Spectral x-ray projection data

Abstract

A computer-implemented method of providing projection data includes receiving spectral computed tomography data (110), receiving spectral X-ray projection data (130) generated using settings for one or more imaging parameters (140), and extracting material projection data (150) from the spectral X-ray projection data (130). Extracting the material projection data (150) from the spectral X-ray projection data (130) or setting the value of one or more imaging parameters (140) is performed using the received spectral CT data (110).

Description

Technical Field

[0001] The present disclosure relates to providing material projection data based on spectral X-ray projection data. A computer-implemented method, computer program product and system are disclosed. Background Art

[0002] Spectral CT imaging is increasingly being used in settings where conventional CT imaging has traditionally been used. For example, spectral CT imaging is currently being used to assess aortic disease, evaluate the coronary artery lumen, assess myocardial perfusion, and detect pulmonary embolism. Spectral CT data contains data from multiple different energy intervals, and processing of this data allows for the differentiation of materials that have similar X-ray attenuation values ​​when measured within a single energy interval and that are indistinguishable in conventional CT data.

[0003] Over time, spectral projection imaging is also expected to find application in environments where conventional X-ray projection imaging systems are already used. For example, spectral projection imaging is expected to find application in environments such as the catheterization laboratory and for investigations of coronary artery disease "CAD" and for procedures such as percutaneous coronary intervention "PCI". Such investigations typically involve the assessment of blood flow through parameters such as FFR, and these assessments are increasingly performed using angiographic data, thereby eliminating the need for invasive measurement devices such as pressure wires.

[0004] Spectral X-ray projection imaging systems acquire spectral X-ray projection data with their source-detector arrangement in a static position relative to the region of interest. This is in contrast to spectral CT imaging systems, in which spectral CT data is acquired while rotating or stepping the source-detector arrangement around the region of interest. Therefore, spectral CT data has sufficient information for reconstructing a volumetric image of the region of interest, while spectral X-ray projection data is typically only used to generate 2D images.

[0005] However, the use of spectral X-ray projection data has several advantages over spectral CT data. For example, spectral X-ray projection data can be generated by spectral X-ray projection imaging systems, which generally have improved versatility, reduced complexity, lower cost, and they can generally generate images at higher image resolution and lower X-ray dose than spectral CT imaging systems. These benefits can be exploited in environments such as catheter laboratories, where maximum proximity to the patient is important when conducting clinical investigations and treatment procedures.

[0006] However, since the spectral X-ray projection data represents the region of interest from a single perspective, the features in the region of interest overlap each other in the spectral X-ray projection data. This complicates the extraction of material data for the features in the region of interest. Therefore, in order to take advantage of the improved benefits of distinguishing between materials of these features provided by the spectral X-ray projection imaging system, improvements are needed in the provision of projection data achieved by the spectral X-ray projection imaging system.

[0007] Document US2019 / 295249 A1 relates to an apparatus and method for determining calcium content by analyzing cardiac spectral CT data. CT projection data (which may be obtained by scanning a cardiac region of a subject using a spectral CT scanning unit) is modeled by applying a material decomposition algorithm to the projection data to provide a calcium-specific component. The projection data is tomographically reconstructed to provide a first 3D image, and the calcium-specific component is tomographically reconstructed to provide a second 3D image. The first 3D image is segmented to provide an image mask corresponding to a cardiovascular structure of interest, a portion of the second 3D image is selected based on the image mask, and the calcium content in the cardiovascular structure of interest is calculated based on the selected portion of the second 3D image.

[0008] Document US2018 / 0192977 A1 relates to the use of silicon-based energy discriminating photon counting detectors, for example for use in X-ray based imaging including computed tomography. The method described addresses the resolution and classification of X-ray photons affected by Compton scattering, which may be detected as having energy levels below their proper levels due to collision or deflection events. Summary of the invention

[0009] According to one aspect of the present disclosure, a computer-implemented method for providing projection data is provided. The method comprises:

[0010] receiving spectral computed tomography (CT) data representing a region of interest, the spectral CT data defining X-ray attenuation within the region of interest in a plurality of different energy intervals;

[0011] receiving spectral X-ray projection data representing a region of interest, the spectral X-ray projection data defining X-ray attenuation within the region of interest in a plurality of energy intervals and generated using set values ​​for one or more imaging parameters; and

[0012] extracting material projection data representing X-ray attenuation of one or more materials within the region of interest from the spectral X-ray projection data to provide projection data; and

[0013] Therein, extracting material projection data from the spectral X-ray projection data or setting values ​​of one or more imaging parameters is performed using the received spectral CT data.

[0014] In the above method, spectral CT data of the region of interest is used to extract material projection data from spectral X-ray projection data or to set the value of one or more imaging parameters. The spectral CT data represents the volume extension of the feature within the region of interest, and thus provides more accurate spatial information about the spectral attenuation caused by the feature. By using spectral CT data to extract material projection data, spatially accurate spectral information of the feature is used to extract material projection data, so the resulting material projection data of the feature is more specific to the intended material. By using spectral CT data to set the value of imaging parameters, such as dose, spectrum of X-rays, and orientation of the imaging system used to generate spectral X-ray projection data, it can be ensured that the spectral X-ray projection data contains sufficient information to accurately extract material projection data of the material. Therefore, both uses of spectral CT data make the extracted material projection data more specific and more accurate for the intended material. This enables doctors to make a more accurate diagnosis of the region of interest based on the extracted material projection data.

[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 providing projection data according to some aspects of the present disclosure.

[0017] Figure 2 is a schematic diagram illustrating an example of a system 200 for providing projection data according to some aspects of the present disclosure.

[0018] Figure 3 is a schematic diagram illustrating an example of a computer-implemented method of providing projection data according to some aspects of the present disclosure.

[0019] Figure 4 is a schematic diagram showing an example heart including blood vessels 180 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 example materials (iodine and water).

[0021] Figure 6 FIG. 1 is a diagram illustrating projecting spectral CT data 110 to provide simulated spectral X-ray projection data 110 according to some aspects of the present disclosure. P ' is a schematic diagram of an example of the operation.

[0022] Figure 7is a schematic diagram illustrating an example of a first orientation parameter a defining an orientation of the imaging system 210 relative to the region of interest 120 according to some aspects of the present disclosure.

[0023] Figure 8 is a schematic diagram illustrating an example of a second orientation parameter b defining the orientation of the imaging system 210 relative to the region of interest 120 according to some aspects of the present disclosure.

[0024] Fig. 9 is an example showing an overlay image 170 including a graphical representation of a performance indicator 160 in accordance with some aspects of the present disclosure.

[0025] Fig.10 is an example of contrast agent projection data 150 representing X-ray attenuation within a region of interest 120 including an identified blood vessel 180 according to some aspects of the present disclosure.

[0026] Fig.11 is an example of contrast agent projection data 150 in which data corresponding to blood vessels other than the identified blood vessel 180 has been removed, in accordance with some aspects of the present disclosure.

[0027] Fig.12 FIG. 1 is a diagram illustrating the relative position of the 3D blood vessel model 190 to the virtual X-ray source 230 according to some aspects of the present disclosure. v and virtual X-ray detector 240 v Schematic diagram of an example of operations for orienting and adjusting the estimated contrast agent distribution C(x, y, z) in the 3D vessel model 190 . DETAILED DESCRIPTION

[0028] Examples of the present disclosure are provided with reference to the following description and accompanying drawings. In the description, many specific details of some examples are set forth for the purpose of explanation. "An example", "an implementation" or similar language mentioned in the specification means that the features, structures or characteristics described in connection with the example are included in at least one example. It should also be understood that the features described in connection with one example can also be used in another example, and for the sake of brevity, it is not necessary to repeat all features in each example. For example, the features described in connection with the computer-implemented method can be implemented in a computer program product and system in a corresponding manner.

[0029] In the following description, reference is made to a method of providing projection data. The projection data is provided using spectral CT data and spectral X-ray projection data, both of which represent a region of interest. In some examples, the region of interest is the heart. However, it should be understood that these are examples only, and the region of interest may alternatively be another anatomical region, such as a leg, arm, brain, etc. Therefore, the spectral CT data, the spectral X-ray projection data, and the projection data may generally represent any anatomical region.

[0030] In some examples, the projection data provided by the method is then analyzed to provide values ​​of a blood flow parameter for one or more vessels. In some examples, the vessels are coronary arteries in a region of interest. However, it should be appreciated that the vessel may generally be any artery, vein, or ventricle in the anatomy. It should also be appreciated that determining values ​​of a blood flow parameter for a plurality of vessels may include determining individual values ​​of a blood flow parameter for a plurality of vessels, or determining a single value of a blood flow parameter for a plurality of vessels.

