Blood flow parameter

Abstract

A computer-implemented method for determining values of blood flow parameters of one or more blood vessels is provided. The method includes receiving spectral CT attenuation data by a host processing system, identifying by the host processing system one or more anatomical structures within the spectral CT attenuation data, determining a subset of the spectral CT attenuation data for use in calculating values of the blood flow parameters based on the identified one or more anatomical structures, transmitting the subset from the host processing system to a remote processing system, calculating values of the blood flow parameters by the remote processing system, and receiving by the host processing system the values of the blood flow parameters calculated by the remote processing system.

Description

Technical Field

[0001] The present disclosure relates to determining values of blood flow parameters of one or more blood vessels. A computer-implemented method, computer program product, system, and processing configuration are disclosed.

Background Art

[0002] Various clinical studies involve the evaluation of blood flow in the vascular system. For example, investigations of coronary artery disease "CAD" often involve the evaluation of blood flow to assess the amount of blood supplied to regions of the heart. In this regard, various blood flow parameters have been investigated, including blood flow velocity, blood pressure, fractional flow reserve "FFR", instantaneous wave-free ratio "iFR", coronary flow reserve "CFR", thrombolysis in myocardial infarction "TIMI" blood flow grade, index of microvascular resistance "IMR", and hypertensive microvascular resistance index "HMR". Some blood flow parameters are calculated for individual blood vessels, while others are calculated for a network of blood vessels.

[0003] Historically, 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 fractional flow reserve "FFR" is often determined in CAD investigations to assess the impact of stenosis on oxygen delivery to the myocardium. FFR is defined by the ratio Pd / Pa, where Pd represents the distal pressure at a distal location relative to the stenosis and Pa represents the proximal pressure relative to the stenosis. FFR is typically calculated using values of Pd and Pa averaged over the cardiac cycle. An FFR value greater than 0.8 is typically considered clinically insignificant, and a value less than 0.8 is typically considered to represent increased clinical significance. Historically, these pressure values have been determined by placing invasive devices such as pressure wires at respective locations within the vascular system.

[0004] More recently, angiographic techniques for determining the value of blood flow parameters, including FFR, have been developed. According to fluid flow theory, pressure changes are related to changes in fluid velocity. In the case of FFR, an angiographic image of the injected contrast agent is analyzed to determine blood flow velocity. The FFR can then be calculated by using a hemodynamic model to estimate the pressure value within the blood vessel from the blood flow velocity.

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

[0006] Some angiographic techniques for measuring blood flow parameters involve the use of computational fluid dynamics "CFD" models. Here, a technique for determining the FFR value using a CFD model is disclosed in the literature by C. A. Taylor et al., "Computational Fluid Dynamics Applied to Cardiac Computed Tomography for Noninvasive Quantification of Fractional Flow Reserve: Scientific Basis, JACC, Vol. 61, 22, 2013". Another literature describing angiographic techniques for measuring blood flow parameters using a computational fluid dynamics "CFD" model is the literature by Koo, B. K. et al., "Diagnosis of Ischemia―Causing Coronary Stenoses by Noninvasive Fractional Flow Reserve Computed From Coronary Computed Tomographic Angiograms: 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 angiography technique for measuring blood flow parameters involves the use of a lumped parameter model. The lumped parameter model can be used to represent blood flow within a vascular region as an electrical circuit, where the volumetric blood flow rate is represented as current, the blood pressure is represented as voltage, and the blood volume is represented by charge. In the lumped parameter model, the resistance to blood flow is represented by a linear or non-linear electrical resistance, the vascular wall compliance is represented by a capacitor, and the blood inertia is represented by an inductor. A machine learning-based approach for setting the values of the linear and non-linear resistors in a static lumped parameter model based on measurements derived from angiography of the vascular geometry is disclosed in Nickisch, H. et al., "learning Patient Specific Lumped Models for Interactive Coronary Blood Flow Simulations, MICCAI 2015, Part II, LNCS 9350, pp. 433 to 441, 2015", and in document WO 2016 / 001017. Then, in order to determine the value of the FFR of the blood vessel, the values of the blood flow rate and blood pressure in the model are solved. Compared to a computational fluid dynamics "CFD" model, the use of a lumped parameter model for calculating the values of blood flow parameters offers the advantage of a reduction in both computational load and model complexity.

Summary of the Invention

Problems to be Solved by the Invention

[0008] Angiography techniques for determining the value of blood flow parameters typically use CT attenuation data because they provide three-dimensional geometric information about the blood vessels within the vascular system. Spectral CT attenuation data is also used to determine the value of blood flow parameters. Spectral CT attenuation data includes data from a plurality of different energy intervals, and the processing of such data enables discrimination between materials that have similar X-ray attenuation values when measured within a single energy interval and are indistinguishable with conventional CT attenuation data. This advantage can be utilized to provide a more accurate value of the blood flow parameter by improving the discrimination between the attenuation resulting from the contrast agent material and the attenuation resulting from the background material. However, the calculation of the value of the blood flow parameter from CT attenuation data is a computationally complex task. Further, for a given region of interest, the spectral CT attenuation data set can be significantly larger than their conventional CT counterparts. This exacerbates the problem of determining the value of the blood flow parameter from spectral CT attenuation data.

[0009] Therefore, there remains room for improvement in the method of determining the value of the blood flow parameter from spectral CT attenuation data.

Means for Solving the Problem

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

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

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

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

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

[0015] Receiving, by the host processing system, the value of the blood flow parameter calculated by the remote processing system and having.

[0016] As described above, the calculation of 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 load on the host processing system. In the above method, instead of conventional CT data, the use of spectral attenuation data for calculating the value of the blood flow parameter facilitates an improved separation between the attenuation resulting from the injected contrast agent and the attenuation resulting from other media that may be present in the anatomical region. Thus, the use of spectral attenuation data in the above method facilitates the accurate calculation of the value of the blood flow parameter while reducing the computational load on the host processing system. However, the inventors have observed that the size of the spectral CT data set presents an issue when transmitting the spectral CT data to a remote processing system to determine the value of the blood flow parameter in 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 X-ray attenuation at multiple different energy intervals. In the above method, a subset of the spectral CT attenuation data is transmitted from the host processing system to the remote processing system via a communication channel, so that the remote processing system can calculate the value of the blood flow parameter with a reduced requirement for the bandwidth of the communication channel and / or calculate the value of the blood flow parameter in a reduced amount of time. Thus, the present method facilitates the provision of accurate blood flow parameter values with reduced latency.

