Measurement of blood flow parameters

Abstract

A computer-implemented method for measuring blood flow parameters in a vasculature is provided, the method comprising the steps of analyzing spectral CT projection data to isolate contrast agent projection data representative of flow of an injected contrast agent from the spectral CT projection data, sampling the contrast agent projection data at one or more regions of interest in the vasculature to provide temporal blood flow data in the one or more regions of interest, and calculating values ​​of one or more blood flow parameters in the one or more regions of interest from the temporal blood flow data.

Description

Technical Field

[0001] The present disclosure relates to the measurement of blood flow parameters in the vascular system. A computer-implemented method, a computer program product, and a system are disclosed.

Background Art

[0002] Various clinical studies involve the evaluation of blood flow in the vascular system. For example, the investigation of coronary artery disease "CAD" often involves the evaluation of blood flow. In this regard, various blood flow parameters have been investigated, including fractional flow reserve "FFR", instantaneous flow reserve ratio "iFR", coronary flow reserve "CFR", thrombolysis in myocardial infarction "TIMI" flow grade, index of microvascular resistance "IMR", and hyperemic microvascular resistance index "HMR".

[0003] Historically, such blood flow parameters have been measured using invasive devices such as pressure wires. However, more recently, angiographic measurements have been used. As an example, the fractional flow reserve "FFR" is often determined to evaluate the impact of stenosis on oxygen delivery to the myocardium in CAD evaluation. FFR is defined by the ratio P d / P a where P d represents the distal pressure at a distal position relative to the stenosis and P a represents the proximal pressure relative to the stenosis. Historically, these pressure values have been determined by placing invasive devices such as pressure wires at respective positions within the vascular system. However, more recently, angiographic techniques for determining FFR have been developed. According to fluid flow theory, pressure changes are related to changes in fluid velocity. In the case of FFR, angiographic images of the injected contrast agent can be analyzed to determine the blood flow velocity. Then, FFR can be calculated by using a hemodynamic model to estimate the pressure values within the blood vessel from the blood flow velocity. Thus, FFR as well as other blood flow parameters can be determined by angiography.

[0004] Known angiographic techniques for measuring blood flow velocity sample the intensity of an injected contrast agent over time in computer tomography "CT" image data and apply a mathematical model to the sampled data. In this regard, a technique for sampling a CT image reconstructed to determine blood flow velocity is disclosed in the document by Barfett, J. J. et al., "Intravascular Blood Flow Velocity and Volume Flow Rate Calculated from Dynamic 4D CT Angiography Using Time-of-Flight" (Int J Cardiovasc Imaging, 2014, 30:1383-1392). A technique for sampling raw, i.e., "projection", CT data to determine blood flow velocity is disclosed in "CT Angiographic Measurement of Vascular Blood Flow Velocity by Using Projection data" by Prevrhal, S. et al. (Radiology, Vol. 261: No. 3, December 2011, pp. 923-929).

[0005] Document WO 2016 / 001017 A1 relates to an apparatus for determining a fractional flow reserve "FFR" value of a biological coronary artery system. The coronary blood flow reserve ratio value determination unit determines the FFR value by using an FFR value determination algorithm adapted to determine the FFR value based on boundary conditions and a provided representation of the coronary artery system. The boundary conditions are specific to the organism and are determined by a boundary condition determination unit. The boundary condition determination unit determines the boundary conditions specific to the living body, and the coronary blood flow reserve ratio value determination unit uses not only the provided representation of the coronary artery system but also the living body, which is a specific boundary condition for determining the FFR value, so that the accuracy of the non-invasively determined FFR value can be improved. SUMMARY OF THE INVENTION PROBLEMS TO BE SOLVED BY THE INVENTION

[0006] However, there remains room for improvement in the accuracy of angiographic measurements of blood flow parameters, such as blood flow velocity, pressure, FFR, iFR, CFR, TIMI flow grade, IMR, and HMR.

Means for Solving the Problem

[0007] According to one aspect of the present disclosure, a computer-implemented method for measuring blood flow parameters in a vascular system is provided. The method includes A computer-implemented method for measuring blood flow parameters in a vascular system, the method comprising Receiving spectral computed tomography (CT) projection data representing the flow of an injected contrast agent in the vascular system, the spectral CT projection data representing X-ray attenuation in the vascular system at a plurality of energy intervals; Analyzing the spectral CT projection data to separate contrast agent projection data representing the flow of the injected contrast agent from the spectral CT projection data; Sampling the contrast agent projection data in one or more regions of interest in the vascular system to provide temporal blood flow data in the one or more regions of interest; Calculating values of one or more blood flow parameters in the one or more regions of interest from the temporal blood flow data and.

[0008] In the above method, since the contrast agent projection data is separated by analyzing the spectral CT projection data, improved separation is provided between the attenuation caused by the contrast agent and the attenuation caused by background substances such as fat, water, soft tissue, bone, and metal that may be present in the vicinity of the vascular system. Further, the analysis and sampling are performed on the projection data, for example, as opposed to reconstructing the spectral CT projection data and sampling the reconstructed contrast agent projection data, so that potential inaccuracies introduced by reconstructing the spectral CT projection data are avoided. Thus, the method provides more reliable blood flow data, which, as a result, leads to a more accurate calculation of blood flow parameters.

[0009] Further aspects, features, and advantages of the present disclosure will become apparent from the following description of the embodiments taken in conjunction with the accompanying drawings.

Brief Description of the Drawings

[0010]

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DETAILED DESCRIPTION OF THE INVENTION

[0011] Embodiments of the present disclosure are provided with reference to the following description and drawings. In this description, for purposes of explanation, many specific details of several examples are set forth. In this specification, references to the same language as “embodiment,” “aspect,” or related to an embodiment, which means that at least one of the characteristics, structures, or properties described is included in that embodiment. Also, it should be understood that features described in connection with one example may be used in another example, and for the sake of brevity, not all features are necessarily replicated in each example. For example, features described in connection with a computer-implemented method may be implemented in a corresponding manner in a computer program product and in a system.

[0012] In the following description, reference is made to a method for measuring blood flow parameters in one or more regions of interest within the vascular system. In some examples, the vascular system is the coronary vascular system and the region of interest is a coronary vessel. In some examples, the vessel is a coronary artery. However, it should be understood that the vessel may alternatively be a coronary vein. More generally, the method may be used to measure blood flow parameters in a vascular system in another part of the body other than the heart. For example, the region of interest may alternatively be a vessel located in the leg, arm, brain, etc., i.e., a vein or an artery.

