Intravascular tissue analysis system

Abstract

An intravascular tissue analysis system (100) comprises one or more processors (110) configured to: receive optical data (120) representing an optical property of an intravascular tissue region (130) over one or more wavelength intervals; analyze the optical data (120) to determine i) a blood clot component content (140) of the intravascular tissue region (130), and ii) a lipid content (150) of the intravascular tissue region (130); and output an indication of the blood clot component content and the lipid content, and / or an indication of one or more tissue composition parameters derived therefrom.

Description

[0001]2023PF00657 1INTRAVASCULAR TISSUE ANALYSIS SYSTEMTECHNICAL FIELDThe present disclosure relates to performing intravascular tissue analysis. Anintravascular tissue analysis system, a computer-implemented method of performing intravascular tissueanalysis, and a computer program product, are disclosed.BACKGROUND Stroke is a devastating disease, and a leading cause of disability worldwide. Approximately 85% of strokes are caused by ischemia; i.e. the deprivation of blood flow to the brain. The remaining 15% of strokes are caused by a brain haemorrhage; i.e. bleeding in or around the brain. The main cause of ischemic stroke is thromboembolism. Thromboembolism occurs when a thrombus, also known as a blood clot, develops outside of the brain and is carried via the vasculature to the brain where it forms a blockage. The blockage, also known as an occlusion, leads to cerebral ischemia, and hence to ischemic stroke. Another possible cause of ischemic stroke is atherosclerosis. Atherosclerosis is a process in which fatty deposits accumulate on the inner walls of the arteries. The fatty deposits are known as atheromas, or plaques. Plaque can grow within a cerebral artery to form a plaque-type occlusion that deprives the brain of blood, similarly leading to cerebral ischemia, and hence to ischemic stroke. This type of atherosclerosis is referred-to as intracranial atherosclerosis. Intracranial atherosclerosis is rare in Western populations but more prevalent in Asian, Black and Hispanic populations. It is also possible for ischemic stroke to have a mixed origin. The current treatment options for ischemic stroke include thrombolysis, i.e. the injection of a clot-busting medicine to disburse a blood clot-type occlusion; and mechanical thrombectomy, i.e. an interventional procedure in which an occlusion is mechanically removed from the body. Mechanical thrombectomy involves the insertion of a catheter into the vasculature. The catheter includes a device that captures the occlusion. A variety of mechanical thrombectomy devices are available, including aspiration catheters that are used to suck the occlusion from the vasculature, stent retrievers that include an expandable stent which is used to capture and subsequently extract the occlusion from the body, and expandable mesh disks. In order to evaluate the different treatment options for ischemic stroke, it is necessary to be able to distinguish between different types of intravascular tissue. This is because different types of occlusions require different treatment options. For instance, stenting is typically used to treat plaque-type occlusions, whereas mechanical thrombectomy is typically used to blood clot-type occlusions. It is 2023PF00657 2 possible to further optimize the choice of treatment for blood clot-type occlusions based on the type of tissue present in an occlusion. For example an aspiration catheter is typically better-suited to removingred blood cell-poor blood clots, whereas most stent retrievers are typically better-suited to removing redblood cell-rich blood clots. Conventionally, the various treatment options for ischemic stroke have been evaluated using non-invasive techniques such as projection X-ray imaging, computed tomography “CT” imaging, and magnetic resonance imaging “MRI”. However, these techniques have limitations in terms of their ability to distinguish between different types of occlusions. Consequently, there is a need to distinguish between different types of intravascular tissue, such as occlusions, in order to assist physicians in evaluating treatment options for ischemic stroke. Aside from the evaluation of treatment options for ischemic stroke, the ability to distinguish between different types of intravascular tissue would be useful in other clinical situations aswell. For instance, this distinction would be useful in assessing treatment options for cardio- and alsoperipheral vascular disease. Adocument US2012 / 101391 A1 relates to a blood vessel wall analyzing apparatusprovided with a structure enabling accurate measurement of plaque components in a blood vessel wall in a state that reduces the burden on a patient. In the blood vessel wall analyzing apparatus, measurement light is illuminated onto a measured portion within a blood vessel such as a carotid artery from a light illuminating unit provided outside the blood vessel, while light from the measured portion is detected in a light receiving unit provided outside the blood vessel. Thus, since the status of the blood vessel wall can be analyzed without inserting an apparatus involved in measurement into the blood vessel, the burden on the patient is reduced during measurement. In addition, as a result of carrying out measurement using near infrared light (a light component in the wavelength range of 780 nm to 2750 nm) that exhibits characteristics that differ according to the compositions of substances such as plaque adhered within the blood vessel, analyses can be carried out that distinguish compositions such as plaque using an analyzing apparatus provided outside the blood vessel. A document by Rico-Jimenez, J., et al., “Automatic classification of atheroscleroticplaques imaged with intravascular OCT”, Biomed Opt. Express. 2016 Sep 15;7(10):4069–4085, disclosesthat Intravascular optical coherence tomography (IV-OCT) allows evaluation of atherosclerotic plaques; however, plaque characterization is performed by visual assessment and requires a trained expert forinterpretation of the large data sets. A computational method for automated IV-OCT plaquecharacterization is presented. The method is based on the modeling of each A-line of an IV-OCT data setas a linear combination of a number of depth profiles. After estimating these depth profiles by means of an alternating least square optimization strategy, they are automatically classified to predefined tissuetypes based on their morphological characteristics. The performance of the method was evaluated withIV-OCT scans of cadaveric human coronary arteries and corresponding tissue histopathology. The results 2023PF00657 3 suggest that this methodology allows automated identification of fibrotic and lipid-containing plaques. Moreover, the computational method has the potential to enable high throughput atherosclerotic plaque characterization. Adocument US2004 / 111016 A1 discloses methods for the detection of inflammationassociated with vulnerable atherosclerotic plaque to prevent heart attack and stroke. The methods are also applicable to detection of infection, cancer, wounds or auto-immune disease in the body. Certain embodiments of the new methods provide a way of predicting the level of vulnerability of an atherosclerotic plaque to rupture or thrombus formation by assessing via fiber optic NIR spectrophotometry the status of two or more parameters associated with inflamed atherosclerotic plaque in a vessel of a living patient. From these measurements such conditions as low pH, hypoxia, low glucose, oxidative stress or compounds abundant in vulnerable plaque such as oxidized LDL cholesterol and oxidized metabolites of NO, significant active macrophage population, thin plaque cap, as well as senescence and / or apoptosis of smooth muscle or endothelial cells are determined with the assistance of a suitably programmed microprocessor. By considering together the status of some or all of these conditions with respect to successive sites along a vessel wall, particular plaques which are at significantrisk of rupturing or thrombosing can be distinguished from “normal” vessel wall and from “intermediate”and relatively stable or “lower risk” plaques. Sites having more of the indicator conditions would beconsidered most in need of prompt intervention, and certain combinations of parameter levels would be suggestive of relatively stable plaque. Also disclosed is a multi-parameter catheter and analytical processing assembly for use in the methods. SUMMARY According to one aspect of the present disclosure, an intravascular tissue analysis system100, is provided. The system includes one or more processors 110 configured to:receive S110 optical data 120 representing an optical property of an intravascular tissue region 130 over one or more wavelength intervals; analyze S120 the optical data 120 to determine i) a blood clot component content 140 ofthe intravascular tissue region 130, and ii) a lipid content 150 of the intravascular tissue region 130; and output S130 an indication of the blood clot component content 140 and the lipid content 150, and / or an indication of one or more tissue composition parameters derived therefrom. The above system provides the blood clot component content of an intravascular tissue region in combination with the lipid content of the intravascular tissue region. This information facilitates a distinction between intravascular tissue regions that include blood clots, and intravascular tissue regions that include lipids. An example of an intravascular tissue region is an occlusion. The inventors have determined that the lipid content of an occlusion is correlated with the presence of plaque. By providing the blood clot component in addition to the lipid content, the system facilitates a distinction between a blood clot-type occlusion and a plaque-type occlusion. This distinction enables a physician to determine a 2023PF00657 4 course of treatment for the occlusion. For instance, if the system indicates that the blood clot component content is relatively high, the physician may conclude that the occlusion is a blood clot-type occlusion.The physician may then choose to remove the occlusion using a mechanical thrombectomy device. Bycontrast, if the system indicates that the lipid content is relatively high, the physician may conclude that the occlusion is a plaque-type occlusion. The physician may then choose to stent the occlusion. The provision of one or more tissue composition parameters derived from the blood clot component content and the lipid content, similarly facilitates a distinction between intravascular tissue regions that include blood clots, and intravascular tissue regions that include lipids. The tissue composition parameter may for instance be a ratio of the blood clot component content to the lipid content, or an indication of the intravascular tissue region comprising a blood clot, or an indication of the intravascular tissue region comprising plaque. Tissue composition parameters such as these similarly facilitate a distinction between a blood clot-type occlusion and a plaque-type occlusion, and consequently they similarly enable aphysician to determine a course of treatment for the occlusion.Further aspects, features, and advantages of the present disclosure will become apparentfrom the following description of examples, which is made with reference to the accompanying drawings.BRIEF DESCRIPTION OF THE DRAWINGSFig. 