[0017] Further aspects, features, and advantages of the present disclosure will become apparent from the following description of embodiments made with reference to the accompanying drawings.

Brief Description of the Drawings

[0018]

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[0019] Embodiments of the present disclosure are provided with reference to the following description and drawings. In this description, for purposes of explanation, many specific details of several examples are set forth. In this specification, references to the same language as "example", "embodiment", or features, structures, or characteristics described in connection with an example are meant to be included in at least one example. Also, features described in connection with one example may be used in another example, and it should be understood that not all features are necessarily replicated in each example for the sake of brevity. For example, features described in connection with a computer-implemented method may be implemented in a corresponding manner in a computer program product, in a system, and in a processing configuration.

[0020] In the following description, a method for determining the value of a blood flow parameter of a blood vessel is referred to. In some examples, the blood vessel is a coronary artery disposed within a thoracic anatomical region. However, it should be understood that the coronary artery functions only as an example, and the blood vessel may generally be any type of blood vessel, i.e., an artery, a vein, or a ventricle. The thoracic anatomical region functions only as an example of an anatomical region where a vasculature may be located, and it should also be understood that the vasculature may generally be located in any anatomical region. For example, the blood vessel may be located in an anatomical region such as the head region, the abdominal region, the upper limb region, the lower limb region, etc.

[0021] In this specification, examples of determining the FFR value of a blood vessel using this method are also referred to. However, it should be understood that FFR functions only as an example of a blood flow parameter, and the method disclosed in this specification 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 blood flow velocity, blood pressure, blood flow transit time, volume blood flow rate value, measurement of blood perfusion, iFR value, CFR value, TIMI blood flow grade, IMR value, and HMR value, congestive stenosis resistance "HSR" value, zero flow pressure "ZFP" value, and instantaneous congestive diastolic blood flow velocity - blood pressure slope "IVDS" value, but is not limited thereto.

[0022] Furthermore, in this specification, examples where the value of a blood flow parameter is determined for a single blood vessel are referred to, but the methods and systems disclosed in this specification may alternatively be used to calculate the values of blood flow parameters for multiple blood vessels. In this regard, the individual values of the blood flow parameter may be determined for each of the multiple blood vessels, or a single value of the blood flow parameter may be determined for multiple blood vessels. For example, the method disclosed in this specification may be used to calculate a measured value of blood perfused by a network of blood vessels, in which case the individual values of the blood flow parameter may be calculated for each of the multiple blood vessels within the network, or a single value of the blood flow parameter may be calculated for the network.

[0023] Note that the computer-implemented method disclosed herein can 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 execute the method. In other words, the computer-implemented method can be implemented in a computer program product. The computer program product can be provided by dedicated hardware or by hardware capable of executing software in association with appropriate software. When provided by a processor, the functions of the features of the method can be provided by a single dedicated processor, or by a single shared processor, or by a plurality of individual processors, some of which can be shared. One or more of the functions of the method features can be provided, for example, by a processor shared within a networked processing architecture such as a client / server architecture, a peer-to-peer architecture, the Internet, or the cloud.

[0024] The explicit use of the terms "processor" or "processing system" or "controller" should not be construed as exclusively referring to hardware capable of executing software, but implicitly includes, but is not limited to, digital signal processor "DSP" hardware, read-only memory "ROM" for storing software, random access memory "RAM", non-volatile memory devices, etc. Further, examples of the present disclosure can take the form of a computer-usable storage medium, or a computer-readable storage medium accessible computer program product, the computer program product providing program code for use by a computer or any instruction execution system, or in connection therewith. For the purposes of this description, a computer-usable storage medium or a computer-readable storage medium can be any device capable of storing, communicating, propagating, or transporting a program for use by or in connection with an instruction execution system, apparatus, or device. The medium can be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system or device or propagation medium. Examples of computer-readable media include semiconductor or solid state memory, magnetic tape, removable computer disk, random access memory "RAM", read-only memory "ROM", rigid magnetic disk, and optical disk. Current examples of optical disks include compact disk read-only memory "CD-ROM", compact disk read / write "CD-R / W", Blu-Ray (trademark), and DVD.

[0025] As described above, there remains room for improvement in the method by which the value of the blood flow parameter is determined from the spectral CT attenuation data.

[0026] FIG. 1 is a flowchart showing an example of a computer-implemented method for determining values 110 of blood flow parameters of one or more blood vessels 120, according to some aspects of the present disclosure. FIG. 2 is a schematic diagram showing an example of a system 300 for determining values 110 of blood flow parameters of one or more blood vessels 120, according to some aspects of the present disclosure. The operations described in connection with the method shown in FIG. 1 may also be performed by the system 300 shown in FIG. 2. Similarly, the operations described in connection with the system 300 may also be performed in the method described with reference to FIG. 1. Referring to FIG. 1, a computer-implemented method for determining values 110 of blood flow parameters of one or more blood vessels 120 is In S110, a step of receiving, by a host processing system 130, spectral CT attenuation data 140 representing an injected contrast agent in an anatomical region including one or more blood vessels 120, wherein the spectral CT attenuation data 140 defines X-ray attenuation in the anatomical region within a plurality of different energy intervals DE 1..m is a step of In S120, a step of identifying, by the host processing system 130, one or more anatomical structures 150 in the spectral CT attenuation data 140 1..i is a step of In S130, based on the identified one or more anatomical structures 150 1..i a step of determining a subset 140 of the spectral CT attenuation data 140 for use in calculating the values 110 of the blood flow parameters S is a step of In S140, a step of transmitting, by the host processing system 130, the subset 140 of the spectral CT attenuation data via a communication channel 160 to a remote processing system 170, wherein the remote processing system is configured to calculate the values 110 of the blood flow parameters from the subset of the spectral CT attenuation data In S150, a step of receiving, by the host processing system 130, the values 110 of the blood flow parameters calculated by the remote processing system 170 is included.

