Determining values for blood flow parameters

Abstract

A computer-implemented method of determining a value of a blood flow parameter of a blood vessel is provided. The method comprises: calculating a value of a blood flow parameter of a blood vessel from spectral attenuation data and using a plurality of different techniques; and providing a value of the blood flow parameter of the blood vessel based on the calculated value of the blood flow parameter.

Description

Technical Field

[0001] The present disclosure relates to determining a value of a blood flow parameter for a blood vessel. A computer-implemented method, computer program product, and system are disclosed. Background Art

[0002] Various clinical studies involve the assessment of blood flow in the vasculature. For example, studies of coronary artery disease "CAD" often involve the assessment of blood flow in order to assess the amount of blood supplied to the cardiac region. In this regard, various blood flow parameters have been studied, including blood flow velocity, blood pressure, fractional flow reserve "FFR", instantaneous wave-free ratio "iFR", coronary flow reserve "CFR", thrombolysis in myocardial infarction "TIMI" flow grade, microvascular resistance index "IMR" and hyperemic microvascular resistance index "HMR".

[0003] Historically, values ​​for blood flow parameters such as those mentioned above have been measured using invasive devices such as pressure wires. For example, values ​​for the fractional flow reserve "FFR" are often determined to assess the effect of stenosis on the delivery of oxygen to the myocardium in CAD studies. The ratio P d / P a To define FFR, where P d represents the distal pressure at the distal location relative to the stenosis, and P a P represents the pressure proximal to the stenosis. P is usually averaged over the cardiac cycle. d and P a FFR is calculated based on values ​​of . FFR values ​​above 0.8 are generally considered to be clinically insignificant, and values ​​below 0.8 are generally considered to indicate increased clinical significance. Historically, values ​​for 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, pressure changes are related to changes in fluid velocity, among other effects. In the example of FFR, angiographic images with injected contrast are analyzed in order to determine the blood flow velocity. The FFR is then calculated by using a hemodynamic model to estimate the pressure value in the blood vessels based on the blood flow velocity. Thus, values ​​for FFR as well as other blood flow parameters can be determined angiographically.

[0005] A known angiographic technique for measuring blood flow velocity is to sample the intensity of the injected contrast agent in the 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 a reconstructed CT image 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: Number 3-December 2011, pp. 923-929 by Prevrhal, S. et al. A technique for sampling raw, i.e. "projected" CT data to determine blood flow velocity is disclosed.

[0006] Some known angiography techniques for determining the values ​​of blood flow parameters use computational fluid dynamics "CFD" models. In this regard, a technique for determining FFR values ​​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 complex fluid dynamics "CFD" model is 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-F LOW (Diagnosis of Ischemia-Causing Stenoses Obtained Via Noninvasive Fractional Flow Reserve) Study", JACC, Vol. 58, No. 19, 2011. Another technique is disclosed in Kim, HJ et al., "Patient-Specific Modeling of Blood Flow and Pressure in Human Coronary Arteries", Annals of Biomedical Engineering, Vol. 38, No. 10, October 2010, pp. 3195-3209.

[0007] 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 an electrical 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, vessel wall compliance is represented by a capacitor, and blood inertia is represented by an inductor. A machine learning-based method is disclosed in the document "Learning Patient-Specific Lumped Models for Interactive Coronary BloodFlow Simulations" by Nickisch, H. et al., MICCAI 2015, Part II, LNCS9350, pp. 433-441, 2015, and the document WO2016 / 001017, which is used to set the values ​​of linear and nonlinear resistors in a static lumped parameter model based on angiographic geometry measurements of blood vessels in a vascular region. The values ​​for blood flow rate and pressure in the model are then solved to determine the value of FFR for the blood vessel. Using a lumped parameter model to calculate the values ​​of blood flow parameters offers the advantages of both reduced computational burden and model complexity compared to a computational fluid dynamics "CFD" model.

[0008] Typically, the basis for providing accurate values ​​for blood flow parameters from angiographic image data is a correct anatomical delineation or segmentation of the lumen. This image processing task can be performed manually, which is time consuming and risks introducing user bias, or automatically, and in this case depends on the technique used to segment the angiographic data. As a result, there are variations in the values ​​of the angiographically derived values ​​of the blood flow parameters.

[0009] Therefore, there is still room for improving the reliability of angiographic measurements of blood flow parameters such as blood flow velocity, blood pressure, FFR, iFR, CFR, TIMI flow grade, IMR, and HMR. 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 a blood vessel. The method comprises:

[0011] Spectral attenuation data is received, the spectral attenuation data representing an injected contrast agent in a blood vessel region including the blood vessel, the spectral attenuation data being in a plurality of different energy intervals DE 1..m defining X-ray attenuation within the blood vessel region;

[0012] calculating a value of a blood flow parameter for the blood vessel based on the spectral attenuation data and using a plurality of different techniques; and

[0013] The value of the blood flow parameter for the blood vessel is provided based on the calculated value of the blood flow parameter.

[0014] Compared with the use of conventional X-ray attenuation data, the use of spectral attenuation data in the above method 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 vascular region. The attenuation caused by other media (such as calcium lesions in the blood vessels) may be interpreted as contrast agents in other ways, thereby reducing the accuracy of the calculated blood flow parameters. The use of spectral attenuation data also helps to reduce image artifacts caused by media other than contrast agents, and the image artifacts may also be misunderstood as contrast agents, and the accuracy of the calculated blood flow parameters is also reduced. For example, calcium lesions may also cause "calcium floating" in conventional CT data, which causes the image intensity value to have a risk of being misunderstood as a result of attenuation caused by contrast agents. Therefore, the use of spectral attenuation data helps to calculate the blood flow parameter value more accurately. However, the blood flow parameters calculated using spectral attenuation data can still be subject to changes. For example, the selection of energy intervals for reconstructing images (and calculating the value of blood flow parameters based on the images) can also affect the values ​​of the calculated blood flow parameters. The values ​​of model parameters (such as boundary conditions) of the blood flow calculation algorithm used to calculate the blood flow parameters can also affect their values. Similarly, the selection of a segmentation algorithm for segmenting the reconstructed image data and calculating the value of the blood flow parameter based on the reconstructed image data and the selection of a blood flow calculation algorithm for calculating the value of the blood flow parameter can also affect its value. In the above method, since a plurality of different techniques are used to calculate the value of the blood flow parameter for a blood vessel and the value of the blood flow parameter is provided based on the calculated value, the reliability of the value of the blood flow parameter provided is improved.

[0015] 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

[0016] Figure 1 is a flow chart illustrating an example of a computer-implemented method of determining a value of a blood flow parameter for a blood vessel according to some aspects of the present disclosure.

[0017] Figure 2 is a schematic diagram illustrating an example of a system 200 for determining a value of a blood flow parameter for a blood vessel, according to some aspects of the present disclosure.

[0018] Figure 3 is a schematic diagram illustrating an example heart including blood vessels 110 according to some aspects of the present disclosure.

[0019] Figure 4is a schematic diagram illustrating an example of a computer-implemented method of determining a value of a blood flow parameter for a blood vessel according to some aspects of the present disclosure.

[0020] Figure 5 is a graph showing the dependence of the mass attenuation coefficient on X-ray energy for two exemplary materials: iodine and water.

[0021] Figure 6 is a schematic diagram illustrating an example of a lumped parameter model 140 including a set of model parameters 1501 according to some aspects of the present disclosure.

[0022] Figure 7 is a schematic diagram illustrating an example of the operation of generating S140 a contrast-adjusted reconstructed image according to some aspects of the present disclosure. DETAILED DESCRIPTION

[0023] 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 the features, structures, or characteristics described in conjunction with the examples are included in at least the examples. It should also be understood that features described with respect to one example may also be used in another example, and that for the sake of brevity, all features are not necessarily replicated in each example. For example, features described with respect to computer-implemented methods may be implemented in computer program products and systems in a corresponding manner.

