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] SUMMARY A computer-implemented method, computer program product, and system are disclosed that relate to measuring blood flow parameters in the vascular system. [Background technology]

[0002] Various clinical studies involve performing an assessment of blood flow in the vasculature. For example, investigations of coronary artery disease "CAD" often involve an assessment of blood flow. In this regard, various blood flow parameters have been investigated, including fractional coronary flow reserve "FFR", instantaneous fractional flow reserve "iFR", coronary flow reserve "CFR", Thrombolysis in Myocardial Infarction "TIMI" flow grade, Index of Coronary 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; more recently, however, angiographic measurements have been used. As an example, fractional coronary flow reserve "FFR" is often determined to assess the effect of a stenosis on the delivery of oxygen to the myocardium in CAD evaluation. FFR is the ratio P d / P a is defined by P d represents the distal pressure at the location distal to the stenosis, and P a represents the proximal pressure to the stenosis. Historically, the values ​​of these pressures have been determined by placing invasive devices, such as pressure wires, at the respective locations in the vasculature. However, more recently, angiographic techniques have been developed to determine the FFR. According to fluid flow theory, pressure changes are related to changes in fluid velocity. In the example of FFR, angiographic images of an injected contrast agent can be analyzed to determine the blood flow velocity. The FFR can then be calculated by using a hemodynamic model to estimate the pressure value in the vessel from the blood flow velocity. Thus, the FFR as well as other blood flow parameters can be determined by angiography.

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

[0005] Document WO 2016 / 001017 A1 relates to a device for determining a flow reserve ratio "FFR" value of a coronary artery system of an organism. A fractional flow reserve value determination unit determines the FFR value by using an FFR value determination algorithm adapted to determine the FFR value on the basis of boundary conditions and a provided representation of the coronary artery system, the boundary conditions being specific to the organism and being determined by the boundary condition determination unit. The boundary condition determination unit determines the boundary conditions specific to the organism, and the fractional flow reserve value value determination unit uses not only the provided representation of the coronary artery system but also the organism specific boundary conditions for determining the FFR value, so that the accuracy of the non-invasively determined FFR value can be improved. Summary of the Invention [Problem 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 vasculature is provided, the method comprising: 1. A computer-implemented method for measuring a blood flow parameter in a vasculature, the method comprising: receiving spectral computed tomography CT projection data representative of flow of an injected contrast agent within the vasculature, the spectral CT projection data representative of x-ray attenuation within the vasculature at a plurality of energy intervals; analyzing the spectral CT projection data to separate from the spectral CT projection data contrast agent projection data representative of flow of the injected contrast agent; sampling contrast agent projection data at one or more regions of interest within the vasculature to provide temporal blood flow data in the one or more regions of interest; calculating values ​​of one or more blood flow parameters in said one or more regions of interest from said temporal blood flow data; has.

[0008] In the above method, because the contrast agent projection data is separated by analyzing the spectral CT projection data, improved separation is provided between attenuation resulting from the contrast agent and attenuation resulting from background materials such as fat, water, soft tissue, bone, and metal that may also be present in the vicinity of the vasculature. Furthermore, because the analysis and sampling is performed on the projection data as opposed to, for example, reconstructing the spectral CT projection data and sampling the reconstructed contrast agent projection data, potential inaccuracies that may be introduced by reconstructing the spectral CT projection data are avoided. Thus, the method provides more reliable blood flow data, which in turn results in more accurate calculation of blood flow parameters.

[0009] Further aspects, features, and advantages of the present disclosure will become apparent from the following detailed description of the embodiments which proceeds with reference to the accompanying drawings. [Brief description of the drawings]

[0010] [Figure 1] 1 is a flow chart illustrating an example of a computer-implemented method for measuring blood flow parameters in a vasculature, according to some aspects of the present disclosure. [Diagram 2] FIG. 2 is a schematic diagram illustrating an example of a system 200 for measuring blood flow parameters in a vascular system, according to some embodiments of the present disclosure. [Diagram 3] 2 is a schematic diagram illustrating a heart, including an example of a blood vessel 260, according to some embodiments of the present disclosure. [Figure 4] 2 is a schematic diagram illustrating an example of a spectral CT imaging system 220 including an X-ray detector 230, according to some embodiments of the present disclosure. [Diagram 5] 1 is a graph showing the dependence of mass attenuation coefficient on X-ray energy for two materials, iodine and water. [Figure 6] 1 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 embodiments of the present invention. [Figure 7]1 is an example of a reconstructed image 110' obtained from spectral CT projection data, according to some aspects of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] Examples of the present disclosure are provided with reference to the following description and drawings. In this description, for the purpose of explanation, many specific details of some examples are described. In this specification, reference to "examples", "implementations", or similar language means that a feature, structure, or characteristic described in connection with an example is included in at least one of the examples. It should also be appreciated that features described in connection with one example may also be used in other examples, and that for the sake of brevity, not all features are necessarily duplicated in each example. For example, features described in connection with a computer-implemented method may be implemented in a computer program product and in a system in a corresponding manner.

