System and method for displaying coverage of stent struts in a vessel

Abstract

In part, a computer-based method of visualizing stent placement in a blood vessel is provided.SOLUTION: The method includes identifying stented region (s) from background noise using fusion of angular stent-strut information for a neighborhood of a given frame. The GUI may include a view of the vessel generated using the distance measurements and demarcating the actual stent region, which provides a visualization of the stent region. The present disclosure also relates to the display of intravascular diagnostic information, such as indicators. The indicator can be generated and displayed with an image generated using the intravascular data collection system. The indicator may include one or more viewable graphical elements suitable for indicating diagnostic information, such as stent information.SELECTED DRAWING: None

Description

[Technical Field]

[0001] [Related Applications] This application claims priority to and the benefit of U.S. Provisional Patent Application No. 62 / 162,795, filed May 17, 2015, U.S. Provisional Patent Application No. 62 / 196,997, filed June 25, 2015, U.S. Provisional Patent Application No. 62 / 322,578, filed April 14, 2016, and U.S. Patent Application No. 14 / 975,516, filed December 18, 2015, the disclosures of which are incorporated herein by reference in their entireties.

[0002] FIELD OF THE DISCLOSURE The present disclosure relates generally to intravascular measurement and feature detection and related diagnostic methods and devices. [Background technology]

[0003] Coronary artery disease is one of the leading causes of death worldwide. The ability to better diagnose, monitor, and treat coronary artery disease could be of life-saving importance. Intravascular optical coherence tomography (OCT) is a catheter-based imaging modality that uses light to peer into the coronary artery wall and generate images of it for examination. Utilizing coherent light, interferometry, and micro-optics, OCT can provide video-rate in vivo tomography of diseased vessels with micrometer-level resolution. The high-resolution view of subsurface structures using a fiber-optic probe makes OCT particularly useful for minimally invasive imaging of internal tissues and organs. This level of detail possible with OCT allows clinicians to not only monitor the progression of coronary artery disease, but also diagnose it. OCT images provide high-resolution visualization of coronary artery morphology and can be used alone or in combination with other information, such as angiographic data, and other sources of target data to aid in diagnosis and planning, such as stent delivery planning.

[0004] OCT imaging of parts of a patient's body provides a useful diagnostic tool for doctors and others. For example, imaging of coronary arteries with intravascular OCT can reveal the location of narrowing or stenosis. This information helps cardiologists choose between invasive coronary artery bypass surgery and less invasive catheter-based procedures such as angioplasty or stent delivery. Although a popular option, stent delivery has its own associated risks.

[0005] Stents are tubular structures, often made of mesh, that can be inserted into vessels and expanded to address stenotic conditions that restrict blood flow. Stents are typically made of metal or polymer scaffolds. They can be deployed at the site of the stenosis via a catheter. During cardiovascular procedures, stents can be delivered to the site of the stenosis through a catheter via a guidewire and expanded using a balloon. Typically, stents are expanded using a preset pressure to widen the lumen of the narrowed blood vessel. Angiography systems, intravascular ultrasound systems, and OCT systems, either in combination or independently, can be used to facilitate stent delivery planning and stent deployment.

[0006] Several factors influence patient outcomes when deploying a stent. In some procedures, the stent should be expanded to a diameter corresponding to the diameter of the adjacent healthy vessel segment. Overexpansion of the stent may cause significant damage to the vessel, predisposing it to dissection, disarticulation, and intramural hemorrhage. Insufficient expansion of the stent may inappropriately dilate the vessel. If portions of the stent do not contact the vessel wall, the risk of thrombosis may increase. An underexpanded or incompletely malapposed stent may fail to restore normal flow. Once the stent is in place, malapposition and insufficient expansion of the stent may result in a variety of problems.

[0007] There are other challenges associated with stent placement and related procedures. Visualizing stent deployment against the wall of a blood vessel using an angiography system is difficult to do by inspection. Furthermore, manually reviewing angiography images to determine stent position on an image-by-image basis is also prone to error.

[0008] In addition, after deploying a stent, clinicians may image the treatment site to verify that the stent was correctly deployed. However, background noise, caused by, for example, uncleared blood cells, can appear as stent struts in OCT image data, making it difficult to accurately detect the stent. While clinicians can sometimes identify the stented area, requiring user intervention can result in significant variability, be subject to user error, and significantly increase the length of the procedure. Additionally, different stents can have different geometries and mesh patterns, which can complicate their evaluation.

[0009] The present disclosure addresses these and other challenges. Summary of the Invention [Means for solving the problem]

[0010] In part, this disclosure relates to angiography and intravascular data acquisition systems, such as OCT and / or IVUS, that can be used to generate and display diagnostic information of interest for planning stent delivery or for other purposes. This disclosure also relates to the generation of various indicators and their integration into the display of image data. As one example, longitudinal indicators, such as apposition bars, may be used alone or in conjunction with stent strut indicators for diagnostic processes such as stent planning and overlaid on an angiography frame co-registered with an intravascular data set, such as a set of OCT scan lines or images generated therefor.

[0011] In part, this disclosure relates to a system and method for displaying the results of data analysis applied to an intravascular data set to a user of an intravascular data collection system, and in one embodiment, on an angiography system. In part, this disclosure describes a user interface and a graphical user interface (GUI) that provides graphical data representations that can be applied to one or more generated vascular images or angiography images so that regions of interest, such as areas of stent crimping and the like, can be easily found and understood on OCT and angiography images.

[0012] In part, this disclosure relates to a data acquisition system, such as an intravascular data acquisition system, suitable for use in a cath lab, such as an optical coherence tomography system. In part, this disclosure relates to a data acquisition system including a processor adapted to display intravascular image data. The displayed image data includes data or images generated based on depth measurements. In one embodiment, the image data is generated using optical coherence tomography. The system may also display a user interface for displaying intravascular information, such as data regarding stent malapposition in a longitudinal mode on a stent strut-by-stent strut basis for one or more stents in a vessel, or a bar having regions corresponding to stent, no stent, or stent crimp levels of potential interest.

[0013] One or more indicators, such as longitudinal indicators, may be generated in response to the stent detection process and lumen boundary detection, as one non-limiting example, and may be displayed on the angiography, OCT, and IVUS images. These may be viewed by a user to plan stent delivery and to adjust stent delivery by reviewing the co-registered OCT and angiography images along with the associated indicators of interest. In part, the systems and methods described herein relate to methods for avoiding or reducing the possibility of data misinterpretation by replacing regions of missing data with indicators such as hashing, colored regions, or other visual indicators. In this way, end users are notified when data is missing rather than misinterpreting black regions as shadows or side branches. Thus, regions of missing data are coded with indicators that prevent the regions from being misinterpreted as side branches, stents, or other features of interest to the diagnostician. In one embodiment, the method may include displaying indicia of stent struts on a graphical user interface and indicia indicating one or more regions in the intravascular image where data was unavailable for display. In one embodiment, the crimp bar is displayed such that it is independent of the intravascular view so that the crimp bar is displayed when no image containing an indicator or stent is present. In one embodiment, the present disclosure relates to a crimp bar aligned with a stented region, the stented region including a located stent strut, and the crimp bar being rotationally agnostic or persistent.

[0014] In part, the present disclosure relates to stent detection and shadow detection in the context of intravascular data sets obtained using probes such as, for example, optical coherence tomography probes or intravascular ultrasound probes.

[0015] In part, the present disclosure relates to a system and method for accurately identifying the offset or location of metallic stent struts within a shadow cast in OCT image data. A method for stent strut detection may include accessing multiple frames of intravascular imaging data, the multiple frames including optical coherency tomography (OCT) scan lines, identifying shadow regions corresponding to candidate stent struts, identifying scan lines corresponding to the candidate stent strut shadow regions to generate candidate strut shadow scan lines, and analyzing the candidate strut shadow scan lines to identify the location of the stent struts.

[0016] The method for stent strut detection may also include storing a plurality of frames of intravascular imaging data; detecting a stent strut within a first group of frames of the plurality of frames; detecting one or more shadow regions within the first group of frames, one or more of the shadow regions adjacent to the detected stent strut; determining on a shadow region-by-shadow region basis whether a given shadow region is due to a guidewire or a side branch, and generating a set of candidate stent strut shadow regions, each candidate stent strut shadow region including a shadow boundary; and identifying a scan line of the candidate stent strut shadow region within the shadow boundary.

[0017] The methods of the present invention may include additional steps or features. For example, the method may include identifying shadow regions corresponding to candidate stent struts by eliminating shadow regions corresponding to non-stent features. The non-stent features may be selected from the group consisting of, for example, guidewires, side branches, and combinations thereof.

[0018] The method may include the step of eliminating candidate strut shadow scanlines that contain spillage from lumen pixels. The method may include the substep of determining a projection over each of the candidate strut shadow scanlines, or portions or samples of the scanline, by summing signal responses over the candidate strut shadow scanlines. The method may include the substep of identifying up to three local maxima within the projection.

[0019] The method may include the substep of ranking the maxima based on peak signal strength to generate a peak score. The ranking substep may be an ordinal ranking, where maxima having higher peak signal strength receive higher peak scores.

[0020] The method may include the substep of ranking the maxima based on their proximity to the vessel wall to generate a proximity score. The ranking substep may be an ordinal ranking, with maxima closer to the vessel wall receiving a higher proximity score. The method may also include the step of assigning a poor contact score to each maxima. The poor contact score may be binary, with poorly crimped maxima receiving a score of zero.

[0021] The method may include summing the peak score, the proximity score and the malapposition score, and the maximum with the highest total score is designated as the location of the stent strut.

[0022] The method may include identifying a plurality of shadow regions corresponding to candidate stent struts, identifying a scan line corresponding to each candidate stent strut shadow region, and identifying a location of a stent strut within each candidate stent strut shadow region. The method may also include performing a cross-frame analysis to validate designated stent struts across a plurality of optical coherence tomography (OCT) imaging frames.

[0023] In part, this disclosure relates to intravascular data acquisition systems and angiography systems, as well as the exchange of data between two or more of the foregoing, and the generation and display of diagnostic information, such as indicators. One or more indicators may be generated and displayed, for example, by overlaying or otherwise combining such indicators with images generated using the intravascular data acquisition system. The indicators may include longitudinal, transverse, and other indicator types, such as one or more indicators or graphical elements suitable for indicating diagnostic information of interest. The indicators may be used to guide users during stent delivery planning and other operations. This disclosure also relates to stent detection and shadow detection in the context of intravascular data sets acquired using probes, such as optical coherence tomography probes or intravascular ultrasound probes.

[0024] This disclosure relates, in part, to computer-based visualization of stent placement within a blood vessel. The stent may be visualized using OCT data and subsequently displayed as stent struts or portions of the stent as part of one or more graphic user interfaces (GUIs). Among other things, the present invention provides a computer algorithm that identifies the stented region(s) from background noise. The GUI may include one or more views of the blood vessel generated using OCT distance measurements and defining the boundaries of the actual stented region(s), which provides a visualization of the stented region(s).

[0025] In one embodiment, the present disclosure relates to automated detection of one or more stented regions in pullback. In one embodiment, OCT, IVUS, or other intravascular modalities are used to collect data during pullback. In one embodiment, the present disclosure relates to automated detection of one or more stents in a given pullback and removal of false positive strut detection from frames that are not part of a stent. One objective of some implementations described herein is that it automatically detects the start and end frames of one or more stents in a given pullback without user input. The algorithm uses angle metrics as a threshold and frame-by-frame strut detection to determine which frames belong to the stent and which are outside the stent region.

[0026] A multi-frame processing algorithm automatically detects one or more stents in the pullback based on struts detected during a single frame step. In this step, cross-frame information is incorporated to identify the set of frames belonging to a particular stent and eliminate false positives in non-stented regions. In one embodiment, the method includes a step of removing detections in guide catheter frames, determined after identifying the placement of the guide catheter.

