System and method for automatic pullback triggering

Abstract

Various aspects of the present disclosure relate to identifying when blood in a blood vessel has been sufficiently cleared in order to capture intravascular images of the vessel wall. The disclosed systems and methods allow for identifying initial blood clearance and final blood clearance based on identification of edges within scan lines of a plurality of image frames. Edges of a plurality of scan lines can be analyzed to determine an average edge offset for each image frame and the average edge offsets of the plurality of image frames are averaged over different time windows to determine when an initial blood clearance event and a final blood clearance event have occurred. Once the final blood clearance event has been identified, the disclosed systems can automatically initiate a catheter pullback procedure in order to capture intravascular images over a length of a blood vessel that has been sufficiently cleared of blood.

Description

[0001] Cross-references to related applications

[0002] This application claims the benefit of U.S. Provisional Application No. 63 / 155,833, filed March 3, 2021, entitled “Systems and Methods For An Automatic Pullback Trigger,” the entire disclosure of which is incorporated herein by reference. Technical Field

[0003] This disclosure generally relates to the field of systems and methods for vascular system imaging and data acquisition. More specifically, this disclosure relates to a system and method for determining the presence of specific vascular conditions before initiating intravascular image acquisition along a length of the blood vessel. Background Technology

[0004] Coronary artery disease is a leading cause of death worldwide. Better capabilities to diagnose, monitor, and treat coronary artery disease are crucial for saving lives. Intravascular optical coherence tomography (OCT) is a catheter-based imaging technique that uses light to visualize the walls of coronary arteries and generate images for research. Utilizing coherent light, interferometry, and micro-optics, OCT provides in vivo tomographic video rates with micrometer-level resolution within diseased vessels. The use of fiber optic probes to observe subsurface structures at high resolution makes OCT particularly suitable for minimally invasive imaging of internal tissues and organs. This level of detail in OCT enables users to diagnose and monitor the progression of coronary artery disease.

[0005] OCT imaging of parts of a patient's body provides doctors and others with a useful diagnostic tool. For example, imaging of the coronary arteries via intravascular OCT can indicate the location of constriction or narrowing. This information helps cardiologists choose between invasive coronary artery bypass grafting and less invasive catheter-based procedures such as angioplasty or stent delivery.

[0006] OCT combines the principles of ultrasound, the imaging capabilities of a microscope, and the shape-factor familiar to clinicians. While ultrasound generates images through backscattered sound "echoes," OCT utilizes infrared light waves that reflect the internal microstructures of biological tissue. Infrared light has a frequency and bandwidth several orders of magnitude higher than medical ultrasound signals, resulting in significantly improved image resolution; approximately 8 to 25 times greater than ultrasound- or X-ray-based methods. OCT uses coherent gating to detect single-scattered photons, achieving tomographic imaging similar to ultrasound or computed tomography (X-ray), but with much higher resolution. Although standard electronics are sufficient to handle ultrasound echoes traveling at the speed of sound, interferometry is required to extract the reflected optical signals from the light used in OCT. The output measured by the interferometer is processed by a computer to generate high-resolution, real-time, cross-sectional, or three-dimensional tissue images. This powerful technology provides in-situ tissue images with near-histological resolution without the need for specimen removal or manipulation.

[0007] A potential limitation of cardiovascular OCT is that it cannot generate clear images of the lumen when blood is present, because the components of red blood cells strongly scatter near-infrared light, making image reconstruction difficult. Therefore, there is a need for systems and methods that facilitate the detection of when blood has been removed from the lumen. The aspects and embodiments of the invention discussed below address this need. Summary of the Invention

[0008] The systems and methods disclosed herein process intravascular image frames to identify, substantially in real time, when blood has been adequately removed from the vessel to allow imaging of the vessel wall. The disclosed systems and methods allow identification of an initial blood removal state, corresponding to the moment when blood removal from the vessel has begun. Furthermore, a final blood removal state can be identified based on the determination that visibility within the vessel no longer improves. Visibility within the vessel is determined based on the identification of edges within scan lines of multiple image frames captured consecutively or over a given period. As blood is removed, the edges identified within each image frame will, on average, have a greater offset from the central axis of the vessel lumen. The edges of multiple scan lines can be analyzed to determine the average edge offset for each image frame, and the average edge offset of multiple image frames can be averaged over different time windows to determine when a specific blood removal state has occurred. Blood removal from the vessel can be categorized into three states: 1) a “pre-removal state,” corresponding to a period of time with a normal amount of blood within the vessel; 2) a “currently being removed state,” corresponding to a period of time when blood is being removed from the vessel; and 3) a “fully removed state,” corresponding to a period of time when blood has been adequately removed from the vessel. The transition from the pre-cleaning state to the current cleaning state can be referred to as the initial cleaning moment, and the transition from the current cleaning state to the fully cleaned state can be referred to as the final cleaning moment. The vessel can also regress to a previous state; for example, the vessel can regress from the current cleaning state to the pre-cleaning state without reaching the fully cleaned state. Once the final cleaning transition to the fully cleaned state has been identified, the disclosed system can automatically initiate a catheter pull-back procedure to capture intravascular images along a length of the cleared blood vessel. Other features and advantages of the disclosed embodiments will become apparent in the following description and drawings.

[0009] This document discloses a method and system for identifying intraluminal blood clearance in a blood vessel. According to various aspects of this disclosure, one or more processors are configured to: acquire multiple intravascular image frames of the lumen; calculate the average edge offset of multiple scan lines within the multiple intravascular image frames; identify a first clearance event based on an increase in the average edge offset of a first set of image frames according to one or more initial conditions; identify a second clearance event based on the occurrence of the initial clearance event and based on the fact that the average edge offset of a second set of image frames has not increased according to one or more final conditions; and automatically initiate a catheter retraction procedure based on the identification of the final clearance event and the identification that the lumen is in a sufficiently cleared state.

[0010] According to other aspects of this disclosure, the aforementioned one or more initial conditions may include at least one of the following: 1) the short-term time window average edge offset is greater than a predetermined maximum value; and 2) the short-term time window average edge offset is greater than the baseline time window average edge offset by at least one specific offset. Furthermore, the predetermined maximum value may be set to be greater than the maximum duct outer diameter offset. For example, this specific offset may be 40 micrometers. The short-term time window average edge offset can be calculated using a predetermined number of recently captured image frames. The baseline time window average edge offset can be calculated using image frames captured over a time period greater than 0.5 seconds.