[0024] In the following description, reference is made to a method of determining a value of a blood flow parameter for a vessel. The vessel is disposed within a vessel region. In some examples, the vessel region is the heart. For example, in some examples, the vessel is a coronary artery. However, it should be understood that the vessel may generally be an artery or a vein. More generally, the method may be used to measure a blood flow parameter for a vessel in another part of the body other than the heart. For example, alternatively, the region(s) of interest may be a vessel located in a peripheral region, such as in a leg, an arm, a brain, etc.

[0025] Reference is also made herein to examples where the calculated blood flow parameter is an FFR value for a blood vessel. However, it should be understood that this is used as an example only, and the methods and systems disclosed herein may alternatively be used to calculate values ​​for other blood flow parameters, such as, but not limited to, blood flow velocity, blood pressure, blood flow transit time, volumetric blood flow value, iFR value, CFR value, TIMI flow grade, IMR value and HMR value, hyperemic stenosis resistance "HSR" value, zero flow pressure "ZFP" value, and transient hyperemic diastolic velocity-pressure slope "IHDVPS" value.

[0026] Note that the computer-implemented method disclosed herein may be provided as a non-transitory computer-readable storage medium including computer-readable instructions stored thereon, which, when executed by at least one processor, causes 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 may be provided by a single dedicated processor or by a single shared processor or by a plurality of individual processors (some of which may be shared). For example, the functionality of one or more method features may be provided by a processor shared within a network processing architecture such as a client / server architecture, a peer-to-peer architecture, the Internet, or a cloud.

[0027] The explicit use of the term "processor" 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 may 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 may be any device that may include, store, transmit, propagate, or transmit a program for use by or in conjunction with an instruction execution system, device, or apparatus. The medium may 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", hard 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 TM and DVD.

[0028] As discussed above, there is still room for improvement in the reliability of angiographic measurements of blood flow parameters such as blood flow velocity, blood pressure, FFR, iFR, CFR, TIMI flow grade, IMR, and HMR.

[0029] Figure 1 is a flow chart illustrating an example of a computer-implemented method of determining a value of a blood flow parameter for a blood vessel according to some aspects of the present disclosure. Figure 2 is a schematic diagram illustrating an example of a system 200 for determining a value of a blood flow parameter for a blood vessel according to some aspects of the present disclosure. Figure 1 The operations described by the methods shown can also be performed by Figure 2The system 200 shown performs the same, and vice versa. Figure 1 , a computer-implemented method for determining a value of a blood flow parameter for a blood vessel 110 includes:

[0030] Receive S110 spectral attenuation data 120, the spectral attenuation data representing the injected contrast agent in the blood vessel region including the blood vessel 110, the spectral attenuation data being in a plurality of different energy intervals DE 1..m defining X-ray attenuation within the blood vessel region;

[0031] calculating S120 a value of a blood flow parameter for the blood vessel 110 based on the spectral attenuation data 120 and using a plurality of different techniques; and

[0032] The value of the blood flow parameter for the blood vessel 110 is provided S130 based on the calculated value of the blood flow parameter.

[0033] Compared with the use of conventional X-ray attenuation data, the use of spectral attenuation data in the above method 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 vascular region. The attenuation caused by other media (such as calcium lesions in the blood vessels) may be interpreted as contrast agents in other ways, thereby reducing the accuracy of the calculated blood flow parameters. The use of spectral attenuation data also helps to reduce image artifacts caused by media other than contrast agents, and the image artifacts may also be misunderstood as contrast agents, and the accuracy of the calculated blood flow parameters is also reduced. For example, calcium lesions may also cause "calcium floating" in conventional CT data, which causes the image intensity value to have a risk of being misunderstood as a result of attenuation caused by contrast agents. Therefore, the use of spectral attenuation data helps to calculate the blood flow parameter value more accurately. However, the blood flow parameters calculated using spectral attenuation data can still be subject to changes. For example, the selection of energy intervals for reconstructing images (and calculating the value of blood flow parameters based on the images) can also affect the values ​​of the calculated blood flow parameters. The values ​​of model parameters (such as boundary conditions) of the blood flow calculation algorithm used to calculate the blood flow parameters can also affect their values. Similarly, the selection of a segmentation algorithm for segmenting the reconstructed image data and calculating the value of the blood flow parameter based on the reconstructed image data and the selection of a blood flow calculation algorithm for calculating the value of the blood flow parameter can also affect its value. In the above method, since a plurality of different techniques are used to calculate the value of the blood flow parameter for a blood vessel and the value of the blood flow parameter is provided based on the calculated value, the reliability of the value of the blood flow parameter provided is improved.

[0034] Also refer to Figure 3 The above method is described, Figure 3 is a schematic diagram illustrating an example heart including blood vessels 110 according to some aspects of the present disclosure. Figure 3 The heart is shown labeled with the left coronary artery, LCA, right coronary artery, RCA, and the LCA and RCA foramina that define the openings of these arteries in the aorta, respectively. Figure 1 The operations described herein are used to determine the value of a blood flow parameter in a cardiovascular region. For example, they can be used to determine Figure 3 The value of a blood flow parameter in the blood vessel 110 of the heart shown. For example, the blood flow parameter may be an FFR value. The following equation may be used for the distal position Pos in the left coronary artery. d Calculate the FFR value:

[0035] FFR=P d / P a Equation 1

[0036] The remote position Pos can be determined using Equation 1 d Blood pressure P d The value and the proximal position Pos p Pressure P a The FFR value is calculated based on the value of p It is seen as the orifice of the left coronary artery.

[0037] Back to Figure 1 In operation S110, spectral attenuation data 120 is received. The spectral attenuation data 120 represents the injected contrast agent in the blood vessel region including the blood vessel 110, and is in a plurality of different energy intervals DE 1..m Defines X-ray attenuation within the vascular region.

[0038] The spectral attenuation data 120 received in operation S110 may generally be projection X-ray data, i.e., 2D data that can be used to generate a 2D image, or it may be volumetric X-ray data, i.e., 3D data that can be used to generate a 3D image. In the latter case, the data may be raw data, i.e., data that has not yet been reconstructed into a 3D image, or it may be image data, i.e., data representing a reconstructed 3D image.

[0039] Can be Figure 2 The one or more processors 210 shown receive the spectral attenuation data 120. The spectral attenuation data 120 may be received via any form of data communication, including wired, optical, and wireless communications. By way of some examples, when wired or optical communications are used, the communication may occur via signals transmitted over electrical or optical cables, and when wireless communications are used, the communication may occur via RF or optical signals. In general, the one or more processors 210 may receive the spectral attenuation data 120 from a spectral X-ray projection imaging system, or from a spectral CT imaging system, or from another source, such as a computer-readable storage medium, the Internet, or the cloud.

[0040] Continue to refer Figure 1 , the spectral attenuation data 120 received in operation S110 represents the injected contrast agent in the blood vessel region including the blood vessel 110. Figure 3 As described, for example, the spectral attenuation data 120 may represent an injected contrast agent in a cardiac region. In this regard, the spectral attenuation data received in operation S110 may be generated after the contrast agent is injected into the vasculature. The contrast agent may include a substance such as iodine, or a lanthanide such as gadolinium, or another substance that provides visibility of the bloodstream into which the contrast agent is injected.

[0041] Continue to refer Figure 1 The spectral attenuation data 120 received in operation S110 may generally be generated by a spectral X-ray imaging system, ie, it may be generated by a spectral CT imaging system or a spectral X-ray projection imaging system.

[0042] The spectral CT imaging system generates spectral attenuation 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. By way of example, the spectral attenuation data 120 may be generated by a spectral CT 7500 sold by Philips Healthcare of Best, The Netherlands.

[0043] Figure 2 An example of a spectral CT imaging system 220 that may be used to generate the spectral attenuation data 120 received in operation S110 is shown in FIG. Figure 2 The spectral CT imaging system 220 shown includes an X-ray source 230 and an X-ray detector 240. The X-ray source 230 and the X-ray detector 240 are mechanically coupled to a gantry ( Figure 2 In operation, the X-ray source 230 and the X-ray detector 240 are rotated about the rotation axis 250 by means of the gantry while acquiring spectral attenuation data 120, which defines the X-ray attenuation in the region of interest set in the imaging region of the imaging system 220. The spectral attenuation data obtained from multiple rotation angles about the rotation axis 250, or more specifically, the volumetric X-ray data (i.e., 3D data), can then be reconstructed into a volumetric image.

