Determining status of vascular disease

Abstract

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

Description

Technical Field

[0001] The present disclosure relates to determining the status of vascular disease.Disclosed herein are computer-implemented methods, computer program products, and systems. Background Art

[0002] Vascular disease refers to a range of conditions that affect the circulatory system (or, in other words, the blood vessels that circulate blood around the body). Some types of vascular disease affect the arteries, while other types 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 the pathology of the coronary arteries and the deterioration of their ability to deliver oxygenated blood to the heart muscle. In the case of CAD, ultimately, the lack of oxygen supply can lead to myocardial ischemia, symptoms of which can include shortness of breath, angina, or even myocardial infarction.

[0003] Various imaging-based biomarkers have been developed to predict the risk associated with vascular disease. One such biomarker involves detecting inflammation in perivascular adipose tissue (PVAT), and is disclosed in WO 2016 / 024128 A1. This document defines a method for volumetric characterization of perivascular adipose tissue (PVAT) using data collected via computed tomography (CT) scans. Volumetric characterization of perivascular adipose tissue allows the inflammatory status of the underlying vessels to be established via CT scans. This can be used for the diagnosis, prognosis, and management of coronary artery and vascular disease.

[0004] Another document by Antoniades C. et al., "State-of-the-art review article. Atherosclerosis affecting fat: What can we learn by imagingperivascular adipose tissue?" (Journal of Cardiovascular Computed Tomography, September-October 2019, Vol. 13, No. 5, pp. 288-296) describes a biomarker derived from CT attenuation in PVAT around human coronary arteries. The biomarker disclosed in this document is designed to capture the spatial variation of PVAT attenuation around human coronary arteries - the fat attenuation index "FAI". FAI has predictive value for cardiac death and non-fatal heart attacks in stable patients.

[0005] PVAT surrounds the coronary arteries and is located within or adjacent to the adventitial layer (i.e., in a layer outside the medial layer). The extent of PVAT, radially outward from the vessel centerline, is roughly equal to the diameter of the artery. In CT images, PVAT can be identified by the elevated X-ray attenuation levels immediately outside the vessel wall.

[0006] As described in the literature cited above 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 found in PVAT. This produces relatively high X-ray attenuation values ​​near the vessel wall, followed by a relatively steep gradient as the radial distance from the vessel wall increases. The literature describes the evaluation of a biomarker (fat attenuation index "FAI") for vascular segments. FAI quantifies a weighted measure of attenuation in the perivascular tissue of a 1 mm concentric layer surrounding the human arterial wall, thereby capturing the corresponding perivascular attenuation gradient, which in turn reflects the biological changes in PVAT that occur due to vascular inflammation. FAI has predictive value for cardiac death and non-fatal heart attack in stable patients.

[0007] WO 2016 / 024128 A1, cited above, discloses another CAD biomarker for predicting the presence of CAD (called the Volume Perivascular Characterization Index (VPCI-i)). The VPCI-i biomarker for a vascular segment is calculated based on a plot of changes in the radiodensity of perivascular adipose tissue surrounding the right coronary artery versus distance from the vessel's outer wall. The area under the curve technique is used to determine the VPCI-i value for the vascular segment, thereby determining the presence of CAD.

[0008] Document US2022 / 401050A1 discloses a method for characterizing coronary plaque tissue data and perivascular tissue data using image data collected based on a computed tomography scan along a blood vessel, the image information including radiodensity values ​​of the coronary plaque and perivascular tissue located near the coronary plaque, the method including: quantifying the radiodensity in an area of ​​the coronary plaque, quantifying the radiodensity in at least one area of ​​the corresponding perivascular tissue near the coronary plaque, determining the gradient of the quantified radiodensity values ​​within the coronary plaque and the quantified radiodensity values ​​within the corresponding perivascular tissue, and determining the ratio of the quantified radiodensity values ​​within 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 tissue and / or the ratio of the radiodensity value of the coronary plaque to the radiodensity value of the corresponding perivascular tissue.

[0009] However, there is still a need to improve the assessment of the vascular disease status of blood vessels by measuring X-ray attenuation in PVAT. Summary of the Invention

[0010] According to one aspect of the present disclosure, a computer-implemented method for determining a state of a vascular disease is provided. The method comprises:

[0011] receiving computed tomography (CT) data representing a portion of a blood vessel;

[0012] determining, based on the CT data, a distribution of X-ray attenuation values ​​at each of a plurality of locations within one or more concentric layers of perivascular adipose tissue (PVAT) surrounding the portion of the blood vessel, along a path through the one or more concentric layers;

[0013] analyzing the distribution of X-ray attenuation values ​​along the path through the one or more concentric layers to provide a spatial distribution of disease state values ​​indicative of a state of vascular disease around the portion of the blood vessel; and

[0014] outputting a graphical representation of the spatial distribution of disease state values, and / or outputting a predicted time when the state of vascular disease around the portion of the blood vessel is expected to reach a predefined state, the predicted time being predicted based on the spatial distribution of disease state values.

[0015] In the above-described 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. This contrasts with providing a single biomarker value for a portion of a blood vessel. The additional information provided by the spatial distribution of disease state values ​​can, for example, be used to track changes in disease at a specific location, thereby facilitating improved decision-making regarding treatment or monitoring of the blood vessel. Similarly, because the output predicted time at which the state of vascular disease around the portion of the blood vessel is expected to reach a predefined state is predicted based on the spatial distribution of disease state values, a more reliable predicted time can be provided.

[0016] Other aspects, features and advantages of the present disclosure will become apparent from the following description of examples with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 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 aspects of the present disclosure.

[0018] Figure 2 is a flow chart illustrating an example of a computer-implemented method for determining the status of vascular disease, according to aspects of the present disclosure.

[0019] Figure 3 is a schematic diagram illustrating an example of a system 200 for determining the status of vascular disease, according to some aspects of the present disclosure.

[0020] Figure 4 is a diagram illustrating a portion of a blood vessel 120 (including one or more concentric layers 130 of perivascular adipose tissue (PVAT) surrounding the vessel) according to some aspects of the present disclosure. 1…n ) is a schematic diagram of an example.

[0021] Figure 5 is an illustration of a method for determining (S120) X-ray attenuation values ​​along one or more concentric layers 130 through a PVAT according to some aspects of the present disclosure. 1…n Schematic diagram of an example of the technique of path distribution.

[0022] Figure 6 Illustrated are a) examples of graphs of X-ray attenuation values ​​along a path extending from a blood vessel centerline CL in a radial direction R, and b) examples of normalized graphs of the X-ray attenuation values ​​illustrated in a), according to aspects of the present disclosure.

[0023] Figure 7 is a schematic diagram illustrating a first example of a graphical representation 150a of a spatial distribution of disease state values ​​140 according to aspects of the present disclosure.

[0024] Figure 8 is a schematic diagram illustrating a second example of a graphical representation 150b of a spatial distribution of disease state values ​​140 , according to aspects of the present disclosure.

[0025] Figure 9 is a schematic diagram illustrating an example of a graphical representation of spatial distributions of disease state values ​​140 representing the state of a vascular disease around a portion of a blood vessel 120 at two different time points t0, t1 that are registered with each other.

[0026] Figure 10 is a schematic diagram illustrating an example of a spatial distribution of graphical representations 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, t1, according to aspects of the present disclosure. DETAILED DESCRIPTION

[0027] Examples of the present disclosure are provided with reference to the following description and accompanying drawings. In this specification, many specific details of certain examples are set forth for the purpose of explanation. References in the specification to "example," "implementation," or similar language mean that the features, structures, or characteristics described in conjunction with the example are included in at least one of the examples. It should also be understood that features described with respect to one example may also be used in another example, and for the sake of brevity, all features are not necessarily repeated in each example. For example, features described with respect to a computer-implemented method may be implemented in a corresponding manner in a computer program product and system.