[0011] According to other aspects of this disclosure, one or more of the aforementioned final conditions may include at least one of the following: 1) for image frames captured after the initial clearing event, the per-frame increment of the average edge offset within the intermediate time window is less than the maximum per-frame increment of the average edge offset within the intermediate time window; and 2) a predetermined time period has elapsed since the maximum cleared region has been observed in one or more of the plurality of image frames. The average edge offset within the intermediate time window may be based on image frames captured within a time period of less than 0.5 seconds. The maximum cleared region may be based on the average edge offset for one or more image frames, and wherein the predetermined time period is at least 40 milliseconds.

[0012] In other aspects of this disclosure, edge thresholds can be calculated based on the identification of minimum and maximum pixel intensity values ​​within the range of scan lines of one or more image frames.

[0013] In other aspects of this disclosure, multiple intravascular images are captured by a catheter device having a sheath with a transparent layer and a doped layer, and wherein calculating an edge threshold includes: identifying a minimum pixel intensity value within a scan line range corresponding to the transparent layer, and identifying a maximum pixel intensity value within a scan line range corresponding to the doped layer. Edges within the multiple intravascular image frames can be identified based on at least one pixel intensity value of a selected scan line having an intensity greater than a first edge threshold, and based on at least one pixel intensity value of a selected scan line having an intensity value less than a second edge threshold, wherein the first edge threshold is greater than the second edge threshold. Attached Figure Description

[0014] Figure 1 This is a schematic diagram of an OCT imaging and data acquisition system according to various aspects of this disclosure.

[0015] Figure 2 This is a diagram illustrating the removal of blood from the lumen as observed from various aspects of this disclosure.

[0016] Figures 3 to 5These are annotated OCT image frames representing different time periods during blood clearance according to various aspects of this disclosure.

[0017] Figure 6 It is a flowchart for identifying the initial clearing state and the final clearing state in accordance with various aspects of this disclosure.

[0018] Figure 7 This is a flowchart for calculating the edge threshold based on various aspects of this disclosure.

[0019] Figure 8 It is a flowchart for calculating blood clearance information based on various aspects of this disclosure.

[0020] Figure 9 These are OCT image frames according to various aspects of this disclosure. Detailed Implementation

[0021] Figure 1 This is a schematic diagram depicting the components of OCT system 10. OCT system 10 may include any suitable light source that meets the coherence and bandwidth requirements for the applications and data collection described herein. The vessel of interest 20 includes a vessel wall 21 defining the vessel lumen, which is imaged using catheter 25. A portion of catheter 25 has a fiber-optic imaging probe 30 disposed therein. Catheter 25 may include a flushing subsystem with a flushing port 32. The flushing system may be of any suitable type or kind that drains a sufficient volume of blood to allow for in vivo OCT data acquisition using probe 30. System 10 includes an OCT system or subsystem 36 connected via fiber optic cable to imaging probe 30. OCT system or subsystem 36 may include a light source (such as a laser), interferometer, various optical paths, clock generator, photodiode, and other OCT system components.

[0022] One or more computers or processors may be part of the OCT system 36 or may be included as a separate subsystem 40 communicating with the OCT system 36. The computer or processor 40 may include memory, storage devices, buses, and other components suitable for processing data and performing flushing processes or software-triggered methods for lumen detection and pullback data collection, as discussed below. In one embodiment, the computer or processor includes a software implementation or program 41 of the methods described herein, which is stored in memory and executed using a processor. The display 42 may also be part of the overall system 10 for displaying cross-sectional scan data as a longitudinal scan or in other suitable formats.

[0023] One limitation of cardiovascular OCT is that it cannot capture clear images of the lumen through the blood due to the scattering of near-infrared light by blood cells. Therefore, when capturing OCT images before clearing the blood from vessel 20, proper image reconstruction of the lumen wall 21 is not possible. Accordingly, in order to capture OCT images of the lumen wall 21, the blood in vessel 20 is temporarily cleared for a period of time. The blood can be drained via a flushing solution (such as physiological saline used through port 32), but the flushing rate must be sufficient to overcome the native flow, which is relatively high in coronary arteries, at 1-5 ml per second. Using a flushing-based method, a clear image time of approximately 3-5 seconds can be established.

[0024] The time required to establish clarity with common reagents (10-20 ml) depends on many factors, such as local blood flow velocity, artery size / imaging location, and the incidence of collateral circulation. However, it is typically in the range of approximately 2 to 5 seconds. The time required to acquire an OCT pullback record (OCT data acquisition process) is in the range of approximately 2 to 4 seconds. Therefore, during the pullback process, it is best to initiate OCT data acquisition once sufficient clearance has been established.

[0025] Computer-based methods preferably process scanned images substantially in real time (or within a time period suitable for a given application, as specified by other OCT systems) and trigger pullback when sufficient clearance has been detected. Computer system 40 can perform the methods described herein. In one embodiment, the methods and systems described herein can analyze complex image data at least 180 frames per second in real time and use the analyzed OCT frames to identify when a vessel is sufficiently cleared in order to initiate pullback.

[0026] For a system, accurately determining when blood in a blood vessel has been adequately cleared is crucial, and physicians may consider even a 400-millisecond delay in identifying the adequate clearance status undesirable. By minimizing the delay at the start of image acquisition, current systems can fully utilize the injected flushing medium, maximize the cleared portion during pullback, minimize the possibility of blood re-entering the vessel distal to the pullback, and maximize recording utility, thereby providing information for optimal vascular treatment. Furthermore, false positives in determining a cleared state can lead to initiating the OCT pullback procedure before the blood in the vessel has been cleared. Therefore, the systems and methods of this disclosure aim for accurate identification of the clearance status, preventing OCT pullback from occurring too early or too late. A suitable method for detecting flushing clearance reliably and in real-time using a system or method based on automated software is a feature of this invention.

[0027] The currently disclosed systems and methods are also designed to operate in a wider range of environments, including those where the exact shape and size of the lumen are unknown. Furthermore, the systems and methods of this disclosure can also allow for the detection of blood clearance in small lumens, such as lumens with a diameter of approximately two millimeters. Other blood clearance detection methods typically cannot identify the clearance status of such small lumens because they require the detection of a certain degree of change in lumen wall displacement to determine that the lumen has been cleared.

