Flow measurement by OCT

Abstract

The present disclosure provides systems and methods for determining the mean transit time of an agent within a blood vessel by passing the agent through the blood vessel while an intravascular imaging probe remains stationary. The probe can collect a plurality of image frames as the agent passes through the probe. The cross-sectional area of the agent within an image frame can be determined by thresholding each image frame, creating a vessel mask, and creating a contrast mask by applying an element-wise AND operation on the thresholded image and the vessel mask. The cross-sectional area of the agent within the image frame can be plotted on an area dilution curve. Various fits can be applied to the area dilution curve, and various points can be identified on the area dilution curve. The various fits and the various points can be used to determine the mean transit time.

Description

[0001] Cross-references to related applications

[0002] This application claims the benefit of U.S. Provisional Application No. 63 / 127,615, entitled “Flow Measurement via OCT,” filed on December 18, 2020, the entire disclosure of which is incorporated herein by reference. Background Technology

[0003] Identifying a patient's microvascular resistance may require one or more data collection systems. For example, physicians can use pressure guidewires, angiography, intravascular imaging, and other techniques to collect data to identify microvascular disease. Angiography provides insight into what is happening throughout the heart, while pressure guidewires provide measurements of data within the blood vessels.

[0004] The mean transit time corresponding to the blood flow rate in a blood vessel is calculated by passing a dose of cold saline through the vessel. As this saline passes through, its temperature is measured by proximal and distal temperature sensors on a pressure guidewire inserted separately from an imaging catheter (such as an optical coherence tomography (“OCT”) catheter). A thermodilution profile is then plotted based on the temperature of the saline as it passes through the temperature sensors, providing a flow rate indication as well as the mean transit time. However, this requires additional steps and instrumentation and can be cumbersome and dangerous in practice. Summary of the Invention

[0005] This disclosure generally relates to systems and methods for determining average transit time using an intravascular imaging probe. For example, the intravascular imaging probe may be an OCT probe, an intravascular ultrasound (“IVUS”) probe, a miniature OCT probe, a near-infrared spectroscopy (NIRS) sensor, or any other device suitable for vascular imaging. Average transit time may be the flow rate within the blood vessel. In an example using an OCT probe, a dose of lumen flushing fluid may be passed through the blood vessel. The lumen flushing fluid may, for example, be a contrast agent. In an example using an IVUS probe, a medium capable of being imaged by the IVUS probe may be passed through the blood vessel. For clarity, the examples described herein may refer to an OCT probe. However, the use of an OCT probe is merely an example and is not intended to be limiting.

[0006] During retraction, the OCT probe can be stopped, thus holding the OCT probe stationary in a given position. While the OCT probe remains stationary, multiple image frames can be collected as the lumen flushing agent passes through. The cross-sectional area of ​​the lumen flushing agent in one or more image frames can be determined. The cross-sectional area can be used to determine and / or create an area dilution profile. The area dilution profile can be used to determine the mean transit time within the vessel. In some examples, the area dilution profile may allow for use with other downstream flow parameters, such as coronary flow reserve (“CFR”), microcirculatory resistance index (“IMR”), etc.

[0007] One aspect of this disclosure includes a method for determining the average transport time of a drug agent within a blood vessel. The method includes storing intravascular imaging data of the blood vessel in a memory device, the intravascular data including multiple image frames collected by one or more processors of an intravascular imaging probe coupled to a location within the blood vessel, and the one or more processors determining the average transport time of the drug agent within the blood vessel based on the intravascular imaging data. The method may further include determining at least one of a coronary flow reserve (“CFR”) value or an microcirculatory resistance index (“IMR”) value by the one or more processors based on the determined average transport time. The intravascular imaging data may be optical coherence tomography (“OCT”) imaging data, intravascular ultrasound imaging data, miniature OCT imaging data, or near-infrared spectral imaging data.

[0008] Each of the multiple image frames may include a portion of the drug injected into the bloodstream. The method may further include having one or more processors determine the cross-sectional area of ​​the drug portion in each of the multiple image frames, and having one or more processors create a distribution curve based on the determined cross-sectional area of ​​the drug portion in each of the multiple image frames. The average transport time of the blood within the blood vessel may be further determined based on the distribution curve. Determining the average transport time may further include integrating the distribution curve by one or more processors.

[0009] Determining the cross-sectional area of ​​the drug delivery portion may include segmenting each frame of a plurality of image frames by one or more processors using a threshold; determining a vascular mask for each frame of the plurality of image frames by one or more processors, wherein the determination is based on at least one of lumen offset, catheter offset, or wire offset; and determining a contrast mask by one or more processors based on each frame of the plurality of segmented image frames and the vascular mask for each frame of the plurality of image frames. The pixel region of the contrast mask for each frame of the plurality of image frames may correspond to the cross-sectional area of ​​the drug delivery portion. Segmenting each frame of the plurality of image frames by a threshold may include at least one of the following: calculating a Gaussian mixture model by one or more processors; comparing each frame of the one or more image frames to a predetermined threshold by one or more processors; or applying an Otsu threshold by one or more processors.

[0010] Another aspect of the invention includes a system comprising an intravascular imaging probe and one or more processors communicating with the intravascular imaging probe. The one or more processors may be configured to acquire a plurality of intravascular image frames at a location within the blood vessel, determine an average transit time of blood within the blood vessel based on the plurality of intravascular image frames, and determine at least one of a coronary flow reserve (“CFR”) value or an microcirculation resistance index (“IMR”) value based on the determined average transit time.

[0011] Another aspect of this disclosure includes a non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to receive from an intravascular imaging probe a plurality of intravascular image frames of a location within a blood vessel, determine an average transit time of blood within the blood vessel based on the intravascular image frames, and determine at least one of a coronary flow reserve (“CFR”) value or an microcirculation resistance index (“IMR”) value based on the determined average transit time.

[0012] One aspect of this disclosure includes a method comprising storing intravascular data in a storage device, the intravascular data including a plurality of image frames collected by one or more processors at locations within the intravascular vessel, the one or more processors being coupled to an optical imaging probe, wherein each of the plurality of image frames includes a portion of a drug injected into the blood vessel, the one or more processors determining a cross-sectional area of ​​the drug portion in each of the plurality of image frames, the one or more processors creating a distribution curve based on the determined cross-sectional area of ​​the drug portion in each of the plurality of image frames, and the one or more processors determining an average transport time of blood within the blood vessel based on the distribution curve.

[0013] Determining the cross-sectional area of ​​the drug delivery portion may include segmenting each frame of a plurality of image frames by one or more processors using a threshold; determining a vascular mask for each frame of the plurality of image frames by one or more processors, wherein the determination is based on at least one of lumen offset, catheter offset, or lead offset; and determining a contrast mask by one or more processors based on each frame of the plurality of segmented image frames and the vascular mask for each frame of the plurality of image frames. The pixel region of the contrast mask for each frame of the plurality of image frames may correspond to the cross-sectional area of ​​the drug delivery portion. Segmenting each frame of the plurality of image frames by a threshold may include at least one of the following: calculating a Gaussian mixture model by one or more processors; comparing each frame of the one or more image frames to a predetermined threshold by one or more processors; or applying an Otsu threshold by one or more processors.

[0014] The method may further include smoothing the distribution curve using a median filter by one or more processors, determining the peak of the smoothed distribution curve by one or more processors, applying Gaussian fitting to the smoothed distribution curve by one or more processors, determining the zero values ​​outside the four standard deviations of the Gaussian fitting by one or more processors, and applying a log-normal fitting to the applied Gaussian fitting based on the zero values ​​by one or more processors.

[0015] The method may further include one or more processors determining the initial time of the drug's arrival at the optical imaging probe by identifying a first image frame containing at least a portion of the drug among multiple image frames. The initial time and a log-normal fit of the application can be used to determine the average transport time. Determining the average transport time may further include one or more processors integrating the distribution curve.

[0016] Another aspect of the invention includes a system comprising an optical imaging probe and one or more processors in communication with the optical imaging probe. The one or more processors may be configured to collect a plurality of image frames at a location within a blood vessel, wherein each of the plurality of image frames includes a portion of a drug injected into the blood vessel, determine a cross-sectional area of ​​the drug portion in each of the plurality of image frames, create a distribution curve based on the determined cross-sectional area of ​​the drug portion in each of the plurality of image frames, and determine the average transport time of blood within the blood vessel based on the distribution curve.