[0028] In the following description, reference is made to a computer-implemented method for determining the state of a vascular disease. In some examples, reference is made to a vascular disease in the form of coronary artery disease "CAD". However, it will generally be understood that the method is not limited to this type of vascular disease. The method can also be used to determine the state of a vascular disease in other parts of the body besides the heart. The method can also be used to determine the state of a vascular disease in other types of blood vessels besides arteries. Generally, the method can be used to determine the state of a vascular disease in any anatomical region, and the blood vessel can be any type of blood vessel. Thus, the blood vessel can be an artery or a vein, and the artery or vein can be located anywhere in the body (e.g., in the heart, in the brain, or in peripheral regions, such as in an arm, a leg, etc.).

[0029] Note that the computer-implemented methods disclosed herein can be provided as a non-transient computer-readable storage medium comprising computer-readable instructions stored thereon, which, when executed by at least one processor, cause at least one processor to perform the method. In other words, the computer-implemented methods can be implemented in a computer program product. The computer program product can be provided by dedicated hardware or hardware capable of running software associated with appropriate software. When provided by a processor, the functionality of the method features can be provided by a single dedicated processor, or by a single shared processor, or by multiple individual processors, some of which can be shared. The functionality of one or more of the method features can be provided, for example, by a processor shared within a networked processing architecture such as a client / server architecture, a peer-to-peer architecture, the Internet, or a cloud.

[0030] The explicit use of the term "processor" or "controller" should not be interpreted as referring exclusively to hardware capable of running software, and 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 devices, and the like. Furthermore, examples of the present disclosure can take the form of a computer program product accessible from a computer-usable or computer-readable storage medium that provides program code for use by or in conjunction with a computer or any instruction execution system. For the purposes of this specification, a computer-usable or computer-readable storage medium can be any device that can contain, store, communicate, propagate, or transmit a program for use by or in conjunction with an instruction execution system, device, or apparatus. The medium can be an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system or device, or a 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 TM and DVD.

[0031] As mentioned above, there remains a need for improvements in the assessment of the status of vascular disease in blood vessels by measuring X-ray attenuation in PVAT.

[0032] Figure 1 is a schematic diagram illustrating an example of a portion of a blood vessel 120 including perivascular adipose tissue (PVAT) surrounding the blood vessel, according to aspects of the present disclosure. For example, Figure 1 The blood vessel 120 shown may represent a coronary artery. PVAT surrounds the blood vessel (e.g., the coronary artery) and Figure 1 Shown are within or adjacent to the adventitial layer (ie, in a layer external to the media layer).

[0033] As mentioned above, document WO2016 / 024128A1 defines a method for volumetric characterization of perivascular adipose tissue using data collected by computed tomography. Volumetric characterization of perivascular adipose tissue allows the establishment of the inflammatory state of the underlying blood vessels by CT scanning. This can be used for the diagnosis, prognosis and treatment of coronary artery and vascular diseases. The above-mentioned document by Antoniades C. et al. describes another biomarker that is derived from CT attenuation in PVAT surrounding human coronary arteries. The document discloses a biomarker designed to capture spatial variations in PVAT attenuation around human coronary arteries - the fat attenuation index "FAI". FAI has predictive value for cardiac death and non-fatal heart attacks in stable patients.

[0034] Figure 2 is a flow chart illustrating an example of a computer-implemented method of determining the status of vascular disease according to aspects of the present disclosure. Figure 3 is a schematic diagram illustrating an example of a system 200 for determining the status of vascular disease according to some aspects of the present disclosure. The system 200 includes one or more processors 210. Note that with respect to reference Figure 2 The operations described in the described methods can also be performed by Figure 3 Similarly, the operations described with respect to the one or more processors 210 of the system 200 may also be performed by the one or more processors 210 of the system 200. Figure 2 The method described in the reference Figure 2 , a computer-implemented method for determining a status of a vascular disease comprises:

[0035] Receiving S110 computed tomography CT data 110 representing a portion of a blood vessel 120 ;

[0036] Determine S120 X-ray attenuation values ​​of one or more concentric layers 130 of perivascular adipose tissue (PVAT) surrounding a portion of a blood vessel 120 based on the CT data 110 . 1…n At each of a plurality of positions within a range of 1…n The distribution of paths;

[0037] Analyze S130 X-ray attenuation values ​​along one or more concentric layers 130 1…n to provide a spatial distribution of disease state values ​​140 representing a state of a vascular disease around a portion of the blood vessel 120; and

[0038] Output S140 is a graphical representation 150 a , 150 b of the spatial distribution of the disease state values ​​140 , and / or output S150 is an expected time for the state of the vascular disease around the portion of the blood vessel 120 to reach a predefined state, the predicted time being predicted based on the spatial distribution of the disease state values ​​140 .

[0039] In the above-described method, providing a spatial distribution of disease state values ​​representing the state of vascular disease around a portion of a vessel provides detailed information about the disease state. This contrasts with providing a single biomarker value for a portion of a vessel. The additional information provided by the spatial distribution of disease state values ​​can, for example, be used to track changes in disease at a specific location, thereby facilitating improved decision-making regarding treatment or monitoring of the vessel. Similarly, because the output predicted time at which the state of vascular disease around the portion of the vessel is expected to reach a predefined state is predicted based on the spatial distribution of disease state values, a more reliable predicted time can be provided.

[0040] refer to Figure 2 1. According to the flowchart shown, in operation S110, CT data 110 representing a portion of a blood vessel 120 is received.

[0041] The CT data 110 received in operation S110 can be generated after a contrast agent is injected into the vasculature. The contrast agent can include a substance such as iodine, a lanthanide such as gadolinium, or another substance that provides visibility of the blood flow into which the contrast agent is injected. The CT data 110 can typically represent a static image of the blood vessel 120, or alternatively, the CT data 110 can represent a time series of images of the blood vessel 120. In the latter case, the time series of images can be generated substantially in real time, and the above-described method can be performed substantially in real time. Therefore, the determination operation S120, the analysis operation S130, and the outputting S140 of the graphical representation of the spatial distribution of the disease state values ​​140 can be performed substantially in real time. Similarly, the outputting S150 of the predicted time at which the state of vascular disease surrounding the portion of the blood vessel 120 is expected to reach a predefined state can also be performed in real time.

[0042] In general, the CT data 110 received in operation S110 may be raw data, i.e., data that has not yet been reconstructed into a volumetric or 3D image, or it may be image data, i.e., data that has been reconstructed into a volumetric image. CT data may also be referred to as volumetric data. CT data 110 may be generated by a CT imaging system, or, as described in more detail below, may 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.

[0043] A CT imaging system generates CT data by rotating or stepping an X-ray source-detector arrangement around a target and acquiring X-ray attenuation data of the target from multiple rotational angles relative to the target. The CT data can then be reconstructed into a 3D image of the target. Examples of CT imaging systems that can be used to generate CT data 110 include cone-beam CT imaging systems, photon-counting CT imaging systems, dark-field CT imaging systems, and phase-contrast CT imaging systems. Figure 3 An example of a CT imaging system 220 that may be used to generate the CT data 110 received in operation S110 is illustrated in For example, the CT data 110 may be generated by a CT 5000 Ingenuity CT scanner sold by Philips Healthcare of Best, The Netherlands.

[0044] As described above, the CT data 110 received in operation S110 may alternatively be generated by rotating or stepping an X-ray source and an 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 that supports the X-ray source and the X-ray detector, for example, a so-called "C-arm". The X-ray projection imaging system may alternatively include a support arm having a shape different from that in this example, for example, an O-arm. Other types of X-ray projection imaging systems may alternatively be used, in which the X-ray source and the X-ray detector are mounted or supported in a different manner. In contrast to a CT imaging system, an X-ray projection imaging system generates X-ray attenuation data of a target, in which the X-ray source and the X-ray detector are in a static position relative to the target. In contrast to the volume data generated by a CT imaging system, the X-ray attenuation data may be referred to as projection data. The X-ray attenuation data generated by an X-ray projection imaging system is typically used to generate a 2D image of the target. However, an X-ray projection imaging system can generate CT data, i.e., volumetric data, by rotating or stepping its X-ray source and X-ray detector around the target and acquiring projection data of the target from multiple rotational angles relative to the target. Image reconstruction techniques can then be used to reconstruct a volumetric image from the projection data acquired from the multiple rotational angles 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 can be generated by a CT imaging system, or alternatively, it can be generated by an X-ray projection imaging system. An 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 Best, Netherlands.