[0012] In the following description, reference is made to a method for measuring blood flow parameters in one or more regions of interest in a vasculature. In some examples, the vasculature is the coronary vasculature and the regions of interest are coronary vessels. In some examples, the vessels are coronary arteries. However, it should be understood that the vessels may alternatively be coronary veins. More generally, the method may be used to measure blood flow parameters in vasculature in other parts of the body other than the heart. For example, the regions of interest may alternatively be vessels, i.e., veins or arteries, located in the legs, arms, brain, etc.

[0013] Reference is also made herein to examples in which the measured blood flow parameters are vascular velocity, pressure, or FFR values. However, it should be appreciated that these serve as examples only, and that the systems and methods disclosed herein may alternatively be used to measure other blood flow parameters, such as, but not limited to, iFR values, CFR values, TIMI flow grades, IMR values, and HMR values, volumetric blood flow values, hyperemic stenosis resistance "HSR" values, zero flow pressure "ZFP" values, and instantaneous diastolic blood flow velocity-pressure gradient "IHDVPS" values.

[0014] It should be noted that the computer-implemented methods disclosed herein may be provided as a non-transitory computer-readable storage medium storing computer-readable instructions that, when executed by at least one processor, cause the at least one processor to perform the method. In other words, the computer-implemented methods may be implemented in a computer program product. The computer program product may be provided by dedicated hardware or hardware capable of executing software in association with appropriate software. When provided by a processor, the functions of the method features may be provided by a single dedicated processor, or by a single shared processor, or by multiple individual processors, some of which may be shared. One or more functions of the method features may be provided by a processor shared within a networked processing architecture, such as, for example, a client / server architecture, a peer-to-peer architecture, the Internet, or a cloud.

[0015] 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, and the like. Furthermore, examples of the present disclosure may take the form of a computer usable storage medium, or a computer program product accessible from a computer readable storage medium, the computer program product providing program code for use by or in connection with a computer or any instruction execution system. For purposes of this description, a computer usable storage medium or computer readable storage medium may be any apparatus that can comprise, store, transmit, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. The medium may be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system or apparatus or propagation medium. Examples of computer readable media include semiconductor or solid state memory, magnetic tape, removable computer disks, random access memory "RAM", read only memory "ROM", rigid magnetic disks, and optical disks. Current examples of optical disks include compact disk-read only memory "CD-ROM," compact disk-read / write "CD-R / W," Blu-Ray™, and DVD.

[0016] As discussed 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 flow chart illustrating an example of a computer-implemented method for measuring blood flow parameters in a vasculature according to some aspects of the present disclosure. FIG 2 is a schematic diagram illustrating an example of a system 200 for measuring blood flow parameters in a vasculature according to some aspects of the present disclosure. Operations described in connection with the method illustrated in FIG 1 may be performed in the system 200 illustrated in FIG 2, and vice versa. With reference to FIG 1, the computer-implemented method for measuring blood flow parameters in a vasculature includes: A step S110 of receiving spectral computed tomography CT projection data 110a, 110b representative of the flow of an injected contrast agent in the vascular system, the spectral CT projection data being divided into a plurality of energy intervals DE 1..m representing X-ray attenuation in the vasculature at A step S120 of analyzing the spectral CT projection data to separate contrast agent projection data representative of the flow of the injected contrast agent from the spectral CT projection data; One or more regions of interest 120 within the vasculature 1..n Sampling the contrast agent projection data in the one or more regions of interest at step S130 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 a step S140 of calculating values ​​of one or more blood flow parameters in Includes.

[0018] In the above method, because the contrast agent projection data is separated by analyzing the spectral CT projection data, improved separation is provided between attenuation resulting from the contrast agent and attenuation resulting from background materials such as fat, water, soft tissue, bone, and metal that may also be present in the vicinity of the vasculature. Furthermore, because the analysis and sampling is performed on the projection data as opposed to, for example, reconstructing the spectral CT projection data and sampling the reconstructed contrast agent projection data, potential inaccuracies that may be introduced by reconstructing the spectral CT projection data are avoided. Thus, the method provides more reliable blood flow data, which in turn results in 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 a 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, right coronary artery, RCA, and the LCA ostium and RCA ostium, which respectively define the openings 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 in a region of interest, such as a proximal location 1201, or a distal location 1202 in the left coronary artery, i.e., the blood vessel 260 shown in FIG. 3.

[0020] Returning to FIG. 1 , in operation S110, the spectral CT projection data 110a, 110b are received. The spectral CT projection data represents the flow of the injected contrast agent in 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 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 be via signals transmitted over electrical or optical cables, and when wireless communication is used, the communication may be via, for example, RF or optical signals. In general, the one or more processors 210 may receive the spectral CT projection data 110a, 110b from the spectral X-ray imaging system or from another source, such as, for example, a computer-readable storage medium, the Internet, or the cloud.