[0013] In this specification, examples are also referred to where the measured blood flow parameter is the velocity, pressure, or FFR value of a blood vessel. However, these are for illustrative purposes only, and it should be understood that the systems and methods disclosed herein may alternatively be used to measure other blood flow parameters such as iFR values, CFR values, TIMI flow grades, IMR values, and HMR values, volume blood flow values, hyperemic stenosis resistance “HSR” values, zero flow pressure “ZFP” values, and instantaneous diastolic blood flow velocity - pressure gradient “IHDVPS” values, and are not limited thereto.

[0014] Note that the computer-implemented method disclosed in this specification can be provided as a non-transitory computer-readable storage medium storing computer-readable instructions that cause at least one processor to execute the method when executed by the at least one processor. In other words, the computer-implemented method can be implemented in a computer program product. The computer program product can be provided by dedicated hardware or by hardware capable of executing software in association with appropriate software. When provided by a processor, the functions of the features of the method can be provided by a single dedicated processor, or by a single shared processor, or by a plurality of individual processors some of which can be shared. One or more of the functions 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 the cloud.

[0015] The explicit use of the terms "processor" or "controller" should not be construed as exclusively referring to hardware capable of executing software, but may 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, etc. Further, examples of the present disclosure can take the form of a computer-usable storage medium, or a computer-readable storage medium accessible computer program product, the computer program product providing program code for use by or in connection with a computer or any instruction execution system. For the purposes of this description, a computer-usable storage medium or a computer-readable storage medium can be any device capable of storing, transmitting, propagating, or transporting a program for use by or in connection with an instruction execution system, apparatus, or device. The medium can be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system or device or propagation medium. Examples of computer-readable media include semiconductor or solid state memory, magnetic tape, removable computer disk, random access memory "RAM", read-only memory "ROM", rigid magnetic disk, and optical disk. Current examples of optical disks include compact disk read-only memory "CD-ROM", compact disk read-write "CD-R / W", Blu-Ray (trademark), and DVD.

[0016] As described above, there remains room for improving the accuracy of angiographic measurements of blood flow parameters, such as blood flow velocity, pressure, FFR, iFR, CFR, TIMI flow grade, IMR, and HMR.

[0017] FIG. 1 is a flowchart showing an example of a computer-implemented method for measuring blood flow parameters in a vascular system according to some aspects of the present disclosure. FIG. 2 is a schematic diagram showing an example of a system 200 for measuring blood flow parameters in a vascular system according to some aspects of the present disclosure. The operations described in relation to the method shown in FIG. 1 may be executed in the system 200 shown in FIG. 2, and vice versa. Referring to FIG. 1, a computer-implemented method for measuring blood flow parameters in a vascular system includes step S110 of receiving spectral computed tomography (CT) projection data 110a, 110b representing the flow of an injected contrast agent in the vascular system, the spectral CT projection data representing X-ray attenuation in the vascular system at a plurality of energy intervals DE 1..m and a step; step S120 of analyzing the spectral CT projection data to separate contrast agent projection data representing the flow of the injected contrast agent from the spectral CT projection data; one or more regions of interest 120 in the vascular system 1..n step S130 of sampling the contrast agent projection data in to provide temporal blood flow data in one or more regions of interest; from the temporal blood flow data, one or more regions of interest 120 1..n step S140 of calculating values of one or more blood flow parameters in; and includes.

[0018] In the above method, since the contrast agent projection data is separated by analyzing the spectral CT projection data, improved separation is provided between the attenuation caused by the contrast agent and the attenuation caused by background substances such as fat, water, soft tissue, bone, and metal that may be present in the vicinity of the vascular system. Further, the analysis and sampling are performed on the projection data, as opposed to, for example, reconstructing the spectral CT projection data and sampling the reconstructed contrast agent projection data, thus avoiding potential inaccuracies that may be introduced by reconstructing the spectral CT projection data. Accordingly, the method provides more reliable blood flow data, which, as a result, leads to a more accurate calculation of blood flow parameters.

[0019] The above method is also described with reference to FIG. 3, which is a schematic diagram illustrating the heart, including an example of a blood vessel 260 according to some aspects of the present disclosure. The heart shown in FIG. 3 is labeled with the left coronary artery, LCA, the right coronary artery, RCA, and the LCA orifice and RCA orifice that respectively define the orifices of these arteries in the aorta. As an example, the operations described above with reference to FIG. 1 may be performed to measure blood flow parameters in the cardiovascular system. For example, the blood flow parameters may be measured at a proximal location 1201 or a distal location 1202 within the left coronary artery, i.e., in a region of interest such as the blood vessel 260 shown in FIG. 3.

[0020] Returning to FIG. 1, in operation S110, spectral CT projection data 110a, 110b are received. The spectral CT projection data represents the flow of an injected contrast agent within the vasculature. The spectral CT projection data may also be referred to as raw spectral CT data. The spectral CT projection data may be received by one or more processors 210 as shown in FIG. 2. The spectral CT projection data may be received via any form of data communication, including wired communication, optical communication, and wireless communication. As some examples, when wired or optical communication is used, the communication may occur via signals transmitted over an electrical or optical cable, and when wireless communication is used, the communication may occur via, for example, RF or optical signals. Generally, one or more processors 210 may receive the spectral CT projection data 110a, 110b from a spectral X-ray imaging system or from another source such as, for example, a computer-readable storage medium, the Internet, or the cloud.

[0021] Continuing to refer to FIG. 1, the spectral CT projection data 110a, 110b received in operation S110 represents the flow of an injected contrast agent within the vasculature. In this regard, the spectral CT projection data may be generated after injection of the contrast agent into the vasculature. The contrast agent may include a substance such as iodine, or a lanthanide such as gadolinium, or any other actual substance that provides visibility of the flow within the vasculature into which the contrast agent is injected.

[0022] Continuing to refer to FIG. 1, the spectral CT projection data 110a, 110b received in operation S110 may generally be generated by a spectral X-ray imaging system, i.e., a spectral CT imaging system, or a spectral X-ray projection imaging system.

[0023] A spectral CT imaging system generates spectral CT projection data while rotating or stepping an X-ray source detector device around an imaging region. Examples of spectral CT imaging systems include cone beam spectral CT imaging systems, photon counting spectral CT imaging systems, dark field spectral CT imaging systems, and phase contrast spectral CT imaging systems. As an example, spectral CT projection data 110a, 110b can be generated by a spectral CT 7500 commercially available from Philips Healthcare, Best, The Netherlands.