[0017] Another aspect of this disclosure includes a non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to receive from an optical imaging probe a plurality of image frames of a location within a blood vessel, wherein each of the plurality of image frames includes a portion of a drug injected into the blood vessel, determines a cross-sectional area of ​​the drug portion in each of the plurality of image frames, creates a distribution curve based on the determined cross-sectional area of ​​the drug portion in each of the plurality of image frames, and determines the average transport time of blood within the blood vessel based on the distribution curve. Attached Figure Description

[0018] Figure 1 This is an exemplary system based on various aspects of this disclosure.

[0019] Figures 2A to 2F These are exemplary image frames captured by an OCT probe while it remains stationary in a blood vessel, according to various aspects of this disclosure.

[0020] Figure 3 This is an exemplary chart comparing each pixel in a bin with the image intensity, based on various aspects of this disclosure.

[0021] Figure 4A These are exemplary image frames captured by an OCT probe while it remains stationary in a blood vessel, according to various aspects of this disclosure.

[0022] Figure 4B Based on all aspects of this disclosure Figure 4A An example of a blood vessel mask in an image frame.

[0023] Figure 5 Is using Figure 4A Image frames and Figure 4B An example of using a blood vessel mask to create a contrast mask.

[0024] Figure 6 These are exemplary area dilution curves calculated based on various aspects of this disclosure.

[0025] Figures 7A to 7F This is applied to various aspects of this disclosure. Figure 6 Exemplary method steps for area dilution curves.

[0026] Figure 8 This is an example of the average transit time based on various aspects of this disclosure.

[0027] Figure 9 This is a flowchart of a method for determining the average transit time of blood within a blood vessel, based on various aspects of this disclosure.

[0028] Figure 10This is an example of the average transit time based on various aspects of this disclosure.

[0029] Figure 11 This is a flowchart of a method for determining intravascular microvascular resistance based on various aspects of this disclosure. Detailed Implementation

[0030] This disclosure provides one or more aspects of using a single intravascular imaging probe to identify and / or diagnose intravascular microvascular disease. By using a single intravascular imaging probe, patient risk during surgery and / or procedures may be reduced because there may be fewer incisions, fewer instruments inserted into the patient, less time spent in the surgical / operating room, and so on. In some examples, the efficiency of diagnosing a patient's microvascular disease may be improved by using a single intravascular imaging probe because the physician is able to collect more data at once.

[0031] According to some examples, intravascular imaging probes can be used to collect intravascular image data of blood vessels. Intravascular image data can be used to determine the average transit time of blood within the vessel. In some examples, the average transit time can be determined at rest and / or during congestion. The average transit time determined from intravascular image data can be used to determine coronary flow reserve (“CFR”), microcirculatory resistance index (“IMR”), etc. CFR and / or IMR values ​​can be used to diagnose microvascular disease. According to some examples, intravascular image data and / or any values ​​determined using intravascular image data can be used to assess intravascular injury, evaluate potential stent implantation, diagnose microvascular disease, etc.

[0032] An exemplary use case could be when a physician can use an intravascular imaging probe to assess damage within a blood vessel. Simultaneously, or essentially simultaneously, the physician can use the intravascular imaging probe to determine the mean transit time of intravascular medications. The determined mean transit time can be used to determine coronary flow reserve (“CFR”), microcirculatory resistance index (“IMR”), etc. Therefore, a single intravascular imaging probe can enable a physician to determine one or more aspects of intravascular microvascular disease.

[0033] Based on some examples, additional factors, combined with established CFR and / or IMR, can be used to identify one or more aspects of intravascular microvascular disease. Additional factors may include, for example, the patient's age, sex, body mass index (“BMI”), medical history, vessel type, vascular condition, treatment history, etc. Medical history may include, for example, known heart failure, previously diagnosed diabetes, hypertension, etc. Vessel type may include the left anterior descending artery (“LAD”), left circumflex artery (“LCX”), right coronary artery (“RCA”), left marginal artery, diagonal branch artery, right marginal artery, etc. Treatment history may include, for example, previous percutaneous coronary intervention (“PCI”), coronary artery bypass grafting (“CABG”), etc.

[0034] Figure 11 An exemplary method for determining the average transport time of a drug within the blood vessel is described. The following operations need not be performed in the exact order described below. Instead, the various operations can be performed in a different order or simultaneously, and operations can be added or omitted.

[0035] In block 1110, a data acquisition system (e.g., system 100) can collect intravascular image data. The collected intravascular image data can be stored in memory 114. According to some examples, the intravascular image frames can be optical coherence tomography (“OCT”) imaging data, intravascular ultrasound imaging data, miniature OCT imaging data, or near-infrared spectroscopy imaging data. The intravascular image data may include one or more intravascular image frames. In some examples, the intravascular image data can be collected while the intravascular imaging probe remains stationary in the blood vessel.

[0036] According to some examples, intravascular imaging data may include one or more intravascular image frames that include a portion of a drug injected into the blood vessel. For example, when the injected drug passes through an intravascular imaging probe, one or more image frames may include a portion of the drug.

[0037] In box 1120, the average transport time of a drug within a blood vessel can be determined based on intravascular imaging data. According to some examples, the average transport time of blood within a blood vessel can be determined by… Figure 9 The methods shown and discussed herein are used to determine this.

[0038] In some examples, determining the average transport time of a drug within a blood vessel may include determining the cross-sectional area of ​​the drug portion in each of a plurality of image frames. Determining the cross-sectional area of ​​the drug portion may include segmenting each of the plurality of image frames by thresholding. Segmenting each of the plurality of image frames by thresholding may include calculating a Gaussian mixture model, comparing each of one or more image frames to a predetermined threshold, or applying at least one of the Otsu thresholds.

[0039] Alternatively or additionally, determining the cross-sectional area of ​​the drug delivery portion may include determining a vascular mask for each of a plurality of image frames. The vascular mask may be based on at least one of lumen offset, catheter offset, or wire offset. In some examples, determining the cross-sectional area of ​​the drug delivery portion may include determining a contrast mask. The contrast mask may be based on a vascular mask for each of a plurality of segmented image frames and / or for each of a plurality of image frames. According to some examples, the pixel region of the contrast mask for each of the plurality of image frames may correspond to the cross-sectional area of ​​the drug delivery portion.

[0040] The cross-sectional area of ​​the drug portion in each of multiple image frames can be used to create a distribution. This distribution curve can be, for example, an area dilution curve. Based on some examples, the distribution curves can be integrated to determine the average transport time of the drug.

[0041] Based on some examples, at least one of the CFR or IMR values ​​can be determined based on a known average transit time. For example, the CFR value can be determined by dividing the known average transit time at rest by the average transit time during congestion. The IMR value can be determined by multiplying the average transit time during congestion by the distal pressure during congestion.

[0042] In identifying microvascular disease and potential treatments, established CFR and / or IMR values ​​can be used in conjunction with one or more patient factors. For example, physicians may consider a patient's age, sex, BMI, medical history, vascular type, vascular condition, previous treatments, etc., in conjunction with established CFR and / or IMR values ​​to determine the presence of any microvascular disease and / or potential treatments.

[0043] In one embodiment, microvascular resistance can be determined based on a predetermined mean transport time, CFR value, and / or IMR value, as well as the patient's age. For example, the patient's age can be input and processed along with the CFR and / or IMR values ​​to determine the microvascular resistance of the vessel. According to some examples, one or more age-specific parameters can be introduced based on training data. In some examples, the age-specific parameter can be a range of ages to classify patients. Training data can be collected and used as input to a machine learning model. Based on one or more age-specific parameters, along with the mean transport time, CFR value, and / or IMR value, the machine learning model can determine the microvascular resistance of the vessel.

[0044] In one embodiment, microvascular resistance can be determined based on a predetermined mean transport time, CFR value, and / or IMR value, as well as the patient's sex. For example, the patient's sex can be input and processed along with the CFR and / or IMR values ​​to determine the microvascular resistance of the vessel. In some examples, the mean transport time, CFR value, and / or IMR value may vary based on the patient's sex. The patient's sex can be incorporated based on training data. Training data can be collected and used as input to a machine learning model. Based on the patient's sex and the mean transport time, CFR value, and / or IMR value, the machine learning model can determine the microvascular resistance of the vessel.