[0031] In some examples, the blood flow parameter provided by the method is the blood flow reserve fraction "FFR". However, it should be understood that this is only by way of example, and the methods disclosed herein may alternatively be used to calculate values ​​of 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.

[0032] It should be noted that the computer-implemented method disclosed herein can be provided as a non-transitory computer-readable storage medium, the 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 can be implemented in a computer program product. The computer program product can be provided by dedicated hardware, or can be provided by hardware capable of running software associated with appropriate software. When provided by a processor, the functionality 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 separate processors, some of which can be shared. For example, one or more of the features of the method can be provided by a processor shared in a network processing architecture (such as a client / server architecture, a peer-to-peer architecture, the Internet, or a cloud).

[0033] The explicit use of the term "processor" or "controller" should not be interpreted as referring only to hardware capable of running software, it 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 that can be accessed from a computer-usable storage medium or a computer-readable storage medium, which provides program code for use by or in connection with a computer or any instruction execution system. For the purpose of description, a computer-usable storage medium or a computer-readable storage medium can be any device that can contain, store, communicate, propagate or transmit a program for use by or in connection with an instruction execution system, device or apparatus. The medium can be an electronic, magnetic, optical, electromagnetic, infrared or semiconductor system or device or a propagation medium. Examples of computer-readable media include semiconductor or solid-state memory, magnetic tape, removable computer disk, random access memory "RAM", read-only memory "ROM", 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.

[0034] As noted above, there is a need for improvements in the provision of projection data achieved by spectral X-ray projection imaging systems.

[0035] Figure 1 is a flow chart illustrating an example of a computer-implemented method of providing projection data according to some aspects of the present disclosure. Figure 2 is a schematic diagram illustrating an example of a system 200 for providing projection data according to some aspects of the present disclosure. Figure 1 The operations described in relation to the method shown may also be performed by Figure 2 Similarly, the operations described in relation to the system 200 may also be performed in conjunction with the system 200 shown in FIG. Figure 1 The method described is performed as described. Figure 1 , a computer-implemented method for providing projection data comprises:

[0036] Receiving S110 spectral computed tomography CT data 110 representing a region of interest 120, the spectral CT data being defined at a plurality of different energy intervals DE 1..m X-ray attenuation within the region of interest in ;

[0037] Receive S120 spectral X-ray projection data 130 representing a region of interest 120, the spectral X-ray projection data being defined in a plurality of energy intervals DE 1..m X-ray attenuation within the region of interest in and is generated using set values ​​of one or more imaging parameters 140; and

[0038] extracting S130 material projection data 150 representing X-ray attenuation of one or more materials within the region of interest 120 from the spectral X-ray projection data 130 to provide projection data; and

[0039] Therein, extracting S130 material projection data 150 from the spectral X-ray projection data 130 or setting values ​​of one or more imaging parameters 140 is performed using the received spectral CT data 110 .

[0040] In the above method, spectral CT data of the region of interest is used to extract material projection data from spectral X-ray projection data or to set the value of one or more imaging parameters. The spectral CT data represents the volume extension of the feature within the region of interest, and thus provides more accurate spatial information about the spectral attenuation caused by the feature. By using spectral CT data to extract material projection data, spatially accurate spectral information of the feature is used to extract material projection data, so the resulting material projection data of the feature is more specific to the intended material. By using spectral CT data to set the value of imaging parameters, such as dose, spectrum of X-rays, and orientation of the imaging system used to generate spectral X-ray projection data, it can be ensured that the spectral X-ray projection data contains sufficient information to accurately extract material projection data of the material. Therefore, both uses of spectral CT data make the extracted material projection data more specific and more accurate for the intended material. This enables doctors to make a more accurate diagnosis of the region of interest based on the extracted material projection data.

[0041] Also refer to Figure 3 describe Figure 1 The method shown in Figure 3 is a schematic diagram illustrating an example of a computer-implemented method of providing projection data according to some aspects of the present disclosure.

[0042] Reference Figures 1 to 3 In operation S110, spectral CT data 110 is received. The spectral CT data 110 represents a region of interest 120 and is defined in a plurality of different energy intervals DE 1..m The X-ray attenuation within the region of interest in the spectral CT data 110. The region of interest 120 represented by the spectral CT data 110 may generally be any portion of an anatomical structure. In some examples, the spectral CT data represents a heart. Figure 4 is a schematic diagram illustrating an example of a heart including a blood vessel 180 according to some aspects of the present disclosure. Figure 4 The heart shown in FIG. 1 is labeled with a left coronary artery LCA, a right coronary artery RCA, and an LCA ostium and a RCA ostium that respectively define the openings of these arteries in the aorta. The spectral CT data 110 received in operation S110 may, for example, represent Figure 4In some examples described later, the value of the blood flow parameter is determined based on the projection data acquired for the heart. For example, Figure 4 The blood vessel 180 of the heart shown in FIG. 1 (ie, the left coronary artery) is used to determine the value of the blood flow parameter.

[0043] The spectral CT data 110 received in operation S110 may generally be raw data, i.e., data that has not been reconstructed into a volume image, or may be image data, i.e., data representing a reconstructed volume image. The spectral CT data 110 may be obtained by Figure 2 1. The spectral CT data 110 may be received by one or more processors 220 shown in FIG. The spectral CT data 110 may be received by any form of data communication, including wired, optical, and wireless communications. For example, when wired or optical communications are used, the communication may be performed by signals transmitted over electrical or optical cables, and when wireless communications are used, the communication may be performed by RF or optical signals. In general, the one or more processors 220 may receive the spectral CT data 110 from an imaging system (e.g., a spectral CT imaging system) or from other sources (e.g., a computer-readable storage medium, the Internet, or the cloud).

[0044] Continue to refer to Figure 1 In some examples, the spectral CT data 110 received in operation S110 may be angiography data. In these examples, the angiography spectral CT data represents the injected contrast agent within the vascular region of interest. Figure 4 As described, the angiographic spectral CT data may, for example, represent an injected contrast agent within the heart. In this regard, the angiographic spectral CT data may be generated after the contrast agent is injected into a blood vessel. The contrast agent may include a substance such as iodine, or a lanthanide such as gadolinium, or other substances that provide visibility of the blood flow into which the contrast agent is injected.

[0045] Continue to refer to Figure 1 The spectral CT data 110 received in operation S110 may be generated by a spectral CT imaging system. The spectral CT imaging system generates spectral CT data while rotating or stepping an X-ray source-detector device around an imaging region. Examples of spectral CT imaging systems include cone-beam spectral CT imaging systems, photon counting spectral CT imaging systems, dark-field spectral CT imaging systems, and phase contrast spectral CT imaging systems. As an example, the spectral CT data 110 may be generated by a spectral CT 7500 sold by Philips Healthcare of Best, The Netherlands.

[0046] The spectral CT data 110 received in operation S110 may alternatively be generated by a spectral X-ray projection imaging system instead of a spectral CT imaging system. A spectral X-ray projection imaging system typically includes a support arm, such as a so-called "C-arm", also referred to as a gantry, which supports an X-ray source and an X-ray detector. The spectral X-ray projection imaging system may alternatively include a support arm having a shape different from that of the example, such as an O-shaped arm. The spectral X-ray projection imaging system typically generates projection data by keeping the support arm in a static position relative 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 acquire spectral CT data, i.e., data that can be reconstructed into a volumetric image. The spectral X-ray projection imaging system can acquire spectral CT data of a region of interest by rotating its support arm around a rotation axis and acquiring spectral X-ray projection data from a plurality of rotation angles around the rotation axis. The set of spectral X-ray projection data from a plurality of rotation angles represents spectral CT data because the data can be reconstructed to provide a volumetric image. Various image reconstruction techniques can be used to reconstruct the set of spectral X-ray projection data from a plurality of rotation angles into a volumetric image. These techniques process spectral CT data in a similar manner as spectral CT data generated by a spectral CT imaging system.Thus, the spectral CT data 110 received in operation S110 may alternatively be generated by a spectral X-ray projection imaging system.

