Blood flow parameters

Abstract

A computer-implemented method of determining a value (110) of a blood flow parameter of one or more blood vessels (120) is provided. The method comprises: receiving, by a main processing system (130), spectral CT attenuation data (140); identifying, by the main processing system (130), one or more anatomical structures (1501... i) in the spectral CT attenuation data (140); determining a subset (140S) of the spectral CT attenuation data (140) for calculating the value (110) of the blood flow parameter based on the identified one or more anatomical structures (1501... i); transmitting the subset (140S) from the main processing system (130) to a remote processing system (170), the remote processing system being configured to calculate the value (110) of the blood flow parameter from the subset (140S); and receiving, by the main processing system (130), the value (110) of the blood flow parameter calculated by the remote processing system (170).

Description

Technical Field

[0001] The present disclosure relates to determining a value of a blood flow parameter of one or more blood vessels. A computer-implemented method, computer program product, system, and processing device are disclosed. Background Art

[0002] Various clinical studies involve evaluating blood flow in the vascular system. For example, studies of coronary artery disease "CAD" typically involve the evaluation of blood flow in order to assess the amount of blood supplied to the heart region. In this regard, various blood flow parameters have been studied, including blood flow velocity, blood pressure, fractional flow reserve "FFR", instantaneous waveform-free ratio "iFR", coronary flow reserve "CFR", Thrombolysis in Myocardial Infarction "TIMI" flow grade, microvascular resistance index "IMR", hyperemic microvascular resistance index "HMR". Some blood flow parameters are calculated for individual vessels, while other blood flow parameters are calculated for vascular networks.

[0003] Historically, the values ​​of blood flow parameters such as those described above have been measured using invasive devices such as pressure wires. For example, the value of the fractional flow reserve "FFR" is often determined to assess the effect of stenosis on the delivery of oxygen to the myocardium in the study of CAD. FFR is determined by the ratio P d / P a Define, where P d represents the distal pressure at the distal location relative to the stenosis, and P a Indicates the pressure proximal to the stenosis. Usually P is averaged over the cardiac cycle. d and P a FFR is calculated using values ​​of 0.8. FFR values ​​above 0.8 are generally considered clinically insignificant, and values ​​below 0.8 are generally considered to indicate increased clinical significance. Historically, the values ​​of these pressures have been determined by positioning invasive devices (such as pressure wires) at corresponding locations in the vasculature.

[0004] Recently, angiographic techniques have been developed to determine the values ​​of blood flow parameters, including FFR. According to fluid flow theory, changes in pressure are associated with changes in fluid velocity. In the example of FFR, angiographic images injected with contrast media are analyzed in order to determine blood flow velocity. FFR can then be calculated by using a hemodynamic model to estimate the pressure value in the blood vessel based on the blood flow velocity.

[0005] A known angiographic technique for measuring blood flow velocity is to sample the intensity of the injected contrast agent in computed tomography "CT" angiography image data over time and apply a mathematical model to the sampled data. In this regard, a technique for sampling a reconstructed CT image to determine blood flow velocity is disclosed in the document "Intra-vascular blood velocity and volumetric flow rate calculated from dynamic 4D CT angiography using a time of flight technique" (Int J Cardiovasc Imaging, 2014, 30: 1383-1392) by Barfett, JJ et al. A technique for sampling raw (i.e., "projection") CT data to determine blood flow velocity is disclosed in the document "CT Angiographic Measurement of Vascular Blood Flow Velocity by Using Projection Data" (Radiology, Vol. 261: No. 3 - December 2011, pp. 923-929) by Prevrhal, S. et al.

[0006] Some angiography techniques for measuring blood flow parameters involve the use of computational fluid dynamics "CFD" models. In this regard, the technique of determining FFR values ​​using CFD models is disclosed in the document "Computational Fluid Dynamics Applied to Cardiac Computed Tomography for Noninvasive Quantification of Fractional Flow Reserve: Scientific Basis" by Taylor. CA et al. (JACC, Vol. 61, No. 22, 2013). Another document describing angiographic techniques for measuring blood flow parameters using a computational fluid dynamics “CFD” model is the document by Koo, BK et al., “Diagnosis of Ischemia-Causing Coronary Stenoses by Noninvasive Fractional Flow Reserve Computed From Coronary Computed Tomographic Angiogram: Results From the Prospective Multicenter DISCOVER-FLOW (Diagnosis of Ischemia-Causing Stenoses Obtained Via Noninvasive Fractional Flow Reserve) Study” (JACC, Vol. 58, No. 19, 2011).

[0007] Yet another angiographic technique for measuring blood flow parameters involves the use of a lumped parameter model. A lumped parameter model can be used to represent blood flow in a vascular region as a circuit; the volume flow rate of blood is represented as a current, the pressure of blood is represented as a voltage, and the blood volume is represented by a charge. In a lumped parameter model, resistance to blood flow is represented by a linear or nonlinear resistor, vascular wall compliance is represented by a capacitor, and blood inertia is represented by an inductor. A machine learning-based method for setting the values ​​of linear and nonlinear resistors in a static lumped parameter model is disclosed in the document "Learning Patient-Specific Lumped Models for Interactive Coronary BloodFlow Simulations" (MICAI 2015, Part II, LNCS9350, pages 433-441, 2015) by Nickisch, H. et al. and the document WO2016 / 001017, which is based on angiographically derived measurements of vascular geometry. The values ​​of blood flow rate and pressure in the model are then solved to determine the value of the FFR of the vessel. Using a lumped parameter model to calculate the values ​​of blood flow parameters offers the advantage of reduced computational burden and model complexity compared to a computational fluid dynamics "CFD" model.

[0008] Angiographic techniques for determining the values ​​of blood flow parameters typically use CT attenuation data because it provides three-dimensional geometric information about the blood vessels in the vasculature. Spectral CT attenuation data has also been used to determine the values ​​of blood flow parameters. Spectral CT attenuation data includes data from multiple different energy intervals, and processing of such data allows for differentiation between materials that have similar X-ray attenuation values ​​when measured within a single energy interval, and which would be indistinguishable in conventional CT attenuation data. This benefit can be exploited to provide more accurate values ​​for blood flow parameters by improving the distinction between attenuation caused by contrast material and attenuation caused by background material. However, calculating the values ​​of blood flow parameters from CT attenuation data is a computationally complex task. In addition, for a given region of interest, the set of spectral CT attenuation coefficients can be significantly larger than their conventional CT counterparts. This exacerbates the challenge of determining the values ​​of blood flow parameters from spectral CT attenuation data.

[0009] Therefore, there still remains room for improving the way in which values ​​of blood flow parameters are determined from spectral CT attenuation data. Summary of the invention

[0010] According to one aspect of the present disclosure, there is provided a computer-implemented method for determining a value of a blood flow parameter of one or more blood vessels. The method comprises:

[0011] receiving, by a host processing system, spectral computed tomography (CT) attenuation data representing an injected contrast agent within an anatomical region including one or more blood vessels, the spectral CT attenuation data defining X-ray attenuation within the anatomical region in a plurality of different energy intervals;

[0012] identifying, by a host processing system, one or more anatomical structures in the spectral CT attenuation data;

[0013] determining a subset of values ​​of the spectral CT attenuation data for use in calculating a blood flow parameter based on the identified one or more anatomical structures;

[0014] transmitting the subset of the spectral CT attenuation data from the host processing system to a remote processing system via a communication channel, the remote processing system being configured to calculate a value of a blood flow parameter based on the subset of the spectral CT attenuation data; and

[0015] The value of the blood flow parameter calculated by the remote processing system is received by the host processing system.

