Near-infrared autofluorescence imaging system and method

Abstract

A computer-implemented method for diagnosing a medical condition in a patient is provided, the method including: using one or more processors, causing an excitation source to emit excitation light toward a region of interest in an artery, using one or more processors and a detector, receiving imaging data of the region of interest in the artery, generating an image of the region of interest using the one or more processors and the imaging data, determining, using the one or more processors, a region at risk for atherosclerotic plaque based on the imaging data, and determining, using the one or more processors, that the patient has a severe symptom of atherosclerotic plaque based on the determined region at risk for atherosclerotic plaque.

Description

[Technical Field]

[0001] <Reference to Related Applications> This application is related to and claims priority to U.S. patent application Ser. No. 63 / 041,728, filed June 19, 2020, and entitled "Near-Infrared Autofluorescence (NIRAF) of Atherosclerosis Produced by Oxidized Lipids." This application is related to and claims priority to U.S. patent application Ser. No. 63 / 113,124, filed November 12, 2020, and entitled "Near-Infrared Imaging System and Method," which is incorporated herein by reference in its entirety.

[0002] STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT This invention was made with government support under R01-HL-137913 and R01-HL-150538, both awarded by the National Institutes of Health. The U.S. Government has certain rights in this invention. [Background technology]

[0003] Cardiovascular disease due to atherosclerosis is a leading cause of death and disability worldwide and can lead to complications including myocardial infarction, ischemic stroke, and peripheral arterial disease. Atherosclerosis is typically characterized by inflammation and the chronic accumulation of fibrofatty plaques in arterial walls. The growth of atherosclerotic plaques is variable, including remaining clinically asymptomatic for most (or all) of an individual's lifetime; however, in some cases, atherosclerotic plaque growth can cause gradual narrowing of the arterial lumen, which can lead to tissue ischemia. In more severe cases, deposited atherosclerotic plaques can rupture, and thrombi can form around the deposited plaques, causing ischemia and infarction (e.g., myocardial infarction, stroke, etc.).

[0004] While stents and other interventional techniques can be deployed to restore adequate blood flow through a patient's arteries, these techniques are deployed after downstream tissue has been damaged by the lack of adequate blood flow. Current imaging approaches cannot adequately characterize high-risk plaques that may cause an event. Therefore, it would be desirable to provide a near-infrared fluorescence imaging system and method that aids in the diagnosis of atherosclerotic plaques before an ischemic event. Summary of the Invention

[0005] Some non-limiting examples of the present disclosure provide a computer-implemented method for diagnosing a medical condition in a patient, the method including: causing an excitation source, using one or more processors, to emit excitation light toward a region of interest in an artery; receiving, using one or more processors and a detector, imaging data of the region of interest in the artery; generating an image of the region of interest using the one or more processors and the imaging data; determining, using the one or more processors, a region at risk for atherosclerotic plaque based on the imaging data; and determining, using the one or more processors, that the patient has a severe symptom of atherosclerotic plaque based on the determined region at risk for atherosclerotic plaque.

[0006] In some non-limiting examples, the method can further include determining, with one or more processors, a maximum intensity value of the imaging data, and determining, with the one or more processors, a threshold based on the maximum intensity value, The pixel having the maximum intensity value can be defined within a region at risk of atherosclerotic plaque.

[0007] In some non-limiting examples, an at-risk area of ​​an atherosclerotic plaque can have a higher amount of insoluble lipid than an area of ​​the atherosclerotic plaque that does not include the at-risk area. An at-risk area of ​​an atherosclerotic plaque can have a higher amount of insoluble iron than an area of ​​the atherosclerotic plaque that does not include the at-risk area.

[0008] In some non-limiting examples, the method can further include filtering the image with one or more processors to generate an image of risk regions of atherosclerotic plaque.

[0009] In some non-limiting examples, the method may further include using one or more processors to determine a size of the risk area of ​​the atherosclerotic plaque based on the image of the risk area of ​​the atherosclerotic plaque; using one or more processors to determine that the size of the risk area is greater than a size threshold; and using one or more processors to determine that the patient has a severe symptom of atherosclerotic plaque based on the size of the risk area being greater than a size threshold.

[0010] In some non-limiting examples, filtering the image using one or more processors to generate an image of the region at risk of atherosclerotic plaque can include thresholding the image of the region of interest according to a pixel intensity threshold using one or more processors to generate an image of the region at risk of atherosclerotic plaque.

[0011] In some non-limiting examples, a pixel intensity threshold can reject pixels having intensities above the pixel intensity threshold, and can be at least one of a first range defined between a peak signal intensity value in the imaging data and a first pixel value that is 0.25 times the peak signal intensity value, a second range defined between the peak signal intensity value and a second pixel value that is 0.5 times the peak signal intensity value, or a third range defined between the peak signal intensity value and a first pixel value that is 0.75 times the peak signal intensity value.

[0012] In some non-limiting examples, the signal strength peak value is a global peak signal strength value.

[0013] In some non-limiting examples, the image is a first image. The method can further include filtering, with one or more processors, the first image to generate a second image of the atherosclerotic plaque excluding the region at risk, and subtracting, with one or more processors, the second image from the first image to generate a difference image that is an image of the region at risk of the plaque.

[0014] In some non-limiting examples, a portion of the risk area of ​​an atherosclerotic plaque can include the ceroid of the atherosclerotic plaque.

[0015] In some non-limiting examples, filtering, using one or more processors, the first image to generate a second image of the atherosclerotic plaque excluding the region at risk can include thresholding the first image according to a threshold to generate the second image.

[0016] In some non-limiting examples, the patient is not administered an imaging agent before exciting the region of interest with the excitation source and before receiving imaging data from the detector.

[0017] In some non-limiting examples, the artery is a carotid artery.

[0018] In some non-limiting examples, the method can include using one or more processors to determine a shape of the atherosclerotic plaque; using one or more processors to determine that the shape of the atherosclerotic plaque exceeds a shape threshold; and using one or more processors to notify a user based on the shape of the risk area of ​​the atherosclerotic plaque exceeding the shape threshold.

[0019] Some non-limiting examples of the present disclosure provide a computer-implemented method for diagnosing or treating atherosclerosis in a patient, the method including: causing an excitation source, using one or more processors, to emit excitation light toward a first region of interest in an artery; receiving, using one or more processors and detectors, first imaging data of the first region of interest in the artery; generating, using the one or more processors, a baseline image of the first region of interest including atherosclerotic plaque in the artery from the first imaging data; causing the excitation source, using the one or more processors, to emit excitation light toward at least a portion of the first region of interest in the artery; receiving, using the one or more processors and detectors, second imaging data of at least a portion of the first region of interest in the artery; generating, using the one or more processors, a diagnostic image of the artery including the atherosclerotic plaque from the second imaging data; and comparing, using the one or more processors, the baseline image and the diagnostic image to determine an increase, decrease, or maintenance of signal intensity between corresponding regions of the baseline image and the diagnostic image. An imaging agent may not be administered to the patient for the acquisition of the first imaging data and for the acquisition of the second imaging data.

[0020] In some non-limiting examples, the method can include administering an antioxidant to the patient after receiving the first imaging data.

[0021] In some non-limiting examples, administering an antioxidant may be performed after receiving the first imaging data and before the excitation light is emitted toward at least a portion of the first region of interest. The antioxidant may be configured to reduce signal intensity of the atherosclerotic plaque by reducing the level of oxidative stress within the atherosclerotic plaque.

[0022] In some non-limiting examples, the antioxidant can be at least one of alpha-tocopherol or N-acetylcysteine.

[0023] In some non-limiting examples, the method can include using one or more processors to determine stabilization of the atherosclerotic plaque based on a decrease in signal intensity between corresponding regions of the baseline image and the diagnostic image.

[0024] In some non-limiting examples, the method can include subtracting, with one or more processors, the diagnostic image from the baseline image.

[0025] In some non-limiting examples, the method can include using one or more processors to determine progression of atherosclerosis based on an increase in signal intensity between corresponding regions of the baseline image and the diagnostic image.

[0026] In some non-limiting examples, the method can include determining, with one or more processors, that an area at risk of atherosclerotic plaque exceeds a size threshold.

[0027] Some non-limiting examples of the present disclosure provide a computer-implemented method for screening atherosclerotic plaque stabilizing or anti-inflammatory compounds, the method including the steps of: acquiring, using one or more processors, a first image of a region of interest of a sample using an excitation source and a detector; after acquiring the first image, administering a proposed atherosclerotic plaque stabilizing or anti-inflammatory compound to contact the sample; acquiring, using the one or more processors, a second image of at least a portion of the region of interest of the sample using the excitation source and a detector; comparing, using the one or more processors, the first image and the second image; determining, using the one or more processors, a decrease in signal intensity between corresponding regions of the first image and the second image based on the comparison of the first image and the second image; and determining, using the one or more processors, that the proposed atherosclerotic plaque stabilizing or anti-inflammatory compound is an atherosclerotic plaque stabilizing or anti-inflammatory compound based on the decrease in signal intensity.

[0028] In some non-limiting examples, the sample is a biological sample.

[0029] In some non-limiting examples, the method can include determining, with one or more processors, a magnitude of the reduction in signal intensity; and determining, with one or more processors, the effectiveness of a proposed atherosclerotic plaque stabilizing or anti-inflammatory compound based on the magnitude of the reduction in signal intensity.

[0030] Some non-limiting examples of the present disclosure provide an imaging system for imaging a patient's blood vessel. The imaging system can include an excitation source configured to emit excitation light toward the patient's blood vessel, a detector configured to sense light emitted from the patient's blood vessel, and a computing device in communication with the excitation source and the detector. The computing device can be configured to cause the excitation source to emit the excitation light toward at least a portion of a region of interest in the blood vessel, receive imaging data of the region of interest in the blood vessel using the detector, and determine that the patient has a severe symptom of atherosclerotic plaque based on the imaging data.

[0031] In some non-limiting examples, the computing device may be further configured to generate an image of the region of interest using the imaging data.

[0032] In some non-limiting examples, the computing device may be further configured to threshold the image according to a pixel threshold to generate an image of a region at risk of atherosclerotic plaque, determine at least one of a size or a shape of the region at risk of atherosclerotic plaque, determine at least one of a size of the atherosclerotic plaque exceeding a size threshold or a shape of the atherosclerotic plaque exceeding a shape threshold, and determine that the patient has a severe symptom of atherosclerotic plaque based on determining that at least one of the size or shape of the atherosclerotic plaque exceeds a corresponding threshold.

[0033] In some non-limiting examples, the computing device may be further configured to generate an image of a risk-free area of ​​plaque, determine an oxidative stress value based on the risk-free area of ​​plaque, and determine that the patient has a severe symptom of atherosclerotic plaque based on the oxidative stress value.

[0034] In some non-limiting examples, the computing device may be further configured to determine that the oxidative stress value exceeds an oxidative stress threshold and, based on determining that the oxidative stress value exceeds an oxidative stress threshold, determine that the patient has severe symptoms of atherosclerotic plaque.

[0035] Some non-limiting examples of the present disclosure provide a method for diagnosing a medical condition in a patient. The patient can have a blood vessel. The method can include emitting excitation light toward at least a portion of a region of interest in the patient's blood vessel using an excitation source, receiving imaging data of the region of interest using a detector, determining that the patient has a severe symptom of atherosclerotic plaque based on the imaging data, and determining that the patient requires a therapeutic treatment plan involving monitoring or treating oxidized lipid-induced oxidative stress based on determining that the patient has a severe symptom of atherosclerotic plaque.

[0036] In some non-limiting examples, the method can further include determining an area at risk for atherosclerotic plaque based on the imaging data, and determining that the patient has a severe symptom of atherosclerotic plaque based on the presence of the area at risk for atherosclerotic plaque.

[0037] In some non-limiting examples, the method can further include determining a size or shape of a risk area of ​​an atherosclerotic plaque based on the imaging data, determining at least one of the size of the risk area exceeding a size threshold or the shape of the risk area exceeding a shape threshold, and determining that the patient has a severe symptom of an atherosclerotic plaque based on determining that the size or shape of the risk area exceeds the corresponding threshold.

[0038] In some non-limiting examples, the therapeutic treatment plan may be at least one of a surgical intervention on the patient, a pharmaceutical intervention on the patient, or an imaging intervention that includes acquiring additional imaging data from a different imaging modality.

[0039] In some non-limiting examples, the therapeutic treatment plan may be a surgical intervention, which may be at least one of deploying a stent at the location of the atherosclerotic plaque or removing the atherosclerotic plaque by cutting it from the blood vessel.

[0040] In some non-limiting examples, the excitation light has a wavelength between 600 nm and 800 nm, and the imaging data may be acquired from light having a wavelength between 680 nm and 880 nm.

[0041] Some non-limiting examples of the present disclosure provide a computer-implemented method for diagnosing atherosclerosis, which may indicate the presence of ceroid in a patient's artery. The method may include using one or more processors to cause an excitation source to emit excitation light having a wavelength between 550 nm and 900 nm toward a region of interest in the artery, and receiving, using one or more processors and a detector, imaging data of the region of interest in the artery. The imaging data may be obtained using light having a wavelength between 600 nm and 980 nm. The method may also include determining an atherosclerosis risk value by analyzing the imaging data using the one or more processors. The atherosclerosis risk value may indicate the amount or concentration of ceroid in the patient's artery.

[0042] In some non-limiting examples, the method can further include using the one or more processors to suggest a medical treatment based on the atherosclerosis risk value, which may indicate a concentration of ceroid in the patient's arteries.

[0043] In some non-limiting examples, the medical treatment may be a treatment designed to monitor or treat oxidized lipid-induced oxidative stress.

[0044] In some non-limiting examples, the excitation light may have a wavelength between substantially 600 nm and substantially 1000 nm.

[0045] In some non-limiting examples, the excitation light may have a wavelength of substantially 633 nm.