[0047] Figure 2 An example of a spectral X-ray projection imaging system 210 is shown. Figure 2 The spectral X-ray projection imaging system 210 shown in the figure 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 connected to a support arm, and in the example shown, the support arm is in the form of a C-arm. In use, the X-ray source 230 and the X-ray detector 240 are positioned by the support arm so as to provide a desired orientation of the imaging system 210 relative to the region of interest. When the desired orientation is reached, the spectral X-ray projection imaging system 210 generates spectral X-ray projection data 130 with its support arm remaining stationary along the orientation relative to the region of interest. As described above, the spectral X-ray projection imaging system 210 can generate spectral computed tomography CT data 110 by rotating its support arm about a rotation axis and acquiring spectral X-ray projection data from multiple rotation angles about the rotation axis. An example of the rotation axis is shown by Figure 2 The dotted lines AA' and BB' are shown in FIG.

[0048] The spectral CT data 110 received in operation S110 is defined in a plurality of different energy intervals DE 1..mThe X-ray attenuation in the region of interest in . In general, there may be two or more energy intervals; that is, m is an integer, and m ≥ 2. Generate a DE defined in multiple different energy intervals 1..m The ability to process X-ray attenuation data from multiple energy intervals distinguishes a spectral CT imaging system from a conventional CT imaging system. By processing data from multiple different energy intervals, media having similar X-ray attenuation values ​​when measured within a single energy interval and indistinguishable from attenuation data generated by a conventional CT imaging system can be distinguished. In this regard, a variety of different configurations of X-ray sources and X-ray detectors can be used in a spectral CT imaging system to generate the spectral CT data 110 received in operation S110, now referring to Figure 2 Some of these configurations are described with reference to the spectral X-ray projection imaging system 210 shown in FIG.

[0049] Generally, the spectral X-ray projection imaging system includes an X-ray source 230 and an X-ray detector 240. The X-ray source 230 and the X-ray detector 240 together generate X-rays at a plurality of different energy intervals DE 1..m X-ray attenuation data in . Figure 2 The X-ray source 230 shown may generally be provided by a plurality of monochromatic sources, or by one or more polychromatic sources, and the X-ray detector 230 may be provided by a common detector for detecting a plurality of different X-ray energy intervals, or by each detector detecting a different X-ray energy interval DE. 1..m A plurality of detectors are provided, or a multi-layer detector that detects X-rays having energies in different X-ray energy intervals by corresponding layers, or a photon counting detector that classifies detected X-ray photons into one of a plurality of energy intervals based on their respective energies. In the photon counting detector, the relevant energy interval of each received X-ray photon can be determined by detecting the pulse height caused by electron-hole pairs generated in response to absorption of the X-ray photon in the direct conversion material.

[0050] Reference Figure 2 In general, different X-ray energy intervals DE can be provided at the X-ray source by temporally switching the tube voltage of a single X-ray source 230 (i.e., by "fast kVp switching"), or by temporally switching or filtering the X-ray emissions from multiple X-ray sources 230. 1..m In such a configuration, a common X-ray detector 240 may be used to detect X-rays across a plurality of different energy intervals, generating energy intervals DE in a time-sequential manner. 1..m Alternatively, different X-ray energy intervals DE may be provided at the X-ray detector 240 by using a multi-layer detector or a photon counting detector. 1..mSuch detectors can detect X-rays from multiple X-ray energy intervals DE nearly simultaneously. 1..m 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 X-ray attenuation data in .

[0051] Other configurations of the X-ray source 230 and the X-ray detector 240 described above may alternatively be used to provide a plurality of different energy intervals DE 1..m For example, in yet another configuration, by mounting the X-ray source-detector pair on a support arm or gantry at a rotationally offset position about a rotation axis, the need to sequentially switch between different X-ray sources 230 emitting X-rays in different energy intervals can be avoided. In this configuration, each source-detector pair operates independently, and by rotating the source-detector pair, the different energy intervals DE can be conveniently switched. 1..m This configuration can be used to achieve separation between the X-ray attenuation data in the X-ray detector 240 by applying an energy selective filter to the X-ray detector 240 to reduce the effect of X-ray scatter. 1..m Improved separation between the X-ray attenuation data.

[0052] In yet another configuration, a plurality of different energy intervals DE may be provided by utilizing one or more spectral filters to intercept the X-ray beam in a conventional X-ray imaging system. 1..m For example, a single filter may be mechanically moved in and out of the X-ray beam generated by the polychromatic X-ray source 230 in order to temporarily control the spectrum of X-rays detected by the X-ray detector 240. The filter may, for example, transmit only a portion of the spectrum of X-rays emitted by the polychromatic X-ray source 230, thereby providing X-ray attenuation data for a first energy interval when the filter intercepts the beam. When the filter is removed from the beam, X-ray attenuation data for a second energy interval is provided, for example, X-ray attenuation data for the complete spectrum emitted by the polychromatic X-ray source. In this way, different energy intervals DE are generated in a time-sequential manner. 1..m Multiple filters, each with a different X-ray transmission spectrum, can be switched in and out of the X-ray beam sequentially to increase the number of energy intervals.

[0053] It is worth noting that, although the generation of the spectral CT data 110 received in operation S110 is described above with reference to a spectral X-ray projection imaging system, the spectral CT data 110 may alternatively be generated by a spectral CT imaging system as described above. It is also worth noting that a similar configuration of the X-ray source 230 and the X-ray detector 240 may be used in the spectral CT imaging system to provide the spectral CT data 110.

[0054] Back to Figure 1 In operation S120, spectral X-ray projection data 130 is received. The spectral X-ray projection data 130 represents the region of interest 120 and is defined in a plurality of different energy intervals DE 1..m The X-ray attenuation within the region of interest in . There may generally be two or more energy intervals; that is, m is an integer, and m ≥ 2, as described above with respect to the spectral CT data 110. The spectral X-ray projection data 130 may be composed of Figure 2 The spectral X-ray projection data 130 may be received by one or more processors 220 shown in FIG. 1 . The spectral X-ray projection data 130 may be received by any form of data communication, as described above with respect to the spectral CT data 110. In general, the one or more processors 220 may receive the spectral X-ray projection data 130 from a spectral X-ray projection imaging system, or may receive the spectral X-ray projection data 130 from another source (e.g., a computer-readable storage medium, the Internet, or the cloud).

[0055] Continue to refer to Figure 1 In some examples, the spectral X-ray projection data 130 received in operation S120 may be angiography data. In these examples, the angiography spectral X-ray projection data represents an injected contrast agent within a vascular region of interest. Thus, the angiography spectral X-ray projection data may be generated after a contrast agent is injected into a blood vessel, as described above with respect to angiography spectral CT data.

[0056] Continue to refer to Figure 1 The spectral X-ray projection data 130 received in operation S120 may be generated by a spectral X-ray projection imaging system. The spectral X-ray projection data 130 may be generated by, for example, the spectral X-ray projection imaging system described above. Figure 2 The spectral X-ray projection imaging system 210 described above generates the spectral X-ray projection data 130 and the spectral CT data 110. As described above, various different configurations of the X-ray source 230 and the X-ray detector 240 can be used in the spectral X-ray projection imaging system 210. In one example, the same spectral X-ray projection imaging system 210 is used to generate the spectral X-ray projection data 130 and the spectral CT data 110. This has the advantage of limiting the amount of motion and the amount of shape change of the region of interest between the acquisition of the two sets of data. This improves their correspondence by eliminating the need to move the subject between the imaging systems in order to acquire the two sets of data.

[0057] Continue to refer to Figure 1 In operation S130, material projection data 150 representing X-ray attenuation of one or more materials within the region of interest 120 is extracted from the spectral X-ray projection data 130 to provide projection data of the one or more materials.

[0058] Typically, the spectral X-ray projection data 130 received in operation S110 represents X-ray attenuation from a variety of materials within the region of interest. The goal of the extraction operation S130 is to extract material projection data 150 of one or more expected materials from the spectral X-ray attenuation data. In the example described below, material projection data 150 of an injected contrast agent containing iodine is extracted. However, it should be understood that material projection data 150 of other types of materials (e.g., fat, bone, water, soft tissue, vascular calcification, metal, and air) may be extracted alternatively or additionally.

[0059] In operation S130, various material decomposition algorithms may be used to extract material projection data 150. An example of a material decomposition algorithm that may be used in operation S130 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 SPI Evol. 7258, 72583Y). Another suitable material decomposition algorithm 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 algorithm is disclosed in the published PCT patent application WO / 2007 / 034359A2. Another suitable material decomposition algorithm is disclosed in Silva, AC et al., “Dual-energy (spectral) CT: applications in abdominal imaging” (Radio Graphics 2011; 31(4): 1031-1046).