[0024] An example of a spectral CT imaging system 220 that can be used to generate the spectral CT projection data received in operation S110 is shown in FIG. 2. Another example of such a spectral CT imaging system 220 is shown in FIG. 4. FIG. 4 is a schematic diagram showing an example of a spectral CT imaging system 220 including an X-ray detector 230 according to some aspects of the present disclosure. The spectral CT imaging system 220 shown in FIG. 4 includes an X-ray source 270 and an X-ray detector 230. The X-ray source 270 and the X-ray detector 230 are mechanically coupled to a gantry (not shown in FIG. 4). During operation, the X-ray source 270 and the X-ray detector 230 are rotated by a gantry around a rotation axis 250, while acquiring spectral CT projection data representing X-ray attenuation within a portion of an object such as an object 290. The spectral CT projection data obtained from a plurality of rotation angles around the rotation axis can then be reconstructed into a volume image. As shown in FIG. 4, the X-ray detector 230 includes a plurality of detector elements 240 arranged along the rotation axis 250 of the X-ray detector 1..k and can include. The detector element 240 1..k may be arranged, for example, parallel or at an acute angle to the rotation axis. With this device, the X-ray detector 230 can capture projection data representing the flow of a contrast agent in a direction along the rotation axis, such as may occur in some of the blood vessels within the heart.

[0025] As described above, the spectral CT projection data received in operation S110 may alternatively be generated by a spectral X-ray projection imaging system. The spectral X-ray projection imaging system typically includes a support arm, such as a so-called "C-arm", that supports an X-ray source and an X-ray detector. The spectral X-ray projection imaging system may alternatively include a support arm having a different shape than this example, such as an O-arm, for example. The spectral X-ray projection imaging system typically generates projection data using a support arm held in a stationary position with respect to the imaging region during acquisition of image data. However, the spectral X-ray projection imaging system can also acquire spectral CT projection data while rotating the support arm around the axis of rotation. Subsequently, this projection data can be reconstructed into a volume image in a manner similar to that of the spectral CT imaging system. Therefore, the spectral CT projection data received in operation S110 may alternatively be generated by a spectral X-ray projection imaging system.

[0026] The spectral CT projection data 110a, 110b received in operation S110 represent X-ray attenuation within the vasculature at a plurality of energy intervals DE. 1..m Generally, two or more energy intervals, i.e., m is an integer and m ≧ 2 may be used. The ability to generate X-ray attenuation data at a wide variety of energy intervals DE 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 within a single energy interval and are indistinguishable in conventional X-ray image data. In this regard, various different settings of the spectral X-ray imaging system can be used to generate the spectral CT projection data received in operation S110, some of which are described with reference to FIG. 4. 1..m

[0027] Referring to FIG. 4, generally, the X-ray source 270 can include a plurality of monochromatic sources, or one or more polychromatic sources, and the X-ray detector 230 can be a common detector for detecting a plurality of different X-ray energy intervals, or each detector can detect a different X-ray energy interval DE 1..m a plurality of detectors for detecting, 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 for classifying the detected X-ray photons into one of a plurality of energy intervals based on their individual energies. In a photon counting detector, the associated energy interval can be determined for each received X-ray photon by detecting the pulse height induced by the electron-hole pairs generated in response to the absorption of the X-ray photons in the direct conversion material.

[0028] Using the various configurations of the X-ray source 270 and detector 230 described above, X-rays within various X-ray energy intervals DE 1..m can be detected. Generally, discrimination between different X-ray energy intervals can be provided to the source 270 by temporally switching the X-ray tube potential of a single X-ray source 270, i.e., "rapid kVp switching", or by temporally switching or filtering the emission of X-rays from a plurality of X-ray sources. In such an arrangement, a common X-ray detector can be used to detect X-rays over a plurality of different energy intervals, and attenuation data for each energy interval is generated in a time series. Alternatively, in the detector 230, a multi-layer detector or a photon counting detector may be used to identify different X-ray energy intervals. Such a detector can detect X-rays from a large number of X-ray energy intervals that are performed almost simultaneously DE 1..m and thus there is no need to perform temporal switching in the source 270. In this way, a multi-layer detector, or a photon counting detector, can be used in combination with a polychromatic source to generate X-ray attenuation data at various X-ray energy intervals DE 1..m .

[0029] Other combinations of the foregoing X-ray sources and detectors can also be used for a plurality of energy intervals DE1..m can be used to provide desired spectral CT projection data. For example, in a further setting, the need to sequentially switch different X-ray sources that emit X-rays at different energy intervals can be avoided by attaching the X-ray source detector pair to the gantry at a rotational offset position around the axis of rotation. In this setting, each source-detector pair operates independently, and the separation between spectral CT projection data at various energy intervals DE 1..m is facilitated by the rotational offset of the source-detector pair. In this setting, to reduce the influence of X-ray scatter, by applying an energy selection filter to the X-ray detector, improved separation between spectral CT projection data at various energy intervals DE 1..m can be achieved.

[0030] Returning to FIG. 1, in operation S120, the received spectral CT projection data 110a, 110b are analyzed to separate contrast agent projection data representing the flow of the injected contrast agent from the spectral CT projection data. This operation may include identifying, as the contrast agent projection data, a portion of the spectral CT projection data corresponding to the material of the contrast agent, based on the energy-dependent X-ray attenuation signature of the material, and / or based on the energy-dependent X-ray attenuation signatures of one or more background materials represented in the spectral CT projection data.

[0031] In order to be used when analyzing spectral CT projection data in operation S120, the use of various "material decomposition" techniques is contemplated. The spectral CT projection data represents the flow of an injected contrast agent within the vascular system. The contrast agent to be injected can include materials such as iodine or gadolinium. Such materials are often used as contrast agents in consideration of their attenuation at the X-ray energies used in diagnostic X-ray imaging systems. In addition to the attenuation arising from the contrast agent, the spectral CT projection data can also represent the attenuation arising from one or more background materials such as fat, water, bone, soft tissue, vascular calcification, air, and metals such as gold, titanium, tungsten, and platinum. Such materials are often also present in the vicinity of the vascular system, and thus the attenuation arising from these materials can also be represented in the spectral CT projection data. For example, when imaging the cardiovascular system, the bones in the form of parts of the spine or ribs are often within the field of view of the spectral CT imaging system. Similarly, fiducial markers, implanted medical devices, and interventional devices are typically formed from metals such as those cited above, and the attenuation arising from these materials can also be captured in the spectral CT projection data. By separating the contrast agent projection data from the spectral CT projection data, more reliable data regarding the flow of the injected contrast agent can be obtained.

[0032] An example of a material decomposition technique that can be used in operation S120 to separate the contrast agent projection data from the spectral CT projection data is the "Empirical, projection-based-basis-component decomposition method" by Brendel, B. et al. (Medical Imaging 2009, Physics of Medical Imaging, edited by Ehsan Samei and Jiang Hsieh, Proc. of SPIE Vol. 7258, 72583Y.). Another suitable material decomposition technique is to refer to "K-edge imaging in x-ray computed tomography using multi-bin photon counting detectors" by Roessl, E. and Proksa, R. (Phys Med Biol. 2007 Aug 7, 52(15):4679-96). Another suitable material decomposition technique is disclosed in the published PCT patent application WO / 2007 / 034359 A2. Another suitable material decomposition technique is disclosed in the literature of "Dual-energy(spectral)CT: applications in abdominal imaging" by Silva, A. C. et al. (RadioGraphics 2011; 31(4):1031-1046).