[0027] As described above, the calculation of 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 load on the host processing system by using a remote processing system to calculate the value of the blood flow parameter. In the above method, instead of conventional CT data, the use of spectral attenuation data for calculating the value of the blood flow parameter facilitates an improved separation between the attenuation resulting from the injected contrast agent and the attenuation resulting from other media that may be present in the anatomical region. Therefore, the use of spectral attenuation data in the above method facilitates the accurate calculation of the value of the blood flow parameter while reducing the computational load on the host processing system. However, the inventors have observed that the size of the spectral CT data set presents a problem when transmitting the spectral CT data to a remote processing system to determine the value of the blood flow parameter in 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 X-ray attenuation at multiple different energy intervals. In the above method, a subset of the spectral CT attenuation data is transmitted from the host processing system to the remote processing system via a communication channel, so that the remote processing system can calculate the value of the blood flow parameter with a reduced requirement for the bandwidth of the communication channel and / or calculate the value of the blood flow parameter in a reduced amount of time. Therefore, this method facilitates the provision of accurate blood flow parameter values with reduced latency.

[0028] The method shown in FIG. 1 will also be described with reference to FIG. 3, which is a schematic diagram showing an example of a computer-implemented method for determining the value 110 of the blood flow parameter of one or more blood vessels 120 according to some aspects of the present disclosure.

[0029] Referring to FIGS. 1 through 3, in operation S110, spectral CT attenuation data 140 is received by host processing system 130. The spectral CT attenuation data represents an injected contrast agent within an anatomical region that includes 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. The contrast agent may be injected manually or by an injector such as injector 370 shown in FIG. 2, for example. The anatomical region represented by spectral CT attenuation data 140 can generally be any portion of an anatomical structure that includes one or more blood vessels. In some examples, spectral CT attenuation data 110 represents a thoracic anatomical region. The thoracic anatomical region includes anatomical structures such as the heart, lungs, and portions of such anatomical structures may be represented in spectral CT data 140. FIG. 4 is a schematic diagram showing the heart, including an example of blood vessel 120, according to some aspects of the present disclosure. The heart shown in FIG. 4 is labeled with the left coronary artery, LCA, right coronary artery, RCA, and LCA inlet and RCA inlet that respectively define the openings of these arteries in the aorta. The spectral CT attenuation data 140 received in operation S110 can represent, among other things, the blood vessel 120 within the heart shown in FIG. 4, i.e., the contrast agent injected into the left coronary artery. As will be described in more detail below, in some examples of the method shown in FIG. 1, values of blood flow parameters 110 are calculated from spectral CT attenuation data 140 for one or more blood vessels. The values of the blood flow parameters can be calculated, for example, for the blood vessel 120 shown in FIG. 4.

[0030] The spectral CT attenuation data 140 received in operation S110 can generally be raw data, i.e., data that has not been reconstructed into a volume image, or image data, i.e., data that represents a reconstructed volume image. The spectral CT attenuation data 140 is received by host processing system 130. This is shown on the left side of FIG. 3, where the multi - energy interval DE 1..mSpectral CT data including the reconstructed image data in each of is received by the host processing system 130. The host processing system 130 includes one or more processors 310 as shown in the system 300 shown in FIG. 2. In general, the host processing system 130, or more specifically, one or more of its processors 310 can receive the spectral CT attenuation data 140 from an imaging system such as the spectral CT imaging system 320 shown in FIG. 2, or from another source such as, for example, a computer-readable storage medium, the Internet, or the cloud. The spectral CT attenuation data 140 can 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 can be performed via signals transmitted over electrical or optical cables, and when wireless communication is used, the communication can be performed via RF or optical signals.

[0031] The host processing 130 system can generally be provided by any device including a processor. For example, the host processing 130 system may be provided by a computer such as a tablet, laptop, desktop computer, or mainframe computer. The host processing device may alternatively be provided in the form of a communication device such as a mobile phone.

[0032] The spectral CT attenuation data 140 received in S110 is a plurality of energy intervals DE 1..mDefines the X-ray attenuation within the anatomical region. In this regard, the spectral CT attenuation data 140 can 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 device around the imaging region. Examples of spectral CT imaging systems 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 attenuation data 140 can be generated by a spectral CT 7500 commercially available from Philips Healthcare, Best, The Netherlands.

[0033] An example of a spectral CT imaging system 320 that can be used to generate the spectral CT attenuation data 140 received in operation S110 is shown in FIG. 2. The spectral CT imaging system 320 shown in FIG. 2 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 (not shown in FIG. 2). During operation, the X-ray source 330 and the X-ray detector 340 are rotated by the gantry around the rotation axis 350, while acquiring the spectral CT attenuation data 140 that defines the X-ray attenuation in the region of interest disposed in the imaging region of the imaging system 320. The spectral CT attenuation data 140 obtained from a plurality of rotation angles around the rotation axis 350 can then be reconstructed into a volume image using various image reconstruction techniques.

[0034] The spectral CT attenuation amount 140 received in S110 defines the X-ray attenuation amount within a plurality of energy intervals DE 1..m Generally, there may be two or more energy intervals, that is, m is an integer and m ≧ 2. The ability to generate data that defines X-ray attenuation data differentiates the spectral CT imaging system from a conventional CT imaging system by DE 1..mThis is the case. By processing data from multiple different energy intervals, it is possible to distinguish between media that have similar X-ray attenuation values when measured within a single energy interval and are indistinguishable from the attenuation data generated by conventional CT imaging systems. In this regard, various different settings of the spectral CT imaging system can be used to generate spectral CT attenuation data received in operation S110, some of which will be described with reference to FIG. 2.

[0035] Referring to the exemplary spectral CT imaging system 320 shown in FIG. 2, generally, the X-ray source 330 can be provided by a plurality of monochromatic sources or by one or more polychromatic sources, and the X-ray detector 330 can be a common detector for detecting X-ray radiation over a plurality of different X-ray energy intervals, or each detector can detect X-ray radiation within a different X-ray energy interval DE 1..m within, or a multi-layer detector in which the 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 that classifies the detected X-ray photons into one of a plurality of energy intervals based on their individual energies. In a photon counting detector, the associated energy interval can be determined for each received X-ray photon by detecting the pulse height induced by the electron-hole pairs generated in response to the absorption of the X-ray photons in the direct conversion material.