[0046] These and other aspects and advantages of the present disclosure will become apparent from the following description. In this specification, reference is made to the accompanying drawings, which form a part hereof and which show, by way of example, preferred configurations of the present disclosure. However, such configurations do not necessarily represent the full scope of the present disclosure, and therefore, reference should be made to the claims and this specification for interpreting the scope of the present disclosure. [Brief explanation of the drawings]

[0047] [Figure 1] FIG. 1 is a schematic diagram of an imaging system. [Figure 2] FIG. 1 is a schematic diagram of a compound screening system. [Figure 3] 1 illustrates an example of an imaging system. [Figure 4] 1 shows a portion of a flowchart of a process for diagnosing a medical condition (e.g., atherosclerosis) in a patient. [Figure 5] 5 illustrates another portion of the flowchart of the process of FIG. 4. [Figure 6] 1 shows a graph of pixel values ​​for each pixel of a pixel distribution of imaging data. [Figure 7] 1 is a schematic diagram of an image of an atherosclerotic plaque in a patient. [Figure 8] 1 shows a flowchart of a process for diagnosing atherosclerosis in a patient. [Figure 9] 1 shows a portion of a flowchart of a process for diagnosing (or treating) a medical condition (e.g., atherosclerosis) in a patient or for screening for atherosclerotic plaque stabilizing or anti-inflammatory compounds. [Figure 10] 10 shows another portion of the flowchart of the process of FIG. 9. [Figure 11] A schematic diagram of the two images and the resulting difference image is shown. [Figure 12] The first column shows NIRAF images, and the second column shows fluorescein isothiocyanate channel autofluorescence images. [Figure 13] 1 shows a histogram showing the frequency of NIRAF mean fluorescence intensity values ​​for acquired NIRAF images. [Figure 14] 1 shows a graph of normalized NIRAF mean fluorescence intensity ("AU") signal versus distance from the carotid artery bifurcation. [Figure 15] Various images of carotid artery plaque NIRAF 90% area are shown. [Figure 16]Various images of carotid plaque sections are shown, showing the % positive areas for NIRAF 90%, SB, and GPA. [Figure 17] Two graphs are shown: (1) the correspondence between NIRAF90% and GPA (intraplaque hemorrhage) and (2) the correspondence between NIRAF90% and SB area (lipid), which were obtained from n = 15 carotid artery sections from seven plaques with adjacent sections stained. [Figure 18A] Representative human carotid artery atheroma images are shown. The middle panel contains a fluorescence microscopy image of the same carotid artery specimen, showing the 650 nm NIRAF signal (grayscale) and a high-magnification NIRAF image, as well as corresponding SB, NIRAF, and GPA staining from an adjacent section. [Figure 18B] Higher magnifications of the NIRAF / SB / GPA patterns from the green dotted box (i.e., i), orange dotted box (i.e., ii), and red dotted box (i.e., iii) in Figure 18A are shown. [Figure 19] High-power fields of SB, NIRAF, GPA, and bilirubin are shown, demonstrating consistent colocalization of SB with NIRAF, moderate colocalization of NIRAF 90% with GPA, and minor colocalization of bilirubin with NIRAF. [Figure 20] Both diffuse and punctate SB and NIRAF signals are evident in two fresh-frozen adjacent carotid sections (separated by lines) (left column). [Figure 21] High-power fields are shown showing frequent localization of NIRAF-positive areas containing plaque iron and CD68-positive plaque macrophages. [Figure 22] Histochemical detection of plaque iron using PB and DAB enhancement in adjacent sections is shown. [Figure 23A] 1 shows a NIRAF image and a NIRAF image overlaid on a bright field ("BF") image. [Figure 23B] A flow cytometry graph corresponding to the image in Figure 23A is shown. [Figure 24A]High magnification images of NIRAF nuclear staining in THP-1 macrophages incubated with medium alone (control), 50 μg / ml native LDL, or 50 μg / ml oxLDL for 5 days, or with 0.5 mg / ml human hemoglobin for 24 hours are shown. [Figure 24B] Representative time courses of NIRAF signal evolution at days 1, 3, and 5 in LDL- and oxLDL-treated MDMs are shown. [Figure 24C] The NIRAF signal at day 5 is compared for MDMs incubated with 20 μg / ml oxLDL and MDMs incubated with 50 μg / ml oxLDL. [Figure 24D] A graph of the quantitative data from Figure 24C is shown, presented as the mean value ± SE of three independent experiments. [Figure 25] NIRAF, Hoechst and BODIPY493 images and their combinations are shown. [Figure 26] Two graphs are shown: BODIPY-positive area and BODIPY co-localized area quantified with NIRAF adjusted for cell number. [Figure 27] NIRAF, BODIPY493 and Hoechst images and their combinations are shown. [Figure 28A] Confocal microscopy images used to assess the relationship between NIRAF and ROS are shown. [Figure 28B] Control flow cytometry graphs are shown. [Figure 28C] 1 shows a flow cytometry graph of LDL. [Figure 28D] Flow cytometry graphs of oxLDL are shown. [Figure 29A] Representative images of C11-BODIPY staining showing increased lipid peroxidative stress after incubation of MDM with oxLDL are shown. [Figure 29B]1 shows a graph depicting quantification of C11-BODIPY lipid peroxidation signal obtained from confocal microscopy, demonstrating a significant increase in lipid peroxidation after incubation with oxLDL compared to LDL or control. [Figure 30A] NIRAF and BF images of THP-1MDMs co-incubated with oxLDL and 5 mM NAC or 1 mM α-Toc for 5 days are shown, showing a decrease in the NIRAF signal (magenta) compared to cells incubated with oxLDL alone. [Figure 30B] 1 shows flow cytometry analysis graphs showing the percentage reduction in NIRAF+ cells after incubation with either the antioxidant compounds N-acetylcysteine ​​("NAC") or α-tocopherol ("α-Toc"). The x-axis of each graph is NIRAF signal (in increments of powers of 10, up to approximately 10), and the y-axis is cell number (in increments of thousands, up to approximately 4,000). [Figure 31] Various confocal microscope images are shown. [Figure 32A] Flow cytometry graphs showing the reduction in the number of CellROX-positive and NIRAF-positive cells corresponding to the image categories in FIG. 31, with FIG. 32A corresponding to oxLDL. [Figure 32B] Flow cytometry graphs showing the reduction in the number of CellROX-positive and NIRAF-positive cells corresponding to the image categories in Figure 31, Figure 32B corresponds to oxLDL+NAC. [Figure 32C] Flow cytometry graphs showing the reduction in the number of CellROX-positive and NIRAF-positive cells corresponding to the image categories in FIG. 31, and FIG. 32C corresponds to oxLDL+α-tocopherol. DETAILED DESCRIPTION OF THE INVENTION

[0048] As mentioned above, atherosclerotic plaques can cause ischemic events, including acute myocardial infarction, sudden cardiac death, and angina pectoris. To better understand the relationship of atherosclerotic plaques to myocardial infarction events, intracoronary imaging studies have revealed that in select patients with acute myocardial infarction, the "culprit" atherosclerotic lesion typically exhibits a ruptured overlying thin cap, a large plaque burden, and a necrotic lipid-rich core. While these studies are helpful in clarifying the relationship between myocardial infarction and atherosclerotic plaque composition, they are not specifically useful for predicting or preventing ischemic events associated with atherosclerotic plaques. Indeed, the ability of intravascular and noninvasive imaging to predict plaque-specific complications remains limited in general.

[0049] In recent years, near-infrared autofluorescence ("NIRAF") has been used to analyze human carotid and aortic atherosclerotic specimens, particularly correlating NIRAF with intraplaque hemorrhage ("IPH"). NIRAF has also been used in conjunction with optical coherence tomography ("OCT") to analyze fibroatheroma in patients with coronary artery disease ("CAD") in vivo. While useful, these studies still lack the predictive power necessary to identify (and therefore address) high-risk atherosclerotic plaques.

[0050] Some non-limiting examples of the present disclosure address these shortcomings (and others) by providing near-infrared autofluorescence imaging systems and methods that can identify high-risk regions (or, in other words, risk areas) of atherosclerotic plaques and monitor changes in the stability (and oxidative stress) of atherosclerotic plaques (e.g., ceroid in atherosclerotic plaques). For example, some non-limiting examples of the present disclosure show the relationship between high NIRAF signal intensity corresponding to high-density regions of insoluble lipids and iron, which can indicate high-risk regions of atherosclerotic plaques. As another example, some non-limiting examples of the present disclosure show the relationship between NIRAF signal and ceroid in atherosclerotic plaques, which are insoluble complexes of lipids and proteins that are produced under conditions of oxidative stress. As yet another example, some non-limiting examples of the present disclosure show the relationship between NIRAF signal and oxidative stress, including the presence of iron—particularly, an increase in NIRAF signal (e.g., amplitude) indicates increased oxidative stress (and vice versa).

[0051] 1 is a schematic diagram of an imaging system 100. The imaging system 100 may include a computing device 102, an excitation source 104, and a detector 106. The computing device 102 may include typical computing components, such as a processor device, memory, a communication system, a display, inputs (e.g., a mouse, keyboard, touch screen, sensors, etc.), a power source, etc. In some cases, the computing device 102 may take a variety of specific forms, including a desktop, a laptop, a mobile device (e.g., a tablet or smartphone), etc. For example, in some cases, the computing device may be located outside of a housing that includes the excitation source 104 and the detector 106 located therein. In other cases, the computing device 102 may be located within the same housing as the excitation source 104, the detector 106, or both.

[0052] In some non-limiting examples, the computing device 102 may include a processor device, memory, a communication system, etc. to communicate with other computing devices (e.g., a desktop computer). In other cases, the computing device 102 may simply be implemented as a processor. Regardless of the implementation of the computing device 102, the computing device 102 may communicate with the excitation source 104 and the detector 106. In this manner, the computing device 102 may cause each of these components (or others) to perform specific tasks (e.g., by sending instructions to each component) and may receive data from each of these components. For example, the computing device 102 may cause the excitation source 104 to emit visible light (i.e., electromagnetic waves having wavelengths substantially in the range of 380 nm to substantially 600 nm), including red light, or near-infrared light (i.e., electromagnetic waves having wavelengths substantially in the range of 600 nm to substantially 2500 nm). In some non-limiting examples, the excitation source 104 can emit light within a range between 500 nm and 1100 nm, or more specifically between 550 nm and 900 nm, or more specifically between 600 nm and 800 nm. In some specific cases, the computing device 102 can cause the excitation source 104 to emit red light having a wavelength between 600 nm and 700 nm, more specifically between 630 nm and 635 nm, or substantially (i.e., with less than a 20% deviation, less than a 10% deviation, or less than a 5% deviation) 630 nm, or red light having a wavelength between 720 nm and 780 nm, or substantially 750 nm. In some non-limiting examples, the excitation source 104 emits light within a wavelength range to excite ceroids, where the excitation is in the red and / or near-infrared region. As another example, the computing device 102 can cause the detector 106 to acquire imaging data. The detector 106 can be configured to detect light within a particular range that is lower in energy than the range of the excitation range.For example, excitation source 104 can be configured to emit light having a first wavelength (e.g., red light, infrared light), and detector 106 can be configured to detect light having a second wavelength that is (or substantially) greater than the first wavelength. In some non-limiting examples, the detection range for excitation of light between 550 nm and 900 nm corresponds to a detection wavelength range between 600 nm and 980 nm, and the detection wavelength range for excitation of light between 600 nm and 800 nm is between 650 nm and 880 nm. In some non-limiting examples, the detection range for excitation of light at 630 nm is 680 nm to 720 nm, or substantially 700 nm. The detection range for excitation of light at 740 nm is 770 nm to 810 nm, or substantially 790 nm (e.g., the detection range may be 40, 20, 10, or narrower). The specific detection range may depend, for example, on the specific filter used and may differ from the ranges listed above.

[0053] In some non-limiting examples, excitation source 104 can emit light having a wavelength between substantially 600 nm and substantially 1000 nm, or between 600 nm and 1000 nm. In some cases, the light emitted from excitation source 104 can have a wavelength of substantially 633 nm, or can have a wavelength of 633 nm.

[0054] In some non-limiting examples, the computing device 102 can communicate with other computing devices. For example, the computing device 102 can communicate with another computing device (not shown), which can be a computer, laptop, smartphone, server, etc., that causes the computing device 102 to perform specific tasks, including causing the excitation source 104 to emit light and the detector 106 to acquire imaging data. As another example, the other computing device can receive data from the computing device 102, including raw data from the excitation source 104 (e.g., characteristics of the emitted light, including duration, wavelength(s), intensity(s), etc.) and raw data from the detector 106 (e.g., location of the catheter system emitting light within the blood vessel, orientation of the catheter within the blood vessel, acquired imaging data, etc.). In some cases, the other computing device can receive processed data from the computing device 102, including images (e.g., reconstructed by the computing device 102), analyzed imaging data (e.g., filtered), analyzed images (e.g., thresholded), etc. In some cases, other computing devices (including the other computing devices described above) may communicate with computing device 102 (e.g., via a communications network including WiFi) to share the computational load (e.g., by performing portions of the processes described below). In this manner, computing device 102 may perform some or all portions of the processes described below (e.g., retrieved from memory) as needed.

[0055] The excitation source 104 and the detector 106 can be of typical configuration. For example, the excitation source 104 can be implemented to provide the required excitation wavelength of the required light (e.g., substantially 630 nm light, substantially 740 nm light, etc.), and the detector 106 can be implemented to sense the required emission wavelength (e.g., 700 nm light for a 630 nm light excitation wavelength, 790 nm light for a 740 nm light excitation wavelength). In some non-limiting examples, the excitation source 104 can be a laser, a light-emitting diode ("LED"), a tungsten-halogen lamp, a mercury or xenon arc lamp, or the like. In some cases, the detector 106 can include multiple light sources (e.g., LEDs), each configured to emit light having a different wavelength or substantially different wavelengths. For example, the excitation source 104 can include a first light source configured to emit light having a first wavelength (e.g., 630 nm) and a second light source configured to emit light having a second wavelength. The second light source can be configured to emit light at a second wavelength that corresponds to the excitation wavelength of an exogenous probe, such as the probes described in US 2010 / 0092389 or US 2018 / 055953. Additionally, multiple different excitation wavelengths of light can provide greater specificity.

[0056] In some non-limiting examples, the detector 106 can be a two-dimensional ("2D") sensor array (e.g., sensor elements) including a charge-coupled device ("CCD"), an active pixel sensor (e.g., a CMOS sensor), or the like. In some configurations, the detector 106 can include one or more optical filters that can attenuate (or block) the transmission of one or more wavelengths of light received by the detector 106, which can include wavelengths of excitation light from the excitation source 104. As shown in FIG. 1 , the excitation source 104 and the detector 106 can be coupled to a catheter system 108 using single or multimode optical fibers, such as those described in U.S. Pat. Nos. 9,332,942, 10,912,462, or 10,952,616, each of which is incorporated herein by reference for its teachings. However, in an alternative configuration, the excitation source 104 and the detector 106 can be integrated into a system external to the patient (e.g., for acquiring imaging data of cell cultures, tissue slides, etc.). For example, in this case, excitation source 104 and detector 106 may form part of an imaging station including a Kodak Image-Station 4000 (see, eg, Carestream Health, Rochester, NY).

[0057] In some non-limiting examples, the detector 106 can be configured to acquire three-dimensional ("3D") imaging data of a region of interest of the biological target 110 (e.g., a patient's artery). In this case, for example, the computing device 102 can receive the 3D imaging data and generate a 3D volume of the region of interest. In some cases, the imaging system 100 can be configured similar to the fluorescence-mediated tomography imaging system described in U.S. Pat. No. 6,615,063 ("Ntziachristos"), which is incorporated herein by reference for its teachings. For example, Ntziachristos describes acquiring 3D images, and thus the imaging system 100, including the excitation source 104 and the detector 106, can be configured to acquire one or more images, including a 3D image, or 3D imaging data of the biological target 110.

[0058] 1 , excitation source 104 emits light having an excitation wavelength toward biological target 110. Biological target 110 absorbs the excitation light and emits light at an emission wavelength (lower energy or longer wavelength than the excitation wavelength), which is detected by detector 106. In some configurations, such as when excitation source 104 and detector 106 are implemented integrally with catheter system 108, biological target 110 may be a patient's blood vessel, particularly a patient's artery. In other configurations, such as when excitation source 104 and detector 106 are implemented as an imaging station, biological target 110 may be tissue slide(s), cell culture(s) (e.g., multiple dishes), or other ex-vivo biological system.

[0059] FIG. 2 is a schematic diagram of a compound screening system 120. The compound screening system 120 can include an imaging system 122, a computing device 124, and a sample manipulation system 126. The imaging system 122 can be implemented similarly to the imaging system 100. For example, the imaging system 122 can include an excitation source 128, a detector 130, and a housing 132 that contains and supports the excitation source 128 and the detector 130. In some cases, the excitation source 128 can be implemented similarly to the excitation source 104, and the detector 130 can be implemented similarly to the detector 106. Although the detector 130 is shown in FIG. 2 as being located on the same side of the sample 134 as the excitation source 128, in an alternative configuration, the excitation source 128 can be located on a first side of the sample 134 and the detector 130 can be located on a second side of the sample 134 opposite the first side. In this case, for example, the sample holder 136 supporting the sample 134 can be transparent to the wavelength(s) of light that can be detected by the detector 130. Thus, the sample holder 136 does not block the light that is to be received by the detector 130 .

