting that in the representative embodiment, the ratio of the PV-iFR index is shown, but other indices are also considered. As per [reference to...] Figure 14 To understand this, pressure data is collected for several cardiac cycles at each location, and a diagnostic time window is detected within each cardiac cycle to mark the intervals of relevant pressure data. For example, within each diagnostic time window, the average pressure (or maximum pressure) is taken, resulting in N pressure values ​​upstream of the lesion and M pressure values ​​downstream of the lesion. Then, based on the averaging method described above, all N pressure values ​​upstream of the lesion and all M pressure values ​​downstream of the lesion are averaged to obtain two values, one upstream of the lesion and one downstream of the lesion. The index, as a ratio, is determined from these two values.

[0126] As will be understood, the first and second curves 1406 and 1408, and the diagnostic time window 1410, are continuously defined, and the PV-iFR index is also continuously updated. Therefore, the method for determining the diagnostic time window 1410 and the PV-iFR index is defined by… Figure 1 and Figure 2 The components of the system are applied in a continuous, real-time manner and are too complex to be determined manually and presented in real time as in the systems and methods described in the various representative embodiments.

[0127] Furthermore, it should be noted that, according to various representative embodiments, a distal pressure sensor can be tracked in an image (e.g., an X-ray image), and the PV-iFR index can be displayed on the X-ray image. This is similar to the iFR co-registration guided by Philips Healthcare's advanced guidelines. This can be accomplished by tracking the pressure sensor on the image to determine the PV-iFR, displaying the PV-iFR value on the X-ray image at the sensor location, and moving the sensor. This allows tracking the PV-iFR index at multiple locations of the pressure sensor.

[0128] While peripheral vascular pressure gradient determination has been described with reference to several exemplary embodiments, it should be understood that the terms used are descriptive and illustrative, not restrictive. Modifications, as described and modified herein, may be made within the scope and spirit of the various aspects of peripheral vascular pressure gradient determination without departing from the scope and spirit of the appended claims. Although peripheral vascular pressure gradient determination has been described with reference to specific methods, materials, and embodiments, it is not intended to be limited to the specific details disclosed; rather, it extends to all functionally equivalent structures, methods, and uses, for example, 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 various embodiments. These illustrations are not intended to be a complete description of all elements and features of the disclosure described herein. Many other embodiments will likely be apparent to those skilled in the art after reviewing this disclosure. Other embodiments can be utilized and derived from this disclosure, allowing structural and logical substitutions and changes to be made without departing from the scope of this disclosure. Furthermore, these illustrations are representative only and may not be drawn to scale. Some scales in the illustrations may be enlarged, while others may be minimized. Therefore, this disclosure and the accompanying drawings should be considered illustrative rather than restrictive.

[0130] The term "invention" may be used independently and / or collectively for convenience only, but this does not imply an intentional limitation of the scope of this application to any particular invention or inventive concept. Furthermore, while specific embodiments have been illustrated and described herein, it should be understood that any subsequent arrangements designed to achieve the same or similar purpose may replace the specific embodiments shown. This disclosure is intended to cover any and all subsequent modifications or variations of the various embodiments. Combinations of the above embodiments, as well as other embodiments not specifically described herein, will be apparent to those skilled in the art upon review of the specification.

[0131] The abstract of this disclosure provided conforms to 37 CFR § 1.72(b), and it should be understood at the time of submission that the abstract is not intended to interpret or limit the scope or meaning of the claims. Furthermore, in the foregoing detailed description, various features may be combined together or described in a single embodiment for the purpose of simplifying this disclosure. This disclosure should not be construed as reflecting an intention that the claimed embodiments require more features than expressly recited in each claim. Rather, as reflected in the following claims, the inventive subject matter may refer to fewer than all features of any of the disclosed embodiments. Therefore, the following claims are incorporated into the detailed description, wherein each claim independently defines the claimed subject matter.