[0028] Software detection for vascular lumen flushing and clearance is performed as described below, using, for example, all or a subset of the methods described below. Once a suitable clearance state is achieved, the software automatically triggers an OCT intravascular pullback data collection process or the acquisition of a record. Software-based methods can be used to detect the clearance state of an artery. This software-based method processes OCT images of the artery essentially in real time to determine clearance radius and quality metrics for each image. Pullback and data acquisition can be automatically initiated when the clearance radius and other potential quality metrics meet predefined "complete clearance of the artery" criteria. Pullback refers to the retraction of probe 30 and / or catheter 25 through vessel 20 to collect data for vascular assessment and treatment. When probe 30 and / or catheter 25 are pulled back, OCT data is collected and sent to OCT system 36 and / or computer system 40. When probe 30 is longitudinally stationary, data is sent to the computer system for execution of the clearance state detection method after flushing initialization.

[0029] refer to Figure 1 If the vessel of interest 20 is a coronary artery, an OCT fiber optic imaging catheter (such as catheter 25 with probe 30) can be used to perform OCT imaging of the coronary artery. The OCT imaging catheter 25 is placed in the artery at the location where retraction recording will begin, and the computer-based OCT software method of flushing and clearing detection is initialized (enabled). The OCT system operator then injects a clearing medium (flushing solution), such as saline, contrast agent solution, dextran, or equivalent, into the artery to clear it for imaging. The flushing and clearing detection method, then executed on computer 40, determines when the injected flushing solution has provided sufficient clearing in the artery to allow the OCT system to acquire good images. When such a determination has been made, retraction is triggered by the computer. The determination of sufficient clearing can be made in real time by processing each frame of images acquired by OCT system 36 or computer system 40.

[0030] Figure 1The system shown is configurable to process each captured OCT image frame within the vessel 20. While processing each image frame, the image can be analyzed to identify edges or boundaries in the image and determine the offset of the identified edges from the center of the lumen. When blood is present within the vessel 20, light from the imaging catheter 25 is scattered by blood cells, causing any identified edges to be slightly offset from the central axis along the radial axis. As the blood in the vessel 20 is cleared, the identified edges in the OCT image will be offset more significantly from the catheter's central axis.

[0031] Figure 2 It is an image representation of the edge shift observed at a specific location within the blood vessel over a period of time as blood is removed from the vessel. Figure 2 In the diagram, the dashed line 210 represents the central axis of the imaging catheter, while the boundary 220 of the dark area represents the observed edge, identified by analyzing light from the OCT catheter as intravascular image frames are captured over a period of time. The distance between the dashed line 210 and the boundary 220 represents the average edge offset observed at a given time. Figure 2 As can be seen, the initial edge offset is relatively small. This is due to the presence of blood within the lumen. As the blood is removed from the lumen, the observed edge boundary 220 moves away from the vessel's central axis 210. According to the present disclosure, the point where the edge offset begins to widen is identified as the initial clearance moment. Once initial clearance is observed, the vessel is in a currently clearing state, and the disclosed system can then determine whether the vessel has entered a fully cleared state or has regressed to a pre-cleared state. Figure 2 In this process, the blood vessel undergoes "final clearance," transitioning from the current clearance phase to a fully cleared phase. As described below, the fully cleared phase is determined based on the average edge offset reaching a relatively cleared and stable state within a predetermined time period. Once a blood vessel is identified as having undergone final clearance and thus entering the fully cleared phase, the above-mentioned... Figure 1 The system can be configured to automatically initiate a pull-back procedure in which the catheter is pulled along a predetermined length of the blood vessel, capturing OCT images along that length. Because the blood within the blood vessel is cleared during the pull-back procedure, the degree to which the captured OCT images are obscured by blood within the blood vessel is minimized.

[0032] Based on currently available information, Figure 1 The system can process captured OCT image frames in order to calculate the average edge offset (“AEO”) for each image frame. Figures 3 to 5 Here are three examples of annotated OCT image frames, which can be viewed through... Figure 1 The catheter-based data collection probe 30 captures these image frames at specific locations within the patient's artery. Figure 3This is OCT image frame 300 where blood has not yet been cleared from the blood vessels. When processing OCT image frame 300, each scan line of the image can be analyzed to identify edge offsets relative to the center 302 of the catheter. The OCT image can be analyzed using polar coordinates so that each scan line is processed as an A-line relative to the center 302 of the catheter. Each annotation point 304 represents the edge observed for each scan line. For clarity, only two annotation points 304 are given reference numerals in image 300. As shown in image 300, the identified points 304 form a circle around the center 302, with each point 304 being approximately close to the center 302. The distance between each point 304 and the center 302 can be calculated and stored as an “edge offset.” This distance can be based on the number of image pixels between point 304 and the center 302. The image is represented in two-dimensional scan-transformed coordinates, thus having a representation that physically resembles the catheter and blood field. However, offset measurements are typically performed radially in polar coordinates, and all image processing can be performed in this polar coordinate system. Based on the edge offset of each point 304, an average edge offset can be calculated, representing the average distance from the edge identified for all scan lines within the image frame to the center 302. The ring 306 observed around the center 302 is a result of the catheter sheath having a doped layer and a transparent layer, which produce different levels of pixel intensity. As described below, the disclosed system and method can use an edge detection process that interprets these doped and transparent layers of the catheter sheath. The doped and transparent layers of the catheter sheath can be used to identify the outer edge of the catheter sheath, and the search for image edges can be configured to start radially outward from just beyond the outer edge of the catheter sheath. By starting just beyond the outer edge of the catheter sheath, the system avoids identifying any thin layer of blood still attached to the catheter sheath as an edge. Furthermore, the doped and transparent layers of the catheter sheath can be used to sample the image pixel intensity to identify an appropriate threshold for edge detection within the image.

[0033] Figure 4 This is OCT image frame 400, captured during the period when the contrast agent begins flushing the blood vessels to clear the blood. Some edges observed in some of the scan lines are related to... Figure 4 In image 300, the edges identified maintain the same distance from the center 302. Some of these edges are identified as annotation points 304a in image 400. However, some scan line edges in image 400 now have a greater offset from the center 302, as can be seen from annotation point 304b. Due to the increased offset of some scan line edges, the average edge offset of image 400 will be greater than the average edge offset of image 300. This increase in average edge offset of image 400 relative to image 300 can be used to determine that the vessel has undergone initial clearance, as the vessel has transitioned from a pre-clearing state to a currently clearing state, as described above. Figure 2 As stated above.