[0060] Like the imaging system 122, the computing device 124 can be implemented similarly to the computing device 102. For example, the computing device 124 may be a computer, a smartphone, a laptop, or simply a processor. The computing device 124 may be in communication with the imaging system 122 and the sample manipulation system 126, and may therefore send instructions to each of these components. For example, the computing device 124 may cause the excitation source 128 to emit light toward the sample 134 (or other sample) and the detector 130 to acquire imaging data of the sample 134.

[0061] The sample manipulation system 126 can be configured to move the sample holder 136 to different positions relative to the imaging system 122. For example, the sample manipulation system 126 can include actuators (not shown), each in communication with the computing device 124, to retract and extend to move the sample holder 136 to different positions relative to the imaging system 122. For example, the sample manipulation system 126 can include a first actuator that can extend and retract along a first line and a second actuator that can extend and retract along a second line that is substantially perpendicular to the first line. In this manner, such as when the sample holder 136 includes a 2D array of samples, the computing device 124 can be configured to selectively retract and extend the first and second actuators to move the sample holder 136, thereby aligning each sample in the sample holder 136 with the imaging system 122 so that imaging data for each sample is acquired one at a time. In some cases, such as when the sample holder 136 includes a 1D array of samples, the sample manipulation system 126 may include a single actuator that may be selectively retracted and extended by the computing device 124 to align each sample with the imaging system 122 so that imaging data for each sample is acquired one sample at a time.

[0062] Alternatively, the sample manipulation system 126 can include a robotic arm in communication with the computing device 124 that can move each sample into alignment with the imaging system 122 so that imaging data is acquired one sample at a time. In this case, for example, each sample 134, 138, 140 can have a sample holder such that the sample holders are separated from one another, and each sample holder is then grasped and subsequently moved into and out of alignment with the imaging system 122 by the robotic arm. Alternatively, the robotic arm can grasp and subsequently move the sample holder 136 to different positions, thereby aligning each sample with the imaging system 122 one at a time. In some non-limiting examples, the sample manipulation system 126 can include a dispenser system (not shown). The dispenser system can include actuators, valves, etc., and can dispense a compound (or multiple compounds, such as a cocktail of compounds) into each sample 134, 138, 140. In this manner, the computing device 124 can operate the dispenser system to dispense a quantity of each compound (eg, into a well supporting the respective sample) into contact with each sample 134 , 138 , 140 .

[0063] In some non-limiting examples, the sample holder 136 can be implemented in different ways. For example, the sample holder 136 can be a multi-well plate, including a microplate, a microwell plate, etc. In this manner, each sample 134, 138, 140 (and others) can be loaded into a respective well within each plate. In other cases, the sample holder 136 can be a holder that supports multiple Petri dishes. In this case, for example, each sample 134, 138, 140 (and others) can be loaded into a respective Petri dish and supported by the holder. In some non-limiting examples, each sample 134, 138, 140 (and others) can include a cell culture (e.g., the same type and amount of cell culture). Furthermore, regardless of whether the dispenser system of the sample manipulation system 126 delivers compounds to each sample 134, 138, 140 (and others), each sample 134, 138, 140 can contain different compounds that have the potential to be anti-inflammatory compounds, or atherosclerotic plaque stabilizing compounds, or different amounts of the same compound (e.g., a compound that has already been determined to be an anti-inflammatory compound or an atherosclerotic plaque stabilizing compound). For example, each sample 134, 138, 140 can be loaded with different compounds, but each different compound can have the same amount across the different compounds (e.g., to control the amount of compound). As another example, each sample 134, 138, 140 can be loaded with the same compound (a compound that has already been determined to have anti-inflammatory or atherosclerotic plaque stabilizing properties), but each sample 134, 138, 140 can be loaded with different amounts of the same compound (e.g., such that each sample is serially diluted). In this manner, the amount (eg, concentration) of a compound determined to be anti-inflammatory or atherosclerotic plaque stabilizing can be optimized based on its anti-inflammatory or atherosclerotic plaque stabilizing properties.

[0064] In some non-limiting examples, the sample manipulation system 126 can move the sample holder 136 (containing different samples 134, 138, 140) so that only one sample 134, 138, 140 at a time is aligned with the excitation source 128 and the detector 130. In other words, the sample manipulation system 126 can move the sample holder 136 (e.g., to a fixed position) so that imaging data for sample 134 can be collected by the imaging system 122 (e.g., at the direction of the computing device 124) at a certain time, while imaging data for samples 138, 140 cannot be collected at that time. Subsequently, such as after the imaging system 122 has acquired imaging data for sample 134, the sample manipulation system 126 can move the sample holder 136 (e.g., to a different position) so that imaging data for sample 138 can be acquired by the imaging system 122 at another time while imaging data for samples 134, 140 cannot be acquired. In this manner, multiple different samples, each having (different amounts of) potential stabilizing or anti-inflammatory compounds, can be screened in a high-throughput manner (e.g., with little or no external monitoring by sample handling system 126). In some non-limiting examples, housing 132 of imaging system 122 can define walls that can block light emitted from excitation source 128 from being directed toward samples that are not aligned with imaging system 122 and can block light emitted from samples that are not aligned with imaging system 122 from being received by detector 130.

[0065] 3 shows an example of an imaging system 150, which may be a specific embodiment of imaging system 100. Imaging system 150 may be implemented as a catheter system that may be placed within a patient's artery (or other blood vessel). Imaging system 150 may include an excitation source 152, a detector 154, and an optical fiber 156 that directs light from excitation source 152 into a blood vessel, such as artery 158. The fluorescent light is collected and guided by optical fiber 156 to detector 154. In some cases, the portion of the catheter that emits the excitation light and the portion of the catheter where the fluorescent light (emission light) is collected and sent to detector 156 may be oriented in the same or opposite directions.

[0066] As shown in FIG. 3 , the imaging system 150 can be rotationally rotated about an axial axis 160 of the artery 158, which can extend along the length of the artery 158 and be centered within the artery 158. In some cases, the axis 160 can follow the curvature of the artery 158 and thus the axis 160 can also be curved. In some non-limiting examples, the imaging system 150 can be advanced along the axis 160 (or an axis parallel or substantially parallel to the axis 160), thereby enabling the imaging system 150 to acquire imaging data of additional longitudinal sections of the artery 158, such as during pullback along a guidewire (not shown). In any event, the imaging system 150 can acquire imaging data including multiple fields of view (FOVs) of the artery 158. For example, the detector 154 can acquire a first set of imaging data corresponding to multiple FOVs by acquiring imaging data while the catheter is rotating. As the catheter is pulled back along the guidewire, the imaging data provides a 360° view of the interior of the arterial wall.

[0067] 4 illustrates a flowchart of a process 200 for diagnosing a medical condition (e.g., atherosclerosis) in a patient, all or a portion of which may be executed on one or more computing devices (e.g., a processor device of computing device 102). At 202, process 200 may include a computing device causing an excitation source (e.g., excitation source 104) to emit excitation light toward a blood vessel of interest (e.g., an artery). In some cases, such as when excitation source 104 is implemented as part of a catheter system (e.g., catheter system 108), the catheter system 108 is deployed within the patient, and an optical fiber (e.g., optical fiber 156) is advanced through the vascular system (e.g., the patient's arterial system) until it reaches a region of interest in the artery.

[0068] In some non-limiting examples, process 200 can be performed without the use of an imaging agent. An imaging agent can refer to an imaging contrast agent that absorbs or alters external electromagnetic or ultrasonic radiation, a radiopharmaceutical that emits radiation that is detected by an imaging system, etc. In some cases, an imaging agent can refer to a diagnostic imaging agent that enhances the contrast of an image within the body. Thus, in some cases, an imaging agent is not administered to a patient before or during excitation of a region of interest with excitation light, an imaging agent is not administered to a patient before or during receipt of imaging data from a detector, or both.

[0069] At 204, process 200 may include a computing device receiving (e.g., from a detector) imaging data of a first region of interest of an artery. In some cases, the imaging data may include a single image having a single corresponding FOV of the portion of the blood vessel, or multiple images, each having a corresponding FOV including a respective portion of the blood vessel. For example, fluorescence received by the detector may be used to generate some (or all) of the imaging data (e.g., corresponding to one FOV) while the detector is fixed. Regardless of the configuration, imaging data including multiple corresponding FOVs may collectively span the entire atherosclerotic plaque area, even if the atherosclerotic plaque area is larger than the 2D surface of the detector. In some non-limiting examples, receiving imaging data at block 204 may include a computing device receiving 3D imaging data of the first region of interest of the artery.

[0070] At 206, process 200 may include a computing device generating one or more images of the first region of interest using the imaging data. The one or more images may be a single image corresponding to a single FOV, or multiple images, some of which may be partially overlapping, not overlapping, or fully overlapping. For example, the computing device may reconstruct multiple images from different FOVs and combine them (e.g., by image stitching) to form a composite image. In some non-limiting examples, including when the imaging data is 3D imaging data, block 206 of process 200 may include generating a 3D volume of the first region of interest using the 3D imaging data.

[0071] At 208, process 200 may include a computing device determining a peak intensity value based on the imaging data. In some cases, the computing device may determine the peak intensity value from a generated image (or, in other words, a reconstructed image), which may include the computing device determining a maximum pixel value in the image. In other cases, the computing device may determine the peak intensity using the imaging data itself (e.g., raw imaging data), which may include the computing device determining a highest signal intensity value. In still other cases, the peak intensity value may be a voxel value (e.g., a maximum voxel value in the 3D imaging data).

[0072] In some non-limiting examples, at block 208, process 200 may include a computing device determining intensity values ​​based on the imaging data. In some cases, this may include a computing device determining a distribution of intensity values ​​of the imaging data and determining whether the distribution of intensity values ​​is multimodal. If the computing device determines that the distribution is multimodal, the computing device may identify each peak and a corresponding intensity for each peak. The computing device may then determine an intensity value based on the intensity value of each peak. For example, the intensity value may be a value located between two peaks, including the peak with the highest intensity value and the peak with the next highest intensity value. In some non-limiting examples, a similar process may be used to determine pixel values. For example, the computing device may determine a pixel value distribution across pixel locations. In other words, the pixel value distribution may span two dimensions (e.g., corresponding to the pixel locations). The computing device may then determine whether the pixel value distribution is multimodal, and if so, each peak and the corresponding pixel value for each peak. A pixel value can then be determined based on the pixel intensity values ​​of each peak, including pixel intensity values ​​that lie between two peaks, including the peak having the highest pixel intensity value. Although this discussion is written with reference to pixels, in some cases, voxels can be used instead of pixels, and thus voxel intensity values ​​can be determined using the process described with reference to pixels.

[0073] At 210, process 200 may include a computing device determining a threshold (e.g., a signal intensity threshold, a pixel threshold, a voxel threshold, etc.). For example, the computing device may determine the threshold based on a determined intensity value (e.g., a peak intensity value determined in block 208) or a determined pixel intensity value (e.g., a maximum pixel intensity value). In some cases, the threshold may correspond to an intensity value (e.g., an intensity value, a pixel intensity value, etc.) located between two intensity value peaks, where one peak is the peak with the highest intensity value. In this manner, and as described below, some non-limiting examples of the present disclosure have shown that intensity values ​​close to the maximum intensity value correspond to high-risk regions (or, in other words, risk regions) of atherosclerotic plaque in a patient's artery. Therefore, by setting a threshold to exclude intensity values ​​outside the highest range, the image can be refined to include only high-risk regions of atherosclerotic plaque that may be or contain lesions. Therefore, by identifying and extracting high-risk regions, the high-risk regions themselves can be more easily evaluated. In some cases, the high-risk area can indicate the amount (e.g., concentration) of ceroid within the atherosclerotic plaque. In other words, the high-risk area can be correlated with the amount of ceroid within the atherosclerotic plaque, such that an increased size in the high-risk area indicates a greater amount of ceroid (and vice versa). In some non-limiting examples, the high-risk area of ​​the atherosclerotic plaque can correspond to the narrowed portion of the atherosclerotic plaque. Correspondingly, the remaining portion of the atherosclerotic plaque (e.g., the portion not including the high-risk area) can correspond to the non-narrowed portion of the atherosclerotic plaque.

[0074] In some non-limiting examples, the threshold may be a value (e.g., an intensity value) or a range (e.g., a plurality of intensity values). For example, the range may be defined by a range that is substantially or exactly between a maximum intensity value (e.g., a maximum pixel value, a maximum voxel value) and a second intensity value (e.g., a second pixel value, a second voxel value different from the maximum pixel value). The second intensity value may be a percentage (e.g., 5%, 10%, 20%, 25%, 50%, 75%, 90%, etc.) of the maximum intensity value. In other cases, the threshold may be an intensity value located between two intensity peaks (e.g., a multimodal distribution), where one intensity peak is the peak having the highest intensity. For example, the threshold may be an intensity value that exists in a valley (e.g., of an intensity distribution) between two intensity peaks, one of which is the peak having the highest intensity. As another example, the threshold may be an intensity value that is the intensity value of the second highest peak in the distribution. In this way, all intensity values ​​below the second highest peak in the distribution can be removed by the computing device.

[0075] In some non-limiting examples, block 210 may include the computing device determining a threshold based on a change in intensity values ​​of the distribution being greater than a threshold (e.g., magnitude of slope). In other words, the threshold may be the intensity value of the distribution at a location within the distribution where the magnitude of the slope of the change in intensity values ​​is greater than the threshold. In this manner, the threshold may be set such that pixels, intensity values, voxels, etc. greater than (or equal to) the threshold (e.g., corresponding to high-risk regions) can be retained, while pixels, voxels, or intensity values ​​less than the threshold can be removed (or, in other words, rejected).

[0076] In some non-limiting examples, the threshold in block 210 can be a predetermined threshold. For example, the predetermined threshold can be an intensity value indicative of a high-risk region of plaque. In other words, the threshold can be predetermined based on a determination that intensity values ​​equal to or greater than the threshold are indicative of (or characteristic of) a high-risk region of plaque. In this case, for example, block 210 can be omitted where appropriate.

[0077] FIG. 6 illustrates a graph 250 of pixel values ​​(corresponding to intensity values) for each pixel of a pixel distribution of imaging data (e.g., of a reconstructed image). The pixel distribution has a first group of pixels that defines a peak and includes a maximum pixel value. The pixel distribution also includes a second group of pixels that defines a peak but does not include a maximum pixel value. As shown in FIG. 6, an intensity threshold 252 can be defined as an intensity value within a valley 254 between a peak 256 of the first group of pixels and a peak 258 of the second group of pixels. In some cases, the intensity threshold 252 can be the intensity value of the lowest intensity value of the valley 254. In other cases, the intensity threshold 260 can be the intensity value of the peak 258, which may be the maximum intensity value of the peak 258.

[0078] In some non-limiting examples, including when the peaks of the intensity distribution are relatively close or largely overlap, the computing device can present a graph (such as graph 250) on the display for the user to evaluate. In this manner, the user can adjust the intensity threshold by adjusting the thresholding intensity value or intensity range (in other words, the intensity window). Accordingly, the computing device can receive user input that defines (or indicates) the threshold.