[0060] Typically, the X-ray attenuation spectrum of a material includes contributions from Compton scattering and contributions from 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 exhibit energy dependence, and the material decomposition algorithm exploits this effect to analyze the spectral X-ray projection data in order to distinguish between different materials.

[0061] Typically, material decomposition algorithms operate by decomposing the attenuation spectrum of an absorbing medium into contributions from a set of hypothetical "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.

[0062] 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 may help identify the contribution of the base material. The k-edge energy of a material is defined as the minimum energy required to cause a photoelectric event with a k-shell electron. For each material, the k-edge occurs at a characteristic energy. The k-edge energy of a material is marked by a sharp increase in its X-ray attenuation spectrum at an X-ray energy corresponding to the k-edge energy value. The k-edge energies of many materials present in the human body are too low to be detected in a diagnostic X-ray imaging system. For example, the k-edge energies of hydrogen, carbon, oxygen, and nitrogen are at energies less than 1 keV. However, the k-edge energy values ​​of 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) allow their differentiation in spectral X-ray attenuation data.

[0063] For example, Figure 5 is a graph illustrating the dependence of the mass attenuation coefficient on X-ray energy for two example materials (iodine and water). Figure 5The mass attenuation coefficient shown in represents the X-ray attenuation. The sharp increase in the X-ray attenuation of iodine at 33.2 keV helps to separate the contribution of each of these materials to the combined attenuation spectrum. In this example, the energy interval DE can be provided close to the k-edge energy of 33.2 keV. 1 , and another energy interval DE significantly higher than the k-edge energy 2 Therefore, operation S130 may involve extracting material projection data 150 of the iodine-based contrast agent from the spectral X-ray projection data 130 containing both iodine and water using a material decomposition algorithm.

[0064] Reference Figure 1 and Figure 3 The method shown in , which covers two alternative uses of the spectral CT data 110 received in operation S110. Figure 3 In the first alternative, along Figure 3 In this alternative, the received spectral CT data (110) is used to set the values ​​of one or more imaging parameters (140) used to generate the spectral X-ray projection data (130). In a second alternative, along Figure 3 In this option, the operation of extracting S130 material projection data 150 from the spectral X-ray projection data 130 is performed using the spectral CT data 110 received in operation S110. These options are now described in more detail.

[0065] In a first option, the operation of setting the values ​​of one or more imaging parameters 140 for generating the spectral X-ray projection data 130 is performed using the spectral CT data 110 received in operation S110. The inventors have observed that by setting the values ​​of one or more imaging parameters 140 using the spectral CT data 110 according to these examples, it is possible to ensure that the resulting material projection data is more specific to the intended material. This improvement in the material projection data then facilitates a physician to make a more accurate diagnosis of the region of interest.

[0066] Also refer to Figures 6 to 9 Examples related to the first option are described. In these examples, the setting of the values ​​of one or more imaging parameters 140 may include controlling the spectrum of X-rays emitted by the X-ray source 230 of the imaging system 210 for generating the spectral X-ray projection data 130 and / or controlling the dose of X-ray radiation emitted by the X-ray source 230 of the imaging system 210 and / or controlling the orientation of the imaging system 210 relative to the region of interest 120.

[0067] In these examples, setting values ​​of one or more imaging parameters 140 using the received spectral CT data 110 includes:

[0068] The received spectral CT data 110 is projected to provide simulated spectral X-ray projection data 110 P ', projecting the received spectral CT data 110 using the estimated orientation of the imaging system 210 used to generate the spectral X-ray projection data 130 relative to the region of interest 120;

[0069] According to the simulated spectral X-ray projection data 110 P ' Calculate the spatial correlation value of the performance indicator 160; and

[0070] The values ​​of one or more imaging parameters 140 are set such that the spatial correlation value of performance indicator 160 meets a predetermined criterion.

[0071] First refer to Figure 6 Describe these examples, Figure 6 FIG. 1 is a diagram illustrating projecting spectral CT data 110 to provide simulated spectral X-ray projection data 110 according to some aspects of the present disclosure. P ' is a schematic diagram of an example of the operation of Figure 6 In FIG. 1 , the spectral CT data 110 is shown in the form of a volumetric image arranged between an X-ray source 230 and an X-ray detector 240 of a projection X-ray imaging system 210. The projection X-ray imaging system 210 has the same geometry as the imaging system used to generate the spectral X-ray projection data 130. For example, the projection X-ray imaging system 210 has the same X-ray detector size as used in the imaging system that generated the spectral X-ray projection data 130, and the same spacing between the X-ray source 230 and the X-ray detector 240. The dashed line connecting the X-ray source 230 and the X-ray detector 240 shows the extension of the imaging beam (over which the imaging system 210 generates X-ray attenuation data). The spectral CT data 110 is mathematically projected using a ray tracing technique in which the line integral of the attenuation of a virtual X-ray passing through the spectral CT data is determined for multiple paths between the X-ray source 230 and the X-ray detector 240. The line integral is determined for a path starting from the X-ray source 230 and ending at different positions on the X-ray radiation sensitive surface of the X-ray detector 240. For each position on the X-ray radiation sensitive surface of the X-ray detector 240, an integrated attenuation value representing the total attenuation along the path is provided. The integrated attenuation value is multiplied by the X-ray radiation emitted by the X-ray source 230 at each energy interval DE 1..m The result of the multiplication may be referred to as the simulated spectral X-ray projection data 110 of the region of interest 120. P', because it represents the projection intensity of the X-ray radiation data obtained by imaging the region of interest 120 using the projection X-ray imaging system 210 at its current orientation relative to the region of interest.

[0072] Simulated spectral X-ray projection data 110 P ' is obtained from the spectral CT data using the estimated orientation of the imaging system 210 used to generate the spectral X-ray projection data 130. In some cases, the region of interest 120 may be conventionally imaged using the same orientation (i.e., a known orientation). In these cases, the estimated orientation of the imaging system 210 may be estimated based on the known orientation of the imaging system 210 relative to the region of interest 120. Alternatively, the orientation may be estimated based on a measurement of the orientation when the spectral X-ray projection data 130 was generated. For example, a rotary encoder, a position encoder, or a position tracking system may be used to estimate the orientation of the imaging system 210. Figure 7 and Figure 8 The orientation of the imaging system 210 is measured using the first orientation parameter a and the second orientation parameter b shown in FIG. Figure 7 is a schematic diagram illustrating an example of a first orientation parameter a defining the orientation of the imaging system 210 relative to the region of interest 120 according to some aspects of the present disclosure. The first orientation parameter a defines a rotation angle of a central ray of the X-ray imaging system 210 around a longitudinal axis of the subject 250. Figure 8 is a schematic diagram illustrating an example of a second orientation parameter b defining the orientation of the imaging system 210 relative to the region of interest 120 according to some aspects of the present disclosure. The second orientation parameter b defines the tilt angle of the central ray of the X-ray imaging system 210 relative to the cranio-caudal axis of the subject 250. Thus, the orientation parameters a and b may be measured when generating the spectral X-ray projection data 130 and subsequently used to project the spectral CT data 110 from the same orientation relative to the region of interest.

[0073] Then, using the simulated spectral X-ray projection data 110 P ' to calculate the spatial correlation value of the performance indicator 160. The performance indicator can represent various factors. In one example, the performance indicator represents the performance of the simulated spectral X-ray projection data 110 by using a material decomposition algorithm. P The material decomposition accuracy achieved by decomposing the spectral X-ray projection data 110 into one or more material types. The performance indicator may, for example, represent the accuracy of material decomposition achieved by decomposing the spectral X-ray projection data 110 into one or more material types using one of the material decomposition algorithms described above. P' A spatially correlated value of the signal-to-noise ratio obtained by decomposing a material such as a contrast agent. The signal-to-noise ratio can be determined by comparing a simulated signal, intensity or line integral value with the expected noise introduced by the stochasticity of imaging with photons given a specific simulated dose level. Alternatively, the performance indicator may represent the confidence that the material type was correctly identified by the material decomposition algorithm. The confidence value can be calculated by comparing a given material data from a simulation and ray projection through the CT data with the material data obtained from the material decomposition algorithm applied to the simulated projection data. A high confidence is achieved if the two material data show the same or similar values, or if the difference is below a predetermined threshold and if such similarity remains independent of expected variations in the simulated projection data (e.g., from photon noise or typical inaccuracies in the imaging system). Since the calculated value of the performance indicator takes into account the parameters used to generate the spectral X-ray projection data 110 P ' spectrum of the X-ray source 230, and the spatial distribution of attenuation of the region of interest represented in the spectral CT data 110, so that the performance indicator provides an accurate prediction of the performance that will be obtained when generating the spectral X-ray projection data 130 using the estimated orientation of the imaging system 210 using the current settings of the imaging system 210.