[0016] As described above, calculating the value of the blood flow parameter based on the CT attenuation data is a computationally complex task. In the above method, using a remote processing system to calculate the value of the blood flow parameter reduces the computational burden at the main processing system by using the remote processing system to calculate the value of the blood flow parameter. In the above method, using spectral attenuation data instead of conventional CT data to calculate the value of the blood flow parameter helps to improve the separation between the attenuation caused by the injection of contrast agent and the attenuation caused by other media that may be present in the anatomical region. Therefore, using spectral attenuation data in the above method helps to accurately calculate the value of the blood flow parameter while reducing the computational burden of the main processing system. However, the inventors have observed that the size of the spectral CT data set presents challenges when transmitting the spectral CT data to the remote processing system so as to determine the value of the blood flow parameter at the remote processing system. This is because, for a given region of interest, the spectral CT data set is significantly larger than its conventional CT counterpart. This is a result of the spectral CT data representing the X-ray attenuation at multiple different energy intervals. Since in the above method, a subset of the spectral CT attenuation data is transmitted from the main processing system to the remote processing system via the communication channel, the remote processing system calculates the value of the blood flow parameter with a reduced demand on the bandwidth of the communication channel and / or it calculates the value of the blood flow parameter in a reduced amount of time. Therefore, the method facilitates providing accurate blood flow parameter values ​​with reduced delay.

[0017] Other aspects, features and advantages of the present disclosure will become apparent from the following description of examples with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 is a flow chart illustrating an example of a computer-implemented method of determining a value 110 of a blood flow parameter for one or more blood vessels 120 in accordance with some aspects of the present disclosure.

[0019] Figure 2 is a schematic diagram illustrating an example of a system 300 for determining a value 110 of a blood flow parameter of one or more blood vessels 120 according to some aspects of the present disclosure.

[0020] Figure 3 is a schematic diagram illustrating an example of a computer-implemented method of determining a value 110 of a blood flow parameter for one or more blood vessels 120 in accordance with some aspects of the present disclosure.

[0021] Figure 4 is a schematic diagram illustrating an example heart including blood vessels 120 according to some aspects of the present disclosure.

[0022] Figure 5 is a diagram showing identifying S120 one or more anatomical structures 150 in the spectral CT attenuation data 140 1…i , and determining S130 a subset 140 of the values ​​110 of the spectral CT attenuation data 140 for calculating a blood flow parameter S A schematic diagram of an example of operation.

[0023] Figure 6 is a graph showing the dependence of the mass attenuation coefficient on X-ray energy for two example materials (iodine and water). DETAILED DESCRIPTION

[0024] Examples of the present disclosure are provided with reference to the following description and accompanying drawings. In this specification, for the purpose of explanation, many specific details of certain examples are set forth. References in the specification to "examples," "implementations," or similar language mean that features, structures, or characteristics described in conjunction with the examples are included in at least that example. It should also be understood that features described with respect to one example may also be used in another example, and for the sake of brevity, not all features need to be repeated in each example. For example, features described with respect to computer-implemented methods may be implemented in a corresponding manner in computer program products and systems and in processing devices.

[0025] In the following description, reference is made to a method of determining a value of a blood flow parameter of a vessel. In some examples, the vessel is a coronary artery arranged in a chest anatomical region. However, it should be understood that the coronary artery is used only as an example, and the vessel can generally be any type of vessel, i.e., an artery, or a vein, or a ventricle. It should also be understood that the chest anatomical region is used only as an example of an anatomical region in which a vessel can be located, and the vessel can generally be located in any anatomical region. For example, the vessel can be located in an anatomical region such as a head region, an abdominal region, an upper limb region, a lower limb region, etc.

[0026] Reference is also made herein to examples in which the method is used to determine the FFR value of a blood vessel. However, it should be understood that FFR is used only as an example of a blood flow parameter, and the method disclosed herein may alternatively be used to calculate the values ​​of other blood flow parameters. For example, the method may be used to determine the values ​​of blood flow parameters, such as, but not limited to, blood flow velocity, blood pressure, blood flow transit time, volumetric blood flow value, a measure of blood perfusion, iFR value, CFR value, TIMI blood flow grade, IMR value and HMR value, hyperemic stenotic resistance "HSR" value, zero flow pressure "ZFP" value, and transient hyperemic diastolic velocity-pressure slope "IHDVPS" value.

[0027] Furthermore, while reference is made herein to examples of determining a value of a blood flow parameter for a single vessel, the methods and systems disclosed herein may alternatively be used to calculate values ​​of a blood flow parameter for a plurality of vessels. In this regard, individual values ​​of a blood flow parameter may be determined for each of a plurality of vessels, or a single value of a blood flow parameter may be determined for a plurality of vessels. For example, the methods disclosed herein may be used to calculate a measure of blood perfused by a network of vessels, and in such cases, individual values ​​of a blood flow parameter may be calculated for each of a plurality of vessels in the network, or a single value of a blood flow parameter may be calculated for the network.

[0028] Note that the computer-implemented methods disclosed herein may be provided as a non-transitory computer-readable storage medium including computer-readable instructions stored thereon, which, when run by at least one processor, cause the at least one processor to perform the method. In other words, the computer-implemented method may be implemented in a computer program product. The computer program product may be provided by dedicated hardware or hardware capable of running software in association with appropriate software. When provided by a processor, the functionality of the method features can be provided by a single dedicated processor, or by a single shared processor, or by a plurality of separate processors, some of which may be shared. The functionality of one or more method features may be provided, for example, by a processor shared within a networked processing architecture (such as a client / server architecture, a peer-to-peer architecture, the Internet, or a cloud).

[0029] The explicit use of the term "processor" or "processing system" or "controller" should not be interpreted as referring exclusively to hardware capable of running software, and 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. In addition, examples of the present disclosure can take the form of a computer program product accessible from a computer-usable storage medium or a computer-readable storage medium, which provides program code for use by or in conjunction with a computer or any instruction execution system. For the purposes of this specification, a computer-usable storage medium or a computer-readable storage medium can be any device that can include, store, communicate, propagate or transmit a program for use by or in conjunction with an instruction execution system, device or apparatus. The medium can be an electronic, magnetic, optical, electromagnetic, infrared or semiconductor system or device or a propagation medium. Examples of computer-readable media include semiconductor or solid-state memory, magnetic tape, removable computer 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 and DVD.

[0030] As mentioned above, there is still room for improving the way in which values ​​of blood flow parameters are determined from spectral CT attenuation data.

