Determining vascular disease status

Abstract

A computer-implemented method for determining a vascular disease state is provided. The method includes determining, from CT data, a distribution of X-ray attenuation values ​​along a path through one or more concentric layers of perivascular adipose tissue (PVAT) surrounding a blood vessel at each of a plurality of locations spanning the one or more concentric layers 1301...n, and analyzing the distribution of X-ray attenuation values ​​to provide a spatial distribution of disease state values ​​140 representing the vascular disease state around the portion of the blood vessel. A graphical representation 150a, 150b of the spatial distribution of the disease state values ​​140 is output. Alternatively or additionally, a predicted time at which the vascular disease state around the portion of the blood vessel is expected to reach a predetermined state is output. The predicted time is predicted based on the spatial distribution of the disease state values ​​140.

Description

[Technical Field]

[0001] FIELD OF THE DISCLOSURE A computer-implemented method, computer program product, and system are disclosed that relate to determining vascular disease status. [Background technology]

[0002] Vascular disease refers to a variety of conditions that affect the circulatory system, or in other words, the blood vessels that circulate blood throughout the body. Some types of vascular disease affect the arteries, while others affect the veins. Vascular disease can affect various parts of the body, including the heart and peripheral areas such as the legs. For example, coronary artery disease "CAD" refers to disease in the coronary arteries and their impaired ability to transport oxygenated blood to the heart muscle. In the case of CAD, the lack of oxygen supply ultimately leads to myocardial ischemia, the symptoms of which may include shortness of breath, angina, or even myocardial infarction.

[0003] Various imaging-based biomarkers have been developed for use in predicting risk for vascular disease. One such biomarker, directed to the detection of inflammation in perivascular tissue (PVAT), is disclosed in WO 2016 / 024128 A1. This document defines a method for the volumetric characterization of perivascular adipose tissue using data collected by computed tomography (CT) scans. The volumetric characterization of perivascular adipose tissue allows the inflammatory state of the blood vessel in question to be established on the CT scan. This aids in the diagnosis, prognosis, and treatment of coronary artery disease and vascular disease.

[0004] Another article by Antoniades C. et al., "State-of-the-art review article, Atherosclerosis affecting fat: What can we learn by imaging perivascular adipose tissue?" J.Cardiovasc.Comput.Tomogr. 2019 Sep-Oct;13(5):288-296, describes a biomarker derived from CT attenuation in PVAT surrounding human coronary arteries. This article discloses a biomarker, the fat attenuation index (FAI), designed to capture spatial variations in PVAT attenuation around human coronary arteries. The FAI has predictive value in stable patients for cardiac mortality and nonfatal heart attack. Summary of the Invention [Problem to be solved by the invention]

[0005] PVAT surrounds the coronary arteries and is found in the adventitia layer or continuous with it, i.e., in the outer layer of the media. The extent of PVAT extending radially outward relative to the vessel centerline is approximately equal to the diameter of the artery. PVAT is identifiable in CT images via increased levels of x-ray attenuation just outside the vessel wall.

[0006] As described in the above-cited publication by Antoniades C. et al., in the absence of vascular inflammation, the X-ray attenuation of PVAT decreases with increasing radial distance from the vessel wall. In contrast, in the presence of vascular inflammation, relatively high X-ray attenuation values ​​are observed in PVAT. This results in relatively high X-ray attenuation values ​​near the vessel wall, followed by a relatively steep gradient with increasing radial distance from the vessel wall. This publication describes the evaluation of the fat attenuation index (FAI), a biomarker of vessel segments. The FAI quantifies the weighted degree of attenuation in concentric 1-millimeter layers of perivascular tissue around the human arterial wall and captures the distinct perivascular attenuation gradient that reflects changes in PVAT biology that occur as a result of vascular inflammation. The FAI has predictive value in stable patients for cardiac mortality and nonfatal heart attacks.

[0007] The above-cited document WO2016 / 024128A1 discloses another biomarker for CAD, called the volumetric perivascular characteristic index (VPCI-i), for predicting the presence of CAD. The biomarker VPCI-i is calculated for a cross-section of a vessel from a plot of the radiodensity change of perivascular adipose tissue surrounding the right coronary artery versus the distance from the vessel outer wall. The area under the curve method is used to determine the value of VPCI-i in the vessel cross-section and confirm the presence or absence of CAD.

[0008] Document US 2022 / 401050 A1 discloses a method for characterizing coronary plaque tissue data and perivascular tissue data using image data collected from a computed tomography scan along a blood vessel, the image information including radiodensity values ​​of coronary plaque and perivascular tissue adjacent to the coronary plaque, the method comprising the steps of quantifying the radiointensity of a coronary plaque region, quantifying the radiointensity of at least one region of corresponding perivascular tissue adjacent to the coronary plaque, determining a gradient of the quantified radiointensity values ​​in the coronary plaque and the corresponding perivascular tissue, determining a ratio of the quantified radiodensity values ​​in the coronary plaque and the corresponding perivascular tissue, and characterizing the coronary plaque by analyzing the gradient of the quantified radiodensity values ​​in the coronary plaque and the corresponding perivascular area and / or the ratio of the radiodensity value of the coronary plaque to the corresponding perivascular tissue.

[0009] However, there remains a need for improved methods to assess vascular disease status from measurements of X-ray attenuation in PVAT. [Means for solving the problem]

[0010] According to one aspect of the present disclosure, there is provided a computer-implemented method for determining vascular disease status, the method comprising: receiving computed tomography CT data representing a portion of a blood vessel; determining from the CT data a distribution of x-ray attenuation values ​​along a path through one or more concentric layers of perivascular adipose tissue (PVAT) surrounding a portion of a blood vessel at each of a plurality of locations spanning the one or more concentric layers; analyzing the distribution of x-ray attenuation values ​​along a path through one or more concentric layers to provide a spatial distribution of disease state values ​​representative of a vascular disease state around the portion of the blood vessel; and outputting a graphical representation of the spatial distribution of the disease state values ​​and / or a predicted time at which the vascular disease state around the portion of the blood vessel is expected to reach a predetermined state, the predicted time being predicted based on the spatial distribution of the disease state values.

[0011] In the above method, providing a spatial distribution of disease state values ​​representing the state of vascular disease around a portion of a blood vessel provides detailed information about the state of the disease, as opposed to providing a single value of a biomarker for the portion of a blood vessel. The additional information provided by the spatial distribution of disease state values ​​can be used, for example, to track disease changes at specific locations, thereby facilitating improved decision-making regarding treatment or monitoring of the blood vessel. Similarly, an output predicted time at which the state of vascular disease around a portion of a blood vessel is expected to reach a predetermined state can be predicted based on the spatial distribution of disease state values, thereby providing a more reliable predicted time.

[0012] Further aspects, features and advantages of the present disclosure will become apparent from the following description of examples which proceeds with reference to the accompanying drawings. [Brief explanation of the drawings]

[0013] [Figure 1] 1 is a schematic diagram illustrating an example of a portion of a blood vessel 120 including perivascular adipose tissue (PVAT) surrounding the vessel, according to some embodiments of the present disclosure. [Figure 2] 1 is a flowchart illustrating an example of a computer-implemented method for determining vascular disease status, according to some aspects of the present disclosure. [Figure 3] 2 is a schematic diagram illustrating an example of a system 200 for determining a vascular disease state, according to some embodiments of the present disclosure. FIG. [Figure 4] 1 is a schematic diagram illustrating an example of a portion of a blood vessel 120 including one or more concentric layers 1301..n of perivascular adipose tissue PVAT surrounding the vessel, according to some embodiments of the present disclosure. [Figure 5]13 is a schematic diagram illustrating an example of a technique for determining a distribution S120 of x-ray attenuation values ​​along a path through one or more concentric layers 1301 ..n of PVAT, according to some aspects of the present disclosure. [Figure 6] 1A-1C illustrate a) an example graph of X-ray attenuation values ​​along a path extending in a radial direction R from a vascular centerline CL, and b) an example normalized graph of the X-ray attenuation values ​​shown in a), in accordance with some aspects of the present disclosure. [Figure 7] 1 is a schematic diagram illustrating a first example 150a of a graphical representation of a spatial distribution of disease state values ​​140, according to some embodiments of the present disclosure. [Figure 8] FIG. 10 is a schematic diagram illustrating a second example 150b of a graphical representation of a spatial distribution of disease state values ​​140, according to some embodiments of the present disclosure. [Figure 9] 1 is a schematic diagram illustrating an example of a graphical representation of a co-registered spatial distribution of disease state values ​​140 representing the state of vascular disease around a portion of a blood vessel 120 at two different time points t0, t1. [Figure 10] 1 is a schematic diagram illustrating an example of a graphical representation of a spatial distribution of disease state values ​​representing changes in the state of vascular disease around a portion of a blood vessel 120 between two time points t0 and t1, according to some embodiments of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0014] Examples of the present disclosure are provided with reference to the following description and figures. For purposes of explanation, several specific details of particular examples are described herein. Reference herein to an "example," "implementation," or similar expressions means that a feature, structure, or characteristic described in connection with that example is included in at least that example. It should be understood that features described in connection with one example may also be used in other examples, and that for purposes of brevity, not all features are necessarily duplicated in each example. For example, features described in connection with a computer-implemented method may be correspondingly implemented in a computer program product and a system.