[0044] As described above, alternatively, the spectral attenuation data received in operation S110 may be generated by a spectral X-ray projection imaging system. Spectral X-ray projection imaging systems typically include a support arm, such as a so-called "C-arm" that supports an X-ray source and an X-ray detector. Alternatively, the spectral X-ray projection imaging system may include a support arm having a shape different from that of this example, such as an O-shaped arm. Spectral X-ray projection imaging systems typically generate projection data while the support arm remains in a static position relative to the imaging area during the acquisition of image data. Therefore, the spectral X-ray projection imaging system can be used to acquire projection X-ray data, i.e., 2D data. However, the spectral X-ray projection imaging system can also acquire volumetric X-ray data, i.e., 3D data, by acquiring projection X-ray data while rotating its support arm around a rotation axis. Image reconstruction techniques can then be used to reconstruct the projection X-ray data obtained from multiple rotation angles around the rotation axis into a volumetric image in a manner similar to that of a spectral CT imaging system. Therefore, alternatively, the spectral attenuation data received in operation S110 may be generated by a spectral X-ray projection imaging system.

[0045] The spectral attenuation data 120 received in operation S110 is in a plurality of different energy intervals DE 1..m Define the X-ray attenuation within the blood vessel region. In general, there may be two or more energy intervals; that is, m is an integer, and m ≥ 2. Generate a DE with multiple different energy intervals 1..m The ability to define data for the X-ray attenuation data distinguishes a spectral X-ray imaging system from a conventional X-ray imaging system. By processing data from multiple different energy intervals, it is possible to distinguish between media 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 X-ray imaging system. In this regard, a variety of different configurations of the spectral X-ray imaging system may be used to generate the spectral attenuation data received in operation S110, some of which refer to Figure 2 Give a description.

[0046] refer to Figure 2 The exemplary spectral CT imaging system 220 shown, in general, may provide an X-ray source 230 by a plurality of monochromatic sources or by one or more polychromatic sources, and the X-ray detector 240 may include: a common detector for detecting 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, or a multi-layer detector in which X-rays having energies in different X-ray energy intervals are detected by corresponding layers, or a photon counting detector that bins detected X-ray photons into one of a plurality of energy intervals based on their individual energies. In a photon counting detector, the relevant energy interval can be determined for each received X-ray photon by detecting the pulse height induced by electron-hole pairs generated in response to absorption of the X-ray photon in the direct conversion material.

[0047] The above-mentioned various configurations of X-ray sources and X-ray detectors can be used in a spectral X-ray imaging system to generate X-rays with multiple different energy intervals DE. 1..m Spectral attenuation data defining the attenuation of the X-rays. In general, the discrimination between different X-ray energy intervals can be provided at the X-ray source 230 by temporally switching the X-ray tube potential of a single X-ray source 230, i.e., by "fast kVp switching," or by temporally switching or filtering the emission of X-rays from multiple X-ray sources 230. In such a configuration, a common X-ray detector 240 can be used to detect X-rays across multiple different energy intervals, with DE for each energy interval being 100 kVp. 1..m The attenuation data is generated in a time-sequential manner. Alternatively, different X-ray energy intervals DE can be provided at the X-ray detector 240 by using a multi-layer detector or a photon counting detector. 1..m Such a detector can detect X-rays from multiple energy ranges DE almost simultaneously. 1..m X-rays, and therefore, there is no need to perform time switching at the X-ray source 230. Therefore, the multi-layer X-ray detector 240 or the photon counting X-ray detector 240 can be used in combination with the polychromatic X-ray source 230 to generate a plurality of different energy intervals DE 1..m Spectral attenuation data in .

[0048] Alternatively, other combinations of the above X-ray source 230 and the X-ray detector 240 may be used in the spectral X-ray imaging system to obtain images in a plurality of different energy intervals DE. 1..m For example, in another configuration, the need to sequentially switch different X-ray sources 230 emitting X-rays in different energy intervals can be avoided by mounting the X-ray source-detector pair to the gantry at a rotationally offset position about the rotation axis 250. In this configuration, each source-detector pair operates independently, and the rotational offset of the source-detector pair facilitates the detection of different energy intervals DE. 1..m By applying an energy selective filter to (one or more) X-ray detectors 240 to reduce the impact of X-ray scatter, this configuration can be used to achieve separation of spectral attenuation data for different energy intervals DE. 1..mImproved separation between the spectral attenuation data.

[0049] In yet another configuration, a plurality of different energy intervals DE may be provided by intercepting the X-ray beam in a conventional X-ray imaging system using one or more spectral filters. 1..m spectral attenuation data in. For example, a single filter can be mechanically moved in and out of the X-ray beam generated by the polychromatic X-ray source 230 in order to temporally control the spectrum of X-rays detected by the X-ray detector 240. For example, the filter can transmit only a portion of the spectrum of X-rays emitted by the polychromatic X-ray source 230 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 complete spectrum emitted by the polychromatic X-ray source) is provided. In doing so, spectral attenuation data for energy intervals DE are generated in a time-sequential manner. 1..m 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 bins.

[0050] Back to Figure 1 In operation S120, a plurality of different techniques are used to calculate the value of a blood flow parameter for the blood vessel 110. As some examples, the blood flow parameter may be a blood flow velocity, a blood pressure, an FFR value, or an iFR value. For example, the different techniques used in operation S120 may include using different techniques to reconstruct the received spectral attenuation data, using different segmentation algorithms to segment an image reconstructed from the spectral attenuation data, using different blood flow calculation algorithms to calculate the value of the blood flow parameter, and using different sets of model parameters for the blood flow calculation algorithm. Further details on these techniques are provided below. Combinations of these and other techniques may also be used to provide different values ​​for the blood flow parameter.

[0051] Continue to refer Figure 1 In operation S130, a value of a blood flow parameter for the blood vessel 110 is provided based on the calculated value of the blood flow parameter. In one example, operation S130 may include providing the value of the blood flow parameter for the blood vessel 110 as a weighted average of the various calculated values. By providing the value of the blood flow parameter as a weighted average, the reliability of the measurement is improved. In another example, the weighted average is calculated, and outliers are removed before calculating the weighted average. In this example, the operation of providing S130 the value of the blood flow parameter for the blood vessel 110 based on the calculated value of the blood flow parameter includes:

[0052] analyzing the calculated values ​​of the blood flow parameter to identify abnormal values; and

[0053] The value of the blood flow parameter for the vessel 110 is provided as a weighted average of the calculated values ​​that were not identified as outliers.

[0054] Excluding outliers from the weighted average calculated in this example has the effect of reducing the impact of potentially inaccurate values ​​of the blood flow parameter on the provided value of the blood flow parameter. It therefore reduces the amount of variation in the calculated value of the blood flow parameter and therefore improves the reliability of the provided value.

[0055] In this example, analyzing the calculated values ​​of the blood flow parameter to identify outliers can include performing a statistical analysis on the calculated values ​​of the blood flow parameter. For example, a mean of the calculated values ​​can be determined, and values ​​that deviate from the mean by more than a predetermined amount can be identified as outliers. As another example, a standard deviation can be determined for the calculated values, and values ​​that are outside of, for example, two standard deviations from the mean can be identified as outliers. As another example, a RANSAC method can be used to identify and exclude outliers from a weighted average.

[0056] Since the value of the blood flow parameter for the blood vessel 110 provided in operation S130 is determined based on the calculated value of the blood flow parameter (which has been calculated using a different technique), the reliability of the provided value of the blood flow parameter for the blood vessel is improved.

[0057] The above-described computer-implemented methods may include one or more additional operations.

[0058] In one example, reference Figure 1 The described method may also include outputting a value representing a change in the calculated value for the blood flow parameter. The output of the value representing the change provides an indication of the level of confidence in the result; a small change indicates a high confidence and vice versa. The measure of the change may be output numerically or graphically, for example via error bars.