[0031] Figure 1 is a flow chart illustrating an example of a computer-implemented method of determining a value 110 of a blood flow parameter for one or more blood vessels 120 in accordance with some aspects of the present disclosure. Figure 2 is a schematic diagram illustrating an example of a system 300 for determining a value 110 of a blood flow parameter of one or more blood vessels 120 according to some aspects of the present disclosure. Figure 1 The operations described by the methods shown can also be performed by Figure 2 Similarly, the operations described with respect to the system 300 may also be performed in reference to Figure 1 The method described in the reference Figure 1 , a computer-implemented method for determining a value 110 of a blood flow parameter of one or more blood vessels 120 includes:

[0032] Spectral computed tomography (CT) attenuation data 140 representing an injected contrast agent within an anatomical region including one or more blood vessels 120 is received S110 by a host processing system 130, the spectral CT attenuation data 140 defining a plurality of different energy intervals DE 1…m X-ray attenuation within the anatomical region of the

[0033] One or more anatomical structures 150 are identified S120 by the host processing system 130 in the spectral CT attenuation data 140 1…i;

[0034] Based on the identified one or more anatomical structures 150 1…i , determining S130 a subset 140 of the values ​​110 of the spectral CT attenuation data 140 for calculating a blood flow parameter S ;

[0035] The subset 140 of the spectral CT attenuation data is transmitted via the communication channel 160 S transmitting S140 from the host processing system 130 to a remote processing system 170, the remote processing system being configured to calculate a value 110 of a blood flow parameter from a subset of the spectral CT attenuation data; and

[0036] The value 110 of the blood flow parameter calculated by the remote processing system 170 is received S150 by the host processing system 130 .

[0037] As described above, calculating the value of the blood flow parameter from the CT attenuation data is a computationally complex task. In the above method, using a remote processing system to calculate the value of the blood flow parameter reduces the computational burden at the main processing system by using the remote processing system to calculate the value of the blood flow parameter. In the above method, using spectral attenuation data instead of conventional CT data to calculate the value of the blood flow parameter helps to improve the separation between the attenuation caused by the injection of contrast agent and the attenuation caused by other media that may be present in the anatomical region. Therefore, using spectral attenuation data in the above method helps to accurately calculate the value of the blood flow parameter while reducing the computational burden of the main processing system. However, the inventors have observed that the size of the spectral CT data set is a challenge when transmitting the spectral CT data to the remote processing system so as to determine the value of the blood flow parameter at the remote processing system. This is because, for a given region of interest, the spectral CT data set is significantly larger than its CT counterpart. This is a result of the spectral CT data representing the X-ray attenuation at multiple different energy intervals. Since in the above method, a subset of the spectral CT attenuation data is transmitted from the main processing system to the remote processing system via a communication channel, the remote processing system calculates the value of the blood flow parameter with a reduced demand for the bandwidth of the communication channel and / or it calculates the value of the blood flow parameter with a reduced amount of time. Thus, the method facilitates providing accurate blood flow parameter values ​​with reduced delay.

[0038] Also refer to Figure 3 Describes Figure 1 The method shown, Figure 3 is a schematic diagram illustrating an example of a computer-implemented method of determining a value 110 of a blood flow parameter for one or more blood vessels 120 in accordance with some aspects of the present disclosure.

[0039] refer to Figures 1 to 3In operation S110, spectral CT attenuation data 140 is received by the main processing system 130. The spectral CT attenuation data represents an injected contrast agent in an anatomical region including one or more blood vessels 120. The contrast agent may include a substance such as iodine, or a lanthanide such as gadolinium, or another substance that provides visibility of the blood flow into which the contrast agent is injected. For example, the contrast agent may be injected manually or with the aid of a syringe (e.g., Figure 2 The spectral CT attenuation data 140 may be used to inject contrast agent using a syringe 370 (shown in FIG. 1 ). The anatomical region represented by the spectral CT attenuation data 140 may generally be any portion of an anatomical structure including one or more blood vessels. In some examples, the spectral CT attenuation data 110 represents a chest anatomical region. The chest anatomical region includes anatomical structures such as the heart, lungs, etc., and portions of such anatomical structures may be represented in the spectral CT data 140. Figure 4 is a schematic diagram illustrating an example heart including blood vessels 120 according to some aspects of the present disclosure. Figure 4 The heart is shown with the left coronary artery LCA, the right coronary artery RCA, and the LCA ostium and the RCA ostium respectively defining the openings of these arteries in the aorta. The spectral CT attenuation data 140 received in operation S110 may particularly represent Figure 4 1, wherein contrast agent is injected into a blood vessel 120 (i.e., the left coronary artery) in the heart. As described in more detail below, Figure 1 In some examples of the method shown, a value 110 of a blood flow parameter of one or more blood vessels is calculated based on the spectral CT attenuation data 140. For example, Figure 4 The values ​​of the blood flow parameters of the blood vessel 120 are shown.

[0040] The spectral CT attenuation data 140 received in operation S110 may generally be raw data, i.e., data that has not yet been reconstructed into a volumetric image, or it may be image data, i.e., data representing a reconstructed volumetric image. The spectral CT attenuation data 140 is received by the main processing system 130. Figure 3 , and wherein the spectral CT data (in this example including multiple energy intervals DE 1…m The reconstructed image data at each of the locations in the image processing unit 10 is received by the main processing system 130. The main processing system 130 includes one or more processors 310, such as Figure 2 Typically, the host processing system 130, or more specifically, one or more processors 310 thereof, may be configured to receive, for example, a signal from an imaging system (such as from a processor 310 of the imaging system). Figure 2The spectral CT attenuation data 140 may be received by the spectral CT imaging system 320 shown in the figure or from another source such as a computer readable storage medium, the Internet, or the cloud. The spectral CT attenuation data 140 may be received via any form of data communication, including wired, optical, and wireless communications. As some examples, when wired or optical communications are used, communication may be via signals transmitted over electrical or optical cables, and when wireless communications are used, communication may be via RF or optical signals.

[0041] The main processing 130 system may generally be provided by any device including a processor. For example, the main processing 130 system may be provided by a computer such as a tablet or a laptop or a desktop computer or a mainframe computer. The main processing device may alternatively be provided in the form of a mobile communication device such as a mobile phone.

[0042] The spectral CT attenuation data 140 received in operation S110 defines a plurality of different energy intervals DE 1…m In this regard, the spectral CT attenuation data 140 may be generated by a spectral CT imaging system. The spectral CT imaging system generates spectral CT data while rotating or stepping an X-ray source-detector arrangement 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, the spectral CT attenuation data 140 may be generated by a Spectral CT7500 sold by Philips Healthcare, Best of the Netherlands.

[0043] Figure 2 An example of a spectral CT imaging system 320 that may be used to generate the spectral CT attenuation data 140 received in operation S110 is shown in FIG. Figure 2 The spectral CT imaging system 320 shown includes an X-ray source 330 and an X-ray detector 340. The X-ray source 330 and the X-ray detector 340 are mechanically coupled to a gantry ( Figure 2 In operation, the X-ray source 330 and the X-ray detector 340 are rotated about the rotation axis 350 by means of the gantry while acquiring the spectral CT attenuation data 140, which defines the X-ray attenuation in the region of interest arranged in the imaging region of the imaging system 320. The spectral CT attenuation data 140 obtained from multiple rotation angles about the rotation axis 350 can then be reconstructed into a volumetric image using various image reconstruction techniques.