[0015] In the following description, reference is made to a computer-implemented method for determining the status of vascular disease. In some instances, reference is made to vascular disease in the form of coronary artery disease ("CAD"). However, it should be understood that, in general, the method is not limited to this type of vascular disease. The method can also be used to determine the status of vascular disease in other parts of the body besides the heart. The method can also be used to determine the status of vascular disease in other types of blood vessels besides arteries. In general, the method can be used to determine the status of vascular disease in any anatomical region, and the blood vessels can be any type of blood vessel. Thus, the blood vessels can be arteries or veins, and the arteries or veins can be located anywhere in the body, such as the heart, brain, or peripheral regions such as the arms and legs.

[0016] It should be noted that the computer-implemented methods disclosed herein may be provided as a non-transitory computer-readable storage medium including computer-readable instructions stored thereon that, when executed by at least one processor, cause the at least one processor to perform the method. In other words, the computer-implemented methods may be embodied in a computer program product. The computer program product may be provided by dedicated hardware or hardware capable of executing software in association with appropriate software. When provided by a processor, the functionality of the method features may be provided by a single dedicated processor, by a single shared processor, or by multiple individual processors, some of which may be shared. The functionality of one or more method features may be provided by a processor shared within a network processing architecture, such as a client / server architecture, a peer-to-peer architecture, the Internet, or the cloud.

[0017] Explicit use of the terms "processor" or "controller" should not be construed to refer solely to hardware capable of executing software, but can implicitly include, but is not limited to, digital signal processor "DSP" hardware, read-only memory "ROM" for storing software, random access memory "RAM," non-volatile storage, etc. Furthermore, examples of the present disclosure may take the form of a computer-usable storage medium or computer program product accessible from a computer-readable storage medium, the computer program product providing program code for use by or in connection with a computer or any instruction execution system. For purposes herein, a computer-usable storage medium or computer-readable storage medium may be any apparatus that can contain, store, communicate, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The medium may be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or propagation medium. Examples of computer-readable media include semiconductor or solid state memory, 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. Thus, there remains a need for improved methods for assessing vascular disease state from measurements of x-ray attenuation in PVAT.

[0018] 1 is a schematic diagram illustrating an example of a portion of a blood vessel 120 including perivascular adipose tissue (PVAT) surrounding the vessel, according to some embodiments of the present disclosure. The blood vessel 120 shown in FIG. 1 may represent, for example, a coronary artery. PVAT surrounds blood vessels such as coronary arteries and is found in or continuous with the adventitia layer, i.e., in the outer layer of the tunica media, as shown in FIG. 1.

[0019] As mentioned above, WO2016 / 024128A1 specifies a method for volumetric characterization of perivascular adipose tissue using data collected by computed tomography scans. Volumetric characterization of perivascular adipose tissue allows the inflammatory state of the blood vessel in question to be established by CT scan. This is useful for the diagnosis, prognosis, and treatment of coronary artery disease and vascular disease. The aforementioned publication by Antoniades C. et al. describes another biomarker derived from CT attenuation in PVAT around human coronary arteries. This publication discloses a biomarker, the Fat Attenuation Index (FAI), designed to capture spatial variations in attenuation in PVAT around human coronary arteries. The FAI has predictive value in stable patients with regard to cardiac mortality and non-fatal heart attacks.

[0020] FIG. 2 is a flowchart illustrating an example of a computer-implemented method for determining a state of vascular disease according to some aspects of the present disclosure. FIG. 3 is a schematic diagram illustrating an example of a system 200 for determining a state of vascular disease according to some aspects of the present disclosure. The system 200 includes one or more processors 210. It should be noted that the operations described in connection with the method described with reference to FIG. 2 may also be performed by one or more processors 210 of the system 200 shown in FIG. 3. Similarly, the operations described in connection with one or more processors 210 of the system 200 may also be performed in the manner described with reference to FIG. 2. Referring to FIG. 2, the computer-implemented method for determining a state of vascular disease includes: a step S110 of receiving computed tomography CT data 110 representing a portion of a blood vessel 120; a step S120 of determining from the CT data 110 a distribution of x-ray attenuation values ​​along a path through one or more concentric layers 1301...n of perivascular adipose tissue PVAT surrounding a portion of a blood vessel 120 at each of a plurality of locations spanning the one or more concentric layers 1301...n; a step S130 of analyzing the distribution of x-ray attenuation values ​​along a path through one or more concentric layers 1301 ..n to provide a spatial distribution of disease state values ​​140 representative of the state of vascular disease around a portion of the blood vessel 120; The method includes a step S140 of outputting a graphical representation 150a, 150b of the spatial distribution of the disease state values ​​140, and / or a step S150 of outputting a predicted time at which the state of the vascular disease around the portion of the blood vessel 120 is expected to reach a predetermined state, the predicted time being predicted based on the spatial distribution of the disease state values ​​140.

[0021] In the above method, providing a spatial distribution of disease state values ​​representing the state of vascular disease around a portion of a blood vessel provides detailed information about the state of the disease, as opposed to providing a single value of a biomarker for the portion of a blood vessel. The additional information provided by the spatial distribution of disease state values ​​can be used, for example, to track disease changes at specific locations, thereby facilitating improved decision-making regarding treatment or monitoring of the blood vessel. Similarly, an output predicted time at which the state of vascular disease around a portion of a blood vessel is expected to reach a predetermined state can be predicted based on the spatial distribution of disease state values, thereby providing a more reliable predicted time.

[0022] Referring to the flowchart shown in FIG. 2, in act S110, CT data 110 representing a portion of a blood vessel 120 is received.

[0023] The CT data 110 received in act S110 may be generated following the injection of a contrast agent into the vascular system. The contrast agent may include iodine, a lanthanide such as gadolinium, or another substance that visualizes the blood flow into which the contrast agent has been injected. The CT data 110 may generally represent a still image of the blood vessel 120, or alternatively, it may represent a temporal sequence of images of the blood vessel 120. In the latter case, the temporal sequence of images may be generated in substantially real time, and the above-described method may be performed in substantially real time. Consequently, act S120 of determining, act S130 of analyzing, and act S140 of outputting a graphical representation of the spatial distribution of the disease state value 140 may be performed in substantially real time. Similarly, act S150 of outputting a predicted time at which the state of the vascular disease around the portion of the blood vessel 120 is expected to reach a predetermined state may also be performed in real time.

[0024] In general, the CT data 110 received in act S110 can be raw data, i.e., data that has not yet been reconstructed into a volumetric or 3D image, or it can be image data, i.e., data that has already been reconstructed into a volumetric image. CT data is sometimes referred to as volumetric data. The CT data 110 can be generated by a CT imaging system, or, as described further below, it can be generated by rotating or stepping an X-ray source and X-ray detector of an X-ray projection imaging system around a blood vessel.

[0025] A CT imaging system generates CT data by rotating or stepping an X-ray source and detector arrangement around an object to obtain X-ray attenuation data of the object from multiple rotational angles relative to the object. The CT data is then reconstructed into a 3D image of the object. Examples of CT imaging systems that may be used to generate the CT data 110 include a cone-beam CT imaging system, a photon-counting CT imaging system, a dark-field CT imaging system, and a phase-contrast CT imaging system. An example of a CT imaging system 220 that may be used to generate the CT data 110 received in operation S110 is shown in FIG. 3. As an example, the CT data 110 may be generated by a CT 5000 Ingenuity CT scanner sold by Philips Healthcare of Vest, The Netherlands.