[0027] In one embodiment, a one-dimensional plot of angular coverage is used as a proxy or threshold to filter stented regions from non-stented regions. If a frame does not have the expected configuration, the angular coverage will be low, which is interpreted as the edge of the stent. The one-dimensional plot is used to amalgamate data from one adjacent frame into a local neighborhood. In one embodiment, all of the struts in a multi-frame neighborhood are used to jointly calculate the coverage metric. Angles are measured for each strut detected in each frame. In such a multi-frame algorithm, angular positions for sets of struts combined over a fixed neighborhood are used. In one embodiment, the method combines the detected struts to create a superframe, and then performs coverage analysis / filtering for stents on the superframe.

[0028] In one embodiment, even though struts from the neighborhood around frame k are used to calculate the maximum angular gap and angular coverage metrics, they are assigned to frame k because that is where the neighborhood window is centered. In one embodiment, the angular metric threshold is on a frame-by-frame basis, and the neighborhood is the frame one before the current frame and the frame one after the current frame. In one embodiment, the number of neighboring frames on either side of the frame under review may include multiple frames, without any restrictions.

[0029] In one embodiment, angles are measured for each strut detected in each frame. In a multi-frame algorithm implementation, angular positions for sets of struts combined over a fixed neighborhood are used. In one embodiment, a neighborhood of frames is processed against an angular metric threshold as described herein, and then the threshold is used to perform a "signal in stented regions" filtering process. In one embodiment, the neighborhood includes two frames. In one embodiment, the neighborhood includes three frames. In one embodiment, the neighborhood includes two or more frames.

[0030] In one embodiment, the present disclosure relates to a non-transitory machine-readable storage medium encoded with a plurality of processor-executable instructions for performing a method for detecting stented regions within a blood vessel, including processor instructions for performing the steps described and depicted herein.

[0031] The present invention relates, in part, to a method for detecting stented regions in a blood vessel, which may include receiving optical coherence tomography data for a stented blood vessel, the optical coherence tomography data including a plurality of image frames; storing the optical coherence tomography data in a storage device of an intravascular data acquisition system; analyzing the plurality of image frames to identify stent struts on a per frame basis; demarcating angular offsets of the identified stent struts to create amalgamated angular gap data across adjacent ones of the plurality of image frames; and determining the maximum angular gap between any two adjacent struts in the adjacent frames.

[0032] The method may include one or more of the following features: The method may include classifying the frame as a stent-containing frame if the maximum angular gap is less than a threshold angular gap.

[0033] The method may include identifying zones containing the stent by identifying clusters of adjacent frames that contain a maximum angular gap that is less than a threshold angular gap.

[0034] The method may include determining a centroid value for the stented vessel and computing a maximum angular gap for the vessel centroid for frame k. The maximum angular gap θ for a given frame k max,k is the angular gap metric Ψ for frame k according to (Equation 1). kAn angular gap metric closer to 1 indicates that the frame contains a stent.

number

[0035] The method may include classifying the frame as a stent-containing frame if the angle gap metric is greater than a threshold angle gap (eg, about 0.25 to about 0.65).

[0036] The method may include calculating an angular gap metric for frame k and at least one neighboring frame k+1. The method may include iteratively calculating the angular gap metric for successive neighboring frames.

[0037] The method may include repeating one or more of the steps of the method to sequentially classify multiple frames in the optical coherence tomography data. The method may include sequentially classifying a frame as a stent-containing frame if the angular gap metric for the given frame is greater than a threshold angular gap. The method may include aggregating adjacent stent-containing frames into a stented region, including a first frame and a last frame. The method may include terminating a first end of the stented region if a frame adjacent to the first frame has an angular gap metric lower than a threshold angular gap.

[0038] The method may include terminating a second end of the stented region if a frame adjacent to the last frame has an angular gap metric lower than a threshold angular gap. The disclosure also relates, in part, to a method for detecting a stented region in a stented vessel, the method including: using an intravascular imaging system to store one or more intravascular image datasets of the vessel, each intravascular dataset including a plurality of frames; using the intravascular imaging system to store one or more intravascular image datasets of the vessel, each intravascular dataset including a plurality of frames; defining a neighborhood, the neighborhood including frame k and one or more frames in a neighborhood of frame k; determining an angular gap for frame k by combining all of the struts detected on all frames in the neighborhood; and using the determined angular gap to calculate an angular coverage metric Ψ for frame k. k generating a signal from the signal.

[0039] The angular coverage metric is of the form: where θmax,k is the largest angular gap between adjacent struts.

number

[0040] The method may include sequentially classifying a frame as a stent-containing frame if the angular coverage metric for the given frame is greater than a threshold angular gap. The present disclosure also relates, in part, to a programmable processor-based computer device of an intravascular imaging system for detecting one or more stented regions. The programmable processor-based computer device may include one or more data access channels for receiving intravascular imaging data; and a processor and associated memory in electronic communication with the one or more data access channels.

[0041] In one embodiment, the processor uses an intravascular imaging system to store one or more intravascular image datasets of a blood vessel, each intravascular dataset including a plurality of frames; defines a neighborhood, the neighborhood including frame k and one or more frames near frame k; determines an angular gap for frame k by combining all of the struts detected on all frames in the neighborhood; and calculates an angular coverage metric Ψ for frame k using the determined angular gap. k and classifying a frame as a stent-containing frame if the angular coverage metric for a given frame is greater than a threshold angular gap.

[0042] In one embodiment, the present disclosure relates to detecting the maximum stent malapposition distance, defined as the widest separation between the surface of a stent strut and the vessel wall along the entire length of the stent. Minimizing this distance is necessary, particularly for drug-eluting stents, to ensure that the stent is firmly anchored to the vessel wall and provides sufficient radial support to prevent vessel collapse.

[0043] In one embodiment, the present disclosure relates to detecting the maximum stent malapposition distance, defined as the widest distance between the surface of a stent strut and the vessel wall along the entire length of the stent. Minimizing this distance is necessary, particularly for drug-eluting stents, to ensure that the stent is firmly anchored to the vessel wall and provides sufficient radial support to prevent vessel collapse.

[0044] In part, the present disclosure relates to a computer interface with a three-dimensional depiction in a top panel of a misplaced stent within a lumen of interest. Areas of stent malapposition may be indicated as hatched areas or using other indicia. Thus, in one embodiment, the methods of the present invention and features described herein are directed to a computer-based user interface that allows for OCT views in multiple panels. Furthermore, stent malapposition may be shown in three dimensions. Additionally, in the case of simulated stent placement, a user may reposition the stent to remove the area of ​​malapposition and simulate correct stent placement before implanting the stent in a real patient.

[0045] The method may include displaying the identified stent struts on a graphical user interface. The present disclosure also includes a computer-readable medium including non-transitory instructions that, when executed, cause a processor to perform any of the foregoing steps.

[0046] However, it is understood that the present invention relates to different aspects and embodiments, and that the different aspects and embodiments disclosed herein may be integrated together, in whole or in part, as needed. Accordingly, the embodiments disclosed herein may be incorporated into each aspect to varying degrees as appropriate for a given implementation. Furthermore, although some aspects and embodiments are described using the term "means for," it is understood that all aspects, embodiments, and other concepts disclosed herein may serve as support for means-plus-function claims, even if the specific "means for" language is not used in a particular portion of the written description. [Brief explanation of the drawings]

[0047] The drawings are not necessarily to scale, emphasis instead being placed generally on illustrative principles. The drawings are to be considered in all respects illustrative and not intended as limiting the disclosure, the scope of which is defined solely by the claims.

[0048] [Figure 1] FIG. 1 shows a schematic diagram of an intravascular imaging and data collection system according to one exemplary embodiment of the present disclosure.

[0049] [Figure 2A] 2A-2E show additional details regarding a user interface display and an intravascular data collection system and appropriate indicators, and an angiography system for diagnostic processes including stent delivery planning, according to one exemplary embodiment of the present disclosure. [Figure 2B] 2A-2E show additional details regarding a user interface display and an intravascular data collection system and appropriate indicators, and an angiography system for diagnostic processes including stent delivery planning, according to one exemplary embodiment of the present disclosure. [Figure 2C] 2A-2E show additional details regarding a user interface display and an intravascular data collection system and appropriate indicators, and an angiography system for diagnostic processes including stent delivery planning, according to one exemplary embodiment of the present disclosure. [Figure 2D] 2A-2E show additional details regarding a user interface display and an intravascular data collection system and appropriate indicators, and an angiography system for diagnostic processes including stent delivery planning, according to one exemplary embodiment of the present disclosure. [Figure 2E]2A-2E show additional details regarding a user interface display and an intravascular data collection system and appropriate indicators, and an angiography system for diagnostic processes including stent delivery planning, according to one exemplary embodiment of the present disclosure.

[0050] [Figure 3A] 3A-6 illustrate various user interfaces and data representations, including various indicia and co-described features, relating to one or more imaging modalities, according to one exemplary embodiment of the present disclosure. [Figure 3B] 3A-6 illustrate various user interfaces and data representations, including various indicia and co-described features, relating to one or more imaging modalities, according to one exemplary embodiment of the present disclosure. [Figure 4A] 3A-6 illustrate various user interfaces and data representations, including various indicia and co-described features, relating to one or more imaging modalities, according to one exemplary embodiment of the present disclosure. [Figure 4B] 3A-6 illustrate various user interfaces and data representations, including various indicia and co-described features, relating to one or more imaging modalities, according to one exemplary embodiment of the present disclosure. [Figure 5] 3A-6 illustrate various user interfaces and data representations, including various indicia and co-described features, relating to one or more imaging modalities, according to one exemplary embodiment of the present disclosure. [Figure 6] 3A-6 illustrate various user interfaces and data representations, including various indicia and co-described features, relating to one or more imaging modalities, according to one exemplary embodiment of the present disclosure.

[0051] [Figure 7A]7A and 7B show a three-dimensional representation of side branch markings generated using intravascular imaging data, such as OCT data, according to one exemplary embodiment of the present disclosure. [Figure 7B] 7A and 7B show a three-dimensional representation of side branch markings generated using intravascular imaging data, such as OCT data, according to one exemplary embodiment of the present disclosure.

[0052] [Figure 8A] 8A-9B show additional details regarding a user interface display and an intravascular data collection system and their appropriate indicators, and an angiography system for the diagnostic process, according to one exemplary embodiment of the present disclosure. [Figure 8B] 8A-9B show additional details regarding a user interface display and an intravascular data collection system and their appropriate indicators, and an angiography system for the diagnostic process, according to one exemplary embodiment of the present disclosure. [Figure 9A] 8A-9B show additional details regarding a user interface display and an intravascular data collection system and their appropriate indicators, and an angiography system for the diagnostic process, according to one exemplary embodiment of the present disclosure. [Figure 9B] 8A-9B show additional details regarding a user interface display and an intravascular data collection system and their appropriate indicators, and an angiography system for the diagnostic process, according to one exemplary embodiment of the present disclosure.

[0053] [Figure 10A] FIG. 10A illustrates an exemplary intravascular data collection system and associated intravascular data collection probe, as well as related image processing, detection and other software components, according to an exemplary embodiment of the present disclosure.

[0054] [Figure 10B]FIG. 10B is a cross-sectional OCT image of a stented vessel according to one exemplary embodiment of the present disclosure.

[0055] [Figure 11] FIG. 11 is a process flow diagram for detecting struts in OCT image data, according to one exemplary embodiment of the present disclosure.

[0056] [Figure 12] FIG. 12 is a scanline OCT image of a stented vessel in polar coordinates and logarithmic scale, according to one exemplary embodiment of the present disclosure.