[0036] The various configurations of the above-described X-ray source and X-ray detector can be used in a spectral CT imaging system to generate spectral attenuation data that defines X-ray attenuation at a plurality of different energy intervals DE 1..m In general, discrimination between different X-ray energy intervals can be achieved at the X-ray source 330 by temporally switching the X-ray anode potential of a single X-ray source 330, i.e., by "rapid kVp switching", or by temporally switching or filtering the emission of X-rays from a plurality of X-ray sources 330. In such a setup, a common X-ray detector 340 can be used to detect X-rays over a number of different energy intervals, and the energy interval DE1..m Attenuation data for each of them is generated in time series. Alternatively, in the X-ray detector 340, a multi-layer detector or a photon counting detector may be used to distinguish between X-ray energy intervals DE 1..m This type of detector can detect X-rays from the X-ray energy interval DE 1..m almost simultaneously, so there is no need to perform temporal switching at the X-ray source 330. Therefore, by using the multi-layer X-ray detector 340 or the counting X-ray detector 340 in combination with the polychromatic X-ray source 330, spectral attenuation data can be generated at a plurality of different energy intervals DE 1..m .

[0037] Other configurations of the aforementioned X-ray source 330 and X-ray detector 340 are alternatively used in a spectral CT imaging system to provide desired X-ray attenuation data at a plurality of various energy intervals DE 1..m . For example, in a further setting, the need to sequentially switch different X-ray sources 330 that emit X-rays at different energy intervals can be avoided by attaching the X-ray source detector pair to the gantry at a rotational offset position around the rotation axis 350. In this setting, each source-detector pair operates independently, and the rotational offset of the source-detector pair facilitates the separation between X-ray attenuation data at various energy intervals DE 1..m . In this setting, in order to reduce the influence of X-ray scattering, by applying an energy selection filter to the X-ray detector 340, improved separation between spectral attenuation data at various energy intervals DE 1..m can be achieved.

[0038] In another setting, a plurality of different energy intervals DE 1..mThe spectral CT attenuation data 140 in [the device] may be provided by blocking the X-ray beam in a conventional X-ray imaging device having one or more spectral filters. For example, in order to temporally control the spectrum of the X-rays detected by the X-ray detector 340, a single filter can be mechanically inserted into and removed from the X-ray beam generated by the polychromatic X-ray source 330. The filter can, for example, transmit only a part of the spectrum of the X-rays emitted by the polychromatic X-ray source 330, thereby providing spectral attenuation data for a first energy interval when the filter blocks the beam. When the filter is removed from the beam, the spectral attenuation data is provided for a second energy interval, for example, the complete spectrum emitted by the polychromatic X-ray source. Thereby, X-ray attenuation data for the energy interval DE 1..m is generated in time series. A plurality of filters, each having a different X-ray transmission spectrum, can be sequentially switched into and out of the X-ray beam in order to increase the number of energy intervals.

[0039] Instead of being generated by a spectral CT imaging system, the spectral CT attenuation data 140 received in operation S110 may alternatively be generated by a spectral X-ray projection imaging system. The spectral X-ray projection imaging system typically includes a support arm, also known as a gantry, that supports an X-ray source and an X-ray detector, such as a so-called "C-arm". The spectral X-ray projection imaging system may alternatively include a support arm having a different shape than this example, such as an O-arm. The spectral X-ray projection imaging system typically generates projection data using a support arm held in a stationary position with respect to the imaging region during acquisition of the projection data. The projection data can be used to generate a 2D image. However, the spectral X-ray projection imaging system can also generate spectral CT attenuation data, i.e., data that can be reconstructed into a volume image. The spectral X-ray projection imaging system can generate spectral CT attenuation data for a region of interest by rotating the support arm around an axis of rotation and acquiring spectral X-ray projection data of the region of interest from a plurality of rotation angles around the axis of rotation. A set of spectral X-ray projection data from a plurality of rotation angles can be reconstructed to provide a volume image and thus represents spectral CT attenuation data. Various image reconstruction techniques can be used to reconstruct a set of spectral X-ray projection data from a plurality of rotation angles into a volume image. These techniques process the set of spectral X-ray projection data in the same way as spectral CT data generated by a spectral CT imaging system. Accordingly, the spectral CT attenuation data 140 received in operation S110 may alternatively be generated by a spectral X-ray projection imaging system.

[0040] Returning to FIG. 1, in operation S120, one or more anatomical structures 150 within the spectral CT attenuation data 140 1..i are identified by the host processing system 130.

[0041] Anatomical structures can generally refer to organs such as the heart, lungs, brain, etc., or to sub-elements of organs. For example, in some instances, the anatomical structure may be the heart, or an atrium of the heart, the myocardium of the heart, at least one lumen of one or more blood vessels 120, one or more walls of one or more blood vessels 120, or one or more atherosclerotic plaques of one or more blood vessels 120. One or more anatomical structures 150 in spectral CT attenuation data 1..i The operation S120 of identifying 1..i can generally be performed by executing a segmentation operation on the spectral CT attenuation data 140. For this purpose, various segmentation techniques are known, which include computer vision methods such as feature clustering, model-based segmentation, the use of feature detectors, and trained neural networks.

[0042] FIG. 5 is a schematic diagram showing an example of the operation of S120 for identifying one or more anatomical structures 150 within the spectral CT attenuation data 140, according to some aspects of the present specification, and a subset 140 of the spectral CT attenuation data 140 used to calculate the value 110 of the blood flow parameter. 1..i FIG. 5 shows an example of the operation of S120 for identifying 1..i and shows S130 for determining a subset 140 of the spectral CT attenuation data 140 used to calculate the value 110 of the blood flow parameter. The spectral CT attenuation data 140 shown in FIG. 5 represents a chest region and includes the heart as an example of an anatomical structure. In this embodiment, the sub-elements of the heart, namely the myocardium 1501, the coronary artery 1502, and the left atrium 1503, are identified using model-based segmentation of the spectral CT attenuation data 140. S Returning to the method shown in FIG. 1, after identifying one or more anatomical structures 150 in the spectral CT attenuation data 140, in operation S130, a subset 140 of the spectral CT attenuation data 140