[0034] Figure 5 This is OCT image frame 500 taken while the contrast agent solution continues to flush the blood vessels to further remove blood from them. Figure 5 As can be seen, image frame 500 contains a large number of scan lines, where the edge offset is greater than that shown in image frames 300 or 400. Specifically, annotation point 304c represents scan lines where the observed edges correspond to the vessel wall. Therefore, even as blood continues to be flushed from the vessel, the edge offsets of these scan lines do not increase significantly. Annotation point 304a represents scan lines where backscattering occurs relatively close to the center 302. However, the number of scan lines containing annotation point 304a is relatively small (less than 20%) compared to those containing annotation point 304c. Therefore, the average edge offset of image frame 500 will begin to approach a relatively stable value, as most edge offsets no longer change significantly from one image frame to the next. According to various aspects of this disclosure, the relative stability of the calculated average edge offset across a series of OCT image frames can be used to determine whether a vessel has transitioned from a currently clearing state to a fully cleared state.

[0035] Figure 6-8 This is a flowchart that provides an example, in which... Figure 1 The system shown can identify the initial and final clearance of blood vessels according to this disclosure. Although Figure 6-8 The operation boxes in the document are provided in a specific order, but one or more processors of the exposed system can be configured to add, remove, or switch operations in a specific order depending on various aspects of the method and the system.

[0036] Figure 6 This is flowchart 600, where OCT image frames are used to identify whether the vessel has undergone initial and final clearance. OCT images can be processed frame-by-frame via a real-time image pipeline, with image frames and catheter offset data provided as input in box 602. In box 604, the image data can be smoothed to reduce noise. This smoothing can be done using a filter kernel (e.g., a 5x5 spatial moving average boxcar filter kernel). In box 606, edge thresholds for the image data are calculated so that edge offsets and associated blood clearance information can be compiled in box 608. Figure 7 and Figure 8Examples of the procedures for calculating edge thresholds (box 606) and blood clearance information (box 608) are provided respectively. As described above, the application of edge thresholds to an image frame may include the identification of edges at each scan line, and the offset of each identified edge from the center of the blood vessel is calculated as an edge offset, which represents blood clearance information. The edge offset for each scan line of the image frame can be used to calculate a single average edge offset for each image frame. This average edge offset may be the average of edge offsets, or it may be the result of other forms of statistical analysis of multiple edge offsets.

[0037] In box 610, a check can be performed to determine if too many edge offsets are within a predefined distance of the catheter's outer diameter offset. For example, it can be determined whether the edge offset is within 120 micrometers of the catheter's outer diameter offset, and if a certain percentage (e.g., 80%) of the edge offset is within this predefined distance, the system will skip the current image frame and continue processing the next image frame in the real-time pipeline. If the edge offset of an image frame is not too close to the catheter's outer diameter offset, the system can continue processing the image frame by calculating the Time Window Average Edge Offset (TWAEO), as provided in box 612. TWAEO represents the time window average of all average edge offsets calculated over a specific time period or a specific number of image frames. Specifically, the system can calculate multiple TWAEOs based on windows with different time periods or different numbers of image frames. For example, the Baseline Time Window Average Edge Offset (TWAEO1) can be calculated by taking the time window average of the average edge offsets of image frames captured over the past 1 second or other time periods sufficient to establish a baseline. The baseline time window typically needs to be at least 0.5 seconds or longer to establish a long-term average edge offset. The intermediate time window average (TWAEO2) can be calculated based on the average edge offset already calculated for image frames captured within a time window of less than 0.5 seconds. For example, the intermediate time window could be 0.2 seconds. Furthermore, the short-term time window average (TWAEO3) can be calculated based on a fixed number of the most recently captured frames. For example, TWAEO3 could be the time window average of the average edge offset of the last four image frames in the pipeline.

[0038] TWAEO1 can be used to represent a baseline time window because it is based on the average edge offset of a large number of image frames captured over a relatively long time domain. The number of image frames included in this 1-second time window will be based on the frame rate of the OCT imaging device, but for some OCT imaging devices, the 1-second window will include approximately 180 image frames, representing a high-speed rotation of 180 frames per second. Regarding TWAEO2, this average time window can be used to examine edge offset increments to assess the trend of clearing improvement and determine whether final clearing has occurred. The short window of TWAEO3 can be used to identify instantaneous edge offset changes occurring across a predetermined number of image frames.

[0039] One or more TWAEOs calculated in box 612 can be used in box 614 to determine whether a vessel has entered the initial clearance state. As previously mentioned, initial clearance refers to the commencement of blood removal from the vessel, thus the vessel can be considered to have transitioned from a pre-clearance state to a currently clearing state. Initial clearance can be identified based on an increase in the average edge offset observed in the image frame. The determination of initial clearance can be based on one or more of the calculated TWAEOs, or on determining that at least one of a plurality of initial conditions has been met. The first condition can be based on the absolute amount of the average edge offset consistent with the vessel undergoing initial blood clearance, while the second condition can represent an increase in the average edge offset of the short window relative to the baseline of a longer time window. For example, in box 614, it can be determined whether a vessel has undergone initial clearance if either of the following conditions is met: 1) TWAEO3 is 120 micrometers larger than the maximum value of the catheter outer diameter offset of all A lines in the image frame; or 2) TWAEO3 is more than 40 micrometers larger than the baseline offset of TWAEO1. The 120-micrometer and 40-micrometer values ​​used for the first and second conditions are merely examples, and different values ​​can be used based on the construction of the OCT catheter and the catheter sheath. For example, the first condition can be modified so that any TWAEO3 value greater than the outer diameter offset of the catheter satisfies the condition, and the second condition can be modified to be based on any increase in TWAEO3 relative to TWAEO1, provided that the increase is sufficient to indicate an actual increase in the average edge offset.