[0057] [Figure 13] FIG. 13 is a graph illustrating the detection of multiple potential struts in a single shadow, according to one exemplary embodiment of the present disclosure.

[0058] [Figure 14A] FIG. 14A is a user interface representation of intravascular image data of a stented vessel region prior to removal of false-positive stent struts, according to one exemplary embodiment of the present disclosure.

[0059] [Figure 14B] FIG. 14B is a user interface representation of intravascular image data of a stented vessel region after removal of false-positive stent struts, according to an exemplary embodiment of the present disclosure.

[0060] [Figure 15A] 15A and 15B are schematic depictions of an automated stent detection algorithm that combines frames as part of an assessment of whether a frame is part of a stented region, according to one exemplary embodiment of the present disclosure. [Figure 15B]15A and 15B are schematic depictions of an automated stent detection algorithm that combines frames as part of an assessment of whether a frame is part of a stented region, according to one exemplary embodiment of the present disclosure.

[0061] [Figure 16] FIG. 16 is a graph of an angular coverage plot demarcating the frame positions of two stents, according to one exemplary embodiment of the present disclosure.

[0062] [Figure 17] FIG. 17 is a flow chart illustrating a multi-frame stent region detection algorithm according to one exemplary embodiment of the present disclosure.

[0063] [Figure 18A] FIG. 18A is a user interface representation of intravascular image data of a stented vessel region prior to removal of false positive struts, according to one exemplary embodiment of the present disclosure.

[0064] [Figure 18B] FIG. 18B is a user interface representation of intravascular image data of a stented vessel region after removal of false positive struts, according to an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0065] In part, this disclosure relates to intravascular data acquisition systems, such as OCT, IVUS, and angiography systems, and the exchange of data between two or more of the foregoing, and the generation and display of diagnostic information, such as, for example, indicators. In one embodiment, intravascular data, such as OCT, is collected while angiography data is simultaneously collected. The indicators may include one or more one- or two-dimensional graphic elements and one or more associated indicia, such as color, grayscale or scale gradations, hashes, symbols, or other visual elements.

[0066] One or more indicators may be generated and displayed, for example, by overlaying or otherwise combining such indicators with images generated using an intravascular data acquisition system. Indicators may include longitudinal, transverse, and other indicator types, such as one or more indicia or graphical elements suitable for indicating diagnostic information of interest, such as tracking to user-selected landmarks. Stent strut indicators may also be used. Methods of stent and shadow detection are described herein, which may be used to display such intravascular features in a user interface and to display indicators, indicia, or other overlays thereon. Angiographic data may also be integrated and displayed with various common indicators as part of a co-registered display. In one embodiment, shadows and other elements that may be misinterpreted as dissections, side branches, or other vascular features may be shaded or otherwise altered to distinguish them and facilitate user review and analysis of image frames and data according to one embodiment.

[0067] Suitable diagnostic information may include stent apposition information, such as stent malapposition relative to the vessel wall or luminal boundary, user-selected OCT placement and associated angiographic frame position within the vessel, and other intravascular diagnostic information or other information generated to facilitate stent delivery planning. The system includes a processor configured to communicate with and send instructions to the graphical user interface. One or more software programs are used to perform one or more of the following: co-registering data such as image data, generating and displaying longitudinal indicators showing stent placement relative to the determined luminal boundary, coding or labeling data-missing regions for an end user, transferring user-selected OCT placement information to the angiographic display using one or more graphical elements to facilitate co-registration, and visually identifying the stent and simulated stent for planning purposes, and other matters described herein.

[0068] In part, this disclosure relates to graphical user interface (GUI) elements or indicators that are represented on a display for subject data, such as image data or other intravascular parameters measured for a subject. Any clinically useful parameter, such as one that varies longitudinally or transversely over the course of an optical coherence tomography pullback recording or IVUS or other intravascular or angiographic system, may be evaluated and displayed as an indicator or indicia. Each indicator / indicia may be used by interventional cardiologists to quickly view clinically useful information for the entire pullback recording in a single view without having to manually manipulate the image. The indicators may guide users to specific points of interest within the vessel based on the parameter exceeding or falling below a clinically meaningful threshold. For example, by encoding the parameter value on a continuous color map or other scale using appropriate indicia, varying degrees of parameter severity can be easily summarized for the entire vessel in a single, easily interpretable view. These features are indicated using various crimping bars, stent indicators, and other indicators for angiographic and other intravascular data collection images.

[0069] FIG. 1 illustrates a system suitable for implementing some of these features. FIG. 2A shows four panels, with the top right panel showing an angiographic display along with various indicators, including a first user-selected position US1, a second user-selected position US2, and an active frame AF. These indicators are also shown in the bottom L-mode or longitudinal panel, with US1 and US2 corresponding to the indicated vertical lines and the active frame AF corresponding to the vertical line between them. The active frame is shown in cross-section in the top right panel. The center panel shows the values ​​in millimeters of the US1 and US2 positions, as well as the vessel position and the calculated MLA. FIG. 2B shows an enlarged view of the angiographic image of FIG. 1, along with the OCT data of FIG. 1. These user interfaces include movable elements C1 and C2, which can be controlled by a user using a mouse, joystick, or other control device and can be operated using one or more processors and memory storage elements. Movable elements C1 and C2 are control devices and can be rotated or moved as part of the interface. They are also shown in FIG. 2C, FIG. 3B, and others. In FIG. 3B, the control devices C1, C2 are also represented as semicircles relative to the stent struts and as line segments in the right panel.

[0070] FIG. 2C shows a crimp bar / indicator bar 111 as an indicator with regions R1 through R7, which are shown in the upper right angiographic view shown in more detail in FIG. 2D. The upper right panel shows R3, which indicates an area of ​​crimping above a threshold of interest. In L-mode, stent struts are coded using indicia, such as symbols or colors. The crimp area of ​​interest in the crimp bar remains on the display even when the data set is rotated to draw the user's attention to areas important for stent planning and patient assessment. In this sense, the indicator can be persistent to direct the user's focus during planning or other procedures. FIG. 2E shows additional details regarding an exemplary crimp bar or indicator bar 111. The indicator bar 111 may be used for stent planning and review, and may also be used to indicate areas of the intravascular image where crimping or another metric for stent struts is present. In one embodiment, the indicator bar 111 is persistent within the user interface view to alert the user to the stent region even if it is not visible based on the view selected by the user - 3D, cross-sectional, longitudinal, viewing angle, etc.

[0071] With reference to FIG. 2C, two user-selected points of interest are shown as U1 and U2. R3 corresponds to the area of ​​malapposition of interest. R2 corresponds to the first stent, and R6 corresponds to the second stent. R5 is the gap between them. This data is co-registered with the angiography data as shown in FIG. 2D to facilitate stent planning. The data collection elements of the probe DC are shown in the image. R1 and R7 are the distal and proximal segments where no stent is present and correspond to the vessel lumen. U1 and U2 serve as user-placed landmarks that can be used by a user viewing a live angiogram to provide them with a frame of reference for the vessel segments labeled with U1 and U2. One or more displays may be used, such as the live angiogram and OCT pullback data with previously acquired pullback frames.

[0072] Using these and other indicators, the images and indicators can serve as tools to guide stent delivery based on the data shown in FIGS. 2C and 2D. Indicators may also be provided in cross- or longitudinal-section views with color-coded or otherwise coded stent strut markings to more specifically indicate when the stent needs to be expanded. In FIG. 2E, the areas of first stent 222a and second stent 222b are indicated by crimping bar 111. Lumen- or stent-free area 224 and malapposition area 223 are also shown. This bar 111 may be displayed on any angiographic, OCT, or IVUS image of interest. In one embodiment, the crimping bar is displayed such that it is independent or persistent from the intravascular view, so that the crimping bar is displayed when no indicators or images containing stents are present. Various indicators and markings may be generated based on stent detection, lumen detection, stent crimping measurements, and various graphic overlays, for example, generated using the systems of FIGS. 1 and 10A.

[0073] FIG. 3A shows the interface with a longitudinal section or L-mode view, showing a crimp bar above the stent strut indicators coded based on a crimp threshold. The indicator bar 111 is shown in the center of the GUI, with stent-free areas 224 and malapposition areas 223 indicated. Lumen boundary data from OCT or IVUS is used to determine crimp issues, such as thresholds being exceeded given detected strut data, as input to the crimp bar generation software module. In FIG. 3B, the interface screen depicts one example of an indicator measuring a high level of crimp for metal stent struts, as shown above the L-mode display in the illustrated GUI screenshot. The crimp indicator allows summary information about clinical parameters to be displayed without the need to manually manipulate or inspect image data. The stent crimp bar and other indicators shown herein and their co-delineation with angiograms provide many benefits to the user.

[0074] In Figures 3A, 3B, 4A, and 4B, some embodiments of the user interface depict endovascular and angiographic data (if applicable) along with one exemplary indicator for stent strut crimping and other indicator-based data display. In one embodiment, the crimping bar is shown on top of L-mode and angiographic images, 3D flow images, and the like. Figure 4B shows a longitudinal cross-section of the crimping bar 111 simultaneously with an angiographic image with stent data showing threshold information along the outer boundary of the vessel. Region 161a in the angiographic portion of the user interface is also aligned with region 161b of the indicator bar 111. In one embodiment, the angiographic image is aligned or registered with the crimping bar. One feature of the crimp bar 111 is that it is persistent within the user interface, so that if a stent is present in the 2D or 3D image but does not appear based on the cut plane or viewing angle, the crimp bar will persist and indicate that the stent and any associated malappositions are present even though the 2D or 3D stent is not visible within the GUI, a useful feature for stent planning and diagnosis.

[0075] In FIG. 4A, an indicator bar 111 is shown indicating the stent struts and crimped areas of interest 157. These areas of interest 157 indicating crimps may be grouped with a representation of the stent struts themselves, or may be color-coded or coded using other indicia that may be found in the GUI. One example of grouping stent strut codes with indicia and indicator bars is shown by areas 188 in FIGS. 3A, 3B, 5, and 6. In these areas 188, indicator bars 111 are shown aligned with a representation of the stent and a series of struts, along with various indicia corresponding to the crimps against the vessel wall. The detected lumen boundary is used to compare the stent placement against it. Additional details regarding stent detection are included herein.

[0076] FIG. 5 shows another GUI with indicators or image data processing features that allow software to correct missing data, such as unclear data or data missing due to a guidewire shadow, and replace it with a gray mask or other indicator. To avoid user confusion with side branches, dissections, or missing data, indicator MD is used to indicate areas where data is missing. This has the advantage of preventing users from mistaking missing data, dissection areas, or side branches. In one embodiment, areas where data is missing as a result of a shadow or for other reasons are displayed using a mark or indicator, such as a gray area, a colored area, hashing, or other visible marking. The central two-arrowhead arrow icon allows the view to be rotated. This user control, along with the crimp bar and guidewire identification by color coding or other markings, completely improves and expands the diagnostic scope of image data from intravascular data collection probes and / or angiography data.

[0077] In another embodiment, indicator W is used to point to the guidewire image, as shown in FIG. 5. In one embodiment, indicator W may be used to identify the guidewire within the GUI or to select and remove it from the image. FIG. 6 shows a three-dimensional fly-through, with the crimping bar shown as a track in front of the user's viewing plane corresponding to the cross-section on the right. Indicator bar 111, showing the stent struts and crimped area of ​​interest included in the 3D fly-through view and any other views of the region of interest, remains visible during rotation to alert the user to critical vessel regions during stent planning.