[0045] In one embodiment, microvascular resistance can be determined based on a predetermined mean transit time, CFR value, and / or IMR value, as well as the patient's BMI. For example, the patient's BMI can be input and processed along with the CFR and / or IMR values ​​to determine the microvascular resistance of the vessel. According to some examples, one or more BMI parameters can be introduced based on training data. In some examples, the BMI parameter can be an age range to classify patients. Training data can be collected and used as input to a machine learning model. Based on one or more BMI parameters, along with the mean transit time, CFR value, and / or IMR value, the machine learning model can determine the microvascular resistance of the vessel.

[0046] In one embodiment, microvascular resistance can be determined based on a determined mean transport time, CFR value, and / or IMR value, as well as the patient's medical history. For example, the patient's medical history (e.g., known heart failure) can be input and processed along with the CFR and / or IMR values ​​to determine the microvascular resistance of the vessel. According to some examples, one or more state-specific parameters can be introduced based on training data. In some examples, state-specific parameters can be based on the patient's medical history. For example, the patient's medical history may indicate a history of heart failure. Training data can be collected and used as input to a machine learning model. Based on one or more state-specific parameters, along with the mean transport time, CFR value, and / or IMR value, the machine learning model can determine the microvascular resistance of the vessel.

[0047] As another example, a patient's medical history (e.g., a prior diagnosis of known diabetes) can be input and processed along with CFR and / or IMR values ​​to determine the microvascular resistance of a blood vessel. According to some examples, one or more state-specific parameters can be introduced based on training data. In some examples, state-specific parameters can be based on the patient's medical history. For example, the patient's medical history might indicate a history of diabetes. Training data can be collected and used as input to a machine learning model. Based on one or more state-specific parameters, along with mean transit time, CFR, and / or IMR values, the machine learning model can determine the microvascular resistance of a blood vessel.

[0048] As another example, a patient's medical history (e.g., a prior diagnosis of known hypertension) can be input and processed along with CFR and / or IMR values ​​to determine the microvascular resistance of the vessel. According to some examples, one or more state-specific parameters can be introduced based on training data. In some examples, state-specific parameters can be based on the patient's medical history. For example, the patient's medical history might indicate a history of hypertension. Training data can be collected and used as input to a machine learning model. Based on one or more state-specific parameters, along with mean transit time, CFR, and / or IMR values, the machine learning model can determine the microvascular resistance of the vessel.

[0049] In one embodiment, microvascular resistance can be determined based on a determined mean transit time, CFR value, and / or IMR value, as well as the type of vessel being imaged and / or diagnosed. For example, a patient's vessel type (e.g., left anterior descending artery (“LAD”)) can be input and processed along with CFR and / or IMR values ​​to determine the vessel's microvascular resistance. According to some examples, one or more vessel-specific parameters can be introduced based on training data. In some examples, the vessel-specific parameters can be based on vessel type. For example, the type could be LAD. Training data can be collected and used as input to a machine learning model. Based on one or more vessel-specific parameters, along with the mean transit time, CFR value, and / or IMR value, the machine learning model can determine the vessel's microvascular resistance.

[0050] As another example, a patient's vessel type (e.g., left circumflex artery (“LCX”)) can be input and processed along with CFR and / or IMR values ​​to determine the vessel's microvascular resistance. According to some examples, one or more vessel-specific parameters can be introduced based on training data. In some examples, the vessel-specific parameters can be based on vessel type. For example, the type could be LCX. Training data can be collected and used as input to a machine learning model. Based on one or more vessel-specific parameters, along with mean transit time, CFR, and / or IMR values, the machine learning model can determine the vessel's microvascular resistance.

[0051] As another example, a patient's vessel type (e.g., right coronary artery (“RCA”)) can be input and processed along with CFR and / or IMR values ​​to determine the vessel's microvascular resistance. According to some examples, one or more vessel-specific parameters can be introduced based on training data. In some examples, the vessel-specific parameters can be based on vessel type. For example, the type could be RCA. Training data can be collected and used as input to a machine learning model. Based on one or more vessel-specific parameters, along with mean transit time, CFR, and / or IMR values, the machine learning model can determine the vessel's microvascular resistance.

[0052] As another example, a patient's vessel type (e.g., left marginal artery) can be input and processed along with CFR and / or IMR values ​​to determine the vessel's microvascular resistance. According to some examples, one or more vessel-specific parameters can be introduced based on training data. In some examples, vessel-specific parameters can be based on vessel type. For example, the type could be left marginal artery. Training data can be collected and used as input to a machine learning model. Based on one or more vessel-specific parameters, along with mean transit time, CFR, and / or IMR values, the machine learning model can determine the vessel's microvascular resistance.

[0053] As another example, a patient's vessel type (e.g., diagonal branch artery) can be input and processed along with CFR and / or IMR values ​​to determine the vessel's microvascular resistance. According to some examples, one or more vessel-specific parameters can be introduced based on training data. In some examples, vessel-specific parameters can be based on vessel type. For example, the type could be a diagonal branch artery. Training data can be collected and used as input to a machine learning model. Based on one or more vessel-specific parameters, along with mean transit time, CFR, and / or IMR values, the machine learning model can determine the vessel's microvascular resistance.

[0054] As another example, a patient's vessel type (e.g., right marginal artery) can be input and processed along with CFR and / or IMR values ​​to determine the vessel's microvascular resistance. According to some examples, one or more vessel-specific parameters can be introduced based on training data. In some examples, vessel-specific parameters can be based on vessel type. For example, the type could be right marginal artery. Training data can be collected and used as input to a machine learning model. Based on one or more vessel-specific parameters, along with mean transit time, CFR, and / or IMR values, the machine learning model can determine the vessel's microvascular resistance.

[0055] In one embodiment, microvascular resistance can be determined based on established mean transit time, CFR values, and / or IMR values, as well as the patient's treatment history. For example, the patient's treatment history (e.g., previous percutaneous coronary intervention (“PCI”)) can be input and processed along with CFR and / or IMR values ​​to determine the vessel's microvascular resistance. According to some examples, one or more treatment parameters can be introduced based on training data. In some examples, treatment parameters can be based on the patient's treatment history. For example, the patient may have already undergone PCI. The previous PCI may have caused microvascular damage, which may therefore need to be taken into account when determining the vessel's microvascular resistance. Training data can be collected and used as input to a machine learning model. Based on one or more vessel-specific parameters, along with mean transit time, CFR values, and / or IMR values, the machine learning model can determine the vessel's microvascular resistance.

[0056] As another example, a patient's treatment history (e.g., previous coronary artery bypass grafting (“CABG”)) can be input and processed along with CFR and / or IMR values ​​to determine the microvascular resistance of the vessel. According to some examples, one or more treatment parameters can be introduced based on training data. In some examples, treatment parameters can be based on the patient's treatment history. For example, the patient may have already undergone CABG. CABG may alter the physiological structure of the heart and / or may cause changes in the microvessels, and therefore may need to be taken into account when determining the microvascular resistance of the vessel. Training data can be collected and used as input to a machine learning model. Based on one or more vessel-specific parameters, along with mean transport time, CFR, and / or IMR values, the machine learning model can determine the microvascular resistance of the vessel.

[0057] Figure 1 A data collection system 100 for collecting intravascular data is described. This system may include a data collection probe 104 for imaging a blood vessel 102. The data collection probe 104 may be an OCT probe, an IVUS catheter, a miniature OCT probe, a near-infrared spectroscopy (NIRS) sensor, or any other device suitable for imaging the blood vessel 102. While the examples provided herein refer to an OCT probe, the use of an OCT probe is not intended to be limiting. An IVUS catheter may be used in conjunction with or instead of an OCT probe. A lead (not shown in the figures) may be used to introduce the probe 104 into the blood vessel 102. The probe 104 may be introduced and withdrawn along the length of the blood vessel while data is collected. According to some examples, the probe 104 may remain stationary during withdrawal, allowing multiple scans of OCT and / or IVUS datasets to be collected. These datasets, or image data frames, may be used to identify features such as the cross-sectional area of ​​a drug.