[0033] Generally, the X-ray attenuation spectrum of a material includes contributions from Compton scattering and from the photoelectric effect. The attenuation due to Compton scattering is relatively similar for different materials, but the attenuation from the photoelectric effect is strongly material-dependent. Both Compton scattering and the photoelectric effect exhibit energy dependence, and this effect is utilized by material decomposition techniques to analyze spectral CT projection data in order to distinguish different materials.

[0034] In general, material decomposition algorithms operate by decomposing the attenuation spectrum of an absorption medium into contributions from a set of assumed "basis" materials. The energy-dependent x-ray attenuation of the assumed basis materials is typically modeled as a combination of absorption from Compton scattering and the photoelectric effect. Some materials also have k-absorption edge ("k-edge") energies within the energy range used by diagnostic x-ray imaging systems, and this effect can also be utilized to distinguish different materials. The spectral decomposition algorithm then seeks to estimate the amount of each of the basis materials required to produce the x-ray attenuation measured at two or more energy intervals. Water and iodine are examples of basis substances that are often separated using so-called dual-material decomposition algorithms in clinical practice. Non-fat soft tissue, fat, and iodine are examples of basis materials that are often separated in clinical practice using three-material decomposition algorithms.

[0035] As noted above, if any of the k-edge energies of the basis materials are within the energy range used by a diagnostic x-ray imaging system, i.e., within about 30 to 120 keV, this can be useful for identifying the contributions of the basis materials. The k-edge energy of a material is defined as the minimum energy required for a photoelectric event to occur with a k-shell electron. The k-edge occurs at a characteristic energy for each material. The k-edge energy of a material is characterized by a sharp increase in its x-ray attenuation spectrum at the x-ray energy corresponding to the k-edge energy value. The k-edge energies of many materials present in the human body are too low to be detected in diagnostic x-ray imaging systems. For example, the k-edge energies of hydrogen, carbon, oxygen, and nitrogen are energies below 1 keV. However, materials such as iodine (k-edge = 33.2 keV), gadolinium (50.2 keV), gold (80.7 keV), platinum (78.4 keV), tantalum (67.4 keV), holmium (55.6 keV), and molybdenum (k-edge = 20.0 keV) have k-edge energy values that enable their distinction in spectral CT projection data acquired from diagnostic x-ray imaging systems.

[0036] As described above, materials such as iodine and gadolinium may be present within the vascular system as injected contrast agents, and materials such as gold, platinum, tantalum, holmium, and molybdenum may be present near the vascular system as reference markers, implanted medical devices, and interventional devices. Thus, the k-edge energies of such materials can be used to identify their presence in the spectral CT projection data 110a, 110b. As an example, FIG. 5 is a graph showing the dependence of the mass attenuation coefficient on X-ray energy for two example materials, iodine and water. The mass attenuation coefficient shown in FIG. 5 represents X-ray attenuation. The sharp increase in X-ray attenuation of iodine at 33.2 keV facilitates the separation between the contributions of each of these materials to the combined attenuation spectrum. In this embodiment, iodine and water can be separated by arranging one energy interval DE1 close to the k-edge energy of 33.2 keV and another energy interval DE2 significantly higher than the k-edge energy.

[0037] As a result, by using such material decomposition techniques, contrast agent projection data representing the flow of the injected contrast agent can be separated from the spectral CT projection data.

[0038] Returning to FIG. 1, in operation S130, the contrast agent projection data is then sampled in one or more regions of interest 120 within the vascular system 1..n to provide time-resolved blood flow data in the one or more regions of interest. Note that both the operations of analysis S120 and sampling S130 are performed on the spectral CT projection data, i.e., the raw data. In contrast to the reconstructed data, performing the sampling operation S130 on the projection data has the advantages of improved accuracy and a simplified workflow, as it avoids the inaccuracies and complexities introduced by operations such as image reconstruction and vessel segmentation. These operations are particularly error-prone and complex when the vasculature is affected by motion.

[0039] A technique for performing a sampling operation S130 on CT projection data, which is used in operation S130 and can provide temporal blood flow data in a region of interest, is disclosed in the literature by Prevrhal, S. et al. cited above. This literature discloses a technique for measuring the flow velocity from multi-detector CT projection data from row to row obtained during a single gantry rotation when a bolus of contrast agent flows through a vascular phantom. According to the present disclosure, the principle known from this document for CT projection data is applied to the contrast agent projection data separated from the spectral CT projection data in operation S120. In particular, this literature discloses a technique for determining the flow velocity for a contrast agent trace represented in the projection, i.e., the "Radon" space. In this literature, the projection data representing the contrast agent trace is generated using an imaging device having a row of X-ray detector elements arranged laterally with respect to the flow direction of the contrast agent. The flow velocity is determined based on the time at which the center of the contrast agent trace is detected in consecutive detector rows. According to the present disclosure, a similar projection space analysis can be performed by sampling the contrast agent projection data obtained in operation S120 over time in the region of interest.

[0040] Next, referring to FIG. 6, a technique S130 for sampling contrast agent projection data in one or more regions of interest in spectral CT projection data will be described. FIG. 6 is an example of a sinogram including spectral CT projection data representing a) X-ray attenuation within a first energy interval DE1 and b) X-ray attenuation within a second energy interval DE2, according to some aspects of the present invention. The sinogram shown in FIG. 6 represents spectral CT projection data acquired during rotation of the gantry of a spectral CT imaging system around the vasculature. Thus, the rotation angle q in FIGS. 6a and 6b also represents time. FIG. 6a represents the range of X-ray energy in a relatively low energy portion of the X-ray spectrum, and FIG. 6b shows projection data for a second energy interval DE2, which may represent the range of X-ray energy in a relatively high energy portion of the X-ray spectrum, with respect to the first energy interval DE1. As seen in FIGS. 6a and 6b, the intensity of the projection data in each of the two illustrated energy intervals changes with time. The change in intensity represents the change in the amount of contrast agent within the vasculature over time. The period shown in FIG. 6 includes a so-called inflow phase when the contrast agent enters the region of interest and a so-called washout phase when the contrast agent exits the region of interest. In the example shown in FIG. 6, the spectral CT projection data represents X-ray attenuation at two energy intervals. Such data may be referred to as "dual energy" data. Note that the spectral CT projection data may alternatively represent X-ray attenuation at three or more energy intervals.