1 is a schematic diagram illustrating an example of an intravascular tissue analysissystem 100, in accordance with some aspects of the present disclosure.Fig. 2 is a flowchart illustrating an example of a computer-implemented method ofperforming intravascular tissue analysis, in accordance with some aspects of the present disclosure.Fig.3 is an example of the optical signatures of various chromophores that are typically present in the vasculature, in accordance with some aspects of the present disclosure. Fig.4 is a schematic diagram illustrating an example of an outputted indication of a red blood cell content 140 and a lipid content 150 of an intravascular tissue region, in accordance with some aspects of the present disclosure. Fig.5 is an example of the optical signatures of various types of blood clot, blood, a vessel wall, and plaque, in accordance with some aspects of the present disclosure. Fig.6 is a schematic diagram illustrating an example of an outputted indication of various types of blood clots, in accordance with some aspects of the present disclosure. Fig.7 is a schematic diagram illustrating a first example of an optical probe 180 for insertion into the vasculature, in accordance with some aspects of the present disclosure. Fig.8 is a schematic diagram illustrating a second example of an optical probe 180 for insertion into the vasculature, in accordance with some aspects of the present disclosure. Fig.9 is a schematic diagram illustrating an example of an optical probe 180 disposed within an intravascular tissue region 130 within a blood vessel 260, in accordance with some aspects of the present disclosure. 2023PF00657 5DETAILED DESCRIPTIONExamples of the present disclosure are provided with reference to the followingdescription and Figures. In this description, for the purposes of explanation, numerous specific details ofcertain examples are set forth. Reference in the specification to “an example”, “an implementation” orsimilar language means that a feature, structure, or characteristic described in connection with theexample is included in at least that one example. It is also to be appreciated that features described inrelation to one example may also be used in another example, and that all features are not necessarilyduplicated in each example for the sake of brevity. For instance, features described in relation to a system,may be implemented in a computer-implemented method, and in a computer program product, in acorresponding manner.In the following description, reference is made to examples of an intravascular tissueanalysis system. The system provides an analysis of an intravascular tissue region. In some examples,reference is made to the use of the system to analyze intravascular tissue in the form of an occlusionwithin an artery in the brain for the purpose of enabling a physician to select a treatment device for removing the occlusion and thereby treat ischemic stroke. However, it is to be appreciated that unlessexplicitly stated, the use of the intravascular tissue analysis system disclosed herein is not limited to theseexamples. Thus, the intravascular tissue analysis system may be used in the analysis of intravasculartissue within the anatomy in general, and the intravascular tissue analysis may be used in various clinicalapplications. For instance, the intravascular tissue analysis system may be used to analyze intravasculartissue within blood vessels such as arteries, or veins, and such blood vessels may be in anatomical regionssuch as the brain, the neck, the arm, the leg, and so forth. Moreover, the intravascular tissue analysissystem may be used in clinical applications such as the analysis of treatment options for stroke, peripheralvascular disease, and so forth.It is noted that the computer-implemented methods disclosed herein may be provided as anon-transitory computer-readable storage medium including computer-readable instructions storedthereon, which, when executed by at least one processor, cause the at least one processor to perform themethod. In other words, the computer-implemented methods may be implemented in a computer programproduct. The computer program product can be provided by dedicated hardware, or hardware capable ofrunning the software in association with appropriate software. When provided by a processor, thefunctions of the method features can be provided by a single dedicated processor, or by a single sharedprocessor, or by a plurality of individual processors, some of which can be shared. The functions of oneor more of the method features may for instance be provided by processors that are shared within anetworked processing architecture such as a client / server architecture, a peer-to-peer architecture, theInternet, or the Cloud.The explicit use of the terms “processor” or “controller” should not be interpreted asexclusively referring to hardware capable of running software, and can implicitly include, but is not 2023PF00657 6limited to, digital signal processor “DSP” hardware, read only memory “ROM” for storing software,random access memory “RAM”, a non-volatile storage device, and the like. Furthermore, examples of thepresent disclosure can take the form of a computer program product accessible from a computer-usablestorage medium, or a computer-readable storage medium, the computer program product providingprogram code for use by or in connection with a computer or any instruction execution system. For thepurposes of this description, a computer-usable storage medium or a computer readable storage mediumcan be any apparatus that can comprise, store, communicate, propagate, or transport a program for use byor in connection with an instruction execution system, apparatus, or device. The medium can be anelectronic, magnetic, optical, electromagnetic, infrared, or a semiconductor system or device orpropagation medium. Examples of computer-readable media include semiconductor or solid-statememories, magnetic tape, removable computer disks, random access memory “RAM”, read-only memory“ROM”, rigid magnetic disks and optical disks. Current examples of optical disks include compact disk-read only memory “CD-ROM”, compact disk-read / write “CD-R / W”, Blu-Ray™ and DVD.It is also noted that some operations that are described as being performed by the one ormore processors of the systems disclosed herein may be implemented using artificial intelligencetechniques. Suitable techniques may include machine learning techniques, deep learning techniques, andneural networks. For instance, one or more neural networks, may be trained in a supervised, or in somecases unsupervised, manner, to implement the operations that are performed by the one or moreprocessors. As mentioned above, there is a need to distinguish between different types ofintravascular tissue. Fig. 1 is a schematic diagram illustrating an example of an intravascular tissueanalysis system 100, in accordance with some aspects of the present disclosure. Fig. 2 is a flowchartillustrating an example of a computer-implemented method of performing intravascular tissue analysis, inaccordance with some aspects of the present disclosure. It is noted that operations that are described asbeing performed by the one or more processors 110 of the intravascular tissue analysis system 100illustrated in Fig. 1, may also be performed in the method illustrated in Fig. 2. Likewise, operations thatare described in relation to the method described with reference to Fig. 2 may also be performed by theone or more processors 110 of the intravascular tissue analysis system 100 illustrated in Fig. 1. Withreference to Fig. 1, and Fig. 2, the intravascular tissue analysis system 100, comprises one or moreprocessors 110 configured to:receive S110 optical data 120 representing an optical property of an intravascular tissue region 130 over one or more wavelength intervals; analyze S120 the optical data 120 to determine i) a blood clot component content 140 ofthe intravascular tissue region 130, and ii) a lipid content 150 of the intravascular tissue region 130; and output S130 an indication of the blood clot component content 140 and the lipid content 150, and / or an indication of one or more tissue composition parameters derived therefrom. 2023PF00657 7 The above system provides the blood clot component content of an intravascular tissue region in combination with the lipid content of the intravascular tissue region. This information facilitates a distinction between intravascular tissue regions that include blood clots, and intravascular tissue regions that include lipids. An example of an intravascular tissue region is an occlusion. The inventors have determined that the lipid content of an occlusion is correlated with the presence of plaque. By providing the blood clot component in addition to the lipid content, the system facilitates a distinction between a blood clot-type occlusion and a plaque-type occlusion. This distinction enables a physician to determine a course of treatment for the occlusion. For instance, if the system indicates that the blood clot component content is relatively high, the physician may conclude that the occlusion is a blood clot-type occlusion. The physician may then choose to remove the occlusion using a mechanical thrombectomy device. By contrast, if the system indicates that the lipid content is relatively high, the physician may conclude that the occlusion is a plaque-type occlusion. The physician may then choose to stent the occlusion. The provision of one or more tissue composition parameters derived from the blood clot component content and the lipid content, similarly facilitates a distinction between intravascular tissue regions that include blood clots, and intravascular tissue regions that include lipids. The tissue composition parameter may for instance be a ratio of the blood clot component content to the lipid content, or an indication of the intravascular tissue region comprising a blood clot, or an indication of the intravascular tissue region comprising plaque. Tissue composition parameters such as these similarly facilitate a distinction between a blood clot-type