[0058] The probe 104 can be connected to the subsystem 108 via optical fiber 106. The subsystem 108 may include a light source (such as a laser), an interferometer with a sample arm and a reference arm, various optical paths, a clock generator, a photodiode, and other OCT and / or IVUS components.

[0059] The probe 104 can be connected to the optical receiver 110. According to some examples, the optical receiver 110 can be a system based on a balanced photodiode. The optical receiver 110 can be configured to receive the light collected by the probe 104.

[0060] The subsystem may include a computing device 112. The computing device may include one or more processors 113, memory 114, instructions 115, data 116, and one or more modules 117.

[0061] One or more processors 113 can be any conventional processor, such as a commercially available microprocessor. Alternatively, one or more processors can be special-purpose devices, such as application-specific integrated circuits (ASICs) or other hardware-based processors. Although Figure 1 While the processor, memory, and other components of device 110 are shown functionally within the same block, those skilled in the art will understand that a processor, computing device, or memory may actually include multiple processors, computing devices, or memories, which may or may not be stored in the same physical housing. Similarly, the memory may be a hard drive or other storage medium located in a housing different from that of device 112. Therefore, references to processors or computing devices will be understood to include references to a collection of processors or computing devices or memories that may or may not operate in parallel.

[0062] Memory 114 may store processor-accessible information, including instructions 115 executable by processor 113, and data 116. Memory 114 may be a type of memory operable to store processor-accessible information, including non-transitory computer-readable media, or other media storing data readable by electronic devices, such as hard disks, memory cards, read-only memory (“ROM”), random access memory (“RAM”), optical discs, and other writable and read-only memories. The subject matter disclosed herein may include different combinations of the above, whereby different portions of instructions 115 and data 116 are stored on different types of media.

[0063] Memory 114 can be retrieved, stored, or modified by processor 113 according to instructions 115. For example, although this disclosure is not limited to any particular data structure, data 116 can be stored in a computer register, in a relational database as a table with multiple different fields and records, an XML document, or a flat file. Data 116 can also be in a computer-readable format, such as, but not limited to, binary values, ASCII, or Unicode. By further example only, data 116 can be stored as a bitmap composed of pixels (stored in a compressed or uncompressed manner), or various image formats (e.g., JPEG), vector-based formats (e.g., SVG), or computer instructions for drawing graphics. Furthermore, data 116 can include information sufficient to identify relevant information, such as numbers, descriptive text, proprietary code, pointers, references to data stored in other memory (including other network locations), or information used by functions to calculate relevant data.

[0064] Instruction 115 can be any set of instructions (e.g., machine code) that is executed directly by processor 113 or indirectly (e.g., a script). In this regard, the terms "instruction," "application," "step," and "program" are used interchangeably. Instructions can be stored in object code format for direct processing by the processor, or stored in any other computing device language, including scripts or collections of independent source code modules that are interpreted on demand or compiled in advance. The function, methods, and routines of the instructions will be explained in detail below.

[0065] Module 117 may include a plaque (e.g., calcified plaque) detection module, a display module, a flow rate or average transit time module, a stent detection module, or other detection and display modules. For example, computing device 112 may access a flow rate module for detecting the average transit time of blood in a vessel. According to some examples, these modules may include an image data processing channel or a component module thereof. The image processing channel may be used to convert collected OCT data into two-dimensional (“2D”) and / or three-dimensional (“3D”) views and / or representations of the vessel, stent, and / or detection area.

[0066] Subsystem 108 may include a display 118 for outputting content to a user. As shown, display 118 is separate from computing device 112; however, according to some examples, display 118 may be part of computing device 112. Display 118 may output image data relating to one or more features detected in a blood vessel. For example, output may include, but is not limited to, cross-sectional scan data, longitudinal scans, diameter maps, image masks, lumen boundaries, plaque size, plaque circumference, visual indications of plaque location, visual indications of the risk to stent expansion, flow rate, etc. Display 118 may identify features using text, arrows, color coding, highlighting, outlines, or other suitable human or machine-readable markings.

[0067] According to some examples, display 118 may be a graphical user interface (“GUI”). One or more steps may be performed automatically or without user input to navigate images, input information, select and / or interact with input, etc. Display 118, alone or in conjunction with computing device 112, may allow switching between one or more viewing modes in response to user input. For example, a user may be able to switch between different branches on display 118, such as by selecting a specific branch and / or by selecting a view associated with a specific branch.

[0068] In some examples, the display 118, either alone or in conjunction with the computing device 112, may include a menu. This menu can allow the user to show or hide various features. There may be more than one menu. For example, there may be a menu for selecting which vascular features to display. Additionally, or alternatively, there may be a menu for selecting the virtual camera angle of the display.

[0069] Figures 2A to 2F This illustrates an exemplary sequence of image frames acquired when the intravascular imaging probe remains stationary within the blood vessel. The intravascular imaging probe can be an OCT probe or an IVUS catheter.

[0070] For example, the retraction of the OCT probe can be stopped at a given location. When the retraction stops, the OCT probe can remain stationary so that it can collect multiple image frames at that location. A lumen flushing agent can be injected into a blood vessel at a location, allowing the agent to flow toward the OCT probe. The lumen flushing agent can be, for example, a contrast agent. Although the examples provided herein refer to the lumen flushing agent as a contrast agent, the use of the term contrast agent is not intended to be limiting. In examples using an IVUS catheter instead of an OCT probe, the agent injected into the blood vessel may include a medium that can be imaged by the IVUS catheter. This medium can be, for example, a microbubble contrast agent. A sequence of image frames can show the state of the blood vessel before, during, and after the agent approaches the OCT probe. The sequence of image frames can be used to determine the flow rate of the agent, and therefore also the flow rate of blood within the blood vessel. For example, each image may include a timestamp to indicate when the image frame was captured by the OCT probe. The cross-sectional area of ​​the agent within the image frame can be determined. The determined cross-sectional area of ​​the agent within the blood vessel can be used, along with the timestamp, to determine blood flow, and thus the average transit time of blood within the blood vessel.

[0071] Figures 2A to 2F They are arranged in order, among which Figure 2A It is the first image frame 200A in the image frame sequence obtained at time t1, that is, before the drug reaches the OCT probe, and Figure 2F It is the last image frame 200F in the image frame sequence obtained at time t6, that is, after the drug passes through the OCT probe. Although Figures 2A to 2F This describes the sequence of image frames captured by the OCT probe while it remains stationary, but the OCT probe can... Figures 2A to 2F One or more image frames are captured between each image frame shown. For example, an OCT probe can capture multiple images. Figures 2A to 2F The sequence of image frames shown may be only six (6) of these multiple image frames. Therefore, showing the sequence of image frames as six (6) image frames is merely exemplary and is not intended to be limiting.

[0072] Figure 2A An exemplary first image frame 200A is illustrated when the OCT probe is stationary. Image frame 200A can be captured at a time t1 before the drug is injected into the blood vessel. Image frame 200A may include the lumen boundary 202 of the blood vessel, catheter 204, and lead wire 206. Image frame 200A may also be captured before the drug is injected, and may not include the drug.

[0073] Figure 2B An example of a second image frame 200B taken when the OCT probe is stationary at time t2 is illustrated. After the drug is injected into the blood vessel, it can move along the vessel towards the OCT probe. Image frame 200B shows the drug 208 as it reaches the OCT probe. The drug 208 is shown as the dark portion within the lumen 202 in image frame 200B. Figure 2B As shown, the agent 208 is not solid, but is shown as multiple portions within the luminal boundary 202 of the blood vessel. These portions can be, for example, a liquid contrast agent divided into smaller parts within the blood vessel. This may be because the agent 208B passes alongside the lead wire 206 and the catheter 204. In some examples, the agent 208 may appear as multiple portions due to factors such as the speed of travel of the agent 208 within the blood vessel, light reflection from the agent 208, etc.

[0074] Figure 2C An exemplary third image frame 200C is illustrated at time t3 when the OCT probe remains stationary in the blood vessel. Image frame 200C can be captured by the OCT probe as the drug 208 passes by and as the drug 208 surrounds the OCT probe. For example, when the drug 208 is close to the OCT probe, the drug 208 in each image frame can occupy more space within the blood vessel defined by the lumen 202.