[0043] is determined that corresponds to the identified one or more anatomical structures 150. 1..i After identifying one or more anatomical structures 150 in the spectral CT attenuation data 140, in operation S130, a subset 140 of the spectral CT attenuation data 140 S is determined that corresponds to the identified one or more anatomical structures 150. 1..iIt is determined for use in calculating the value 110 of the blood flow parameter based thereon. Generally, operation S130 can be performed by the host processing system 130 or by the remote processing system 170. Subset 140 S can be determined, for example, using a look-up table or a neural network, based on one or more identified anatomical structures 150 1..i In such a case, the look-up table or neural network identifies, for one or more anatomical structures 150 1..i the corresponding subset 140 of spectral CT attenuation data necessary for the calculation of the blood flow parameter S For example, with respect to FIG. 5, if the desired blood flow parameter is FFR and the anatomical structure is the heart, the look-up table may identify that subset 140 S includes a contrast agent map for the myocardium 1501, data from a relatively low energy interval, "low mono e", i.e., data from two energy intervals DE1, DE2 for the coronary arteries, and the DE1 of a dual energy spectral CT imaging system including an electron density image for the left atrium 1503. The look-up table may similarly identify the subset 140 of spectral CT attenuation data 140 required for calculating the values of other blood flow parameters, such as iFR, CFR, TIMI flow grade, IMR value, and HMR value, congestive stenosis resistance "HSR" value, zero flow pressure "ZFP" value, and instantaneous congestive diastolic velocity-pressure gradient "IHDVPS" value, etc. S The look-up table can also identify the subset 140 of spectral CT attenuation data 140 required for other anatomical regions S The subset 140 of spectral CT attenuation data 140 required to calculate the numerical value of the desired blood flow parameter

[0044] Instead of using a look-up table to identify the subset 140 of spectral CT attenuation data 140 required to calculate the numerical value of the desired blood flow parameter S a neural network identifies the subset 140 Scan be trained to identify. In this case, the neural network may be input with an image of an anatomical region, along with an input indicating the desired blood flow parameter, and the neural network calculates the corresponding subset 140 required to calculate the value of the blood flow parameter S may be trained to output. Training data for this neural network can include a plurality of images of the anatomical region and a corresponding list of data required to calculate the values of the blood flow parameters. Subset 140 S can be determined based on the data required by the technique used to calculate the numerical value of the blood flow parameter.

[0045] Returning to the method shown in FIG. 1, in operation S140, a subset 140 of the spectral CT attenuation data S is transmitted from the host processing system 130 to the remote processing system 170 via the communication channel 160. This operation is shown in FIGS. 2 and 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. That is, when using a wired communication channel, communication can be performed via signals transmitted through an electrical cable or an optical cable, and when using a wireless communication path, communication can be performed via an RF signal or an optical signal.

[0046] In one embodiment, the data within the subset 140 of the spectral CT attenuation data S is transmitted according to priority. In this embodiment, the subset 140 of the spectral CT attenuation data S includes a plurality of data elements, and the data elements correspond to different anatomical structures 150 1..i and / or different materials within the anatomical region. In this example, the method described with reference to FIG. 1 includes the step of assigning transmission priorities to the plurality of data elements within the subset by the host processing system 130 or by the remote processing system 170 and the subset 140 of the spectral CT attenuation data SThe step of transmitting includes transmitting data elements in the order of the assigned transmission priorities.

[0047] The transmission priority can be defined based on factors such as the type of blood flow parameter to be calculated, the anatomical site represented by the spectral CT data 140, the anatomical structure 150 identified in operation S120 1..i and the substance included in the anatomical structure 150 1..i For example, when the FFR is calculated by the remote processing system 170, the host processing system 130 prioritizes the transmission of data representing the coronary artery 1502 over the data representing the myocardium 1501, so that a subset 140 of the spectral CT attenuation data S can be transmitted. This allows the remote processing system 170 to start by processing the data from the most relevant regions such as the coronary artery 1502 without waiting for the transmission of data for other anatomical structures such as the myocardium 1501, thus facilitating a more time-efficient transmission of the subset 140 S In this way, by prioritizing the transmission of the subset 140 S the time required to calculate the blood flow parameter can be shortened.

[0048] The transmission priority can be assigned by the host processing system 130 or by the remote processing system 170. In the latter case, the remote processing system 170 can operate in a "pull" / "on-demand" mode, and the remote processing system 170 uses the result of processing the current data element, for example, the result of the processed data for the coronary artery 1502, to identify the subsequent data elements required by the remote processing system 170 to calculate the numerical value 110 of the blood flow parameter.

[0049] The remote processing system then selects a subset 140 of the spectral CT attenuation data SCalculate the numerical value 110 of the blood flow parameter. In this regard, the value 110 of the blood flow parameter can be calculated using various techniques cited above. For example, techniques for sampling a reconstructed CT image to determine the blood flow velocity disclosed in the literature cited above by Barfett, J. J. et al. may be used, or techniques for sampling raw, i.e., "projected", CT data to determine the blood flow velocity disclosed in the literature cited by Prevrhal, S. et al. may be used. Other techniques such as techniques for determining the FFR value using a CFD model are disclosed in the cited references by Taylor. C. A. et al., or the computational fluid dynamics "CFD" model in the cited references by Koo, B. K. et al., or the lumped parameter model in the cited references by Nickisch, H. et al., and in WO 2016 / 001017, the FFR value of a blood vessel can be determined using an angiography technique for measuring a blood flow parameter using a computational fluid dynamics "CFD" model. Such techniques can be computationally complex, and thus, by using a remote processing system to calculate the value 110 of the blood flow parameter instead of the host processing system 130, the processing burden on the host processing system 130 is reduced. Further, a subset 140 S of the spectral CT attenuation data 140 is transmitted to the remote processing system 170 instead of the complete set of the spectral CT attenuation data 140, so that the remote processing system 170 calculates the value of the blood flow parameter with a reduced requirement for the bandwidth of the communication channel and / or calculates the value of the blood flow parameter in a reduced amount of time. Thus, the present method facilitates the provision of a blood flow parameter value with reduced latency.

[0050] Next, the value 110 of the blood flow parameter calculated by the remote processing system 170 is transmitted to the host processing system 130 via 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 host processing system 130.

[0051] The value 110 of the blood flow parameter can then be output by the host processing system 130. The value 110 can be output in various ways. For example, the value of the blood flow parameter can be output to a display device such as the monitor 370 of the system 300 shown in FIG. 2. Alternatively, the value may be output to, for example, 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 a plurality of positions along the length of the blood vessel 120 and output graphically. For example, an image showing the blood vessel 120 may be output as a color-coded overlay on the image together with the values of the blood flow parameter displayed at corresponding positions 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 subject.