[0059] In another example, the operations of calculating S120 the value of the blood flow parameter for the blood vessel 110 and providing S130 the value of the blood flow parameter for the blood vessel 110 are performed at a plurality of locations along the blood vessel.

[0060] For example, FFR values ​​for a vessel may be provided for multiple locations along the vessel and thus used by a physician to assess the severity of a stenosis along the vessel.

[0061] As described above, the operation of calculating S120 the value of the blood flow parameter for the blood vessel 110 using a number of different techniques may be performed in various ways.

[0062] In one example, the computing S120 operation includes reconstructing the received spectral attenuation data 120 using a plurality of different image reconstruction techniques to provide a plurality of corresponding reconstructed images 130 1..n , and the value of the blood flow parameter is calculated based on each of the reconstructed images.

[0063] refer to Figure 4 The example is described, Figure 4 is a schematic diagram illustrating an example of a computer-implemented method for determining a value of a blood flow parameter for a blood vessel according to some aspects of the present disclosure. Figure 4 The left side of FIG. 1 shows the spectral attenuation data 120 received in the above operation S110. In this example, the spectral attenuation data 120 is volume X-ray data generated by a spectral CT imaging system. In operation S120, the spectral attenuation data 120 is reconstructed using a plurality of different image reconstruction techniques to provide a plurality of corresponding reconstructed images 130. 1..n ,like Figure 4 In this example, the reconstructed image can be reconstructed in the following way:

[0064] applying different material decomposition algorithms to the spectral attenuation data 120; and / or

[0065] Reconstruct spectral attenuation data 120, the spectral attenuation data includes energy intervals DE 1..m different options.

[0066] The resulting reconstructed image represents the injected contrast agent in the vascular region. The injected contrast agent may include materials such as iodine or gadolinium. Such materials are commonly used as contrast agents due to their attenuation at the X-ray energies used in diagnostic X-ray imaging systems. By reconstructing an image representing the injected contrast agent from the spectral attenuation data, more reliable data regarding the flow of the injected contrast agent may be obtained compared to using conventional X-ray attenuation data.

[0067] In addition to the attenuation caused by contrast agents, the spectral attenuation data may also represent attenuation caused by one or more background materials, such as fat, water, bone, soft tissue, vascular calcification, air, and metals such as gold, titanium, tungsten, and platinum. Such materials are also typically present near blood vessels, so the attenuation caused by these materials may also be represented in the spectral attenuation data. For example, when imaging the cardiac vasculature, bones (in the form of portions of the spine) or ribs are typically within the field of view of the spectral CT imaging system. Similarly, fiducial markers, implantable medical devices, and interventional devices are typically formed of metals such as those described above, and the attenuation caused by these materials may also be captured in the spectral attenuation data. Therefore, the reconstructed image generated in operation S120 may also represent one or more materials, such as these, in addition to contrast agents.

[0068] An example of a material decomposition technique that can be used in operation S120 to reconstruct an image representing an injected contrast agent based on spectral attenuation data is disclosed in the document “Empirical, projection-based basis-component decomposition method” by Brendel, B. et al., Medical Imaging 2009, Physics of Medical Imaging, edited by Ehsan Samei and Jiang Hsieh, Proc. of SPIE Vol. 7258, 72583Y. Another suitable material decomposition technique is disclosed in the document “K-edge imaging in X-ray computed tomography using multi-bin photon counting detectors” by Roessl, E. and Proksa, R., Phys Med Biol 2007 Aug 7, 52(15): 4679-96. Another suitable material decomposition technique is disclosed in the published PCT patent application WO / 2007 / 034359A2. Another suitable material decomposition technique is disclosed in the document “Dual-energy (spectral) CT: applications in abdominal imaging” by Silva, AC et al., Radio Graphics 2011; 31(4): 1031-1046.

[0069] Thus, in operation S120, a number of different material decomposition algorithms such as these may be applied to the spectral attenuation data 120 in order to reconstruct an image representing the injected contrast agent.

[0070] Typically, the X-ray attenuation spectrum of a material includes contributions from Compton scattering and contributions from the photoelectric effect. For different materials, the attenuation due to Compton scattering is relatively similar, while the attenuation from the photoelectric effect strongly depends on the material. Both Compton scattering and the photoelectric effect show energy dependence, and material decomposition techniques use this effect to distinguish between 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 hypothetical 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 for a material is defined as the minimum energy required to undergo a photoelectric event with a k-shell electron. The k-edge occurs at a characteristic energy for each material. The k-edge energy for 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 projection data acquired from a diagnostic X-ray imaging system.

[0073] For example, Figure 5 is a graph showing the dependence of the mass attenuation coefficient on X-ray energy for two exemplary materials: iodine and water. Figure 5 The mass attenuation coefficient represented in represents the X-ray attenuation. The sharp increase in the X-ray attenuation of iodine at 33.2 keV facilitates the separation between the contribution of each of these materials in the combined attenuation spectrum, which includes the attenuation caused by both materials. For example, the X-ray attenuation caused by iodine and water can be separated by using a first selection of energy intervals ES1, where one energy interval DE1 of the spectral attenuation data is close to the k-edge energy of 33.2 keV and another energy interval DE2 is significantly higher than the k-edge energy.

[0074] In operation S120, a plurality of different material decomposition algorithms may be used to reconstruct separate images representing the injected contrast agent, wherein the value of the blood flow parameter is then calculated based on each of the reconstructed images to determine the value of the provided blood flow parameter. In this regard, the plurality of different material decomposition algorithms used may be provided by the material decomposition algorithm cited above. Alternatively or additionally, in operation S120, the energy interval DE may be used 1..m Different selections of are used to reconstruct separate images representing the injection of contrast agent, wherein the value of the blood flow parameter is calculated from each of these reconstructed images. Figure 5 An example of a selection of energy intervals ES1 for generating a reconstructed image by separating iodine from water is shown in FIG. Alternative selections of energy intervals for separating iodine from water can be provided by changing the width or center energy of energy intervals DE1 and / or DE2. Different selections of energy intervals can also be provided, such as Figure 5 The second selection ES2 of the energy interval in the embodiment is shown, and wherein different numbers of energy intervals and different ranges of energy intervals (ie, energy interval DE 1..5 ) is used to separate iodine from water. After selecting the energy interval, a material decomposition algorithm (such as one of those cited above) can be used in operation S120 to reconstruct each of the images representing the injected contrast agent.

[0075] Continue to refer Figure 4 In the example shown, in operation S120, after reconstructing a plurality of images each representing the injection of a contrast agent, a value of a blood flow parameter is determined for each of the reconstructed images. Figure 4 The operation is shown in the central part of FIG. 1 , and wherein a blood flow calculation algorithm 140 is used to calculate the blood flow according to the reconstructed image 130 1..3 In each frame, determine the value of the blood parameter. Figure 4 In the example shown, the blood flow calculation algorithm 140 includes a lumped parameter model. The lumped parameter model can be used to represent the blood flow in the vascular region as a circuit; the volume flow rate of the blood in the vascular region is represented by the current in the circuit, and the pressure of the blood in the vascular region is represented by the voltage in the circuit. The lumped parameter model is described in more detail below.

[0076] Instead of using a lumped parameter model in operation S120 to calculate the value of the blood flow parameter from each of the reconstructed images, other techniques may be used alternatively. These include the time-of-flight method disclosed in the references of Barfett, JJ et al. cited above for sampling the reconstructed CT images to determine the blood flow velocity, the technique disclosed in the references of Prevrhal, S. et al. for sampling the original or "projected" CT data to determine the blood flow velocity, and computational fluid dynamics "CFD" techniques, such as the techniques disclosed in the references of Taylor. CA et al. cited above, the references of Koo, BK et al. cited above, and the references of Kim, HJ et al. cited above for determining FFR values.