[0021] 1, the spectral CT projection data 110a, 110b received in act 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 a contrast agent into the vasculature. The contrast agent may include a substance such as iodine, or a lanthanide such as gadolinium, or indeed another 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 act S110 may generally be generated by a spectral X-ray imaging system, ie, a spectral CT imaging system or a spectral X-ray projection imaging system.

[0023] The spectral CT imaging system generates the spectral CT projection data while rotating or stepping the x-ray source-detector device around the imaging region. Examples of the spectral CT imaging system include a cone-beam spectral CT imaging system, a photon-counting spectral CT imaging system, a dark-field spectral CT imaging system, and a phase-contrast spectral CT imaging system. As an example, the spectral CT projection data 110a, 110b may 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 may 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 illustrating 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). In operation, the X-ray source 270 and the X-ray detector 230 are rotated by the gantry about a rotation axis 250 while acquiring spectral CT projection data representative of X-ray attenuation within a portion of an object, such as the object 290. The spectral CT projection data obtained from multiple rotation angles about the rotation axis may then be reconstructed into a volumetric image. As shown in FIG. 4, the X-ray detector 230 includes multiple detector elements 240 arranged along the rotation axis 250 of the X-ray detector. 1..k The detector element 240 may include 1..k For example, the x-ray detector 230 may be positioned parallel to the axis of rotation or at an acute angle to the axis of rotation, allowing the x-ray detector 230 to capture projection data representative of the flow of contrast agent in a direction along the axis of rotation, such as may occur in some of the blood vessels in 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. A 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. A spectral X-ray projection imaging system may alternatively include a support arm having a different shape from this example, such as an O-arm. A spectral X-ray projection imaging system typically generates projection data using a support arm that is held in a stationary position relative to the imaging region during acquisition of image data. However, the spectral X-ray projection imaging system may also acquire the spectral CT projection data while rotating the support arm around a rotation axis. This projection data can then be reconstructed into a volumetric image using image reconstruction techniques in a manner similar to that of a spectral CT imaging system. Thus, 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 act S110 is divided into a plurality of energy intervals DE 1..m represents the X-ray attenuation in the vasculature at m. In general, there may be two or more energy intervals, i.e., m is an integer and m≧2. A wide variety of energy intervals DE 1..m The ability to generate x-ray attenuation data in a single energy interval distinguishes a spectral x-ray imaging system from a conventional x-ray imaging system. 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 that are indistinguishable with conventional x-ray image data. In this regard, a variety of different settings of the spectral x-ray imaging system may be used to generate the spectral CT projection data received in act S110, some of which are described with reference to FIG.

[0027] Referring to FIG. 4, generally, the X-ray source 270 may include multiple monochromatic sources, or one or more polychromatic sources, and the X-ray detector 230 may include a common detector for detecting multiple different X-ray energy intervals, or each detector may include a different X-ray energy interval DE. 1..m The detector may include a plurality of detectors that detect X-rays having energies within different X-ray energy intervals, 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 classifies 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 may be determined for each received X-ray photon by detecting a pulse height induced by electron-hole pairs that are generated in response to absorption of the X-ray photon in the direct conversion material.

[0028] Using the various configurations of X-ray source 270 and detector 230 described above, various X-ray energy intervals DE 1..m Typically, the source 270 can be provided with discrimination between different X-ray energy intervals by switching the X-ray tube potential of a single X-ray source 270 in time, i.e., "rapid kVp switching," or by switching or filtering the X-ray emission from multiple X-ray sources in time. In such a setup, a common X-ray detector can be used to detect X-rays across multiple different energy intervals, with attenuation data for each energy interval being generated in a time sequence. Alternatively, a multi-layer detector or photon counting detector may be used in the detector 230 to discriminate between different X-ray energy intervals. Such detectors detect nearly simultaneous DE 1..m X-rays can be detected from multiple X-ray energy intervals, and therefore no time switching needs to be performed in source 270. In this way, a multi-layer detector, or a photon counting detector, can be used in conjunction with a polychromatic source to detect X-rays from various X-ray energy intervals DE 1..m can generate X-ray attenuation data.

[0029] Other combinations of the aforementioned X-ray sources and detectors may also be used in multiple energy intervals DE1..m For example, in a further setup, the need to sequentially switch between different x-ray sources emitting x-rays at different energy intervals can be avoided by mounting the x-ray source-detector pairs on the gantry at rotationally offset positions about the axis of rotation. In this setup, each source-detector pair operates independently and can provide the desired spectral CT projection data for the various energy intervals DE 1..m The separation between the spectral CT projection data of each is facilitated by the rotational offset of the source-detector pair. In this setup, the projection data of various energy intervals DE are separated by applying an energy selection filter to the X-ray detector to reduce the effect of X-ray scatter. 1..m Improved separation between the spectral CT projection data of the two images can be achieved.

[0030] 1, in operation S120, the received spectral CT projection data 110a, 110b is analyzed to separate contrast agent projection data representative of the flow of injected contrast agent from the spectral CT projection data. This operation may include identifying portions of the spectral CT projection data corresponding to the contrast agent material as the 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 signature of one or more background materials represented in the spectral CT projection data.