[0079] Returning to FIG. 4 , at 212, process 200 may include the computing device filtering the imaging data. In some cases, this may include the computing device filtering intensity values ​​(e.g., raw intensity values), filtering pixel values ​​(e.g., spatially), filtering voxel values, etc. In other cases, this may include the computing device filtering one or more images (e.g., one or more images generated or otherwise received in block 206). In some non-limiting examples, block 212 may include the computing device thresholding the images. For example, block 212 may include the computing device thresholding the imaging data based on the threshold determined in block 210. This may include the computing device removing (or replacing) intensity value(s) (e.g., raw data points, pixels, etc. from the imaging data) that exceed the threshold determined in block 210. For example, if the threshold is a threshold intensity value, the computing device may remove all pixels having intensity values ​​below the threshold. As another example, the computing device may replace each pixel having an intensity value below the threshold with an intensity value (e.g., an intensity value of 0). Regardless of the setting, filtering the imaging data (or image(s)) according to the determined threshold may remove portions of the imaging data (or image(s)) that are not high-risk regions, leaving only high-risk regions (or vice versa). In this manner, the imaging data (or image(s)) may be separated into groups that share common characteristics, thereby enabling better analysis of these respective regions. While this discussion is described with respect to pixels, in other configurations, these processes may be performed to filter voxels of a 3D volume (e.g., generated in block 206).

[0080] At 214, process 200 may include the computing device generating image(s) of the high-risk region of atherosclerotic plaque in the artery based on the imaging data, particularly the filtered imaging data. For example, if one or more images of the region of interest have not already been generated (e.g., if block 206 is omitted), the computing device may filter (e.g., threshold) the imaging data to generate filtered imaging data having only intensity values ​​greater than an intensity threshold. The computing device may then use this filtered imaging data to generate images of the high-risk region of atherosclerotic plaque. In other cases, for example, if one or more images of the region of interest in the artery have already been generated (or, as the case may be, received) in block 206 and thresholded by the computing device (e.g., in block 212), the resulting image(s) are images of the high-risk region of atherosclerotic plaque in the artery. In some non-limiting examples, block 214 may include the computing device generating a 3D volume of the high-risk region of atherosclerotic plaque. For example, the computing device may generate this 3D volume by utilizing one or more images of the high-risk region, or may generate this 3D volume as a result of filtering the voxels in block 212.

[0081] At 216, process 200 may include the computing device determining a size of the high-risk region of atherosclerotic plaque based on the image of the high-risk region of interest of the atherosclerotic plaque. In some cases, this area may be calculated and appropriately scaled based on a measure relating one or more dimensions of the area (or images) to real-world size (e.g., the ratio between columns or rows of pixels and millimeters). In some cases, this may include the computing device determining the size of each pixel of the detector and determining the number of adjacent pixels (or simply the total pixels) to determine the area of ​​the high-risk region. In some cases, including when multiple images of the high-risk region of atherosclerotic plaque are generated (e.g., at block 214), the computing device may combine the multiple images (e.g., by image stitching) to generate a composite image of the high-risk region of atherosclerotic plaque. The computing device may then determine the size of the high-risk region of atherosclerotic plaque from the composite image. In some cases, the computing device can determine the size of the high-risk region from a 3D volume of the high-risk region (and in some cases from real-world scaling values ​​relating voxels to real-world reference values).

[0082] At 218, process 200 may include the computing device generating an image of the remaining portion of the atherosclerotic plaque (e.g., the region of the atherosclerotic plaque that does not include the high-risk region). For example, in some cases, the computing device may subtract the image of the high-risk region of the atherosclerotic plaque (e.g., acquired at block 214) from the image of the region of interest of the artery (e.g., acquired at block 206) to generate the image of the remaining region of the atherosclerotic plaque. In other cases, a threshold (e.g., acquired at block 210) may be applied to the imaging data, image(s), etc. to remove imaging data (pixels) corresponding to the high-risk region of the atherosclerotic plaque to generate the image(s) of the remaining portion of the atherosclerotic plaque. In some non-limiting examples, this may include the computing device generating a 3D volume of the remaining portion of the atherosclerotic plaque. For example, this may involve the computing device subtracting the 3D volume of the high-risk region from the 3D volume of the atherosclerotic plaque to generate a 3D volume of the remaining portion of the atherosclerotic plaque.

[0083] At 220, process 200 may include a computing device determining an oxidative stress value (e.g., quantifying the amount of oxidative stress, or in other words, the degree of oxidative stress) based on the image(s) (or imaging data forming the image(s)) of atherosclerotic plaque that does not include high-risk regions. As described below, some non-limiting examples of the present disclosure indicate that signal intensity is related to oxidative stress in atherosclerotic plaque (e.g., an area of ​​atherosclerotic plaque that does not include high-risk regions). Thus, the computing device may determine an oxidative stress value related to the degree of oxidative stress by summing all intensity values ​​(e.g., pixel values) in the image(s) (or imaging data used to form the image(s)) acquired in block 218 of process 200, averaging all pixel values, or the like. In some non-limiting examples, the computing device may display or compare this oxidative stress value to an oxidative stress threshold, and based on the oxidative stress value exceeding the threshold, a warning may be presented on a display or otherwise notified to a user, and may be used to generate a therapeutic treatment plan. In some cases, including when multiple images of the remaining region of the atherosclerotic plaque (e.g., a non-high-risk region of the atherosclerotic plaque) are generated, the computing device can combine the multiple images (e.g., by image stitching) to generate a composite image of the remaining region of the atherosclerotic plaque. The computing device can then determine an oxidative stress value for the remaining region of the atherosclerotic plaque based on the composite image. In some non-limiting examples, block 220 can include the computing device determining the oxidative stress value based on a 3D volume of the remaining portion of the atherosclerotic plaque. For example, this can include the computing device summing (or averaging) all voxels in the 3D volume of the remaining portion of the atherosclerotic plaque.

[0084] FIG. 7 shows schematic diagrams of images 270, 272, and 274 of atherosclerotic plaque in a patient's artery to illustrate the image processing steps described above. For example, image 270 includes an atherosclerotic plaque 276 having a high-risk region 278. While the cross-sections of images 270, 272, and 274 are exaggerated for simplicity, images of the interior wall of a blood vessel (e.g., an artery) are contemplated. Using the above process, for example, the high-risk region 278 can be extracted from image 270 to generate image 272, which contains only the high-risk region 278 of atherosclerotic plaque 276. Similarly, using the above process, for example, the high-risk region 278 of atherosclerotic plaque 276 can be removed to generate image 274 of atherosclerotic plaque 276 containing only the remaining region 280 (e.g., atherosclerotic plaque 276 minus the high-risk region 278).

[0085] Referring back to FIG. 5 , at 222, process 200 may include a computing device determining whether a high-risk region of atherosclerotic plaque (e.g., generated in block 214) exceeds a threshold. For example, the threshold may include a size of the high-risk region of atherosclerotic plaque, a shape of the high-risk region of atherosclerotic plaque, or both. As a more specific example, the computing device may compare the size of the high-risk region of atherosclerotic plaque (e.g., determined in block 216) to determine whether the size exceeds a size threshold, thereby facilitating a determination of the presence or extent of an atherosclerotic pathology. In some cases, the size threshold may be a value equal to or greater than 0.5 cm (e.g., 0.5 cm or substantially 0.5 cm), or may be a value within a range between substantially 0.5 cm and substantially 2 cm. In some cases, the size threshold may be equal to or greater than 3.5 cm. 2 In some configurations, the size thresholds may be a radial thickness (e.g., substantially 75 mm), a volume value (e.g., 3.5 mm 2 , 4mm 2In other cases, the size threshold may be a percentage of the remaining luminal cross-sectional area and the total cross-sectional area. For example, the computing device may divide the remaining luminal cross-sectional area for a location of the artery that does not contain tissue (including atherosclerotic plaque) by the total cross-sectional area at the location of the artery to generate a percentage (e.g., an occlusion percentage). In some cases, this percentage may be substantially or exactly 60%. In yet other cases, the size threshold may be the circumferential extent of the atherosclerotic plaque. For example, in this case, the size threshold may be substantially or exactly 180 degrees circumferential around the artery at the location of the artery.

[0086] In some non-limiting examples, if the computing device determines at block 222 that the size of the high-risk region of the atherosclerotic plaque exceeds (e.g., is larger than) the size threshold, process 200 may proceed to block 226. However, if the computing device determines that the size of the high-risk region of the atherosclerotic plaque does not exceed (e.g., is smaller than) the size threshold, process 200 may proceed to block 232. In some cases, the size threshold may be a multiple of (or the same size as) the size of a high-risk region of atherosclerotic plaque previously acquired as a baseline image for the same patient. For example, this baseline image may be acquired prior to process 200 (e.g., before block 204 of process 200). In this manner, the size threshold may be tailored to a particular patient to be used as a patient-specific reference point, whereby, for example, the size threshold may be set by the computing device to be related to the size of the pre-determined high-risk region (e.g., 10%, 20%, 30%, 40%, 50% larger than the size of the pre-determined high-risk region). Thus, for example, the image(s) generated in block 214 can be subtracted from the baseline image(s) of the high-risk region of atherosclerotic plaque to generate a difference image. In this manner, any positive intensity value indicates an increase in the size of the high-risk region, while any negative intensity value indicates a decrease in the size of the high-risk region. Correspondingly, the magnitude of the intensity values ​​in the difference image can correspond to an increased risk for the high-risk region.

[0087] In some non-limiting examples, block 222 may involve the computing device determining the shape of the high-risk region of the atherosclerotic plaque and determining whether the shape of the high-risk region exceeds a shape threshold. In some cases, determining the shape of the high-risk region may include the computing device determining the uniformity or deviation of uniformity of the high-risk region. For example, this may include the computing device determining the extent of the high-risk region of the plaque that corresponds to a uniform shape (e.g., an ellipse), where the extent may include the amount of area overlap between the uniform shape and the high-risk region. As another example, this may include the computing device determining the perimeter of the high-risk region and determining each curvature of the perimeter and the corresponding radius of curvature of the curvature. As yet another example, the shape threshold may include the shape of the high-risk region of the atherosclerotic plaque previously acquired as a baseline image. For example, the shape threshold may be the amount of area (or volume) overlap between previously acquired high-risk regions of atherosclerotic plaques scaled to different sizes (e.g., by increasing or decreasing the total size of the previously acquired high-risk regions) and the current high-risk region. In this manner, the shape of a diseased or relatively benign high-risk region of atherosclerotic plaque can be used to determine whether a change in shape is indicative of atherosclerotic disease. Thus, the shape threshold may be the percentage of area (or volume) overlap between the shape (e.g., a uniform shape) and the high-risk region, a radius of curvature, or the like. In some cases, if the computing device determines that the shape of the high-risk region of atherosclerotic plaque exceeds the shape threshold, process 200 may proceed to block 226. However, if the computing device determines that the shape of the atherosclerotic plaque exceeds the shape threshold, process 200 may proceed to block 232.

[0088] In some non-limiting examples, process 200 may proceed to block 226 only if, at block 222, the size of the atherosclerotic plaque exceeds the size threshold and the shape of the atherosclerotic plaque exceeds the shape threshold. Thus, if the size of the atherosclerotic plaque does not exceed the size threshold or the shape of the atherosclerotic plaque does not exceed the shape threshold, process 200 may proceed to block 232. However, in other cases, process 200 may proceed to block 226 (or block 232) only if the size of the atherosclerotic plaque exceeds the size threshold or if the shape of the atherosclerotic plaque exceeds the shape threshold.

[0089] At 224, process 200 may include a computing device determining whether the oxidative stress value exceeds a threshold. In some cases, this threshold may be a multiple (or the same) of an oxidative stress value previously obtained from a non-high-risk area of ​​atherosclerotic plaque for the same patient (e.g., the multiple may be 1.1, 1.2, 1.3, 1.4, 1.5, etc., times the previously obtained oxidative stress value). Thus, similar to the size threshold, the oxidative stress threshold may be tailored specifically to a particular patient. In other cases, the oxidative stress value may be a default value indicating a high oxidative status. If, at block 224, the computing device determines that the oxidative stress value exceeds (e.g., is greater than) the oxidative stress threshold, process 200 may proceed to block 226. However, if, at block 224, the computing device determines that the oxidative stress value does not exceed (e.g., is less than) the oxidative stress threshold, process 200 may proceed to block 232. In some cases, if one or both of the thresholds are exceeded at blocks 222 and 224, process 200 may proceed to block 226. In some cases, for example, the image(s) generated at block 218 may be subtracted from a baseline image of the remaining region of the atherosclerotic plaque to generate a difference image. In this manner, any positive intensity value in the difference image(s) may indicate an increase in oxidative stress for that region, any negative intensity value in the difference image(s) may indicate a decrease in oxidative stress for that region, and any void regions (e.g., no change in intensity) may indicate a maintenance of oxidative stress values ​​for that region. Accordingly, correspondingly, the magnitude of the intensity value in the difference image may correspond to the amount of increase (or decrease) in oxidative stress for the remaining region of the atherosclerotic plaque. While this has been described with reference to images, in some configurations, this may be accomplished using the 3D volume described above.For example, the 3D volume of the remaining area of ​​the atherosclerotic plaque can be subtracted from the baseline 3D volume of the remaining area of ​​the atherosclerotic plaque to generate a resultant 3D volume.

[0090] At 226, process 200 may include the computing device determining that the patient has a severe symptom of atherosclerotic plaque based on exceeding one or more thresholds at blocks 222 and 224. For example, this may include the computing device determining that the patient has a severe symptom of atherosclerotic plaque based on the determined high-risk areas of atherosclerotic plaque, or based on the determined non-high-risk areas of atherosclerotic plaque, or both. As an example, this may include determining that the patient has a sufficient risk of atherosclerotic plaque (e.g., the risk is above a particular amount), which may include determining that one or more thresholds are exceeded at blocks 222 and 224.

[0091] At 232, process 200 may include the computing device determining that the patient does not have a severe symptom of atherosclerotic plaque, which may include the computing device determining that one or more thresholds of blocks 222 and 224 have not been exceeded. Correspondingly, this may include, for example, determining that the patient's atherosclerotic plaque does not pose a sufficient risk (e.g., the risk is less than a certain amount) if one or more thresholds of blocks 222 and 224 have not been exceeded. In some cases, block 232 may also include the computing device determining that the atherosclerotic plaque has stabilized. For example, if multiple (e.g., two, three, etc.) iterations of process 200 are performed over a period of time (e.g., a week, a month, a year), each at different time periods spaced apart by more than one day, and each results in the computing device determining that the patient does not have severe symptoms of atherosclerotic plaque, then the computing device can determine that the atherosclerotic plaque has stabilized (e.g., therefore, no therapeutic treatment is required).

[0092] At 228, process 200 may include input from a user that, based on the computing device's determination that the patient has severe symptoms of atherosclerotic plaque, a therapeutic treatment plan is needed for the patient. In some cases, the therapeutic treatment plan may include pharmaceutical intervention, acquisition of additional imaging data (e.g., additional autofluorescence imaging, OCT imaging, utilizing a different imaging modality, etc.), surgical intervention (e.g., resecting some or all of the atherosclerotic plaque, deploying a stent at the location of the atherosclerotic plaque, etc.). In particular, the therapeutic treatment plan may include monitoring oxidized lipid-induced oxidative stress, such as by increasing patient monitoring and / or imaging frequency. Alternatively or additionally, the therapeutic treatment plan may include treatment of oxidized lipid-induced oxidative stress, such as by pharmaceutical treatment, including, but not limited to, a hyperlipidemic drug or an anti-inflammatory drug. In some cases, pharmaceutical treatments may include cholesterol-reducing drugs, beta-blockers, blood thinning drugs (e.g., heparin, warfarin, etc.), angiotensin-converting enzyme ("ACE") inhibitors, calcium channel blockers, antiplatelet drugs, diuretics, etc. Once it has been determined that a patient has severe symptoms of atherosclerotic plaque and that the plaque exhibits oxidized lipid-induced oxidative stress, a therapeutic treatment plan can be specifically targeted to the patient's needs.