[0041] The spectral CT projection data 110a, 110b shown in FIGS. 6a and 6b can be analyzed using one of the material decomposition techniques described above in operation S120. The result of operation S120 is to provide contrast agent projection data representing the flow of the injected contrast agent. Isolated contrast agent projection data is not shown in FIG. 6, but may be represented similarly in the formation of the sinogram. In operation S130, the contrast agent projection data is then used to provide temporal blood flow data in one or more regions of interest within the vasculature, in one or more regions of interest 120 1..nis sampled over time. As described above, by sampling the contrast agent projection data, more reliable blood flow data can be provided, as opposed to, for example, sampling the reconstructed image data.

[0042] Generally, the region of interest represents a location within the vasculature where it is desired to determine temporal blood flow data. Region of interest 120 1,2 can represent, for example, proximal and distal locations within a coronary artery where it is desired to calculate a blood flow parameter such as FFR. Performing the sampling operation S130 on the region of interest within the projection data also requires that the region of interest be defined within the projection data. In this regard, the regions of interest may be defined directly in the projection data, or they may be defined indirectly in the projection data via the reconstructed image. In the first situation, the regions of interest can be defined directly in the spectral CT projection data or directly in the isolated contrast agent projection data. Landmarks are identified within this data and can then be used to identify the regions of interest within the projection data. The second situation will be described later. In the first situation, the landmarks can represent features of the vasculature such as bifurcations, or bony regions such as portions of the ribs or spine. Such landmarks produce characteristic patterns in the projection data, for example, in the sinograms shown in FIGS. 6a and 6b, and can be used to determine the location of the regions of interest within the vasculature in the sinogram. For example, a bifurcation in the vasculature can produce a characteristic pattern of two phase-shifted waves that enables the determination of the regions of interest within the vasculature. Any metal landmark appears as a prominent sine wave within the sinogram and can be identified within the projection data by applying a threshold to the intensity values within the sinogram. Thus, in the first situation, the method described with reference to FIG. 2 is identifying one or more anatomical landmarks within the spectral CT projection data; identifying the location of one or more regions of interest 120 within the spectral CT projection data based on the one or more identified anatomical landmarks 1..n and includes.

[0043] As described above, in the second situation, the sampling operation S130 is performed on the projection data, and the region of interest is defined indirectly within the projection data via the reconstructed image. In this second situation, the spectral CT projection data 110a, 110b shown in FIGS. 6a and 6b, or alternatively, the isolated contrast agent projection data obtained from operation S120, is reconstructed into one or more reconstructed images representing the vasculature. This image can then be used to identify the region of interest, the location of which is then mapped back to the projection data. As an example, FIG. 7 is an example of a reconstructed image 110' obtained from spectral CT projection data according to some aspects of the present disclosure. The reconstructed image 110' shown in FIG. 7 can be obtained by reconstructing the projection data shown in FIGS. 6a and 6b. The reconstruction of either the spectral CT projection data or the separated contrast agent projection data provides a volume image of the vasculature, which can be used to help identify the regions of interest 1201, 1202 within the projection data. Various known image reconstruction techniques can be used to reconstruct the projection data. Reconstructing the isolated contrast agent projection data obtained from operation S120 represents only the vasculature and provides a reconstructed image in which materials other than the contrast agent material are omitted. Thus, if attenuation from the bone region is represented in the spectral CT projection data, the attenuation from the bone is omitted from the reconstructed image 110'. The absence of such features can provide a clearer view of the vasculature. In contrast, the reconstruction of the spectral CT projection data 110a, 110b received in operation S110 provides a more complete image of the anatomical landmarks surrounding the vasculature. Such additional landmarks can also facilitate the identification of the region of interest.

[0044] Thus, in some examples, the method reconstructing the spectral CT projection data, or the contrast agent projection data, into one or more reconstructed images representing the vasculature; identifying the position of one or more regions of interest 1201, 1202 in one or more reconstructed images; mapping the positions of the one or more regions of interest 1201, 1202 to the spectral CT projection data or the contrast agent projection data, respectively; having Sampling S130 is performed on the contrast agent projection data at positions corresponding to the one or more mapped regions of interest.

[0045] In this second state where the regions of interest are defined indirectly in the projection data via the reconstructed images, the positions of the one or more regions of interest 1201, 1202 can be identified manually or automatically within the reconstructed image 110'. Manual identification of the regions of interest 1201, 1202 may be performed by means of the user operating a user input device in combination with the displayed reconstructed image 110'. Automatic identification of the regions of interest 1201, 1202 can be performed using a feature detector or a trained neural network. If the spectral CT projection data is reconstructed, segmentation may be performed on the reconstructed image to assist in identifying the regions of interest. Thus, it is also possible to segment one or more reconstructed images and perform the operation of identifying the position of one or more regions of interest 1201, 1202 in the one or more reconstructed images within one or more segmented reconstructed images. For this purpose, various segmentation algorithms are known.

[0046] As described above, a region of interest within the reconstructed image can be automatically identified using a feature detector or a trained neural network. The region of interest may be a blood vessel or a stenosis within a blood vessel. The neural network or feature detector can automatically identify potential locations for measuring blood flow parameters within the reconstructed image. As an example, the reconstructed image 110' shown in FIG. 7 represents a portion of the cardiovascular system and includes the left coronary artery 260. The feature detector or trained neural network can identify the left coronary artery and a stenosis therein, and indicate regions of interest 1201, 1202 as potential proximal and potential distal locations, respectively, for use in performing blood flow velocity measurements. Subsequently, the blood flow velocity measurement values can be used to determine the FFR value of the stenosis.

[0047] In this second state, after identifying the location of one or more regions of interest 1201, 1202 within the reconstructed image(s), the location of the region(s) of interest 1201, 1202 is mapped to the spectral CT projection data or the contrast agent projection data. This operation can be performed using the known spatial correspondence between the location within the reconstructed image and the projection data from which the image was reconstructed. Subsequently, the above-described sampling operation S130 is performed on the contrast agent projection data at locations corresponding to the one or more mapped regions of interest to provide temporal blood flow data at the one or more regions of interest.

[0048] Returning to FIG. 1, in operation S140, one or more regions of interest 120 1..nValues of one or more blood flow parameters in the region of interest are calculated from the temporal blood flow data. This operation can include, for example, calculating blood flow velocity in the region of interest. This operation can be performed according to the above-cited publication by Prevrhal, S. et al. Alternatively or additionally, blood flow parameters other than blood flow velocity can be determined in operation S140. For example, one or more of pressure, transit time, fractional flow reserve (FFR) value, instantaneous flow reserve (iFR) value, coronary flow reserve (CFR) value, thrombolysis in myocardial infarction (TIMI), flow grade value, microvascular resistance index (IMR) value, and hyperemia microvascular resistance index (HMR) value can be calculated for a blood vessel 260 in the vasculature in operation S140. Examples of these are described in more detail below.