[0077] Continue to refer Figure 4 After calculating the value of the blood flow parameter according to each of the reconstructed images using the blood flow calculation algorithm in operation S120, Figure 4 The illustrated method continues with operation S130, where a value of a blood flow parameter for the vessel 110 is provided based on the calculated value of the blood flow parameter. This may be performed as described above, for example by calculating the provided value of the blood flow parameter as a weighted average of the individual values ​​from the reconstructed image. This may also involve the identification of outliers, and their exclusion from the weighted average, also as described above.

[0078] In operation S120, alternative methods may also be used to calculate the value of the blood flow parameter for the blood vessel 110 based on the spectral attenuation data 120. In addition to or instead of using a plurality of different image reconstruction techniques to provide a plurality of corresponding reconstructed images 130 1..n , and calculating the value of the blood flow parameter based on each of the reconstructed images, which can be performed as described in the above method.

[0079] In one example method, in operation S120, the received spectral attenuation data 120 is reconstructed to provide a reconstructed image 1301, and a plurality of values ​​for blood flow parameters are calculated from the image using the blood flow calculation algorithm 140. Model parameters 150 using the blood flow calculation algorithm 1..i The value of the blood flow parameter is calculated using a set of different values ​​of .

[0080] In this example, the model parameters may include geometric parameters of the model and / or boundary conditions of the model. The blood flow calculation algorithm may include a lumped parameter model or a CFD model, as described above. For example, if the model is a lumped parameter model, the value of the blood flow parameter for the model may be determined using different values ​​of a set of geometric parameters of the model or using different values ​​of a set of boundary conditions of the model. Figure 4 , geometric parameters for the model can be calculated based on the reconstructed image (for example, based on the reconstructed image 1301). For example, the model parameters 1501..i The set of may represent the diameter of the lumen at different locations along its length. Different values ​​for the diameter may be determined by applying different thresholds to the image intensity values ​​in the reconstructed image, these thresholds being used to distinguish contrast agent in the vessel from the vessel wall, the cross-sectional area of ​​the vessel representing the lumen diameter. Alternatively, a different segmentation algorithm may be used. Alternatively, the model parameter 150 1..i The set of may represent boundary conditions for the model, such as pressure values ​​at the inlet and outlet of a lumen in a vascular region. Different values ​​for the boundary conditions may be provided by making different assumptions about the actual pressure values ​​in the model. Such assumptions are detailed in the literature cited for each model. As an example, the global total pressure drop boundary condition may be varied to estimate the sensitivity of the model.

[0081] In another example method, in operation S120, the received spectral attenuation data 120 is reconstructed to provide a reconstructed image, a segmentation algorithm is applied to the reconstructed image, and a plurality of values ​​for a blood flow parameter are calculated from the segmented image using a blood flow calculation algorithm. The values ​​of the blood flow parameter are calculated from the segmented image obtained by applying a plurality of different segmentation algorithms to the reconstructed image. A variety of different segmentation algorithms (such as model-based segmentation, watershed-based segmentation, region growing or level set or graph cuts) may be used for this purpose. Alternatively, different parameter settings for a single algorithm may be used.

[0082] In another example method, in operation S120 , a value of a blood flow parameter is calculated based on the received spectral attenuation data 120 using each of a plurality of different blood flow calculation algorithms.

[0083] In this example, different blood flow calculation algorithms may be provided by, for example, different lumped parameter models or different CFD models.

[0084] The above-described exemplary methods may be used alone or in combination to calculate multiple values ​​of the blood flow parameter for the blood vessel 110 using different techniques in operation S120 .

[0085] As mentioned above, in some examples, such as Figure 4 In the example shown, the operation of calculating S120 the value of the blood flow parameter for the blood vessel 110 is performed using the blood flow calculation algorithm 140, and the blood flow calculation algorithm 140 includes a lumped parameter model. The lumped parameter model includes a set of equations representing the blood flow in the model, and the equations are solved to calculate the value of the blood flow parameter.

[0086] Typically, a lumped parameter model can be used to represent blood flow in a vascular region as a circuit. The volume flow rate of blood in the vascular region is represented by current in the circuit, and the pressure of blood in the vascular region is represented by voltage in the circuit. The blood volume in the vascular region is represented by charge. As described above, in the lumped parameter model, resistance to blood flow is represented by resistance, compliance of the vascular wall is represented by capacitance, and blood inertia is represented by inductance. A machine learning-based method for setting the values ​​of resistance, capacitance, and inductance in such a lumped parameter model based on angiographic geometry measurements of blood vessels in a vascular region is disclosed in the literature cited above by Nickisch, H. et al. and in the document WO2016 / 001017. The values ​​for blood flow rate and pressure in the model are then solved to determine the value of FFR for the blood vessel. Another technique for providing and determining parameters of a lumped parameter model is described in the literature cited above by Kim, HJ et al.

[0087] In more detail, as described in the above-cited document by Nickisch, H. et al., geometric measurements from reconstructed angiographic images can be used to set the values ​​of model parameters in a lumped parameter model. These model parameters include the values ​​of electrical components in a circuit, and the values ​​of voltages or currents at nodes in the circuit, i.e., boundary conditions. Such model parameters are determined based on geometric measurements in the reconstructed image. For example, such measurements include the length, diameter, and curvature of the lumen in a vascular region.

[0088] Figure 6 is a schematic diagram illustrating an example of a lumped parameter model 140 including a set 1501 of model parameters according to some aspects of the present disclosure. Figure 6 The lumped parameter model shown represents branches in the vessel region as vessel segments, and bifurcations in the vessel region are represented as nodes. As described in the above-cited literature by Nickisch, H. et al., generally, the lumped parameter model includes two types of macroscopic components: nonlinear vessel segment resistance (white tube) and boundary conditions (black boxes). Figure 6 The vessel segments are labeled with boundary conditions: "LCX" (left circumflex artery), "M1" (marginal artery branch of the left circumflex artery), "LAD" (left anterior descending artery), "D1" (first diagonal artery), etc. The boundary conditions can be pressure or flow sources driving the network; but any (lumped) boundary condition driving a conventional FE model can be used. The challenge is to convert the local geometry of the vessel (radius, perimeter, cross-sectional area) into the parameters of the nonlinear resistor.

[0089] The hydraulic analogue to Ohm's law is Poiseuille's law, which states that the pressure drop p and flow rate f through a long, narrow pipe are related by the linear transfer function p F Linearly related, Among them, the resistance constant w P Depends on the length of the pipe and its cross-sectional area. Poiseuille friction is simply a hydraulic effect that causes a pressure drop (or energy loss) in a hydraulic network. Friction between the blood and the vessel wall is caused by the cross-sectional area, bifurcations, and changes in ellipticity and curvature of the vessel. In the adopted framework shown in Table 1, the piecewise polynomial (reversible and point-symmetric) effect transfer function It is used to model blood vessels with d based only on local vessel geometry and material constants such as blood density r and viscosity m. e Series and geometry-specific weights The hydraulic effect. If d e =1, then we restore the resistor, and if d e ≠1, then we are talking about a rheostat. The functional form of the effect transfer function represented in Table 1 below is taken from the fluid mechanics literature, where individual hydraulic effects have been studied individually, both experimentally and analytically. In order to combine multiple interdependent effects e=1..E, it is assumed that the local effects are superimposed Effect specificity coefficient α e A graphical representation of the local effects superposition and tree segment compression is given at the bottom of Table 1. A total of 6 different hydraulic effects are included, of which the first 5 have a single coefficient and the last has three coefficients, resulting in a total of 8 coefficients. In order to compress the size of the hydraulic network, the transfer function The sum along the center line of the tree segment c = 1..C is the transfer function of a single tree segment This is possible because the flow through a tree segment is constant within the segment. Once the simulation is complete and the value of f is known, the compression operation can be reversed by expansion. The hydraulic effects are also location specific, as the first 5 effects are operative everywhere except at bifurcations, and the last effect is operative only at bifurcations. This representation of the hydraulic network as a collection of transfer functions encoding the hydraulic effects is then used to simulate blood flow.