[0040] If initial clearing has not yet been identified, the vessel remains in the pre-clearing state, and the system can return to box 602, where the next image frame in the pipeline is processed. If initial clearing has been detected, the system can proceed to box 618, where it is determined whether the vessel has undergone final clearing in order to transition from the current clearing state to the fully cleared state. The determination of the final clearing event can be based on one or more calculated TWAEOs that indicate that the average edge offset has reached a stable condition within a predetermined time period, and this determination can be based on one or more final conditions being met. A stable state of the average edge offset can be identified when it is determined that the average edge offset no longer improves by a certain predetermined value or rate. For example, if either of the following two conditions is found to be met, it can be determined that the vessel has undergone "no improvement": 1) the TWAEO2 increment per frame is less than a predetermined amount than the maximum increment recorded during the processing of previous image frames, or 2) a predetermined time period has elapsed since the maximum cleared area was recorded. For example, in conjunction with the first condition, it can be determined that blood clearance has reached a steady state when the TWAEO2 increment per frame is less than one-third of the maximum increment recorded during the processing of previous image frames. Other comparisons of the average edge offset over a time window can be used, provided that the comparison indicates that the increase in the average edge offset has sufficiently slowed down, thus indicating that blood clearance has reached a final steady state. For the second condition, the predetermined time period can be, for example, at least 40 milliseconds, so that if the maximum average edge offset of one or more image frames was recorded 40 milliseconds prior, it is identified as final clearance. Furthermore, the predetermined time period can be precisely adjusted so that a pullback procedure is initiated immediately upon reaching final clearance without causing false positives for final clearance. For example, the predetermined time period can be selected as 44.44 milliseconds.

[0041] If the "no improvement" condition is not met at box 620, the system can return to box 602 by continuing to process the next image frame in the pipeline. However, if at least one of the "no improvement" conditions has been met (box 620), the system can determine whether the vessel is in a sufficiently cleared state (box 622). This determination can be based on whether the cleared area of ​​the currently processed image frame or the group of most recently captured image frames is larger than the initial cleared area. For example, the average edge offset of the current image frame can be compared with the average edge offset observed when or shortly after the initial clearing occurred. If the current cleared area is significantly larger than the initial cleared area, the vessel can be determined to have undergone final clearing and is thus in a sufficiently cleared state. Specifically, it can be determined whether the current cleared area is greater than or equal to the initial cleared area multiplied by a value "K", where K is greater than or equal to 1.0. This K value can be adjusted to adjust the sensitivity of the final clearing detection, thereby identifying the moment when blood has returned to the vessel. The larger the K value, the higher the threshold for determining that final clearing has been achieved. The system can then be configured to automatically initiate a pullback procedure, at which point the catheter is pulled to a predetermined length of the vessel while capturing an image of the vessel wall. Images captured during pullback can be saved and processed for vascular diagnosis. If a sufficiently cleared state is not found in box 622, the initial cleared state can be reset to false, and the system can return to box 602 to process the next image frame in the pipeline.

[0042] Figure 7 This is flowchart 700, which provides an example of edge threshold calculation and can be combined with... Figure 6 Box 606 executes this example. In box 702, smoothed image frames and catheter offset data are received. When calculating the edge threshold, the system can use information associated with detectable layers of the OCT image representing different layers of the catheter sheath. In particular, the catheter sheath may include a doped layer corresponding to the area surrounding the center of the OCT image, and a transparent layer surrounding the doped layer.

[0043] Turning Figure 9 In OCT image 900, the doped layer and the transparent layer are visible. Specifically, the doped layer of the duct sheath can be identified by ring 902 appearing in image 900. Outside ring 902, ring 904 is visible. This ring 904 corresponds to the transparent layer of the duct sheath. As described below, the doped layer and the transparent layer can be used to calibrate a threshold level used to identify edges within the OCT image.

[0044] The doped layer can be a scattering layer filled with TiO2. Image 900 is a negative of the OCT image; correspondingly, the doped layer appears as a dark ring, but in the standard image, the doped layer appears as a bright band in the OCT image. The outer diameter layer is the farthest boundary of the imaging catheter. The catheter sheath offset can be calculated using the OPTIS catheter sheath detection algorithm. The offsets of the doped layer and the catheter sheath outer diameter can be used to calculate the edge threshold.

[0045] Back Figure 7 In box 704, the system iterates to another scan line / A line in the image frame. In box 706, the minimum / valley intensity value "I" in the duct transparency layer is identified. min In box 708, identify the maximum / peak value of the doped layer "I". max The maximum value ranges between the inner and outer boundaries of the doped layer. For example, for some duct sheaths, the inner boundary location can be identified as approximately 0.0015 inches or approximately 38 micrometers inward from the outer boundary of the doped layer, ranging from the outer boundary to the outer diameter layer boundary. The iterative process of boxes 704 to 708 is then repeated on all scan lines of the OCT image frame.

[0046] In box 710, I can be calculated. min Value and I max The average value, and then based on I min Value and I max The average value is used to estimate the threshold "T". tissue (Box 712). For example, the threshold "T" tissue "It can be estimated based on the following formula:"

[0047] Starting from the first scan line "α = 1" to the last scan line "N", the above summation is performed on all scan lines of the image. This threshold can then be used to identify edges within the image frame and... Figure 6 The clearing information is calculated in box 608.

[0048] Figure 8 It provides a sample flowchart 800, combined with Figure 6 Box 608 allows for the calculation of clearance information using this example. The clearance information is based on the lumen and blood edge offsets of the blood vessels observed in the image frame. Combined with... Figure 6Box 608 allows for the application of an edge detection algorithm as a one-dimensional edge-finding core along each scan line of the image's polar coordinates to detect rising and falling edges relative to the catheter sheath offset input. The search range for edges can be a predetermined distance from the outer diameter offset of the catheter sheath to the end of each A-line. Specifically, the edge search range can be set by a controllable variable sufficient to avoid identifying any remaining traces of blood. For example, the edge search range can be set 70 micrometers outward from the outer diameter offset to the end of each A-line. This 70-micrometer offset can be increased to avoid potential blooming at the edge of the catheter sheath's outer diameter layer and to prevent small amounts of residual blood that might adhere to the catheter sheath. The edge detection algorithm can be configured to identify rising and falling edges using a threshold calculated in conjunction with flowchart 700. Specifically, T... tissue It can be used as the blood edge (“BE”) threshold, and the vessel edge (“VE”) threshold can be defined as T. tissue Part of, such as T tissue Half of it.

[0049] Figure 8 Flowchart 800 provides an example of calculating this cleansing information data. In block 802 of flowchart 800, the smoothed image frame and the calculated edge threshold data are accessed. In block 804, the system iterates to the next A-line of the image frame to be analyzed. The system can then scan the pixel intensity from an outer diameter offset of 70 micrometers to the end of the selected A-line (block 806) and then perform edge detection processing on the selected A-line.