[0031] Various "material decomposition" techniques are contemplated for use in analyzing the spectral CT projection data in operation S120. The spectral CT projection data represents the flow of an injected contrast agent within the vasculature. The injected contrast agent may include materials such as iodine or gadolinium. Such materials are often used as contrast agents given their attenuation at the x-ray energies used in diagnostic x-ray imaging systems. In addition to attenuation resulting from the contrast agent, the spectral CT projection data may also represent attenuation resulting 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 vasculature, and thus attenuation resulting from these materials may also be represented in the spectral CT projection data. For example, when imaging the cardiovascular system, bone in the form of portions of the spine or ribs is 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 attenuation resulting from these materials may 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 is obtained.

[0032] One example of a material decomposition technique that may be used in act S120 to separate the contrast agent projection data from the spectral CT projection data is Brendel, B. et al., "Empirical, projection-based-basis-component decomposition method," 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 described in Roessl, E. and Proksa, R., "K-edge imaging in x-ray computed tomography using multi-bin photon counting detectors," Phys Med Biol. 2007 Aug 7, 52(15):4679-96. Another suitable material decomposition technique is disclosed in published PCT patent application WO / 2007 / 034359 A2. Another suitable material decomposition technique is disclosed in Silva, AC et al., "Dual-energy (spectral) CT: applications in abdominal imaging," RadioGraphics 2011; 31(4):1031-1046.

[0033] In general, the X-ray attenuation spectrum of a material contains contributions from Compton scattering and the photoelectric effect. The attenuation from Compton scattering is relatively similar for different materials, while the attenuation from the photoelectric effect is strongly material-dependent. Both Compton scattering and the photoelectric effect show energy dependence, and this effect is exploited by material decomposition techniques to analyze spectral CT projection data to distinguish different materials.

[0034] In general, material decomposition algorithms operate by decomposing the attenuation spectrum of an absorbing 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-edge "k-edge" energies that are in the energy range used by diagnostic x-ray imaging systems, and this effect can also be exploited to distinguish between different materials. The spectral decomposition algorithm then seeks to estimate the amount of each of the basis materials required to produce the measured x-ray attenuation at two or more energy intervals. Water and iodine are examples of basis materials that are often separated in clinical practice using so-called two-material decomposition algorithms. Non-fatty soft tissue, fat, and iodine are examples of basis materials that are often separated in clinical practice using three-material decomposition algorithms.

[0035] As mentioned above, if the k-edge "k-edge" energy of any of the basic materials is within the energy range used by diagnostic X-ray imaging systems, i.e., within about 30 to 120 keV, this can help identify the contribution of the basic material. The k-edge energy of a material is defined as the minimum energy required for a photoelectric event to occur in 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 less than 1 keV in energy. 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 allow for their differentiation in spectral CT projection data acquired from a diagnostic x-ray imaging system.

[0036] As mentioned above, materials such as iodine and gadolinium may be present in the vasculature as injected contrast agents, and materials such as gold, platinum, tantalum, holmium, and molybdenum may be present in the vicinity of the vasculature as fiducial markers, implanted medical devices, and interventional devices. Thus, the k-edge energies of such materials may 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 mass attenuation coefficients with x-ray energy for two example materials, iodine and water. The mass attenuation coefficients shown in FIG. 5 represent x-ray attenuation. The sharp increase in iodine x-ray attenuation at 33.2 keV facilitates separation between the contributions of each of these materials to the combined attenuation spectrum. In this embodiment, iodine and water can be separated by placing one energy interval DE1 close to the k-edge energy of 33.2 keV and another energy interval DE2 significantly above the k-edge energy.

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

[0038] Returning to FIG. 1, in act S130, the contrast agent projection data is then processed to generate one or more regions of interest 120 within the vasculature. 1..n to provide temporal blood flow data in one or more regions of interest. Note that the analysis S120 and sampling S130 operations are both performed on spectral CT projection data, i.e., raw data. Performing the sampling operation S130 on projection data, as opposed to reconstructed data, has the advantages of improved accuracy and simplified workflow, since it avoids the inaccuracies and complexities introduced by operations such as image reconstruction and vessel segmentation, which can be error-prone and complex, especially when the vasculature is affected by motion.

[0039] A technique for performing a sampling operation S130 on CT projection data, which can be used in operation S130 to provide temporal blood flow data in a region of interest, is disclosed in the above-cited publication by Prevrhal, S. et al., which discloses a technique for measuring flow velocities from row-to-row multi-detector CT projection data acquired during a single gantry rotation as a bolus of contrast agent flows through a vascular phantom. According to the present disclosure, principles known from this document for CT projection data are applied to the contrast agent projection data separated from the spectral CT projection data in operation S120. In particular, this publication discloses a technique in which flow velocities are determined for projections, i.e., contrast agent traces represented in "Radon" space. In this publication, projection data representing contrast agent traces are generated using an imaging device having rows of X-ray detector elements arranged transversely to the flow direction of the contrast agent. Flow velocities are determined based on the times at which the center of the contrast agent trace is detected in successive detector rows. According to the present disclosure, a similar projection space analysis can be performed on the contrast agent projection data acquired in act S120 by sampling the data over time in a region of interest.