[0090]

[0091] Table 1: Transfer functions and hydraulic effects. The geometric weights are obtained from the local cross-sectional area A, perimeter P, length l, radius r and curvature κ of the vessel. The blood density is represented by r and the viscosity by m. Note that the cross section is not circular. Local effect specific transfer function are linearly combined to produce the local transfer function Local transfer function along the center line c = 1..C Sum to produce the tree segment transfer function

[0092] A modified nodal analysis technique described in more detail in the above cited reference Nickisch, H. et al. is then used to solve the circuit diagram representation of the model in order to determine the values ​​of pressure and volume flow rate at each node in the model. The model parameters a in Table 1 are then learned by comparing the lumped model predictions with the CFD reference simulations. This produces values ​​for pressure and flow at locations along the centerline of the blood vessel.

[0093] Therefore, in the example of performing the operation of calculating S120 the value of the blood flow parameter for the blood vessel 110 using the lumped parameter model, the above reference Figure 1 The described method may also include:

[0094] extracting from each of the reconstructed images model parameters of a corresponding lumped parameter model representing blood flow within the vessel region 150; and

[0095] The calculation S120 of the value of the blood flow parameter for the blood vessel 110 is performed using the corresponding lumped parameter model and the model parameter 150 .

[0096] In these examples, the operation of extracting model parameters 150 includes:

[0097] segmenting the reconstructed image to provide a three-dimensional model representing the vascular region;

[0098] Determining geometric data for the blood vessel region according to the three-dimensional model; and

[0099] The values ​​of the model parameters 150 are determined based on the geometric data.

[0100] In these examples, various known segmentation algorithms can be used to segment the reconstructed image. For example, the determined geometric data can include the dimensions of the (one or more) lumens in the vessel region, the measure of the curvature of the (one or more) lumens in the vessel region, the cross-sectional area and the shape of the (one or more) lumens, etc., as described above with reference to Table 1. Image intensity values ​​in the segmented image can also be used to provide values ​​for model parameters. For example, flow velocity boundary conditions can be determined based on the image intensity within the segmented blood vessel.

[0101] Return to Figure 1 The value of the blood flow parameter provided in operation S130 may be provided in various ways. Typically, the value of the blood flow parameter is output. For example, the value of the blood flow parameter may be output to a display device such as a Figure 2The blood flow parameter may be provided by a computer readable storage medium or a printer device, for example. In some examples, the value of the blood flow parameter is output as a numerical value, and in other examples, the value of the blood flow parameter may be output graphically. For example, the value of the blood flow parameter may be output as a color-coded icon. The value of the blood flow parameter may also be calculated at multiple locations along the length of the vessel and output graphically. For example, an image showing a vessel or a region of a vessel may be output, wherein the value of the blood flow parameter at a corresponding location along the length of the vessel is displayed as a color-coded overlay on the image. The physician may then use the provided value(s) of the blood flow parameter to inform a clinical diagnosis about the subject.

[0102] The inventors have also observed that, despite the use of reconstructed spectral attenuation data to visualize stenosis in a vessel region, it may be difficult to communicate to a physician some potential anatomical causes of the stenosis in the reconstructed image. For example, if a vessel includes a stenosis, it is useful to communicate to the physician the relative amounts of calcification, soft tissue, plaque, vessel, and lumen in order to assist the physician in performing a diagnosis of the vessel. However, these amounts may represent relatively small portions of the reconstructed image, which makes them difficult to identify. In response to this observation, in some examples, the contrast in the reconstructed image is adjusted based on the value of a blood flow parameter provided for the vessel. This has the effect of drawing the physician's attention to the anatomical causes of clinically significant values ​​of the blood flow parameter.

[0103] In one example, the reference above Figure 1 The described methods also include:

[0104] Based on the provided values ​​of the blood flow parameters for the blood vessel 110 and the angiography image reconstructed based on the spectral attenuation data 120 or the angiography image reconstructed based on conventional X-ray attenuation data representing the blood vessel 110, a contrast-adjusted reconstructed image is generated S140, wherein the contrast in the reconstructed image is adjusted based on the values ​​of the blood flow parameters provided for the blood vessel 110.

[0105] In this example, the contrast may be adjusted throughout the contrast-adjusted reconstructed image, or the contrast may be adjusted only in a predetermined region 160 thereof. In the latter case, the contrast may be enhanced in a predetermined region around a location where the value of a blood flow parameter provided in the reconstructed angiographic image meets a predetermined criterion. For example, if the value of the blood flow parameter is less than a threshold at a location in a blood vessel, the contrast may be adjusted only within a circular region surrounding the location. By adjusting the contrast locally in this manner, the physician's attention is focused on features in the anatomical region surrounding a clinically significant finding where the value of the blood flow parameter exceeds the threshold, while keeping the appearance of the rest of the reconstructed image unchanged. For example, the contrast may be increased in a contrast-adjusted reconstructed image.

[0106] The contrast can be adjusted in various ways. For example, a "windowing" technique, also known as grayscale mapping, contrast stretching, histogram correction, or contrast enhancement, can be used. For example, a so-called "level and window" method can be used, in which values ​​are set for a window width (i.e., the range of X-ray attenuation values ​​to be mapped between grayscale black values ​​and grayscale white values) and a window level (i.e., the center of the range of X-ray attenuation values). By setting values ​​for the window width and the window level, image intensity values ​​in the reconstructed image can be mapped to new values ​​in the contrast-adjusted reconstructed image based on the value of the blood flow parameter. For example, by reducing the window level, the brightness of the image can be increased in the contrast-adjusted reconstructed image, thereby improving the visibility of anatomical features.

[0107] The angiographic image for generating the contrast-adjusted angiographic image may be generated based on the spectral attenuation data 120 received in operation S110 or based on conventional X-ray attenuation data representing the blood vessel 110. In the former case, the angiographic image may be a reconstructed image used to calculate the value of the blood flow parameter for the blood vessel 110 in operation S120, or another image reconstructed based on the spectral attenuation data. In either case, a classification algorithm (such as a random forest or a support vector machine) may be used to make a decision about which of a plurality of image reconstruction techniques to use to generate the angiographic image, or about which of a plurality of windowing techniques to use to provide the contrast-adjusted image. The classification algorithm may decide which technique to use to adjust the contrast based on rules related to, for example, the type of anatomical region represented in the image, the value of the blood flow parameter, and the like. For example, at a strong local pressure drop in the reconstructed image, a lesion is expected to be present. Therefore, plaques and stenotic contrast-filled blood vessels are expected to be present. According to the rules, a predetermined local area surrounding the pressure drop may be displayed simply as a contrast image in the contrast-adjusted image. The remainder of the contrast-adjusted image may be displayed as classical Hounsfield unit X-ray attenuation values. Additional layers may also be provided within predetermined localized regions where plaque characteristics are identified. For example, material images such as calcium images may be provided to highlight the presence of plaque near a lesion. As another example, in an anatomical region with a relatively high level of perfusion, the contrast between perfused muscle and contrast-filled blood vessels will be different. In this anatomical region, the rule may provide for material with contrast agent not to be displayed because there is too much contrast agent in the tissue.

[0108] In one example, a neural network is used to adjust contrast in a contrast-adjusted image. The neural network may directly adjust contrast in a reconstructed image, or alternatively, the neural network may be used to identify which one or more windowing techniques of a plurality of reconstruction techniques to apply in order to provide a contrast-adjusted image. Figure 7Describe the example, Figure 7 is a schematic diagram showing an example of an operation of generating S140 a reconstructed image with contrast adjustment according to some aspects of the present disclosure. In this example, the operation of generating a reconstructed image with contrast adjustment includes:

[0109] Inputting the provided values ​​of the blood flow parameters for the blood vessel 110 and the corresponding reconstructed image or the corresponding angiographic image reconstructed from conventional X-ray attenuation data representing the blood vessel 110 into the neural network; and

[0110] The neural network is trained to generate a contrast-adjusted reconstructed image based on the input value of the blood flow parameter and the corresponding reconstructed image.

[0111] exist Figure 7 In the example shown, the blood flow parameter is an FFR value, and the contrast is adjusted only within a predetermined region 160 surrounding locations in the vessel where the FFR value is less than a threshold value of 0.8. The neural network may be provided by various architectures, such as a convolutional neural network, "CNN", a recurrent neural network "RNN", or a transformer, or the like.