[0050] Based on defined VE and BE thresholds, the scan line can be processed to determine its edge offset. As part of this determination, the algorithm attempts to calculate any presented blood offset and vessel edge offset. If both calculations are successful, the calculations resolve to approximately the same offset; however, in cases where there is blood but some clearance behind it, the calculations will not resolve to the same offset. The determination of these offsets is further described below. When both blood and vessel edge offsets are found simultaneously, a search is performed between the two offsets to determine if clearance exists, indicated by an average intensity between the offsets being less than a clearance threshold. This clearance threshold can be the blood edge threshold divided by 3.3. If this condition is met, it indicates substantial clearance between the two offsets, and the more distant offset (vessel edge offset) is selected as the scan line's edge offset. Otherwise, the blood edge offset is selected as the edge offset.

[0051] For example, in box 808, by searching outward from the catheter, it is determined whether the pixel intensity of the selected line A is greater than the blood edge offset. If the intensity of the pixel sample is indeed greater than the BE threshold, the blood edge offset is identified at the index of the first pixel with an intensity greater than the threshold (box 810), and line A is marked as having a detected edge offset (box 812). In box 814, the pixel intensity can be checked from the end of line A inward toward the center of the blood vessel, and it is determined whether the pixel intensity is greater than the VE threshold (box 816). If the pixel intensity is greater than the VE threshold, the search for line A continues to find the first pixel with an intensity less than the VE threshold after the sample with an intensity greater than the VE threshold, thus indicating the presence of an ascending edge (box 818). If box 818 determines that an ascending edge exists, the ascending edge is set as a vessel wall edge offset (box 820). If the determination in box 816 or box 818 is not met, it is determined that no vessel wall edge offset was found (box 822).

[0052] Returning to box 808, if the pixel intensity along line A is not greater than the BE threshold, it is determined whether a peak exists within the search range (box 824). If it exists, the blood edge offset is associated with the index position of the peak (box 826). If no peak is identified within the search range in box 824, the blood edge offset is associated with the starting position of the pixel index (box 828). In box 830, line A is marked as having no detected blood vessel wall edge offset.

[0053] The edge offset generated by each scan line / A-line can be used to calculate the average edge offset of the image frame. In box 832, the cleanup information is compiled, including the edge offset, average edge offset, identification of whether the A-line is cleaned, and all other edge information. Then, the system can return to box 804 to iterate over the next A-line of the image frame. Once all A-lines of the image frame have been processed, the cleanup information for the entire image frame can be summarized in box 834, and the summarized cleanup information in box 836 can be used in flowchart 600 to proceed as described above. Figure 6 The process is carried out in box 610. Therefore, the flowchart above allows for the detection of initial and final clearance events within the blood vessel, thereby identifying conditions that can automatically initiate the OCT pull-back procedure.

[0054] Non-limiting software features and embodiments for implementing the disclosed systems and methods The foregoing description is not intended to limit the applicable environment or the scope of this disclosure. Similarly, hardware and other operating components may be suitably incorporated as part of the described apparatus. This disclosure can be implemented through other system configurations, including personal computers, multiprocessor systems, microprocessor-based or programmable electronic devices, network PCs, microcomputers, mainframe computers, and other system configurations. This disclosure can also be implemented in a distributed computing environment where tasks are performed by remote processing devices (e.g., processing devices in different rooms of a catheterization lab or cardiovascular laboratory) linked via a communication network.

[0055] Some parts of the detailed description are presented in the form of algorithms and notations for manipulating data bits in computer memory. Those skilled in the art of computers and software can use these algorithms to illustrate and represent them. In one embodiment, an algorithm is generally considered herein as a structured sequence of operations that yields a desired result. The operations performed herein as method steps or otherwise described are those that require physical manipulation of physical quantities. Typically, though not always, these quantities are in the form of electrical or magnetic signals that can be stored, transmitted, combined, converted, compared, and otherwise manipulated.

[0056] Unless otherwise specified in the discussion below, it is understood that throughout the description, discussions using terms such as “processing” or “computing” or “searching” or “detecting” or “measuring” or “calculating” or “comparing”, “generating” or “determining” or “displaying”, or Boolean logic or other set-related operations or similar operations, refer to the behavior and processing of a computer system or electronic device that manipulates and converts data represented as physical (electronic) quantities in the registers of the computer system or the registers and memories of the electronic device into other data similarly represented as physical quantities in electronic memory or registers or other such information storage, transmission or display devices.

[0057] In some embodiments, this disclosure also relates to apparatus for performing the operations described herein. Such apparatus may be specifically constructed for the desired purpose, or it may also include a general-purpose computer that can be selectively activated or reconfigured by a computer program stored in a computer.

[0058] Embodiments of this disclosure may be embodied in a variety of different forms, including but not limited to computer program logic used with a processor (e.g., a microprocessor, microcontroller, digital signal processor, or general-purpose computer), programmable logic used with a programmable logic device (e.g., a field-programmable gate array (FPGA) or other programmable logic device), discrete components, integrated circuits (e.g., application-specific integrated circuits (ASICs)), or any other means including any combination thereof. In common embodiments of this disclosure, part or all of the processing of data collection using an OCT probe and a processor-based system is implemented in the form of a computer program instruction set, which is converted into a computer-executable form, stored in such a form on a computer-readable medium, and executed by a microprocessor under the control of an operating system. Thus, query responses and input data are converted into processor-understandable instructions adapted to generate imaging data, detect vascular boundaries, detect stent struts, compare measured distances relative to set thresholds, and otherwise perform image comparison, signal processing, vascular detection, and other features and embodiments described above.

[0059] The computer program logic that implements all or part of the functionality described earlier in this document can be embodied in various forms, including but not limited to source code, computer-executable form, and various intermediate forms (e.g., forms generated by an assembler, compiler, linker, or locator). Source code may include a set of computer program instructions implemented in any of various programming languages ​​(e.g., object code, assembly language, or high-level languages ​​such as Fortran, C, C++, JAVA, or HTML) for various operating systems or operating environments. Source code may define and use various data structures and communication information. Source code may be in computer-executable form (e.g., via an interpreter), or source code may be transformed (e.g., via a decoder, assembler, or compiler) into computer-executable form.