[0026] As described above, the CT data 110 received in operation S110 may alternatively be generated by rotating or stepping the X-ray source and X-ray detector of an X-ray projection imaging system around the blood vessel 120. The X-ray projection imaging system may include a support arm, such as a so-called "C-arm," that supports the X-ray source and X-ray detector. The X-ray projection imaging system may alternatively include a support arm of a different shape than this example, such as an O-arm. Other types of X-ray projection imaging systems may alternatively be used, in which the X-ray source and X-ray detector are mounted or supported in a different manner. In contrast to CT imaging systems, X-ray projection imaging systems generate X-ray attenuation data about an object with the X-ray source and X-ray detector in a stationary position relative to the object. The X-ray attenuation data is sometimes referred to as projection data, in contrast to the volumetric data generated by a CT imaging system. The X-ray attenuation data generated by an X-ray projection imaging system is typically used to generate a 2D image of the object. However, an X-ray projection imaging system can generate CT data, i.e., volumetric data, by rotating or stepping an X-ray source and X-ray detector around an object and acquiring projection data of the object from multiple rotational angles relative to the object. Image reconstruction techniques can then be used to reconstruct the projection data obtained from the multiple rotational angles into a volumetric image in a manner similar to reconstructing a volumetric image using X-ray attenuation data acquired from a CT imaging system. Thus, the CT data 110 received in operation S110 may be generated by a CT imaging system, or alternatively, it may be generated by an X-ray projection imaging system. One example of an X-ray projection imaging system that can be used to generate the CT data 110 is the Azurion 7 X-ray projection imaging system sold by Philips Healthcare of Vest, The Netherlands.

[0027] In some examples described in more detail below, the CT data 110 received in act S110 includes spectral CT data. The spectral CT data defines the X-ray attenuation of the object in each of a plurality of different energy intervals DE1...m. Generally, there are two or more energy intervals. That is, m is an integer, and m>2. In this regard, the spectral CT data 110 received in act S110 may be generated by a spectral CT imaging system or a spectral X-ray projection imaging system. In the latter case, the spectral CT data can be acquired by rotating or stepping the X-ray source and X-ray detector of the spectral X-ray projection imaging system around the blood vessel, as described above. More generally, the spectral CT data 110 received in act S110 may be generated by a spectral X-ray imaging system.

[0028] The ability to generate X-ray attenuation data at multiple different energy intervals DE1...m distinguishes spectral X-ray imaging systems from conventional X-ray imaging systems. By processing data from multiple different energy intervals, it is possible to distinguish between media that have similar X-ray attenuation values ​​when measured at a single energy interval, and that would be indistinguishable using conventional X-ray attenuation data. Examples of spectral X-ray imaging systems that can be used to generate the spectral CT data 110 received in act S110 include cone-beam spectral X-ray imaging systems, photon-counting spectral X-ray imaging systems, dark-field spectral X-ray imaging systems, and phase-contrast spectral X-ray imaging systems. One example of a spectral CT imaging system that can be used to generate the spectral CT data 110 received in act S110 is the Spectral CT 7500 sold by Philips Healthcare of Vest, The Netherlands.

[0029] In general, the spectral CT data 110 can be generated by a variety of different configurations of spectral X-ray imaging systems, including X-ray sources and X-ray detectors. The X-ray source of the spectral X-ray imaging system can include multiple monochromatic light sources or one or more polychromatic light sources, and the X-ray detector of the spectral X-ray imaging system can include a common detector for detecting multiple different X-ray energy intervals, multiple detectors where each detector detects a different X-ray energy interval DE1...m, a multi-layer detector where X-rays with energies within different X-ray energy intervals are detected by corresponding layers, or a photon-count detector that bins detected X-ray photons into one of multiple energy intervals based on their individual energies. In the photon-count detector, the associated energy interval can be determined for each received X-ray photon by detecting the height of a pulse induced by an electron-hole pair generated upon absorption of the X-ray photon in a direct conversion material.

[0030] Various configurations of the aforementioned X-ray source and detector can be used to detect X-rays within different X-ray energy intervals DE1..m. Generally, discrimination between different X-ray energy intervals can be provided at the X-ray source by temporally switching the X-ray tube potential of a single X-ray source, i.e., "fast kVp switching," or by temporally switching or filtering X-ray emissions from multiple X-ray sources. In such configurations, a common X-ray detector can be used to detect X-rays across multiple different energy intervals. X-ray attenuation data for each energy interval is generated in a time series. Alternatively, discrimination between different X-ray energy intervals can be provided at the detector using a multi-layer detector or a photon-count detector. Such a detector can detect X-rays from multiple X-ray energy intervals DE1..m nearly simultaneously and thus eliminate the need for temporal switching at the source. Thus, a multi-layer detector or a photon-count detector can be used in combination with a polychromatic light source to generate X-ray attenuation data in different X-ray energy intervals DE1..m.

[0031] Other combinations of the aforementioned X-ray sources and detectors can also be used to provide the spectral CT data 110. For example, in yet another configuration, the need to sequentially switch between different X-ray sources emitting X-rays at different energy intervals can be avoided by mounting X-ray source-detector pairs on the gantry at positions that are rotationally offset about the axis of rotation. In this configuration, each source-detector pair operates independently, and separation between the spectral CT data of different energy intervals DE1...m is facilitated by the rotational offset of the source-detector pairs. In this configuration, improved separation between the spectral CT data of different energy intervals DE1...m can be achieved by applying an energy-selective filter to the X-ray detector to reduce the effects of X-ray scatter.

[0032] In general, the CT data 110 received in act S110 can be received via any form of data communication, including wired communication, optical communication, and wireless communication. To give some examples, if wired or optical communication is used, the communication can be via signals transmitted over electrical or optical cables, and if wireless communication is used, the communication can be via, for example, RF or optical signals. The CT data 110 received in act S110 can be received from a variety of sources. For example, the CT data 110 can be received from an imaging system, such as one of the imaging systems described above. Alternatively, the CT data 110 can be received from another source, such as, for example, a computer-readable storage medium, the internet, or the cloud.

[0033] The CT data 110 received in act S110 represents a portion of a blood vessel 120. As an example, the CT data 110 may represent a portion of an artery, such as a coronary artery. In this example, the CT data may be used to determine the coronary artery disease status of the artery. The CT data 110 may alternatively represent a portion of another type of blood vessel. For example, the CT data 110 may alternatively represent a portion of a vein. In general, veins or arteries may be located anywhere in the body, such as the heart, the brain, or peripheral regions such as the arms, legs, etc.

[0034] Returning to the flowchart shown in Figure 2, in operation S120, a distribution of x-ray attenuation values ​​along a path through one or more concentric layers 1301...n of perivascular adipose tissue PVAT surrounding a portion of blood vessel 120 is determined at each of a plurality of locations spanning the one or more concentric layers 1301...n. An example of operation S120 will now be described with reference to Figures 4 and 5.

[0035] FIG. 4 is a schematic diagram illustrating an example of a portion of a blood vessel 120 including one or more concentric layers 1301...n of perivascular adipose tissue (PVAT) surrounding the vessel, in accordance with some embodiments of the present disclosure. The blood vessel 120 illustrated in FIG. 4 corresponds to the blood vessel 120 illustrated in FIG. 1, and in addition to the items illustrated in FIG. 1, FIG. 4 illustrates multiple concentric layers 1301...n of PVAT. In this example, the PVAT is disposed in the adventitial layer. In the illustrated example, the concentric layers 1301...n are defined relative to a common center, which in this example is defined as the vessel centerline CL. The concentric layers 1301...n are represented as surfaces formed around the vessel centerline CL. Each surface is bounded axially along the vessel centerline CL at positions 0 and L. These positions define the length of the portion of the vessel. Each surface is bounded by a rotational angle range [0, 2π] in a rotational direction Φ about the vessel centerline CL. The concentric layers 1301..n are bounded in the radial direction R by a radially inner dimension defined by the radially inner position of the PVAT for a given rotation angle Φ about the vascular centerline CL, and a radially outer dimension defined by the radially outer position of the PVAT for a given rotation angle Φ about the vascular centerline CL. Generally, there may be one or more concentric layers. In some instances, there may be two or more concentric layers. When two or more concentric layers are present, the layers may have equal thicknesses. In some instances, there may be 10, 20, or more layers. For example, when there are about 20 layers, each layer may have a thickness R of approximately 1 millimeter in the radial direction R. In contrast, when only a few layers are provided, each layer may have a thickness of several millimeters. These values ​​are provided purely by way of example.