[0093] At 230, the process may include executing a therapeutic treatment plan for the patient. In some cases, this may include administering a drug (e.g., a plaque stabilization compound) to the patient and acquiring additional imaging data (e.g., from an imaging modality different from the imaging system used in block 204). Additionally, this may include surgically removing some (or all) of the atherosclerotic plaque, or (e.g., prophylactically) deploying a stent at the location of the atherosclerotic plaque, etc.

[0094] In some non-limiting examples, process 200 can include the computing device generating a report including information about the atherosclerotic plaque, including the size of the high-risk area of ​​the atherosclerotic plaque, the shape of the high-risk area of ​​the atherosclerotic plaque, the oxidative stress level of the atherosclerotic plaque (e.g., excluding the high-risk area), and whether each of the shape, size, or oxidative stress level exceeds a corresponding threshold, and if so, the amount by which the corresponding threshold is exceeded. Further, the report can include image(s) (3D volume) of the atherosclerotic plaque, a determination of whether the atherosclerotic plaque is severe or not severe, etc. In some cases, in addition to or as an alternative to generating a report, the computing device can also issue an alert, present an alert on a display, or otherwise notify a practitioner based on the computing device's determination that one or more thresholds have been exceeded.

[0095] 8 shows a flowchart of a process 300 for diagnosing atherosclerosis in a patient (e.g., atherosclerosis indicating the presence of ceroid in one or more arteries of the patient). All or some of the portions of process 300 may be performed using one or more computing devices (e.g., one or more processors, including the processor device of computing device 102), as appropriate. Additionally, process 300 is related to process 200 described above, and therefore, content of process 200 is relevant to (and applicable to) process 300, and vice versa. Like process 200, process 300 may be completed without the use of an imaging agent.

[0096] At 302, process 300 may include a computing device causing an excitation source to emit excitation light toward at least a portion of a region of interest in the artery (e.g., an inner wall of the artery), which may be similar to block 202 of process 200.

[0097] At 304, process 300 may include a computing device acquiring one or more images of the arterial region of interest using a detector, which may be similar to blocks 204 and 206 of process 200. For example, this may include the computing device acquiring multiple images of different FOVs of different portions of the arterial region of interest. As another example, this may include acquiring a single image of a single FOV of the arterial region of interest. In some cases, acquiring the one or more images may include the computing device receiving imaging data and generating one or more images from the imaging data. Alternatively, acquiring the one or more images may include the computing device receiving one or more images. In some cases, block 304 of process 300 may include the computing device acquiring (or receiving) 3D imaging data.

[0098] At 306, process 300 may include a computing device determining that atherosclerotic plaque is present for a region of interest in the artery based on one or more images (or 3D imaging data) of the artery (or imaging data defining the one or more images). For example, this may include the computing device summing all of the intensity values ​​of the one or more images, averaging all of the intensity values ​​of the one or more images, etc. to determine an intensity value. The computing device may then compare the intensity value to a threshold intensity value, and if the computing device determines that the intensity value exceeds (e.g., is greater than) the intensity value threshold indicating the presence of atherosclerotic plaque, the computing device may determine that atherosclerotic plaque is present for the patient's artery, and process 300 may proceed to block 308. Alternatively, if the computing device determines that the intensity value does not exceed the intensity value threshold, the computing device may determine that the patient's artery does not have atherosclerotic plaque, and in some cases, process 300 may proceed to block 318. Correspondingly, if the computing device determines that the patient's arteries do not have atherosclerotic plaque, the computing device may present an indication on the display (or provide other notification to the practitioner) that the patient's arteries do not have atherosclerotic plaque.

[0099] At 308, process 300 may include the computing device determining the size, shape, oxidative stress, etc., of one or more regions of the atherosclerotic plaque, which may be similar to blocks 214, 216, 218, and 220 of process 200. For example, this may include determining the size of a high-risk region of the atherosclerotic plaque, determining the shape of a high-risk region of the atherosclerotic plaque, and determining an oxidative stress value for the high-risk region of the atherosclerotic plaque (e.g., this may be similar to the process used to determine an oxidative stress value for the remainder of the atherosclerotic plaque). As another example, this may include determining the size of a remainder of the atherosclerotic plaque (e.g., one that does not include the high-risk region), determining the shape of the remainder of the atherosclerotic plaque, and determining an oxidative stress value for the remainder of the atherosclerotic plaque. As yet another example, this can include determining the overall size of the atherosclerotic plaque, determining the overall shape of the atherosclerotic plaque, and determining an oxidative stress value for the overall atherosclerotic plaque.

[0100] In some non-limiting examples, block 308 of process 300 may include a computing device determining one or more dimensions of the atherosclerotic plaque. As one example, determining the one or more dimensions may include a computing device determining a length of the atherosclerotic plaque based on imaging data (e.g., other than the imaging data of one or more images of block 304). For example, the computing device may receive imaging data of the atherosclerotic plaque from an optical coherence tomography ("OCT") imaging system (e.g., which may be deployed in a manner similar to the imaging systems described herein). The computing device may then determine the length of the atherosclerotic plaque (e.g., a length defined along the arterial flow path), for example, by the computing device reconstructing the imaging data. As another example, determining the one or more dimensions may include a computing device determining a volume of the atherosclerotic plaque based on imaging data (e.g., other than the imaging data of one or more images of block 304). For example, a computing device can receive intravascular ultrasound ("IVUS") imaging data of an atherosclerotic plaque and generate a 3D volume of the atherosclerotic plaque. In some cases, the computing device can determine a ratio between intensity values ​​from one or more images and one or more dimensions (e.g., a determined length, a determined volume, etc.) of the atherosclerotic plaque. For example, the sum of all intensity values ​​of one or more images of the atherosclerotic plaque can be taken as an intensity value, which can be divided by the length of the atherosclerotic plaque or the volume of the atherosclerotic plaque. In this manner, the intensity value can be normalized to a reference dimension(s) of the atherosclerotic plaque by this ratio.In some cases, this may involve the computing device using the 3D imaging data or one or more images to generate one or more 3D volumes of one or more portions of the artery region of interest.

[0101] At 310, process 300 may include a computing device determining an atherosclerosis risk value based on one or more of the size, shape, or oxidative stress for one or more regions of atherosclerotic plaque. For example, the computing device may determine an atherosclerosis risk value for one or more regions of atherosclerotic plaque based on their size, shape, oxidative stress, or a combination thereof to determine risk. For example, exceeding a corresponding threshold and the amount may increase the risk value, while not exceeding the corresponding threshold may decrease the risk value. As a more specific example, the computing device may compare each determined size of a region of atherosclerotic plaque with a corresponding size threshold to determine whether the size exceeds the threshold and, if so, to what extent (e.g., used to increase the risk value). This may also be completed for shape and oxidative stress value. For example, the computing device may compare each determined shape of the region of atherosclerotic plaque with a corresponding shape threshold to determine whether the size exceeds the threshold, and if so, by how much (e.g., to increase the risk value). As another example, the computing device may compare each determined oxidative stress value of the region of atherosclerotic plaque with a corresponding oxidative stress threshold to determine whether the oxidative stress value exceeds the oxidative stress threshold, and if so, by how much (e.g., to increase the risk value).

[0102] In some non-limiting examples, including when a ratio between the intensity value(s) and one or more dimensions of an atherosclerotic plaque is determined, the computing device can determine an atherosclerosis risk value based on this ratio. In some non-limiting examples, blocks 302 through 310 can be performed for multiple atherosclerotic plaques located in the same artery or different arteries. In this case, for example, the computing device can combine each risk value for each atherosclerotic plaque (e.g., by summing the risk values). Thus, the determined risk value can include a combination of risk values, where each risk value in the combination of risk values ​​is from a different atherosclerotic plaque (e.g., in the same artery or in different arteries).

[0103] At 312, process 300 may include the computing device determining whether one or more of the size, shape, and oxidative stress for one or more regions exceed (e.g., are greater than) corresponding thresholds. For example, the computing device may determine whether each of the determined sizes of the regions of atherosclerotic plaque, each of the determined shapes of the regions of atherosclerotic plaque, and each of the determined oxidative stress values ​​of the regions of atherosclerotic plaque exceed (e.g., are greater than) corresponding thresholds. As another example, the determined risk value of atherosclerosis may be compared to a threshold risk value to determine whether the determined risk value exceeds (e.g., is greater than) the threshold risk value. If the computing device determines that one or more thresholds have been exceeded, then process 300 may proceed to block 314. However, if the computing device determines that one or more thresholds have not been exceeded, then process 300 may proceed to block 318.

[0104] At 314, process 300 may include the computing device recommending a therapeutic treatment plan for the patient based on exceeding one or more thresholds. This block 314 may be similar to block 228 of process 200. For example, this may include recommending pharmaceutical intervention, surgical intervention, medical device intervention, imaging intervention, etc. In some cases, pharmaceutical intervention may include prescribing (or indicating the prescription of) a heart attack mitigating drug (e.g., a beta-blocker), a redox compound (e.g., an inflammation-reducing compound, a plaque-stabilizing compound), etc. In some non-limiting examples, this may include the computing device presenting the therapeutic treatment plan on a display, notifying a practitioner of the treatment plan, etc.

[0105] At 316, the process 300 may include executing a therapeutic treatment plan for the patient, which may be similar to block 230 of the process 300.

[0106] At 318, process 300 may include the computing device recommending that the patient not undergo the therapeutic treatment plan based on not exceeding one or more thresholds. For example, this may include the computing device presenting the recommendation on a display, notifying a practitioner of the recommendation, etc.

[0107] 9 shows a flowchart of a process 350 for diagnosing (or treating) a medical condition (e.g., atherosclerosis) in a patient or for screening for atherosclerotic plaque stabilizing or anti-inflammatory compounds. All or part of process 350 may be performed using one or more computing devices (e.g., one or more processors, including the processor device of computing device 102), as appropriate.

[0108] At 352, process 350 may include having a computing device move a sample (e.g., a biological sample) into alignment with an imaging system (e.g., imaging system 122). For example, this may include having a sample manipulation device of a compound screening system move the sample (e.g., via extending an actuator) into alignment with an imaging system (e.g., of a compound screening system). In another case, block 352 of process 350 may include moving an imaging system (e.g., imaging system 150) into a blood vessel (e.g., an artery) of a patient suspected of (or previously identified as) having an atherosclerotic plaque.

[0109] At 354, process 350 can include the computing device causing the excitation source to emit excitation light toward the sample or vessel, which can be similar to block 202 of process 200. For example, after the sample is aligned with the imaging system, the computing device can cause the excitation source to emit light toward the sample. As another example, the imaging system can be advanced along the vessel to a desired location, and the computing device can cause the excitation source to emit light toward the vessel.

[0110] At 356, process 350 may include a computing device receiving first imaging data of a region of interest of a sample or vessel, which may be similar to block 204 of process 300. In some cases, the first imaging data may be 3D imaging data.

[0111] At 358, process 350 may include a computing device generating a first image of a region of interest in the sample or vessel using the first imaging data. This may be similar to block 206 of process 200. In some cases, blocks 352 through 358 may function as a process for generating a baseline image prior to treatment (or compound testing). In some cases, this may include generating a first 3D volume of the region of interest using the first imaging data, where the first imaging data is 3D imaging data.

[0112] At 360, process 350 can include a computing device administering a compound (or multiple compounds, including a cocktail of compounds) to the sample. In some cases, this can include the computing device causing a dispenser system of the compound screening system to deliver an amount of the compound(s) to contact the sample. In other cases, block 360 can include administering the compound(s) to the patient. In some cases, this includes administering the compound (e.g., by using a catheter) to the location of the atherosclerotic plaque. The compound(s) can be a compound suspected (or predetermined) to have anti-inflammatory properties, redox properties, atherosclerotic plaque stabilizing properties, etc. In some cases, this can include administering anti-inflammatory compound(s) (e.g., canakinumab, colchicine, etc.), administering an antioxidant (e.g., icosapent ethyl), etc. to the patient or sample.

[0113] At 362, process 350 can include the computing device waiting a period of time after the compound(s) are administered to the sample (or patient). In some cases, this period of time can be minutes, hours, days, weeks, etc.

[0114] At 364, process 350 may include the computing device causing an excitation source (e.g., excitation source 104) to emit excitation light toward the same (or substantially the same) location on the sample (or blood vessel) as the location when block 354 of process 300 was performed. Thus, block 366 may be similar to block 354, but may be performed at a different time. For example, the excitation light in blocks 354 and 364 may occur at the same (or substantially the same) location.

[0115] At 366, process 350 may include the computing device receiving second imaging data of at least a portion of the region of interest of the sample (or vessel). Like block 354, block 366 may be the same as block 356, but may occur at a different time. In some cases, the second imaging data may be second 3D imaging data.

[0116] At 368, process 350 may include the computing device generating a second image using the second imaging data, which may be the same process used to generate the first image at block 358. In some cases, this may include generating a second 3D volume using the first imaging data, which is 3D imaging data.

[0117] At 370, process 350 may include a computing device comparing the first and second images (or 3D volumes) to determine a change (e.g., a decrease or increase) or maintenance of intensity between corresponding regions. For example, the computing device may subtract the second image (or second volume) from the first image (or first volume), or vice versa, to determine whether a change exists and whether the change is an increase or decrease. For example, in some non-limiting examples of the present disclosure, higher intensity values ​​indicate higher levels of oxidative stress (and vice versa). Thus, when the second image (or second volume) is subtracted from the first image (or first volume) to generate a difference image (or difference 3D volume), areas of positive values ​​in the difference image (or volume) indicate increased oxidative stress, areas of negative values ​​in the difference image (or volume) indicate decreased oxidative stress, and areas of zero value indicate no change in oxidative stress.

[0118] Figure 11 shows a schematic diagram of two images 380, 382 acquired at different times of a patient's atherosclerotic plaque. In particular, image 380 corresponds to a first image (e.g., before administration of a compound), and image 382 corresponds to a second image (e.g., after administration of compound(s) and after a waiting period). As shown in Figure 11, image 382 is subtracted from image 380 to generate difference image 384. The difference image includes regions 386 with positive intensity values ​​indicating increased intensity (and thus increased oxidative stress), regions 388 with negative intensity values ​​indicating decreased intensity (and thus decreased oxidative stress), and regions 390 with no intensity values ​​indicating no change in intensity (and thus no change in oxidative stress).

[0119] 9 , in some cases, rather than comparing images, the computing device can determine a first oxidative stress value from a first image (or first 3D volume) and a second oxidative stress value from a second image (or second 3D volume). This may be similar to determining the oxidative stress values ​​in block 220 of process 200. For example, the oxidative stress value can be the sum of all pixel intensity values ​​or the average of all pixel intensity values. The computing device can then compare the first oxidative stress value with the second oxidative stress value to determine a change in the oxidative stress value.