occlusion and a plaque-type occlusion, and consequently they similarly enable a physician to determine a course of treatment for the occlusion. The operations that are performed by the one or more processors of the intravascular tissue analysis system 100 are described in more detail below. With reference to the method illustrated in Fig. 2, in the operation S110, the one or moreprocessors 110 of the intravascular tissue analysis system 100 receive optical data 120. The optical data120 represents an optical property of an intravascular tissue region 130 over one or more wavelength intervals. In general, the optical data 120 that is received in the operation S110 may represent an optical property such as the reflectance, optical scatter, transmission, absorption, or fluorescence, of the intravascular tissue region 130. In general, the optical data 120 may be generated in response to the irradiation of the intravascular tissue region with optical radiation. In general, the one or more wavelength intervals may occupy a portion of the visible region of the optical spectrum and / or a portion of the near- infrared region of the optical spectrum. In general, the optical data 120 may be generated in real-time, i.e. the optical data 120 may be real-time optical data. Alternatively, the optical data 120 may have been generated historically, i.e. the optical data 120 may be historic optical data. The optical data 120 that is received in the operation S110 may be received from various sources. For example the optical data 120 may be received from a computer readable storage medium, or from the Internet, or the Cloud, and so forth. In the system 100 illustrated in Fig.1, the optical data 120 is 2023PF00657 8 received from an optical detector 190, and the optical data 120 is generated in real-time. In general, theoptical data 120 that is received in the operation S110 may be received via any form of datacommunication, including via wired, or wireless, or optical fiber communication. By way of someexamples, when wired data communication is used, the communication may take place via electricalsignals that are transmitted on an electrical cable. When wireless data communication is used, thecommunication may take place via RF or infrared signals. When an optical fiber data communication isused, the communication takes place via optical signals that are transmitted on an optical fiber.With continued reference to the method illustrated in Fig.2, in the operation S120, theone or more processors 110 of the intravascular tissue analysis system 100, analyze S120 the optical data120 to determine i) a blood clot component content 140 of the intravascular tissue region 130, and ii) a lipid content 150 of the intravascular tissue region 130. An example of the analysis of optical data in the operation S120 is now described with reference to Fig.3, which is an example of the optical signatures of various chromophores that aretypically present in the vasculature, in accordance with some aspects of the present disclosure. In general,the blood clot component may include red blood cells and / or fibrin and / or platelets. The blood clotcomponent content 140 may be determined based on a contribution to the optical property caused by red blood cells and / or fibrin and / or platelets, respectively. Similarly, the lipid content 150 may be determined based on a contribution to the optical property caused by lipids. In the example described with reference to Fig.3, the blood clot component that is determined is the red blood cell component. The content of other blood clot components, such as the fibrin component, and the platelet component, may be determined in a similar manner. The abscissa in Fig.3 represents wavelength in nanometers, and the ordinate in of Fig.3 represents the absorption coefficient of the various chromophores. The chromophores represented in Fig. 3 include water, lipid, collagen, and various haemoglobin chromophores. One of the main constituents of red blood cells is haemoglobin. Haemoglobin is present in the vasculature in various forms, including as deoxygenized haemoglobin, labelled Hb in Fig.3, and as oxygenized haemoglobin, labelled HbO2 in Fig. 3, and as methaemoglobin. As may be seen in Fig. 3, within the wavelength interval from approximately 450nanometers to approximately 800 nanometers, the absorption coefficients of the aforementionedhaemoglobin chromophores are relatively high compared to those of other chromophores that are presentin the vasculature. In this context, the term “approximately” refers to within ± 10 nanometers, or within ±5 nanometers, of these values. Thus, the red blood cell content 140 of an intravascular region may bedetermined based on a contribution to the optical absorption coefficient caused by haemoglobin. Forexample, the red blood cell content of an intravascular region may be determined based on a contributionto the optical absorption coefficient caused by haemoglobin over a wavelength interval within a rangeextending from approximately 450 nanometers to approximately 800 nanometers. The wavelengthinterval may be a portion of this range, or it may correspond to the complete range. As described later, the 2023PF00657 9 red blood cell content may be used to distinguish between a red blood cell rich environment, such as blood, or a red blood cell-rich occlusion, and a red blood cell poor environment such as a red blood cell- poor occlusion. As mentioned above, the lipid content of the intravascular tissue region also provides information that facilitates a distinction between different types of intravascular tissue regions. The inventors have determined that the lipid content of an occlusion is correlated with the presence of plaque. Thus, the lipid content may be used to distinguish between a lipid-rich environment, such as a plaque- type occlusion, and a lipid-poor environment, such as a blood clot-type occlusion. As may be seen in Fig.3, within the wavelength interval from approximately 1100 nanometers to approximately 1300nanometers, the absorption coefficient of the lipid chromophore has a distinctive peak. In this context, theterm “approximately” refers to within ± 10 nanometers, or within ± 5 nanometers, of these values. Thispeak is distinct from the absorption coefficients of other chromophores in the vasculature such as collagen, haemoglobin, and water. Thus, the lipid content 150 of an intravascular region may be determined based on a contribution to the optical absorption coefficient caused by lipids. For example, the lipid content 150 of an intravascular region may be determined based on a measurement of the optical absorption coefficient over a wavelength interval within a range extending from approximately 1100nanometers to approximately 1300 nanometers. The wavelength interval may be a portion of this range,or it may correspond to the complete range. As may be appreciated, instead of measuring the optical absorption (as illustrated in Fig. 3) the measurements of other optical properties of the intravascular tissue region 130 may alternatively be used to determine the red blood cell content and the lipid content of the intravascular tissue region. For instance, measurements of optical properties such as reflectance, or optical scattering, or transmission, or fluorescence, of the intravascular tissue region, are also affected by the chromophores represented in Fig. 3. Consequently measurements of these optical properties may alternatively be used to determine the red blood cell content, and the lipid content, of an intravascular tissue region. The measurements of such optical properties may be obtained using the intravascular tissue analysis system 100 illustrated in Fig.1, as described in more detail below. Various techniques may be used to analyze the optical data 120 in the operation S120 inorder to determine the red blood cell content and the lipid content 140 of the intravascular tissue region. In one example, a model fitting technique is used to fit a model to measurements of the optical property. In this example, a model reported in a document by Farrell, T. J. et al., “A diffusion theory model of spatially resolved, steady-state diffuse reflectance for the noninvasive determination of tissue opticalproperties in vivo”, Medical Physics 19(4): p. 879-888 (1992), is fitted to measurements of absorptionspectra in order to determine the red blood cell content and the lipid content. The Farrell model is derived from diffusion theory and describes the optical properties ofthe tissue via two properties, the absorption coefficient ^^ (^) and the reduced scattering coefficient^,^ (^), both of which are dependent on the wavelength ^ of the optical radiation. The total absorption 2023PF00657 10 coefficient μ^^^^^^ (^) is given by the sum of the absorption coefficients of the various absorbers weightedby their concentrations ^, i.e.: μ^^^^^ ^^^^^^^^^^^ ^^^^^^ (λ) = c^^^^^^^^^^^μ^ (λ) + c^^^^^μ^ (λ) + c^^^^^μ^^^^^^(λ) 1)The wavelength-dependent absorption coefficients of Lipids μ^^^^^^(λ), Water μ^^^^^^(λ), and other absorbers are known from literature. The absorption spectrum of deoxygenated Hb, is different to that of oxygenated haemoglobin, HbO2. The absorption coefficient due ^^^^^^^^^^^ to haemoglobin, μ^ (λ) is given by:μ ^^^^^^^^^^^ ^(λ) = ν(λ)^StO^μ ^^^^^ (λ) + (1 − StO^)μ^^^ (λ)^ (Equation 2) where μ^^^ (λ) and μ ^^^^^ (λ) correspond to absorption coefficients of deoxygenized andoxygenized haemoglobin, and StO^ is the oxygen saturation level of the tissue. The parameter ν(λ) isused to account for the inhomogeneous distribution of haemoglobin and is known as the “pigment packaging factor” and is given by: 1− exp^−2R^StO^μ ^^^^(λ) + (1 − StO )μ^^(λ)^^ν(λ) = ^ ^ ^2R^StO^^^^ (Equation 3)^μ^ (λ) + (1 − StO^)μ^^^(λ)^ as reported in a document by Verkruysse, W., et al., “Modelling light distributions of homogeneous versus discrete absorbers in light irradiated turbid media”, Phys. Med. Biol.42, 51, and wherein R corresponds to the average radius of a haemoglobin concentration, e.g., a blood vessel. Thereduced scattering coefficient is empirically modeled as the sum of Mie and Rayleigh scattering: μ^ = α ^ρ ^λ ^^ ^^ ,(λ)^+ (1 − ρ) ^λ ^^ (Equation 4) λ^ = 800 nm to a wavelength normalization value, α is the reducedscattering amplitude at λ^ the Mie scattering slope is b, and ρ denotes the Mie-fraction of the reducedscattering in the tissue. The Farrell model may be fitted to the absorption spectra using a Levenberg-Marquardtnon-linear inversion algorithm, for example. The result of the fitting is to determine the values of the concentrations of the haemoglobin and lipid chromophores, i.e.c^^^^^^^^^^^andc^^^^^, and which 2023PF00657 11represent the red blood cell content of the intravascular tissue region, and the lipid content of theintravascular tissue