[0078] 7A and 7B show another indicator SB corresponding to the side branch. A rendering of the vessel wall VW is also shown for the side branch. These and other indicators may be used to highlight regions of the 2D and 3D data. As shown in the depicted user interface, such as FIG. 9B, the various circles / line segments C1, C2 in the upper right view may be rotated to navigate through various views of the image. FIGS. 8A-9B show additional interface and control information for navigating the image dataset and performing diagnostics, such as stent planning. Various proximal and distal views and other perspective views may be navigated using the tools shown herein. In one or more embodiments, indicators such as the crimping bar 111 are persistent, so they remain in view even when navigated away from the area of ​​malapposition.

[0079] In this manner, some indicators are rotationally agnostic, so that if an indicator includes a region or length that includes a parameter that exceeds a threshold, then that region will remain indicated even if the image data is altered such that a rotated view obscures the region, such as an incompletely crimped stent region. Thus, if one side of the vessel has a crimping problem, the user remains aware of their location within the vessel. The crimp bar may be displayed as an indicator in one or more views of the angiogram or OCT image or user interface.

[0080] As shown in the various figures, the crimping bar 111 may be subdivided into various regions or lengths to indicate the presence of a stent or more stents in the vessel, or malposition or gaps between multiple stents for a multi-stented vessel. The angiography data and associated image frames may be co-registered with the OCT data. Additionally, as shown in the figures, user-selected vertical lines corresponding to specific longitudinal distances on the artery may be set to guide stent planning. The rotationally agnostic or persistent nature of the bar provides further assistance and error reduction during stent planning.

[0081] During the stent delivery planning procedure, clinician-specified landmarks may be used for stent planning by providing a reference for the user to select a stent size and for the vessel, which the user can refer to during stent deployment using live angiograms. Given the level and location of the malapposition, the user can refer to the OCT and annotated angiograms to further expand or move the stent as part of the delivery plan. These system features and methods may be implemented, for example, using System 3 shown in FIG. 1 and the system of FIG. 10A.

[0082] 1 shows a system 3 including various data acquisition subsystems suitable for collecting data or detecting characteristics of, sensing conditions of, or otherwise diagnosing a subject 4. In one embodiment, the subject is placed on a suitable support 19, such as a table, bed, chair, or other suitable support. Typically, the subject 4 is a human or other animal having a particular region of interest 25.

[0083] The data acquisition system 3 includes a non-invasive imaging system, such as nuclear magnetic resonance, X-ray, computer-aided tomography, or other suitable non-invasive imaging technique. As one non-limiting example of such a non-invasive imaging system, an angiography system 21 suitable for generating cines is shown. The angiography system 21 may also include a fluoroscopy system. The angiography system 21 is configured to non-invasively image the subject 4 such that frames of angiography data are generated, typically in the form of frames of image data, while a pullback procedure is performed using the probe 30 such that blood vessels within the region 25 of the subject 4 are imaged using angiography in one or more imaging techniques, such as OCT or IVUS.

[0084] The angiography system 21 is in communication with an angiography data storage and image management system 22, which in one embodiment may be implemented as a workstation or a server. In one embodiment, data processing on the collected angiography signals is performed directly on the detector of the angiography system 21. Images from the system 21 are stored and managed by the angiography data storage and image management system 22.

[0085] In one embodiment, system server 50 or workstation 87 handles the functions of system 22. In one embodiment, the entire system 21 generates electromagnetic radiation, such as x-rays. System 21 also receives such radiation after it passes through object 4. Data processing system 22 then uses signals from angiography system 21 to image one or more regions, including region 25, of object 4. This system enables the angiography data to be shown on displays 82 and 82, along with endovascular data and various indicators and detected stent struts and shadows, as described herein.

[0086] As shown in this particular example, the region of interest 25 is a subset of the vasculature, such as a particular blood vessel, or peripheral vasculature, which can be imaged using OCT. A catheter-based data collection probe 30 is introduced into the subject 4 and positioned in the lumen of a particular blood vessel, such as a coronary artery. The probe 30 can be various types of data collection probes, such as an OCT probe, an FFR probe, an IVUS probe, a probe combining features of two or more of the foregoing, and other probes suitable for intravascular imaging. The probe 30 typically includes a probe tip, one or more radiopaque markers, an optical fiber, and a torque wire. In addition, the probe tip includes one or more data collection subsystems, such as an optical beam director, an acoustic beam director, a pressure detector sensor, other transducers or detectors, and combinations of the foregoing.

[0087] For intravascular probes that include an optical beam director, the optical fiber 33 is in optical communication with the probe using the beam director. The torque wire defines a bore in which the optical fiber is positioned. In FIG. 1, the optical fiber 33 is shown without the torque wire surrounding it. In addition, the probe 30 also includes a sheath, such as a polymer sheath (not shown), that forms part of the catheter. The optical fiber 33, which in the context of an OCT system is part of the sample arm of an interferometer, is optically coupled to a patient interface unit (PIU) 35 as shown.

[0088] The patient interface unit 35 includes a probe connector suitable for receiving and optically coupling to the end of the probe 30. Typically, the data collection probe 30 is disposable. The PIU 35 includes appropriate joints and elements based on the type of data collection probe being used. For example, a combination OCT and IVUS data collection probe requires an OCT and IVUS PIU. The PIU 35 also typically includes a motor suitable for retracting the torque wire, sheath, and optical fiber 33 disposed therein as part of a pullback procedure. In addition to being retracted, the probe tip is also typically rotated by the PIU 35. In this manner, the blood vessels of the subject 4 can be imaged longitudinally or through a cross-section. The probe 30 may also be used to measure specific parameters, such as FFR or other pressure measurements. The image data may be used to generate various 2D and 3D views, which may be navigated as shown in the user interface depictions.

[0089] The PIU 35 is then connected to one or more intravascular data acquisition systems 42. The intravascular data acquisition system 42 may be an OCT system, an IVUS system, other imaging systems, or combinations of the foregoing. For example, in the context of the probe 30 being an OCT probe, the system 42 may include an interferometer sample arm, an interferometer reference arm, a photodiode, a control system, and a patient interface unit. Similarly, as another example, in the context of an IVUS system, the intravascular data acquisition system 42 may include ultrasound signal generation and processing circuitry, noise filters, a rotatable joint, a motor, and an interface unit. In one embodiment, the data acquisition system 42 and the angiography system 21 have a shared clock or other timing signal configured to synchronize angiography video frame timestamps and OCT image frame timestamps.

[0090] In addition to the invasive and non-invasive image data collection systems and devices of Figure 1, various other types of data may be collected regarding the region of interest 25 of the subject and other parameters of interest of the subject. For example, the data collection probe 30 may include one or more pressure sensors, such as pressure wires. Pressure wires may be used without the addition of OCT or ultrasound components. Pressure readings may be obtained along segments of blood vessels within the region of interest 25 of the subject 4.

[0091] Such readings may be relayed either by a wired connection or via a wireless connection. As shown in the fractional flow reserve FFR data collection system, the wireless transceiver 48 is configured to receive pressure readings from the probe 30 and transmit them to the system to generate FFR measurements or more locations along the vessel measured. One or more displays 82, 83 may also be used to show angiography frames of data, OCT frames, user interfaces for the OCT and angiography data, shadows, indicators, missing data, and other controls and features of interest.

[0092] Intravascular imaging data, such as frames of intravascular data generated using the data acquisition probe 30, may be routed via the PIU 35 to a data acquisition and processing system 42 coupled to the probe. Noninvasive image data generated using the angiography system 22 may be transmitted to, stored in, and processed by one or more servers or workstations, such as a co-registration server 50 workstation 87. A video frame grabber device 55, such as a computer board configured to acquire angiography image data from the system 22, may be used in various embodiments.

[0093] In one embodiment, server 50 includes one or more co-registering software modules 60 stored in memory 70 and executed by processor 80. Server 50 may include other typical components for a processor-based computing server. Alternatively, more databases, such as database 90, may be configured to receive generated image data, subject parameters, and other information generated by one or more system devices or components shown in FIG. 1 and received by or transferred to database 90. While database 90 is shown as being stored in memory at workstation 87 and connected to server 50, this is merely one exemplary configuration. For example, software module 60 may be running on a processor at workstation 87, and database 90 may be located in memory at server 50. Devices or systems for operating various software modules are provided as examples. In various combinations, the hardware and software described herein may be used to acquire frames of image data, process such image data, and register such image data.

[0094] As otherwise noted herein, software modules 60 may include software such as pre-processing software, transformations, matrices, lumen detection, stent detection, shadow detection, indicator generators and displays, and other software-based components used to process image data or responsive to patient triggers to facilitate or otherwise perform co-registration of different types of image data by other software-based components 60. Modules may include lumen detection using scan line based or image-based techniques, stent detection using scan line based or image-based techniques, indicator generation, crimp bar generation for stent planning, guidewire shadow indicators to prevent confusion with dissections, side branches, and missing data, and others.

[0095] The database 90 may be configured to receive and store angiography image data 92, such as image data generated by the angiography system 21 and acquired by the frame grabber 55 server 50. The database 90 may be configured to receive and store OCT image data 95, such as image data generated by the OCT system 42 and acquired by the frame grabber 55 server 50.

[0096] Additionally, subject 4 may be electrically coupled via one or more electrodes to one or more monitors, such as monitor 49. Monitor 49 may include, but is not limited to, an electrocardiogram monitor configured to generate data regarding cardiac function and indicating various states of the subject, such as systole and diastole. Because the geometry of the heart, including the coronary arteries, is approximately the same in a particular cardiac phase, even across different cardiac cycles, knowing the cardiac phase may be used to assist in tracking the vascular centerline.

[0097] Therefore, when angiography data spans a small number of cardiac cycles, first-order matching of vascular centerlines at the same cardiac phase can aid in tracking the centerline throughout the pullback. Additionally, because the majority of cardiac motion occurs during systole, vasomotion is expected to be higher near systole and attenuate toward diastole. This provides data to one or more software modules as an indication of the amount of motion expected between successive angiography frames. Knowledge of the expected motion may be used by one or more software modules to improve tracking quality and vascular centerline quality by enabling adaptive constraints based on the expected motion.

[0098] [Shadow Detection Embodiment] The present disclosure provides, in part, methods and systems for identifying within a detected stent shadow the exact offset or location of the strut that resulted in the detected shadow. Sometimes within the shadow there is a single possible strut location corresponding to a bright strut bloom or peak against a dark shadow background in the scan line. However, multiple strut peaks are often detected within the strut shadow, making it difficult to identify the correct location of the stent strut. Spurious peaks can be caused, for example, by blood pooling, insufficient blood clearing in the pullback zone, or ringing artifacts resulting from imaging optics interacting with the metal struts. The present disclosure provides methods and systems for identifying the best candidate for a true stent within a stent shadow.

[0099] FIG. 10A is a high-level schematic diagram depicting a blood vessel 5, such as an artery, a data collection probe 7, and an intravascular data collection and processing system 10. The method described with respect to system 10 of FIG. 10A may also be performed using system 3 of FIG. 1 and other systems. Specifically, system 10 of FIG. 10A may include, for example, an OCT, intravascular ultrasound (IVUS), or other intravascular imaging system. A stent 12 is shown within blood vessel 5. The stent includes multiple struts. Some stents may generate a shadow or shadow region SR as part of the process of imaging the blood vessel with an intravascular probe. System 10 may include various software modules suitable for performing side branch detection, stent detection, peak detection, shadow region detection and processing, error correction, indicator bar generation and display, model comparison, lumen detection, and various other processes as described herein. Additional details related to some exemplary stent detection features are described in more detail with respect to FIGS. 14A-18B. System 10 may include a suitable light source to meet the coherence and bandwidth requirements of the applications and data collection described herein. System 10 may include an ultrasound imaging system. Probe 7 may include a catheter 20 having a catheter portion with one or more optical fibers 15 and a probe tip 17 disposed therein. In one embodiment, probe tip 17 includes a beam director.