[0052] As described above, in operation S130, a subset 140 of the spectral CT attenuation data (140) S is determined based on the identified anatomical structure 150 1..i In this regard, the identified anatomical structure 150 1..i can be used in various ways to determine the subset 140 S In one embodiment, the subset 140 of the spectral CT attenuation data S includes only the spectral CT attenuation data corresponding to one or more identified anatomical structures 150 1..i Referring to FIG. 5, in this example, the subset 140 of the spectral CT attenuation data S can include only the spectral CT attenuation data corresponding to, for example, the heart, or the myocardium 1501, or the coronary artery 1502, or the left atrium 1503, or a combination of these anatomical structures 150 1..3 By omitting the spectral CT attenuation data 140 for other anatomical structures, the amount of data transmitted in operation S140 is reduced.

[0053] In another embodiment, the subset 140 of spectral CT attenuation S The one or more identified anatomical structures 150 identified in operation S120. 1..i and the spectral CT attenuation data corresponding to the identified anatomical structure or structures 150. 1..i and compressing or defaulting the spectral CT attenuation data corresponding to one or more regions outside of the identified anatomical structure 150. 1..i 5. Alternatively, a compression value may be provided for a region such as this background region. The compression value may be calculated based on the identified anatomical structure or structures 150. 1..i The spectral CT attenuation data corresponding to one or more regions outside of the identified anatomical structure 150 is 1..i The compressed value may alternatively be represented at a relatively lower spatial resolution than the spectral CT attenuation data corresponding to the identified anatomical structure 150. This may be achieved, for example, by mapping the image intensity values of a group of image pixels in the background region to an average value of the group. 1..i The spectral CT attenuation data corresponding to one or more regions outside of the identified anatomical structure 150 is 1..i The background regions may be represented at a relatively lower bit depth than the spectral CT attenuation data corresponding to the background regions. This may be achieved, for example, by quantizing the image intensity values of the pixels in the background regions using fewer threshold levels. Thus, these examples reduce the amount of data of the background regions that is transmitted over the communication channel 160 in act S140, which reduces the bandwidth requirements thereof.

[0054] 150 Anatomical Structures Identified 1..i is a subset of the spectral CT attenuation data. S In another example, the subset of spectral CT attenuation data 140 S one or more anatomical structures 1501..i corresponding to a subset 140 of the spectral CT attenuation data S has one or more anatomical structures 150 having a subset of a plurality of energy intervals DE 1..m and represents at least one of them. For example, with respect to FIG. 5, the subset 140 of the spectral CT attenuation data 1..i may correspond to the coronary artery 1502, and in this case the subset 140 S may represent the coronary artery 1502 having a subset of the energy interval DE S . In this example, since the coronary artery data does not include the data of all the energy intervals DE 1..m , in operation S140, the reduced component of the coronary artery data is transmitted via the communication channel 160, which reduces the requirement for its bandwidth. 1..m

[0055] In another embodiment, the subset 140 of the spectral CT attenuation data S corresponds to one or more anatomical structures 150 identified in operation S120 1..i , and the spectral CT attenuation data represents a plurality of different substances within the anatomical region. The subset 140 of the spectral CT attenuation data S represents at least one of one or more anatomical structures 150 having a subset of the substances represented by the spectral CT attenuation data 1..i . In this example, the spectral CT attenuation data may represent, for example, an iodine contrast agent, bone, soft tissue, and water in an anatomical region. The identified anatomical structures 150 1..i may include the myocardium 1501, the coronary artery 1502, and the left atrium 1503. Instead of transmitting data for all of the materials represented in each of these anatomical structures, in this example at least one of the anatomical structures, for example the coronary artery, may be represented using only the data for the contrast agent material, iodine. In this example, since the data for other materials such as soft tissue for the coronary artery is omitted, the amount of data transmitted in operation S140 is reduced.

[0056] ​In the above example, the material can be identified by applying a material decomposition algorithm to the spectral CT attenuation data 140.

[0057] An example of a material decomposition technique that can be used to identify materials is "Empirical, projection - based - basis - component decomposition method" by Brendel, B. et al. (Medical Imaging 2009, Physics of Medical Imaging, edited by Ehsan Samei and Jiang Hsieh, Proc., SPIE Vol. 7258, 72583Y.). See also "K - edge imaging in x - ray computed tomography using multi - bin photon counting detectors" by Roessl, E. and Proksa, R. (Phys Med Biol. 2007 Aug 7, 52(15):4679 - 4696). Another suitable material decomposition technique is disclosed in the published PCT patent application WO / 2007 / 034359 A2. Another suitable material decomposition technique is disclosed in the literature "Energy - selective reconstructions in X - ray computerized tomography" by Alvarez et al. (Physics in medicine and biology, Vol. 21, No. 5, page 733 - 744).

[0058] Generally, the X - ray attenuation spectrum of a material includes contributions from Compton scattering and the photoelectric effect. The attenuation due to 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 it is this effect that is utilized by material decomposition techniques to distinguish different materials.

[0059] Generally, a material decomposition algorithm operates by decomposing the attenuation spectrum of an absorption medium into contributions from a set of assumed "basis" materials. The energy-dependent x-ray attenuation of the assumed basis materials is typically modeled as a combination of absorption from Compton scattering and the photoelectric effect. Some materials also have k-absorption edge "k-edge" energies within the energy range used by diagnostic x-ray imaging systems, and this effect can also be utilized to distinguish different materials. The spectral decomposition algorithm then seeks to estimate the amount of each of the basis materials required to produce the measured x-ray attenuation at two or more energy intervals. Water and iodine are examples of basis substances that are often separated in clinical practice using so-called dual-material decomposition algorithms. Non-fat soft tissue, fat, and iodine are examples of basis materials that are often separated in clinical practice using three-material decomposition algorithms.

[0060] As described above, when the "k-edge" energy of any of the base materials is within the energy range used by the diagnostic X-ray imaging system, i.e., within about 30 to 120 keV, this can be utilized to help distinguish 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 with a k-shell electron. The k-edge occurs at the characteristic energy of 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 a diagnostic X-ray imaging system. For example, the k-edge energies of hydrogen, carbon, oxygen, and nitrogen are 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 enable their distinction in spectral CT attenuation data obtained from a diagnostic X-ray imaging system.

[0061] As an example, FIG. 6 is a graph showing the dependence of the mass attenuation coefficient on X-ray energy for two example materials, iodine and water. The mass attenuation coefficient shown in FIG. 5 represents X-ray attenuation. The sharp increase in the X-ray attenuation of iodine at 33.2 keV facilitates the separation between the contributions of each of these materials in a composite attenuation spectrum that includes attenuation from both of these materials. For example, the X-ray attenuation arising from iodine and water can be separated by using 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.