[0075] Image frame 200C illustrates an example where a drug agent 208 has traveled toward and surrounded the tip of an OCT probe. As shown, the maximum cross-sectional diameter of the drug agent 208 can pass through the tip of the OCT probe, thus the drug agent 208 is shown filling the space within a blood vessel as defined by lumen 202. According to some examples, the drug agent 208 may be shaped to conform to or anastomose with the blood vessel as it passes through the OCT probe, catheter 204, and lead 206. Therefore, the drug agent 208 may not have a circular or rectangular shape, but may have a shape based on the shape of the object and / or lumen 202 encountered by the drug agent 208.

[0076] Figure 2D An exemplary fourth image frame 200D is illustrated at time t4 when the OCT probe remains stationary in the blood vessel. Image frame 200D can be captured by the OCT probe after most of the drug 208 has passed through the tip of the OCT probe. For example, most of the drug 208 may have already passed through the OCT probe, so only a portion of the drug 208 is captured in image frame 200D.

[0077] Figure 2EAn exemplary fifth image frame 200E, captured at time t5, is illustrated. Compared to image frame 200D, image frame 200E may include less of the agent 208. This may indicate that the agent 208 continues to pass through the OCT probe.

[0078] Figure 2F An exemplary sixth image frame 200F, captured at time t6, is illustrated. Image frame 200F may include less agent 208 compared to image frame 200E. Any subsequent image frame may include even less agent 208 or may not include any agent 208 at all, as the agent 208 may have already passed completely through the OCT probe.

[0079] The cross-sectional area of ​​drug 208 within image frames 200A to 200F can be used to create an area dilution curve, thus calculating the average intravascular blood transport time "Tmn". To create the area dilution curve, the contrast agent area in each image frame is determined. To determine the contrast agent area in each image frame, each image can be segmented by thresholding. The vascular mask can be determined using offsets of the lumen, catheter, and lead. Element-level operations between the determined vascular mask and the thresholded image can be used to determine the contrast mask. The sum of pixels identified as drug in the contrast mask can be summarized as the contrast agent area in each image frame. The contrast agent area in each image frame can be plotted to create an area dilution curve. One or more operations can be performed on the area dilution curve to determine the average transport time. For example, the area dilution curve can be smoothed by a median filter, the peak closest to the center of the still image frame can be determined, the determined peak can be used to apply Gaussian fitting to the smoothed curve, the zero value of the Gaussian fitting curve can be taken outside the four standard deviations of the Gaussian fitting, the smoothed curve can be log-normal fitted, the initial time of the drug's first arrival in the catheter can be determined using the log-normal fitting and the area dilution curve, and the initial time, area dilution curve and log-normal fitting curve can be used to determine the average transport time.

[0080] Figure 3 An exemplary graph illustrates the threshold for identifying segmented images. The segmented image can be, for example, a region of a blood vessel separated or segmented from other parts of an image. For an image frame captured by an OCT probe while it remains stationary within a blood vessel, graph 300 compares the pixel intensity per compartment on the Y-axis to the image intensity on the X-axis.

[0081] The threshold can be determined using a Gaussian mixture model with two nodes, for example, N=2. The two nodes can be two different Gaussian models, so that the peaks of the models are canceled out. In some examples, the two models can be the contrast and brightness of an image frame. According to some examples, the two modes or models can be noisy values ​​and a lumen region with blood. The noisy low values ​​can be the background, while the lumen region with blood can be a signal-producing region. The two nodes can be superimposed to determine the threshold. The threshold can be the average of each node. Figure 3 As shown, the threshold is represented by line 302 on graph 300.

[0082] In some examples, the threshold can be fixed or predetermined. In other examples, the threshold can be determined using Otsu's thresholding. In still other examples, the threshold can be determined using deep learning. For example, a set or series of images can be stored. The stored images can be the input to a deep learning model. For each image, a threshold can be determined. The determined threshold can be the output of the deep learning model. A deep learning model or convolutional neural network can be trained to perform regression analysis, such that the image can be the input and the threshold can be the output. Furthermore, or alternatively, the threshold can be determined by any combination of Gaussian mixture models, fixed thresholding, Otsu's thresholding, and / or deep learning.

[0083] Figure 4A and 4B This section illustrates an example of using lumen, catheter, and lead offset to determine a vascular mask. A vascular mask defines the space within the luminal boundary that a drug may occupy as it travels through the vessel and passes the OCT probe. For example, a vascular mask can define the largest area within the lumen that a drug might occupy at any given time.

[0084] Image frame 400A may include a lumen boundary 402, a catheter 404, a lead 406, and a drug 408. The offset of the lumen, catheter, and lead can be determined by... Figure 1 The system 100 shown is determined using one or more software modules. For example, the offset of a guide can be determined by searching for peaks, transitions, or relative extrema along the scan line within the shadow. Interpolation can be used to simulate the connection segments of a guide in an image frame. In some examples, interpolation can be performed using one or more modules and various intravascular data processing steps. According to some examples, interpolation can construct guide detection segments in a frame and connect valid segments to create a guide connection model. The validity of a segment can be determined based on various criteria, which can be used to score the segments. The segment scores and segment continuity can be used to generate a representation of the guide in the image frame, and thus can be used to determine the guide offset. A guide can be scored based on its segments. Similar methods can be used to determine the offset of lumens and catheters, and therefore will not be elaborated here.

[0085] After determining the offsets of the lumen, catheter, and lead, the vascular mask 410 can be determined. As shown in image frame 400B, the vascular mask 410 can be defined by the boundaries of the lumen, lead, and catheter. The vascular mask 410 can represent the cross-sectional area of ​​the blood vessel through which the lead and catheter are inserted. Therefore, the vascular mask 410 can represent the cross-sectional area through which the drug can pass.

[0086] Figure 5 An exemplary image frame is shown, in which a vascular mask and a thresholded image can be used to determine a contrast mask. The contrast mask can be determined using element-level operations between a determined threshold and the vascular mask. For example, the contrast mask can determine how much area within the vascular mask is occupied by the agent 208. The contrast mask can be used to determine the cross-sectional area of ​​the agent. The cross-sectional area can be measured in pixels. Image frame 500 can substantially correspond to image frame 400A, which is used to determine vascular mask 410, such that vascular mask 510 substantially corresponds to vascular mask 410. Vascular mask 510 can represent the total cross-sectional area that the agent 508 may occupy within the lumen boundary 502 when passing through the OCT probe. For example, vascular mask 510 can represent the remaining cross-sectional area of ​​the lumen boundary 502 once the OCT probe (including catheter 504 and lead 506) is considered.

[0087] Element-level operations can be applied between the vessel mask and the thresholded image to determine the contrast mask. For example, the logical array operator "AND" can be used between the vessel mask and the thresholded image. Element-level operations can determine, on a pixel-by-pixel basis, whether a pixel within the vessel mask is a portion of the drug. Pixels identified as drug portions can be summed to determine the cross-sectional area of ​​the drug.

[0088] Figure 6 An exemplary area dilution curve is illustrated. Area dilution curve 600 illustrates the cross-sectional area of ​​the drug at a given time as it flows through the blood vessel and passes the OCT probe. For example, area dilution curve 600 can plot the cross-sectional area of ​​each frame, in mm^2, identified by its respective frame index. The frame index can be a number that identifies a specific image frame among multiple image frames. For example, if one hundred (100) image frames were captured, the frame index would correspond to a number between one (1) and one hundred (100). Each image frame may include a timestamp. In some examples, the frame index may correspond to the timestamp of each image frame. In some examples, area dilution curve 600 may plot the cross-sectional area based on the capture time of the image frame rather than the frame index.

[0089] As shown in the figure, area dilution curve 600 is a graph of image frames 200A to 200F. The contrast agent area in each frame of image frames 200A to 200F can be determined through thresholding. A vascular mask can be determined for each image frame 200A to 200F based on the offset of the lumen, catheter, and lead. Alternatively, a contrast mask can be determined for each image frame 200A to 200F. The contrast mask can be determined using element-level operations between the thresholded image and the vascular mask. The contrast mask can be used to determine the cross-sectional area of ​​the drug by determining the sum of the pixels within the contrast mask. The cross-sectional area can then be plotted to create area dilution curve 600.