[0100] As shown, a catheter 20 is introduced into a lumen 11, such as an arterial lumen. The probe 7 may include a rotating or slidable fiber 15 that directs and guides light forward into the lumen L or in a direction perpendicular to the longitudinal axis of the fiber 15. One result is that, with light directed from the side of the probe as the fiber 15 rotates, OCT data is collected about the wall of the blood vessel 5. The wall of the blood vessel 5 defines the lumen boundary. This lumen boundary may be detected using distance measurements obtained from the optical signals collected at the probe tip 17 using a lumen detection software component. Side branches and stent struts, as well as shadow areas and other features, may be identified in scan lines generated by the probe during pullback through the artery.

[0101] In one embodiment, probe 7 may include other imaging modalities in addition to OCT, such as ultrasound in one embodiment. In one embodiment, lumen / luminal boundary refers to the portion of a blood vessel that is first impinged upon by the presence of light or ultrasound and an intravascular imaging probe that generates a signal of interest for imaging the vessel. This excludes any blood flowing within the vessel, which is typically removed using image processing in the form of masking. In one embodiment, lumen or luminal boundary refers to the region of tissue located in front of the vessel wall and facing the blood-containing region of the vessel.

[0102] As shown in FIG. 10A , probe tip 17 is positioned within lumen L such that it is distal to the stented region of blood vessel 5. Probe tip 17 is configured to transmit light and receive backscattered light from objects, such as stent 12 and the wall of blood vessel 5. Probe tip 17 and the remainder of data collection probe 7 are pulled through lumen L and the stented region. As shown in FIG. 10B , probe 17 is shown before and after insertion into the blood vessel. Probe 7 is in optical communication with OCT system 10. OCT system or subsystem 10, which connects to probe 17 via optical fiber 15, 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 system components.

[0103] In one embodiment, an optical receiver 31, such as a balanced photodiode-based system, can receive light exiting the probe 7. A computing device 40, such as a computer, processor, ASIC, or other device, may be part of the OCT system 10 or may be included as a separate subsystem in electrical or optical communication with the OCT system 10. The computing device 40 may include memory, storage, buses, and other components suitable for processing data, as well as software 44, such as an image data processing stage configured for side branch detection, stent strut candidate selection or identification, candidate stent strut shadow region detection, stent region detection, stent strut confirmation, correlation and comparison of stent image data and stent visualization, and pullback data collection as described below. In one embodiment, the software 44 may include a pipeline containing various modules, such as an automated stent detection module that operates on the intravascular data to detect stent struts. This module may include various other software modules, such as a sparse peak detection module, a model strut generation module, and a false positive testing module, as described herein.

[0104] In one embodiment, the computing device 40 includes or has access to software modules or programs 44, such as a side branch detection module, a lumen detection module, a stent detection module, a stent strut verification module, a candidate stent strut identification module, and other software. The software modules or programs 44 may include an image data processing pipeline or its component modules, and one or more graphical user interfaces (GUIs). The various software-based methods described herein may be included as part of a collection of software / programs 44. The modules may be subsets of each other and may be arranged and connected through various inputs, outputs, and data classes. In one embodiment, the software modules 44 include a stent detection module, such as an automated stent detection module.

[0105] One exemplary image processing pipeline and its components may comprise one or more software programs or modules 44. The software modules 44 may include several image processing algorithms tailored to detect vessel lumens, side branches, guidewires, guide catheters, stent struts, and stent regions. This disclosure relates to image processing for determining the location of metal struts within their shadows. The image data processing pipeline, its component software modules, and related methods, as well as any methods described herein, are stored in memory and executed using one or more computing devices, such as a processor, device, or other integrated circuit. The software modules or programs 44 receive image data and convert such data into two-dimensional or three-dimensional views of the vessel and stent, and may include a lumen detection software module, peak detection, a stent detection software module, and a side branch detection software module, among others.

[0106] 10A, a display 46 may also be part of the system 10 for showing information 47, such as cross-sectional and longitudinal views of the vessel generated from the OCT or IVUS imaging data, as well as crimping bars and other indicators. Image processing software algorithms 44 provide data corresponding to detected image features, such as stents, side branches, guidewires, etc., which is input to the GUI, where these features are displayed in a desired format on the cross-sectional, longitudinal and / or 3D view sections of the GUI.

[0107] In addition, the display 46 may also show information 47, such as cross-sectional and longitudinal views of the stented vessel, generated using collected image data, a user interface, images, and various indicators and indicia. A representation of the stent, such as an OCT or IVUS image thereof, may be shown to the user via the display 46. Stent detection is performed prior to displaying these features and coding or tagging them with identifying indicia that may be included in the displayed image. This OCT-based information 47 may be displayed using one or more graphic user interfaces. Images of FIGS. 10B, 14A, 14B, and 18B, as well as other user interfaces and their components depicted herein, are examples of displayed information 47 that can be displayed and interacted with using GUIs and various input devices. Specifically, it shows a 2D cross-sectional view of a coronary artery containing a metal stent.

[0108] Additionally, the displayed information 47 may include, but is not limited to, cross-sectional scan data, longitudinal scans, diameter graphs, image masks, stents, areas of malapposition, lumen boundaries, and other images or representations of the vessel, or underlying distance measurements, acquired using the OCT system and data collection probe. The computing device 40 may also include software or programs 44, which may be stored in one or more memory devices 45 and configured to identify stent struts and levels of malapposition (e.g., based on comparison of measured distances to thresholds), shadowed regions, and struts within the shadowed regions, as well as other vessel features, for example, using text, arrows, color coding, highlighting, contours, or other suitable human- or machine-readable indications. Once the OCT data is acquired using the probe and stored in memory, it may be processed to generate information 47, such as cross-sectional, longitudinal, and / or three-dimensional views, or subsets thereof, of the vessel along the length of the pullback region. These views may be depicted as part of a user interface, for example as shown in Figures 1B, 1C, and 14A and 14B, or may be depicted in other ways as described and depicted herein.

[0109] FIG. 10B is a cross-sectional OCT image of a stented blood vessel in accordance with the present disclosure. The vessel's lumen-lumen boundary L is at the center of the image. A guidewire shadow 12 can be seen at the top of the image, from 12 o'clock to 1 o'clock. Also visible in FIG. 10B are multiple metal stent struts 14, which cast a shadow 16 in the OCT image. Because the coherent light typically used for OCT imaging cannot penetrate the stent struts and is reflected, the metal stent struts cast a shadow against the vessel wall. The present disclosure provides an enhanced method for detecting the precise offset of the struts within the strut shadow. Once detected, the shadows and struts in FIG. 10B may be used to generate the user interfaces and indicators described herein.

[0110] FIG. 11 is a process flow diagram for detecting struts in OCT image data. Method 100 analyzes shadows corresponding to stent struts in multiple OCT pullback frames. Method 100 may include one or more steps described herein. These steps may be performed in any order unless otherwise required. The metal strut detection method operates based on various inputs from other image / endovascular data processing modules, such as information regarding the guidewire (140), side branch (130), and strut shadow location (110). The process flow and associated method steps and stages may operate based on original endovascular or raw data 120 acquired using OCT, IVUS, or other endovascular data acquisition systems. In one embodiment, data 120 is processed by one or more image processing modules in a pipeline configuration.

[0111] In step 110, each shadow in the OCT image data is compared or correlated with data input from side branch detection module 130 and guidewire detection module 140 to determine whether the shadow can be attributed to a side branch vessel or a guidewire. Methods, systems, and devices for detecting strut shadows, side branches, and guidewires are known. See, e.g., U.S. Patent Nos. 8,412,312; 8,478,387; 8,831,321; 9,138,147; and 9,173,591.

[0112] If, in step 150, it is determined that a given shadow can be attributed to a guidewire or side branch, the shadow is discarded and analysis for that shadow terminates. If, in step 160, it is determined that a given shadow can be attributed to a stent strut, either by direct detection or by a process of elimination, the shadow is analyzed to calculate or isolate the shadow's interior. The shadow boundary is cut off or otherwise reduced or suppressed so that only scanlines or A-lines corresponding to the interior (and therefore "darkenest") portion of the shadow are retained. This is because the shadow region, specifically the shadow's start and stop scanlines, can sometimes contain spillover from neighboring lumen pixels. Isolating the shadow's interior and ignoring transition scanlines in the shadow margin improves the estimation of strut offset.

[0113] In step 170, the shadow interior is analyzed to calculate the projection (or sum) of each sample across the scanlines corresponding to the portion of the shadow interior. Each scanline is sampled into discrete pixels or "samples." In the input OCT image data, each scanline refers to data acquired along a particular angular direction centered on the imaging catheter. Each scanline is then radially sampled into a discrete set of pixels or "samples." Each sample in the OCT data is typically a few microns wide and typically uniform in size. "Projection" refers to the process of adding across each scanline. In other words, the two-dimensional shadow in {scanline, sample} space is collapsed into a one-dimensional signal where the i-th index corresponds to the sum of the i-th sample of each scanline involved in the process. The projection encompasses samples at radius R, which are the average of samples from constituent scanlines at that same radius R.

[0114] In step 180, the projection is searched for up to three (e.g., one, two, or three) largest local maxima. The location or offset of each selected maximum may be recorded as a potential strut location, and certain characteristics of the selected maxima are then analyzed to determine which is the best candidate for being the true strut. In various embodiments, only the largest maximum is selected. In other embodiments, two or three of the largest maxima are selected. The initial selection of multiple local maxima increases sensitivity. More than three local maxima may be selected, but this is typically not necessary, as usually one of the three highest maxima will indicate the true strut location. The maximum selection process is illustrated in Figures 12 and 13.

[0115] Figure 12 is an A-line or scan-line OCT image of a stented vessel on a logarithmic scale. The box 155 on the right side of the image indicates the shadow under analysis, and Figure 13 shows a projection graph of this shadow. The vessel lumen L is the dark area at the top of the image, and the vessel wall VW is the light area at the bottom of the image. L is used generally herein to refer to the lumen. Multiple stents 14 and stent shadows 16 can be seen in the image. In one embodiment, the lumen is the boundary between the tissue and the interior of the cleared vessel.

[0116] Figure 13 is a graph showing the detection of multiple potential struts within a single shadow. Figure 13 is a plot of the projection across the interior shadow scan line. There are two maxima 22a and 22b. These maxima correspond to two potential strut locations within the shadow. These locations may be used by an intravascular imaging system, such as an OCT or IVUS system, to display the stent struts on a user interface, as described herein.

[0117] Additional filters may be applied to local maxima to remove false positives. In various embodiments, a local maximum is selected only if it has a signal greater than 1 / 10 (i.e., 10%) of the global peak (the maximum value along the maximum projection). The global peak is the peak with the largest amplitude. The 10% threshold reduces the likelihood of selecting a false local maximum due to noise. The threshold may be set between 5% (i.e., 1 / 20) and 10% of the global peak, for example, 5%, 6%, 7%, 8%, 9%, or 10%, with 10% being preferred. In various embodiments, if multiple peaks are detected close to each other, only the largest peak is selected for further analysis.

[0118] In step 190, the selected local maxima are analyzed to determine which maxima have the highest likelihood of being the true strut, based on information available from the strut's immediate neighborhood. A relative score is assigned to each strut based on one or more of the following criteria: 1. Proximity to the lumen: Selected maxima are scored based on their proximity to the lumen boundary: the maxima closest to the lumen around the strut shadow receive the highest score, and the maxima furthest from the lumen around the strut shadow receive the lowest score. 2. Peak Intensity: The selected maxima are scored based on their peak intensity, with the maxima with the highest peak receiving the highest score and the maxima with the lowest peak receiving the lowest score. 3. Degree of Malapposition: Selected maxima are scored based on their apposition, which refers to their juxtaposition to the lumen. Maxima that are crimped within a predetermined acceptable distance from the lumen or vessel wall receive a higher malapposition score. Struts that are too far from the lumen or vessel wall (determined by a user-specified threshold using one or more interface screens or based on accepted treatment thresholds) are penalized and receive a lower malapposition score as a potential false positive. In one embodiment, a strut may have a malapposition score of either 0 or 1, depending on whether it is poorly crimped or not, respectively.