[0074] The performance indicator 160 may alternatively represent other factors. For example, the performance indicator 160 may represent the performance of the imaging system 210 at one or more energy intervals DE used to generate the spectral X-ray projection data 130. 1..m The expected amount of X-ray dose detected in the detector, or it may represent the signal-to-noise ratio calculated from the X-ray dose and an estimated amount of noise. The amount of noise may be estimated based on measurements of the performance of the X-ray detector.

[0075] The spatial correlation values ​​of the performance indicator 160 may then be output. The spatial correlation values ​​of the performance indicator 160 may be output in various ways, including, for example, output to a display device, a computer-readable storage medium, or a printer device. In one example, the spatial correlation values ​​of the performance indicator 160 are output as an overlay image 170. For example, an overlay image including a graphical representation of the region of interest 120 and the spatial correlation values ​​of the performance indicator 160 may be output. Fig. 9 is an example of an overlay image 170 including a graphical representation of a performance indicator 160 according to some aspects of the present disclosure. Fig. 9 In FIG. 1 , overlay image 170 includes a graphical representation of the left coronary artery, which is overlaid with performance indicator 160. Performance indicator 160 can be provided in a variety of graphical formats, including, for example, Fig. 9The performance indicator can alternatively be provided in the form of, for example, a color-coded overlay image, in which the various colors represent the relative values ​​of the performance indicator. The user can then evaluate the Fig. 9 The overlay images are shown to identify any portions of the region of interest where the performance indicator 160 is insufficient.

[0076] Then, the values ​​of one or more imaging parameters 140 are set so that the spatially correlated values ​​of the performance indicators 160 meet the predetermined criteria. Continuing with the above example, this operation can be performed, for example, by setting the values ​​of the imaging parameters so that the expected amount of material decomposition accuracy or X-ray dose meets the predetermined criteria. For example, the material decomposition accuracy or X-ray dose can be specified to exceed a threshold value in a specific portion of the region of interest. Doing so can ensure that when the spectral X-ray projection data 130 is generated using these settings, the data contains sufficient information to accurately isolate the expected material from other materials that can be represented by the spectral X-ray projection data 130. Alternatively, the X-ray dose in a specific portion of the region of interest can be specified not to exceed another threshold value. Doing so can keep the X-ray dose delivered to the subject within safe limits.

[0077] As described above, the imaging parameters set in this operation may include the spectrum of X-rays emitted by the X-ray source 230 of the imaging system 210 and / or the dose of X-ray radiation emitted by the X-ray source 230 of the imaging system 210 and / or the orientation of the imaging system 210 relative to the region of interest 120.

[0078] The spectrum of X-rays emitted by the X-ray source 230 of the spectral X-ray projection imaging system 210 can be controlled by adjusting the tube voltage of the X-ray source 230. This technique is called kVp switching. The spectrum of X-rays emitted by the X-ray source 230 of the spectral X-ray projection imaging system 210 can alternatively be controlled by other means, including, for example, by switching different X-ray sources 230 of the spectral X-ray projection imaging system 210, or by intercepting the beam of X-ray radiation emitted by the X-ray source 230 using a spectral filter.

[0079] For example, the dose of X-ray radiation emitted by the X-ray source 230 of the imaging system 210 can be controlled by adjusting the duration that the X-ray detector 240 in the spectral X-ray projection imaging system 210 is exposed to the X-ray radiation from the X-ray source 230, or by intercepting the beam of X-ray radiation emitted by the X-ray source 230 using an attenuation filter having a flat spectral response.

[0080] By adjusting the above reference Figure 6The orientation parameters a and b are used to control the orientation of the imaging system 210 relative to the region of interest 120. The orientation of the imaging system 210 relative to the region of interest 120 can be automatically controlled. For example, the one or more processors 220 can transmit control signals to one or more actuators that are mechanically coupled to the Figure 2 The support arm of the spectral X-ray projection imaging system 210 shown in FIG. 1 and combined with position feedback from the rotary encoder, position encoder, or position tracking system described above provides a desired orientation of the imaging system 210 relative to the region of interest 120 .

[0081] The values ​​of the imaging parameters set in this operation may be determined in various ways. In one example, by calculating the simulated spectral X-ray projection data 110 P ' (which is required for the performance indicator 160 to meet the predetermined criteria), back-projecting the change through the spectral CT data 110, and thus calculating at each energy interval DE 1..m The change of X-ray radiation emitted by the X-ray source in the X-ray source (which is to generate the simulated spectral X-ray projection data 110 P ') to determine the values ​​of the X-ray dose and the X-ray spectrum of the X-rays emitted by the X-ray source 230. In another example, the values ​​of the X-ray dose and the X-ray spectrum of the X-rays emitted by the X-ray source 230 are adjusted step by step. After each adjustment, the effect of the adjustment is calculated by the performance indicator 160, and the adjustment is repeatedly performed until the value of the performance indicator 160 meets the predetermined criteria. Instead of stepwise adjusting the values ​​of the X-ray dose and the X-ray spectrum of the X-rays emitted by the X-ray source 230, these values ​​can be iteratively adjusted using a mathematical optimization technique based on the value of an error function. In this case, the error function represents the difference between the expected value of the performance indicator 160 and its value for the current iteration.

[0082] You can also use Figure 6 The simulated spectral X-ray projection data 110 generated by the apparatus shown in P'To determine the values ​​of orientation parameters a and b required for performance indicator 160 to meet predetermined criteria. For example, the estimated orientation of imaging system 210 estimated in the manner described above may be used to calculate an initial value of performance indicator 160. Subsequently, the impact of incremental adjustments to the orientation parameters a and b on performance indicator 160 may be calculated and used to determine the values ​​of the orientation parameters for which performance indicator 160 meets predetermined criteria. Alternatively, mathematical optimization techniques may be used to iteratively adjust the values ​​of orientation parameters a and b in response to the value of an error function. The error function represents the difference between the expected value of performance indicator 160 and its value for the current iteration. After each adjustment, the impact of each adjustment is calculated by performance indicator 160. The value of the error function is also used to repeatedly adjust the values ​​of the orientation parameters until the value of performance indicator 160 meets predetermined criteria.

[0083] Thus, by using the received spectral CT data 110 to set the values ​​of one or more imaging parameters 140 according to the first option, it can be ensured that the extracted material projection data 150 contains sufficient information to accurately represent the intended material.

[0084] A second alternative is now described, in which an operation of extracting S130 material projection data 150 from the spectral X-ray projection data 130 is performed using the spectral CT data 110 received in operation S110. In an example according to this second alternative, reference is made to Figure 1 The methods described include:

[0085] The received spectral CT data 110 is projected to provide simulated spectral X-ray projection data 110 P ', projecting the received spectral CT data 110 using the estimated orientation of the imaging system 210 used to generate the spectral X-ray projection data 130 relative to the region of interest 120; and

[0086] Extracting S130 the material projection data 150 from the spectral X-ray projection data 130 includes subtracting the simulated spectral X-ray projection data 110 from the spectral X-ray projection data 130 P ', so as to provide subtraction data representing the region of interest 120.

[0087] In these examples, the received spectral CT data 110 is projected using the same method as described above with reference to Figures 6 to 8 In some examples, projecting the received spectral CT data 110 may be performed using a subset of the spectral CT data 110. The subset of spectral CT data 110 may represent one or more background materials in addition to the material represented by the material projection data (150). This may produce simulated spectral X-ray projection data 110 of the background material. P'. Therefore, the subtraction performed in the extraction operation S130 removes the attenuation caused by these background materials, and the resulting subtraction data represents the expected material without the background material. Since the subtraction is performed using projection data obtained by projecting the spectral CT data 110 (i.e., volume data), the subtraction data has a spectrally accurate spatial distribution of the attenuation of the expected material.

[0088] In these examples, refer to Figure 1 The described method may also include:

[0089] generating background material data from the spectral CT data 110, the background material data defining X-ray attenuation of one or more materials represented in the spectral CT data other than the one or more materials represented by the material projection data 150; and

[0090] projecting the received spectral CT data 110 using the background material data to provide simulated background material projection data; and

[0091] Extracting S130 material projection data 150 from the spectral X-ray projection data 130 comprises subtracting simulated background material projection data from the spectral X-ray projection data to provide subtracted data.