[0090] Although image frames 200A to 200F are represented only by points 600A to 600F on the area dilution curve 600, additional image frames captured while the OCT probe remains stationary in the blood vessel can be used to create the area dilution curve 600. The area dilution curve 600 can be used to determine the mean transport time "Tmn". The mean transport time can be the blood flow rate within the blood vessel.

[0091] The area dilution curve 600 can represent whether the drug moves quickly or slowly within the blood vessel. For example, the area dilution curve 600 can have a width "w". The width "w" can be defined by a first image frame and a last image frame that includes at least a portion of the drug in a plurality of image frames. If the drug moves quickly within the blood vessel, its width "w" may be smaller than the width "w" in the case where the drug moves slowly within the blood vessel. For example, if the drug moves quickly, image frames capturing a portion of the drug may occur over a shorter time period because the time required for the drug to move a certain distance is shorter. On the other hand, if the drug moves slowly, image frames capturing a portion of the drug may occur over a larger or longer time period because the drug may require more time to move a certain distance. Since time or image frames are on the X-axis of the area dilution curve, shorter time periods will have a smaller width "w", while longer time periods will have a larger width "w".

[0092] Figures 7A to 7F This section describes an exemplary method for determining average transit time using an area dilution curve of 600. The following operations do not necessarily need to be performed in the exact order described below. Instead, the various operations can be performed in a different order or simultaneously, and operations can be added or omitted.

[0093] Figure 7A This illustrates a smoothed area dilution curve. For example, area dilution curve 600 can be smoothed using a median filter. Smoothing the area dilution curve can remove or smooth jagged regions. Jagged regions of the curve may be caused by improper determination of lumen offset. Figure 7AAs shown, the area dilution curve 600 has been smoothed into curve 702.

[0094] Figure 7B This illustrates the peak of curve 702. Peak 704 could be the peak closest to the center of the still image frame capture. When the OCT probe remains stationary, one or more drugs can be injected into the blood vessel. (As...) Figure 7B As shown, only a single dose of the drug is injected into the bloodstream; therefore, there is only one peak 704. According to some examples, in cases where more than one dose of the drug is injected into the bloodstream, graph 700B may include peaks for each dose.

[0095] Figure 7C This illustrates the application of a defined peak to Gaussian fitting on a smooth curve. The defined peak 704 could be, for example, the average value used to apply the Gaussian fitting. Figure 7C The difference between the smooth curve 702 and the Gaussian fitted curve 706 is explained.

[0096] Figure 7D This illustrates the zero value of curve 702 taken outside the four standard deviations of the Gaussian fit. The zero value is represented by points 708 and 710. Any values ​​to the left of point 708 and to the right of point 710 can be removed. Removing values ​​outside points 708 and 710 ensures that any artifacts in the blood vessel before and / or after drug administration do not affect the determination of the mean transport time. According to some examples, artifacts could be from previous doses or the next dose injected into the blood vessel.

[0097] Figure 7E The diagram illustrates the curve 712 generated by applying a log-normal fit to the smoothed curve 702. According to some examples, the curve 712 generated by applying a log-normal fit to the smoothed curve 702 may produce a fit closer to the measured signal. Furthermore, or alternatively, the log-normal fit (curve 712) may be a more accurate fit compared to a Gaussian fit (curve 706).

[0098] Figure 7F This illustrates that the log-normal fitting curve 712 is superimposed on the area dilution curve 600. The log-normal fitting curve 712 and / or the area dilution curve 600 can be used to determine the initial time "To". The initial time "To" can be determined by finding the first increase in the cross-sectional area in the log-normal fitting curve 712 and / or the area dilution curve 600. The initial time "To" can be the initial time when any part of the agent first arrives at the OCT probe.

[0099] As shown in the figure, the initial time "To" can occur at frame index 25. Frame index 25 could be the point where the cross-sectional area of ​​the agent first changes from 0 mm² to a positive value. According to some examples, the frame index can correspond to a specific time. This time could be a timestamp on the image frame.

[0100] like Figure 7F As shown, the center point of the log-normal fitted curve 712 can be to the right of the peak in the area dilution curve 600. In some examples, the peak shift is due to the log-normal fit applied to the area dilution curve 600. For example, the log-normal curve is not symmetrical, while the Gaussian curve is symmetrical. Therefore, the peak may shift due to the asymmetry of the log-normal curve.

[0101] Figure 8 This section explains how to determine the average transport time "Tmn" of a drug flowing in a blood vessel using an area dilution curve 600 and a log-normal fitted curve 712. The average transport time "Tmn" can be determined using an initial time "To," which is determined using the area dilution curve 600 and the log-normal fitted curve 712. Based on some examples, the average transport time "Tmn" can be determined using the following formula:

[0102]

[0103] In this equation, "t" can correspond to time. This time can be the timestamp of an image frame corresponding to a given frame index. "y" in the equation can correspond to the cross-sectional area of ​​the agent at that time "t".

[0104] Figure 10 This section explains how to use thermodilution measurements to determine the average transport time "Tmn" of a drug flowing in a blood vessel. For example, a pressure guidewire can be used to determine the thermodilution measurement. The average transport time "Tmn" can be determined using the time between the initial sensor temperature reading and the time between the peak sensor temperature reading. According to some examples, the initial sensor temperature reading may be at the same time as the midpoint of the injected drug. In some examples, the average transport time "Tmn" may be the time elapsed between the initial sensor temperature reading and the peak sensor temperature reading.

[0105] Figure 1000 illustrates exemplary curves of temperature readings, in degrees Celsius, for the cable “CT” and sensor “ST” as the drug is injected into a blood vessel. For example, when the initial injection volume “Io” occurs at time “to”, the cable temperature “CT” and sensor temperature “ST” may be zero degrees Celsius. At time “Th”, approximately half of the injection volume “Ih” may have been completed. At time “Th”, the initial sensor temperature can be read. The cable temperature “CT” and sensor temperature “ST” may decrease or become colder as the drug is injected. For example, at time “To”, the cable temperature “CT” and sensor temperature “ST” may be -2 degrees Celsius.

[0106] The agent can continue to be injected until the peak injection volume "Ip" is reached. According to some examples, the peak cable temperature "CT" can occur at the same time or very close to the peak injection volume "Ip". According to some examples, the peak cable temperature can be the maximum negative temperature, such as approximately -3 degrees Celsius, as shown in Figure 1000.

[0107] According to some examples, the sensor temperature "ST" may reach the peak sensor temperature "STp" at a certain time "Tp" after the peak injection volume "Ip". The difference between the time "Tp" of the peak sensor temperature "STp" and the time "Th" of half the injection volume "Ih" can be the average transport time "Tmn". According to some examples, the average transport time "Tmn" can be determined by the following formula:

[0108] T mn =T p -T h

[0109] In this formula, "Tp" can correspond to the time of peak sensor temperature "STp", and "Th" can correspond to the time of half injection volume "Ih".

[0110] The average transport time can be determined in both the quiescent and hyperemic states. Determining the average transport time in the quiescent state may involve collecting one or more images while the OCT probe remains stationary in a blood vessel in its natural state. For example, a natural state could be a state where the blood vessel has not undergone any treatment or drug intervention. Determining the average transport time in the hyperemic state may involve collecting one or more images while the OCT probe remains stationary in a blood vessel that has been hyperemic to produce full dilation of the vessel due to drug-induced hyperemia.

[0111] The Tmn determined using an intravascular imaging probe can be the same as or substantially the same as the Tmn determined using a pressure guidewire. Alternatively, the Tmn determined using an intravascular imaging probe can be the same as or substantially the same as the flow rate determined using a flow meter. Therefore, a single intravascular imaging device can be used to determine Tmn as well as identify other aspects of microvascular disease.

[0112] Based on some examples, Tmn, determined using an intravascular imaging probe, can be used to determine CFR. Tmn can be determined both at rest and during hyperemia. CFR can be determined using the established mean transit time at rest (“Tmn at rest”) and the mean transit time during hyperemia (“Tmn during hyperemia”), as shown in the following formula:

[0113]

[0114] According to some examples, the CFR determined using Tmn (determined using an intravascular imaging probe) can be the same as or substantially the same as the CFR determined using a pressure guidewire.