[0119] These scoring criteria are exemplary, and additional scoring criteria based on other strut and shadow features may be used. In one embodiment, candidate stent struts are validated using cross-frame analysis, which indicates a strut is valid if a section of the strut is next to or aligned with other sections in adjacent or nearby frames.

[0120] Each local maximum receives a combined score that is a linear sum of the above criteria. In step 200, the local maximum with the highest score is selected as the active strut. In step 210, the remaining local maxima are saved as alternate or backup struts pending further analysis. In the event of a tie, the local maximum closest to the lumen and / or the brightest local maximum is used as the tiebreaker. Table 1 provides one exemplary ranking of local maxima for a stent shadow. Table 1: Maximum ranking for stent shadows [Table 1]

[0121] As shown in Table 1, Maximum 1 has the highest total score and will therefore be selected as the candidate valid strut. Maximums 2 and 3 are designated as backup struts.

[0122] In step 220, all local maxima (active struts and any backup struts) undergo multi-frame validation. In this step, adjacent frames are compared to verify that the active strut in one frame aligns with the active strut selected for the adjacent frame. If the active strut does not align with the other cross-frame strut, then the active strut may be replaced by the backup strut if the backup strut better fits the cross-frame model. One embodiment of the multi-frame validation step may use stent strut geometry and position information. Other embodiments using a larger set of strut and shadow features may also be used for this step. That is, position and geometry may all be used as features, along with other features, such as previous pullback data or other user-supplied information.

[0123] Once detected, the valid or selected struts may be displayed on a user interface, providing important visual assistance to the clinician regarding the precise location of the stent struts and whether adjustments are needed to optimize and / or speed stent placement and reduce the risk of side effects. The user interface may include cross-sectional images, L-mode images, scan line images, three-dimensional renderings, or any other suitable display format for visualizing the detected struts. The user interface may also include indicator bars, angiography data, and other views and features described and depicted herein.

[0124] The detection algorithm accurately located struts, with a sensitivity ranging greater than about 80% in one embodiment. The detection algorithm accurately located struts, with a sensitivity ranging greater than about 0% in one embodiment. In one embodiment, sensitivity is the percentage of correctly located struts divided by the total number of struts (properly located struts plus missing struts). In one embodiment, positive predictive value is the percentage of correctly detected struts divided by all positive calls (correctly detected struts plus false positives). The various features described herein are suitable for use in several different cath lab systems, such as intravascular imaging systems and pressure measurement systems. The indicators and detection steps described herein provide various advantages to diagnosticians and those planning stent deployment or evaluating deployed stents.

[0125] In part, the present invention provides a computer-based method, system, and apparatus for detecting and displaying stented regions. Specifically, the present invention can identify the first and last frames of a stented region. In this context, a frame refers to a cross-section through a blood vessel being imaged via OCT. The stented region is identified by iteratively processing OCT image frames to determine whether the frame and / or its vicinity exhibits features consistent with an expected configuration of stent struts. Stent struts appear in OCT images as solid structures distinguishable from soft tissue, such as the vessel wall. In addition, the struts of a properly expanded stent are typically positioned adjacent to the vessel wall. Thus, in a cross-section of a stented vessel, multiple stent struts are uniformly distributed around the circumference of the vessel wall. Therefore, frames that exhibit OCT features consistent with a deployed stent—e.g., multiple struts distributed around the entire vessel wall—are candidates for being designated as stent-containing frames.

[0126] In OCT imaging data, artifacts due to uncleared blood cells or catheter walls can have optical properties similar to stent struts. These imaging artifacts are often interpreted by software as stent struts. However, background noise typically does not have the regular geometric shape of a stent, which is composed of a network of struts. For example, uncleared blood cells may be clustered within a single region, distributed randomly, and / or may not be located adjacent to the vessel wall. The challenge is to distinguish frames containing stent struts from frames without stent struts, which contain only false positives due to artifacts.

[0127] Another challenge is to identify whether a valid stented region exists within the OCT image data, and if so, accurately locate the first and last frames and image multiple frames that encompass the stent. In one embodiment, the method described herein is applicable to metallic and bioabsorbable stents as well as other non-metallic stents. Generally, one or more embodiments of the present disclosure provide a method for identifying frames and associated representations of an intravascular pullback that are displayed to an end user, which accurately detects stent struts and accurately identifies sections or regions of the vessel where a stent is not present.

[0128] [Embodiment of stent detection] In part, the present invention provides a computer-based method, system, and apparatus for detecting and displaying stented regions. Specifically, the present invention can identify the first and last frames of a stented region. In this context, a frame refers to a cross-section through a blood vessel being imaged via OCT. The stented region is identified by iteratively processing OCT image frames to determine whether the frame and / or its vicinity exhibit features consistent with an expected configuration of stent struts. Stent struts appear in OCT images as solid structures distinguishable from soft tissue, such as the vessel wall. In addition, the struts of a properly expanded stent are typically positioned adjacent to the vessel wall. Thus, in a cross-section of a stented vessel, multiple stent struts are uniformly distributed around the circumference of the vessel wall. Therefore, frames that exhibit OCT features consistent with a deployed stent, e.g., multiple struts distributed around the entire vessel wall, are candidates for being designated as stent-containing frames.

[0129] In OCT imaging data, artifacts due to uncleared blood cells or catheter walls can have optical properties similar to stent struts. These imaging artifacts are often interpreted by software as stent struts. However, background noise typically does not have the regular geometric shape of a stent, which is composed of a network of struts. For example, uncleared blood cells may be clustered within a single region, distributed randomly, and / or may not be located adjacent to the vessel wall. The challenge is to distinguish frames containing stent struts from frames without stent struts, which contain only false positives due to artifacts.

[0130] Another challenge is to identify whether a valid stented region exists within the OCT image data, and if so, accurately locate the first and last frames and image multiple frames that encompass the stent. In one embodiment, the method described herein is applicable to metallic and bioabsorbable stents as well as other non-metallic stents. Generally, one or more embodiments of the present disclosure provide a method for identifying frames and associated representations of an intravascular pullback that are displayed to an end user, which accurately detects stent struts and accurately identifies sections or regions of the vessel where a stent is not present.

[0131] FIG. 14A is an L-mode or longitudinal view of a stented vessel region before removal of false-positive struts. A longitudinal view is a type of cross-sectional view. The distal (D) end is to the left of the depicted vessel in the L-mode image, and the proximal (P) end is to the right. The actual stented region 101 extends from approximately 12 mm to approximately 45 mm in the L-mode image. The false-positive region 15 is located directly adjacent to the actual stented region 101, from approximately 45 mm to approximately 52 mm in the L-mode image. The false-positive region 15 is caused by background noise with optical properties similar to stent struts. For example, uncleared blood cells swirling within the imaging area sometimes appear as stent struts in OCT images.

[0132] Software programs that analyze these imaging data typically include false positive regions 15 as part of the stented region 101. As one result, the software program may determine that the first stented frame was approximately 12 mm and the last stented frame was approximately 52 mm. Displaying false positive regions 15 as part of the stented region 101 may lead to misinterpretation or confusion in the clinician's understanding of the image, which may further lead to failed interventions (e.g., failed redeployment of an incompletely crimped stent) or unnecessary procedures (e.g., redeploying a correctly deployed stent).

[0133] 14B is an L-mode display of a stented vessel region after elimination of non-stented regions 15 according to the present invention. The present invention does not detect individual false-positive struts, but rather looks at the distribution of detected struts over a fixed longitudinal neighborhood to assess the location of the stented region. The false-positive stented regions 15 can be automatically eliminated, allowing for detection of the actual stented region 101.

[0134] FIG. 15A is a diagrammatic depiction of one embodiment of the Stent Region Detection algorithm, which creates a frame neighborhood for a frame being evaluated, such as frame k. In one embodiment, the frame neighborhood, which may include two adjacent frames k and k+1 (or k and k−1), is used to analyze strut angle coverage and geometry in frame k. The size and rules associated with selecting frames for the neighborhood may vary for a given application. In one embodiment, the Stent Region Detection algorithm uses frames k−1, k, and k+1 to accumulate struts and determine the maximum angle gap and subsequently the angle coverage metric. For a given neighborhood created for frame k, the set of frames on either side of k may be summed to create a superframe. A given superframe is the fusion of struts on frame k with other struts on neighboring frames.

[0135] In one embodiment, stent struts 50 are detected in the OCT image data using known techniques, and an estimate of the vessel wall centroid 54 is also pre-calculated by known methods and used by the algorithm. If the stent is correctly deployed and expanded, the stent struts will typically be adjacent to the vessel wall 52 at the luminal boundary, but this method is equally applicable to frames in which the stent deployment does not properly crimp against the luminal boundary (vessel wall). For a given frame k, we combine that strut with struts detected over a fixed neighborhood (in this embodiment, the neighboring frame k+1). The angular position of each strut is determined using the vessel centroid.

[0136] Figure 15B is a schematic depiction of another exemplary embodiment of the stent region detection algorithm in which a neighborhood of frames is used to create a superframe, or strut fusion, about a neighborhood center on frame k. As shown in Figure 15B, three adjacent frames k-1, k, and k+1 are used to analyze strut angle coverage and geometry in the three-frame neighborhood. As in Figure 15A, stent struts 50 are detected in the OCT image data using known techniques, and an estimate of the vessel wall center of gravity is also pre-computed by known methods and used by the algorithm.

[0137] If the stent is correctly deployed and expanded, the stent struts will typically abut the vessel wall 52 at the luminal boundary, but this method is equally applicable to frames in which the stent deployment does not crimp correctly against the luminal boundary (vessel wall). For a given frame k, we combine that strut with struts detected over a fixed neighborhood (in this embodiment, nearby frames k-1 and k+1). The angular position of each strut is determined using the vessel centroid.

[0138] If a given frame belongs to a stented region, it will contain struts with nearly 360-degree coverage around the circumference of the vessel wall 52. Occasionally, struts are missed during the OCT imaging process, which will appear as gaps in coverage around the lumen. Therefore, in one preferred embodiment, stent information from multiple frames is stacked or combined, and then gaps between struts in the combined data are measured. Using struts detected over a neighborhood facilitates smoothing of the plot and easier use of thresholding methods to separate true stent regions from false positives. Additionally, the presence of guidewires and side branches, which appear as large shadows in the image, can lead to lower angular coverage around the circumference. These features may be identified and taken into account when analyzing gaps between stent struts.

[0139] 15A and 15B, strut information from frame k is stacked or combined with stent information from frame k+1 to create multi-frame stent data. In multi-frame analysis, the orientation of each frame is preserved, and the angular gap between struts is measured around the circumference of the vessel lumen in the multi-frame data. The largest angular gap θ between adjacent struts is max,k is then calculated for the vessel centroid for frame k.

[0140] Figure 15A illustrates a multi-frame analysis based on two adjacent frames k and k+1. Figure 15B illustrates a multi-frame analysis based on three adjacent frames k-1, k, and k+1. Combining multiple struts from adjacent frames provides cross-frame information to the stented region detection method. False-positive struts will tend to be randomly distributed across frames; therefore, frames and multi-frames containing only false-positives are unlikely to exhibit uniform strut coverage around the circumference of the vessel. Therefore, the largest angular gap for a valid stented frame or multi-frame will be smaller, often much smaller, than the largest angular gap for a non-stented frame or multi-frame.