[0049] The pressure at a region of interest within a blood vessel can generally be determined using a hemodynamic model that uses blood flow velocity and dimensions of the vasculature as input parameters. Examples of such models are disclosed in the publication by Nickisch et al., "Learning Patient-Specific Lumped Models for Interactive Coronary Blood Flow Simulations" (MICCAI, 2015). The vessel dimensions used in the model can be determined, for example, by analyzing contrast projection data, or alternatively, by determining the region of interest 120. 1..n can be determined from the projection data from measurements of the reconstructed spectral CT projection data at

[0050] The transit time is T T , is defined as the time it takes for an injected front of contrast agent to travel between a proximal location within a blood vessel and a distal location within the blood vessel. The proximal and distal locations may be defined relative to the ostium of the associated blood vessel and defined as regions of interest 1201, 1202, respectively. The transit time may be calculated using the blood flow velocity determined as described above and using the calculated distance between the regions of interest, which may be determined in the spectral CT projection data or reconstructed images.

[0051] In one example, the vascular system has a blood vessel 260, and one or more regions of interest 120 1..n The operation S140 of calculating the value of one or more blood flow parameters in 1..n includes Calculating the proximal blood flow velocity and the distal blood velocity at respective proximal 1201 and distal 1201 positions within the blood vessel 260; Using a hemodynamic model to calculate a proximal blood pressure P a And a distal blood pressure P d At respective proximal and distal positions within the blood vessel; Proximal blood pressure P a And distal blood pressure P d Calculating one or more of a coronary flow reserve ratio FFR value and an instantaneous flow reserve ratio iFR value from the proximal blood pressure P And distal blood pressure P

[0052] In this example, further inputs to the hemodynamic model may include geometric measurements of the blood vessel obtained from projection data or from a reconstructed image representing the blood vessel. FFR is given by the equation FFR = P d / P a Equation 1 As defined by Here, P d Represents the distal pressure at the distal position within the blood vessel, and P a Represents the proximal pressure at the proximal position within the blood vessel. The hemodynamic model may be used to calculate the pressure values P d And P a Using geometric measurements of the blood vessel. When measured for stenosis, the distal position may be distal to the stenosis. Examples of hemodynamic models that may be used in this embodiment are disclosed in the literature by Nickisch et al. cited above.

[0053] IFR can be calculated using the same equation as FFR. IFR differs from FFR in that FFR is measured during the wave-free period of diastole. This wave-free period can be determined for the vascular system, for example, based on the received electrocardiogram signal.

[0054] The coronary flow reserve "CFR" is defined as the ratio of coronary blood flow at maximum hyperemia to that at the baseline state. CFR represents the capacity of the coronary circulation to respond to the physiological increase in oxygen demand associated with the corresponding increase in blood flow. CFR can be determined from the velocity in the region of interest using the formula CFR = APV h / APV b Equation 2 as described below. APV h is the mean peak velocity measured in centimeters per second during maximum hyperemia, and APV b is the mean peak velocity measured in centimeters per second under baseline conditions.

[0055] As noted above, IMR can be calculated from the transit time T T 0 and is defined by the formula IMR = P d · T T Equation 3 as follows. Here, P d represents the distal pressure at a distal location within the vessel. IMR is typically calculated using the time-averaged value of the distal pressure P d over the cardiac cycle at maximum hyperemia.

[0056] In the case of a vessel undergoing severe epicardial stenosis, an alternative definition of IMR has been proposed, and IMR is calculated using the formula IMR = P d · T T · (P d ― P w ) / (P a ― P w ) Equation 4 as follows.

[0057] In Equation 4, the additional term P a represents the proximal pressure at a proximal location within the vessel, and the additional term P w represents the coronary wedge pressure, i.e., the pressure at the distal location of the stenosis when the vessel is occluded by an inflated balloon. IMR is typically calculated using the proximal pressure P aUsing the time-averaged value, the distal pressure P over the cardiac cycle at the maximum high pressure d Using the time-averaged value, the distal position Pos within the blood vessel when it is occluded by an inflated balloon d Using the time-averaged value of the pressure over the cardiac cycle at the distal position, the wedge pressure P w is used to calculate according to Equation 4. Another equation for calculating IMR can also be used, and these are similarly dependent on the passage T T as well.

[0058] HMR is typically calculated by the equation HMR = P d / V d Equation 5 as follows. Here, P d represents the distal pressure at the distal position within the blood vessel, and V d represents the blood flow velocity at the distal position within the blood vessel. The parameter V d may be determined as described above.

[0059] One or more additional operations may be performed in the computer-implemented method described above with reference to FIG. 1.

[0060] In one example, the volume image is reconstructed from data other than the spectral CT projection data received in operation S110. The reconstructed volume image is then used to identify the region of interest to be sampled in operation S12). In this example, the method receives volume image data representing one or more regions of interest 120 1..n and reconstructs the received volume image data to provide a reconstructed image representing one or more regions of interest 120 1..n and identifies one or more regions of interest 1201, 1202 within the reconstructed image and 1..nmapping the position to spectral CT projection data to provide one or more regions of interest within the spectral CT projection data, and including.

[0061] Various advantages are associated with identifying regions of interest within an image reconstructed from volumetric image data, i.e., data separate from the spectral CT projection data received in operation S110. For example, the volumetric image data may be acquired prior to the spectral CT projection data and enable its use as a planning image for planning measurements of blood flow parameters using the subsequently acquired spectral CT projection data. The volumetric image data may be acquired months, weeks, days, or a shorter period prior to the spectral CT projection data. Alternatively, the volumetric image data may be acquired at a time later than the spectral CT projection data and used for subsequent analysis of the spectral CT projection data. The use of various types of volumetric image data is also contemplated. For example, the volumetric image data may be CT projection data, spectral CT projection data, ultrasound data, and the like. The use of different types of data for the spectral CT projection data can reduce the amount of X-ray dose applied to the subject, or provide reconstructed images with different resolutions, or provide reconstructed images in which different features, such as soft tissue, are more clearly visible.