[0044] The spectral CT attenuation data 140 received in operation S110 defines a plurality of different energy intervals DE 1…mThe X-ray attenuation in . In general, there may be two or more energy intervals; that is, m is an integer, and m ≥ 2. Generate a plurality of different energy intervals DE 1…m The ability to process data from multiple different energy intervals distinguishes a spectral CT imaging system from a conventional CT imaging system. By processing data from multiple different energy intervals, it is possible to distinguish between media having similar X-ray attenuation values ​​when measured within a single energy interval and which are indistinguishable based on attenuation data generated by a conventional CT imaging system. In this regard, various different configurations of a spectral CT imaging system may be used to generate the spectral CT attenuation data received in operation S110, some of which refer to Figure 2 Give a description.

[0045] refer to Figure 2 In the example spectral CT imaging system 320 shown, in general, the X-ray source 330 may be provided by a plurality of monochromatic sources or by one or more polychromatic sources, and the X-ray detector 330 may include: a common detector for detecting X-ray radiation across a plurality of different X-ray energy intervals, or a plurality of detectors, wherein each detector detects a different X-ray energy interval DE 1…m The invention relates to a method for detecting X-ray radiation within a plurality of energy intervals, or a multi-layer detector, wherein X-ray radiation within each of a plurality of different X-ray energy intervals is detected by a corresponding layer, or a photon counting detector, which bins 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 of each received X-ray photon can be determined by detecting the pulse height caused by electron-hole pairs generated in response to absorption of the X-ray photon in the direct conversion material.

[0046] Various configurations of the above-described X-ray sources and X-ray detectors can be used in a spectral CT imaging system to generate a plurality of energy intervals DE defined by 1…m Spectral attenuation data of the X-ray attenuation in the X-ray source 330. In general, discrimination between different X-ray energy intervals can be provided at the X-ray source 330 by temporally switching the X-ray anode potential of a single X-ray source 330, i.e., by "fast kVp switching," or by temporally switching or filtering the X-ray emissions from multiple X-ray sources 330. In such a configuration, a common X-ray detector 340 can be used to detect X-rays across multiple different energy intervals, each energy interval DE 1…m The attenuation data of DE is generated in a time-sequential manner. Alternatively, the attenuation data of DE is generated in a time-sequential manner. 1…m The distinction between can be provided at the X-ray detector 340 by using a multi-layer detector or a photon counting detector. Such a detector is capable of detecting X-rays from multiple X-ray energy intervals DE almost simultaneously. 1…mX-rays, and therefore, there is no need to perform time switching at the X-ray source 330. Therefore, the multi-layer X-ray detector 340 or the photon counting X-ray detector 340 can be used in combination with the polychromatic X-ray source 330 to detect X-rays at multiple different energy intervals DE 1…m Generate spectral attenuation data in .

[0047] Other configurations of the aforementioned X-ray source 330 and X-ray detector 340 may alternatively be used in the spectral CT imaging system to obtain images at multiple different energy intervals DE. 1…m For example, in yet another configuration, the need to sequentially switch between different X-ray sources 330 emitting X-rays at different energy intervals can be eliminated by mounting the X-ray source-detector pairs to the gantry at rotationally offset positions about the rotation axis 350. In this configuration, each source-detector pair operates independently and transmits X-rays at different energy intervals DE. 1…m The separation of the X-ray attenuation data is facilitated by the rotational offset of the source-detector pair. 1…m Improved separation between the spectral attenuation data of can be achieved with this configuration by applying an energy selective filter to the (one or more) X-ray detectors 340 to reduce the effects of X-ray scatter.

[0048] In another configuration, multiple different energy intervals DE 1…m The spectral CT attenuation data 140 in can be provided by intercepting an X-ray beam in a conventional X-ray imaging system having one or more spectral filters. For example, a single filter can be mechanically moved in and out of the X-ray beam generated by the polychromatic X-ray source 330 in order to temporally control the spectrum of X-rays detected by the X-ray detector 340. The filter can, for example, transmit only a portion of the spectrum of X-rays emitted by the polychromatic X-ray source 330 and thereby provide spectral attenuation data for a first energy interval when the filter intercepts the beam. When the filter is removed from the beam, spectral attenuation data for a second energy interval (e.g., for the full spectrum emitted by the polychromatic X-ray source) is provided. In doing so, the energy interval DE 1…m The X-ray attenuation data is generated in a time-sequential manner. Multiple filters, each with a different X-ray transmission spectrum, can be sequentially switched in and out of the X-ray beam to increase the number of energy intervals.

[0049] The spectral CT attenuation data 140 received in operation S110 may alternatively be generated by a spectral X-ray projection imaging system instead of by a spectral CT imaging system. A spectral X-ray projection imaging system typically includes a support arm, such as a so-called "C-arm", also referred to as a gantry, which supports an X-ray source and an X-ray detector. The spectral X-ray projection imaging system may alternatively include, for example, a support arm having a shape different from that of the example, such as an O-arm. A spectral X-ray projection imaging system typically generates projection data while the support arm is maintained in a static position relative to the imaging region during acquisition of the projection data. The projection data may be used to generate a 2D image. However, a spectral X-ray projection imaging system may also generate spectral CT attenuation data, i.e., data that can be reconstructed into a volumetric image. A spectral X-ray projection imaging system may generate spectral CT attenuation data of a region of interest by rotating its support arm around a rotation axis and acquiring spectral X-ray projection data of the region of interest from a plurality of rotation angles around the rotation axis. A collection of spectral X-ray projection data from a plurality of rotation angles represents spectral CT attenuation data, because this may be reconstructed to provide a volumetric image. Various image reconstruction techniques can be used to reconstruct a set of spectral X-ray projection data from multiple rotation angles into a volume image. These techniques process the set of spectral X-ray projection data in the same manner as the spectral CT data generated by the spectral CT imaging system. Therefore, the spectral CT attenuation data 140 received in operation S110 can alternatively be generated by a spectral X-ray projection imaging system.

[0050] Back to Figure 1 In operation S120 , the main processing system 130 identifies one or more anatomical structures 150 in the spectral CT attenuation data 140 1…i .

[0051] The anatomical structure may generally refer to an organ, such as a heart, a lung, a brain, etc., or it may refer to a sub-element of an organ. For example, in some examples, the anatomical structure may be a heart or an atrium of the heart, a myocardium of the heart, a cavity of at least one of the one or more blood vessels 120, a wall of one or more of the one or more blood vessels 120, or a vascular plaque of one or more of the one or more blood vessels 120. Identifying the One or More Anatomical Structures 150 in the Spectral CT Attenuation Data S120 1…i The operation may generally be performed by performing a segmentation operation on the spectral CT attenuation data 140. Various segmentation techniques are known for this purpose and include computer vision methods such as feature clustering, model-based segmentation, use of feature detectors, and trained neural networks.

[0052] Figure 5 is a diagram illustrating identifying S120 one or more anatomical structures 150 in spectral CT attenuation data 140 according to some aspects of the present disclosure. 1…i, and determining S130 a subset 140 of the spectral CT attenuation data 140 S A schematic diagram of an example of operations for calculating a value 110 of a blood flow parameter. Figure 5 The spectral CT attenuation data 140 shown represents the chest region and includes the heart as an example of an anatomical structure. In this example, model-based segmentation of the spectral CT attenuation data 140 has been used to identify sub-elements of the heart: the myocardium 150 1 , Coronary artery 150 2 and left atrium 150 3 .