[0045] In some examples described in more detail below, the CT data 110 received in operation S110 includes spectral CT data. The spectral CT data defines the X-ray attenuation in the target at a plurality of different energy intervals DE. 1…m In each energy interval in . Generally speaking, there may be two or more energy intervals; that is, m is an integer, and m ≥ 2. In this regard, the spectral CT data 110 received in operation S110 may be generated by a spectral CT imaging system or by a spectral X-ray projection imaging system. In the latter case, as described above, the spectral CT data may 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. More generally, the spectral CT data 110 received in operation S110 may be generated by a spectral X-ray imaging system.

[0046] In multiple different energy intervals DE 1…mThe ability to generate X-ray attenuation data distinguishes spectral X-ray imaging systems from conventional X-ray imaging systems. By processing data from multiple different energy intervals, media having similar X-ray attenuation values ​​when measured within a single energy interval, which are indistinguishable using conventional X-ray attenuation data, can be distinguished. Examples of spectral X-ray imaging systems that can be used to generate the spectral CT data 110 received in operation 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. An example of a spectral CT imaging system that can be used to generate the spectral CT data 110 received in operation S110 is the Spectral CT7500 sold by Philips Healthcare of Best, The Netherlands.

[0047] In general, the spectral CT data 110 can be generated by various different configurations of a spectral X-ray imaging system including an X-ray source and an X-ray detector. The X-ray source of the spectral X-ray imaging system can include multiple monochromatic sources or one or more polychromatic 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; or multiple detectors, wherein each detector detects a different X-ray energy interval DE 1…m or a multi-layer detector in which X-rays having energies within different X-ray energy intervals are detected by corresponding layers; or a photon counting detector that bins detected X-ray photons into one of a plurality of energy intervals based on their individual energies. In a photon counting detector, the relevant energy interval for each received X-ray photon can be determined by detecting the pulse height caused by electron-hole pairs generated in response to absorption of the X-ray photon in the direct conversion material.

[0048] The various configurations of the aforementioned X-ray source and detector can be used to detect different X-ray energy ranges DE 1…m Typically, discrimination between different X-ray energy intervals can be provided at the 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 the emission of X-rays from multiple X-ray sources. In such a configuration, a common X-ray detector can be used to detect X-rays across multiple different energy intervals, with X-ray attenuation data for each energy interval being generated in a time-sequential manner. Alternatively, discrimination between different X-ray energy intervals can be provided at the detector by using a multi-layer detector or a photon counting detector. Such a detector is capable of detecting X-rays from multiple X-ray energy intervals DE almost simultaneously. 1…mTherefore, there is no need to perform time switching at the source. Therefore, multi-layer detectors or photon counting detectors can be used in conjunction with a multi-color source to detect X-rays in different X-ray energy ranges. 1…m Generate X-ray attenuation data at

[0049] Other combinations of the aforementioned X-ray sources and detectors may also be used to provide 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 may be avoided by mounting the X-ray source-detector pair to the gantry at rotationally offset positions about the axis of rotation. In this configuration, each source-detector pair operates independently, and the rotational offset of the source-detector pair facilitates the generation of X-rays at different energy intervals DE. 1…m This configuration can be used to achieve separation of different energy intervals DE by applying energy selective filters to (one or more) X-ray detectors to reduce the effects of X-ray scatter. 1…m Improved separation between spectral CT data.

[0050] In general, the CT data 110 received in operation S110 can be received via any form of data communication, including wired communication, optical communication, and wireless communication. For example, when wired communication or optical communication is used, communication can occur via signals transmitted over an electrical or optical cable; and when wireless communication is used, communication can occur, for example, via RF signals or optical signals. The CT data 110 received in operation S110 can be received from a variety of sources. For example, the CT data 110 can be received from an imaging system (e.g., one of the imaging systems described above). Alternatively, the CT data 110 can be received from another source (e.g., a computer-readable storage medium, the internet, or the cloud).

[0051] The CT data 110 received in operation S110 represents a portion of a blood vessel 120. For example, the CT data 110 may represent a portion of an artery (e.g., a coronary artery). In this example, the CT data may be used to determine the status of coronary artery disease in 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, a vein or artery may be located anywhere in the body, such as the heart, brain, or peripheral regions (e.g., arms, legs, etc.).

[0052] Return to Figure 2 In the flowchart shown in FIG. 1 , in operation S120 , the X-ray attenuation value of one or more concentric layers 130 of the perivascular fat tissue PVAT surrounding the portion of the blood vessel 120 is determined. 1…nAt each of a plurality of positions within a range of 1…n The distribution of the path. Figure 4 and Figure 5 An example of operation S120 will be described.

[0053] Figure 4 is a diagram illustrating a portion of a blood vessel 120 (including one or more concentric layers 130 of perivascular adipose tissue (PVAT) surrounding the vessel) according to some aspects of the present disclosure. 1…n ) is a schematic diagram of an example. Figure 4 The blood vessel 120 shown corresponds to Figure 1 The blood vessels 120 are shown, and in addition Figure 1 In addition to the items shown, Figure 4 Also shown are multiple concentric layers 130 of PVAT 1…n In this embodiment, the PVAT is provided in the adventitial layer of the blood vessel. In the illustrated example, the concentric layers 130 1…n are defined relative to a common center, and in this example, the center is defined as the vessel centerline CL. Concentric layers 130 1…n It can be described as a surface formed around the vessel centerline CL. Each surface is bounded by positions 0 and L in the axial direction along the vessel centerline CL. These positions define the length of the portion of the vessel. Each surface is bounded by a rotation angle range [0, 2π] in the rotation direction φ around the vessel centerline CL. Concentric layers 130 1…n In the radial direction R, it is defined by an inner radial dimension, which is defined by the radially innermost portion of the PVAT for a given rotation angle φ about the vessel centerline CL, and an outer radial dimension, which is defined by the radially outermost position of the PVAT for a given rotation angle φ about the vessel centerline CL. Typically, there may be one or more concentric layers. In some examples, there may be two or more concentric layers. If there are two or more concentric layers, the layers may have equal thickness. For example, there may be ten or twenty layers or more. For example, if there are approximately twenty layers, each layer may have a thickness of approximately 1 mm in the radial direction R. In contrast, if only a few layers are provided, each layer may have a thickness of several millimeters. These values ​​are provided purely as examples.

[0054] Continue to refer Figure 4 In operation S120, the X-ray attenuation value is determined in one or more concentric layers 130 of the PVAT. 1…n At each of a plurality of positions within a range of 1…n The operation involves determining the distribution of paths at each layer 130 1…nX-ray attenuation along the path through each layer at each of a plurality of positions within a range of , i.e., X-ray attenuation at each of a plurality of positions within a range of each of the surfaces defined above within the limits of [0, L] along the vessel centerline CL and within a rotation angle range of [0, 2π] in the rotation direction φ. Figure 5 An example of this operation is described, Figure 5 is an illustration of a method for determining (S120) X-ray attenuation values ​​along one or more concentric layers 130 through a PVAT according to some aspects of the present disclosure. 1…n Schematic diagram of an example of the technique of path distribution.

[0055] refer to Figure 5 , X-ray attenuation values ​​can be determined along one or more concentric layers 130 through the PVAT by generating virtual image slices from the CT data. 1…n The distribution of the paths of the slices is arranged transversely with respect to the vessel centerline CL. Figure 5 The upper portion of FIG shows an example of such an image slice. In this example, the slices are arranged perpendicularly to the vessel centerline CL. The virtual slices can be arranged at regular intervals along the vessel centerline CL (e.g., at intervals of approximately 1 mm).