[0120] At 372, process 300 may include the computing device determining (e.g., based on the comparison at block 370) that the administered compound(s) were successful (or not) in reducing oxidative stress, reducing inflammation, or improving atherosclerotic plaque stability. If the computing device determines that the oxidative stress value increased, the computing device may determine that the administered compound(s) did not reduce the oxidation level (and thus do not have anti-inflammatory, redox, or atherosclerotic plaque-stabilizing properties). If the computing device determines that there is no change in the oxidative stress value, the computing device may determine that the administered compound(s) did not reduce the oxidation level (and thus do not have anti-inflammatory, redox, or atherosclerotic plaque-stabilizing properties). In some cases, if the computing device determines that there is no change in the oxidative stress value, the computing device may determine that the atherosclerotic plaque has stabilized, for example, if the first and second images (or volumes) are separated by sufficient time. If the computing device determines that the oxidative stress value is reduced, the computing device may determine that the compound is successful in reducing oxidative stress, reducing inflammation, or improving atherosclerotic plaque stability.

[0121] In some non-limiting examples, the first and second images (or the first and second 3D volumes) can be filtered to identify high-risk regions of atherosclerotic plaque and remove the high-risk regions of atherosclerotic plaque from the first and second images. In this manner, the high-risk regions can be removed from the oxidative stress analysis. Furthermore, including in the case of image subtraction, increased oxidative stress (e.g., by positive intensity values) surrounding the high-risk region can indicate that the high-risk region is at risk of expanding. Thus, in some cases, the computing device can determine whether increased oxidative stress surrounds the high-risk region of atherosclerotic plaque, and if so, the extent of that increase (e.g., the total area of ​​positive values ​​surrounding the high-risk region, the arc length of contiguous regions with positive values ​​surrounding the high-risk region, etc.).

[0122] In some non-limiting examples, process 350 can be completed for multiple samples, each having a different compound, different concentrations of the same compound (e.g., each sample having a different amount of the same compound), and different combinations of multiple compounds. Thus, process 350 can be used to screen compounds to determine whether they have a property (e.g., a property that stabilizes atherosclerotic plaque, a property that reduces inflammation, or a property that reduces oxidation levels), as well as to determine the extent of the property that is quantity-related. Thus, in some cases, the computing device can determine an optimal amount of a compound based on the amount of compound(s) administered to each sample, and can determine the corresponding reduction in oxidative stress levels.

[0123] In some examples, such as when process 350 uses blood vessels, if the computing device determines that the administered compound is not successful, process 300 may proceed to administer a different compound (e.g., the process returns to block 360). Alternatively, process 350 may proceed to execute a therapeutic treatment plan for the patient, which may be similar to block 230 of process 200.

[0124] <Example> The following examples are presented to further illustrate aspects of the present disclosure and are not meant to limit the scope of the disclosure in any way. The following examples are intended as examples of the present disclosure, and they (and other aspects of the present disclosure) are not intended to be limited by theory.

[0125] The relationship between plaque lipids, ceroids, and NIRAF signals in human carotid atherosclerotic specimens was investigated. Furthermore, we investigated whether oxidized low-density lipoprotein ("oxLDL") can cause oxidative stress and whether this correlates with NIRAF signals in cultured human monocyte-derived macrophages ("MDMs").

[0126] Excised carotid atherosclerotic specimens were obtained from 15 patients who underwent carotid endarterectomy for severe carotid artery stenosis (greater than 70% stenosis) diagnosed by duplex ultrasound or CT angiography. Patients undergoing carotid endarterectomy for in-stent restenosis were excluded. After surgical excision, carotid artery specimens were placed on ice before fluorescence reflectance imaging and histopathological evaluation.

[0127] Prior to histopathological processing, fresh carotid artery specimens were subjected to fluorescence reflectance imaging ("FRI"; Kodak Image-Station 4000, Carestream Health, Rochester, NY) using exposure times ranging from 4 to 64 seconds in the fluorescein isothiocyanate autofluorescence (excitation / emission 470 nm / 535 nm) channel and two NIRAF channels (excitation / emission 630 nm / 700 nm and excitation / emission 740 nm / 790 nm). The mean fluorescence intensity of epifluorescence detected by FRI was measured at the carotid bifurcation and then at distances of 1 mm and 2 mm proximal and distal to the carotid bifurcation using Fiji / Image J (see Figure 12 for an example).

[0128] Figure 12 shows NIRAF images of three representative freshly excised human carotid endarterectomy specimens (n ​​= 7 in total) detected by epifluorescence imaging, demonstrating increased intensity of the NIRAF signal (630 nm) at the origin of the internal carotid artery (dashed yellow line) at the common carotid bifurcation. Scale bar = 1 mm.

[0129] After ex vivo imaging, carotid artery specimens were cut into 5 mm rings (approximately 4 to 5 per specimen) from the most proximal to the distal aspect of the specimen. The carotid artery specimens were either placed directly in optical cutting temperature compound (n = 7 carotid artery specimens) or placed in decalcifying solution (Cal-Ex™ Decalcifier, Fisher Chemical, CS510-1D) for 2 hours, then placed in 4% PFA for 18 hours (n = 8 carotid artery specimens), and subsequently embedded in paraffin. The optical cutting temperature compound- and paraffin-embedded tissues were then cut at 10 μm thickness, and 6 to 8 serial sections were collected on glass slides for staining.

[0130] Fluorescence and bright-field microscopy were performed on unstained carotid plaque sections using an epifluorescence microscope (Nikon Eclipse 90i, Tokyo, Japan). Three-channel autofluorescence was detected with excitation / emission filters (exposure times) of 480 nm / 535 nm (50 ms), 650 nm / 710 nm (1 s), and 775 nm / 810 nm (3 s), with the latter two autofluorescence channels depicting NIRAF. Stitched images were obtained at 10x magnification for all culprit and flanking plaques, and selected high-power fields (20x and 40x) were obtained for each plaque.

[0131] Using available image processing software (Fiji / Image J, NIH), a contour was drawn to define the entire plaque region of interest ("ROI") on 10x stitched images of fresh-frozen carotid plaque sections obtained using an epifluorescence microscope (excitation / emission 650 nm / 710 nm). The entire plaque ROI was used to measure the mean fluorescence intensity ("MFI") of the NIRAF signal. A histogram of the mean MFI for 23 sections was then plotted. NIRAF-positive pixels were defined as pixels with a NIRAF signal intensity above the 90th percentile (≥235 arbitrary fluorescence units, NIRAF 90% (See Figure 13).

[0132] Figure 13 shows the cutoff points for defining NIRAF-positive pixels. In particular, Figure 13 shows a histogram of NIRAF mean fluorescence intensity ("MFI") from 23 fresh-frozen plaque sections obtained from seven carotid endarterectomy specimens. NIRAF-positive pixels were defined as those having a fluorescence intensity above the 90th percentile in the histogram (NIRAF 90% , ≥ 235 arbitrary fluorescence units). The NIRAF-positive pixel area was then compared with the Sudan Black-positive pixel area or glycophorin A-positive pixel area on adjacent sections. The mean was 201.9439, the standard deviation was 29.17563, and N (number of samples) was 23.

[0133] Matched histological sections were analyzed for hematoxylin and eosin ("H&E"), Masson's trichrome, Sudan Black B ("SB"), and glycophorin A ("GPA") corresponding to the NIRAF sections. Fresh-frozen (n = 23 sections from n = 7 plaques) and 4% PFA- and paraffin-embedded plaques (n = 65 sections from n = 8 plaques) were analyzed using FM and light microscopy for assessment of NIRAF and plaque composition. Slides stained with Sudan Black B were imaged immediately after drying to avoid fading. Glycophorin A (GPA) immunohistochemical staining was performed (primary antibody: Abcam, ab129024, 1:200 for 4 minutes; secondary antibody: Biocare, MACH2™ Rabbit AP-Polymer, RALPH525; chromogen: Biocare, Warp Red Chromogen, WR806). SB stained triglyceride and complex lipid aggregates, and antibodies against GPA differentiated intraplaque hemorrhage (IPH). To specifically evaluate the relationship between insoluble lipids and NIRAF in human atheromas, a group of adjacent sections was ethanol-fixed (100% for 1 hour) before SB staining. A subset of carotid plaques (n = 5) was immunohistochemically stained for the presence of macrophages (CD68; Abcam, ab955), bilirubin (LS, LS-C664051), and iron using Perl's Prussian Blue stain (potassium hexacyanoferrate(II), Sigma, P3289) containing 3,3'-diaminobenzidine tetrahydrochloride (DAB; Vector Laboratories, SK-4105).

[0134] THP-1 human monocytes ("ATCC") were plated at 5 × 10 on a 35 mm glass-bottom dish (MatTek). 5Cells were seeded at a density of 1 / 3000 and cultured in Gibco RPMI 1640 medium supplemented with glutamine, 10% fetal bovine serum, and 100 U / ml penicillin / streptomycin. Cells were then differentiated into macrophage-like cells (MDMs) by treatment with 100 nM phorbol-12-myristate 13-acetate (PMA, Sigma-Aldrich, P1585) for 72 hours. Following differentiation, MDMs were incubated with medium alone, human hemoglobin (0.5 mg / mL, Sigma-Aldrich, H7379), native low-density lipoprotein (LDL) (50 μg / mL, Kalen Biomedical, 770200), or oxidized LDL (oxLDL) (50 μg / mL, Kalen Biomedical, 770202). For each condition, medium was changed every 2 days. For NIRAF observations, unstained cells or cells nuclear-stained with Hoechst (Life Technologies, 62249) were observed under a Leica SP8 confocal microscope at an excitation wavelength of 638 nm and an emission wavelength range of 643 to 713 nm. Cells were stained with BODIPY493 / 503 (2 μM, 30 min, Life Technologies, D3922) to visualize intracellular lipids. To specifically assess insoluble lipids, a subset of MDMs was treated with 100% ethanol for 15 min to remove soluble lipids, fixed with 4% PFA, and then stained with the fluorescent lipid marker BODIPY493 / 503. Cytosolic reactive oxygen species ("ROS") production was assessed by CellROX-Green staining (2 μM, 30 min, Life Technologies, C10444). Lipid peroxidation in MDM cells was imaged by confocal microscopy following incubation with BODIPY581 / 591 (2 μM, 60 min, Life Technologies, D3861) and excitation with a 488 nm laser. For antioxidant treatment studies, treatment with N-acetyl-L-cysteine ​​(5 mM, NAC, Sigma-Aldrich, A7250) or α-tocopherol (1 mM, Sigma-Aldrich, 258024) was initiated simultaneously with oxLDL incubation.

[0135] After incubating MDMs with medium alone, native LDL, or oxLDL for 5 days, or with hemoglobin for 24 hours, the MDMs were washed with cold PBS and harvested by gentle scraping. Cells were resuspended in cold PBS and subjected to flow cytometry. For CellROX-based ROS detection, cells were incubated with CellROX-Green (Life Technologies, C10444, 2 μM, 30 min), washed twice with cold PBS, and then harvested. Samples were analyzed using a BD SORP8 laser LSRII (BD Biosciences) and software (Flowjo). NIRAF detection used a 640 nm wavelength laser and a 690 / 40 bandpass filter, while CellROX detection used a 488 nm wavelength laser and a 515 / 20 bandpass filter.

[0136] Parametric and nonparametric data parameters were compared between two groups using unpaired Student's t-test or Mann-Whitney U test, respectively. Data normality was assessed using the Shapiro-Wilk test. For carotid artery specimens, fluorescence intensity measured by FRI was compared across bifurcation locations using a Kruskal-Wallis one-way analysis of variance followed by Dunn's multiple comparison test (SPSS, v26; IBM®). Data are presented as mean ± standard error of the mean ("SEM") or median and interquartile range ("IQR"). For cell culture studies, quantitative data analyzed using Fiji / Image J software are presented as mean ± SEM from at least three independent experiments. Differences between groups were examined using one-way analysis of variance followed by a post-hoc Tukey-Kramer or Gaims-Howell test. For all analyses, p < 0.5 was considered statistically significant.

[0137] Culprit lesions requiring carotid endarterectomy are large, lipid-rich, and typically located at the origin of the internal carotid artery; however, the relationship between NIRAF signal intensity and carotid plaque topography is unclear. FRI was performed on N=7 freshly excised carotid plaques (four asymptomatic patients and three patients with transient ischemic attack or stroke; see Figure 12). Analysis of normalized mean FRI signal intensity across carotid specimens showed that the highest NIRAF signal occurred at the common carotid bifurcation compared with the proximal and distal flanking regions (p=0.16; see Figure 14). These results indicate that the relative NIRAF signal peaks at the origin of the internal carotid artery, a common culprit site for patients requiring carotid revascularization.

[0138] Figure 14 shows a graph of normalized NIRAF mean fluorescence intensity ("AU") signal versus distance from the carotid bifurcation. In particular, Figure 14 shows that human plaque NIRAF signal is elevated at the carotid bifurcation. Quantitation of normalized NIRAF epifluorescence showed a significant increase in NIRAF signal intensity within 1 mm of the bifurcation. Data shown are median and interquartile range. * P = 0.16 (by Kruskal-Wallis test). NIRAF images were processed and windowed identically.

[0139] To date, the comparative relationship between plaque NIRAF, lipid content, and IPH has not been determined. Examination of NIR mean fluorescence intensity ("MFI") has been used, with the 90th percentile as the cutpoint for NIRAF-positive pixels, or NIRAF 90% (See Figure 13). To define NIRAF-positive pixels, a cut point of 235 arbitrary fluorescence units or greater was determined. Carotid plaque sections showed NIRAF-positive areas ranging from 1.3% to 20.0% of the total area (N = 23 sections from 7 patients, See Figure 15). For each section, the NIRAF-positive area significantly corresponded to both the SB-positive lipid area (r = 0.53, P = 0.43) and the GPA-differentiated IPH area (r = 0.57, P = 0.23; See Figures 12 and 13, N = 15 sections).

[0140] Figure 15 shows the carotid artery plaque NIRAF 90% In particular, Figure 15 shows various images of the area. 90% Carotid artery sections (n=23 sections from 7 plaques) used to derive the cutoff value (≥235 arbitrary fluorescence units) are shown. Grouped sections from each plaque are indicated by a colored rectangular outline.

[0141] Figure 16 shows various images of a carotid artery plaque section, and NIRAF 90% , SB and GPA% positive areas are shown.

[0142] Figure 17 shows (1) NIRAF 90% and GPA (intraplaque hemorrhage), and (2) NIRAF 90% Two graphs of the corresponding relationship between SB area (lipid) and SB area (lipid) are shown, which were obtained from n = 15 carotid artery sections from seven plaques where adjacent sections were stained ( * P<0.5). NIRAF images were processed and windowed identically.

[0143] Further examination of plaque sections revealed that SB-detected lipids corresponded to focal hyperintense NIRAF in some plaque areas and to a diffuse hypointense signal pattern in other plaque areas (see Figures 18A and 18B). Higher magnification examination of plaque sections showed more pronounced colocalization of NIRAF with a punctate pattern in SB-positive areas (see also Figures 18A and 18B). Immunohistochemical staining for bilirubin, a heme breakdown product formed after IPH and a source of NIRAF, also showed slight colocalization with NIRAF (see Figure 19). Notably, some areas of the NIRAF-positive and SB-positive zones showed no evidence of IPH as defined by GPA or bilirubin staining (see top row of Figure 18B and bottom row of Figure 19).