region, respectively. Instead of the model described in the above example, other models, and also other fittingtechniques, may alternatively be used in the operation S120 to analyze the optical data 120 in order todetermine the red blood cell content and the lipid content 140 of the intravascular tissue region. For example, a machine learning algorithm such as a neural network, a decision tree, a support vectormachine “SVM”, principal component analysis, and so forth, may be trained, and subsequently used, toanalyze the optical data 120. The training data used to train the machine learning algorithm may includemultiple sets of optical spectra of intravascular tissue regions, and for each set of optical spectra, corresponding ground truth data comprising a ground truth value for the red blood cell content, and a ground truth value for the lipid content. The ground truth data, may be determined via histological analysis of the intravascular tissue regions. In the above example, the blood clot component content that was determined was that of red blood cells. However, as mentioned above, the content of other blood clot components, such as the fibrin component, and the platelet component, may also be determined in a similar manner based on their optical signatures. Returning to the method illustrated in Fig. 2, in the operation S130, the one or moreprocessors 110 of the intravascular tissue analysis system 100, output an indication of the blood clotcomponent content 140 and the lipid content 150, and / or an indication of one or more tissue composition parameters derived therefrom. The indication of the blood clot component content 140 and the lipid content 150 may be outputted in various forms in the operation S130. For example, the blood clot component content 140 andthe lipid content 150 may be outputted in numerical form, or in textual form, or in the form of an icon, orin the form of a graph. The indication of the blood clot component content 140 and the lipid content 150 may be outputted in real-time in the operation S130. By way of an example, Fig.4 is a schematic diagram illustrating an example of an outputted indication of a blood clot component content 140 and a lipid content 150 of an intravascular tissue region, in accordance with some aspects of the present disclosure. In the example illustrated in Fig.4, the blood clot component is the red blood cell component. In the upper portion of Fig.4, the red blood cell content and the lipid content are outputted as numerical valuesfor an intravascular tissue region 130 that corresponds to a current position of an optical probe in vessel.In the operation S130, instead of, or in addition to outputting values for the blood clot component content 140 and the lipid content 150, the processor(s) may output an indication of one or more tissue composition parameters derived from the blood clot component content 140 and the lipid content 150. An example of a tissue composition parameter is a ratio of the blood clot component content 140 to the lipid content 150. Another example of a tissue composition parameter is an indication of the intravascular tissue region comprising a blood clot. An indication of the intravascular tissue regioncomprising a blood clot may be determined based on the blood clot component content. For instance, a 2023PF00657 12 threshold may be applied to the blood clot component content, and if the blood clot component content exceeds the threshold, the intravascular tissue region may be deemed to comprise a blood clot. Another example of a tissue composition parameter is an indication of the intravascular tissue region comprising plaque. An indication of the intravascular tissue region comprising plaque may be determined based onthe lipid content. For instance, a threshold may be applied to the lipid content, and if the lipid contentexceeds the threshold, the intravascular tissue region may be deemed to comprise plaque. In one example, the blood clot component content 140 and the lipid content 150 areoutputted for each of multiple positions along a vessel. In this example, the received optical data 120represents the optical property at a plurality of positions along a vessel, and the one or more processors110 output the blood clot component content 140 and the lipid content 150, and / or the indication of theone or more tissue composition parameters derived therefrom, for each of a plurality of the positions along the vessel. An example of the outputting of data in accordance with this example is illustrated by the graph in the lower portion of Fig.4. In this graph, the Fig.4, the blood clot component is the red blood cell content. In this graph, the abscissa represents the position along the vessel, and the ordinate represents the red blood cell content 140 and the lipid content 150 respectively. The optical data 120 in this example may be obtained during a so-called pullback procedure in which an optical probe 180 that is used to obtain the optical data 120 is translated along the vessel. The position along the vessel may be determined based on a known pullback speed of the optical probe. The blood clot component content 140 and the lipid content 150 may be outputted tovarious media in the operation S130, including to a display such as to the display 280 illustrated in Fig. 1,or to a virtual / augmented reality display device, or to a printer, or to a computer-readable storagemedium, or to the Internet, or to the Cloud, and so forth.The above-described system provides the blood clot component content of an intravascular tissue region in combination with the lipid content of the intravascular tissue region. This information facilitates a distinction between intravascular tissue regions that include blood clots, and intravascular tissue regions that include lipids. An example of an intravascular tissue region is an occlusion. The inventors have determined that the lipid content of an occlusion is correlated with the presence of plaque. By providing the blood clot component in addition to the lipid content, the system facilitates a distinction between a blood clot-type occlusion and a plaque-type occlusion. This distinction enables a physician to determine a course of treatment for the occlusion. The provision of one or more tissue composition parameters derived from the blood clot component content and the lipid content, similarly facilitates a distinction between intravascular tissue regions that include blood clots, and intravascular tissue regions that include lipids. The tissue composition parameter(s) similarly facilitate a distinction between a blood clot-type occlusion and a plaque-type occlusion, and consequently they similarly enable a physician to determine a course of treatment for the occlusion. 2023PF00657 13 Various further examples of the intravascular tissue analysis system 100 are described below. In one example, the one or more processors 110 of the intravascular tissue analysissystem 100 are configured to analyze the optical data 120 to determine a type of a blood clot 160 in theintravascular tissue region 130; and to output an indication of the type of the blood clot. By outputting anindication of the type of the blood clot, this example provides additional information to a physician thatmay support the physician in determining a course of treatment for the blood clot.This example is described with reference to Fig. 5, which is an example of the opticalsignatures of various types of blood clot, blood, a vessel wall, and plaque, in accordance with some aspects of the present disclosure. The abscissa of Fig.5 represents wavelength in nanometers, and the ordinate axis of Fig.5 represents the reflectance of the various intravascular media. As may be appreciated from Fig.5, differences between the spectra of a red blood cell-rich blood clot, a red bloodcell-poor blood clot, vessel wall, blood, and plaque, may be used to distinguish these media from oneanother. For instance, differences between the illustrated spectra enable a determination of a type of ablood clot, the type of the clot being a red blood cell-poor blood clot, a red blood cell-rich blood clot, anda mixed clot. A red blood cell-poor blood clot may alternatively be referred-to as a white clot, or a fibrin- rich blood clot. A red blood cell-rich blood clot may alternatively be referred to as a red clot. A clot that is neither red blood cell-poor, nor red blood cell-rich, may also be referred-to as a mixed clot. The indication of the type of the blood clot may be outputted in various forms in thisexample. For example, the type of the blood clot may be outputted in textual form, or in the form of anicon, or in the form of a graph. By way of an example, Fig.6 is a schematic diagram illustrating an example of an outputted indication of various types of blood clots, in accordance with some aspects of the present disclosure. In the example illustrated in Fig.6, the type of the blood clot is outputted in textual form, and also as an icon. The type of the blood clot is also illustrated in the lower portion of Fig.6 for optical data that is obtained at a plurality of positions along a vessel. Providing the type of the blood clot at multiple positions along a vessel in accordance with this example facilitates an assessment of a length of the blood clot. This information is useful to a physician in determining a course of treatment for the ischemic stroke. For instance it may be used to specify the length of a stent to deploy in the vessel. In this example the one or more processors 110 may analyze the optical data 120 todetermine the type of the blood clot based on a measurement of the optical property over a wavelengthinterval within a range extending from approximately 450 nanometers to approximately 800 nanometers.The wavelength interval may be a portion of this range, or it may correspond to the complete range. The type of the blood clot that is determined in this example comprises one or more of: a red blood cell-poorblood clot, a red blood cell-rich blood clot, and a mixed clot. The type of the blood clot is determinedbased on a contribution to the optical property caused by one or more of: a red blood cell-poor blood clot, a red blood cell-rich blood clot, and a mixed clot. 2023PF00657 14 In this example, the contributions to the optical property from the different types of blood clots may be determined by measuring an optical reflectance spectrum of the intravascular tissue region, and then fitting a weighted sum of reference optical signatures for the blood clots, such as the optical signatures of the blood clots illustrated in Fig.5, to the measured optical reflectance spectrum. The model fitting technique described above may be used to in this operation. The type of the blood clot may then be determined by applying threshold values to the contributions of the different