[0062] In this example, the volumetric image data can be reconstructed using known image reconstruction techniques. The regions of interest can be identified within the reconstructed image manually or automatically and in a manner similar to that described above for the reconstruction of images from spectral CT projection data. Region of interest 120 1..nThe operation of mapping the position to the spectral CT projection data can be performed by registering two data sets. For example, when a reconstructed image is generated from the spectral CT projection data, image registration is performed and can be used to map the region of interest. Alternatively, the region of interest may be mapped by registering a reconstructed image from the volume data to the spectral CT projection data. This operation may be performed based on the identification and matching of corresponding landmarks or fiducial markers in both images. Mapping the region of interest to the spectral CT projection data enables accurate identification of landmarks in the reconstructed image from the volume image data and calculation of blood flow parameters using only the spectral CT projection data. This has the advantage of avoiding inaccuracies when reconstructing and segmenting the spectral CT projection data, and as a result, not exposing the calculation of blood flow parameters to such inaccuracies.

[0063] In another embodiment, the operation of S120 for analyzing the spectral CT projection data is selectively performed in one or more regions of interest 1201, 1202. In other words, the analysis operation S120 is performed only in the regions of interest. The regions of interest may be points in the image space represented by the spectral CT projection data, or larger volumes, such as groups of voxels. The regions of interest can include, for example, a portion of a blood vessel. By performing the operation S120 only in the regions of interest, the blood flow parameters can be obtained with a reduced computational load.

[0064] In another example, the method described with reference to FIG. 1 is performed using only the data representing the "uptake" phase when the injected contrast agent flows into the region of interest. In this embodiment, the flow of the injected contrast agent in the vascular system is represented by the uptake phase in which the leading edge of the injected contrast agent flows into one or more regions of interest 120 1..n to. The flow of the injected contrast agent in the vascular system is such that the trailing edge of the injected contrast agent is one or more regions of interest 120 1..ndoes not represent the washout phase flowing out from. Data representing only the uptake phase can be generated by triggering the acquisition of spectral CT data based on the injection time of the contrast agent, or by post-acquisition analysis of the spectral CT projection data acquired to detect the position in front of the injected contrast agent. By omitting data from the washout phase, a reduced amount of data is processed in this way. Also, when there is no need to image the vascular system during the washout phase, the X-ray dose to the subject can be reduced.

[0065] Alternatively, instead of using only the data representing the uptake phase, the method described with reference to FIG. 1 may be performed using only the data representing the "washout" phase when the injected contrast agent flows out from the region of interest. In this example, the flow of the injected contrast agent in the vascular system is such that the trailing edge of the injected contrast agent flows out of one or more regions of interest 120 1..n represents the washout phase. Also, the flow of the injected contrast agent in the vascular system does not represent the uptake phase in which the leading edge of the injected contrast agent flows into one or more regions of interest 120 1..n Data representing only the washout phase can be generated by triggering the acquisition of spectral CT data based on the injection time of the contrast agent, or by post-acquisition analysis of the spectral CT projection data acquired to detect the position of the trailing edge of the injected contrast agent. By omitting data from the uptake phase, a reduced amount of data is processed in this way. The X-ray dose to the subject can also be reduced when there is no need to image the vascular system during the uptake phase.

[0066] In another example, a computer program product is provided. When the computer program product is executed by one or more processors, the one or more processors are provided with instructions to execute a method for measuring blood flow parameters in the vascular system. This method Step S110 of receiving spectral computed tomography (CT) projection data 110a, 110b representing the flow of an injected contrast agent within the vascular system, wherein the spectral CT projection data represents X-ray attenuation within the vascular system at a plurality of energy intervals DE 1..m and a step of representing X-ray attenuation within the vascular system at Step S120 of analyzing the spectral CT projection data to separate contrast agent projection data representing the flow of the injected contrast agent from the spectral CT projection data

[0067] Step S130 of sampling the contrast agent projection data in one or more regions of interest 120 within the vascular system to provide temporal blood flow data in the one or more regions of interest From the temporal blood flow data, one or more regions of interest 120 1..n Step S140 of calculating values of one or more blood flow parameters in and having.

[0068] In another example, a system 200 for measuring blood flow parameters within the vascular system is provided. The system

[0069] Step S110 of receiving spectral computed tomography (CT) projection data 110a, 110b representing the flow of an injected contrast agent within the vascular system, wherein the spectral CT projection data represents X-ray attenuation within the vascular system at a plurality of energy intervals DE 1..m and a step of representing X-ray attenuation within the vascular system at Step S120 of analyzing the spectral CT projection data to separate contrast agent projection data representing the flow of the injected contrast agent from the spectral CT projection data Sampling the contrast agent projection data in one or more regions of interest 120 within the vascular system 1..n to provide temporal blood flow data in the one or more regions of interest in step S130 From the temporal blood flow data, one or more regions of interest 120 1..n Step S140 of calculating values of one or more blood flow parameters in includes one or more processors 210 configured to execute

[0070] An example of system 200 is shown in FIG. 2. System 200 may include, for example, a spectral X-ray imaging system for generating spectral CT projection data received in operation S110, such as the spectral CT imaging system 220 shown in FIG. 2, one or more calculated blood flow parameters, a monitor 300 for displaying reconstructed images, etc., a patient bed 310, an injector (not shown in FIG. 2) for injecting a contrast agent into the vascular system, and one or more of a user input device configured to receive user inputs such as a keyboard, a mouse, a touch screen (not shown in FIG. 2). Note that

[0071] The above embodiments should be understood as illustrative of the present disclosure and not limiting. Further examples are contemplated. For example, the examples described in relation to the computer-implemented method may also be provided by a corresponding method, by a computer program product, or by a computer-readable storage medium, or by system 200. It should be understood that the features described for any one embodiment may be used alone or in combination with other described features, and may be used in combination with one or more other features of another embodiment or a combination of other embodiments. Furthermore, equivalents and modifications not described above may also be used 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 acts, and the indefinite article "a" or "an" does not exclude a plurality. The mere fact that certain features are described in mutually different dependent claims does not indicate that a combination of these features cannot be used advantageously. Any reference signs in the claims should not be construed as limiting their scope.

Claims

1. A computer-implemented method for measuring blood flow parameters in a vascular system, the method comprising: Receiving spectral computed tomography (CT) projection data representing the flow of an injected contrast agent in the vascular system, the spectral CT projection data representing X-ray attenuation in the vascular system at a plurality of energy intervals; Analyzing the spectral CT projection data to separate contrast agent projection data representing the flow of the injected contrast agent from the spectral CT projection data; Sampling the contrast agent projection data in one or more regions of interest in the vascular system to provide temporal blood flow data in the one or more regions of interest; Calculating values of one or more blood flow parameters in the one or more regions of interest from the temporal blood flow data; And Reconstructing the spectral CT projection data or the contrast agent projection data into one or more reconstructed images representing the vascular system; Identifying the locations of one or more regions of interest in the one or more reconstructed images; Mapping the locations of the one or more regions of interest to the spectral CT projection data or the contrast agent projection data respectively; Further comprising: The sampling step is performed on the contrast agent projection data at positions corresponding to the one or more mapped regions of interest; A method.