[0053] Back to Figure 1 The method shown in FIG. 1 is performed after one or more anatomical structures 150 have been identified in the spectral CT attenuation data 140. 1…i Then, in operation S130, based on the identified one or more anatomical structures 150 1…i , determining a subset 140 of the values ​​110 of the spectral CT attenuation data 140 for calculating a blood flow parameter S Generally, operation S130 may be performed by main processing system 130 or by remote processing system 170. Operation S130 may be performed based on the identified one or more anatomical structures 150. 1…i To determine the subset 140 S For example, a lookup table or a neural network is used. In such cases, the lookup table or neural network is one or more anatomical structures 150 1…i Identifying a corresponding subset of the spectral CT attenuation data required to calculate a value for a blood flow parameter 140 S For example, refer to Figure 5 If the desired blood flow parameter is FFR and the anatomical structure is the heart, the lookup table may identify the subset 140 S Includes: For myocardial 150 1 Contrast agent maps from relatively low energy intervals, “low monoE”, i.e., DE of dual-energy CT imaging systems 1 , which includes two energy intervals DE from the coronary arteries 1 ,DE 2 Data from the left atrium 150 3 The lookup table can similarly identify the desired subset of spectral CT attenuation data 140 S , in order to calculate, for example, values ​​for other blood flow parameters, such as iFR, CFR, TIMI flow grade, IMR and HMR, hyperemic stenotic resistance "HSR", zero flow pressure "ZFP" and transient hyperemic diastolic velocity-pressure slope "IHDVPS". Similarly, the lookup table can identify subsets of spectral CT attenuation data 140 required for other anatomical regions. S .

[0054] Neural networks can be trained to recognize subsets 140 S , rather than using a lookup table to identify the subset of spectral CT attenuation data 140 needed to calculate the value of a desired blood flow parameter S In this case, the neural network may be input with an image of the anatomical region, along with input indicative of a desired blood flow parameter, and the neural network may be trained to output the corresponding subset 140 required to calculate the value of the blood flow parameter. S The training data for the neural network may include a plurality of images of the anatomical region and a corresponding list of data required to calculate the value of the blood flow parameter. The subset 140 may be determined based on the data required by the technique for calculating the value of the blood flow parameter. S .

[0055] Return to Figure 1 In the method shown in FIG. 1 , in operation S140 , a subset 140 of the spectral CT attenuation data is obtained. S It is then transmitted from the main processing system 130 to the remote processing system 170 via the communication channel 160. Figure 2 and Figure 3 . The communication channel 160 can generally be provided by any data communication channel, including a wired communication channel, an optical communication channel, and a wireless communication channel. In other words, when a wired communication channel or an optical communication channel is used, communication can be performed via a signal transmitted on an electrical cable or an optical cable, and when a wireless communication channel is used, communication can be performed via an RF or optical signal.

[0056] In one example, subsets of spectral CT attenuation data are sent according to priority 140. S In this example, a subset 140 of the spectral CT attenuation data S Includes multiple data elements, each corresponding to a different anatomical structure 150 1…i and / or different materials in anatomical regions. In this example, reference Figure 1 The methods described include:

[0057] assigning, by the host processing system 130 or by the remote processing system 170, a transmission priority to the plurality of data elements in the subset; and

[0058] Among them, the subset 140 of the transmitted spectral CT attenuation data S Includes transmitting data elements in order of assigned transmission priorities.

[0059] The anatomical structure 150 identified in operation S120 may be selected based on, for example, the type of blood flow parameter to be calculated, the anatomical region represented in the spectral CT data 140, and the anatomical structure 150 identified in operation S120. 1…i and included in the anatomy 1501…i For example, if the remote processing system 170 calculates the FFR value, the main processing system 130 may prioritize the transmission by sending the data representing the coronary artery 150 to the remote processing system 170. 2 The data transmission takes precedence over that of the myocardium 150 1 The data is used to transmit a subset of the spectral CT attenuation data 140 S This helps subset 140 S More time-efficient transmission because the remote processing system 170 can process the data from the most relevant areas (such as the coronary arteries 150 2 ) without waiting for other anatomical structures (such as myocardium 150 1 ) data transmission. By S Prioritizing the transmission of the blood flow parameters can reduce the time required to calculate the values ​​of the blood flow parameters.

[0060] The transmission priority may be assigned by the main processing system 130 or by the remote processing system 170. In the latter case, the remote processing system 170 may operate in a "pull" / "on-demand" mode, wherein the remote processing system 170 uses the results of processing the current data element (e.g., processing the coronary artery 150) to 2 The results of the data are used to identify subsequent data elements required for the remote processing system 170 to calculate the value 110 of the blood flow parameter.

[0061] The remote processing system then processes the subset 140 of the spectral CT attenuation data based on the S Calculate the value of the blood flow parameter 110. In this regard, the various techniques cited above can be used to calculate the value of the blood flow parameter 110. For example, the technique disclosed in the above-cited literature of Barfett, JJ et al. for sampling the reconstructed CT image to determine the blood flow velocity can be used, or the technique disclosed in the cited literature of Prevrhal, S. et al. for sampling the original (i.e., "projection") CT data to determine the blood flow velocity can be used. Other techniques such as techniques for determining FFR values ​​using CFD models are disclosed in the cited literature of Taylor. CA et al., or angiography techniques for measuring blood flow parameters using computational fluid dynamics "CFD" models in the cited literature of de Koo, BK et al., or the lumped parameter model in the cited literature of Nickisch, H. et al. and WO2016 / 001017 can be used to determine the FFR value of the blood vessel. Such techniques may be computationally complex, so by using a remote processing system instead of the main processing system 130 to calculate the value of the blood flow parameter 110, the processing burden on the main processing system 130 is reduced. Furthermore, since the subset 140 of the spectral CT attenuation data 140 SInstead of the complete set of spectral CT attenuation data 140, the remote processing system 170 calculates the value of the blood flow parameter with a reduced demand on the bandwidth of the communication channel and / or it calculates the value of the blood flow parameter in a reduced amount of time. Therefore, the method facilitates providing the blood flow parameter value with reduced delay.

[0062] The value 110 of the blood flow parameter calculated by the remote processing system 170 is then transmitted to the main processing system 130 through the communication channel 160 , and in operation S150 , the value 110 of the blood flow parameter calculated by the remote processing system 170 is received by the main processing system 130 .

[0063] The value 110 of the blood flow parameter may then be output by the main processing system 130. The value 110 may be output in a variety of ways. For example, the value of the blood flow parameter may be output to a display device, such as Figure 2 The monitor 370 of the system 300 shown. Alternatively, for example, the value can be output to a computer-readable storage medium or a printer device. In some examples, the value 110 of the blood flow parameter is output as a numerical value, and in other examples, the value 110 of the blood flow parameter can be output graphically. For example, the value of the blood flow parameter can be output as a color-coded icon. The value of the blood flow parameter can also be calculated at multiple locations along the length of the blood vessel 120 and output graphically. For example, an image showing the blood vessel 120 can be output, wherein the value of the blood flow parameter is displayed as a color-coded overlay on the image at corresponding locations along the length of the blood vessel. The physician can then use the provided value 110 of the blood flow parameter to perform a clinical diagnosis on the object.