[0144] Figures 18A and 18B show that human atheroma NIRAF signals can be differentially associated with lipid-protein aggregates and intraplaque hemorrhage. In particular, Figure 18A shows a representative human carotid artery atheroma image. The center panel contains a fluorescence microscopy image of the same carotid artery specimen, showing the 650 nm NIRAF signal (grayscale) and a high-magnification image of NIRAF, as well as corresponding SB, NIRAF, and GPA staining from adjacent sections. The scale bar is 1 mm (center panel). Figure 18B shows the NIRAF / SB / GPA patterns from the green, orange, and red dotted boxes from Figure 18A at higher magnification, demonstrating (i) the punctate pattern of SB (lipid) spatially overlapping with NIRAF in the absence of GPA (IPH), (ii) evidence of SB and GPA overlapping with punctate NIRAF, and (iii) diffuse SB staining but the absence of both NIRAF and GPA signal. Scale bar is 250 μm. NIRAF images were processed and windowed identically.

[0145] Figure 19 shows partial overlap with bilirubin, a marker of IPH. In particular, Figure 19 shows high-power fields of SB, NIRAF, GPA, and bilirubin, demonstrating consistent colocalization of SB and NIRAF, and NIRAF. 90% Figure 1 shows moderate colocalization of GPA with bilirubin, and minimal colocalization of NIRAF with bilirubin. NIRAF images were processed and windowed identically. Scale bar is 250 μm.

[0146] This novel relationship between NIRAF and plaque lipids led to the speculation that ceroid may contribute to NIRAF. To assess the relative contribution of insoluble versus soluble lipids to the NIRAF signal, fresh-frozen sections were examined for NIRAF by fluorescence microscopy, and then SB staining was performed on the same sections. Adjacent sections were then treated with ethanol for 1 hour to remove soluble lipid components, followed by SB staining of these sections. After ethanol treatment, the SB-stained sections showed a loss of diffuse hypointense NIRAF and SB signals, while the focal hyperintense signal pattern was preserved (see Figure 20).

[0147] Figure 20 shows that SB and NIRAF signals remain localized after ethanol fixation, revealing ceroid as the source of plaque NIRAF. In particular, Figure 20 shows that both diffuse and punctate SB and NIRAF signals are evident in two fresh-frozen adjacent carotid artery sections (separated by blue lines) (left column). After fixing each of the two sections in 100% ethanol for 1 hour (right column), both SB sections showed a loss of diffuse SB signal (soluble lipids). The remaining insoluble lipids, i.e., ceroid, show colocalization with NIRAF.

[0148] Plaque ceroids are products of lipid oxidation and can form under conditions of iron-catalyzed oxidative stress in lysosomes. Perl staining with DAB enhancement was used to assess iron in NIRAF-positive areas on carotid atheroma sections. NIRAF / SB / GPA-positive areas were observed to localize with ferric and ferrous iron detected by Perl / DAB, as well as CD68+ plaque macrophages (see Figures 20 and 21). These findings establish a link between NIRAF and cellular mediators of lipids, iron, and oxidative stress in atheroma, hallmarks of ceroidogenesis in atherosclerosis.

[0149] Figure 21 shows that plaque NIRAF is associated with iron and macrophages. In particular, Figure 21 shows a high-power field showing frequent localization of NIRAF-positive areas containing plaque iron and CD68-positive plaque macrophages. PB+DAB: Prussian blue with diaminobenzidine. NIRAF images were processed and windowed identically. Scale bar is 250 μm.

[0150] Figure 22 shows the localization of iron in NIRAF-positive plaque areas. In particular, Figure 22 shows the histochemical detection of plaque iron using PB and DAB enhancement in adjacent sections. Representative PB+DAB staining shows the presence of iron in NIRAF-positive plaque areas. Scale bar = 250 μm.

[0151] Based on the finding that NIRAF-positive plaque areas colocalize with regions of insoluble and soluble lipids and iron, a source of oxidative stress, it was hypothesized that NIRAF may arise in vitro in human monocyte-derived macrophages ("MDMs") under conditions of oxidative stress. In differentiated THP-1 cells or MDMs incubated with oxidized LDL ("oxLDL") for 5 days or hemoglobin ("Hb") for 24 hours, substantial NIRAF was detected after NIR light excitation at 638 nm (see Figures 22 and 23).

[0152] In contrast, MDMs incubated with LDL did not produce NIRAF, whereas MDMs incubated with oxLDL produced NIRAF in a time- and concentration-dependent manner (see Figure 23A). Next, NIRAF detection in oxLDL- or Hb-treated cells was quantified by flow cytometry, revealing a higher number of NIRAF+ cells compared to controls (see Figure 23B).

[0153] Figure 23 shows oxidized LDL-induced NIRAF production in THP-1 human macrophages. In particular, Figure 23 shows that NIRAF excited by 638 nm light in THP-1 MDMs was assessed by confocal microscopy (Figure 23A) and flow cytometry (Figure 23B). Differentiated THP-1 cells were incubated with medium alone (control), 50 μg / ml native LDL, 50 μg / ml oxLDL for 5 days, or 0.5 mg / ml human hemoglobin for 24 hours. NIRAF detection using confocal microscopy (Figure 23A) and flow cytometry (Figure 23B) is shown.

[0154] Figures 24A to 24D show NIRAF detection in THP-1 MDMs incubated with oxLDL. Figure 24A shows high-magnification images of nuclear-stained NIRAF in THP-1 macrophages incubated with medium alone (control), 50 μg / ml native LDL, or 50 μg / ml oxLDL for 5 days, or with 0.5 mg / ml human hemoglobin for 24 hours. Figure 24B shows a representative time course of NIRAF signal development in LDL- and oxLDL-treated MDMs on days 1, 3, and 5. Figure 24C compares NIRAF signals on day 5 for MDMs incubated with 20 μg / ml oxLDL and MDMs incubated with 50 μg / ml oxLDL. Figure 24D shows quantitative data from Figure 24C, presented as the mean ± SE of three independent experiments.

[0155] To confirm the possibility of lipid-mediated NIRAF generation in MDMs, we visualized intracellular lipids using BODIPY493 / 503 and simultaneously assessed NIRAF production. oxLDL-treated cells showed robust intracellular lipid presence (BODIPY493 positive) colocalizing with NIRAF (see Figure 25), in contrast to LDL-treated cells, which were also BODIPY493 positive but showed no NIRAF signal. In addition, Hb-treated cells showed NIRAF but no evidence of lipid accumulation, indicating that NIRAF production may occur via an oxLDL-specific and hemoglobin-independent pathway. Corroborating the histopathological findings, oxLDL-treated MDMs after ethanol treatment to remove soluble lipids still showed persistent NIRAF colocalizing with BODIPY493 compared to control cells (see Figure 25).

[0156] FIG. 25 shows that in the oxLDL group, intracellular lipids accessed by BODIPY493 / 503 staining co-localized with NIRAF in confocal microscopy.

[0157] Figure 26 shows two graphs: quantified BODIPY-positive area and BODIPY colocalized area, adjusted for NIRAF by cell number, shown as the mean ± standard error of six independent experiments. BODIPY quantification was analyzed by one-way ANOVA followed by the Tukey-Kramer test, and BODIPY / NIRAF was followed by the Games-Howell test. * p<0.5, ** p<0.01. Scale bar is 50 μm. NIRAF images were processed and windowed identically.

[0158] Figure 27 shows that NIRAF colocalizes with insoluble lipids, i.e., ceroid, in human macrophages. Specifically, Figure 27 shows MDMs incubated with 50 μg / ml oxLDL for 5 days, then treated with ethanol for 20 minutes or as a control (no ethanol), and then incubated with BOPIDY493 to stain intracellular lipids. Cell nuclei were visualized with Hoechst staining. After EtOH (ethanol) fixation to remove soluble lipids, the remaining insoluble BODIPY+ lipids were observed to colocalize with the NIRAF signal (yellow, right column).

[0159] Because oxidized LDL is a potent source of oxidative stress and oxidative stress promotes ceroid formation, the role of oxidative stress in the generation of NIRAF after oxLDL exposure was evaluated. Intracellular reactive oxygen species ("ROS") assessed with the CellROX reagent successfully colocalized with oxLDL-induced NIRAF in human MDMs (see Figure 28A). Quantitative analysis by flow cytometry confirmed that CellROX- and NIRAF-positive populations were increased in oxLDL-treated cells compared with LDL-treated and control cells (see Figure 28A). The presence of an oxidative environment was further analyzed to reflect nonenzymatic lipid oxidation mediated by ROS. Using the C11-BODIPY lipid peroxidation sensor, we observed that oxLDL-treated cells exhibited higher levels of lipid peroxidation compared with LDL-treated and control cells (see Figures 28A to 28D).

[0160] Figures 28A to 28D show that oxidized LDL-induced oxidative stress contributes to NIRAF generation. NIRAF and intracellular reactive oxygen species ("ROS") were assessed by CellROX Green staining in THP-1MDMs by confocal microscopy after 5 days of incubation with medium alone (control), 50 μg / ml native LDL, or 50 μg / ml oxLDL (Figure 28A). Representative images from four independent experiments show that NIRAF signal increased under conditions of oxLDL-generated oxidative stress. Figures 28B to 28D show flow cytometry analysis demonstrating that both NIRAF- and CellROX-positive cells increased after incubation with oxLDL. Representative results from three independent experiments are shown.

[0161] Figure 29A shows representative images of C11-BODIPY staining demonstrating increased lipid peroxidation stress after incubation of MDM with oxLDL. Figure 29B shows a graph depicting quantification of C11-BODIPY lipid peroxidation signal obtained from confocal microscopy, demonstrating a significant increase in lipid peroxidation after incubation with oxLDL compared to LDL or control. Data are presented as mean ± SE from three independent experiments.

[0162] To specifically assess the role of oxidative stress in oxLDL-generated NIRAF, we examined the effects of antioxidant treatment on NIRAF production using two different antioxidants, N-acetylcysteine ​​("NAC") and α-tocopherol. We found that simultaneous treatment with oxLDL and NAC or α-tocopherol significantly reduced NIRAF production and ROS levels in MDM at day 5 compared with oxLDL treatment alone.

[0163] Figure 30A shows THP-1MDMs co-incubated with oxLDL and 5 mM NAC or 1 mM α-Toc (tocopherol) for 5 days, demonstrating a decrease in NIRAF signal (magenta) compared to cells incubated with oxLDL alone. Figure 30B shows a flow cytometry analysis graph demonstrating the decrease in the percentage of NIRAF+ cells after incubation with either NAC or α-Toc. Representative results from three independent experiments are shown. Quantitative data were analyzed by one-way ANOVA followed by the Tukey-Kramer test. * p<0.5, *** p<0.001. Scale bar is 50 μm.

[0164] Figure 31 shows that antioxidant treatment reduces ox-LDL-generated NIRAF signals in human macrophages. In particular, Figure 31 shows intracellular ROS production detected by CellROXGreen in day 5 THP-1MDM incubated with oxLDL alone or co-incubated with oxLDL and 5 mM NAC or 1 mM α-tocopherol. Representative confocal microscopy images from three independent observations are shown. NIRAF images were processed and windowed identically.

[0165] Figures 32A to 32C show flow cytometry graphs demonstrating the reduction in the number of CellROX-positive and NIRAF-positive cells.

[0166] In this study, we provide new insights into NIRAF generation by showing that (1) human carotid plaques exhibit NIRAF in lipid-rich zones, regardless of the presence or absence of intraplaque hemorrhage; (2) plaque NIRAF colocalizes with insoluble lipids and iron, which are catalysts for the formation of oxidized lipoproteins, including ceroid; (3) in human monocyte-derived macrophages ("MDMs"), oxidized LDL, but not LDL, generates NIRAF, lipid peroxidation products, and oxidative stress independently of hemoglobin; and (4) antioxidant treatment can suppress oxidized LDL-generated oxidative stress and NIRAF in vitro. Our combined results demonstrate a novel pathway for NIRAF generation via oxidized lipid-induced oxidative stress and support the hypothesis that ceroid may be an additional source of NIRAF in human atherosclerosis. These findings therefore have the potential to further inform future clinical NIRAF imaging studies of human atherosclerosis.

[0167] It was hypothesized that ceroid, a long-standing insoluble lipid complex generated under conditions of oxidative stress, may be a potential source of NIRAF. It was further hypothesized that ceroid may possess an NIRAF signal. Early studies of human atherosclerosis detected ceroid in aortic and coronary plaques across a wide age range of autopsy patients (5 to 88 years), leading to speculation that ceroid may be a marker of past oxidative events and play a role in plaque progression. Preliminary investigations of human coronary and aortic plaques using fluorescence (476 nm excitation) and Raman (830 nm excitation) spectroscopic microscopy further demonstrated that lipids within ceroid were primarily present in the form of peroxidation products derived from the myeloperoxidase (MPO)-hypochlorous acid pathway and the Fenton reaction, highlighting the importance of inflammation and iron-mediated LDL oxidation mechanisms.

[0168] In this study, we studied freshly excised carotid endarterectomy specimens, and our observations support the concept that ceroid may contribute to the NIRAF signal based on the relationship between NIRAF and lipids, particularly insoluble lipids, as well as its relationship to iron, which induces oxidative stress. Free iron released by hemoglobin following IPH may promote the formation of reactive oxygen species and lipid peroxides, a hallmark of ceroid formation. However, while we confirmed a partial association between IPH and NIRAF, we also found that NIRAF+ and SB+ areas lacked evidence of IPH. This finding suggests that a lipid-based mechanism may generate NIRAF via oxidative stress, independent of hemoglobin-based iron supply. Further experiments on in vitro plaque sections and human MDMs using organic solvents to remove soluble lipids indeed demonstrated that a stronger punctate NIRAF signal colocalized with insoluble lipids, or ceroid.

[0169] Although the mechanisms underlying ceroid formation are complex and have been extensively studied, the exact mechanism of NIRAF generation from plaque-based ceroids was previously unknown. Ceroids are observed in various cell types and disease states, and their formation has been linked to oxidative stress and pathological cellular senescence. They are generally described as aggregation of incomplete digestion products resulting from lysosomal and autophagosome dysfunction.

[0170] Importantly, it is well established that excessive accumulation of oxLDL induces oxidative stress through dysfunction of cellular degradation mechanisms, including lysosomes and endoplasmic reticulum, which can be alleviated with N-acetylcysteine ​​(NAC). In this disclosure, it has been elucidated that in vitro oxidized lipids can produce ceroid formation and autofluorescence in the near-infrared (NIR). Additionally, oxLDL further generates reactive oxygen species (ROS) and lipid peroxidation products. Furthermore, NIRAF and ROS generation in this study were inhibited by antioxidant treatment with NAC or α-tocopherol in human MDM. Although atherosclerosis prevention trials of α-tocopherol supplementation have been equivocal, the recently approved icosapent ethyl ester (EI) has been shown to be effective in preventing atherosclerosis. 43 It is possible that more potent antioxidants such as ceroids may be more effective in inhibiting ceroidogenesis, NIRAF, and atheroma progression. Given the relationship between NIRAF and oxidative stress, this study supports further investigation into whether ceroids are directly proatherogenic, an area of ​​debate.