[0040] A technique S130 for sampling contrast agent projection data in one or more regions of interest in the spectral CT projection data will now be described with reference to FIG. 6. FIG. 6 is an example of a sinogram including spectral CT projection data representing a) X-ray attenuation in a first energy interval DE1 and b) X-ray attenuation in a second energy interval DE2, according to some embodiments of the present invention. The sinogram shown in FIG. 6 represents spectral CT projection data acquired during a rotation of a gantry of a spectral CT imaging system around the vasculature. Thus, the rotation angle q in FIG. 6a and FIG. 6b also represents time. Projection data for a second energy interval DE2, which may represent a range of X-ray energies in a relatively low energy portion of the X-ray spectrum in FIG. 6a and a range of X-ray energies in a relatively high energy portion of the X-ray spectrum in FIG. 6b, is shown for a first energy interval DE1. As can be seen in FIG. 6a and FIG. 6b, the intensity of the projection data in each of the two illustrated energy intervals changes with time. The change in intensity represents a change in the amount of contrast agent in the vasculature over time. The time period shown in Figure 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 leaves the region of interest. In the example shown in Figure 6, the spectral CT projection data represents X-ray attenuation in two energy intervals. Such data is sometimes referred to as "dual energy" data. It should be noted that the spectral CT projection data may alternatively represent X-ray attenuation in three or more energy intervals.

[0041] The spectral CT projection data 110a, 110b shown in Figures 6a and 6b may be analyzed in act S120 using one of the material decomposition techniques described above. The result of act S120 is to provide contrast agent projection data representative of the flow of injected contrast agent. Although the isolated contrast agent projection data is not shown in Figure 6, it may similarly be represented in the formation of a sinogram. In act S130, the contrast agent projection data is then segmented into one or more regions of interest 120 within the vasculature to provide temporal blood flow data in the one or more regions of interest. 1..nAs discussed above, sampling the contrast projection data can provide more reliable blood flow data as opposed to, for example, sampling the reconstructed image data.

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

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

[0044] Thus, in some examples, the method comprises: reconstructing the spectral CT projection data or the contrast agent projection data into one or more reconstructed images representative of the vasculature; Identifying the location of one or more regions of interest 1201, 1202 in one or more reconstructed images; Mapping the location of one or more regions of interest 1201, 1202 onto the spectral CT projection data or onto the contrast agent projection data, respectively; having Sampling S130 is performed on the contrast agent projection data at locations corresponding to one or more mapped regions of interest.

[0045] In this second situation, where the regions of interest are defined in the projection data indirectly via the reconstructed image, the location of one or more regions of interest 1201, 1202 may be manually or automatically identified in the reconstructed image 110'. Manual identification of the regions of interest 1201, 1202 may be performed by means of a user manipulating a user input device in combination with the displayed reconstructed image 110'. Automatic identification of the regions of interest 1201, 1202 may be performed using a feature detector or a trained neural network. When the spectral CT projection data is reconstructed, a segmentation may be performed on the reconstructed image to help identify the regions of interest. Thus, one or more reconstructed images may also be segmented and an operation of identifying the location of one or more regions of interest 1201, 1202 in one or more reconstructed images may be performed in one or more segmented reconstructed images. For this purpose, various segmentation algorithms are known.

[0046] As described above, a feature detector, or trained neural network, can be used to automatically identify regions of interest in the reconstructed image. The region of interest may be a blood vessel, or a stenosis in the blood vessel. The neural network or feature detector can automatically identify potential locations for measuring blood flow parameters in the reconstructed image. As an example, the reconstructed image 110' shown in FIG. 7 represents a portion of the cardiovascular system and includes a 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 distal locations, respectively, for use in making blood flow velocity measurements. The blood flow velocity measurements can then be used to determine an FFR value for the stenosis.

[0047] In this second state, after identifying the location of one or more regions of interest 1201, 1202 in the reconstructed image(s), the locations of the region(s) of interest 1201, 1202 are mapped onto the spectral CT projection data, or onto the contrast agent projection data. This operation may be performed using a known spatial correspondence between the locations in the reconstructed image and the projection data from which the image was reconstructed. The above-mentioned sampling operation S130 is then 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 in the one or more regions of interest.

[0048] Returning to FIG. 1, in operation S140, one or more regions of interest 120 are 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, other blood flow parameters besides blood flow velocity can be determined in operation S140. For example, one or more of pressure, transit time, fractional coronary flow reserve FFR value, instantaneous fractional 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 the region of interest in the blood vessels 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 article by Nickisch et al., "Learning Patient-Specific Lumped Models for Interactive Coronary Blood Flow Simulations" (MICCAI, 2015). The dimensions of the vessels used in the model can be determined, for example, by analyzing contrast projection data, or alternatively, by determining the pressure at the region of interest 120. 1..n From measurements of the reconstructed spectral CT projection data at, the projection data can be determined.