[0056] Then use image slices to define the reference above Figure 4 Concentric layers 130 described 1…n . You can Figure 5 This operation may be performed for the image slices as visualized in the upper part of , or alternatively, the CT data of each image slice may be transformed to provide an unfolded representation of the vessel and, in this case, concentric layers are defined in the unfolded representation of the vessel.

[0057] In the former case, for each image slice, the inner and outer boundaries of the PVAT in the slice are initially defined in the slice by performing image segmentation on the slice. As described above, in CT images, PVAT can be identified by the elevated X-ray attenuation level just outside the vessel wall. Figure 5 The dashed line graph in the upper part of FIG shows an example of the resulting segmented image. Concentric boundaries are then defined at regular intervals in the radial direction R between the inner and outer boundaries of the PVAT in each image slice so as to define the concentric layers 130. 1…nCT data from each slice is identified for each concentric layer in the slice. For a given slice, the CT data for each layer is bounded in the radial direction by the inner and outer concentric boundaries of the slice, and in the rotation direction φ by a rotation angle range [0, 2π]. The CT data for each layer is then obtained by concatenating the data from each layer in the slice in a direction along the vessel centerline CL. This provides a layer 130 of X-ray attenuation values ​​along the portion passing through the vessel. 1…n From here, the method may proceed to operation S130 described below.

[0058] exist Figure 5 The center and lower portions of the diagram illustrate the latter case. In this case, image segmentation is performed on the image slices in the same manner as described above. This defines the inner and outer boundaries of the PVAT in each of the image slices. The CT data for each image slice is then transformed to provide an unfolded representation of the blood vessel 120. Figure 5 This operation is illustrated in the center portion of . This operation can be performed by applying a coordinate transformation to the CT data, where data from angular positions around the rotational direction φ are mapped to linear axes. This will be Figure 5 The image slice shown in the upper part is mapped to the Figure 5 The image slices shown in the center portion of . The data from each of the image slices within the limits of [0, L] along the vessel centerline CL are then concatenated along the direction of the vessel centerline to provide an expanded volume representation of the vessel. Starting from the inner surface of the PVAT, a plurality of layers 130 are then defined in the PVAT 1…n , each layer has a defined thickness. This provides X-ray attenuation values ​​along the layer 130 that passes through the portion of the blood vessel. 1…n From here, the method may proceed to operation S130 described below.

[0059] Due to the irregular geometry of blood vessels and the irregular distribution of PVAT, Figure 5 The cascaded data obtained from the image slices illustrated in the central portion of the may have non-planar surfaces. This may complicate subsequent analysis of the X-ray attenuation values ​​along the path through the layer. To avoid this complication, the inner surface of the PVAT, defined by the inner boundary of the PVAT in the slice, may be flattened. This operation may be performed by further transforming the cascaded data so that positions on the inner surface of the PVAT are mapped to a planar surface. Figure 5 As shown in the lower part of , this reshapes the PVAT so that its inner surface is planar. Then, a plurality of layers 130 are defined in the PVAT starting from the inner surface of the PVAT as described above. 1…n, each layer having a defined thickness. This provides CT data in which one or more concentric layers 130 of PVAT surrounding a portion of a blood vessel 120 are shown. 1…n is represented as a plane layer. From here, the method may proceed to operation S130 described below.

[0060] Return to Figure 2 As shown in the flowchart, in operation S130, the X-ray attenuation values ​​are analyzed along the one or more concentric layers 130. 1…n The distribution of the paths of the blood vessels 120 is calculated to provide a spatial distribution of disease state values ​​140 representing the state of the vascular disease around the portion of the blood vessel 120. Typically, the distribution of the disease state values ​​140 is calculated based on the X-ray attenuation values ​​along the path through the one or more concentric layers 130. 1…n The gradient of the path is used to perform the operation. Figure 6 An example of operation S130 is described, Figure 6 Illustrated are a) examples of graphs of X-ray attenuation values ​​along a path extending from a vessel centerline CL in a radial direction R, and b) examples of normalized graphs of the X-ray attenuation values ​​illustrated in a), according to aspects of the present disclosure. Figure 6 The graph shown in a illustrates that for vessels in which CAD has been detected (dashed curve) and vessels in which CAD has not been detected (solid curve), Figure 3 An example of a change in the X-ray attenuation value in the radial direction R is shown in FIG.

[0061] As described in the literature cited above 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. Figure 6 The solid curve in a represents "no CAD". In contrast, relatively high X-ray attenuation values ​​are found in PVAT in the presence of vascular inflammation. This produces relatively high X-ray attenuation values ​​near the vessel wall, followed by a relatively steep gradient with increasing radial distance away from the vessel wall. This situation is represented by Figure 6 The dashed curve "CAD" shown in a.

[0062] Figure 6 The radial direction R shown in a passes approximately perpendicularly through the reference Figure 5 One or more concentric layers 130 are described 1…n These layers 130 1…n The location is also Figure 6 As shown in Figure a. Therefore, Figure 6 The X-ray attenuation values ​​shown in a show the X-ray attenuation values ​​along the concentric layers 130 extending in the radial direction R from the centerline of the blood vessel. 1…nAs can be appreciated, similar X-ray attenuation in each layer of the layer path can be provided for different rotation directions φ and for different positions along the vessel centerline CL. Figure 6 The graph shown in a. The shapes of these curves vary depending on the spatial distribution of CAD in the vessel.

[0063] By targeting concentric layers 130 1…n Normalization of the X-ray attenuation value in the innermost layer Figure 6 The X-ray attenuation values ​​shown in a are obtained Figure 6 Normalized graph of X-ray attenuation values ​​shown in b. Therefore, Figure 6 The normalized X-ray attenuation value at the vessel wall in b corresponds to one (1).

[0064] In operation S130, the spatial distribution of a disease state value 140 representing the state of vascular disease around a portion of the blood vessel 120 is determined. This operation can be performed by determining the spatial distribution of the values ​​of various biomarkers. Examples of biomarkers that can be evaluated in this operation include the fat attenuation index FAI (i.e., a biomarker described in the above-cited document by Antoniades C. et al.) and the volumetric perivascular characterization index "VPCI-i" (i.e., a biomarker described in document WO 2016 / 024128 A1). The spatial distribution of the values ​​of other biomarkers can alternatively be determined in this operation.

[0065] For example, now refer to Figure 6 To describe the spatial distribution of the volumetric perivascular characterization index "VPCI-i" (ie, a biomarker described in document WO 2016 / 024128 A1). In this example, multiple concentric layers 130 for PVAT are shown. 1…n Execute the above reference Figure 2 In the described determination S120 operation and analysis S130 operation. In this example, the analysis operation S130 includes:

[0066] One or more concentric layers 130 based on the PVAT along the portion surrounding the blood vessel 120 1…n The X-ray attenuation value of the path in the innermost layer is calculated for the path along the path passing through one or more concentric layers 130 1…n Normalize the distribution of X-ray attenuation values ​​of the path;

[0067] The plurality of concentric layers 130 extending radially outward from the centerline of the blood vessel 120 through the PVAT 1…n Integrate the normalized X-ray attenuation values ​​of the path; and

[0068] comparing the integrated normalized X-ray attenuation values ​​to a threshold value to provide a spatial distribution of disease state values ​​140 representing a state of vascular disease around the portion of the blood vessel 120; and

[0069] wherein the integration comprises assigning negative values ​​to normalized X-ray attenuation values ​​that exceed the normalized X-ray attenuation value in the innermost layer along the corresponding path, and assigning positive values ​​to normalized X-ray attenuation values ​​that are lower than the normalized X-ray attenuation value in the innermost layer along the corresponding path.

[0070] The above operations can be expressed as Figure 5 The above operations may be performed using CT data representing the vessel as visualized in the upper part of the image (i.e. the vessel is in its actual shape), or alternatively, the above operations may be performed using CT data representing the vessel in an expanded (and optionally further flattened) state (i.e. using CT data representing the vessel as in the image). Figure 5 This is performed using CT data of blood vessels that appear in the central part or lower part of the image.