[0060] Computer programs can be permanently or temporarily embedded in tangible storage media in any form (e.g., source code, computer-executable, or intermediate form), such as semiconductor storage devices (e.g., RAM, ROM, PROM, EEPROM, or flash programmable RAM), magnetic storage devices (e.g., disks or hard disks), optical storage devices (e.g., CD-ROMs), PC cards (e.g., PCMCIA cards), or other storage devices. Computer programs can be embedded in signals that can be transmitted to a computer via various communication technologies, including but not limited to analog, digital, optical, wireless (e.g., Bluetooth), network, and internet technologies. Computer programs can be distributed in any form as removable storage media along with accompanying printed or electronic documentation (e.g., unpacking software), pre-installed on a computer system (e.g., on a system ROM or hard disk), or distributed from a server or electronic bulletin board via a communication system (e.g., the internet or the World Wide Web).

[0061] Hardware logic (including programmable logic used with programmable logic devices) implements all or part of the functions described above and can be designed using conventional manual methods or electronically designed, captured, simulated, or recorded using various tools, such as computer-aided design (CAD), hardware description languages ​​(e.g., VHDL or AHDL), or PLD programming languages ​​(e.g., PALASM, ABEL, or CUPL).

[0062] Programmable logic can be permanently or temporarily embedded in a tangible storage medium, such as semiconductor memory devices (e.g., RAM, ROM, PROM, EEPROM, or flash programmable RAM), magnetic storage devices (e.g., floppy disks or fixed disks), optical storage devices (e.g., CD-ROMs), or other memory devices. Programmable logic can be embedded in signals that can be transmitted to a computer via various communication technologies, including but not limited to analog, digital, optical, wireless (e.g., Bluetooth), network, and internet technologies. Programmable logic can be distributed as a removable storage medium along with accompanying printed or electronic documentation (e.g., unpacking software), pre-installed in a computer system (e.g., on a system ROM or fixed hard drive), or distributed from a server or electronic bulletin board via a communication system (e.g., the internet or the World Wide Web).

[0063] Examples of suitable processing modules will be discussed in more detail below. As used herein, a module refers to software, hardware, or firmware suitable for performing a specific data processing or data transfer task. Typically, in preferred embodiments, a module refers to a software routine, program, or other memory-resident application suitable for receiving, converting, routing, and processing instructions or various types of data, such as OCT scan data, IVUS scan data, interferometer signal data, target stent profile, post-stent lumen profile and image, view of the intubated lumen profile indicating a fully expanded stent, the ratio of the geometry of the expanded stent lumen profile to the fully expanded lumen profile, stent expansion level indicators (color, fill, etc.), highlight / emphasis pixel characteristics, branch position, branch diameter, stent expansion percentage or fraction, pre-stent FFR value, post-stent FFR value, and other pre- and post-stent values ​​and other relevant information.

[0064] The computers and computer systems described herein may include operatively associated computer-readable media, such as memory for storing software applications used in acquiring, processing, storing, and / or communicating data. It will be understood that such memory may be internal, external, remote, or local, in relation to the computer or computer system to which it is operatively associated.

[0065] The memory may also include any means for storing software or other instructions, including, but not limited to, hard disks, optical disks, floppy disks, DVDs (Digital Multifunction Optical Discs), CDs (Optical Discs), memory cards, flash memory, ROMs (Read-Only Memory), RAMs (Random Access Memory), DRAMs (Dynamic Random Access Memory), PROMs (Programmable ROMs), EEPROMs (Extended Erasable PROMs), and / or other similar computer-readable media.

[0066] Generally, a computer-readable storage medium used in conjunction with the embodiments disclosed herein may include any storage medium capable of storing instructions executable by a programmable device. Where applicable, the method steps described herein may be embodied or executed as instructions stored on or on a computer-readable storage medium. These instructions may be software embodied in various programming languages, such as C++, C, Java, and / or various other types of software programming languages ​​that can be used to create instructions according to embodiments of this disclosure.

[0067] Storage media can be non-transitory or include non-transitory devices. Therefore, non-transitory storage media or non-transitory devices can include tangible devices, meaning that the device has a specific physical form, although its physical conditions can change. Thus, for example, non-transitory means that the device remains tangible despite changes in conditions.

[0068] 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 this disclosure.

[0069] The use of headings and sections in this application is not intended to limit this disclosure; each section may be applied to any aspect, embodiment, or feature of this disclosure.

[0070] 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 method steps, it is assumed that the composition of the doctrine of the present invention is also substantially composed of (or constituted by) the said components, and the method of the doctrine of the present invention is also substantially composed of (or constituted by) the method steps.

[0071] In this application, when an element or component is said to be 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 teachings of the invention, whether explicit or implicit.

[0072] Unless otherwise specified, the use of the terms “include,” “includes,” “including,” “have,” “has,” or “having” should be generally understood as open-ended and non-restrictive.

[0073] Unless otherwise specified, the singular as used herein includes the plural (and vice versa). Furthermore, the singular forms “a” (a), “an” (an), and “said” include the plural forms unless the context explicitly specifies otherwise. Additionally, unless otherwise specified, the doctrine of the invention includes the specific quantity itself when the terms “about” or “substantially” are used before a quantity. The terms “about” and “substantially” as used herein refer to variations in a numerical quantity, for example, in the real world through measurement or processing procedures; due to unintentional errors in these procedures; due to differences / failures in the manufacturing process of materials (e.g., synthetic material strips); due to defects; and variations that are considered equivalent by those skilled in the art, provided that such variations do not cover known values ​​practiced in the prior art. Generally, the terms “about” and “substantially” mean greater than or less than 1 / 10 (e.g., ±10%) of the value or a range of the value.

[0074] 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.

[0075] It should be understood that the figures and descriptions of this disclosure have been simplified to illustrate elements relevant to a clear understanding of this disclosure, and other elements have been omitted for clarity. However, those skilled in the art will recognize that these and other elements may be desirable. However, since these elements are well-known in the art and because they are not conducive to a better understanding of this disclosure, they will not be discussed herein. It should be understood that these figures are presented for illustrative purposes and are not intended as structural diagrams. Omitted details and modifications or alternative embodiments are within the scope of knowledge of those skilled in the art.