[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 in a blood vessel and a distal location in a 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 vasculature includes blood vessels 260 and one or more regions of interest 120. 1..n The operation S140 of calculating values ​​of one or more blood flow parameters in calculating proximal and distal blood velocities at respective proximal 1201 and distal 1201 locations within the blood vessel 260; Using a hemodynamic model, the proximal blood pressure P at each proximal and distal location in the blood vessel is calculated. a and distal blood pressure P d and calculating using Proximal blood pressure P a and distal blood pressure P d calculating one or more of a fractional flow reserve (FFR) value and an instantaneous fractional flow reserve (iFR) value from the Equipped with.

[0052] In this example, further inputs to the hemodynamic model may include geometric measurements of the vessel obtained from the projection data or from a reconstructed image representing the vessel. The FFR is calculated using the formula FFR=P d / P a formula 1 is defined as follows: Here, P d represents the distal pressure at the distal location in the vessel, and P a represents the proximal pressure at a proximal location in the vessel. The hemodynamic model uses geometric measurements of the vessel to calculate the pressure value P d and P a When measured for a stenosis, the distal location may be distal to the stenosis. An example of a hemodynamic model that may be used in this example is disclosed in the above-cited article by Nickisch et al.

[0053] The IFR can be calculated using the same formula as the FFR. The IFR differs from the FFR in that the FFR is measured during the waveless period of diastole, which may be determined for the vasculature based on, for example, a received electrocardiogram signal.

[0054] Coronary flow reserve "CFR" is defined as the ratio of coronary blood flow during maximal hyperemia to baseline conditions. CFR represents the capacity of the coronary circulation to respond to a physiological increase in oxygen demand with a corresponding increase in blood flow. CFR is calculated according to the formula: CFR=APV h / APV b formula 2 can be determined from the velocity in the region of interest using APV h is the average peak velocity measured in centimeters per second during maximal hyperemia, and APV b is the average peak velocity measured in centimeters per second under baseline conditions.

[0055] As mentioned above, the IMR is the transit time T T It can be calculated from the formula IMR=P d T T formula 3 is defined as follows: Here, P d represents the distal pressure at a distal location in the vessel. IMR is typically the distal pressure P over the cardiac cycle at maximum hyperemia. d It is calculated using the time average value of

[0056] An alternative definition of IMR has been proposed, whereby IMR is defined as the endocardium-vasopressin (Evasion) ratio (Evasion) of a blood vessel that is subjected to severe epicardial stenosis, as defined by the formula: IMR=P d T T (P d -P w ) / (P a -P w ) Equation 4 It is calculated using

[0057] In Equation 4, the additional term P a represents the proximal pressure at the proximal location in the vessel, and an 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. The IMR is typically the proximal pressure P over the cardiac cycle at maximum high pressure. aThe time-averaged value of the distal pressure P d The time average value of the distal position Pos in the blood vessel when the blood vessel is occluded by the inflated balloon is used. d The wedge pressure P is calculated using the time average value of the pressure over the cardiac cycle at w Other formulas for calculating the IMR can also be used, which are similar to the passing T T Depends on.

[0058] HMR is typically represented by the formula HMR=P d / V d formula 5 It is calculated using: Here, P d represents the distal pressure at the distal location in the vessel, and V d represents the blood flow velocity at the distal location in the blood vessel. The parameter V d may be determined as described above.

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

[0060] In one example, a volumetric image is reconstructed from data other than the spectral CT projection data received in act S110. The reconstructed volumetric image is then used to identify a region of interest that is sampled in act S120. In this example, the method includes: One or more regions of interest 120 1..n receiving volumetric image data representative of The received volumetric image data is reconstructed to define one or more regions of interest 120. 1..n providing a reconstructed image representative of identifying one or more regions of interest 1201, 1202 in the reconstructed image; One or more regions of interest 120 from the reconstructed image. 1..nmapping the location of the first region to the spectral CT projection data to provide one or more regions of interest within the spectral CT projection data; Includes.

[0061] Various advantages are associated with identifying a region of interest in an image reconstructed from the 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, enabling its use as a planning image for planning measurements of blood flow parameters using subsequently acquired spectral CT projection data. The volumetric image data may be acquired months, weeks, days, or a shorter period of time prior to the spectral CT projection data. Alternatively, the volumetric image data may be acquired at a later time than the spectral CT projection data and used for subsequent analysis of the spectral CT projection data. The use of different 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, etc. The use of different types of data for the spectral CT projection data may reduce the amount of x-ray dosage applied to the subject, or provide a reconstructed image with different resolution, or provide a reconstructed image in which different features, such as soft tissue, are more clearly visible.