[0062] In the method described above with reference to FIG. 1, one or more additional operations can also be performed. For example, in one example, the remote processing system 170 Predict a confidence value representing the expected accuracy of the calculated value 110 of the blood flow parameter, and transmit the confidence value to the host processing system 130 via the communication channel 160. It is configured as follows.

[0063] The confidence level may be calculated, for example, based on the magnitude of the noise in the subset 140. S The confidence value may be output by the host processing system 130. The confidence value may be output, for example, as an error bar on the calculated value 110 of the blood flow parameter. A physician can use the confidence value to determine how much reliability to place on the value of the blood flow parameter.

[0064] In another example, the remote processing system 170 identifies the type of data within the subset 140 of the spectral CT attenuation data transmitted to the remote processing system, selects an algorithm to be used by the remote processing system 170 to calculate the value 110 of the blood flow parameter based on the identified data type, and based on the identified type of data, converts 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. It is configured to perform the above.

[0065] In this example, the remote processing system 170, for example, the subset 140 SAnalyze the data therein to determine the anatomical structure to which the transmitted data pertains, and use the anatomical structure to select an algorithm to be used for processing the data. For example, if the result of the analysis indicates that the transmitted data is related to the coronary artery, a first algorithm can be used to calculate the value of the blood flow parameter. In contrast, if the result of the analysis indicates that the transmitted data is related to the peripheral artery, a second algorithm can be used to calculate the value of the blood flow parameter. The remote processing system 170 is the subset 140 S Analyze the data therein, and determine the anatomical structure to which the transmitted data pertains, for example, by segmenting the received data and applying a feature detector to the segmented data. Similarly, if the desired blood flow parameter is the FFR value, the result of the analysis can be used to identify the most appropriate algorithm to be used to determine that value based on the type of the transmitted data. This facilitates the calculation of the accurate value of the blood flow parameter.

[0066] Alternatively, the remote processing system can convert the subset 140 S based on the identification of the data type within the subset 140. In this case, the remote processing system 170 analyzes, for example, the transmitted data to determine the type of the basis function image transmitted to the remote processing system 170, and converts the subset 140, for example, by mapping the data of the basis function representing soft tissue to a different material, or by mapping to different energy intervals required by an algorithm for calculating the value of the blood flow parameter. S This mapping of the data can be performed based on a physical model or using image-to-image conversion with the aid of a trained (convolutional) neural network. This also facilitates the calculation of the accurate value of the blood flow parameter.

[0067] In another example, the method described with reference to FIG. 1 is

[0068] A step of receiving, by the host processing system 130, an input for identifying one or more blood vessels 120 in the spectral CT attenuation data 140, wherein a value 110 of a blood flow parameter is calculated for the identified one or more blood vessels 120 includes

[0069] In this example, the operation of identifying the (one or more) blood vessels 110 can 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. The input for identifying the blood vessels 180 within the region of interest 120 may be provided by the user or may be provided automatically. The user input for identifying the blood vessels 180 can be received from a user input device that operates in combination with the displayed image of the region of interest. The input for identifying the blood vessels 120 can be provided automatically by using a feature detector or a trained neural network to identify the blood vessels 120 within the spectral CT attenuation data 140. A segmentation operation may be performed on such an image to assist in identifying the blood vessels. For this purpose, various segmentation algorithms are known. A feature detector or a trained neural network can automatically identify candidate blood vessels, which can then be selected by the user via the user input device, and a combination of these approaches can also be used. The blood vessels can be identified based on the detection of a contrast agent in such an image or based on the presence of an abnormality such as a stenosis in the blood vessel. As an example, a feature detector or a trained neural network can be used to identify the left coronary artery in a heart image and the stenosis therein, and the blood vessels can be identified as candidate blood vessels

[0070] Accordingly, in this example, the input for identifying one or more blood vessels 120 can be received from a user input device or from a processor that performs the operation of a feature detector or a trained neural network

[0071] In another example, a computer program product is provided. The computer program product A method for determining a value 110 of a blood flow parameter of one or more blood vessels 120 when executed by one or more processors 310, the method comprising: In S110, a step of receiving, by a host processing system 130, spectral computed tomography 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 X-ray attenuation within the anatomical region at a plurality of different energy intervals DE 1..m ; and In S120, a step of identifying, by the host processing system 130, one or more anatomical structures 150 in the spectral CT attenuation data 140 1..i ; and In S130, determining a subset 140 of the spectral CT attenuation data 140 for use in calculating the value 110 of the blood flow parameter based on the identified one or more anatomical structures 150 1..i ; and S In S140, a step of transmitting the subset 140 of the spectral CT attenuation data from the host processing system 130 to a remote processing system 170 via a communication channel 160, the remote processing system being configured to calculate the value 110 of the blood flow parameter from the subset of the spectral CT attenuation data In S150, a step of receiving, by the host processing system 130, the value 110 of the blood flow parameter calculated by the remote processing system 170 S ; and Instructions for causing the method to be executed. In another example, a system 300 for determining a value 110 of a blood flow parameter of one or more blood vessels 120 is provided. The system includes:

[0072] In another example, a system 300 for determining a value 110 of a blood flow parameter of one or more blood vessels 120 is provided. The system includes: In S110, receiving spectral computed tomography attenuation data 140 representing an injected contrast agent within an anatomical region including one or more blood vessels 120, wherein the spectral CT attenuation 140 data defines X-ray attenuation within the anatomical region over a plurality of different energy intervals DE 1..m including a step of defining X-ray attenuation within the anatomical region over a plurality of different energy intervals DE In S120, identifying one or more anatomical structures 150 within the spectral CT attenuation data 140 1..i including a step of identifying In S130, based on the identified one or more anatomical structures 150 1..i determining a subset 140 of the spectral CT attenuation data 140 for use in calculating a value 110 of a blood flow parameter S including a step of determining In S140, transmitting a subset 140 of the spectral CT attenuation data via a communication channel 160 to a remote processing system 170, the remote processing system being configured to calculate a value 110 of a blood flow parameter from the subset of the spectral CT attenuation data, including a step of In S150, receiving the value 110 of the blood flow parameter calculated by the remote processing system 170 and includes one or more processors 310 configured to execute

[0073] An example of the system 300 is shown in FIG. 2. It should be noted that the system 300 may also include one or more of 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 value 110 of the blood flow parameter, 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 (not shown in FIG. 2) configured to receive user input for use in conjunction with methods such as a keyboard, mouse, touch screen, etc.