[0115] Based on some examples, Tmn, determined using an intravascular imaging probe, can be used to determine IMR. Tmn can be determined at rest and / or during hyperemia. For example, IMR can be determined using the following formula:

[0116] IMR = P d在充血时 ×T mn在充血时

[0117] In the formula, "Pd during hyperemia" corresponds to the distal pressure during drug-induced hyperemia. The distal pressure can be determined using volumetric flow rate ("VFR"). When the vessel is in a drug-induced hyperemia state, the VFR can be determined using one or more images collected during OCT pullback. The luminal geometry of the vessel can be determined based on one or more images collected during pullback. The luminal geometry can be used to determine the distal pressure during hyperemia and the aortic pressure during hyperemia. The distal pressure can be determined based on the volumetric lumen geometry. For example, a resistance model can be used to represent the target vessel. Based on Ohm's law, the pressure gradient (ΔP) can be equal to the flow rate (Q) multiplied by the vessel resistance (R), as shown below:

[0118] ΔP=QR

[0119] The flow rate can be calculated using a resistance model based on theoretical aortic and venous pressures.

[0120] In some examples, images collected by intravascular imaging probes and data determined from those images can be used to evaluate potential stent placement. For example, CFR, IMR, VFR, etc., can be determined based on the images. According to some examples, the determined CFR, IMR, VFR, etc., values ​​can be used in conjunction with virtual stents, placement zones, clustering methods, etc., to perform stent planning and other diagnostic and analytical methods.

[0121] Figure 9 An exemplary method for determining the average transport time of a drug within the blood vessel is described. The following operations need not be performed in the exact order described below. Instead, the various operations can be performed in a different order or simultaneously, and operations can be added or omitted.

[0122] For example, in block 910, a data collection system (e.g., system 100) can collect multiple image frames. The collected image frames can be stored in memory 114. Multiple image frames can be collected while the OCT probe remains stationary in the blood vessel. One or more of the multiple image frames may include a portion of the drug injected into the blood vessel. For example, as the drug passes through the OCT probe, one or more of the image frames may include a portion of the injected drug.

[0123] In box 920, the cross-sectional area of ​​the drug portion in each of multiple image frames can be determined. Determining the cross-sectional area of ​​the drug can include, for example, calculating a threshold using a Gaussian mixture model with two components. The threshold can be the average of each component. A vascular mask can be determined using offsets of the lumen, catheter, and lead. The vascular mask can represent the cross-sectional area within the luminal boundary where the drug might occupy. An element-level AND operation can be applied between the vascular mask and the segmented image frames. The result of the element-level AND operation can be a contrast mask. The contrast mask allows the determination of pixel regions of contrast agent in the image. This can be repeated for one or more images captured while the OCT probe remains stationary in the blood vessel.

[0124] In box 930, a distribution curve is created based on the cross-sectional area of ​​the drug portion in one or more image frames. This distribution curve can be an area dilution curve.

[0125] In box 940, the average transport time can be determined based on the distribution curve. For example, the distribution curve can be smoothed using a median filter. The peak closest to the center of the still image frame capture can be identified. The identified peak can be used as the initial average value to perform a Gaussian fit on the smoothed distribution curve. Regions far from four standard deviations of the curve can be removed. This prevents any artifacts, such as those from before or after the agent, from being considered when determining the average transport time. A log-normal fit can be performed on the distribution curve. The initial time “T0” can be determined based on the first increase in the cross-sectional area of ​​the distribution curve. The average transport time can be determined based on the initial time and the log-normal fitted curve.

[0126] Using an OCT probe to determine the mean transport time offers the advantage of eliminating the need for additional hardware or sensors. For example, using an OCT probe to collect image frames and then using those frames to determine the mean transport time can eliminate the need for inserting a pressure guidewire. This can reduce the risk of complications during the procedure. Furthermore, or alternatively, using an OCT probe to determine the mean transport time allows for real-time reading of the mean transport time. In some examples, the mean transport time determined using an OCT system can be used to determine coronary flow reserve (“CFR”) data and / or IMR data. Therefore, an OCT probe can be used to determine both physiological and anatomical information.

[0127] According to the techniques described herein, a method for determining the average transport time may include storing intravascular data of a blood vessel in a memory device, the intravascular data including a plurality of image frames collected by one or more processors of an intravascular imaging probe coupled to a location within the blood vessel, wherein each of the plurality of image frames includes a portion of a drug injected into the blood vessel, the one or more processors determining a cross-sectional area of ​​the drug portion in each of the plurality of image frames, the one or more processors creating a distribution curve based on the determined cross-sectional area of ​​the drug portion in each of the plurality of image frames, and the one or more processors determining the average transport time of blood within the blood vessel based on the distribution curve. Determining the average transport time may include integrating the distribution curve by the one or more processors.

[0128] Determining the cross-sectional area of ​​the drug delivery portion may include segmenting each frame of a plurality of image frames by one or more processors through a threshold; determining a vascular mask for each frame of the plurality of image frames by one or more processors, wherein the determination is based on at least one of lumen offset, catheter offset, or wire offset; and determining a contrast mask by one or more processors based on each frame of the plurality of segmented image frames and the vascular mask for each frame of the plurality of image frames. The pixel region of the contrast mask for each frame of the plurality of frames may correspond to the cross-sectional area of ​​the drug delivery portion. Segmenting each frame of the plurality of image frames by a threshold may include at least one of the following: calculating a Gaussian mixture model by one or more processors; comparing each frame of the one or more image frames to a predetermined threshold by one or more processors; or applying an Otsu threshold by one or more processors.

[0129] The method may further include smoothing the distribution curve using a median filter by one or more processors, determining the peak of the smoothed distribution curve by one or more processors, applying a Gaussian fit to the smoothed distribution curve by one or more processors, determining the zero values ​​outside the four standard deviations of the Gaussian fit by one or more processors, and applying a log-normal fit to the applied Gaussian fit based on the zero values ​​by one or more processors. The initial time of drug arrival at the intravascular imaging probe can be determined by identifying a first image frame containing at least a portion of the drug among multiple image frames. The initial time and the applied log-normal fit can be used to determine the average transport time.

[0130] Another aspect of the technology disclosed herein may include a system comprising an intravascular imaging probe and one or more processors in communication with the intravascular imaging probe. The one or more processors may be configured to collect multiple image frames at a location within a blood vessel, wherein each of the multiple image frames includes a portion of a drug injected into the blood vessel, determine the cross-sectional area of ​​the drug portion in each of the multiple image frames, create a distribution curve based on the determined cross-sectional area of ​​the drug portion in each of the multiple image frames, and determine the average transport time of blood within the blood vessel based on the distribution curve.

[0131] Another aspect of the technology disclosed herein may include a non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to receive multiple image frames of a location within a blood vessel from an intravascular imaging probe, wherein each of the multiple image frames includes a portion of a drug injected into the blood vessel, determines the cross-sectional area of ​​the drug portion in each of the multiple image frames, creates a distribution curve based on the determined cross-sectional area of ​​the drug portion in each of the multiple image frames, and determines the average transport time of blood within the blood vessel based on the distribution curve.

[0132] The average transit time of blood within a vessel can be determined based on intravascular imaging data. CFR and / or IMR values ​​can be determined based on the determined average transit time. According to some examples, CFR and / or IMR values ​​can be used in conjunction with one or more additional factors to identify one or more aspects of microvascular disease within the vessel.

[0133] The aspects, embodiments, features, and examples disclosed in this invention should be considered illustrative in all respects and are not intended to limit the scope of the invention, which is defined only by the claims. Other embodiments, modifications, and uses will be apparent to those skilled in the art without departing from the spirit and scope of the invention.

[0134] Throughout the application, when a composition is described as having, including, or comprising specific components, or when a method is described as having, including, or comprising specific process steps, it can be understood that the composition of the doctrine of the invention also consists substantially of the said components, and the process of the doctrine of the invention also consists substantially of the said process steps.