blood clots. In another example, the one or more processors 110 of the intravascular tissue analysissystem 100 are configured to:analyze the optical data 120 to determine a contribution to the optical property caused bya vessel wall and / or blood 270 in the optical data 120; and output an indication of the detection of the vessel wall and / or blood, the indication being determined based on the corresponding contribution. In this example, the contribution to the optical property caused by a vessel wall and / or blood in the optical data 120 may be determined using the model fitting technique described above. These contributions may be determined using reference optical spectra of the vessel wall and / or blood, respectively, such as those illustrated in Fig.5. As illustrated in Fig.5, these spectra have characteristicfeatures within the wavelength range extending from approximately 450 nanometers to approximately800 nanometers. For instance, these spectra have a characteristic absorption edge that may be used to determine their individual contributions to a measured optical reflectance spectrum. The indication of the detection of the vessel wall and / or blood may be outputted in a similar manner to that described above for the blood clots. For instance, as illustrated in Fig.6, the indication of the detection of the vessel wall and / or blood may be provided in textual form, or in the form of an icon, or in the form of a graph indicating the detection of the vessel wall, or blood, at multiple positions along the vessel. By providing an indication of the detection of the vessel wall and / or blood in accordance with this example, the system informs a physician of the position of the optical probe in the vasculature. For instance, an indication of blood informs a physician that the optical probe is not currently located within an occlusion. This indication may alert the physician of the need to re-position the optical probe within the occlusion so as to obtain optical data for the occlusion. Similarly, an indication of the detection of the vessel wall may alert the physician of the need to rotate a radially-sensing optical probe in order to instead obtain optical data for the occlusion. As illustrated in Fig.1, the optical data 120 that is used to determine the blood clot component content 140 of the intravascular tissue region 130, the lipid content 150 of the intravasculartissue region 130, and likewise the type of a blood clot in the intravascular tissue region 130, may beprovided using at least one optical source 170, an optical probe 180 for insertion into the vasculature, andat least one optical detector 190. These elements are now described with reference to Fig. 1 and Fig. 7 –Fig. 9. 2023PF00657 15 In general, the at least one optical source 170 illustrated in Fig. 1 may generate opticalradiation over a portion of the visible spectrum and / or over a portion of the near-infrared spectrum. In theexample described above, the optical source(s) 170 generate optical radiation over a first wavelengthinterval that is within a range extending from approximately 450 nanometers to approximately 800nanometers, and also over a second wavelength interval that is within a range extending fromapproximately 1100 nanometers to approximately 1300 nanometers. The wavelength intervals may beportions of these ranges, or they may correspond to the complete range. In other examples, the optical source(s) may generate optical radiation over one or more wavelength intervals that are different to thisexample. In general, the optical source(s) may be provided by lamps, or light emitting diodes “LED”s, orlasers. The at least one optical detector 190 illustrated in Fig. 1 may be provided by various types ofdetectors, including for example a silicon detector, a gallium arsenide detector, an indium galliumarsenide detector, and so forth.In some examples, the optical source(s) and the optical detector(s) are configured as aspectrometer. For instance, in one example, a spectrometer may be provided by using a diffractive orrefractive optical element to spatially separate the optical radiation into different portions of thewavelength interval(s), and by using one or more optical detectors to detect the intensity the opticalradiation over the different portions of the wavelength interval(s). The intensity of the collected opticalradiation in each portion of the wavelength interval may be detected using multiple detectors, the spatialpositions of which determine the detected portion of the wavelength interval(s), or alternatively a singledetector may be used, the position of the single optical detector determining the detected portions of thewavelength interval(s).In another example, a spectrometer may be provided by using a diffractive, or arefractive, optical element to select from the optical radiation generated by the optical source(s), opticalradiation over different portions of the wavelength interval(s). In this example, the spectrometer may beprovided by temporally adjusting the position of the diffractive or refractive optical element so as toselect optical radiation from different portions of the wavelength interval(s) over time. A correspondingsingle optical detector may be used to temporally separate the detected optical radiation into the differentportions of the wavelength interval(s).In another example, a spectrometer may be provided by temporally modulating theintensity of different optical sources so as to selectively generate optical radiation over the differentportions of the wavelength interval(s). A single optical detector may then be used to temporally separatethe collected optical radiation into the different portions of the wavelength intervals.In another example, a spectrometer may be provided by temporally switching filters thatselectively transmit optical radiation over each of the different portions of the of the wavelengthinterval(s). The filters provide temporally modulated optical radiation for the different portions of thewavelength interval(s). A single optical detector may then be used to temporally separate the collectedoptical radiation into the different portions of the wavelength interval(s). 2023PF00657 16 In one example, the at least one optical source and the at least one optical detector areconfigured as a Raman spectrometer. In another example, the at least one optical source and the at leastone optical detector are configured to perform diffuse reflectance spectroscopy. In another example, the atleast one optical source and the at least one optical detector are configured to perform fluorescencespectroscopy. Returning to the system 100 illustrated in Fig. 1, the at least one optical source 170 is inoptical communication with the optical probe 180, and the optical probe 180 is configured to irradiate theintravascular tissue region 130 with optical radiation 210 generated by the at least one optical source 170,and to collect optical radiation returned by the intravascular tissue region 130 in response to theirradiation. The at least one optical detector 190 is in optical communication with the optical probe 180,and the at least one optical detector 190 is configured measure an intensity of the collected opticalradiation over the one or more wavelength intervals to provide the optical data 120.Various types of optical probes may be used with the system illustrated in Fig.1. In general, these include optical probes that are configured to measure one or more of: optical reflectance, optical scattering, optical transmission, optical absorption, and fluorescence. In general, the optical probemay include one or more optical fibers. For instance, in one example, the optical probe includes a singleoptical fiber 220, and the single optical fiber is used both to irradiate the intravascular tissue region and tocollect optical radiation returned by the intravascular tissue region in response to the irradiation. Inanother example, the optical probe includes multiple optical fibers, and separate optical fibers are used toirradiate the intravascular tissue region, and to collect optical radiation returned by the intravascular tissueregion in response to the irradiation. The use multiple optical fibers offers improved separation betweenthe irradiating, and collected, optical radiation, at the expense of an increase in diameter, and reducedflexibility, of the optical probe. Some examples of optical probes that include a single optical fiber, andwhich are configured to measure optical reflectance, are described with reference to Fig.7, and Fig.8. Fig.7 is a schematic diagram illustrating a first example of an optical probe 180 for insertion into the vasculature, in accordance with some aspects of the present disclosure. The optical probe 180 illustrated in Fig.7 includes an elongate body 230. The elongate body is flexible, and has a smooth outer surface in order to enable its insertion into the vasculature. The optical probe 180 illustratedin Fig. 7 includes a single optical fiber 240. The optical fiber is used both to irradiate an intravasculartissue region 130 and to collect optical radiation returned by the intravascular tissue region in response tothe irradiation. The optical fiber 240 is arranged within the elongate body 230. Optical radiation passingalong the optical fiber 240 is emitted along a path that is parallel to the longitudinal axis of the optical probe. The optical fiber 240 also collects optical radiation that returns back along the same path as theemitted radiation. This enables the optical probe illustrated in Fig. 7 to analyze an intravascular tissueregion 130 that is disposed distally with respect to a distal end of the optical probe. The optical probe illustrated in Fig.7 may therefore be referred-to as a “forward-sensing” optical probe. 