[0074] In another example, a processing configuration is provided for determining the value 110 of the blood flow parameter of one or more blood vessels 120. The processing apparatus includes the above-described system 300 and the remote processing system 170.

[0075] The above embodiments should be understood as illustrative of the present disclosure and not limiting. Further examples are contemplated. For example, the examples described in connection with the computer-implemented method may also be provided by a corresponding method, by a computer program product, or by a computer-readable storage medium, or by the system 300. It should be understood that the 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 in combination with combinations of other embodiments. Furthermore, equivalents and modifications not described above may also be used without departing from the scope of the invention as defined in the appended claims. In the claims, the word "comprising" does not exclude other elements or acts, and the indefinite article "a" or "an" does not exclude a plurality. The mere fact that certain features are 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. A computer-implemented method for determining values of blood flow parameters of one or more blood vessels, comprising: receiving, by a host processing system, spectral CT attenuation data representing an injected contrast agent within an anatomical region including the one or more blood vessels, the spectral CT attenuation data defining X-ray attenuation within the anatomical region over a plurality of different energy intervals; identifying, by the host processing system, one or more anatomical structures in the spectral CT attenuation data; determining, based on the identified one or more anatomical structures, a subset of the spectral CT attenuation data for use in calculating the values of the blood flow parameters; transmitting, via a communication channel, the subset of the spectral CT attenuation data from the host processing system to a remote processing system, the remote processing system being configured to calculate the values of the blood flow parameters from the subset of the spectral CT attenuation data; receiving, by the host processing system, the values of the blood flow parameters calculated by the remote processing system and a method.

2. The subset of the spectral CT attenuation data includes only the spectral CT attenuation data corresponding to the identified one or more anatomical structures, or the subset comprises the spectral CT attenuation data corresponding to the identified one or more anatomical structures and compression or default values for the spectral CT attenuation data corresponding to one or more regions outside the identified one or more anatomical structures, the method according to claim 1.

3. The compression values represent the spectral CT attenuation data corresponding to one or more regions outside the identified one or more anatomical structures at a relatively lower spatial resolution or a relatively lower bit depth than the spectral CT attenuation data corresponding to the identified one or more anatomical structures, the computer-implemented method according to claim 2.

4. The subset of the spectral CT attenuation data corresponds to the one or more anatomical structures, The subset of the spectral CT attenuation data represents at least one of the one or more anatomical structures in a subset of the plurality of energy intervals, or the spectral CT attenuation data represents a plurality of different materials in the anatomical region, and the subset of the spectral CT attenuation data represents at least one of the one or more anatomical structures in a subset of the materials represented by the spectral CT attenuation data, The method according to any one of claims 1 to 3.

5. The method further comprises applying a material decomposition algorithm to the spectral CT attenuation data to identify the one or more materials, The computer-implemented method according to claim 4, comprising:

6. The step of identifying one or more anatomical structures in the spectral CT attenuation data comprises performing a segmentation operation on the spectral CT attenuation data, according to any one of claims 1 to 5. The computer-implemented method described.

7. The subset of the spectral CT attenuation data is determined by the host processing system or by the remote processing system, the subset is determined based on the one or more identified anatomical structures using a look-up table or a neural network, The look-up table or the neural network provides a corresponding subset of the spectral CT attenuation data necessary for calculating the value of the blood flow parameter for the one or more anatomical structures, according to any one of claims 1 to 6. The computer-implemented method described.

8. The subset of the spectral CT attenuation data has a plurality of data elements, and the data elements correspond to different anatomical structures and / or different materials within the anatomical region, The method further comprises assigning transmission priorities to the plurality of data elements in the subset by the host processing system or by the remote processing system; and The step of transmitting the subset of the spectral CT attenuation data comprises transmitting the data elements in the order of the assigned transmission priorities, The computer-implemented method according to claim 7.

9. The remote processing system further comprises predicting a confidence value representing the prediction accuracy of the calculated value of the blood flow parameter, transmitting the trust value to the host processing system via the communication channel A computer-implemented method according to any one of claims 1 to 8, configured to perform **Claim 10** The remote processing system further identifying the type of data within a subset of the spectral CT attenuation data transmitted to the remote processing system; and selecting an algorithm for use by the remote processing system based on the identified data type to calculate the value of the blood flow parameter, or converting a subset of the spectral attenuation CT data transmitted to the remote processing system into a format for use by the remote processing system based on the identified data type to calculate the value of the blood flow parameter A computer-implemented method according to any one of claims 1 to 9, configured to perform **Claim 11** The anatomical region is a chest region, and / or the one or more anatomical structures include at least one lumen of one or more blood vessels, one or more walls of the one or more blood vessels, and one or more of the one or more atherosclerotic plaques of the one or more blood vessels, an organ, the heart, an atrium of the heart, myocardium of the heart A computer-implemented method according to any one of claims 1 to 10 **Claim 12** The method further receiving, by the host processing system, an input for identifying one or more blood vessels within the spectral CT attenuation data comprising the value of the blood flow parameter is calculated for the identified one or more blood vessels A computer-implemented method according to any one of claims 1 to 11 **Claim 13** The blood flow parameter is a fractional flow reserve FFR, or an instantaneous wave-free ratio iFR, or an index of myocardial resistance IMR, or a coronary flow reserve CFR, a computer-implemented method according to any one of claims 1 to 12 **Claim 14** A computer program product having instructions for causing one or more processors to perform the method according to any one of claims 1 to 13 when executed by the one or more processors **Claim 15** A system for determining a value of a blood flow parameter for one or more blood vessels, the system comprising Receiving spectral CT attenuation data representative of contrast agent injected within an anatomical region containing the one or more blood vessels, wherein the spectral CT attenuation data defines X-ray attenuation within the anatomical region over a plurality of different energy intervals; Identifying one or more anatomical structures in the spectral CT attenuation data; Determining a subset of the spectral CT attenuation data for use in calculating a value of the blood flow parameter based on the identified one or more anatomical structures; Transmitting the subset of the spectral CT attenuation data via a communication channel to a remote processing system, the remote processing system being configured to calculate a value of the blood flow parameter from the subset of the spectral CT attenuation data; Receiving a value of the blood flow parameter calculated by the remote processing system; A system having one or more processors configured to perform the above.

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