[0071] In the above operations, providing a spatial distribution of disease state values ​​140 involves scanning a plurality of concentric layers 130 extending radially outward from the centerline of the blood vessel 120 through the PVAT. 1…n The normalized X-ray attenuation value is integrated along the path. Figure 5 In the upper portion of , the path can be defined at specified intervals in the rotation direction φ and can be defined at specified positions along the blood vessel centerline CL. For example, the path can be defined at regular intervals in the rotation direction φ and can be defined at regular intervals along the blood vessel centerline CL. If an expression such as Figure 5 , the normalized X-ray attenuation value can be obtained by normalizing the X-ray attenuation value along each path with respect to the X-ray attenuation value in the innermost layer, as described above with reference to Figure 6 a and Figure 6 b. The integration operation is then equivalent to performing an integration of the values ​​along each path using the normalized X-ray attenuation values. If the expression is used as in Figure 5 CT data of a blood vessel shown in the lower portion of FIG, the path in this expanded representation of the blood vessel typically extends through layer 130 1…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 in the innermost layer, as described above with reference to Figure 6 b. This is equivalent to the X-ray attenuation value at the corresponding position on the innermost layer along the concentric layer 130 1…nThe X-ray attenuation value in the thickness direction z of the layer is normalized. Then, the integration operation is equivalent to performing integration on the value along the path of the layer (ie, along the thickness direction z of the layer) using the normalized X-ray attenuation value.

[0072] The integration operation further includes assigning negative values ​​to normalized X-ray attenuation values ​​that exceed the normalized X-ray attenuation value in the innermost layer along the corresponding path, and assigning positive values ​​to normalized X-ray attenuation values ​​that are lower than the normalized X-ray attenuation value in the innermost layer along the corresponding path. Figure 6 b), which causes X-ray attenuation values ​​below 1 to be added to the integrated value, and X-ray attenuation values ​​above 1 to be subtracted from the integrated value. Therefore, the integrated value of each path represents Figure 6 The difference between the area indicated by the hatching in b and the area indicated by the dots, the hatching being above the line "normalized X-ray attenuation = 1".

[0073] The result of the above integration is a spatial distribution of integrated, normalized X-ray attenuation values, where each integrated value represents an integral along a different path. In a comparison operation, the integrated value for each path is then compared to a threshold value to provide a disease state value for the path. The disease state value for a path can be an analog or digital value, for example, CAD or no CAD. The threshold value can be determined based on clinical studies that, for example, separate vessels with CAD from those without CAD. Thus, by performing a comparison operation on each of the paths, a spatial distribution of disease state values ​​140 representing the state of vascular disease around a portion of blood vessel 120 is provided.

[0074] Return to Figure 2 Flowchart shown, in operation S140, then outputs graphical representations 150a, 150b of the spatial distribution of the disease state value 140. For example, the graphical representations 150a, 150b may be output to a display, e.g., Figure 3 The display 230 shown. Figure 7-10 Alternatively or additionally, in operation S150 , a predicted time for the state of the vascular disease around the portion of the expected blood vessel 120 to reach a predefined state is output. The predicted time is predicted based on the spatial distribution of the disease state values ​​140 .

[0075] In one example, a graphical representation of a spatial distribution of disease state values ​​140 representing the state of a vascular disease around a portion of the blood vessel 120 is output. In this example, the graphical representation 150a includes a projection of the spatial distribution of disease state values ​​140 onto a surface of revolution formed around the portion of the blood vessel 120. Figure 7 The example is described, Figure 7is a schematic diagram illustrating a first example of a graphical representation 150a illustrating a spatial distribution of disease state values ​​140 according to aspects of the present disclosure. Figure 7 In the example shown, the surface of revolution comprises a cylindrical surface. The disease state value 140 is projected along the path along which the integration is performed, and the corresponding disease state value 140 is displayed on the cylindrical surface at the intersection between the path and the cylindrical surface. Figure 7 In the example shown, dark shaded areas represent areas of CAD, and light shaded areas represent areas where CAD is not present. In this example, the disease state value 140 may alternatively be displayed in a different manner (e.g., via a different color). Figure 7 In addition to the graphical representation shown, an image illustrating a portion of the blood vessel 120 can also be displayed. For example, a reconstructed image of the portion of the blood vessel can be generated based on the CT data 110 and displayed within a cylindrical surface. This helps convey the correspondence between the vascular anatomy and the disease state value. The user can be provided with the ability to manipulate the graphical representation to obtain different perspectives on the disease state value 140.

[0076] 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 the blood vessel 120 is output. In this example, the graphical representation 150b includes a projection of the spatial distribution of disease state values ​​140 onto an unfolded surface, which is an unfolded representation of a surface of revolution formed around the portion of the blood vessel 120. Figure 8 This example is illustrated in Figure 8 is a schematic diagram illustrating a second example of a graphical representation 150b of a spatial distribution of disease state values ​​140 according to aspects of the present disclosure. Figure 7 Compared to the example shown, Figure 7 The cylindrical surface shown has been developed. This can be done by virtually cutting the cylindrical surface along line BB' and developing the surface around the center line CL to Figure 7 The cylindrical surface of Figure 8 The graphical representation is generated on the plane surface shown. Figure 7 Compared to the graphical representation 150a shown, the graphical representation 150b provides a less obstructed view of the disease state value 140. Alternatively, the disease state value 140 may be displayed directly on the PVAT. Figure 5 The data shown in the lower part is used to generate Figure 8 The graphical representation 150b is shown.

[0077] As described above, in operation S150, a predicted time for the state of the vascular disease around the portion of the expected blood vessel 120 to reach a predefined state is output. The predicted time is predicted based on the spatial distribution of the disease state value 140. As an alternative or supplement to operation S140, operation S150 may be performed.

[0078] In this example, the predefined states are defined by one or more of the following:

[0079] a value from the spatial distribution of disease state values ​​140 exceeds a predetermined threshold;

[0080] Onset of symptoms of vascular disease;

[0081] The physiological parameters of the blood vessel 120 meet predetermined threshold conditions; and

[0082] The value of the vascular disease risk metric exceeds a predetermined threshold.

[0083] Since in this example, the prediction time is predicted based on the spatial distribution of the disease state value 140, a more accurate prediction can be obtained. Examples of symptoms whose onset can be predicted according to this example include pain, skin infection, skin ulcer, skin discoloration, numbness, stroke, nausea, blood clots, chest pain, irregular heartbeat, shortness of breath, etc. Examples of physiological parameters that can be used to define predefined states include measurements of vascular geometry (e.g., vascular area or vascular diameter), measurements of blood flow parameters (e.g., blood flow rate, fractional flow reserve "FFR", instantaneous wave-free ratio "iFR", coronary artery flow reserve "CFR"), etc. Examples of vascular disease risk metrics include CAD risk, peripheral arterial disease "PAD" risk, WIFI score, GLASS score, Villata score, Framingham risk score, JBS3, etc.

[0084] In one example, a predicted time can be determined by inputting a spatial distribution of disease state values ​​140 into a neural network trained to predict the time at which the state of an expected vascular disease (e.g., as described in the above example) reaches a predefined state. The neural network can be trained using training data comprising spatial distributions of disease state values ​​140, each spatial distribution having a corresponding ground truth value for the time to reach the state of the associated vascular disease. The training data can include thousands or more spatial distributions and corresponding ground truth values. The training data can be curated based on historical clinical investigations performed on different subjects.

[0085] Also envisioned above reference Figure 2 A variation of the described method.

[0086] In one example, the CT data 110 includes spectral CT data. Spectral CT data defines X-ray attenuation in a medium within multiple energy intervals. As described above, by processing data from multiple energy intervals, it is possible to distinguish between media that have similar X-ray attenuation values ​​when measured within a single energy interval and are indistinguishable using conventional X-ray attenuation data.