[0135] In this application, when an element or component is referred to as being included in and / or selected from the list of enumerated elements or components, it should be understood that the element or component can be any one of the enumerated elements or components and can be selected from a combination of two or more enumerated elements or components. Furthermore, it should be understood that elements and / or features of the compositions, apparatus, or methods described herein can be combined in various ways without departing from the spirit and scope of the doctrine of the invention, whether explicit or implicit.

[0136] The use of the terms “including” or “having” should be generally understood to be open-ended and non-restrictive, unless otherwise specified.

[0137] Unless otherwise specified, the singular as used herein includes the plural (and vice versa). Furthermore, the singular forms “a” and “described” include the plural forms unless the context explicitly specifies otherwise. Additionally, when the term “about” precedes a quantity, the teachings of this invention also include the specific quantity itself unless otherwise specified. Here, the term “about” refers to a variation of ±10% from the nominal value. All numerical values ​​and ranges disclosed herein are to be regarded as including “about” before each value.

[0138] It should be understood that the order of steps or the sequence of actions is not important as long as the doctrine of the invention remains operable. Furthermore, two or more steps or actions can be performed simultaneously.

[0139] When a range or list of values ​​is provided, each interval between the upper and lower limits of that range or list is considered individually and is included in this invention, just as each value is specifically listed herein. Furthermore, ranges between the upper and lower limits of a given range, as well as smaller ranges including the upper and lower limits, are also considered and included in this invention. Listing exemplary values ​​or ranges does not imply the exclusion of other values ​​or ranges between the upper and lower limits of a given range, or including the upper and lower limits.

Claims

1. A method for determining the average transport time of a drug within a blood vessel, comprising: Imaging data of a blood vessel is received by one or more processors, the imaging data comprising multiple image frames, each of which includes a portion of a drug injected into the blood vessel; The cross-sectional area of ​​the drug portion in the plurality of image frames is determined by the one or more processors; as well as The average transport time of the drug within the blood vessel is determined by the one or more processors based on the plurality of image frames, which include at least the drug portion. The determination of the cross-sectional area of ​​the pharmaceutical portion includes: Each frame of the plurality of image frames is segmented by the one or more processors; The one or more processors determine the blood vessel mask for each of the plurality of image frames; and The one or more processors determine a contrast mask based on each of the multiple segmented image frames and a blood vessel mask for each of the multiple image frames. The pixel region of the contrast mask for each of the plurality of image frames corresponds to the cross-sectional area of ​​the pharmaceutical portion.

2. The method according to claim 1, further comprising: The one or more processors determine at least one of the coronary flow reserve ("CFR") value or microcirculation resistance index ("IMR") value based on the determined average transport time.

3. The method according to claim 1 or 2, wherein the imaging data is optical coherence tomography ("OCT") imaging data, intravascular ultrasound imaging data, miniature OCT imaging data, or near-infrared spectral imaging data.

4. The method of claim 1, further comprising: The one or more processors create a distribution curve based on the determined cross-sectional area of ​​the drug portion in each of the plurality of image frames.

5. The method of claim 4, wherein determining the average transport time of the drug within the blood vessel is further based on the distribution curve.

6. The method of claim 4 or 5, wherein determining the average transit time further comprises integrating the distribution curve by the one or more processors.

7. The method of claim 1, wherein when segmenting each of the plurality of image frames, the plurality of image frames are segmented by a threshold.

8. The method of claim 1, wherein determining the vascular mask for each of the plurality of image frames is based on at least one of lumen offset, catheter offset, or wire offset.

9. The method of claim 7 or 8, wherein segmenting each of the plurality of image frames by a threshold comprises at least one of the following: The Gaussian mixture model is computed by the one or more processors. The one or more processors compare each frame of the one or more image frames with a predetermined threshold, or The Otsu threshold is applied by one or more processors.

10. A system for determining the average transport time of a drug within a blood vessel, comprising: One or more processors, said one or more processors being configured to: Receive imaging data of a blood vessel, the imaging data including multiple image frames, each of the multiple image frames including a portion of a drug injected into the blood vessel; Determine the cross-sectional area of ​​the drug portion in the plurality of image frames; Based on the determined cross-sectional area of ​​the pharmaceutical portion, a distribution curve is created; as well as Based on the plurality of image frames including at least the drug component, the average transport time of the drug within the blood vessel is determined. When determining the cross-sectional area of ​​the pharmaceutical portion, the one or more processors are configured to: Segment each frame in the plurality of image frames; Determine a blood vessel mask for each of the plurality of image frames; and A contrast mask is determined based on each of the multiple segmented image frames and a blood vessel mask for each of the multiple image frames. The pixel region of the contrast mask for each of the plurality of image frames corresponds to the cross-sectional area of ​​the pharmaceutical portion.

11. The system of claim 10, wherein the one or more processors are further configured to: determine at least one of a coronary flow reserve ("CFR") value or a microcirculation resistance index ("IMR") value based on the determined average transit time.

12. The system of claim 10 or 11, wherein the imaging data is optical coherence tomography ("OCT") imaging data, intravascular ultrasound imaging data, miniature OCT imaging data, or near-infrared spectral imaging data.

13. The system of claim 10, wherein determining the average transport time of the drug within the blood vessel is further based on the distribution curve.

14. The system of claim 10 or 13, wherein when determining the average transit time, the one or more processors are further configured to integrate the distribution curve.

15. The system of claim 10, wherein when segmenting each of the plurality of image frames, the one or more processors are configured to: Each frame in the plurality of image frames is segmented by using a threshold.

16. The system of claim 10, wherein determining the vascular mask for each of the plurality of image frames is based on at least one of lumen offset, catheter offset, or wire offset.

17. The system of claim 15 or 16, wherein when each of the plurality of image frames is segmented by a threshold, the one or more processors are configured to perform at least one of the following: Calculate the Gaussian mixture model. Each of the one or more image frames is compared with a predetermined threshold, or Apply the Otsu threshold.

18. A non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to: Receive imaging data of a blood vessel, the imaging data including multiple image frames, each of the multiple image frames including a portion of a drug injected into the blood vessel; Determine the cross-sectional area of ​​the drug portion in the plurality of image frames; as well as Based on the image frame including at least the drug component, the average transport time of the drug within the blood vessel is determined. When determining the cross-sectional area of ​​the pharmaceutical portion, the one or more processors are configured to: Segment each frame in the plurality of image frames; Determine a blood vessel mask for each of the plurality of image frames; and A contrast mask is determined based on each of the multiple segmented image frames and a blood vessel mask for each of the multiple image frames. The pixel region of the contrast mask for each of the plurality of image frames corresponds to the cross-sectional area of ​​the pharmaceutical portion.

19. The non-transitory computer-readable medium of claim 18, wherein the one or more processors are further configured to: determine at least one of a coronary flow reserve ("CFR") value or a microcirculation resistance index ("IMR") value based on the determined average transit time.

20. The non-transitory computer-readable medium of claim 18 or 19, wherein the imaging data is optical coherence tomography ("OCT") imaging data, intravascular ultrasound imaging data, miniature OCT imaging data, or near-infrared spectral imaging data.

21. The non-transitory computer-readable medium of claim 18, wherein the one or more processors are further configured to: A distribution curve is created based on the determined cross-sectional area of ​​the drug portion in each of the plurality of image frames.

22. The non-transitory computer-readable medium of claim 21, wherein determining the average transport time of the drug within the blood vessel is further based on the distribution curve.

23. The non-transitory computer-readable medium of claim 21 or 22, wherein when determining the average transit time, the one or more processors are further configured to integrate the distribution curve.

24. The non-transitory computer-readable medium of claim 18, wherein when each of the plurality of image frames is segmented, the one or more processors are configured to: Each frame in the plurality of image frames is segmented by using a threshold.

25. The non-transitory computer-readable medium of claim 18, wherein determining the vascular mask for each of the plurality of image frames is based on at least one of lumen offset, catheter offset, or wire offset.

26. The non-transitory computer-readable medium of claim 24 or 25, wherein when each of the plurality of image frames is segmented by a threshold, the one or more processors are configured to perform at least one of the following: Calculate the Gaussian mixture model. Each of the one or more image frames is compared with a predetermined threshold, or Apply the Otsu threshold.

Images