[0062] In this example, volumetric image data may be reconstructed using known image reconstruction techniques. Regions of interest may 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. Regions of interest 120 1..nThe operation of mapping the location of the CT projection data to the spectral CT projection data may be performed by registering the two data sets. For example, if a reconstructed image is generated from the spectral CT projection data, image registration may be performed and used to map the region of interest. The region of interest may alternatively be mapped by registering a reconstructed image from the volumetric data to the spectral CT projection data. This operation may be performed based on identifying and matching corresponding landmarks or fiducial markers in both images. Mapping the region of interest to the spectral CT projection data allows accurate identification of landmarks in the reconstructed image from the volumetric image data and calculation of blood flow parameters using only the spectral CT projection data. This has the advantage of avoiding inaccuracies in, for example, reconstructing and segmenting the spectral CT projection data, thereby exposing the calculation of the blood flow parameters to such inaccuracies.

[0063] In another embodiment, the operation S120 of analyzing the spectral CT projection data is selectively performed in one or more regions of interest 1201, 1202. In other words, the analyzing 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 a larger volume, e.g., a group of voxels. The regions of interest may, for example, encompass a portion of a blood vessel. By performing the operation S120 only in the regions of interest, blood flow parameters may be obtained with reduced computational load.

[0064] 1 may be performed using only data representing the "uptake" phase as the injected contrast agent flows into the region of interest. In this embodiment, the flow of injected contrast agent within the vasculature is measured as the leading edge of the injected contrast agent passes through one or more regions of interest 120. 1..n The flow of the injected contrast agent within the vasculature represents the uptake phase, where the trailing edge of the injected contrast agent flows into one or more regions of interest 120. 1..nThe data does not represent the washout phase flowing out from the vascular system. Data representing only the uptake phase can be generated by triggering the acquisition of the spectral CT data based on the injection time of the contrast agent or by post-acquisition analysis of the acquired spectral CT projection data to detect the location ahead of the injected contrast agent. By omitting data from the washout phase, a reduced amount of data is processed in this manner. Also, if there is no need to image the vasculature during the washout phase, the x-ray dose to the subject can be reduced.

[0065] 1 may be performed using only data representing a "washout" phase as the injected contrast agent flows out of the region of interest. In this example, the flow of injected contrast agent within the vasculature is measured by measuring the flow of the injected contrast agent through one or more regions of interest 120, 122, 124, 126, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 201, 20 1..n The flow of the injected contrast agent within the vasculature also represents a washout phase in which the leading edge of the injected contrast agent exits one or more regions of interest 120. 1..n The data does not represent the uptake phase in which the vasculature flows into the endothelial cavity. Data representing only the washout phase may be generated by triggering the acquisition of the spectral CT data based on the injection time of the contrast agent, or by post-acquisition analysis of the acquired spectral CT projection data to detect the location 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 manner. The x-ray dose to the subject may also be reduced if there is no need to image the vasculature during the uptake phase.

[0066] In another example, a computer program product is provided that comprises instructions that, when executed by one or more processors, cause the one or more processors to perform a method for measuring a blood flow parameter in a vasculature, the method comprising: A step S110 of receiving spectral computed tomography CT projection data 110a, 110b representative of the flow of an injected contrast agent in the vascular system, the spectral CT projection data being divided into a plurality of energy intervals DE 1..m representing X-ray attenuation in the vasculature at A step S120 of analyzing the spectral CT projection data to separate contrast agent projection data representative of the flow of the injected contrast agent from the spectral CT projection data;

[0067] Sampling contrast agent projection data in one or more regions of interest 120 within the vasculature S130 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 a step S140 of calculating values ​​of one or more blood flow parameters in has.

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

[0069] A step S110 of receiving spectral computed tomography CT projection data 110a, 110b representative of the flow of an injected contrast agent in the vascular system, the spectral CT projection data being divided into a plurality of energy intervals DE 1..m representing X-ray attenuation in the vasculature at A step S120 of analyzing the spectral CT projection data to separate contrast agent projection data representative of the flow of the injected contrast agent from the spectral CT projection data; The contrast agent projection data is then applied to one or more regions of interest 120 within the vasculature. 1..n 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 and a step S140 of calculating values ​​of one or more blood flow parameters. The system includes one or more processors 210 configured to execute the

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

[0071] The above examples should be understood as illustrating the present disclosure, but not limiting it. Further examples are contemplated. For example, the examples described in relation to a computer-implemented method may also be provided by a computer program product, or by a computer-readable storage medium, or by the system 200 in a corresponding manner. It should be understood that features described with respect to 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 the embodiment, or with combinations of other embodiments. Moreover, 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 operations, and the indefinite article "a" or "an" does not exclude a plurality. The mere fact that certain features are recited in mutually different dependent claims does not indicate that a combination of these features cannot be advantageously used. Any reference signs in the claims should not be construed as limiting their scope.