[0092] In these examples, generating background material data from spectral CT data 110 may be performed by applying a material decomposition algorithm to spectral CT data 110 that is configured to identify X-ray attenuation caused by one or more materials represented in spectral CT data 110 other than one or more materials represented by material projection data 150. Any of the material decomposition algorithms described above may be used for this purpose.

[0093] Now describe the imaging scenario in order to illustrate the benefits of this example. In this scenario, the region of interest is the heart, and it is desired to obtain material projection data of the contrast agent in the coronary arteries in order to determine the value of the blood flow parameter. In this scenario, the spectral X-ray projection data 130 of the heart usually also represents bone structures such as ribs and spine. These bone structures inevitably overlap with the heart during imaging. In this scenario, in order to determine the value of the blood flow parameter, it will be advantageous to accurately remove the effect of bone structure attenuation from the spectral X-ray projection data 130. By generating background material data of the bone, projecting the background material data of the bone to provide simulated bone projection data, and extracting contrast agent projection data 150 from the spectral X-ray projection data 130 by subtracting the simulated bone projection data from the spectral X-ray projection data, subtraction data is provided for the contrast agent in the absence of bone. In these examples, the material projection data 150 is provided by the subtraction data. Since the bone is removed, the subtraction data provides an improved visualization of the region of interest for the contrast agent, which also helps to more accurately calculate the blood flow parameters.

[0094] It is worth noting that in these examples, the operation of extracting S130 material projection data may involve applying a material decomposition algorithm to the subtraction data. This results in the material projection data of the desired material being isolated from other materials that may also be present in the subtraction data, the effects of which are not removed in the subtraction operation.

[0095] In these examples, the subtraction of the simulated background material projection data from the spectral X-ray projection data 130 may be performed in a variety of ways.

[0096] In one example, the operation of subtracting the simulated background material projection data from the spectral X-ray projection data 130 includes:

[0097] From the corresponding energy interval DE at the corresponding position within the region of interest 120 in the spectral X-ray projection data 130 1..m The image intensity values ​​in DE are subtracted from the simulated background material projection data at multiple energy intervals DE 1..m The image intensity value in each of .

[0098] As mentioned above, the energy interval DE 1..m The values ​​may be defined by different layers of a multi-layer detector, or they may be defined by the tube voltage of the X-ray source. In this example, for each detector layer, or for each tube voltage used to generate the spectral X-ray projection data 130, simulated background material projection data are generated from the spectral CT data 110. In the corresponding energy interval DE 1..m The simulated background material projection data is subtracted from the spectral X-ray projection data 130 to provide an energy interval DE 1..mIn this way, the obtained subtraction data is spectrally accurate.

[0099] In another example, the operation of subtracting the simulated background material projection data from the spectral X-ray projection data 130 includes:

[0100] Multiple energy intervals DE in the data will be projected from the simulated background material 1..m The image intensity values ​​within the selected energy range are subtracted from the image intensity values ​​in the same energy range and in corresponding positions within the region of interest 120 in the spectral X-ray projection data 130 .

[0101] It can be obtained by 1..m The energy range is defined by selecting the energy interval in the energy interval. In this case, the subtraction is performed for the corresponding range of the energy interval. In this way, the obtained subtraction data is also spectrally accurate.

[0102] A further example related to the second alternative is now described, in which projection data of blood vessels other than the vessel of interest are removed from the subtraction data.

[0103] The inventors have observed that although the extracted material projection data is related to a specific material (e.g., a contrast agent), the extracted material projection data is susceptible to confounding effects caused by the projection properties of the data. For example, overlapping vessels in the extracted material projection data that contain the same contrast agent material will produce overlapping regions in the projection data in which the attenuation from the overlapping vessels is combined. This may be confusing to the reviewing physician. It can also lead to errors in the calculation of blood flow parameters from the extracted material projection data, as such calculations typically rely on accurate values ​​for the contrast agent intensity along the vessel. In the following example, data for vessels other than the vessel of interest are removed from the subtraction data, which provides improved visualization of the injected contrast agent within the vessel of interest and also facilitates calculation of more accurate blood flow parameters for the vessel of interest.

[0104] In these examples, material projection data 150 represents X-ray attenuation of an injected contrast agent in a plurality of blood vessels within region of interest 120 and refers to Figure 1 The described methods also include:

[0105] receiving an input identifying at least one blood vessel 180 within the region of interest 120; and

[0106] Data corresponding to one or more blood vessels other than the identified at least one of blood vessels 180 is removed from the subtraction data.

[0107] Reference Figures 10 to 12 Describe these examples. Fig.10is an example of contrast agent projection data 150 representing X-ray attenuation in a region of interest 120 including an identified blood vessel 180 according to some aspects of the present disclosure. Fig.10 , it is desired to obtain projection data of the identified blood vessel 180. It may be desired to obtain projection data of the blood vessel in order to determine a value of a blood flow parameter of the blood vessel without the confounding influence of overlapping data from adjacent blood vessels. Fig.10 Projection data is depicted before data for blood vessels other than the identified blood vessel 180 is removed. Fig.11 is an example of contrast agent projection data 150 from which data corresponding to blood vessels other than the identified blood vessel 180 has been removed in accordance with some aspects of the present disclosure. Fig.11 It can be seen that by removing the data for the other vessels, the visibility of the identified vessel 180 is improved because the attenuation caused by the other vessels has been removed, which attenuation may overlap with the vessel 180. The data for the identified vessel 180 can then be used to accurately determine the value of the blood flow parameter for the identified vessel 180 without the confounding effects of data for other vessels that include the same contrast agent.

[0108] In these examples, input for identifying a vessel 180 within the region of interest 120 may be provided by a user, or may be provided automatically. User input for identifying the vessel 180 may be received from a user input device operating in conjunction with a displayed image of the region of interest. For example, an image may be generated from the spectral X-ray projection data 130 or the spectral CT data 110. Input for identifying the vessel 180 may be provided automatically by using a feature detector or a trained neural network to identify the vessel 180 in the spectral X-ray projection data 130 or the spectral CT data 110. A segmentation operation may also be performed on such an image to assist in identifying the vessel. Various segmentation algorithms are known for this purpose. A combination of these methods may also be used, wherein a feature detector or a trained neural network automatically identifies a candidate vessel, which the user may then select via a user input device. The vessel may be identified based on the detection of a contrast agent in such an image or based on the presence of an abnormality (e.g., a stenosis in the vessel). For example, a feature detector or a trained neural network may be used to identify the left coronary artery and a stenosis therein of a cardiac image, and identify the vessel as a candidate vessel.

[0109] In one example, the projection operation is used to remove data for one or more blood vessels other than the identified blood vessel 180. In this example, removing data corresponding to one or more blood vessels other than the identified at least one of the blood vessels 180 includes:

[0110] segmenting the received spectral CT data 110 to provide a 3D vessel model 190 representing a plurality of vessels within the region of interest 120;

[0111] The 3D blood vessel model 190 is aligned with a virtual X-ray source 230 representing the imaging system 210. v and virtual X-ray detector 240 v Directed so that by v The virtual X-ray is projected onto the virtual detector 240 through the 3D blood vessel model 190 v The simulated projection of the 3D vascular model obtained above provides simulated projection data 190 P ', the position of the blood vessel in the simulated projection data corresponds to the position of the blood vessel in the spectral X-ray projection data 130;

[0112] adjusting the estimated contrast agent distribution C(x, y, z) in the 3D vessel model 190 so that simulated projections of the estimated contrast agent distribution obtained using the oriented 3D vessel model 190 match the contrast agent distribution in the contrast agent material projection data 150;

[0113] wherein projecting the received spectral CT data 110 is performed using a directional 3D vessel model and an estimated contrast agent distribution; and

[0114] Wherein, removing data corresponding to one or more blood vessels other than at least one identified among the blood vessels 180 from the contrast agent material projection data 150 includes: subtracting simulated spectral X-ray projection data obtained using a directional 3D blood vessel model and an estimated contrast agent distribution of one or more blood vessels other than at least one identified among the blood vessels 180 from the spectral X-ray projection data 130.

[0115] In this example, since the data of blood vessels other than the identified blood vessel 180 is removed using the estimated contrast agent distribution C(x, y, x), which when projected gives simulated projection data that matches the contrast agent distribution in the contrast agent material projection data 150, the volume distribution of the spectral attenuation of the contrast agent in the blood vessels other than the identified blood vessel 180 can be accurately accounted for. The result of the subtraction operation is to provide projection data that accurately removes the data of blood vessels other than the identified blood vessel 180.