[0112] Figure 7 The neural network NN shown can be trained to generate a contrast-adjusted reconstructed image in the following way:

[0113] receiving training data, the training data comprising a plurality of volumetric training images representing the blood vessel region and corresponding values ​​of blood flow parameters for blood vessels in the blood vessel region;

[0114] receiving ground truth data, the ground truth data comprising a corresponding ground truth contrast-adjusted reconstructed image for each of the volumetric training images, wherein contrast in the reconstructed image has been adjusted;

[0115] inputting the training data into the neural network; and

[0116] For each volumetric training image in the plurality of input volumetric training images:

[0117] predicting a corresponding contrast-adjusted reconstructed image using the neural network;

[0118] adjusting parameters of the neural network based on a difference between the predicted contrast-adjusted reconstructed image and the ground truth contrast-adjusted reconstructed image; and

[0119] The predicting and the adjusting are repeated until a stopping criterion is met.

[0120] In this example, for example, the volume training image can be a CT image that has been reconstructed from spectral CT data. The corresponding value of the blood flow parameter can be determined using the above-mentioned techniques or by invasive measurements in the vascular system (for example, using a blood flow sensing line, a pressure line, or a Doppler ultrasound catheter). The reference truth data can be provided by an expert who has adjusted the contrast of a predetermined area around the portion of the blood flow parameter that has clinical significance for the entire image or the blood vessel. If the neural network is trained to adjust the contrast directly, the reference truth data may include the contrast-adjusted image intensity. If the neural network is trained to identify which one or more windowing techniques of a plurality of reconstruction techniques are applied in order to provide a contrast-adjusted image, the reference truth data may include an indication of the type of image reconstruction technique that should be used to generate the contrast-adjusted image, or an indication of the energy interval that should be used to generate the contrast-adjusted image, or a type of windowing method that should be used to generate the contrast-adjusted image.

[0121] Typically, training of a neural network involves inputting a training data set into the neural network and iteratively adjusting the parameters of the neural network until the trained neural network provides accurate outputs. Training is typically performed using a graphics processing unit "GPU" or a dedicated neural processor (such as a neural processing unit "NPU" or a tensor processing unit "TPU"). Training typically employs a centralized approach, where a cloud-based or host-based neural processor is used to train the neural network. After training it with the training data set, the trained neural network can be deployed to a device for analyzing new input data during inference. The processing requirements during inference are significantly less than those required during training, allowing the neural network to be deployed to a variety of systems, such as laptops, tablets, mobile phones, and the like. For example, inference can be performed by a central processing unit "CPU", GPU, NPU, TPU on a server or in the cloud.

[0122] therefore, Figure 7The process of training the neural network NN shown includes adjusting its parameters. Parameters, or more specifically weights and biases, control the operation of activation functions in the neural network. In supervised learning, the training process automatically adjusts the weights and biases so that when input data is provided, the neural network accurately provides the corresponding expected output data. In order to do this, the value of the loss function or error is calculated based on the difference between the predicted output data and the expected output data. The value of the loss function can be calculated using functions such as negative log-likelihood loss, mean absolute error (or L1 norm), mean square error, root mean square error (or L2 norm), Huber loss or (binary) cross entropy loss. During training, the value of the loss function is usually minimized, and training terminates when the value of the loss function meets the stopping criteria. Sometimes, training terminates when the value of the loss function meets one or more of a plurality of criteria.

[0123] Various methods are known for solving the loss minimization problem, such as gradient descent, quasi-Newton methods, etc. Various algorithms have been developed to implement these methods and their variants, including but not limited to stochastic gradient descent "SGD", batch gradient descent, mini-batch gradient descent, Gauss-Newton, Levenberg Marquardt, momentum, Adam, Nadam, Adagrad, Adaddelta, RMSProp and Adamax "optimizers". These algorithms use the chain rule to calculate the derivatives of the loss function with respect to the model parameters. This process is called backpropagation because the derivatives are calculated starting from the last layer or output layer and moving toward the first layer or input layer. These derivatives tell the algorithm how the model parameters must be adjusted in order to minimize the error function. That is, the model parameters are adjusted starting from the output layer and running backwards in the network until the input layer is reached. In the first training iteration, the initial weights and biases are usually randomized. The neural network then predicts the output data, which is also random. Backpropagation is then used to adjust the weights and biases. The training process is iteratively performed by adjusting the weights and biases in each iteration. When the error or difference between the predicted output data and the expected output data is within an acceptable range for the training data or some validation data, the training is terminated. Subsequently, the neural network can be deployed and the trained neural network uses the trained values ​​of its parameters to make predictions on new input data. If the training process is successful, the trained neural network accurately predicts the expected output data based on the new input data.

[0124] In one example, patient data can also be entered into a reference Figure 7 In the neural network NN described, a contrast-adjusted angiography image can also be generated based on the patient data. In this example, when inferring, the operation of generating a contrast-adjusted reconstructed image also includes:

[0125] receiving patient data associated with the blood vessel;

[0126] inputting the patient data into the neural network; and

[0127] generating the contrast-adjusted angiographic image further based on the patient data; and

[0128] The patient data includes electronic health record data related to the blood vessel and / or an identification of an implanted device in the blood vessel.

[0129] The patient data may include information about the patient, such as body mass index, smoking history, age, gender, data identifying implanted devices in blood vessels, etc. In this example, the neural network is trained to generate contrast-adjusted angiographic images further based on the patient data. In doing so, the neural network may provide contrast-adjusted images that are more suitable for reconstructing blood vessels in the image.

[0130] The above method can be performed on a plurality of blood vessels or selected blood vessels in a blood vessel region. In the latter case, in one example, reference Figure 1 The method also includes:

[0131] receiving input identifying a blood vessel 110 in the spectral attenuation data 120; and

[0132] The providing S130 of the value of the blood flow parameter for the blood vessel 110 is performed for the identified blood vessel.

[0133] The operation of identifying the blood vessel 110 may be performed in an image reconstructed from the spectral attenuation data received in operation S110 or in an image reconstructed from conventional X-ray attenuation data. The blood vessel may be identified manually or automatically. For example, manual identification of the blood vessel 110 may be performed by a user operating a user input device in conjunction with the displayed reconstructed image. For example, automatic identification of the blood vessel 110 may be performed using a feature detector or a trained neural network. Segmentation may also be performed on the reconstructed image to help identify the blood vessel. Various segmentation algorithms are known for this purpose. The feature detector or the trained neural network may automatically identify candidate blood vessels in which the value of the blood flow parameter is determined. The blood vessel may be identified based on the type of blood vessel or based on the presence of an abnormality such as a stenosis in the blood vessel. For example, a feature detector or a trained neural network may be used to identify the left coronary artery of the reconstructed image and the stenosis therein, and identify the blood vessel as a candidate blood vessel for blood flow measurement such as FFR. Blood flow measurement for the blood vessel may then be provided at the location of the stenosis.

[0134] In another example, a computer program product is provided. The computer program product includes instructions that, when executed by one or more processors, cause the one or more processors to perform a method of determining a value of a blood flow parameter for a blood vessel 110, including:

[0135] Receiving S110 represents spectral attenuation data 120, the spectral attenuation data including the injected contrast agent in the vascular region of the blood vessel 110, the spectral attenuation data in a plurality of different energy intervals DE 1..m X-ray attenuation within defined vascular regions;

[0136] calculating S120 a value of a blood flow parameter for the blood vessel 110 from the spectral attenuation data 120 and using a number of different techniques; and

[0137] A value of the blood flow parameter for the blood vessel 110 is provided S130 based on the calculated value of the blood flow parameter.

[0138] In another example, a system 200 is provided for determining a value of a blood flow parameter for a blood vessel 110. The system includes one or more processors 210 configured to:

[0139] Receiving S110 represents spectral attenuation data 120, the spectral attenuation data including the injected contrast agent in the vascular region of the blood vessel 110, the spectral attenuation data in a plurality of different energy intervals DE 1..m defining X-ray attenuation within the blood vessel region;

[0140] calculating S120 a value of a blood flow parameter for the blood vessel 110 based on the spectral attenuation data 120 and using a plurality of different techniques; and

[0141] The value of the blood flow parameter for the blood vessel 110 is provided S130 based on the calculated value of the blood flow parameter.