2. The method further comprises: Identifying one or more anatomical landmarks in the spectral CT projection data; Identifying the locations of one or more regions of interest in the spectral CT projection data based on the one or more identified anatomical landmarks; The computer-implemented method according to claim 1.

3. The step of identifying one or more anatomical landmarks in the spectral CT projection data comprises identifying one or more characteristic patterns corresponding to the one or more landmarks in a sinogram representation of the spectral CT projection data. The computer-implemented method according to claim 2.

4. A computer-implemented method for measuring blood flow parameters in a vascular system, the method comprising: Receiving spectral computed tomography (CT) projection data representing the flow of an injected contrast agent within the vascular system, wherein the spectral CT projection data represents X-ray attenuation within the vascular system at a plurality of energy intervals; Analyzing the spectral CT projection data to separate contrast agent projection data representing the flow of the injected contrast agent from the spectral CT projection data; Sampling the contrast agent projection data in one or more regions of interest within the vascular system to provide temporal blood flow data in the one or more regions of interest; Calculating values of one or more blood flow parameters in the one or more regions of interest from the temporal blood flow data; Reconstructing the spectral CT projection data or the contrast agent projection data into one or more reconstructed images representing the vascular system; Identifying the locations of one or more regions of interest within the one or more reconstructed images; Mapping the locations of the one or more regions of interest to the spectral CT projection data or the contrast agent projection data, respectively; comprising; wherein the sampling step is performed on the contrast agent projection data at locations corresponding to the one or more mapped regions of interest, and the method further comprises: segmenting the one or more reconstructed images; comprising further; wherein the step of identifying the locations of one or more regions of interest within the one or more reconstructed images is performed in the one or more segmented reconstructed images; method. **Claim 5** A computer-implemented method for measuring blood flow parameters within a vascular system, the method comprising: Receiving spectral computed tomography (CT) projection data representing the flow of an injected contrast agent within the vascular system, wherein the spectral CT projection data represents X-ray attenuation within the vascular system at a plurality of energy intervals; Analyzing the spectral CT projection data to separate contrast agent projection data representing the flow of the injected contrast agent from the spectral CT projection data; Sampling contrast agent projection data in one or more regions of interest within the vascular system to provide temporal blood flow data in the one or more regions of interest; Calculating values of one or more blood flow parameters in the one or more regions of interest from the temporal blood flow data; Receiving volume image data representing the one or more regions of interest; Reconstructing the received volume image data to provide a reconstructed image representing the one or more regions of interest; Identifying one or more regions of interest within the reconstructed image; Mapping the positions of the one or more regions of interest from the reconstructed image to the spectral CT projection data to provide one or more regions of interest within the spectral CT projection data; A method comprising: "Claim 6" The step of analyzing the spectral CT projection data: Identifying a portion of the spectral CT projection data corresponding to the contrast agent material as contrast agent projection data based on the energy-dependent x-ray attenuation signature of the material and / or based on the energy-dependent x-ray attenuation signatures of one or more background materials represented in the spectral CT projection data; The computer-implemented method according to claim 1, comprising: "Claim 7" The contrast agent material comprises one or more of iodine and gadolinium, and / or The one or more background materials comprise one or more of fat, water, bone, soft tissue, vascular calcification, air, and metal; The computer-implemented method according to claim 6. "Claim 8" The step of analyzing the spectral CT projection data is selectively performed in the one or more regions of interest; the computer-implemented method according to any one of claims 1 to 7. "Claim 9" The spectral CT projection data is generated by a spectral x-ray imaging system comprising an x-ray detector having a plurality of detector elements arranged along the axis of rotation of the x-ray detector, and the spectral CT projection data is acquired while rotating the x-ray detector around the axis of rotation; the computer-implemented method according to any one of claims 1 to 8. "Claim 10" The vascular system comprises blood vessels, and the step of calculating the value of one or more blood flow parameters in the one or more regions of interest comprises calculating one or more of the blood flow parameters for the blood vessels of blood flow velocity, pressure, transit time, fractional flow reserve (FFR) value, instantaneous wave-free ratio (iFR) value, coronary flow reserve (CFR) value, thrombolysis in myocardial infarction (TIMI) blood flow grade value, index of microvascular resistance (IMR) value, and hyperemic microvascular resistance (HMR) value The computer-implemented method according to any one of claims 1 to 9, comprising the step of

11. The vascular system comprises blood vessels, and the step of calculating the value of one or more blood flow parameters in the one or more regions of interest comprises calculating a proximal blood flow velocity and a distal blood flow velocity at each of a proximal position and a distal position within the blood vessel; and calculating a proximal blood pressure and a distal blood pressure at each of the proximal position and the distal position within the blood vessel using a hemodynamic model; and calculating one or more of a fractional flow reserve (FFR) value and an instantaneous wave-free ratio (iFR) value from the proximal blood pressure and the distal blood pressure; and The computer-implemented method according to any one of claims 1 to 9, comprising

12. The flow of the contrast agent injected into the vascular system represents a capture phase in which a leading edge of the injected contrast agent flows into the one or more regions of interest, the flow of the contrast agent injected into the vascular system does not represent a washout phase in which a trailing edge of the injected contrast agent flows out of the one or more regions of interest, or the flow of the contrast agent injected into the vascular system represents a washout phase in which a trailing edge of the injected contrast agent flows out of the one or more regions of interest, the flow of the contrast agent injected into the vascular system does not represent a capture phase in which a leading edge of the injected contrast agent flows into the one or more regions of interest, The computer-implemented method according to any one of claims 1 to 11.

13. A computer program product having instructions that, when executed by one or more processors, cause the one or more processors to perform the method according to any one of claims 1 to 12.

14. A system for measuring blood flow parameters in a vascular system, the system having one or more processors, the one or more processors receiving spectral computed tomography (CT) projection data representative of the flow of an injected contrast agent within the vascular system, the spectral CT projection data representing X-ray attenuation within the vascular system at a plurality of energy intervals, analyzing the spectral CT projection data to separate from the spectral CT data contrast agent projection data representative of the flow of the injected contrast agent, sampling the contrast agent projection data in one or more regions of interest within the vascular system to provide temporal blood flow data in the one or more regions of interest, calculating values of one or more blood flow parameters in the one or more regions of interest from the temporal blood flow data, reconstructing the spectral CT projection data or the contrast agent projection data into one or more reconstructed images representative of the vascular system, identifying the locations of one or more regions of interest within the one or more reconstructed images, mapping the locations of the one or more regions of interest to the spectral CT projection data or the contrast agent projection data, respectively is configured to wherein the sampling step is performed on the contrast agent projection data at locations corresponding to the one or more mapped regions of interest, system.

Images