[0087] In this example, the CT data 110 includes spectral CT data defining X-ray attenuation in a portion of a blood vessel 120 within each of a plurality of different energy intervals. X-ray attenuation values ​​are determined 120 along one or more concentric layers 130 of the PVAT that passes through the portion surrounding the blood vessel 120. 1…n The operation of the distribution of the path includes extracting from the spectral CT data one or more concentric layers 130 representing X-ray attenuation values ​​along the PVAT passing through the portion surrounding the blood vessel 120. 1…n The operation of analyzing S130 the distribution of the X-ray attenuation value is performed using the extracted PVAT attenuation data.

[0088] In this example, spectral CT data can be generated by various configurations of the spectral X-ray imaging system, as described above. Using spectral CT data helps improve the distinction between X-ray attenuation caused by PVAT and X-ray attenuation caused by other media that may be present near the blood vessel 120. This, in turn, provides a more reliable determination of the spatial distribution of the disease state value 140 in operation S130.

[0089] In another example, the CT data received in operation 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 enables identification of changes in the spatial distribution of disease state values ​​140 over time. Thus, this example helps improve understanding of the progression of disease in a blood vessel.

[0090] 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 S120 operation and the analyzing S130 operation are performed on the CT data representing the portion of the blood vessel 120 at each of the time points t0, t1; and referring to Figure 2 The methods described include:

[0091] The mutual registration represents the distribution of X-ray attenuation values ​​of the portion of the blood vessel 120 at each time point t0, t1; and

[0092] Among them, outputting S140 a graphical representation of the spatial distribution of the disease state value 140 includes: outputting the spatial distribution of the disease state value 140 representing the state of the vascular disease around the portion of the blood vessel 120 at each time point t0, t1 after mutual registration, and / or outputting the spatial distribution of the disease state value representing the change in the state of the vascular disease around the portion of the blood vessel 120 between two time points, t0, t1, wherein the change in the state of the vascular disease is calculated using the distributions at the two time points after mutual registration.

[0093] In this example, the time points may be separated by, for example, days, weeks or months.For example, a time point may represent the time of an initial clinical investigation or a follow-up investigation on a blood vessel.

[0094] The spatial distribution of the mutually registered disease state values ​​140 output in this example can be output by superimposing the spatial distributions. Various known techniques can be used to superimpose the spatial distributions.

[0095] The spatial distribution of disease state values ​​representing the change in the state of vascular disease output according to this example can be generated by performing a subtraction operation on the mutually registered spatial distributions. For example, the spatial distribution of disease state values ​​representing the change in the state of vascular disease can indicate the difference between the areas that have been identified as representing CAD.

[0096] The registration performed according to this example can be performed using various techniques. In general, the registration can be a rigid registration or an affine registration or a deformable registration. Various image registration techniques can be used, including intensity-based image registration techniques, feature-based image registration techniques, neural networks, etc. The image registration can be performed using the blood vessels represented in their actual shape (i.e., as they appear in the reconstruction of the received CT data 110), or alternatively, the image registration can be performed using an unfolded representation of the blood vessels (e.g., a reference image). Figure 5 described in the expanded representation) to execute.

[0097] In one example, the registration operation includes identifying the centerline of a blood vessel in the CT data at each time point. The blood vessel centerlines are then registered so as to mutually register the distribution of X-ray attenuation values ​​at each time point. In this example, the X-ray attenuation values ​​are determined S120 along one or more concentric layers 130 through the PVAT. 1…n The operation of the distribution of the paths includes identifying the center line of the portion of the blood vessel 120 in the CT data 110 at each of the multiple time points, and the operation of mutually registering the distribution of the X-ray attenuation values ​​includes mutually registering the center line of the portion of the blood vessel 120 at each of the multiple time points.

[0098] The vessel centerline provides a reference location in the vessel that enables reliable registration. The centerline of the vessel can be identified by identifying the lumen of the vessel in the CT data, defining the centerline of the lumen in the vessel, and defining the centerline of the lumen as the vessel centerline CL. The centerline of the lumen of the vessel can be defined as a line passing through the geometric centers of multiple planes that intersect the vessel lumen transversely. Figure 5 The upper portion of the image slices shown define the vessel centerline in this manner.

[0099] 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, the X-ray attenuation values ​​are determined S120 along one or more concentric layers 130 passing through the PVAT. 1…n The path distribution operations include:

[0100] determining a distribution of X-ray attenuation values ​​along a centerline of the portion of the blood vessel 120 at a selected one of the time points; and

[0101] Based on the determined distribution, for the CT data 110 representing the blood vessel 120 at one or more of the other time points, the X-ray attenuation values ​​are plotted along one or more concentric layers 130 of the PVAT passing through the portion surrounding the blood vessel 120. 1…n The distribution of the paths is normalized.

[0102] X-ray attenuation values ​​in the CT data of a region of interest can vary depending 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 ​​can also vary along a vessel due to natural flow characteristics within the vessel, the natural tapering of the vessel, and so on. Therefore, by normalizing the data using the distribution of X-ray attenuation values ​​along the vessel's centerline, this example provides a reliable correction for such effects. This reduces the impact of variations caused by differences in how the CT data was acquired. In this example, the selected time point can be the time of the first image in the time series, or the time of another image. The distribution of X-ray attenuation values ​​along the vessel's centerline from the selected image is then applied to one or more additional images to provide an identical intensity distribution along the vessel's centerline.

[0103] 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 ​​with each other includes:

[0104] identifying one or more anatomical landmarks in each distribution of x-ray attenuation values; and

[0105] The distributions of X-ray attenuation values ​​are registered with each other based on the identified one or more anatomical landmarks.

[0106] In this example, anatomical landmarks can be identified by performing an image segmentation operation on the CT data. Various image segmentation algorithms can be used for this purpose, including model-based segmentation, watermark-based segmentation, region growing, level set, graph cut, etc. A neural network can also be trained to segment the CT data 110 to identify anatomical landmarks. The one or more anatomical landmarks that can be identified in this example can include, for example, one or more of the following: a branch in the blood vessel 120, a lesion in the blood vessel 120.

[0107] In one example, portions of a vessel can be automatically identified in the CT data. This provides reliable identification of the portion of the vessel and ensures that the same portion of the vessel is analyzed in the distribution of X-ray attenuation values ​​that are mutually registered. This, in turn, facilitates accurate assessment of changes in the spatial distribution of the disease state value 140 over time. In this example, X-ray attenuation values ​​are determined S120 along one or more concentric layers 130 through the PVAT. 1…n The operation of distributing the path of the vessel 120 includes identifying a portion of the vessel 120 in the CT data 110 at each of a plurality of time points. For example, the portion of the vessel may be identified based on the location of landmarks in the image (e.g., a branch in the vessel or a lesion in the vessel). For example, the portion of the vessel may be identified as the portion of the vessel between two branches.

[0108] refer to Figure 9 and Figure 10 An example of performing image registration using an unfolded representation of a blood vessel is described. In this example, reference Figure 2 The methods described include:

[0109] The received CT data 110 is transformed to provide an unfolded representation of the blood vessel 120, wherein one or more concentric layers 130 of PVAT surrounding a portion of the blood vessel 120 are 1…n is represented as a flat layer; and

[0110] Therein, determining S120 X-ray attenuation values ​​along one or more concentric layers 130 of the PVAT passing through the portion surrounding the blood vessel 120 is performed using the transformed CT data. 1…n the distribution of paths; and

[0111] Therein, mutual registration of the distribution of X-ray attenuation values ​​representing the portion of the blood vessel 120 at each time point t0, t1 is performed using the distribution of X-ray attenuation values ​​determined from the transformed CT data.

[0112] In this example, using the reference Figure 5The registration is performed using an expanded representation of the vessel 120 depicted in the lower portion of FIG. Registering the distribution in this manner reduces irregularities that may appear in the distribution due to changes in vessel shape between time points. Such irregularities may confound the assessment of changes in the spatial distribution of the disease state value 140 over time.