[0142] exist Figure 2 An example of the system 200 is shown in FIG. Note that the system 200 may also include one or more of the following: a spectral X-ray imaging system for generating the spectral attenuation data 120 received in operation S110, for example Figure 2 Spectral CT imaging system 220 shown in FIG. 2 ; monitor 260 for displaying the value of the provided blood flow parameter, reconstructed image, etc.; bed 270; syringe ( Figure 2 ), which is used to inject contrast agent into the vascular system; and a user input device, which is configured to receive user input, such as a keyboard, a mouse, a touch screen, etc.

[0143] The above examples should be understood as illustrations of the present disclosure, not limitations. Other examples are also contemplated. For example, examples described with respect to computer-implemented methods may also be provided in a corresponding manner by a computer program product or by a computer-readable storage medium or by system 200. It should be understood that the features described with respect to any one example may be used alone or in combination with the features described with respect to other examples, and may be used in combination with one or more features of another example or in combination with other examples. In addition, without departing from the scope of the present invention, equivalents and modifications not described above may also be adopted, which are defined in the appended claims. In the claims, the word "comprising" does not exclude other elements or operations, and the indefinite article "one" or "an" does not exclude multiple. The mere fact that certain features are described in mutually different dependent claims does not indicate that the combination of these features cannot be used advantageously. Any figure mark in the claims should not be interpreted as limiting its scope.

Claims

1. A computer-implemented method of determining a value of a blood flow parameter for a blood vessel (110), the method comprising: Receive (S110) spectral attenuation data (120), the spectral attenuation data representing the injected contrast agent in the blood vessel region including the blood vessel (110), the spectral attenuation data being in a plurality of different energy intervals (DE 1..m ) defining X-ray attenuation within the blood vessel region; Calculating (S120) a value of a blood flow parameter for the blood vessel (110) based on the spectral attenuation data (120) and using a plurality of different techniques; and The value of the blood flow parameter for the blood vessel (110) is provided (S130) based on the calculated value of the blood flow parameter.

2. The computer-implemented method of claim 1 , wherein: Calculating (S120) the value of the blood flow parameter for the blood vessel (110) includes one or more of the following: The received spectral attenuation data (120) is reconstructed using a plurality of different image reconstruction techniques to provide a plurality of corresponding reconstructed images (130 1..n ), and calculating the value of the blood flow parameter according to each of the reconstructed images; The received spectral attenuation data (120) is reconstructed to provide a reconstructed image (1301), and a plurality of values ​​for the blood flow parameter are calculated from the image using a blood flow calculation algorithm (140), each value of the blood flow parameter being a function of the model parameters (150) of the blood flow calculation algorithm. 1..i ) is calculated based on the different values ​​of the set; reconstructing the received spectral attenuation data (120) to provide a reconstructed image, applying a segmentation algorithm to the reconstructed image, and calculating a plurality of values ​​for the blood flow parameter from the segmented image using a blood flow calculation algorithm, each value of the blood flow parameter being calculated from the segmented image obtained by applying a different segmentation algorithm to the reconstructed image; and A value of the blood flow parameter is calculated based on the received spectral attenuation data (120) using each of a plurality of different blood flow calculation algorithms.

3. The computer-implemented method of claim 2, wherein: Calculating (S120) the value of the blood flow parameter for the blood vessel (110) includes: reconstructing the received spectral attenuation data (120) using a plurality of different image reconstruction techniques to provide a plurality of corresponding reconstructed images; and calculating the value of the blood flow parameter based on each of the reconstructed images; and Wherein, the reconstructed image is reconstructed in the following manner: applying different material decomposition algorithms to the spectral attenuation data (120); and / or Reconstruct spectral attenuation data (120), the spectral attenuation data including the energy interval (DE 1..m ) different options.

4. The computer-implemented method of claim 3, wherein: The method further comprises: extracting from each of the reconstructed images model parameters for a corresponding lumped parameter model representing blood flow within the vascular region (150); and The calculation (S120) of the value of the blood flow parameter for the blood vessel (110) is performed using the corresponding lumped parameter model and the model parameters (150).

5. The computer-implemented method of claim 4, wherein: Extracting model parameters (150) includes: segmenting the reconstructed image to provide a three-dimensional model representing the vascular region; Determining geometric data for the blood vessel region according to the three-dimensional model; and The values ​​of the model parameters (150) are determined based on the geometric data.

6. The computer-implemented method of any one of claims 4-5, wherein: The lumped parameter model represents the blood flow within the vascular region as an electrical circuit; and Therein, the volume flow rate of the blood in the blood vessel region is represented by the current in the circuit, and the pressure of the blood in the blood vessel region is represented by the voltage in the circuit.

7. The computer-implemented method of any one of claims 1 to 6, wherein: Providing (S130) the value of the blood flow parameter for the blood vessel (110) based on the calculated value of the blood flow parameter comprises: analyzing the calculated values ​​of the blood flow parameter to identify abnormal values; and The value of the blood flow parameter for the blood vessel (110) is provided as a weighted average of the calculated values ​​that are not identified as outliers.

8. The computer-implemented method of claim 7, wherein: Analyzing the calculated values ​​of the blood flow parameter includes performing a statistical analysis on the calculated values ​​of the blood flow parameter.

9. The computer-implemented method of any one of claims 1 to 8, wherein: The method also includes outputting a value representative of a change in the calculated value for the blood flow parameter.

10. The computer-implemented method of any one of claims 1 to 9, wherein: Calculating (S120) a value of a blood flow parameter for the blood vessel (110) and providing (S130) the value of the blood flow parameter for the blood vessel (110) are performed at a plurality of locations along the blood vessel.

11. The computer-implemented method of any one of claims 1 to 10, wherein: The method further comprises: A contrast-adjusted reconstructed image is generated (S140) based on the provided value of the blood flow parameter for the blood vessel (110) and an angiography image reconstructed based on the spectral attenuation data (120) or an angiography image reconstructed based on conventional X-ray attenuation data representing the blood vessel (110), wherein the contrast in the reconstructed image is adjusted based on the value of the blood flow parameter provided for the blood vessel (110).

12. The computer-implemented method of claim 11, wherein: Generating a contrast-adjusted reconstructed image includes: inputting the provided value of the blood flow parameter for the blood vessel (110) and the corresponding reconstructed image or a corresponding angiographic image reconstructed from conventional X-ray attenuation data representing the blood vessel (110) into a neural network; and The neural network is trained to generate the contrast-adjusted reconstructed image based on the input value of the blood flow parameter and the corresponding reconstructed image.

13. The computer-implemented method of claim 12, wherein: The neural network is trained to generate the contrast-adjusted reconstructed image by: receiving training data, the training data comprising a plurality of volumetric training images representing the blood vessel region and corresponding values ​​of blood flow parameters for blood vessels in the blood vessel region; receiving ground truth data, the ground truth data comprising a corresponding ground truth contrast-adjusted reconstructed image for each of the volumetric training images, wherein contrast in the reconstructed image has been adjusted; inputting the training data into the neural network; and For each of multiple input volumetric training images: using the neural network to predict a corresponding contrast-adjusted reconstructed image; adjusting parameters of the neural network based on a difference between the predicted contrast-adjusted reconstructed image and the ground truth contrast-adjusted reconstructed image; and The predicting and the adjusting are repeated until a stopping criterion is met.

14. The computer-implemented method of any one of claims 11 to 13, wherein: The contrast is adjusted only in a predetermined area (160) in the reconstructed image.

15. A computer-implemented method according to any preceding claim, wherein: The method further comprises: receiving input identifying the blood vessel (110) in the spectral attenuation data (120); and Therein, providing (S130) the value of the blood flow parameter for the blood vessel (110) is performed for the identified blood vessel.

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