[0036] Continuing with reference to FIG. 4 , in operation S120, a distribution of X-ray attenuation values ​​along a path through one or more concentric layers 1301...n of PVAT is determined at each of a plurality of locations spanning the one or more concentric layers 1301...n. This operation includes determining, at each of a plurality of locations spanning each layer 1301...n, the X-ray attenuation along a path through each layer, i.e., at each of a plurality of locations spanning each of the above-defined surfaces within the range [0,L] along the vascular centerline CL and within the rotational angle range [0,2π] in the rotational direction Φ. An example of this operation is described with reference to FIG. 5 , which is a schematic diagram illustrating an example of a technique for step S120 for determining the distribution of X-ray attenuation values ​​along a path through one or more concentric layers 1301...n of PVAT according to some aspects of the present disclosure.

[0037] Referring to Figure 5, the distribution of x-ray attenuation values ​​along a path through one or more concentric layers 1301...n of PVAT can be determined by generating virtual image slices from the CT data, with the slices positioned transversely to the vascular centerline CL. An example of such an image slice is shown at the top of Figure 5. In this example, the slices are positioned perpendicular to the vascular centerline CL. The virtual slices can be positioned at regular intervals along the vascular centerline CL, for example, approximately 1 mm apart.

[0038] Next, the concentric layers 1301...n described above with reference to Figure 4 are defined using the image slices. This operation can be performed on image slices such as those displayed at the top of Figure 5, or alternatively, the CT data for each image slice can be transformed to provide an unfolded representation of the vessel. In this case, the concentric layers are defined in the unfolded representation of the vessel.

[0039] In the former case, for each image slice, the inner and outer boundaries of the PVAT within the slice are first defined by performing image segmentation at the slice. As previously described, PVAT can be identified in CT images via the elevated level of X-ray attenuation just outside the vessel wall. An example of the resulting segmented image is shown via the dotted lines at the top of Figure 5. To identify the CT data from each slice for each of the concentric layers 1301...n, concentric boundaries are defined at regular intervals in the radial direction R between the inner and outer boundaries of the PVAT in each image slice. For a given slice, the CT data for each layer is bounded in the radial direction by the slice's inner and outer concentric boundaries and in the rotational direction Φ by the rotational angle range [0, 2π]. The CT data for each layer is obtained by concatenating the data for that layer from each slice in the direction along the vessel centerline CL. This provides a distribution of X-ray attenuation values ​​along a path through each layer 1301...n for a portion of the vessel. From here, the method may continue to operation S130, described below.

[0040] The latter case is shown in the center and bottom of FIG. 5. In this case, image segmentation is performed on the image slices, as described above. This defines the inner and outer boundaries of the PVAT in each image slice. The CT data for each image slice is then transformed to provide an unfolded representation of the blood vessel 120. This operation is shown in the center portion of FIG. 5. This operation can be performed by applying a coordinate transformation to the CT data, in which data from angular positions around the rotation direction Φ is mapped to linear axes. This maps the image slice illustrated in the top portion of FIG. 5 to the image slice illustrated in the center portion of FIG. 5. Data from each image slice along the vessel centerline CL in the range [0, L] is concatenated along the direction of the vessel centerline to provide an unfolded volumetric representation of the blood vessel. Starting from the inner surface of the PVAT, multiple layers 1301, each with a defined thickness, are formed in the PVAT. This provides a distribution of X-ray attenuation values ​​along a path through each layer 1301...n for the portion of the blood vessel. From here, the method can continue to operation S130, described below.

[0041] Due to the irregular shape of the blood vessel and the irregular distribution of the PVAT, the concatenated data obtained from the image slice shown in the center portion of FIG. 5 may have a non-planar surface. This can complicate subsequent analysis of X-ray attenuation values ​​along paths through this layer. To avoid such complications, the inner surface of the PVAT, defined by the inner boundary of the PVAT in the slice, may be flattened. This operation can be performed by further transforming the concatenated data so that locations on the inner surface of the PVAT are mapped to a plane. This reshapes the PVAT, resulting in its inner surface becoming planar, as shown in the lower portion of FIG. 5. Next, starting from the inner surface of the PVAT, multiple layers 1301...n are defined in the PVAT, as described above, each with a defined thickness. This provides CT data in which one or more concentric layers 1301...n of PVAT surrounding a portion of the blood vessel 120 are represented as planar layers. From here, the method can continue to operation S130, described below.

[0042] Returning to the flowchart shown in FIG. 2 , in operation S130, the distribution of X-ray attenuation values ​​along a path through one or more concentric layers 1301...n is analyzed to provide a spatial distribution of disease state values ​​140 representative of the state of vascular disease around the portion of blood vessel 120. Generally, this operation is performed based on the gradient of X-ray attenuation values ​​along a path through one or more concentric layers 1301...n. An example of operation S130 is described with reference to FIG. 6 , which shows a) an example graph of X-ray attenuation values ​​along a path extending in a radial direction R from a blood vessel centerline CL, and b) an example normalized graph of the X-ray attenuation values ​​shown in a), in accordance with some embodiments of the present disclosure. The graph shown in FIG. 6 a illustrates an example of the change in X-ray attenuation values ​​in the radial direction R shown in FIG. 3 for a blood vessel in which CAD is detected (dashed line) and a blood vessel in which CAD is not detected (solid line).

[0043] As described in the above-cited article by Antoniades C. et al., when there is no inflammation in the blood vessel, the X-ray attenuation of PVAT decreases with increasing radial distance from the blood vessel wall. This situation is represented by the solid curve "No CAD" shown in Figure 6a. In contrast, when there is inflammation in the blood vessel, relatively high X-ray attenuation values ​​are observed in PVAT. This results in relatively high X-ray attenuation values ​​near the blood vessel wall, which continues to have a relatively steep slope with increasing radial distance from the blood vessel wall. This situation is represented by the dashed curve "CAD" shown in Figure 6a.

[0044] The radial direction R shown in Figure 6a passes approximately normally through one or more of the concentric layers 1301...n described with reference to Figure 5. The locations of these layers 1301...n are also shown in Figure 6a. Thus, the x-ray attenuation values ​​shown in Figure 6a represent the x-ray attenuation in each layer along a path extending in the radial direction R from the vessel centerline through the concentric layers 1301...n. As will be appreciated, graphs similar to those shown in Figure 6a can be provided for different rotational directions Φ and different positions along the vessel centerline CL. The shapes of these curves will vary based on the spatial distribution of CAD within the vessel.

[0045] The normalized graph of X-ray attenuation values ​​shown in Figure 6b is obtained by normalizing the X-ray attenuation values ​​shown in Figure 6a to the X-ray attenuation value of the innermost layer of the concentric layers 1301...n. As a result, the normalized X-ray attenuation values ​​at the vessel wall in Figure 6b correspond to unity values.

[0046] In operation S130, a spatial distribution of disease state values ​​140 representative of the state of vascular disease around a portion of blood vessel 120 is determined. This operation can be performed by determining the spatial distribution of values ​​of various biomarkers. Examples of biomarkers that may be evaluated in this operation include the Fat Attenuation Index (FAI) biomarker described in the above-cited publication by Antoniades C. et al., and the Volumetric Perivascular Characterization Index (VPCI-i) biomarker described in publication WO 2016 / 024128 A1. The spatial distribution of values ​​of other biomarkers can alternatively be determined in this operation.