[0064] As described above, in operation S130, based on the identified anatomical structure(s) 150 1…i To determine a subset of the spectral CT attenuation data (140) S In this regard, the identified anatomical structure(s) 150 1…i The subset 140 may be determined in various ways. S In one example, a subset 140 of the spectral CT attenuation data S Only the data corresponding to the identified one or more anatomical structures 150 are included. 1…i Spectral CT attenuation data of . Figure 5 , in this example, a subset 140 of the spectral CT attenuation data S For example, only the heart, or myocardium 150, may be included. 1 , or coronary artery 150 2 , or left atrium 150 3 , or these anatomical structures 150 1…3By omitting the spectral CT attenuation data 140 of other anatomical structures, the amount of data transmitted in operation S140 is reduced.

[0065] In another example, the subset 140 of spectral CT attenuation S The one or more anatomical structures 150 identified in operation S120 are included 1…i Spectral CT attenuation data corresponding to the identified one or more anatomical structures 150 1…i In this example, the spectral CT attenuation data of one or more regions other than the identified anatomical structure 150 is compressed or defaulted. 1…i Data in background areas outside the range can be converted to a default value, such as a zero intensity value, such as Figure 5 Alternatively, a compression value may be provided for an area such as the background area. The compression value may represent a region corresponding to one or more anatomical structures 150 identified. 1…i Spectral CT attenuation data of one or more regions outside the anatomical structure 150 having a spectral CT attenuation data corresponding to the identified one or more anatomical structures 150 1…i The spectral CT attenuation data may be relatively lower spatial resolution. For example, this may be achieved by mapping the image intensity values ​​of a group of image pixels in the background region to the average value of the group. The compressed value may alternatively represent the image intensity values ​​corresponding to the identified one or more anatomical structures 150. 1…i Spectral CT attenuation data of one or more regions outside the anatomical structure 150 having a spectral CT attenuation data corresponding to the identified one or more anatomical structures 150 1…i The spectral CT attenuation data of the background region is relatively lower in bit depth. For example, this can be achieved by using fewer threshold levels to quantize the image intensity values ​​of pixels in the background region. Therefore, these examples reduce the amount of data of the background region transmitted through the communication channel 160 in operation S140, and this reduces the demand for its bandwidth.

[0066] Identified anatomical structure(s) 150 1…i A subset 140 of the spectral CT attenuation data may be used in other ways to determine S In another example, the subset 140 of spectral CT attenuation data S Corresponding to one or more anatomical structures DE 1…m And the subset 140 of the spectral CT attenuation data S Represents one or more anatomical structures 150 1…i DE with multiple energy intervals 1…m For example, reference Figure 5 , in this example, a subset 140 of the spectral CT attenuation data S Can correspond to coronary artery 1502 , and in this case, subset 140 S It can be expressed as having energy interval DE 1…m A subset of coronary arteries 150 2 Since in this example the data for the coronary arteries do not include all energy intervals DE 1…m In operation S140, a reduced amount of data of the coronary arteries is transmitted through the communication channel 160, and this reduces the demand for its bandwidth.

[0067] In another example, the subset 140 of spectral CT attenuation data S Corresponding to the one or more anatomical structures 150 identified in operation S120 1…i , and the spectral CT attenuation data represents a plurality of different materials in the anatomical region. The subset 140 of the spectral CT attenuation data S Represents one or more anatomical structures 150 1…i At least one anatomical structure in the anatomical region having a subset of the materials represented by the spectral CT attenuation data. In this example, the spectral CT attenuation data may, for example, represent iodinated contrast agent, bone, soft tissue, and water in the anatomical region. The identified anatomical structure 150 1…i Can include myocardium 150 1 , Coronary artery 150 2 and left atrium 150 3 In this example, only data of contrast material iodine may be used to represent at least one of the anatomical structures, such as the coronary arteries, rather than transmitting data of all materials represented in each of these anatomical structures. In this example, since data of other materials (such as soft tissue) is omitted for the coronary arteries, the amount of data transmitted in operation S140 is reduced.

[0068] In the above example, the material may be identified by applying a material decomposition algorithm to the spectral CT attenuation data 140 .

[0069] An example of a material decomposition technique that can be used to identify materials is disclosed in the document “Empirical, projection-based basis-component decomposition method” by Brendl, 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 disclosed in the document “K-edge imaging in x-ray computed tomography using multi-bin photon counting detector” by Roessl, E. and Proksa, R. (Phys Med Biol. August 7, 2007, 52(15): 4679-96). Another suitable material decomposition technique is disclosed in the published PCT patent application WO / 2007 / 034359A2. Another suitable material decomposition technique is disclosed in the document "Energy-selective reconstruction in X-ray computerized tomography" by Alvarez et al. (Physics in medicine and biology, Vol. 21, No. 5, pp. 733-744).

[0070] Typically, the X-ray attenuation spectrum of a material includes contributions from Compton scattering and contributions from the photoelectric effect. The attenuation caused by 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 exhibit energy dependence, and material decomposition techniques exploit this effect in order to distinguish different materials.

[0071] Typically, material decomposition algorithms operate by decomposing the attenuation spectrum of an absorbing medium into contributions from a hypothetical set of "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 a k-absorption edge "k-edge" energy within the energy range used by diagnostic X-ray imaging systems, and this effect can also be exploited in order to distinguish between different materials. The spectral decomposition algorithm then attempts to estimate the amount of each basis material required to produce the measured amount of X-ray attenuation at two or more energy intervals. Water and iodine are examples of basis materials that are commonly separated in clinical practice using so-called two-material decomposition algorithms. Non-fat soft tissue, fat, and iodine are examples of basis materials that are commonly separated in clinical practice using three-material decomposition algorithms.

[0072] As described above, if the k-absorption edge "k-edge" energy of any base material is within the energy range used by the diagnostic X-ray imaging system, i.e., approximately 30-120 keV, this can be used to help identify the contribution of the base material to X-ray attenuation. The k-edge energy of a material is defined as the minimum energy required for a photoelectric event to occur using a k-shell electron. The k-edge occurs at a characteristic energy for each material. The k-edge energy of a material is marked by a sharp increase in its X-ray attenuation spectrum at an 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 a diagnostic X-ray imaging system. For example, the k-edge energies of hydrogen, carbon, oxygen, and nitrogen are at energies less than 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 allow them to be distinguished in spectral CT attenuation data acquired from a diagnostic X-ray imaging system.

[0073] As an example, Figure 6 is a graph showing the dependence of the mass attenuation coefficient on X-ray energy for two example materials (iodine and water). Figure 5 The mass attenuation coefficient denoted in represents the X-ray attenuation. The sharp increase in X-ray attenuation of iodine at 33.2 keV facilitates separation between the contributions of each of these materials in the combined attenuation spectrum, which contains the attenuation due to both materials. For example, the X-ray attenuation due to iodine and water can be separated by using an energy interval DE close to the k-edge energy of 33.2 keV. 1 and another energy interval DE significantly higher than the k-edge energy 2 To separate.