[0141] Although larger neighborhoods may be used, the presence of false positives in larger neighborhoods can hinder the approach. Therefore, small neighborhoods (e.g., 2-3 frames) are preferred. In various embodiments, the algorithm analyzes the OCT data from pullback to determine the stent - i.e., θ max,k is within the expected range of the stented region. Using cross-frame and cross-neighborhood analysis, the algorithm determines the first and last frames of the stented region. False positives outside the stented area are revealed because the false positives are not contiguous with the stented area.

[0142] False positives around the stent ends may be further eliminated by comparing the detected stent length with the known actual stent length. If the detected stent length exceeds the known stent length, the detection algorithm may be improved by using dynamic thresholds to either adjust the stent region or adjust the neighborhood size to give a better estimate of the stent region. The same approach may be applied to situations where the detected stent length is shorter than the known length.

[0143] The detection algorithm may also include a validation step that compares the detected stent geometry and length with known stent geometries and lengths. Frames that represent atypical geometries may be eliminated as false positives and / or deprioritized until the atypical frame is found to be part of a contiguous region of multiple frames.

[0144] The detection algorithm is not limited to analyzing pairs of frames, but may analyze stent information from a single frame if the imaged stent comprises a sufficiently dense mesh network, or may analyze stent information from more than two frames (e.g., 3, 4, 5, or more) if the imaged stent comprises a sparse mesh network. In addition, the frames used in the multi-frame analysis do not need to be adjoining frames, but may be separated by several frames. In one embodiment, this may be implemented using a sliding window type algorithm.

[0145] The maximum angular gap is used to derive an angular coverage metric for each multiframe, which can be plotted against frame number on a graph. Figure 16 is a graph of an angular coverage plot demarcating the frame locations of two stents in the OCT pullback data. Angular coverage metric Ψ for frame k k is defined by the following equation (Equation 3):

number

[0146] Frames with an angular coverage metric below a predetermined threshold are categorized as non-stented frames, while frames with an angular coverage metric above a predetermined threshold are categorized as stented frames. In one preferred embodiment, the angular coverage metric threshold is, for example, from about 0.25 to about 0.65. The angular coverage threshold may be automatically or dynamically set or calculated by software, or it may be user-defined depending on, for example, the geometry of a particular stent. In one embodiment, about 0.8 is an angular coverage metric threshold observed for some stents. In one embodiment, less than about 0.3 is an angular coverage metric threshold observed when frame k is outside the stent region.

[0147] Figure 16 shows an angular coverage plot for one exemplary OCT pullback. The angular coverage metric threshold is set at 0.4. The angular coverage metric is below 0.4 for frames 0 to 20; therefore, these frames are classified by the algorithm as non-stented frames. The angular coverage metric for frames 0 to 20 is less than zero, indicating potential background noise. Frames 20 to 125 have angular coverage metrics between 0.7 and 0.9, which are well above the 0.4 threshold. Therefore, frames 20 to 125 are classified by the algorithm as stented frames. The angular coverage metric drops sharply to zero after frame 125 and remains below the threshold until frame 140, indicating a non-stented region between frames 125 and 140. At frame 140, the angular coverage metric increases above the 0.4 threshold and remains above the threshold from frames 140 to 220. Therefore, frames 140 to 220 are categorized as stented regions.

[0148] 17 is a flow diagram illustrating the stent region detection algorithm 201. As will be appreciated, additional steps or analyses can be introduced without departing from the base detection algorithm. The detection algorithm 200 receives guide catheter data 211 and / or stent strut detection data 212 obtained from a preliminary analysis of OCT pullback data. These input data are often based on analysis of OCT scanlines or single OCT frames. In step 214, these input data are used to clean the frame and / or stent strut detection within the guide catheter region. This step is optional but preferred because guide catheters often create imaging artifacts that are misinterpreted as stent struts.

[0149] Next, in step 216, cross-frame or multi-frame information is generated by combining strut data from neighboring frames k and k+1. In this manner, strut data from one frame near other nearby frames may be fused, aggregated, or combined to perform a type of cross-frame verification. In one preferred embodiment, frames k and k+1 are directly adjacent OCT frames. In other embodiments, a neighborhood of 2n+1 frames may be used, using the frame set {kn, k-n+1,...k-1, k, k+1, k+n-1, k+n}. For n=1, frames k-1, k, and k+1 are used. However, as described above, struts in frame k may be combined with data from frames several microns apart. Additionally, as described above, the detection algorithm may analyze stent information from a single frame.

[0150] In step 218, the detection algorithm calculates the angular gap between consecutive struts around the circumference of the vessel lumen in the multi-frame data. In step 221, the maximum angular gap for a given multi-frame is determined. The maximum angular gap is then used to calculate an angular gap metric for each (e.g., frame) in step 222. In step 224, the angular gap metric is then compared against a threshold angular gap metric. If the angular gap metric for a given multi-frame exceeds the threshold angular gap metric, then that multi-frame is flagged as being within the actual stented region. Finally, in step 226, the detection algorithm determines, based on the multi-frame analysis, which OCT frames correspond to the actual stented region. A confirmation step 228 may be performed. The method may further include displaying a mark for the region of the vessel indicating the stented region. Generally, any of the detected and confirmed struts may be displayed as described and depicted herein. Additionally, in one embodiment, the indicia is a crimping bar aligned with the stent region, the crimping bar being rotationally agnostic or persistent.

[0151] Figure 18A is a user interface representation showing a longitudinal or L-mode view of a stented vessel region 101 before elimination of false-positive struts 18. Numerous false-positive struts were detected around 20 mm and approximately 34 mm. Figure 18B is an L-mode view of the stented vessel region 101 after elimination of the false-positive struts. As shown in Figure 18B, the detection algorithm eliminates the false-positive struts, resulting in a more accurate representation of the actual stented region.

[0152] The use or lack thereof of directional arrowheads in a given diagram is not intended to limit or dictate the direction in which information may flow. For example, for a given connector, such as the illustrated arrows and lines connecting elements shown in Figures 1 and 10A, information may flow in one or more directions or in only one direction as appropriate for a given embodiment. Connections may include various suitable data transmission connections, such as optical, wired, power, wireless, or electrical connections.

[0153] Some portions of the detailed descriptions are presented in terms of algorithms and symbolic representations of operations on data bits within a computer memory. These algorithmic descriptions and representations may be used by those skilled in the related arts of computers and software. In one embodiment, an algorithm is here, and generally, conceived to be a self-consistent sequence of operations leading to a desired result. The operations performed as method steps or otherwise described herein require physical manipulations of physical quantities. These quantities usually, though not necessarily, take the form of electrical or magnetic signals capable of being stored, transferred, combined, transformed, compared, and otherwise manipulated.

[0154] Non-limiting software features and embodiments for implementing interfaces, detection, and other disclosed features The following description is intended to provide a general overview of device hardware and other operating components suitable for carrying out the methods of the present disclosure described herein. This description is not intended to limit the applicable environments or the scope of the present disclosure. Similarly, hardware and other operating components may be suitable as part of the above-described apparatus. The present disclosure may also be practiced with other system configurations, including personal computers, multiprocessor systems, microprocessor-based or programmable electronic devices, network PCs, minicomputers, mainframe computers, etc. The present disclosure may also be practiced in distributed computing environments where tasks are performed by remote processing devices linked through a communications network, such as in different rooms of a catheterization or cath lab.

[0155] Some portions of the detailed descriptions are presented in terms of algorithms and symbolic representations of operations on data bits within a computer memory. These algorithmic descriptions and representations may be used by those skilled in the related arts of computers and software. In one embodiment, an algorithm is here, and generally, conceived to be a self-consistent sequence of operations leading to a desired result. The operations performed as method steps or otherwise described herein require physical manipulations of physical quantities. These quantities usually, though not necessarily, take the form of electrical or magnetic signals capable of being stored, transferred, combined, transformed, compared, and otherwise manipulated.

[0156] Unless otherwise expressly stated, and as will be apparent from the following description, throughout the description, descriptions utilizing terms such as "processing" or "computing" or "searching" or "indicating" or "detecting" or "measuring" or "calculating" or "comparing" or "generating" or "sensing" or "determining" or "displaying", or operations involving Boolean logic or other sets, are clearly understood to refer to operations and processes of a computer system or electronic device that manipulate and convert data represented as physical (electronic) quantities in registers and memories of the computer system or electronic device into other data that are similarly represented as physical quantities in electronic memory or registers or other such information storage, transmission or display devices.

[0157] The present disclosure also relates, in some embodiments, to apparatus for performing the operations herein. This apparatus may be specially constructed for the required purposes, or it may comprise a general-purpose computer selectively activated or reconfigured by a computer program stored on the computer. Various circuits and components thereof may be used to perform some of the data collection, conversion, and processing described herein.

[0158] The algorithms and displays presented herein are not inherently related to any particular computer or other apparatus. Various general-purpose systems may be used with programs in accordance with the teachings herein, or various general-purpose systems may prove advantageous for constructing more specialized apparatus to perform the required method steps. The required structure for a variety of these systems will appear from the description below. In addition, the present disclosure is not described with reference to any particular programming language; thus, various embodiments may be implemented using a variety of programming languages. In one embodiment, the software instructions are configured for operation on a microprocessor or ASIC of an intravascular imaging / data acquisition system.

[0159] Embodiments of the present disclosure may be embodied in many different forms, including, but not limited in any way to, computer program logic for use with a processor (e.g., a microprocessor, microcontroller, digital signal processor, or general-purpose computer), programmable logic for use 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 exemplary embodiments of the present disclosure, some or all of the processing of data collected using the OCT probe and processor-based system or used to generate control signals or initiate user interface commands is implemented as a set of computer program instructions, which are converted into a computer-executable form, themselves stored in a computer-readable medium, and executed by a microprocessor under the control of an operating system.

[0160] In this manner, queries, responses, transmitted probe data, input data, and other data, and signals described herein are converted into processor-understandable instructions and other data suitable for responding to user interface selections, controlling the graphical user interface, processing control and graphic signals, displaying cross-sectional information and images from other data acquisition modalities, generating and displaying stent and crimp bar and other intravascular data, displaying angiograms, OCT, detecting shadows, detecting peaks, and the like, as part of the graphical user interface and other features and embodiments as described above. Data and parameters suitable for display as GUI components or controls or other representations in the graphical user interface include, but are not limited to, malapposition values, crimp bars, stent struts, missing data representations, indicator bars, shadows, angiogram representations, three-dimensional and two-dimensional renders and views, and other features described herein.

[0161] Computer program logic implementing all or a portion of the functionality described herein above may be embodied in various forms, including, but not limited to, source code form, computer-executable form, and various intermediate forms (e.g., forms produced by an assembler, compiler, linker, or locator). Source code may include a series of computer program instructions implemented in any of a variety of programming languages ​​(e.g., object code, assembly language, or higher-level languages ​​such as Fortran, C, C++, JAVA, or HTML) for use with various operating systems or operating environments. Source code may define and use various data structures and communication messages. Source code may be in a computer-executable form (e.g., via an interpreter), or source code may be converted into a computer-executable form (e.g., via a translator, assembler, or compiler).

[0162] A computer program may be permanently or temporarily fixed in any form (e.g., source code form, computer-executable form, or intermediate form) in a tangible storage medium, such as semiconductor storage (e.g., RAM, ROM, PROM, EEPROM, or flash-programmable RAM), magnetic storage medium (e.g., diskette or fixed disk), optical storage (e.g., CD-ROM), PC card (e.g., PCMCIA card), or other storage device. A computer program may also be fixed in any form in a signal that can be transmitted to a computer using any of a variety of communications technologies, including, but not limited to, analog, digital, optical, wireless, networking, and internetworking technologies. A computer program may be distributed in any form, such as a removable storage medium with printed or electronic documentation (e.g., shrink-wrapped software), preloaded with a computer system (e.g., on a system ROM or fixed disk), or distributed over a network.