2023PF00657 17 By way of another example, Fig.8 is a schematic diagram illustrating a second example of an optical probe 180 for insertion into the vasculature, in accordance with some aspects of the present disclosure. As in the Fig.7 example, the optical probe 180 illustrated in Fig.8 includes an elongate body 230. The elongate body is flexible, and has a smooth outer surface in order to enable its insertion into thevasculature. As in the Fig. 7 example, the optical probe 180 illustrated in Fig. 8 includes a single opticalfiber240. The optical fiber 240 is used both to irradiate an intravascular tissue region 130 and to collectoptical radiation returned by the intravascular tissue region in response to the irradiation. The optical fiber240 is arranged within the elongate body 230. The optical fiber is arranged within the elongate body 230. In contrast to the Fig.7 example, the optical probe 180 illustrated in Fig.8 includes a reflective surface at a beveled end face 230 of the optical fiber 240. The reflective surface may be provided by a mirror, or total internal reflection, for example. The reflective surface redirects optical radiation passing along the optical fiber from a longitudinal direction with respect to the longitudinal axis of the optical probe, andinto a radial direction with respect to the longitudinal axis of the optical probe. The emitted opticalradiation 210 then passes through an aperture 250 in the elongate tube 230. The optical fiber 240 also collects optical radiation that returns back along the same path as the emitted radiation. This enables theoptical probe illustrated in Fig. 8 to analyze an intravascular tissue region 130 that is disposed radiallywith respect to a distal end of the optical probe. The optical probe illustrated in Fig.8 may therefore be referred-to as a “radial-sensing” optical probe. The optical probes illustrated in Fig. 7 and Fig. 8 may be used to analyze intravasculartissue regions such as occlusions in blood vessels. By way of an example, Fig.9 is a schematic diagram illustrating an example of an optical probe 180 disposed within an intravascular tissue region 130 within a blood vessel 260, in accordance with some aspects of the present disclosure. The optical probe 180illustrated in Fig. 5 represents an optical probe such as the optical probe 180 illustrated in Fig. 7 or in Fig.8. The intravascular tissue region 130 illustrated in Fig.5 may be an occlusion, such as a plaque-type occlusion, or blood clot-type occlusion, for example. As illustrated in Fig.9, an analysis of the intravascular tissue region 130 may be performed by arranging the distal end of the optical probe 180 from which the optical radiation 210 is emitted, within, abutting, or at least close to, the intravascular tissue region. This analysis may be performed during a so-called pullback procedure in which the optical probe is withdrawn from the vasculature, as illustrated by the arrow labelled Pullback, for example. Instead of the arrangements illustrated in Fig.7 and Fig.8, other types of optical probes may be used with the system illustrated in Fig.1. These alternative optical probes may have different numbers of optical fibers, and different arrangements of optical fibers. For instance, the distal ends of the optical fiber(s) may be oriented in a different direction to those illustrated in Fig.7 and Fig.8 in order to sense a different intravascular tissue region. The distal end(s) of the optical fiber(s) may also beconfigured to measure another optical property of the intravascular tissue region 130. For instance theymay be configured to measure optical scattering, or optical transmission, or optical absorption, or optical fluorescence. 2023PF00657 18 Referring now to the system illustrated in Fig. 1, as mentioned above, the at least oneoptical source 170 is in optical communication with the optical probe 180, and the at least one opticaldetector 190 is also in optical communication with the optical probe 180. This optical communicationmay be provided by one or more optical components such as an optical connector, a beamsplitter, abifurcated optical fiber, and so forth.It is noted that the intravascular tissue analysis system 100 may, as illustrated in Fig. 1,also include one or more of: a display, such as the display 280 illustrated in Fig.1, for displaying the blood clot component content 140, the lipid content 150, the indication of the tissue compositionparameter(s) 160, and other outputs generated by the processor(s) 110; and a user input device (notillustrated in Fig.1) configured to receive user input in relation to the operations performed by the one or more processors 110, such as a keyboard, a mouse, a touchscreen, and so forth. In another example, a computer-implemented method of performing intravascular tissueanalysis, is provided. The method comprises:receiving S110 optical data 120 representing an optical property of an intravascular tissue region 130 over one or more wavelength intervals; analyzing S120 the optical data 120 to determine i) a blood clot component content 140of the intravascular tissue region 130, and ii) a lipid content 150 of the intravascular tissue region 130; and outputting S130 an indication of the blood clot component content 140 and the lipid content 150, and / or an indication of one or more tissue composition parameters derived therefrom. In another example, a computer program product is provided. The computer programproduct comprises instructions which when executed by one or more processors 110, cause the one ormore processors to carry out a method of performing intravascular tissue analysis. The method comprises:receiving S110 optical data 120 representing an optical property of an intravascular tissue region 130 over one or more wavelength intervals; analyzing S120 the optical data 120 to determine i) a blood clot component content 140of the intravascular tissue region 130, and ii) a lipid content 150 of the intravascular tissue region 130; and outputting S130 an indication of the blood clot component content 140 and the lipid content 150, and / or an indication of one or more tissue composition parameters derived therefrom. An alternative system is now described which determines the presence and / or a type of ablood clot in an intravascular tissue region. This system shares many of the features of the system described above with reference to Fig.1 and Fig.2. For instance, it uses the same optical source(s), the same optical detector(s), the same optical probe, and the same optical data as those described above. This alternative system may also include a display and / or a user input device, as described above in relation toFig. 1. In this alternative system, the optical data may also be analyzed in a similar manner to thatdescribed above with reference to Fig.1 and Fig.2. For example, the model fitting technique may be used 2023PF00657 19to analyze the optical data. However, this alternative system does not output a blood clot componentcontent 140 and a lipid content 150. Instead, it provides an indication of the presence and / or the type of ablood clot 160, and an indication of the presence and / or a type of a plaque. By providing these parametersdirectly, the alternative system alleviates the mental burden on the physician of interpreting the meaning of the blood clot component content 140 and lipid content 150. In this intravascular tissue analysis system, the system comprises one or more processorsconfigured to:receive optical data 120 representing an optical property of an intravascular tissue region 130 over one or more wavelength intervals; analyze the optical data 120 to determine a) a presence and / or a type of a blood clot 160in the intravascular tissue region, and b) a presence and / or a type of a plaque in the intravascular tissue region 130; and output an indication of a) the presence and / or the type of the blood clot, and b) the presence and / or the type of the plaque. In this system, the presence of the blood clot is determined based on a contribution to the optical property caused by red blood cells and / or fibrin and / or platelets, and the presence of the plaque is determined based on a contribution to the optical property caused by lipids. The contribution to the optical property caused by red blood cells and / or fibrin and / or platelets, and lipids may be determined using the model fitting approach described above with referenceto Fig. 3. Thus, in one example, the one or more wavelength intervals comprise a first wavelength intervalwithin a range extending from approximately 450 nanometers to approximately 800 nanometers, and asecond wavelength interval within a range extending from approximately 1100 nanometers toapproximately 1300 nanometers; and the one or more processors are configured to analyze the opticaldata 120 to determine the presence of the blood clot based on a measurement of the optical property over the first wavelength interval, and to determine the presence of the plaque based on a measurement of the optical property over the second wavelength interval. The presence of the a blood clot, and the presence of plaque, may be determined byapplying threshold values to each contribution to the optical property. For instance, an indication of thepresence of a blood clot may be provided if the contribution to the optical property caused byhaemoglobin exceeds a specified threshold. Similarly an indication of the presence of plaque may beprovided if the contribution to the optical property caused by lipids exceeds a specified threshold.The type of the blood clot that is determined in this example may be one or more of: a redblood cell-poor blood clot, a red blood cell-rich blood clot, and a mixed clot. The type of the blood clotmay be determined in a similar manner to that described above with reference to Fig. 5. Thus, in oneexample, the one or more wavelength intervals comprise a first wavelength interval within a rangeextending from approximately 450 nanometers to approximately 800 nanometers, and the type of theblood clot type comprises one or more of: a red blood cell-poor blood clot, a red blood cell-rich blood 2023PF00657 20clot, and a mixed clot. In this example, the one or more processors are configured to analyze the opticaldata 120 to determine the type of the blood clot based on a measurement of the optical property over thefirst wavelength interval, and the type of the blood clot is determined based on a contribution to theoptical property caused by one or more of: a red blood cell-poor blood clot, a red blood cell-rich blood clot, and a mixed clot. Similarly, different types of plaque may be identified based on differences in their opticalproperties. An example of the optical properties, in this example, the reflectance, of one type of plaqueare illustrated in Fig. 5, and this has a characteristic absorption band within the wavelength range fromapproximately 1100 nanometers to approximately 1300 nanometers. Other types of plaque such ascalcified plaque, vulnerable plaque, and stable plaque, are expected to have other characteristic featuresthat likewise facilitate their distinction.In another example, the received optical data 120 represents the optical property at aplurality of positions along a vessel; and the one or more processors are configured to output theindication of a) the presence and / or the type of the blood clot in the intravascular tissue region 130, and b) the presence and / or the type of the plaque in the intravascular tissue region 130, for each of a plurality of the positions along the vessel. In this example, output may be provided in a similar manner to the graph of the red blood cell fraction versus position along the vessel illustrated in Fig.4, for example. In another example, a machine learning algorithm is used to generate the predictedindication of the presence and / or the type of the blood clot, and the predicted indication of the presenceand / or the type of the plaque. In this example, the one or more processors are configured to analyze theoptical data 120 to determine a) the presence and / or the type of the blood clot in the intravascular tissue region 130, and b) the presence and / or the type of the plaque in the intravascular tissue region 130 by: inputting the received optical data 120 into a machine learning algorithm; and generating a predicted indication of the presence and / or the type of the blood clot, and a predicted indication of the presence and / or the type of the plaque, in response to the inputting, using the machine learning algorithm; and wherein the machine learning algorithm is trained to generate the predicted indication ofthe presence and / or the type of the blood clot, and the predicted indication of the presence and / or the typeof the plaque, from the optical data 120, using training data comprising a plurality of sets of opticaltraining data representing an optical property of an intravascular tissue region 130 over the one or morewavelength intervals; and for each set of optical training data, corresponding ground truth datarepresenting the indication of the presence and / or the type of the blood clot and the indication of the presence and / or the type of the plaque. In this example, the machine learning algorithm may for example employ a neuralnetwork, a decision tree, a support vector machine “SVM”, principal component analysis, and so forth. 