[0047] By way of example, the spatial distribution of the Volumetric Perivascular Characteristic Index "VPCI-i" biomarker described in document WO2016 / 024128A1 will now be described with reference to Figure 6. In this example, the Determine S120 and Analyze S130 operations described above with reference to Figure 2 are performed on a number of concentric layers 1301...n of PVAT. In this example, the Analyze S130 operation: normalizing a distribution of X-ray attenuation values ​​along a path through the one or more concentric layers 1301 ..n of PVAT surrounding a portion of the blood vessel 120 based on X-ray attenuation values ​​along the path in the innermost layer of the one or more concentric layers 1301 ..n; integrating the normalized x-ray attenuation values ​​along a path extending radially outward from the centerline of the blood vessel 120 through the plurality of concentric layers 1301 ..n of PVAT; comparing the integrated normalized x-ray attenuation value with a threshold to provide a spatial distribution of disease state values ​​140 representative of the state of vascular disease around the portion of the blood vessel 120; The integrating step assigns a negative value to normalized x-ray attenuation values ​​that exceed a normalized x-ray attenuation value at the innermost layer along the corresponding path; Assigning a positive value to normalized x-ray attenuation values ​​that are less than or equal to the normalized x-ray attenuation value at the innermost layer along the corresponding path.

[0048] The above operations can be performed using CT data representing the vessels as they appear in the upper part of Figure 5, i.e., in their actual shape, or alternatively, they can be performed using CT data representing the vessels in an unfolded and optionally further flattened state, i.e., as they appear in the middle or lower part of Figure 5.

[0049] In the above operations, providing the spatial distribution of the disease state value 140 includes integrating normalized X-ray attenuation values ​​along paths extending radially outward from the centerline of the blood vessel 120 through multiple concentric layers 1301...n of PVAT. Referring to the top of FIG. 5 , paths can be defined at predetermined intervals in the rotational direction Φ and at predetermined positions along the vessel centerline CL. For example, paths can be defined at regular intervals in the rotational direction Φ or at regular intervals along the vessel centerline CL. When CT data representing a vessel such as that shown in the top of FIG. 5 is used, normalized X-ray attenuation values ​​can be obtained by normalizing the X-ray attenuation values ​​along each path relative to the X-ray attenuation value of the innermost layer, as described above with reference to FIGS. 6a and 6b. The integration operation corresponds to performing an integration of the values ​​along each path using the normalized X-ray attenuation values. When CT data representing a vessel such as that shown in the bottom of FIG. 5 is used, paths in this unfolded representation of the vessel extend normal through the layers 1301...n. In this case, the normalized X-ray attenuation values ​​are again obtained by normalizing the X-ray attenuation values ​​along the path with respect to the X-ray attenuation value of the innermost layer, as described above with reference to Figure 6b. This corresponds to normalizing the X-ray attenuation values ​​along the thickness direction z of the concentric layers 1301...n with respect to the X-ray attenuation value at the corresponding position of the innermost layer. The integral operation corresponds to performing an integral of the values ​​along the path, i.e., the values ​​along the thickness direction z of the layers, using the normalized X-ray attenuation values.

[0050] The integration operation involves assigning a negative value to normalized X-ray attenuation values ​​that exceed the normalized X-ray attenuation value of the innermost layer along the corresponding path, and assigning a positive value to normalized X-ray attenuation values ​​that are less than or equal to the normalized X-ray attenuation value of the innermost layer along the corresponding path. Referring to Figure 6b), this results in X-ray attenuation values ​​that are less than or equal to unity being added to the integral value, and X-ray attenuation values ​​that are greater than unity being subtracted from the integral value. Therefore, the integral value of each path represents the difference between the hatched area above the “normalized X-ray attenuation=1” line and the dotted area in Figure 6b.

[0051] The result of the above integration provides a spatial distribution of integrated normalized x-ray attenuation values, where each integral value represents an integral along a different path. In a comparison operation, the integral value of each path is compared to a threshold value to provide a disease state value for that path. The disease state value of a path may be an analog value or may be a digital value, such as CAD or no CAD. The threshold value may be determined, for example, from clinical studies that separate vessels with CAD from those without CAD. Thus, performing a comparison operation for each of the paths provides a spatial distribution of disease state values ​​140 representing the state of vascular disease around the portion of blood vessel 120.

[0052] Returning to the flowchart shown in FIG. 2 , in act S140, a graphical representation 150a, 150b of the spatial distribution of the disease state values ​​140 is then output. The graphical representation 150a, 150b may be output, for example, on a display such as the display 230 shown in FIG. 3 . Examples of such graphical representations 150a, 150b are described below with reference to FIGS. 7-10 . Alternatively or additionally, in act S150, a predicted time at which the vascular disease state around the portion of the blood vessel 120 is expected to reach a predetermined state is output. The predicted time is predicted based on the spatial distribution of the disease state values ​​140.

[0053] In one example, a graphical representation of the spatial distribution of disease state values ​​140 representing the state of vascular disease around a portion of a blood vessel 120 is output. In this example, the graphical representation 150a comprises a projection of the spatial distribution of the disease state values ​​140 onto a surface of revolution formed around the portion of the blood vessel 120. This example is described with reference to FIG. 7, which is a schematic diagram illustrating a first example of a graphical representation 150a of the spatial distribution of the disease state values ​​140 according to some aspects of the present disclosure. In the example shown in FIG. 7, the surface of revolution comprises a cylindrical surface. The disease state values ​​140 are projected along the path along which the integration was performed, and the corresponding disease state values ​​140 are displayed on the cylindrical surface at the intersection of the path and the cylindrical surface. In the example shown in FIG. 7, the darkly shaded areas represent areas where CAD is present, and the lightly shaded areas represent areas where CAD is not present. The disease state values ​​140 in this example can alternatively be displayed in different manners, such as different colors. In addition to the graphical representation shown in FIG. 7, an image depicting the portion of the blood vessel 120 can also be displayed. For example, a reconstructed image of a portion of a blood vessel can be generated from the CT data 110 and displayed in a cylindrical plane. This helps convey the correspondence between the vascular anatomy and the disease state values. A user may be provided with the ability to manipulate the graphical representation to obtain different perspectives of the disease state values ​​140.

[0054] In another example, a graphical representation of the spatial distribution of disease state values ​​140 representing the state of vascular disease around a portion of a blood vessel 120 is output. In this example, graphical representation 150b has a projection of the spatial distribution of disease state values ​​140 onto an unfolded surface, where the unfolded surface is an unfolded representation of a surface of revolution formed around the portion of the blood vessel 120. This example is shown in FIG. 8, which is a schematic diagram illustrating a second example of a graphical representation 150b of the spatial distribution of disease state values ​​140, according to some aspects of the present disclosure. In comparison to the example shown in FIG. 7, the cylindrical surface illustrated in FIG. 7 has been unfolded. This graphical representation can be generated by virtually cutting the cylindrical surface along line B-B′ and unfolding the surface around centerline CL to map the cylindrical surface of FIG. 7 onto a plane as shown in FIG. 8. In comparison to graphical representation 150a shown in FIG. 7, graphical representation 150b provides a less obstructed view of the disease state values ​​140. The graphical representation 150b shown in Figure 8 can alternatively be generated directly from the data shown at the bottom of Figure 5 by displaying the disease state values ​​140 directly on the PVAT.

[0055] As described above, in act S150, a predicted time is output for a vascular disease state around a portion of blood vessel 120 to reach a predetermined state. The predicted time is predicted based on the spatial distribution of disease state values ​​140. Act S150 can be performed instead of or in addition to act S140.

[0056] In this example, the predetermined state is a value from the spatial distribution of disease state values ​​140 exceeds a predetermined threshold; development of symptoms of vascular disease; a physiological parameter of the blood vessel 120 satisfies a predetermined threshold condition; and or a value of a vascular disease risk metric exceeding a predetermined threshold.

[0057] In this example, the predicted time is predicted based on the spatial distribution of the disease state values ​​140, thereby providing a more accurate prediction. Examples of symptoms that may be predicted based on this example include pain, skin infection, skin ulcer, skin discoloration, numbness, stroke, nausea, blood clot, chest pain, arrhythmia, shortness of breath, etc. Examples of physiological parameters that may be used to define a given condition include measurements of vessel geometry such as vessel area or vessel diameter, and measurements of blood flow parameters such as blood flow velocity, fractional flow reserve (FFR), instantaneous flow reserve ratio (iFR), coronary flow reserve (CFR), etc. Examples of vascular disease risk metrics include risk of CAD, risk of peripheral arterial disease (PAD), WIFI score, GLASS score, Villata score, Framingham risk score, JBS3, etc.