[0076] It is understood that, in certain aspects of this disclosure, a single component may be replaced by multiple components, and multiple components may be replaced by a single component, to provide an element or structure or to perform a given function or multiple functions. Such substitution is considered to be within the scope of this disclosure unless it is not operable for certain embodiments of this disclosure.

[0077] The embodiments presented herein are intended to illustrate potential and specific implementations of this disclosure. It is understood that these examples are primarily for illustrative purposes to those skilled in the art. These illustrations or operations described herein may be modified without departing from the spirit of this disclosure. For example, in some cases, method steps or operations may be performed or implemented in a different order, or operations may be added, deleted, or modified.

Claims

1. A method for identifying blood clearance within a lumen, comprising: Multiple intravascular image frames of the lumen are accessed by one or more processors; The one or more processors calculate the average edge offset for multiple scan lines within each intravascular image frame; The first clearing event is identified by the one or more processors based on the average edge offset of the time window for the first set of image frames, increased according to one or more initial conditions; The one or more processors identify the second clearing event based on the occurrence of the first clearing event and based on the fact that the average edge offset of the time window of the second set of image frames has not increased according to one or more final conditions.

2. The method according to claim 1, further comprising: The one or more processors automatically initiate a catheter pull-back procedure based on the recognition of the second clearance event and the recognition that the lumen is in a fully cleared state.

3. The method of claim 1, wherein the one or more initial conditions include at least one of the following: 1) The average edge offset of the short-term time window is greater than a predetermined maximum value; and 2) The average edge offset of the short-term time window is at least one specific offset larger than the average edge offset of the baseline time window.

4. The method according to claim 3, wherein the predetermined maximum value is greater than the maximum catheter outer diameter offset.

5. The method of claim 3, wherein the specific offset is 40 micrometers.

6. The method of claim 3, wherein the short-term time window average edge offset is calculated using a predetermined number of recently captured image frames.

7. The method of claim 3, wherein the baseline time window average edge offset is calculated using image frames captured over a time period greater than 0.5 seconds.

8. The method of claim 1, wherein the one or more final conditions include at least one of the following: 1) For image frames captured after the first clearing event, the per-frame increment of the average edge offset within the intermediate time window is less than the maximum per-frame increment of the average edge offset within the intermediate time window; and 2) A predetermined time period has elapsed since the largest cleared area was observed in one or more of the plurality of intravascular image frames.

9. The method of claim 8, wherein the average edge offset of the intermediate time window is based on image frames captured within a time period of less than 0.5 seconds.

10. The method of claim 8, wherein the maximum cleared region is based on the average edge offset over one or more image frames, and wherein the predetermined time period is at least 40 milliseconds.

11. The method of claim 1, further comprising: The edge threshold is calculated by the one or more processors based on the identification of the minimum and maximum pixel intensity values ​​within the range of the scan lines of one or more image frames.

12. The method of claim 11, wherein the plurality of intravascular image frames are captured by a catheter device having a sheath with a transparent layer and a doped layer, and wherein calculating the edge threshold comprises: The minimum pixel intensity value is identified within the scan line range corresponding to the transparent layer, and the maximum pixel intensity value is identified within the scan line range corresponding to the doped layer.

13. The method of claim 1, further comprising: The one or more processors identify edges within the plurality of intravascular image frames based on the identification that at least one pixel intensity value of a selected scan line has an intensity greater than a first edge threshold, and based on the identification that at least one pixel intensity value of a selected scan line has an intensity value less than a second edge threshold, wherein the first edge threshold is greater than the second edge threshold.

14. A system for identifying blood clearance within a lumen, comprising: The memory, used to store image frame data: and one or more processors, the one or more processors communicating with the memory, the one or more processors being operable to: Access multiple intravascular image frames within the lumen; Calculate the average edge offset of multiple scan lines within each intravascular image frame; The first clearing event is identified by increasing the average edge offset of the time window for the first set of image frames according to one or more initial conditions; Based on the occurrence of the first clearing event, and based on the fact that the average edge offset of the second set of image frames within the time window has not increased according to one or more final conditions, a second clearing event is identified.

15. The system of claim 14, wherein the one or more processors are further configured to automatically initiate a catheter pull-back procedure based on the recognition of the second clearing event and based on the recognition that the lumen has been sufficiently cleared.

16. The system of claim 14, wherein the one or more initial conditions include at least one of the following: 1) The average edge offset of the short-term time window is greater than a predetermined maximum value; and 2) The average edge offset of the short-term time window is at least one specific offset larger than the average edge offset of the baseline time window.

17. The system of claim 16, wherein the predetermined maximum value is greater than the maximum catheter outer diameter offset.

18. The system of claim 16, wherein the specific offset is 40 micrometers.

19. The system of claim 16, wherein the average edge offset of the short-term time window is calculated using a predetermined number of recently captured image frames.

20. The system of claim 16, wherein the baseline time window average edge offset is calculated using image frames captured over a time period greater than 0.5 seconds.

21. The system of claim 14, wherein the one or more final conditions include at least one of the following: 1) For image frames captured after the first clearing event, the per-frame increment of the average edge offset within the intermediate time window is less than the maximum per-frame increment of the average edge offset within the intermediate time window; and 2) A predetermined time period has elapsed since the largest cleared area was observed in one or more of the plurality of intravascular image frames.

22. The system of claim 21, wherein the average edge offset of the intermediate time window is based on image frames captured within a time period of less than 0.5 seconds.

23. The system of claim 21, wherein the maximum cleared region is based on the average edge offset over one or more image frames, and wherein the predetermined time period is at least 40 milliseconds.

24. The system of claim 14, wherein the one or more processors are further configured to calculate an edge threshold based on the identification of minimum and maximum pixel intensity values ​​within the range of the scan lines of one or more image frames.

25. The system of claim 24, wherein the plurality of intravascular image frames are captured by a catheter device having a sheath with a transparent layer and a doped layer, and wherein calculating the edge threshold comprises: The minimum pixel intensity value is identified within the scan line range corresponding to the transparent layer, and the maximum pixel intensity value is identified within the scan line range corresponding to the doped layer.

26. The system of claim 14, wherein the one or more processors are further configured to: identify edges within the plurality of intravascular image frames based on the identification that at least one pixel intensity value of a selected scan line has an intensity greater than a first edge threshold, and based on the identification that at least one pixel intensity value of a selected scan line has an intensity value less than a second edge threshold, wherein the first edge threshold is greater than the second edge threshold.

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