[0116] Reference Fig.12 Describe the example, Fig.12 FIG. 1 is a diagram illustrating the relative position of the 3D blood vessel model 190 to the virtual X-ray source 230 according to some aspects of the present disclosure. v and virtual X-ray detector 240 vSchematic diagram of an example of operations for orienting and adjusting the estimated contrast agent distribution C(x, y, z) in the 3D vessel model 190 .

[0117] In this example, the segmentation operation may be performed using various known segmentation algorithms.Segmentation may also include processing the received spectral CT data 110 using a material decomposition algorithm to accurately distinguish blood vessels from other media in the region of interest.

[0118] The 3D blood vessel model is made relative to the virtual X-ray source 230 v and virtual X-ray detector 240 v The purpose of the operation of the orientation 190 is to make the virtual X-ray source 230 v and virtual X-ray detector 240 v The imaging system used to generate the spectral X-ray projection data 130 has the same orientation relative to the region of interest as that used to generate the spectral X-ray projection data 130. This operation can be performed using a known orientation. For example, the orientation of the imaging system used to generate the spectral X-ray projection data 130 relative to the region of interest can be known by measuring the orientation parameters a, b when generating the spectral X-ray projection data 130. Figure 6 The orientation parameters a, b are described in more detail. Alternatively, an iterative method can be used to determine the orientation. The latter method is described in Fig.12 As shown in the figure, the first loss function D is calculated 1 The value of and use this value to iteratively adjust the orientation parameters a, b until the simulated projection data 190 P The difference between ' and the spectral X-ray projection data 130 indicates that the expected accuracy has been achieved. In this method, an optimization is performed, in which a first loss function D is calculated 1 The first loss function D 1 Indicates simulated projection data 190 P ' and the difference between the spectral X-ray projection data 130. Based on the first loss function D 1 The orientation parameters a and b are iteratively adjusted according to the value of until the value of the first loss function meets the specified standard, at which point the expected accuracy is achieved. The first loss function D can be calculated using a loss function such as the Dice coefficient or the root mean square error of the gradient image. 1 The value of .

[0119] Simulated projection data for determining orientation 190 P 'With reference to the above Figure 6 In other words, the 3D blood vessel model 190 is mathematically projected using ray tracing technology, wherein the virtual X-ray source 230 v and virtual X-ray detector 240 v The multiple paths between determine the line integral of the attenuation of the virtual X-ray passing through the 3D blood vessel model 190.

[0120] The estimated contrast agent distribution C(x, y, z) in the oriented 3D vessel model 190 is then adjusted so that the simulated projections of the estimated contrast agent distribution obtained using the oriented 3D vessel model 190 match the contrast agent distribution in the contrast agent material projection data 150. Fig.12 This operation may be performed using an iterative method, wherein the contrast agent distribution C(x, y, z) is adjusted based on the value of the second loss function D2 until the simulated projection of the estimated contrast agent distribution obtained using the oriented 3D vascular model 190 matches the contrast agent distribution in the contrast agent material projection data 150. 2 The contrast agent distribution C(x, y, z) is iteratively adjusted according to the value of the second loss function until the value of the second loss function meets the specified criterion, at which time the expected accuracy is achieved. The simulated projection is used as described above for simulating the projection data 190 P The difference is that in this case, the projection is performed using the estimated contrast agent distribution in the oriented 3D vascular model 190. The second loss function D can be calculated using a loss function such as the Dice coefficient or the root mean square error. 2 The value of .

[0121] Having determined the contrast distribution in the model that gives the projection image data corresponding to the contrast material projection data 150, the simulated spectral X-ray projection data obtained using the oriented 3D vessel model and the estimated contrast distribution for one or more vessels other than the identified at least one of the vessels 180 are subtracted from the spectral X-ray projection data 130. This accurately removes the attenuation effects caused by vessels other than the identified vessel 180 in the region of interest because the subtraction takes into account the 3D distribution of the contrast agent in the other vessels.

[0122] In some examples, reference Figure 1 The described method also includes determining a value of a blood flow parameter. In these examples, the material projection data 150 represents X-ray attenuation of an injected contrast agent in one or more blood vessels in the region of interest 120, and the method includes:

[0123] analyzing the material projection data 150 to provide values ​​of blood flow parameters for one or more blood vessels in the region of interest 120; and

[0124] Output the value of the blood flow parameter.

[0125] Various techniques may be used to analyze the material projection data to provide values ​​for blood flow parameters. The blood flow parameters may, for example, represent various quantities including, for example, 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". The values ​​for the blood flow parameters may be output in various ways.

[0126] For example, the blood flow parameter may be the blood flow reserve fraction "FFR" of the blood vessel. Figure 4 , the distal position Pos of the left coronary artery of the vessel 180 can be calculated using the following formula d FFR value:

[0127] FFR=P d / P a Formula 1

[0128] In formula 1, P d represents the distal pressure at the distal position relative to the stenosis, P a Indicates the pressure proximal to the stenosis. FFR usually uses P averaged over the cardiac cycle. d and P a FFR values ​​above 0.8 are generally considered to be clinically insignificant, while values ​​below 0.8 are generally considered to indicate a clinically significant increase. Figure 4 , the distal position of the left coronary artery can be used d Blood pressure P d The value of and the proximal position of the left coronary artery Pos p Pressure P a The FFR is calculated based on the value of p The pressure values ​​at these locations can be determined based on the material projection data 150 using the techniques disclosed in WO 2018 / 060529A1 and Prevrhal, S. et al., “CT Angiographic Measurement of Vascular Blood Flow Velocity by Using Projection Data” (Radiology, Vol. 261: No. 3-December 2011, pp. 923-929) and US 2017 / 0105694A1.

[0129] Similarly, the values ​​of other blood flow parameters can be calculated based on the material projection data 150. For example, in the document “Quantification of Internal Carotid Artery Flow with Digital Subtraction Angiography: Validation of an Optical Flow Approach with Doppler Ultrasound” (American journal of neuroradiology, 35, pp. 166-163) by Pereira, V. et al., a technique for determining blood flow velocity values ​​based on projection data is disclosed.

[0130] The value of the blood flow parameter may be output in various ways. For example, the value may be output to a display device, such as Figure 2 200 of the system 200 shown in . Alternatively, for example, the value of the blood flow parameter can be output to a computer-readable storage medium or a printer device. In some examples, the value of the blood flow parameter is output as a digital value, while in other examples, the value of the blood flow parameter is output graphically. For example, the value of the blood flow parameter can be output as a color-coded icon. The values ​​of the blood flow parameter at multiple locations along the length of the blood vessel 180 can also be calculated and output graphically. For example, an image of the illustrated blood vessel 180 can be output, wherein the values ​​of the blood flow parameter are displayed as a color-coded overlay on the image at corresponding locations along the length of the blood vessel. The physician can then use the values ​​of the provided blood flow parameter to make a clinical diagnosis of the subject.

[0131] In some examples, reference Figure 1 The described method also includes outputting graphical representations of various data generated or processed. For example, the method may also include outputting graphical representations of one or more of the following: received spectral CT data 110, received spectral X-ray projection data 130, extracted material projection data 150. These graphical representations may also be output to a display device (e.g., Figure 2 230) or a computer readable storage medium or a printer device.

[0132] In another example, a computer program product is provided. The computer program product includes instructions, and when the instructions are executed by one or more processors, the instructions cause the one or more processors to perform a method of providing projection data, including:

[0133] Receiving S110 spectral computed tomography CT data 110 representing a region of interest 120, the spectral CT data being defined at a plurality of different energy intervals DE 1..m X-ray attenuation within the region of interest in ;

[0134] Receive S120 spectral X-ray projection data 130 representing a region of interest 120, the spectral X-ray projection data being defined in a plurality of energy intervals DE 1..m X-ray attenuation within the region of interest in and is generated using set values ​​of one or more imaging parameters 140; and

[0135] extracting S130 material projection data 150 representing X-ray attenuation of one or more materials within the region of interest 120 from the spectral X-ray projection data 130 to provide projection data; and

[0136] Therein, extracting S130 material projection data 150 from the spectral X-ray projection data 130 or setting values ​​of one or more imaging parameters 140 is performed using the received spectral CT data 110 .