[0058] In one example, the predicted time can be determined by inputting the spatial distribution of disease state values ​​140 into a neural network trained to predict the expected time for a vascular disease state to reach a predetermined state, such as those described in the previous examples. The neural network can be trained using training data having spatial distributions of disease state values ​​140, each with a corresponding ground truth value for the time to reach the associated vascular disease state. The training data can include thousands or more spatial distributions and corresponding ground truth values. The training data can be collected from previous clinical trials conducted on different subjects.

[0059] Variations on the method described above with reference to FIG. 2 are also contemplated.

[0060] In one example, the CT data 110 comprises spectral CT data, which defines x-ray attenuation in a medium at multiple different energy intervals. As described above, processing data from multiple different energy intervals can distinguish between media that have similar x-ray attenuation values ​​when measured within a single energy interval, and that would be indistinguishable using conventional x-ray attenuation data.

[0061] In this example, the CT data 110 includes spectral CT data defining x-ray attenuation in a portion of the blood vessel 120 within each of a plurality of different energy intervals. The operation of determining a distribution S120 of x-ray attenuation values ​​along a path through one or more concentric layers 1301...n of PVAT surrounding the portion of the blood vessel 120 includes extracting, from the spectral CT data, PVAT attenuation data representing a distribution of x-ray attenuation values ​​in the PVAT along a path through one or more concentric layers 1301...n of PVAT surrounding the portion of the blood vessel 120. An operation S130 of analyzing the distribution of x-ray attenuation values ​​is performed using the extracted PVAT attenuation data.

[0062] In this example, the spectral CT data can be generated by a variety of differently configured spectral X-ray imaging systems, as described above. The use of spectral CT data facilitates improved differentiation between X-ray attenuation due to PVAT and X-ray attenuation due to other media that may be present in the vicinity of blood vessel 120. This, in turn, provides a more reliable determination of the spatial distribution of disease state value 140 in operation S130.

[0063] In another example, the CT data received in act S110 represents a blood vessel at multiple different time points. The CT data from the different time points are then registered with each other. This allows changes in the spatial distribution of disease state values ​​140 over time to be identified. This example therefore facilitates an improved understanding of disease progression in the blood vessel.

[0064] In this example, the CT data 110 represents a portion of a blood vessel 120 at each of a plurality of time points t0, t1. The determining operation S120 and the analyzing operation S130 are performed on the CT data representing the portion of the blood vessel 120 at each of the time points t0, t1. The method described with reference to FIG. mutually registering distributions of X-ray attenuation values ​​representing portions of the blood vessel 120 at each time point t0, t1; The step S140 of outputting the graphical representation of the spatial distribution of the disease state values ​​140 includes a step of outputting a mutually registered spatial distribution of the disease state values ​​140 representing the state of vascular disease around the portion of the blood vessel 120 at each time point t0, t1, and / or a step of outputting a spatial distribution of the disease state values ​​representing a change in the state of vascular disease around the portion of the blood vessel 120 between two time points t0, t1, wherein the change in the state of vascular disease is calculated using the mutually registered distributions at the two time points.

[0065] In this example, the time points may be, for example, days, weeks, or months apart, and may represent, for example, the initial clinical examination of the vessel, or the time of a follow-up examination.

[0066] The co-registered spatial distributions of disease state values ​​140 output in this example can be output by overlaying the spatial distributions. Various known techniques can be used to overlay the spatial distributions.

[0067] Based on this example, an output spatial distribution of disease state values ​​representing changes in vascular disease state can be generated by performing a subtraction operation on the mutually registered spatial distributions. The spatial distribution of disease state values ​​representing changes in vascular disease state can show, for example, differences between regions identified as representing CAD.

[0068] The registration performed according to this example can be performed using various techniques. In general, the registration can be rigid, affine, or deformable registration. Various image registration techniques can be used, including intensity-based image registration techniques, feature-based image registration techniques, neural networks, etc. Image registration can be performed using vessels represented in their actual shape, i.e., as they appear in a reconstruction of the received CT data 110, or alternatively, it can be performed using an unfolded representation of the vessels, such as the unfolded representation described with reference to FIG. 5.

[0069] In one example, the registration operation includes identifying centerlines of blood vessels in the CT data at each time point. The centerlines of the blood vessels are then registered to mutually register the distributions of X-ray attenuation values ​​at each time point. In this example, the operation S120 of determining the distribution of X-ray attenuation values ​​along a path through one or more concentric layers 1301...n of PVAT includes identifying centerlines of portions of blood vessels 120 in the CT data 110 at each of the multiple time points, and the operation of mutually registering the distributions of X-ray attenuation values ​​includes mutually registering the centerlines of portions of blood vessels 120 at each of the multiple time points.

[0070] The vascular centerline provides a reliable reference position in the vessel for registration. The vascular centerline can be identified by locating the vessel lumen in the CT data, defining the centerline of the lumen in the vessel, and defining the centerline of the lumen as the vascular centerline CL. The vascular lumen centerline can be defined as a line passing through the geometric centers of multiple planes that transversely intersect the vascular lumen. The vascular centerline can be defined in this manner using the image slice shown at the top of FIG. 5.

[0071] In a related example, the distribution of x-ray attenuation values ​​along the centerline of the blood vessel 120 is used to normalize the distribution of x-ray attenuation values ​​in the CT data. In this example, operation S120 of determining the distribution of x-ray attenuation values ​​along a path through one or more concentric layers 1301..n of PVAT includes: determining a distribution of X-ray attenuation values ​​along a centerline of a portion of a blood vessel 120 at a selected time point; and normalizing the distribution of X-ray attenuation values ​​along a path through one or more concentric layers 1301..n of PVAT surrounding a portion of the blood vessel 120 based on the determined distribution for CT data 110 representing the blood vessel 120 at one or more other time points.

[0072] X-ray attenuation values ​​in CT data of a region of interest vary based on factors such as the CT imaging system used to acquire the CT data, as well as settings such as X-ray energy, CT dose, and gantry rotation speed. Furthermore, the distribution of X-ray attenuation values ​​may vary along a vessel due to natural flow characteristics within the vessel, natural vessel tapering, and the like. Thus, by normalizing the data using the distribution of X-ray attenuation values ​​along the vessel centerline, this example provides a reliable correction for such effects. Consequently, it reduces the impact of variability arising from differences in the manner in which the CT data is acquired. In this example, the selected time point may be the time point of the first image in the time series, or the time point of another image. The distribution of X-ray attenuation values ​​along the vessel centerline from the selected image is applied to one or more additional images to provide the same intensity distribution along the vessel centerline.

[0073] In one example, the registration operation is performed using anatomical landmarks. In this example, the operation of registering the distributions of X-ray attenuation values ​​to each other is performed using: identifying one or more anatomical landmarks in each distribution of x-ray attenuation values; and co-registering the distributions of X-ray attenuation values ​​based on the identified anatomical landmark(s).

[0074] In this example, anatomical landmarks may be identified by performing an image segmentation operation on the CT data. For this purpose, various image segmentation algorithms may be used, including model-based segmentation, watershed-based segmentation, region growing, level set, graph cut, etc. A neural network may be trained to segment the CT data 110 to identify anatomical landmarks. One or more anatomical landmarks that may be identified in this example may include, for example, at least one of a branch in a blood vessel 120, a lesion in the blood vessel 120, etc.

[0075] In one example, portions of a blood vessel are automatically identified in the CT data. This provides reliable identification of portions of a blood vessel and ensures that the same portions of the blood vessel are analyzed in the co-registered distributions of X-ray attenuation values. This, in turn, facilitates accurate assessment of changes in the spatial distribution of disease state values ​​140 over time. In this example, operation S120 of determining the distribution of X-ray attenuation values ​​along a path through one or more concentric layers 1301...n of PVAT includes identifying portions of a blood vessel 120 in the CT data 110 at each of a plurality of time points. The portions of a blood vessel can be identified based on the location of landmarks in the image, such as, for example, a branch in the blood vessel or a lesion in the blood vessel. For example, the portion of the blood vessel can be identified as the portion of the blood vessel between two branches.