[0171] Detection of NIRAF in patients with CAD was recently reported using a clinically approved, dual-modality NIRAF optical coherence tomography (OCT) intravascular catheter. The findings indicated that the highest NIRAF signal occurred at the origin of the internal carotid artery, with typical culprit sites indicating advanced atheroma with evidence of IPH or plaque rupture. Therefore, the current findings may inform future intracoronary NIRAF studies in human CAD, as NIRAF can report on ceroid and intraplaque hemorrhage. Intracoronary catheter-based detection of ceroid in the NIRAF range has important advantages over detection in the visible range, including greater penetration of NIR light into plaque and avoidance of confounding visible autofluorescence from other entities, such as elastin and collagen. Future studies are planned to determine whether intravascular NIRAF signal intensity predicts plaque progression in coronary-sized arteries. In addition, the current findings support the potential value of intravascular NIRAF-OCT to detect the plaque-stabilizing effects of antioxidant or anti-inflammatory therapy by tracking ceroid / IPH signals over time.

[0172] Ceroid is insoluble in water or organic solvents and is difficult to extract from human atheroma for analytical NIRAF testing. Nevertheless, the persistence of NIRAF colocalization with insoluble lipids after ethanol fixation in both plaque sections and oxLDL-treated human MDM provides evidence that ceroid contributes to the NIRAF signal within human atheroma. Further mechanistic studies beyond the scope of this study are needed to understand the specific molecular components of ceroid that generate NIRAF. Finally, although ceroid appears to be another source of NIRAF beyond heme breakdown products (e.g., bilirubin and protoporphyrin IX), additional moieties exhibiting NIRAF may exist in atheroma. Discovery of these molecules will require more extensive screening approaches utilizing lipidomic, metabolomic, and proteomic approaches.

[0173] In summary, this combined human atherosclerosis and in vitro human MDM study demonstrated that NIRAF occurs in areas of insoluble plaque lipid, or ceroid, and that NIRAF production can occur in vitro via the oxidized LDL pathway, which generates oxidative stress and lipid peroxidation products. The overall results demonstrate a novel pathway for NIRAF production via oxidized lipid-induced oxidative stress and support the role of ceroid as a source of NIRAF in human atherosclerosis. These findings may inform future clinical intracoronary imaging studies of NIRAF in patients with CAD.

[0174] The mechanisms underlying NIRAF generation have not been fully characterized. Here, we investigated NIRAF generation in atherosclerosis and the role of lipids and oxidative stress in human monocyte-derived macrophages (MDMs) in vitro. The spatial distribution of lipids, IPH, and NIRAF (excitation / emission 630 nm / 650 nm) was examined in N = 15 human carotid endarterectomy specimens. Plaque NIRAF was associated with both Sudan Black (SB)-positive lipids (r = 0.53, P = 0.43) and glycophorin A (GPA)-positive IPH (r = 0.57, P = 0.23). Plaque NIRAF also colocalized with lipids, particularly insoluble lipids (ceroid), and iron. Interestingly, some NIRAF-positive areas were SB-positive but GPA-negative. To further investigate the role of lipids in NIRAF generation, human MDMs were examined. Oxidized low-density lipoprotein (oxLDL) and hemoglobin, but not LDL, generated NIRAF in MDM. In oxLDL-treated MDM, NIRAF colocalized with lipid peroxidation products and intracellular oxidative stress markers. The antioxidants α-tocopherol and N-acetylcysteine ​​suppressed NIRAF generation and oxidative stress in oxLDL-treated MDM. In human atherosclerosis and in vitro human MDM, NIRAF colocalized with lipids, particularly insoluble lipids, or ceroid. In vitro studies further demonstrated that oxidized LDL generates NIRAF, oxidative stress, and lipid peroxidation products. Our combined results demonstrate a novel pathway for NIRAF generation via oxidized lipid-induced oxidative stress and support the role of ceroid as a source of NIRAF in human atherosclerosis. These findings may inform further clinical intracoronary imaging studies of NIRAF in patients with CAD.

[0175] Near-infrared autofluorescence (NIRAF) is detectable in patients with coronary artery disease (CAD) and can distinguish plaques at risk for future ischemic events. NIRAF is associated with insoluble lipids (ceroids) in human carotid atherosclerotic arteries and in human macrophages in vitro, independent of intraplaque hemorrhage or hemoglobin. Oxidized LDL generates NIRAF, oxidative stress, and lipid peroxidation products in vitro. Antioxidant treatment can suppress NIRAF production in human macrophages. These findings may inform future clinical intracoronary imaging studies of NIRAF in patients with CAD.

[0176] While these systems and methods have been described and illustrated in the foregoing illustrative, non-limiting examples, it will be understood that the present disclosure has been described by way of example only, and that numerous changes in the details of the implementation of these systems and methods can be made without departing from the spirit and scope of these systems and methods, which are limited only by the claims that follow. Features of the disclosed, non-limiting examples can be combined and rearranged in various ways.

[0177] Furthermore, non-limiting examples of the disclosure provided herein are not limited in application to the details of construction and arrangements of parts set forth in the following description or illustrated in the following drawings. Other non-limiting examples are possible and these systems and methods may be variously implemented or performed. It is also understood that the phraseology and terminology used herein are for descriptive purposes and should not be considered limiting. The use of "including," "comprising," or "having" and variations thereof herein is meant to encompass the items listed thereafter and equivalents thereof, as well as additional items. Unless otherwise expressly stated or limited, the terms "mounted," "connected," "supported," and "coupled," and variations thereof, are used broadly and encompass both direct and indirect mounting, connecting, supporting, and coupling. Furthermore, "connected" and "coupled" are not limited to physical or mechanical connections or couplings.

[0178] Additionally, the use of phraseology and terminology used herein is for descriptive purposes and should not be considered limiting. The use of "right," "left," "front," "rear," "top," "bottom," "upper," "lower," "top," or "bottom," and variations thereof, herein is for descriptive purposes and should not be considered limiting. Unless otherwise specified or limited, the terms "attached," "connected," "supported," and "coupled," and variations thereof, are used broadly and include direct and indirect attaching, connecting, supporting, and coupling. Furthermore, "connected" and "coupled" are not limited to physical or mechanical connections or couplings.

[0179] Unless otherwise specified or limited, phrases such as "at least one of A, B, and C," "one or more of A, B, and C," and similar phrases are meant to refer to A or B or C, or any combination of A, B, and / or C, including multiple combinations or single instances of A, B, and / or C.

[0180] In some non-limiting examples, aspects of the present disclosure, including computer-implemented methods, can be implemented as a system, method, apparatus, or article of manufacture using standard programming or engineering techniques to produce software, firmware, hardware, or any combination thereof for controlling a processor device, a computer (e.g., a processor device operably coupled to a memory), or another electronically operated controller, to accomplish aspects described in detail herein. Thus, for example, non-limiting examples of these systems and methods can be implemented as a set of instructions tangibly embodied on a non-transitory computer-readable medium, and a processor device can execute the instructions based on reading the instructions from the computer-readable medium. Some non-limiting examples of these systems and methods can include automated devices consistent with (or utilizing) the following description, as well as special-purpose or general-purpose computers, including various computer hardware, software, firmware, etc.

[0181] As used herein, the term "article of manufacture" is intended to encompass a computer program accessible from any computer-readable device, carrier (e.g., a non-transitory signal), or medium (e.g., a non-transitory medium). For example, computer-readable media include, but are not limited to, magnetic storage devices (e.g., hard disks, floppy disks, magnetic strips, etc.), optical disks (e.g., compact disks (CDs), digital versatile disks (DVDs), etc.), smart cards, and flash memory devices (e.g., cards, sticks, etc.). Furthermore, it should be understood that carrier waves can be used to transmit computer-readable electronic data, such as those used to send and receive email or access networks such as the Internet or local area networks. Those skilled in the art will recognize that many modifications can be made to these structures without departing from the scope or spirit of the claimed subject matter.

[0182] Certain operations of methods according to these systems and methods, or of systems implementing such methods, may be illustrated schematically in figures or described herein. Unless otherwise expressly stated or limited, the representation in a figure of certain operations in a particular spatial order may not necessarily require that those operations be performed in a particular order corresponding to the particular spatial order. Correspondingly, certain operations illustrated or otherwise disclosed herein may be performed in orders different from those explicitly illustrated or described, as appropriate for particular non-limiting examples of these systems and methods. Furthermore, in some non-limiting examples, certain operations may be performed in parallel, including by dedicated parallel processor devices or separate computing devices configured to interoperate as part of a larger system.

[0183] When used herein in the context of computer implementation, unless otherwise specified or limited, terms such as "component," "system," "module," and the like are intended to encompass hardware, software, a combination of hardware and software, or part or all of a computer-related system including running software. For example, a component may be, but is not limited to, a processor device, a process running (or executable by) a processor device, an object, an executable, a thread of execution, a computer program, or a computer. For example, both an application running on a computer and a computer can be a component. One or more components (or systems, modules, etc.) can reside within a process or thread of execution, can be localized on one computer, can be distributed among two or more computers or other processor devices, or can be included within another component (or system, module, etc.).

[0184] As used herein, the terms "controller," "processor," and "computer" include any device capable of executing a computer program or any device containing logic gates configured to perform the described functions. For example, this includes processors, microcontrollers, field programmable gate arrays, programmable logic controllers, etc. As another example, these terms may include one or more processors and memory, and / or one or more programmable hardware elements, such as any type of processor, CPU, microcontroller, digital signal processor, or other device capable of executing software instructions.

[0185] While the above description has framed the above operations with respect to particular computing devices that execute those processes (as appropriate), it will also be understood that non-transitory computer-readable media (e.g., the articles of manufacture described above) can store computer-executable code for the above processes. For example, processes 200, 300 (or others) can be efficiently stored on non-transitory computer-readable media.

[0186] As used herein, the term "imaging agent" refers to imaging contrast agents that absorb or alter external electromagnetic or ultrasonic radiation, radiopharmaceuticals that emit radiation that is detected by an imaging system, etc. In some cases, an "imaging agent" can be a diagnostic imaging agent that enhances the contrast of images inside the body.

Claims

1. 1. A method of operating an apparatus for analyzing imaging data, comprising: the apparatus comprises one or more processors; The operating method includes: receiving, by the one or more processors, imaging data of a region of interest of a lumen; generating, by the one or more processors, at least one image of the region of interest using the imaging data; the one or more processors separating, from at least one image of the region of interest, imaging data that does not include regions at risk of atherosclerotic plaque and imaging data that includes regions at risk of atherosclerotic plaque based on an intensity threshold; the one or more processors determining a size, shape, or oxidative stress value of the at-risk area of ​​atherosclerotic plaque; the one or more processors: the size of the risk area of ​​the atherosclerotic plaque is greater than a threshold size; the shape of the risk area is greater than a threshold shape; or the oxidative stress value in the risk area is greater than a threshold oxidative stress value; determining at least one of the following: and generating, by the one or more processors, a report containing information about the atherosclerotic plaque that a threshold has been exceeded.

2. the one or more processors determining a maximum intensity value of the imaging data; the one or more processors determining a threshold based on the maximum intensity value; The method of claim 1 , wherein the pixel having the maximum intensity value is defined within the risk region of the atherosclerotic plaque.

3. the at-risk area of ​​the atherosclerotic plaque has a higher amount of insoluble lipids than an area of ​​the atherosclerotic plaque that does not include the at-risk area; 10. The method of claim 1, wherein the at-risk area of ​​the atherosclerotic plaque has a higher amount of insoluble iron than the area of ​​the atherosclerotic plaque that does not include the at-risk area.

4. The method of claim 1 further comprising the step of the one or more processors filtering the image to generate an image of the at-risk region of the atherosclerotic plaque.

5. determining, by the one or more processors, a size of the area at risk of the atherosclerotic plaque based on the image of the area at risk of the atherosclerotic plaque; the one or more processors determining that the size of the risk area is greater than a size threshold; The method of claim 4 , further comprising: the one or more processors determining that the image has a severe symptom of atherosclerotic plaque based on the size of the risk region being greater than the size threshold.

6. filtering the image by the one or more processors to generate an image of the region at risk of the atherosclerotic plaque; The method of claim 4 , further comprising the step of: thresholding the image of the region of interest according to a pixel intensity threshold to generate the image of the region at risk of the atherosclerotic plaque.

7. the pixel intensity threshold rejects pixels having intensities above the pixel intensity threshold; The pixel intensity threshold is: a first range defined as between a peak signal intensity value in the imaging data and a first pixel value that is 0.25 times the peak signal intensity value; a second range defined as between the peak signal intensity value and a second pixel value that is 0.5 times the peak signal intensity value; or and a third range defined as between the peak signal intensity value and a first pixel value that is 0.75 times the peak signal intensity value.

8. the image is a first image, The operating method includes: the one or more processors filtering the first image to generate a second image of the atherosclerotic plaque excluding the region at risk; 2. The method of claim 1, further comprising: the one or more processors subtracting the second image from the first image to generate a difference image that is an image of the risk region of the atherosclerotic plaque.

9. The method of claim 8 , wherein the portion of the at-risk area of ​​the atherosclerotic plaque comprises ceroid of the atherosclerotic plaque.

10. 9. The method of claim 8, wherein the one or more processors filtering the first image to generate the second image of the atherosclerotic plaque excluding the risk area comprises thresholding the first image according to a threshold to generate the second image.

11. the one or more processors determining the shape of the atherosclerotic plaque; the one or more processors determining that the shape of the atherosclerotic plaque exceeds a shape threshold; The method of claim 1 , further comprising the step of the one or more processors notifying a user based on the shape of the risk area of ​​the atherosclerotic plaque exceeding the shape threshold.

12. 1. An imaging system for imaging a lumen, comprising: The imaging system includes: an excitation source configured to emit excitation light toward the lumen; a detector configured to sense light emitted from the lumen; a computing device in communication with the excitation source and the detector; the computing device, causing the excitation source to emit excitation light toward at least a portion of the region of interest of the lumen; receiving imaging data of the region of interest of the lumen using the detector; generating at least one image of the region of interest using the imaging data; separating imaging data that does not include regions at risk of atherosclerotic plaque and imaging data that includes regions at risk of atherosclerotic plaque from at least one image of the region of interest based on an intensity threshold; determining the size, shape or oxidative stress value of the at-risk area of ​​the atherosclerotic plaque; the size of the risk area of ​​the atherosclerotic plaque is greater than a threshold size; the shape of the risk area is greater than a threshold shape; or the oxidative stress value in the risk area is greater than a threshold oxidative stress value; determining at least one of The imaging system is configured to generate a report containing information about the atherosclerotic plaque, the threshold crossing report.

13. The imaging system of claim 12 , wherein the computing device is further configured to generate an image of the region of interest using the imaging data.

14. the computing device: thresholding the image according to a pixel threshold to generate an image of the atherosclerotic plaque risk region; determining at least one of the size or shape of the at-risk area of ​​the atherosclerotic plaque; determining at least one of the size of the atherosclerotic plaque exceeding a size threshold or the shape of the atherosclerotic plaque exceeding a shape threshold; 14. The imaging system of claim 13, further configured to determine that the image has a severe symptom of the atherosclerotic plaque based on determining that at least one of the size or the shape of the atherosclerotic plaque exceeds a corresponding threshold.

15. the computing device: generating an image of a risk-free area of ​​the atherosclerotic plaque; determining an oxidative stress value based on the risk-free area of ​​the atherosclerotic plaque; The imaging system of claim 13 , further configured to determine that the image has a severity of the atherosclerotic plaque based on the oxidative stress value.

16. the computing device: determining that the oxidative stress value exceeds an oxidative stress threshold; 16. The imaging system of claim 15, further configured to determine that the image has a severe symptom of the atherosclerotic plaque based on determining that the oxidative stress value exceeds the oxidative stress threshold.

Images