[0074] You can also refer to the above Figure 1 One or more additional operations are performed in the method described. For example, in one example, the remote processing system 170 is further configured to:

[0075] predicting a confidence value representing the expected accuracy of the calculated value 110 of the blood flow parameter; and

[0076] The confidence value is communicated to the host processing system 130 via the communication channel 160 .

[0077] The subset 140 may be based on, for example SThe confidence value may be output by the main processing system 130. For example, the confidence value may be output as an error bar on the calculated value 110 of the blood flow parameter. A physician may use the confidence value to determine the degree of reliance on the value of the blood flow parameter.

[0078] In another example, the remote processing system 170 is configured to:

[0079] Identify a subset of spectral CT attenuation data for transmission to a remote processing system 140 S The data types in ; and

[0080] Based on the identified data type, selecting an algorithm for use by the remote processing system 170 to calculate the value 110 of the blood flow parameter; or

[0081] Based on the identified data type, a subset 140 of the spectral attenuation CT data is transmitted to a remote processing system 170. S Converted into a format used by a remote processing system to calculate a value 110 of a blood flow parameter.

[0082] In this example, remote processing system 170 may, for example, analyze subset 140 S The remote processing system 170 may analyze the data in the subset 140 to determine the anatomical structure to which the transmitted data belongs, and use the anatomical structure to select an algorithm for processing the data. For example, if the result of the analysis indicates that the transmitted data relates to a coronary artery, a first algorithm may be used to calculate the value of a blood flow parameter. Conversely, if the result of the analysis indicates that the transmitted data relates to a peripheral artery, a second algorithm may be used to calculate the value of a blood flow parameter. The remote processing system 170 may analyze the subset 140 S The data in the image may be analyzed and the anatomical structure to which the transmitted data belongs may be determined, for example, by segmenting the received data and applying a feature detector to the segmented data. Similarly, if the desired blood flow parameter is an FFR value, the results of the analysis may be used to identify the most appropriate algorithm for determining its value based on the type of data that has been transmitted. This helps in calculating an accurate value for the blood flow parameter.

[0083] The remote processing system may alternatively be based on a subset 140 S Identification of data types in the conversion subset 140 S In this case, the remote processing system 170 may, for example, analyze the transmitted data to determine the type of basis function image that has been transmitted to the remote processing system 170 and, if the type of basis function image has been identified, convert the subset 140 by, for example, mapping the basis function data representing the soft tissue to different energy intervals required for different materials or algorithms to calculate the value of the blood flow parameter. SThe mapping of this data can be performed based on a physical model or using image-to-image translation with the help of a trained (convolutional) neural network. This also helps in calculating accurate values ​​for blood flow parameters.

[0084] In another example, reference Figure 1 The described methods also include:

[0085] receiving, by a host processing system 130, input identifying one or more blood vessels 120 in the spectral CT attenuation data 140; and

[0086] Therein, a value 110 of a blood flow parameter is calculated for the identified one or more blood vessels 120 .

[0087] In this example, the operation of identifying the vessel(s) 110 may be performed in an image reconstructed from the spectral CT attenuation data 140, or alternatively in an image representing an anatomical region reconstructed from conventional CT attenuation data. Input to identify the vessel(s) 180 in the region of interest 120 may be provided by a user, or may be provided automatically. User input to identify the vessel(s) 180 may be received from a user input device operating in combination with a displayed image of the region of interest. Input to identify the vessel(s) 120 may be provided automatically by using a feature detector or a trained neural network to identify the vessel(s) 120 in the spectral CT attenuation data 140. Segmentation operations may also be performed on such an image to aid in identifying the vessel. Various segmentation algorithms are known for this purpose. A combination of these methods may also be used, wherein a feature detector or a trained neural network automatically identifies a candidate vessel and may then be selected by a user via a user input device. The vessel may be identified based on the detection of contrast agent in such an image or based on the presence of an abnormality such as a stenosis in the vessel. As an example, a feature detector or a trained neural network may be used to identify the left coronary artery of a cardiac image and a stenosis therein, and identify the vessel as a candidate vessel.

[0088] Thus, in this example, input identifying one or more blood vessels 120 may be received from a user input device or from a processor performing operation of a feature detector or operation of a trained neural network.

[0089] In another example, a computer program product is provided. The computer program product includes instructions that, when executed by one or more processors 310, cause the one or more processors to perform a method of determining a value 110 of a blood flow parameter of one or more blood vessels 120, the method comprising:

[0090] Spectral computed tomography (CT) attenuation data 140 representing an injected contrast agent within an anatomical region including one or more blood vessels 120 is received S110 by a host processing system 130, the spectral CT attenuation data 140 defining a plurality of different energy intervals DE 1…m X-ray attenuation within the anatomical region of the

[0091] One or more anatomical structures 150 are identified S120 by the host processing system 130 in the spectral CT attenuation data 140 1…i ;

[0092] Based on the identified one or more anatomical structures 150 1…i , determining S130 a subset 140 of the values ​​110 of the spectral CT attenuation data 140 for calculating a blood flow parameter S ;

[0093] The subset 140 of the spectral CT attenuation data is transmitted via the communication channel 160 S transmitting S140 from the host processing system 130 to a remote processing system 170, the remote processing system being configured to calculate a value 110 of a blood flow parameter from a subset of the spectral CT attenuation data; and

[0094] The value 110 of the blood flow parameter calculated by the remote processing system 170 is received S150 by the host processing system 130 .

[0095] In another example, a system 300 is provided for determining a value 110 of a blood flow parameter of one or more blood vessels 120. The system includes one or more processors 310 configured to:

[0096] Receive S110 spectral computed tomography CT attenuation data 140 representing an injected contrast agent within an anatomical region including one or more blood vessels 120, the spectral CT attenuation data 140 defining a plurality of different energy intervals DE 1…m X-ray attenuation within the anatomical region of the

[0097] Identify S120 one or more anatomical structures 150 in the spectral CT attenuation data 140 1…i ;

[0098] Based on the identified one or more anatomical structures 150 1…i , determining S130 a subset 140 of the values ​​110 of the spectral CT attenuation data 140 for calculating a blood flow parameter S ;

[0099] The subset 140 of the spectral CT attenuation data is transmitted via the communication channel 160 S transmitting S140 to a remote processing system 170, the remote processing system being configured to calculate a value 110 of a blood flow parameter from a subset of the spectral CT attenuation data; and

[0100] The value 110 of the blood flow parameter calculated by the remote processing system 170 is received S150 .

[0101] Figure 2 An example of the system 300 is shown in FIG. 1 . Note that the system 300 may also include one or more of the following: a spectral CT imaging system 320 for generating the spectral CT attenuation data 140 received in operation S110; a monitor 360 for displaying the calculated values ​​110 of the blood flow parameters and other data described according to the method; an injector 370 for injecting a contrast agent into the vascular system; a patient bed; and a user input device ( Figure 2 ), which is configured to receive user input used in conjunction with the method, such as a keyboard, mouse, touch screen, etc.

[0102] In another example, a processing device is provided for determining a value 110 of a blood flow parameter of one or more blood vessels 120. The processing device includes the system 300 described above and a remote processing system 170.