Claims

1. 1. A computer-implemented method for measuring a blood flow parameter in a vasculature, the method comprising: receiving spectral computed tomography CT projection data representative of flow of an injected contrast agent within the vasculature, the spectral CT projection data representative of x-ray attenuation within the vasculature at a plurality of energy intervals; analyzing the spectral CT projection data to separate from the spectral CT projection data contrast agent projection data representative of flow of the injected contrast agent; sampling contrast agent projection data at one or more regions of interest within the vasculature 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; The method comprising:

2. The method further comprises: identifying one or more anatomical landmarks within the spectral CT projection data; identifying locations of one or more regions of interest within the spectral CT projection data based on the identified one or more anatomical landmarks; 2. The computer-implemented method of claim 1, comprising:

3. 3. The computer-implemented method of claim 2, wherein 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.

4. reconstructing the spectral CT projection data or the contrast agent projection data into one or more reconstructed images representative of the vasculature; identifying the location of one or more regions of interest within the one or more reconstructed images; mapping the location of the one or more regions of interest onto the spectral CT projection data or onto the contrast agent projection data, respectively; and the step of sampling is performed on the contrast agent projection data at locations corresponding to the one or more mapped regions of interest.

2. The computer-implemented method of claim 1.

5. The method comprises: Segmenting the one or more reconstructed images. and and wherein the step of identifying locations of one or more regions of interest in the one or more reconstructed images is performed on the one or more segmented reconstructed images.

5. The computer-implemented method of claim 4.

6. The method comprises: receiving volumetric image data representative of the one or more regions of interest; reconstructing the received volumetric image data to provide a reconstructed image representative of the one or more regions of interest; identifying one or more regions of interest within the reconstructed image; mapping locations of one or more regions of interest from the reconstructed image onto the spectral CT projection data to provide one or more regions of interest within the spectral CT projection data; 2. The computer-implemented method of claim 1, comprising:

7. The step of analyzing the spectral CT projection data includes: identifying a portion of the spectral CT projection data that corresponds to a material of a contrast agent as contrast agent projection data based on an energy-dependent X-ray attenuation signature of the material and / or based on an energy-dependent X-ray attenuation signature of one or more background materials represented in the spectral CT projection data.

2. The computer-implemented method of claim 1, comprising:

8. the contrast agent material comprises one or more of iodine and gadolinium; and / or the one or more background materials include one or more of fat, water, bone, soft tissue, vascular calcification, air, and metal; 8. The computer-implemented method of claim 7.

9. The computer-implemented method of claim 1 , wherein analyzing the spectral CT projection data is performed selectively in the one or more regions of interest.

10. 10. The computer-implemented method of claim 1, wherein the spectral CT projection data is generated by a spectral X-ray imaging system including an X-ray detector having a plurality of detector elements arranged along an axis of rotation of the X-ray detector, and the spectral CT projection data is acquired while rotating the X-ray detector about the axis of rotation.

11. The vasculature comprises blood vessels, and calculating values ​​of one or more blood flow parameters in the one or more regions of interest comprises: Blood flow velocity, pressure, transit time, fractional coronary flow reserve FFR value, instantaneous flow reserve iFR value, coronary flow reserve CFR value, TIMI flow grade for thrombolysis in myocardial infarction, index of coronary microvascular resistance IMR value, and index of hyperemic microvascular resistance HMR value 11. A computer-implemented method according to claim 1, comprising calculating one or more of the blood flow parameters for said blood vessels.

12. The vasculature comprises blood vessels, and calculating values ​​of one or more blood flow parameters in the one or more regions of interest comprises: calculating proximal and distal blood flow velocities at respective proximal and distal locations within the blood vessel; calculating proximal and distal blood pressures at respective proximal and distal locations within said blood vessel using a hemodynamic model; From the proximal blood pressure and distal blood pressure, the fractional coronary flow reserve FFR value and the instantaneous fractional flow reserve iFR value are calculated. and calculating one or more of 11. A computer-implemented method according to any one of claims 1 to 10, comprising:

13. a flow of injected contrast agent within the vasculature exhibiting an uptake phase during which a leading edge of the injected contrast agent flows into the one or more regions of interest; the flow of injected contrast agent within the vasculature does not exhibit a washout phase in which the trailing edge of the injected contrast agent flows out of the one or more regions of interest; or a flow of injected contrast agent within the vasculature exhibiting 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 injected contrast agent within the vasculature does not exhibit an uptake phase during which a leading edge of the injected contrast agent flows into the one or more regions of interest; 13. A computer-implemented method according to any one of claims 1 to 12.

14. A computer program product comprising instructions which, when executed by one or more processors, cause the one or more processors to perform a method according to any one of claims 1 to 13.

15. 1. A system for measuring blood flow parameters in a vascular system, the system comprising one or more processors, the one or more processors comprising: receiving spectral computed tomography CT projection data representative of flow of an injected contrast agent within the vasculature, the spectral CT projection data representative of x-ray attenuation within the vasculature at a plurality of energy intervals; analyzing the spectral CT projection data to isolate from the spectral CT data contrast agent projection data representative of flow of the injected contrast agent; sampling contrast agent projection data at one or more regions of interest within the vasculature to provide temporal blood flow data at 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; It is configured as follows: system.

Images