[0076] An example of image registration using an expanded representation of blood vessels will be described with reference to Figures 9 and 10. In this example, the method described with reference to Figure 2 is transforming the received CT data 110 to provide an unfolded representation of the blood vessel 120 in which one or more concentric layers 1301..n of PVAT surrounding a portion of the blood vessel 120 are represented as planar layers; a step S120 of determining a distribution of X-ray attenuation values ​​along a path through one or more concentric layers 1301..n of PVAT surrounding a portion of a blood vessel 120 is performed using the transformed CT data; The step of co-registering the distributions of X-ray attenuation values ​​representing the portions of the blood vessel 120 at each time point t0, t1 is carried out using the distributions of X-ray attenuation values ​​determined from the transformed CT data.

[0077] In this example, registration is performed using an expanded representation of the vessels 120, as described with reference to the bottom of Figure 5. Registering the distributions in this form reduces irregularities that may appear in the distributions as a result of changes in vessel shape between time points, which would interfere with the assessment of changes in the spatial distribution of disease state values ​​140 over time.

[0078] 9 is a schematic diagram illustrating an example of a graphical representation of a co-registered spatial distribution of disease state values ​​140 representing the state of vascular disease around a portion of a blood vessel 120 at two different time points, t0 and t1. In FIG. 9, the shaded area enclosed by the solid curve indicates the spatial distribution of the disease state values ​​140 at time t0, in this example, the range of CAD. The area enclosed by the dotted curve indicates the range of CAD at time t1. The graphical representation shown in FIG. 9 provides a clear illustration of the changes in the spatial distribution of the disease state values ​​140 without the confounding effect of vessel shape.

[0079] 10 is a schematic diagram illustrating an example of a graphical representation of a spatial distribution of disease state values ​​representing changes in vascular disease state around a portion of a blood vessel 120 between two time points t0 and t1, according to some embodiments of the present disclosure. The hatched regions in FIG. 10 represent changes in the spatial distribution of CAD over the time period from t0 to t1. These regions are determined by subtracting the areas of the regions representing CAD at times t0 and t1 shown in FIG. 9.

[0080] In another example, one or more recommended treatment or monitoring steps are determined for the blood vessel 120 based on the spatial distribution of the disease state values ​​140. The one or more recommended treatment or monitoring steps may be referred to as a treatment plan. In this example, the method described with reference to FIG. determining one or more recommended treatment or monitoring steps for the vessel 120 based on the spatial distribution of the disease state values ​​140; and outputting one or more recommended treatment or monitoring steps.

[0081] Examples of treatment steps according to this example include recommending treating the blood vessel with a drug, recommending treating the blood vessel with a stent, recommending more exercise, etc. Examples of monitoring steps according to this example include recommending blood pressure monitoring, recommending a visit to a cardiologist, etc.

[0082] In one example, the recommended treatment or monitoring steps are determined from a database. In this example, the database stores multiple reference graphical representations from past studies and corresponding recommended treatment or monitoring steps. For a given graphical representation, the database is searched to identify a matching graphical representation, which results in a recommended treatment or monitoring step. Matching graphical representations can be identified by comparing the graphical representation with the reference graphical representation based on the value of a similarity metric, such as Dice coefficient or cross-entropy.

[0083] In another example, the spatial distribution of disease state values ​​140 is input into a predictive model to determine recommended treatment or monitoring steps. The predictive model can be based on artificial intelligence, machine learning, or decision trees, for example. The predictive model can be trained using training data representing, for each of a plurality of blood vessels, the spatial distribution of disease state values ​​140 representing the state of vascular disease around a portion of the blood vessel 120 and corresponding ground truth data representing one or more past recommended treatment or monitoring steps for the blood vessel 120.

[0084] The act of determining one or more recommended treatment or monitoring steps for the vessel 120 may also be based on additional information.

[0085] In one example, patient data related to the blood vessel 120 is received, and the operation of determining one or more recommended treatment or monitoring steps for the blood vessel 120 is further based on the patient data. The patient data may represent factors such as the patient's age, obesity level, sex, blood pressure, blood cholesterol level, an indication of a family history of vascular disease, an indication of smoking, diabetes, or dyspnea, etc.

[0086] In another example, additional data is extracted from the CT data and used to determine one or more recommended treatment or monitoring steps for the blood vessel 120. In this example, the distribution of plaque within the blood vessel 120 and / or values ​​of one or more blood flow parameters of the blood vessel 120 are determined from the received CT data 110, and the operation of determining one or more recommended treatment or monitoring steps for the blood vessel 120 is further based on the distribution of plaque or the determined values ​​of the one or more blood flow parameters, respectively.

[0087] In this example, the additional data may be provided as further input to the predictive model, and the corresponding additional training data may be used to train the predictive model to predict one or more recommended treatment or monitoring steps for the blood vessel 120. The distribution of plaque within the blood vessel may be identified based on the values ​​of X-ray attenuation in the CT data 110. By providing the CT data 110 as spectral CT data 110, improved separation of plaque regions in the CT data is achieved.

[0088] In one example, an x-ray projection imaging system is used to navigate an intravascular treatment device to a portion of a blood vessel, and a graphical representation is used to position the intravascular treatment device within the blood vessel. In this example, the method described with reference to FIG. receiving x-ray image data representative of an endovascular treatment device within a blood vessel; registering the x-ray image data to a graphical representation of the spatial distribution of disease state values ​​140; and indicating the position of the endovascular treatment device in the graphical representation.

[0089] In this example, the X-ray image data is provided by an X-ray projection imaging system. Registration can be performed by registering the X-ray image data to the CT image data. The position of the endovascular treatment device in the graphical representation can be indicated in various manners, such as by displaying an overlay of the X-ray image data with the graphical representations 150a and 150b, or by displaying a marker. By indicating the position of the endovascular treatment device in the graphical representation, this example facilitates precise positioning of the treatment device relative to the diseased region within the blood vessel. In this manner, treatment can be selectively delivered to the diseased region. To provide a record of the treatment, the position of the treatment device in the graphical representation at the time of the delivered treatment can also be recorded. Examples of treatments that can be delivered to the blood vessel according to this example include injections of drugs such as dexamethasone and canakinumab. An example of an endovascular treatment device that can be used according to this example is the Bullfrog Micro-Infusion Device, available from Mercator MedSystems, Inc., of California, USA.

[0090] In another example, a computer program product is provided having instructions that, when executed by one or more processors, cause the one or more processors to perform a method for determining a vascular disease status, the method comprising: a step S110 of receiving computed tomography CT data 110 representing a portion of a blood vessel 120; a step S120 of determining from the CT data 110 a distribution of X-ray attenuation values ​​along a path through one or more concentric layers 1301...n of perivascular adipose tissue PVAT surrounding a portion of the blood vessel 120 at each of a plurality of locations spanning the one or more concentric layers 1301...n; a step S130 of analyzing the distribution of x-ray attenuation values ​​along a path through one or more concentric layers 1301 ..n to provide a spatial distribution of disease state values ​​140 representative of the state of vascular disease around a portion of the blood vessel 120; The method includes a step S140 of outputting a graphical representation 150a, 150b of the spatial distribution of the disease state values ​​140, and / or a step S150 of outputting a predicted time at which the state of the vascular disease around the portion of the blood vessel 120 is expected to reach a predetermined state, the predicted time being predicted based on the spatial distribution of the disease state values ​​140.

[0091] In another example, a system 200 for determining a vascular disease status is provided. The system includes one or more processors 210, which: receiving S110 computed tomography CT data 110 representing a portion of a blood vessel 120; determining S120 from the CT data 110 a distribution of x-ray attenuation values ​​along a path through one or more concentric layers 1301...n of perivascular adipose tissue PVAT surrounding a portion of the blood vessel 120 at each of a plurality of locations spanning the one or more concentric layers 130i; analyzing S130 the distribution of x-ray attenuation values ​​along a path through one or more concentric layers 1301 ..n to provide a spatial distribution of disease state values ​​140 representative of a vascular disease state around a portion of the blood vessel 120; The method is configured to output S140 a graphical representation 150 a, 150 b of the spatial distribution of the disease state values ​​140 and / or to output S150 a predicted time at which the state of the vascular disease around the portion of the blood vessel 120 is expected to reach a predetermined state, the predicted time being predicted based on the spatial distribution of the disease state values ​​140.