2023PF00657 21 Examples of neural networks that may be used to generate the predicted indication of thepresence and / or the type of the blood clot include recurrent neural networks “RNN”s, encoder / decoderarchitectures, variational autoencoders, and so forth. In general, the training of a neural network involvesinputting the training data into the neural network, and iteratively adjusting the neural network’sparameters until the trained neural network provides an accurate output. The parameters, or moreparticularly the weights and biases, control the operation of activation functions in the neural network. Insupervised learning, the training process automatically adjusts the weights and the biases, such that whenpresented with the input data, the neural network accurately provides the corresponding expected outputdata. In order to do this, the value of the loss functions, or errors, are computed based on a differencebetween predicted output data and the expected output data. The value of the loss function may becomputed using functions such as the negative log-likelihood loss, the mean absolute error (or L1 norm),the mean squared error, the root mean squared error (or L2 norm), the Huber loss, or the (binary) crossentropy loss. During training, the value of the loss function is typically minimized, and training isterminated when the value of the loss function satisfies a stopping criterion. Sometimes, training isterminated when the value of the loss function satisfies one or more of multiple criteria.Various methods are known for solving the loss minimization problem, includinggradient descent, Quasi-Newton methods, and so forth. Various algorithms have been developed toimplement these methods and their variants, including but not limited to Stochastic Gradient Descent“SGD”, batch gradient descent, mini-batch gradient descent, Gauss-Newton, Levenberg Marquardt,Momentum, Adam, Nadam, Adagrad, Adadelta, RMSProp, and Adamax “optimizers”. These algorithmscompute the derivative of the loss function with respect to the model parameters using the chain rule. Thisprocess is called backpropagation since derivatives are computed starting at the last layer or output layer,moving toward the first layer or input layer. These derivatives inform the algorithm how the modelparameters must be adjusted in order to minimize the error function. That is, adjustments to modelparameters are made starting from the output layer and working backwards in the network until the inputlayer is reached. In a first training iteration, the initial weights and biases are often randomized. Theneural network then predicts the output data, which is likewise, random. Backpropagation is then used toadjust the weights and the biases. The training process is performed iteratively by making adjustments tothe weights and biases in each iteration. Training is terminated when the error, or difference between thepredicted output data and the expected output data, is within an acceptable range for the training data, orfor some validation data. Subsequently the neural network may be deployed, and the trained neuralnetwork makes predictions on new input data using the trained values of its parameters. If the trainingprocess was successful, the trained neural network accurately predicts the expected output data from thenew input data.The training of a neural network is often performed using a Graphics Processing Unit“GPU” or a dedicated neural processor such as a Neural Processing Unit “NPU” or a Tensor ProcessingUnit “TPU”. Training often employs a centralized approach wherein cloud-based or mainframe-based 2023PF00657 22neural processors are used to train a neural network. Following its training with the training dataset, thetrained neural network may be deployed to a device for analyzing new input data during inference. Theprocessing requirements during inference are significantly less than those required during training,allowing the neural network to be deployed to a variety of systems such as laptop computers, tablets,mobile phones and so forth. Inference may for example be performed by a Central Processing Unit“CPU”, a GPU, an NPU, a TPU, on a server, or in the cloud.In the above example, the neural network may be trained to generate the predictedindication of the presence and / or the type of the blood clot, and the predicted indication of the presenceand / or the type of the plaque from the optical data 120, by:for each of a plurality of sets of the optical training data:inputting the set of optical training data into the neural network;generating a predicted indication of the presence and / or the type of the blood clot, and apredicted indication of the presence and / or the type of the plaque, in response to the inputting, using theneural network; andadjusting parameters of the neural network based on a difference between the predictedindication of the presence and / or the type of the blood clot, and the predicted indication of the presenceand / or the type of the plaque, generated by the neural network, and the corresponding indication of thepresence and / or the type of the blood clot, and indication of the presence and / or the type of the plaque,from the ground truth data; andrepeating the inputting, the generating, and the adjusting, until a stopping criterion is met.In this alternative system, the or more processors may also analyze the optical data 120 todetermine a contribution to the optical property caused by a vessel wall and / or blood 270 in the opticaldata 120, and output an indication of the detection of the vessel wall and / or blood, the indication being determined based on the corresponding contribution. This operation may be performed using the model fitting technique described above. A machine learning algorithm may also be used to generated a predicted indication of the detection of the vessel wall and / or blood from the optical data. In this example, the machine learning algorithm may be trained to generate the predicted indication of the detection of thevessel wall and / or blood, from the optical data 120, using training data comprising a plurality of sets ofoptical training data representing an optical property of an intravascular tissue region 130 over the one ormore wavelength intervals; and for each set of optical training data, corresponding ground truth datarepresenting the indication of the detection of the vessel wall and / or blood. The optical training data, and the corresponding ground truth data in this example may be provided from intravascular measurements of the optical property using an optical probe that is known to be in contact with a vessel wall and / or disposed in blood. In another example, a computer-implemented method of performing intravascular tissueanalysis, is provided. The method comprises: 2023PF00657 23 receiving S110 optical data 120 representing an optical property of an intravascular tissue region 130 over one or more wavelength intervals; analyzing S120 the optical data 120 to determine a) a presence and / or a type of a bloodclot 160 in the intravascular tissue region, and b) a presence and / or a type of a plaque in the intravascular tissue region 130; and outputting an indication of a) the presence and / or the type of the blood clot, and b) the presence and / or the type of the plaque. In another example, a computer program product is provided. The computer programproduct comprises instructions which when executed by one or more processors 110, cause the one ormore processors to carry out a method of performing intravascular tissue analysis. The method comprises:receiving S110 optical data 120 representing an optical property of an intravascular tissue region 130 over one or more wavelength intervals; analyzing S120 the optical data 120 to determine a) a presence and / or a type of a bloodclot 160 in the intravascular tissue region, and b) a presence and / or a type of a plaque in the intravascular tissue region 130; and outputting an indication of a) the presence and / or the type of the blood clot, and b) the presence and / or the type of the plaque. The above examples are to be understood as illustrative of the present disclosure, and notrestrictive. Further examples are also contemplated. For instance, the examples described in relation to asystem, may also be provided by the corresponding computer-implemented method, or by thecorresponding computer program product, or by a computer-readable storage medium, in a correspondingmanner. It is to be understood that a feature described in relation to any one example may be used alone,or in combination with other described features, and may be used in combination with one or morefeatures of another of the examples, or a combination of other examples. Furthermore, equivalents andmodifications not described above may also be employed without departing from the scope of theinvention, which is defined in the accompanying claims. In the claims, the word “comprising” does notexclude other elements or operations, and the indefinite article “a” or “an” does not exclude a plurality.The mere fact that certain features are recited in mutually different dependent claims does not indicatethat a combination of these features cannot be used to advantage. Any reference signs in the claimsshould not be construed as limiting their scope.