[0132] The foregoing description of the disclosed embodiments is provided to enable any person skilled in the art to practice the concepts described in this disclosure. Therefore, the subject matter disclosed above should be considered illustrative rather than restrictive, and the appended claims are intended to cover all such modifications, enhancements, and other embodiments falling within the true spirit and scope of this disclosure. Accordingly, to the fullest extent permitted by law, the scope of this disclosure shall be determined by the broadest permissible interpretation of the following claims and their equivalents, and shall not be construed as limited or restricted by the foregoing detailed description.

Claims

1. A system (100) for assessing a patient's non-coronary artery vessels (260), comprising: Memory (151), its storage instructions, and A processor (153) executes the instructions, wherein the instructions, when executed by the processor (153), cause the system (100) to: Receive pressure measurements obtained by an intravascular pressure sensing instrument at a first location and a second location of the non-coronary artery (260) during the patient's cardiac cycle; Based on the pressure measurements taken from the non-coronary artery (260) during the patient's cardiac cycle, a diagnostic time window (1006) for the pressure measurements is identified; and The index is calculated based on the pressure measurement results within the diagnostic time window (1006).

2. The system (100) according to claim 1, wherein, The third position of the non-coronary artery (260) is located between the first position and the second position of the non-coronary artery (260) and includes the diseased portion (266) of the non-coronary artery (260).

3. The system (100) according to claim 1, wherein, The diagnostic time window (1006) of the pressure measurement results includes a symmetrical window around the peak pressure (540).

4. The system (100) according to claim 1, wherein, The diagnostic time window (1006) for the pressure includes an asymmetric time window around the peak pressure (540).

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

6. The system (100) according to claim 1, wherein, The intravascular pressure sensing instrument is a first intravascular pressure sensing instrument, and the system (100) also includes a second intravascular pressure sensing instrument (170).

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

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

9. A method (1100) for assessing a patient's non-coronary artery vessels (260), the method (1100) comprising: Receive pressure measurements obtained by an intravascular pressure sensing instrument at a first location and a second location of the non-coronary artery (260) during the patient's cardiac cycle; Based on the pressure measurements taken from the non-coronary artery (260) during the patient's cardiac cycle, a diagnostic time window (1006) for the pressure measurements is identified; and The index is calculated based on the pressure measurement results within the diagnostic time window (1006).

10. The method (1100) according to claim 9, wherein, The third position of the non-coronary artery (260) is located between the first position and the second position of the non-coronary artery (260) and includes the diseased portion (266) of the non-coronary artery (260).

11. The method (1100) according to claim 9, wherein, The diagnostic time window (1006) of the pressure measurement results includes a symmetrical window around the peak pressure (540).

12. The method (1100) according to claim 9, wherein, The diagnostic time window (1006) for the pressure includes an asymmetric time window around the peak pressure (540).

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

14. A tangible, non-transient computer-readable medium (14) storing instructions that, when executed by a processor (153), cause the processor (153) to: Receive pressure measurements obtained by an intravascular pressure sensing instrument at a first position and a second position of the non-coronary artery (260) during the patient's cardiac cycle; Based on the pressure measurements taken from the non-coronary artery (260) during the patient's cardiac cycle, a diagnostic time window (1006) for the pressure measurements is identified; and The index is calculated based on the pressure measurement results within the diagnostic time window (1006).

15. The tangible, non-transient computer-readable medium according to claim 14, wherein, The third position of the non-coronary artery (260) is located between the first position and the second position of the non-coronary artery (260) and includes the diseased portion (266) of the non-coronary artery (260).

16. The tangible, non-transient computer-readable medium according to claim 14, wherein, The diagnostic time window (1006) of the pressure measurement results includes a symmetrical window around the peak pressure (540).

17. The tangible, non-transient computer-readable medium according to claim 14, wherein, The diagnostic time window (1006) for the pressure includes an asymmetric time window around the peak pressure (540).

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

19. The tangible, non-transient computer-readable medium according to claim 14, wherein, The intravascular pressure sensing instrument includes a first intravascular pressure sensing instrument and a second intravascular pressure sensing instrument.

20. The tangible, non-transient computer-readable medium (14) according to claim 15, wherein, The first intravascular pressure sensing instrument (160) is positioned at the first location in the patient's non-coronary artery (260), and the second intravascular pressure sensing instrument (170) is introduced at the second location in the patient's non-coronary artery (260).

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