[0092] An example of a system 200 is shown in Figure 3. It should be noted that the system 200 may also include one or more of a CT imaging system 220 for generating the CT data 110 received in operation S110; a monitor 230 for displaying the output graphical representations 150a, 150b, reconstructed CT images generated from the CT data 110, etc.; a patient bed 240; an injector (not shown in Figure 2) for injecting contrast agent into the vasculature; and a user input device configured to receive user input, such as a keyboard, mouse, touch screen, etc.

[0093] The above examples are to be understood as illustrative of the present disclosure and not limiting. Further examples are contemplated. For example, an example described in connection with a computer-implemented method may also be provided by a corresponding computer program product, a corresponding computer-readable storage medium, or a corresponding system 200. It should be understood that features described in connection with any one example can be used alone or in combination with other described features, and can be used in combination with one or more features of another example or in combination with other examples. Furthermore, equivalents and modifications not described above may be employed without departing from the scope of the invention as defined in the appended claims. In the claims, the word "comprises" does not exclude other elements or operations, and the indefinite articles "a" or "an" do not exclude a plurality. The mere fact that certain features are recited in mutually different dependent claims does not indicate that a combination of these features cannot be advantageously used. Any reference signs in the claims should not be construed as limiting the scope of the invention.

Claims

1. 1. A computer-implemented method for determining vascular disease status, comprising: receiving CT data representing a portion of a blood vessel; determining from the CT data a distribution of x-ray attenuation values ​​along a path through one or more concentric layers of perivascular adipose tissue, or PVAT, surrounding a portion of the blood vessel, at each of a plurality of locations spanning the one or more concentric layers; analyzing a distribution of x-ray attenuation values ​​along a path through the one or more concentric layers to provide a spatial distribution of disease state values ​​representative of a vascular disease state around the portion of the blood vessel; and outputting a graphical representation of the spatial distribution of the disease state values ​​and / or a predicted time at which a vascular disease state around the portion of the blood vessel is expected to reach a predetermined state, wherein the predicted time is predicted based on the spatial distribution of the disease state values.

2. the CT data comprises spectral CT data defining x-ray attenuation in the portion of the blood vessel at each of a plurality of different energy intervals; Determining a distribution of x-ray attenuation values ​​along a path through one or more concentric layers of PVAT surrounding the portion of the blood vessel comprises extracting from the spectral CT data PVAT attenuation data representing a distribution of x-ray attenuation values ​​in PVAT along a path through one or more concentric layers of PVAT surrounding the portion of the blood vessel; The computer-implemented method of claim 1 , wherein analyzing the distribution of X-ray attenuation values ​​is performed using the extracted PVAT attenuation data.

3. the CT data representing a portion of the blood vessel at each of a plurality of time points; the determining and analyzing steps are performed on CT data representing the portion of the blood vessel at each time point; the method further comprising the step of mutually registering distributions of X-ray attenuation values ​​representing portions of the blood vessel at each time point; 3. The computer-implemented method of claim 1, wherein outputting the graphical representation of the spatial distribution of disease state values ​​comprises outputting a mutually registered spatial distribution of disease state values ​​representing a vascular disease state around the portion of the blood vessel at each time point, and / or outputting a spatial distribution of disease state values ​​representing a change in vascular disease state around the portion of the blood vessel between two time points, wherein the change in vascular disease state is calculated using the mutually registered distribution of X-ray attenuation values ​​at the two time points.

4. the method further comprising transforming the received CT data to provide an unfolded representation of the vessel in which one or more concentric layers of PVAT surrounding a portion of the vessel are represented as planar layers; determining a distribution of x-ray attenuation values ​​along a path through one or more concentric layers of PVAT surrounding the portion of the blood vessel is performed using the transformed CT data; 4. The computer-implemented method of claim 3, wherein the step of mutually registering distributions of X-ray attenuation values ​​representing the portions of the blood vessel at each time point is performed using distributions of X-ray attenuation values ​​determined from the transformed CT data.

5. the step of mutually registering the distributions of X-ray attenuation values, identifying one or more anatomical landmarks in each distribution of x-ray attenuation values; and mutually registering the distribution of X-ray attenuation values ​​based on the identified one or more anatomical landmarks.

6. The computer-implemented method of claim 5 , wherein the one or more anatomical landmarks represent at least one of a bifurcation in a blood vessel, a lesion in a blood vessel.

7. The computer-implemented method of claim 3 , wherein the determining step comprises identifying portions of the blood vessel in the CT data at each of the plurality of time points.

8. the determining step includes identifying a centerline of the portion of the blood vessel in the CT data at each of the plurality of time points; and registering the distributions of X-ray attenuation values ​​to each other comprises registering centerlines of the blood vessel portions to each other at each of the plurality of time points. and / or The determining step further comprises: determining a distribution of X-ray attenuation values ​​along a centerline of the portion of the blood vessel at a selected time point; and normalizing a distribution of X-ray attenuation values ​​along a path through one or more concentric layers of PVAT surrounding a portion of the blood vessel based on the determined distribution to CT data representing the blood vessel at one or more other time points.

9. the graphical representation of the spatial distribution of disease state values ​​representative of the state of vascular disease around the portion of the blood vessel comprises a projection of the spatial distribution of disease state values ​​onto a surface of revolution formed around the portion of the blood vessel; or a graphical representation of a spatial distribution of disease state values ​​representative of a vascular disease state around the portion of the blood vessel comprising a projection of the spatial distribution of disease state values ​​onto an unfolded surface; 9. The computer-implemented method of claim 1, wherein the unfolded surface is an unfolded representation of a surface of revolution formed around the portion of the blood vessel.

10. receiving x-ray image data representative of an endovascular treatment device in the blood vessel; registering the x-ray image data to a graphical representation of the spatial distribution of the disease state value; 10. The computer-implemented method of claim 1, further comprising: indicating the position of the endovascular treatment device in the graphical representation.

11. The method includes outputting a predicted time at which a state of vascular disease around the portion of the blood vessel is expected to reach a predetermined state, the predetermined state comprising: a value from the spatial distribution of disease state values ​​exceeding a predetermined threshold; the development of symptoms of vascular disease, the physiological parameter of the blood vessel satisfies a predetermined threshold condition; and 3. The computer-implemented method of claim 1 or 2, wherein the vascular disease risk metric is defined by one or more of: a) the value of a vascular disease risk metric exceeding a predetermined threshold;

12. determining one or more recommended treatment or monitoring steps for the vessel based on the spatial distribution of the disease state values; 12. The computer-implemented method of claim 1, further comprising: outputting the one or more recommended treatment or monitoring steps.

13. determining a distribution of plaque in the blood vessel and / or values ​​of one or more blood flow parameters of the blood vessel from the received CT data; 13. The computer-implemented method of claim 12, wherein determining one or more recommended treatment or monitoring steps for the blood vessel is further based on the distribution of the plaque or the determined values ​​of the one or more blood flow parameters, respectively.

14. 14. The computer-implemented method of claim 12 or 13, wherein determining one or more recommended treatment or monitoring steps for the vessel comprises inputting the spatial distribution of the disease state values ​​into a predictive model.

15. The determining and analyzing steps are performed for a plurality of concentric layers of PVAT, and the analyzing step further comprises: normalizing a distribution of x-ray attenuation values ​​along paths through one or more concentric layers of PVAT surrounding the portion of the blood vessel based on x-ray attenuation values ​​along paths in an innermost layer of the one or more concentric layers; integrating the normalized x-ray attenuation values ​​along paths extending radially outward from a centerline of the blood vessel through a plurality of concentric layers of PVAT; comparing the integrated normalized x-ray attenuation value to a threshold to provide a spatial distribution of disease state values ​​representative of a vascular disease state around the portion of the blood vessel; 15. The computer-implemented method of claim 1, wherein the integrating step comprises assigning a negative value to normalized X-ray attenuation values ​​that exceed the normalized X-ray attenuation value of the innermost layer along the corresponding path, and assigning a positive value to normalized X-ray attenuation values ​​that are equal to or less than the normalized X-ray attenuation value of the innermost layer along the corresponding path.

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