[0103] The above examples should be understood as illustrating the present disclosure, rather than being restrictive. Other examples are also contemplated. For example, the examples described about the computer-implemented method may also be provided by a computer program product or by a computer-readable storage medium or by the system 300 in a corresponding manner. It should be understood that the features described about any one example may be used alone, or in combination with the features described with other features, and may be used in combination with one or more features of another example or a combination of other examples. In addition, equivalents and modifications not described above may also be adopted without departing from the scope of the present invention defined in the appended claims. In the claims, the word "including" does not exclude other elements or operations, and the word "one" or "an" does not exclude multiple. The fact that certain features are recorded in mutually different dependent claims does not indicate that the combination of these features cannot be used advantageously. Any figure marks in the claims should not be interpreted as limiting their scope.

Claims

1. A computer-implemented method for determining a value (110) of a blood flow parameter of one or more blood vessels (120), the method comprising: Spectral computed tomography (CT) attenuation data (140) representing an injected contrast agent in an anatomical region including the one or more blood vessels (120) is received (S110) by a main processing system (130), the spectral CT attenuation data (140) defining a plurality of different energy intervals (DE 1…m ) in the anatomical region; One or more anatomical structures (150) in the spectral CT attenuation data (140) are identified (S120) by the main processing system (130). 1…i ); Based on the identified one or more anatomical structures (150 1…i ), determining (S130) a subset (140) of the spectral CT attenuation data (140) for calculating the value (110) of the blood flow parameter S ); The subset (140) of the spectral CT attenuation data is transmitted via a communication channel (160) S ) transmitting (S140) from the main processing system (130) to a remote processing system (170), the remote processing system being configured to calculate the value (110) of the blood flow parameter based on the subset of the spectral CT attenuation data; as well as The value (110) of the blood flow parameter calculated by the remote processing system (170) is received (S150) by the main processing system (130).

2. The computer-implemented method of claim 1 , wherein: The subset of spectral CT attenuation data (140 S ) includes only the regions corresponding to the identified one or more anatomical structures (150 1…i ) of the spectral CT attenuation data; or The subset (140 S ) includes a corresponding to the identified one or more anatomical structures (150 1…i ) and the spectral CT attenuation data corresponding to the identified one or more anatomical structures (150 1…i )Compression or default value of the spectral CT attenuation data of one or more regions outside.

3. The computer-implemented method of claim 2, wherein: The compression value corresponds to the identified one or more anatomical structures (150 1…i ) of the spectral CT attenuation data corresponding to the identified one or more anatomical structures (150) 1…i )The spectral CT attenuation data of the one or more regions outside.

4. The computer-implemented method according to any one of claims 1 to 3, wherein: The subset of spectral CT attenuation data (140 S ) corresponds to the one or more anatomical structures (150 1…i ), and where: The subset of spectral CT attenuation data (140 S ) represents the one or more anatomical structures (150 1…i ) in the plurality of energy intervals (DE 1…m ) of at least one anatomical structure of a subset of ); or The spectral CT attenuation data represents a plurality of different materials in the anatomical region, and wherein the subset (140) of the spectral CT attenuation data S ) represents the one or more anatomical structures (150 1…i ) having at least one anatomical structure having a subset of said material represented by said spectral CT attenuation data.

5. The computer-implemented method of claim 4, wherein: The method further comprises: A material decomposition algorithm is applied to the spectral CT attenuation data to identify one or more materials.

6. A computer-implemented method according to any preceding claim, wherein: Identifying one or more anatomical structures in the spectral CT attenuation data (150 1…i ) includes performing a segmentation operation on the spectral CT attenuation data.

7. A computer-implemented method according to any preceding claim, wherein: The subset of spectral CT attenuation data (140 S ) is determined by the main processing system (130) or by the remote processing system (170); and wherein a lookup table or a neural network is used based on the identified one or more anatomical structures (150 1…i ) to determine the subset (140 S );and wherein the lookup table or the neural network is the one or more anatomical structures (150 1…i ) provides a corresponding subset of the spectral CT attenuation data required for calculating the value of the blood flow parameter (140 S ).

8. The computer-implemented method of claim 7, wherein: The subset of spectral CT attenuation data (140 S ) includes a plurality of data elements corresponding to different anatomical structures in the anatomical region (150 1…i ) and / or different materials; and Wherein, the method further comprises: assigning, by the host processing system (130) or by the remote processing system (170), transmission priorities to the plurality of data elements in the subset; and wherein the subset of the spectral CT attenuation data is transmitted (140 S ) comprises transmitting the data elements in the order of the assigned transmission priorities.

9. A computer-implemented method according to any preceding claim, wherein: The remote processing system (170) is also configured to: predicting a confidence value representing the expected accuracy of the calculated value (110) of the blood flow parameter; and The confidence value is transmitted to the host processing system (130) via the communication channel (160).

10. A computer-implemented method according to any preceding claim, wherein: The remote processing system (170) is also configured to: identifying the subset of the spectral CT attenuation data transmitted to the remote processing system (140 S ) in the data type; as well as selecting, based on the identified data type, an algorithm for use by the remote processing system (170) to calculate the value (110) of the blood flow parameter; or Based on the identified data type, the subset (140) of the spectral attenuation CT data transmitted to the remote processing system (170) S ) into a format for use by the remote processing system to calculate the value (110) of the blood flow parameter.

11. A computer-implemented method according to any preceding claim, wherein: The anatomical region is the thoracic region; and / or wherein the one or more anatomical structures (150 1…i ) includes one or more of the following: an organ, a heart, an atrium of the heart, the myocardium of the heart, a lumen of at least one of the one or more blood vessels (120), a wall of one or more of the one or more blood vessels (120), or a vascular plaque of one or more of the one or more blood vessels (120).

12. A computer-implemented method according to any preceding claim, wherein: The method further comprises: receiving, by the host processing system (130), input identifying the one or more blood vessels (120) in the spectral CT attenuation data (140); and Therein, the value (110) of the blood flow parameter is calculated for the identified one or more blood vessels (120).

13. A computer-implemented method according to any preceding claim, wherein: The blood flow parameter is the fractional flow reserve FFR, or the instantaneous waveform-free ratio iFR, or the myocardial resistance index IMR, or the coronary blood flow reserve CFR.

14. A computer program product comprising instructions which, when executed by one or more processors (310), cause the one or more processors to perform the method according to any one of claims 1-13.

15. A system (300) for determining a value (110) of a blood flow parameter of one or more blood vessels (120), the system comprising one or more processors (310), the one or more processors being configured to: receiving (S110) spectral computed tomography (CT) attenuation data (140) representing an injected contrast agent in an anatomical region including the one or more blood vessels (120), the spectral CT attenuation data (140) defining a plurality of different energy intervals (DE 1…m ) in the anatomical region; Identifying (S120) one or more anatomical structures (150) in the spectral CT attenuation data (140) 1…i ); Based on the identified one or more anatomical structures (150 1…i ), determining (S130) a subset (140S) of the spectral CT attenuation data (140) for calculating the value (110) of the blood flow parameter; The subset (140) of the spectral CT attenuation data is transmitted via a communication channel (160) S ) transmitting (S140) to a remote processing system (170), the remote processing system being configured to calculate the value (110) of the blood flow parameter based on the subset of the spectral CT attenuation data; as well as The value (110) of the blood flow parameter calculated by the remote processing system (170) is received (S150).

Images