Claims

2023PF00657 24 CLAIMS:

1. An intravascular tissue analysis system (100), the system comprising one or moreprocessors (110) configured to:receive (S110) optical data (120) representing an optical property of an intravascular tissue region (130) over one or more wavelength intervals; analyze (S120) the optical data (120) to determine i) a blood clot component content(140) of the intravascular tissue region (130), and ii) a lipid content (150) of the intravascular tissue region (130); and output (S130) an indication of the blood clot component content (140) and the lipid content (150), and / or an indication of one or more tissue composition parameters derived therefrom; wherein the blood clot component comprises red blood cells and / or fibrin and / or platelets,wherein the blood clot component content (140) is determined based on a detection of a signature in theoptical property that is characteristic of red blood cells and / or fibrin and / or platelets, respectively, and wherein the lipid content (150) is determined based on a contribution to the optical property caused by lipids.

2. The intravascular tissue analysis system according to claim 1, wherein the one or morewavelength intervals comprise a first wavelength interval within a range extending from approximately450 nanometers to approximately 800 nanometers, and a second wavelength interval within a rangeextending from approximately 1100 nanometers to approximately 1300 nanometers; andwherein the one or more processors (110) are configured to analyze the optical data (120)to determine i) the blood clot component content (140) based on a measurement of the optical property over the first wavelength interval, and to determine ii) the lipid content (150), based on a measurement of the optical property over the second wavelength interval.

3. The intravascular tissue analysis system according to any previous claim, wherein the oneor more tissue composition parameters comprise one or more of: an indication of the intravascular tissue region (130) comprising a blood clot, and an indication of the intravascular tissue region (130) comprising plaque.

4. The intravascular tissue analysis system according to any previous claim, wherein the oneor more processors (110) are further configured to:2023PF00657 25 analyze the optical data (120) to determine iii) a type of a blood clot (160) in theintravascular tissue region (130); andoutput an indication of the type of the blood clot.

5. The intravascular tissue analysis system according to claim 4, wherein the one or morewavelength intervals comprise a first wavelength interval within a range extending from approximately450 nanometers to approximately 800 nanometers, and wherein the type of the blood clot comprises oneor more of: a red blood cell-poor blood clot, a red blood cell-rich blood clot, and a mixed clot; and wherein the one or more processors (110) are configured to analyze the optical data (120)to determine iii) the type of the blood clot based on a measurement of the optical property over the firstwavelength interval, and wherein the type of the blood clot is determined based on a contribution to theoptical property caused by one or more of: a red blood cell-poor blood clot, a red blood cell-rich blood clot, and a mixed clot.

6. The intravascular tissue analysis system according to any previous claim, wherein thereceived optical data (120) represents the optical property at a plurality of positions along a vessel; and wherein the one or more processors (110) are configured to output the blood clotcomponent content (140) and the lipid content (150), and / or the indication of the one or more tissue composition parameters derived therefrom, for each of a plurality of the positions along the vessel.

7. An intravascular tissue analysis system, the system comprising one or more processorsconfigured to:receive optical data (120) representing an optical property of an intravascular tissue region (130) over one or more wavelength intervals; analyze the optical data (120) to determine a) a presence and / or a type of a blood clot(160) in the intravascular tissue region, and b) a presence and / or a type of a plaque in the intravascular tissue region (130); and output an indication of a) the presence and / or the type of the blood clot, and b) the presence and / or the type of the plaque; wherein a) the presence and / or a type of a blood clot (160) in the intravascular tissueregion is determined based on a detection of a signature in the optical property that is characteristic of red blood cells and / or fibrin and / or platelets, and wherein b) the presence and / or a type of a plaque in theintravascular tissue region (130) is determined based on a contribution to the optical property caused bylipids.

8. The intravascular tissue analysis system according to claim 7, wherein the one or moreprocessors are configured to analyze the optical data (120) to determine a) the presence and / or the type of2023PF00657 26 the blood clot in the intravascular tissue region (130), and b) the presence and / or the type of the plaque in the intravascular tissue region (130) by: inputting the received optical data (120) into a machine learning algorithm; and generating a predicted indication of the presence and / or the type of the blood clot, and a predicted indication of the presence and / or the type of the plaque, in response to the inputting, using the machine learning algorithm; and wherein the machine learning algorithm is trained to generate the predicted indication ofthe presence and / or the type of the blood clot, and the predicted indication of the presence and / or the typeof the plaque, from the optical data (120), using training data comprising a plurality of sets of opticaltraining data representing an optical property of an intravascular tissue region (130) over the one or morewavelength intervals; and for each set of optical training data, corresponding ground truth datarepresenting the indication of the presence and / or the type of the blood clot and the indication of the presence and / or the type of the plaque.

9. The intravascular tissue analysis system according to claim 8, wherein the machinelearning algorithm comprises a neural network, and wherein the neural network is trained to generate thepredicted indication of the presence and / or the type of the blood clot, and the predicted indication of thepresence and / or the type of the plaque from the optical data (120), by:for each of a plurality of sets of the optical training data:inputting the set of optical training data into the neural network;generating a predicted indication of the presence and / or the type of the blood clot, and apredicted indication of the presence and / or the type of the plaque, in response to the inputting, using theneural network; andadjusting parameters of the neural network based on a difference between the predictedindication of the presence and / or the type of the blood clot, and the predicted indication of the presenceand / or the type of the plaque, generated by the neural network, and the corresponding indication of thepresence and / or the type of the blood clot, and indication of the presence and / or the type of the plaque,from the ground truth data; andrepeating the inputting, the generating, and the adjusting, until a stopping criterion is met.

10. The intravascular tissue analysis system according to any one of claims 7 – 9, wherein thetype of the blood clot comprises one or more of: a red blood cell-poor blood clot, a red blood cell-rich blood clot, and a mixed clot.

11. The intravascular tissue analysis system according to any previous claim, wherein the oneor more processors (110) are further configured to:2023PF00657 27 analyze the optical data (120) to determine a contribution to the optical property causedby a vessel wall and / or blood (270) in the optical data (120); and output an indication of the detection of the vessel wall and / or blood, the indication being determined based on the corresponding contribution.

12. The intravascular tissue analysis system according to any previous claim, furthercomprising: at least one optical source (170);an optical probe (180) for insertion into the vasculature; andat least one optical detector (190);wherein the at least one optical source (170) is in optical communication with the opticalprobe (180), and the optical probe (180) is configured to irradiate the intravascular tissue region (130)with optical radiation (210) generated by the at least one optical source (170), and to collect opticalradiation returned by the intravascular tissue region (130) in response to the irradiation;wherein the at least one optical detector (190) is in optical communication with theoptical probe (180), and the at least one optical detector (190) is configured measure an intensity of thecollected optical radiation over the one or more wavelength intervals to provide the optical data (120).

13. A computer-implemented method of performing intravascular tissue analysis, the methodcomprising: receiving (S110) optical data (120) representing an optical property of an intravascular tissue region (130) over one or more wavelength intervals; analyzing (S120) the optical data (120) to determine i) a blood clot component content(140) of the intravascular tissue region (130), and ii) a lipid content (150) of the intravascular tissue region (130); and outputting (S130) an indication of the blood clot component content (140) and the lipid content (150), and / or an indication of one or more tissue composition parameters derived therefrom; wherein the blood clot component comprises red blood cells and / or fibrin and / or platelets,wherein the blood clot component content (140) is determined based on a detection of a signature in theoptical property that is characteristic of red blood cells and / or fibrin and / or platelets, respectively, and wherein the lipid content (150) is determined based on a contribution to the optical property caused by lipids.

14. A computer program product comprising instructions which when executed by one ormore processors (110), cause the one or more processors to carry out the method according to claim 13.

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