[0113] Figure 9 is a schematic diagram illustrating an example of a graphical representation of spatial distribution of disease state values ​​140 representing the state of a vascular disease around a portion of a blood vessel 120 at two different time points t0, t1 that are registered with each other. Figure 9 , the shaded area bounded by the solid curve illustrates the spatial distribution of disease state values ​​140 (in this example, the range of CAD) at time t0. The area bounded by the dashed curve illustrates the range of CAD at time t1. Figure 9 The graphical representation shown provides a clear illustration of changes in the spatial distribution of disease state values ​​140 without the confounding effects of vessel shape.

[0114] Figure 10 is a schematic diagram illustrating an example of a spatial distribution of graphical representations 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, t1, according to aspects of the present disclosure. Figure 10 The shaded areas in the figure illustrate the change in the spatial distribution of CAD from time period t0 to t1. These areas are obtained by subtracting Figure 9 The areas of the regions representing CAD at times t0 and t1 are shown.

[0115] In another example, one or more recommended treatment or monitoring steps for the blood vessel 120 are determined 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. Figure 2 The methods described include:

[0116] determining one or more recommended treatment or monitoring steps for the blood vessel 120 based on the spatial distribution of the disease state values ​​140 ;

[0117] The one or more recommended treatment or monitoring steps are output.

[0118] Examples of treatment steps according to this example include: recommending treatment of a blood vessel with medication, recommending treatment of a blood vessel with a stent, recommending more exercise, etc. Examples of monitoring steps according to this example include: recommending blood pressure monitoring, recommending consultation with a cardiologist, etc.

[0119] In one example, a database is used to determine the recommended treatment or monitoring step(s). In this example, the database stores a plurality of reference graphical representations from historical surveys and the corresponding recommended treatment or monitoring step(s). For a given graphical representation, the database is searched to identify a matching graphical representation and, accordingly, provide the recommended treatment or monitoring step(s). The matching graphical representation can be identified by comparing the graphical representation with the reference graphical representation based on a similarity metric such as a dice coefficient or cross entropy.

[0120] In another example, the recommended treatment or monitoring step(s) are determined by inputting the spatial distribution of disease state values ​​140 into a predictive model. For example, the predictive model can be based on artificial intelligence, machine learning, or a decision tree. The predictive model can be trained using training data, wherein, for each of a plurality of blood vessels, the training data represents 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 historically recommended treatment or monitoring steps for the blood vessel 120.

[0121] The operation of determining one or more recommended treatment or monitoring steps for blood vessel 120 may also be based on additional information.

[0122] In one example, receiving patient data related to blood vessel 120 and determining one or more recommended treatment or monitoring steps for blood vessel 120 is further based on the patient data. The patient data may represent factors such as the patient's age, body mass index, gender, blood pressure, blood cholesterol level, indication of a family history of blood vessel disease, indication of smoking, diabetes, or breathing difficulties.

[0123] 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, a plaque distribution in the blood vessel 120 and / or values ​​of one or more blood flow parameters for the blood vessel 120 are determined based on the received CT data 110, and the determination of the one or more recommended treatment or monitoring steps for the blood vessel 120 is further based on the plaque distribution or the determined values ​​of the one or more blood flow parameters, respectively.

[0124] In this example, additional data may be provided as further input to the prediction model, and the corresponding additional training data may be used to train the prediction model to predict one or more recommended treatment or monitoring steps for the blood vessel 120. Plaque distribution in the blood vessel may be identified based on the values ​​of X-ray attenuation in the CT data 110. Improved isolation of plaque regions in the CT data may be achieved by providing the CT data 110 as spectral CT data 110.

[0125] 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 in the blood vessel. Figure 2 The methods described include:

[0126] receiving X-ray image data representing an intravascular treatment device in a blood vessel 120;

[0127] registering the X-ray image data to a graphical representation of the spatial distribution of disease state values ​​140; and

[0128] The location of the intravascular treatment device is indicated in the graphical representation.

[0129] In this example, X-ray image data can be provided by an X-ray projection imaging system. Registration can be performed by registering the X-ray image data to the CT image data. For example, the position of the intravascular treatment device in the graphical representation can be indicated in various ways (e.g., by displaying an overlay of the X-ray image data and the graphical representations 150a, 150b, or by displaying a marker). By indicating the position of the intravascular treatment device in the graphical representation, this example facilitates accurate positioning of the treatment device relative to the diseased area in the blood vessel. Therefore, the treatment can be selectively delivered to the diseased area. The position of the treatment device in the graphical representation can also be recorded when the treatment is delivered to provide a record of the treatment. Examples of treatments that can be delivered to the blood vessel according to this example include injectable drugs (e.g., dexamethasone and canakinumab). An example of an intravascular treatment device that can be used according to this example is the Bullfrog microinfusion device sold by Mercator MedSystems, Inc. of California, USA.

[0130] In another example, a computer program product is provided. The computer program product includes instructions that, when executed by one or more processors, cause the one or more processors to perform a method for determining a state of vascular disease. The method includes:

[0131] Receiving S110 computed tomography CT data 110 representing a portion of a blood vessel 120 ;

[0132] Determine S120 X-ray attenuation values ​​of one or more concentric layers 130 of perivascular adipose tissue (PVAT) surrounding a portion of a blood vessel 120 based on the CT data 110 . 1…n At each of a plurality of positions within a range of 1…n The distribution of paths;

[0133] Analyze S130 X-ray attenuation values ​​along one or more concentric layers 1301…n to provide a spatial distribution of disease state values ​​140 representing a state of a vascular disease around a portion of the blood vessel 120; and

[0134] Output S140 is a graphical representation 150 a , 150 b of the spatial distribution of the disease state values ​​140 , and / or output S150 is an expected time for the state of the vascular disease around the portion of the blood vessel 120 to reach a predefined state, the predicted time being predicted based on the spatial distribution of the disease state values ​​140 .

[0135] In another example, a system 200 for determining a state of vascular disease is provided. The system includes one or more processors 210 configured to:

[0136] Receiving S110 computed tomography CT data 110 representing a portion of a blood vessel 120 ;

[0137] Determine S120 X-ray attenuation values ​​of one or more concentric layers 130 of perivascular adipose tissue (PVAT) surrounding a portion of a blood vessel 120 based on the CT data 110 . 1…n At each of a plurality of positions within a range of 1…n The distribution of paths;

[0138] Analyze S130 X-ray attenuation values ​​along one or more concentric layers 130 1…n to provide a spatial distribution of disease state values ​​140 representing a state of a vascular disease around a portion of the blood vessel 120; and

[0139] Output S140 is a graphical representation 150 a , 150 b of the spatial distribution of the disease state values ​​140 , and / or output S150 is an expected time for the state of the vascular disease around the portion of the blood vessel 120 to reach a predefined state, the predicted time being predicted based on the spatial distribution of the disease state values ​​140 .

[0140] Figure 3 An example of the system 200 is illustrated in FIG. Note that the system 200 may further include one or more of the following: 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, a reconstructed CT image generated based on the CT data 110, etc.; a bed 240; an injector ( Figure 2 which is used to inject contrast agent into the vascular system; and a user input device configured to receive user input, such as a keyboard, a mouse, a touch screen, etc.

[0141] The above examples should be understood as illustrative of the present disclosure, rather than limiting. Additional examples are also contemplated. For example, the examples described with respect to computer-implemented methods may also be provided by corresponding computer program products or corresponding computer-readable storage media or corresponding systems 200. It should be understood that the features described with respect to any one example may be used alone or in combination with the features described with respect to the other examples, or in combination with one or more features of another of these examples, or in combination with the other examples. In addition, equivalents and modifications not described above may also be adopted without departing from the scope of the invention as defined in the appended claims. In the claims, the word "comprising" does not exclude other elements or operations, and the word "one" or "an" does not exclude a plurality. The fact that certain features are recited in mutually different dependent claims does not indicate that a combination of these features cannot be used to advantage. Any figure marks in the claims should not be interpreted as limiting their scope.