[0163] Hardware logic (including programmable logic for use with a programmable logic device) implementing all or a portion of the functionality described hereinabove may be designed using conventional manual methods, or may be designed, captured, simulated, or documented electronically using a variety of 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).

[0164] Programmable logic may be permanently or temporarily fixed in a tangible storage medium, such as semiconductor memory (e.g., RAM, ROM, PROM, EEPROM, or flash-programmable RAM), magnetic memory (e.g., diskette or fixed disk), optical memory (e.g., CD-ROM), or other storage device. Programmable logic may also be fixed in a signal transmittable to a computer using any of a variety of communications technologies, including, but not limited to, analog, digital, optical, wireless (e.g., Bluetooth), networking, and internetworking technologies. Programmable logic may be distributed as a removable storage medium with printed or electronic documentation (e.g., shrink-wrapped software), preloaded with a computer system (e.g., on a system ROM or fixed disk), or distributed from a server or electronic bulletin board via a communications system (e.g., the Internet or World Wide Web).

[0165] Various examples of suitable processing modules are described in more detail below. As used herein, a module refers to software, hardware, or firmware suitable for performing a particular data processing or data transmission task. Typically, in one preferred embodiment, 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, user interface data, control signals, angiography data, user actions, frequencies, interferometer signal data, detected stents, candidate stent struts, FFR data, IVUS data, shadows, pixels, intensity patterns, scores, projections, side branch data, and guidewire data, as well as other information of interest as described herein.

[0166] The computers and computer systems described herein may include machine-readable media, such as operably associated computer-readable media, such as memory for storing software applications used to acquire, process, store, and / or communicate data. It will be understood that such memory may be internal, external, remote, or local with respect to the operably associated computer or computer system.

[0167] Memory may also include any means for storing software or other instructions, including, but not limited to, for example, a hard disk, an optical disk, a floppy disk, a DVD (digital versatile disk), a CD (compact disk), a memory stick, flash memory, a ROM (read only memory), a RAM (random access memory), a DRAM (dynamic random access memory), a PROM (programmable ROM), an EEPROM (extendable erasable programmable read only memory), and / or other similar computer readable medium.

[0168] Generally, computer-readable storage media applied in connection with the embodiments of the present disclosure described herein may include any storage medium capable of storing instructions executed by a programmable device. Where applicable, method steps described herein may be embodied or performed as instructions stored on a computer-readable storage medium or memory 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 may be adapted to create instructions according to embodiments of the present disclosure.

[0169] The terms "machine-readable medium" or "computer-readable medium" include any medium capable of storing, encoding, or retaining a set of instructions for execution by a machine, causing the machine to perform any one or more of the methodologies of this disclosure. While the machine-readable medium is shown in one exemplary embodiment as a single medium, the term "machine-readable medium" should be taken to include a single medium or multiple media (e.g., a database, one or more centralized or distributed databases and / or associated caches and servers) that store one or more sets of instructions.

[0170] A storage medium may be non-transitory or may include a non-transitory device. Accordingly, a non-transitory storage medium or a non-transitory device may include a tangible device, meaning that the device has a concrete physical form, although it can change its physical state. Thus, for example, non-transitory refers to a device that remains tangible despite this change in state.

[0171] The aspects, embodiments, features, and examples of the present disclosure are to be considered in all respects illustrative and are not intended to limit the disclosure, the scope of which is defined solely 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 claimed disclosure.

[0172] The use of headings and paragraphs in this application is not meant to limit the disclosure, and each paragraph may apply to any aspect, embodiment, or feature of the disclosure.

[0173] Throughout this application, when compositions are described as having, including, or comprising particular components, or when processes are described as having, including, or comprising particular process steps, it is contemplated that compositions of the present teachings may consist essentially of or consist of the recited components, or that processes of the present teachings may consist essentially of or consist of the recited process steps.

[0174] When an element or component is referred to in this application as being included in and / or selected from a 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 group consisting of two or more of the enumerated elements or components. Furthermore, it should be understood that the elements and / or features of the compositions, devices, or methods described herein, whether expressly or implicitly stated herein, can be combined in various ways without departing from the spirit and scope of the present teachings.

[0175] The use of the terms "include," "includes," "including," or "have," "has," "having" should generally be understood to be open-ended and non-limiting unless specifically stated otherwise.

[0176] The use of the singular herein includes the plural (and vice versa) unless specifically stated otherwise. Moreover, the singular forms "a," "an," and "the" or "when not limiting a number" include the plural unless the context clearly dictates otherwise. In addition, when the term "about" is used before a quantitative value, the present teachings also include ±10% of the specific quantitative value itself, unless specifically stated otherwise.

[0177] It should be understood that the order of steps or order for performing certain actions is immaterial so long as the present teachings remain operable. Moreover, two or more steps or actions may be conducted simultaneously.

[0178] When a range or list of values ​​is provided, each intervening value between the upper and lower limits of that range or list of values ​​is individually contemplated and encompassed within the disclosure as if each value were specifically recited herein. Additionally, smaller ranges between and including the upper and lower limits of a given range are contemplated and encompassed within the disclosure. The listing of exemplary values ​​or ranges is not exclusive of other values ​​or ranges between and including the upper and lower limits of a given range.

[0179] It should be understood that the drawings and descriptions of the present disclosure have been simplified to show relevant elements for a clear understanding of the present disclosure, while omitting other elements for clarity. Those skilled in the art will recognize, however, that these and other elements may be desirable. However, because such elements are well known in the art and because they do not facilitate a better understanding of the present disclosure, descriptions of such elements are not provided herein. It should be understood that the drawings are presented for illustrative purposes and not as structural diagrams. Omitted details and modifications or alternative embodiments are within the knowledge of one skilled in the art.

[0180] It can be clearly understood that in certain aspects of the present 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 functions. Except insofar as such a substitution would not be effective for practicing a particular embodiment of the present disclosure, such substitution is deemed to be within the scope of the present disclosure.

[0181] The examples presented herein are intended to illustrate potential and specific implementations of the present disclosure. It can be clearly understood that these examples are intended primarily for the purpose of illustrating the present disclosure for those skilled in the art. There may be changes to these diagrams or the operations described herein without departing from the spirit of the present disclosure. For example, in some cases, method steps or operations may be executed or performed in a different order, or operations may be added, deleted, or modified.

[0182] Furthermore, since particular embodiments of the present disclosure have been described herein for purposes of illustrating the disclosure and not for purposes of limiting the disclosure, it will be clearly understood by those skilled in the art that numerous changes in the details, materials and configurations of elements, steps, structures and / or parts may be made within the principles and scope of the present disclosure without departing from the disclosure as set forth in the claims.

Claims

1. 1. A method for detecting a stented region in a blood vessel, the method comprising: receiving optical coherence tomography data about the stented vessel; wherein the optical coherence tomography data comprises a plurality of image frames. storing the optical coherence tomography data in a storage device of the intravascular data collection system; a step of: analyzing the plurality of image frames and identifying stent struts on a frame-by-frame basis; Step The angular gap data fused across adjacent frames of the plurality of image frames is Delimiting the angular offset of the identified stent struts to create the data Steps, and The maximum angular gap between any two adjacent struts in the neighboring frame Steps to determine A method comprising:

2. If the maximum angular gap is less than a threshold angular gap, the stent-containing frame The method of claim 1 further comprising classifying the frame as:

3. The class of adjacent frames that encompasses a maximum angular gap that is smaller than the threshold angular gap. and further comprising identifying an area containing a stent by identifying the area containing the stent. The method of claim 2.

4. determining a center of gravity for the stented vessel and determining the center of gravity for frame k; 2. The method of claim 1, further comprising the step of: calculating the maximum angular gap relative to a vessel centroid. How to post.

5. The maximum angular gap, θ, for a given frame k ｍａｘ，ｋ According to (Equation 1), and the angular gap metric Ψ for frame k ｋ used to calculate the The method according to claim 1. [Equation 1]

6. An angular gap metric closer to 1 indicates that the frame contains a stent. The method according to claim 5 .

7. displaying indicia on the region of the blood vessel indicating the stented region. The method according to claim 1.

8. The indicia are apposition bars aligned with the stent region, the apposition bars being 8. The method of claim 7, wherein the method is transmissive or persistent.

9. The angular gap measurement for frame k and at least one neighboring frame k+1 6. The method of claim 5, further comprising the step of calculating tricks.

10. Iteratively calculating the angular gap metric for successive neighboring frames. The method of claim 6, further comprising the steps of:

11. the method for sequentially classifying a plurality of frames in the optical coherence tomography data.

3. The method of claim 2, further comprising repeating one or more of the steps of the method. Law.

12. If the angular gap metric for a given frame is greater than a threshold angular gap, if not, then classifying the frame as a stent-containing frame.

10. The method according to claim 10.

13. Within the stented area, including the first and last frames, there are adjacent stents.

13. The method of claim 12, including the step of assembling a tent-containing frame.

14. Frames adjacent to the first frame have angular gaps lower than the threshold angular gap. and terminating a first end of the stented region when the stented region has a metric. Item 14. The method according to item 13.

15. The frame adjacent to the last frame has an angular gap lower than the threshold angular gap. terminating a second end of the stented region when the stented region has a metric The method of claim 13, comprising:

16. One or more regions in an intravascular image for which data was not available for display The method of claim 7 further comprising the step of displaying an indicia indicating:

17. The angular coverage metric is of the form: where θ ｍａｘ，ｋ is adjacent 10. The method of claim 9, wherein the largest angular gap between the struts. [Equation 2]

18. The angular coverage metric for a given frame is greater than the threshold angular gap and further comprising the step of sequentially classifying the frame as a stent-containing frame if Item 9. The method according to item 9.

19. 1. A method for detecting a stented region in a stented vessel, comprising: The method comprises: One or more intravascular image datasets of a blood vessel are obtained using an intravascular imaging system. storing a set of intravascular data, each set comprising a plurality of frames; defining a neighborhood, said neighborhood being one of frame k and a neighborhood of frame k. or more frames, By combining all of the struts detected on all frames in the neighborhood, determining the angular gap for frame k by Using the determined angular gap, the angular coverage metric Ψ for frame k is calculated as ｋ generating a A method comprising:

20. The angular coverage metric is of the form: ｍａｘ，ｋ is adjacent 20. The method of claim 19, wherein the angular gap between adjacent struts is the largest angular gap between adjacent struts. [Equation 3]

21. Title: Intravascular imaging system for detecting one or more stented regions A programmable processor-based computer device, The processor-based computing device: one or more data access channels for receiving intravascular imaging data; and, a processor in electrical communication with said one or more data access channels; and associated memory, the processor One or more intravascular image datasets of a blood vessel are obtained using an intravascular imaging system. , each endovascular data set contains multiple frames, Define a neighborhood, where the neighborhood is frame k and one or more frames near frame k. Including Rehm, By combining the struts detected on all frames in the neighborhood, Determine the angular gap for frame k; Using the determined angular gap, the angular coverage metric Ψ for frame k is calculated as ｋ Generates If the angular coverage metric for a given frame is greater than a threshold angular gap, classifying the frame as a stent-containing frame if a processor and associated memory programmed to: A computer device comprising:

22. The angular coverage metric is of the form: ｍａｘ，ｋ is adjacent 22. The method of claim 21, wherein the angular gap between adjacent struts is the largest angular gap between adjacent struts. [Equation 4]

Images