Claims

1. A computer-implemented method for determining a status of a vascular disease, the method comprising: receiving (S110) computed tomography (CT) data (110) representing a portion of a blood vessel (120); Determining (S120) X-ray attenuation values ​​in one or more concentric layers (130) of perivascular adipose tissue (PVAT) surrounding the portion of the blood vessel (120) based on the CT data (110) 1…n ) at each of a plurality of locations within a range of the length of the plurality of locations along the path through the one or more concentric layers (130 1…n )’s path distribution; Analyze (S130) X-ray attenuation values ​​along the path through the one or more concentric layers (130 1…n ) to provide a spatial distribution of disease state values ​​(140) representing a state of vascular disease around the portion of the blood vessel (120); and Outputting (S140) a graphical representation (150a, 150b) of the spatial distribution of disease state values ​​(140), and / or outputting (S150) a predicted time at which the state of vascular disease around the portion of the blood vessel (120) is expected to reach a predefined state, the predicted time being predicted based on the spatial distribution of disease state values ​​(140).

2. The computer-implemented method of claim 1 , wherein: The CT data (110) includes spectral CT data defining X-ray attenuation in the portion of the blood vessel (120) within each of a plurality of different energy intervals; and wherein determining (S120) X-ray attenuation values ​​along one or more concentric layers (130) passing through the portion of the PVAT surrounding the blood vessel (120) 1…n ) includes extracting PVAT attenuation data from the spectral CT data, the PVAT attenuation data representing the X-ray attenuation values ​​in the PVAT along the one or more concentric layers (130) of the PVAT that pass through the portion surrounding the blood vessel (120). 1…n ) of the paths; and Herein, analyzing ( S130 ) the distribution of X-ray attenuation values ​​is performed using the extracted PVAT attenuation data.

3. The computer-implemented method of claim 1 or claim 2, wherein: The CT data (110) represents the portion of the blood vessel (120) at each of a plurality of time points (t0, t1); and wherein the determining (S120) and the analyzing (S130) are performed on the CT data representing the portion of the blood vessel (120) at each time point (t0, t1); and The method further comprises: mutual registration representing the distribution of X-ray attenuation values ​​of the portion of the blood vessel (120) at each time point (t0, t1); and Wherein, outputting (S140) the graphical representation of the spatial distribution of the disease state value (140) includes: outputting the spatial distribution of the disease state value (140) representing the state of the vascular disease around the portion of the blood vessel (120) at each time point (t0, t1) after being aligned with each other, and / or outputting the spatial distribution of the disease state value representing the change of the state of the vascular disease around the portion of the blood vessel (120) between two time points in the time points (t0, t1), the change of the state of the vascular disease being calculated using the distribution of the X-ray attenuation values ​​at the two time points after being aligned with each other.

4. The computer-implemented method of claim 3, wherein: The method further includes transforming the received CT data (110) to provide an unfolded representation of the blood vessel (120), wherein the one or more concentric layers (130) of PVAT surrounding the portion of the blood vessel (120) are 1…n ) is represented as a plane layer; and wherein determining (S120) X-ray attenuation values ​​along one or more concentric layers (130) passing through the portion of the PVAT surrounding the blood vessel (120) 1…n ) is performed using the transformed CT data; and wherein mutual registration represents that the distribution of X-ray attenuation values ​​of the portion of the blood vessel (120) at each time point (t0, t1) is performed using the distribution of X-ray attenuation values ​​determined from the transformed CT data.

5. A computer-implemented method according to claim 3 or claim 4, wherein: Registering the distributions of X-ray attenuation values ​​with each other comprises: identifying one or more anatomical landmarks in each distribution of x-ray attenuation values; and The distributions of X-ray attenuation values ​​are registered with each other 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 branch in the blood vessel (120), a lesion in the blood vessel (120).

7. The computer-implemented method of any one of claims 3 to 6, wherein: The determining (S120) includes identifying the portion of the blood vessel (120) in the CT data (110) at each of the plurality of time points.

8. The computer-implemented method of any one of claims 3 to 7, wherein: The determining (S120) includes identifying a centerline of the portion of the blood vessel (120) in the CT data (110) at each of the plurality of time points; and wherein mutually registering the distribution of X-ray attenuation values ​​comprises mutually registering the centerline of the portion of the blood vessel (120) at each of the plurality of time points; and / or The determining (S120) further includes: determining a distribution of X-ray attenuation values ​​along the centerline of the portion of the blood vessel (120) at a selected one of the time points; and Based on the determined distribution, for the CT data (110) representing the blood vessel (120) at one or more of the other time points, X-ray attenuation values ​​are calculated along the one or more concentric layers (130) passing through the portion of the PVAT surrounding the blood vessel (120). 1…n ) is normalized for the distribution of the paths.

9. The computer-implemented method of any one of claims 1 to 8, wherein: The graphical representation of the spatial distribution of disease state values ​​(140) representing the state of vascular disease around the portion of the blood vessel (120) comprises: a projection of the spatial distribution of disease state values ​​(140) onto a surface of revolution formed around the portion of the blood vessel (120); or wherein the graphical representation of the spatial distribution of disease state values ​​(140) representing the state of vascular disease around the portion of the blood vessel (120) comprises a projection of the spatial distribution of disease state values ​​(140) onto an unfolded surface, the unfolded surface being an unfolded representation of a surface of revolution formed around the portion of the blood vessel (120).

10. The computer-implemented method of any one of claims 1 to 9, wherein: The method further comprises: receiving X-ray image data representing an intravascular treatment device in the blood vessel (120); registering the X-ray image data to the graphical representation of the spatial distribution of disease state values ​​(140); and A position of the intravascular treatment device is indicated in the graphical representation.

11. The computer-implemented method of claim 1 or claim 2, wherein: The method comprises outputting (S150) a predicted time at which the state of vascular disease around the portion of the blood vessel (120) is expected to reach a predefined state, and wherein the predefined state is defined by one or more of: a value from the spatial distribution of disease state values ​​(140) exceeds a predetermined threshold; Onset of symptoms of vascular disease; The physiological parameters of the blood vessel (120) meet predetermined threshold conditions; and The value of the vascular disease risk metric exceeds a predetermined threshold.

12. The computer-implemented method of any one of claims 1 to 11, wherein: The method further comprises: determining one or more recommended treatment or monitoring steps for the blood vessel (120) based on the spatial distribution of disease state values ​​(140); The one or more recommended treatment or monitoring steps are output.

13. The computer-implemented method of claim 12, further comprising: determining a distribution of plaque in the blood vessel (120) and / or values ​​of one or more blood flow parameters for the blood vessel (120) based on the received CT data (110); and in, Determining one or more recommended treatment or monitoring steps for the blood vessel (120) is also based on the plaque distribution or the determined values ​​of the one or more blood flow parameters, respectively.

14. A computer-implemented method according to claim 12 or claim 13, wherein: Determining one or more recommended treatments or monitoring steps for the blood vessel (120) includes inputting the spatial distribution of disease state values ​​(140) into a predictive model.

15. The computer-implemented method of any one of claims 1 to 14, wherein: The determination (S120) and the analysis (S130) are for a plurality of concentric layers (130) of PVAT. 1…n ), and wherein the analysis (S130) further comprises: Based on the one or more concentric layers (130) along the PVAT surrounding the portion of the blood vessel (120) 1…n ) of the innermost layer of the path to the X-ray attenuation value along the path through the one or more concentric layers (130 1…n ) is normalized for the distribution of the paths; The plurality of concentric layers (130) extending radially outward from the centerline of the blood vessel (120) through the PVAT 1…n ) path integrates the normalized X-ray attenuation value; and comparing the integrated normalized X-ray attenuation values ​​to a threshold value to provide the spatial distribution of disease state values ​​(140) representing a state of vascular disease around the portion of the blood vessel (120); and wherein the integration comprises assigning negative values ​​to normalized X-ray attenuation values ​​that exceed the normalized X-ray attenuation value in the innermost layer along the corresponding path, and assigning positive values ​​to normalized X-ray attenuation values ​​that are lower than the normalized X-ray attenuation value in the innermost layer along the corresponding path.

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