[0137] In another example, a system 200 for providing projection data is provided. The system includes one or more processors 220 configured to perform the following:

[0138] Receiving S110 spectral computed tomography CT data 110 representing a region of interest 120, the spectral CT data being defined at a plurality of different energy intervals DE 1..m X-ray attenuation within the region of interest in ;

[0139] Receive S120 spectral X-ray projection data 130 representing a region of interest 120, the spectral X-ray projection data being defined in a plurality of energy intervals DE 1..m X-ray attenuation within the region of interest in and is generated using set values ​​of one or more imaging parameters 140; and

[0140] extracting S130 material projection data 150 representing X-ray attenuation of one or more materials within the region of interest 120 from the spectral X-ray projection data 130 to provide projection data; and

[0141] Therein, the one or more processors are configured to extract material projection data 150 from the spectral X-ray projection data 130 or to set values ​​of one or more imaging parameters 140 using the received spectral CT data 110 .

[0142] Figure 2 An example of the system 200 is shown. It is worth noting that the system 200 may also include one or more of the following: a spectral X-ray projection imaging system 210 for generating the spectral X-ray projection data 130 and the spectral CT data 110, a spectral CT imaging system ( Figure 2), a monitor 230 for displaying the output data, a syringe 260 for injecting contrast agent into the blood vessels represented in the area of ​​interest, a patient bed 270, and a user input device configured to receive user input for the method, such as a keyboard, a mouse, a touch screen, etc.

[0143] The above examples should be understood as illustrative examples of the present disclosure, rather than restrictive examples. Other examples are also contemplated. For example, the examples described in connection with the computer-implemented method may also be provided by a computer program product or a computer-readable storage medium or system 200 in a corresponding manner. It should be understood that the features described in connection with any one example may be used alone, or may be used in combination with the features described elsewhere, and may be used in combination with one or more features of another example, or in combination with other examples. In addition, equivalent schemes and modifications not described above may also be adopted without departing from the scope of the present invention. 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 fact that some features are recorded in mutually different dependent claims does not indicate that the combination of these features cannot be used to advantage. Any figure mark in the claims should not be understood as limiting its scope.

Claims

1. A computer-implemented method for providing projection data, the method comprising: Receive (S110) spectral computed tomography (CT) data (110) representing a volumetric image of a region of interest (120), the spectral CT data defined at a plurality of different energy intervals (DE 1..m ) in the X-ray attenuation within the region of interest; Receive (S120) spectral X-ray projection data (130) representing a two-dimensional image of the region of interest (120), the spectral X-ray projection data defined in the plurality of energy intervals (DE 1..m ) and is generated using set values ​​of one or more imaging parameters (140); and extracting (S130) material projection data (150) representing X-ray attenuation of one or more materials within the region of interest (120) from the spectral X-ray projection data (130) to provide the projection data; and Therein, extracting (S130) material projection data (150) from the spectral X-ray projection data (130) or setting the value of the one or more imaging parameters (140) is performed using the received spectral CT data (110).

2. The computer-implemented method of claim 1 , wherein: Setting the values ​​of the one or more imaging parameters (140) is performed using the received spectral CT data (110) and includes: The received spectral CT data (110) is projected to provide simulated spectral X-ray projection data (110 P '), projecting the received spectral CT data (110) using an estimated orientation of an imaging system (210) for generating the spectral X-ray projection data (130) relative to the region of interest (120); According to the simulated spectrum X-ray projection data (110 P ') calculating the spatial correlation value of the performance indicator (160); and The values ​​of the one or more imaging parameters (140) are set so that the spatial correlation value of the performance indicator (160) meets a predetermined criterion.

3. The computer-implemented method of claim 2, wherein: The method further comprises: A graphical representation of the region of interest (120) and the spatial correlation values ​​of the performance indicator (160) are output as an overlay image (170).

4. A computer-implemented method according to claim 2 or 3, wherein: Setting the value of the one or more imaging parameters (140) includes: controlling the spectrum of X-rays emitted by the X-ray source (230) of the imaging system (210) and / or controlling the dose of X-ray radiation emitted by the X-ray source (230) of the imaging system (210) and / or controlling the orientation of the imaging system (210) relative to the region of interest (120).

5. The computer-implemented method of any one of claims 1 to 4, wherein: Extracting (S130) material projection data (150) from the spectral X-ray projection data (130) is performed using the received spectral CT data (110); and the method comprises: The received spectral CT data (110) is projected to provide simulated spectral X-ray projection data (110 P '), projecting the received spectral CT data (110) using an estimated orientation of an imaging system (210) used to generate the spectral X-ray projection data (130) relative to the region of interest (120); and wherein extracting (S130) the material projection data (150) from the spectral X-ray projection data (130) comprises subtracting the simulated spectral X-ray projection data (110) from the spectral X-ray projection data (130) P ') to provide subtraction data representing the region of interest (120).

6. The computer-implemented method of claim 5, wherein: The method further comprises: generating background material data based on the spectral CT data (110), the background material data defining X-ray attenuation of one or more materials represented in the spectral CT data other than one or more materials represented by the material projection data (150); and wherein projection of the received spectral CT data (110) is performed using the background material data to provide simulated background material projection data; and Wherein, extracting (S130) material projection data (150) from the spectral X-ray projection data (130) comprises subtracting the simulated background material projection data from the spectral X-ray projection data to provide the subtracted data.

7. The computer-implemented method of claim 6, wherein: Subtracting the simulated background material projection data from the spectral X-ray projection data (130) comprises: The corresponding energy intervals (DE) at corresponding positions in the region of interest (120) in the spectral X-ray projection data (130) are 1..m ) minus the multiple energy intervals (DE) in the simulated background material projection data 1..m ) in each of the image intensity values.

8. The computer-implemented method of claim 6, wherein: Subtracting the simulated background material projection data from the spectral X-ray projection data (130) comprises: The plurality of energy intervals (DE) in the projection data from the simulated background material 1..m ) is subtracted from image intensity values ​​in the same energy range and at corresponding positions in the region of interest (120) in the spectral X-ray projection data (130).

9. A computer-implemented method according to any one of claims 6 to 8, wherein: Generating the background material data according to the spectral CT data (110) comprises: A material decomposition algorithm is applied to the spectral CT data (110), wherein the material decomposition algorithm is configured to identify X-ray attenuation caused by one or more materials represented in the spectral CT data other than one or more materials represented by the spectral X-ray projection data (130).

10. The computer-implemented method of claim 5, wherein: The material projection data (150) represents X-ray attenuation of an injected contrast agent in a plurality of blood vessels within the region of interest (120), and the method further comprises: receiving an input identifying at least one of the blood vessels (180) within the region of interest (120); and Data corresponding to one or more blood vessels other than the identified at least one of the blood vessels (180) is removed from the subtracted data.

11. The computer-implemented method of claim 10, wherein: Removing data corresponding to one or more blood vessels other than the identified at least one of the blood vessels (180) includes: segmenting the received spectral CT data (110) to provide a 3D vessel model (190) representing the plurality of vessels within the region of interest (120); The 3D blood vessel model (190) is oriented relative to a virtual X-ray source (230) representing the imaging system (210). v ) and virtual X-ray detector (240 v ) is oriented so that by v ) passes through the 3D blood vessel model (190) and is projected onto the virtual detector (240 v ) and the simulated projection of the 3D blood vessel model obtained provides simulated projection data (190 P '), the position of the blood vessel in the simulated projection data corresponds to the position of the blood vessel in the spectral X-ray projection data (130); adjusting an estimated contrast agent distribution (C(x, y, z)) in the 3D vascular model (190) such that the simulated projections of the estimated contrast agent distribution obtained using the oriented 3D vascular model (190) match the distribution of the contrast agent in the contrast agent material projection data (150); wherein the received spectral CT data (110) is projected using the oriented 3D blood vessel model and the estimated contrast agent distribution; and Wherein, removing data corresponding to one or more blood vessels other than the at least one identified one of the blood vessels (180) from the contrast agent material projection data (150) comprises: subtracting the simulated spectral X-ray projection data obtained using the oriented 3D blood vessel model and the estimated contrast agent distribution of the one or more blood vessels other than the at least one identified one of the blood vessels (180) from the spectral X-ray projection data (130).

12. The computer-implemented method of any one of claims 1 to 11, wherein: The material projection data (150) represents X-ray attenuation of an injected contrast agent in one or more blood vessels within the region of interest (120), and the method further comprises: analyzing the material projection data (150) to provide values ​​of blood flow parameters of the one or more blood vessels within the region of interest (120); and The value of the blood flow parameter is output.

13. The computer-implemented method of any one of claims 1 to 12, wherein: The method also includes outputting a graphical representation of one or more of the following: The received spectral CT data (110), the received spectral X-ray projection data (130), and the extracted material projection data (150).

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

15. A system (200) for providing projection data, the system comprising one or more processors (220), the one or more processors being configured to perform the following: Receive (S110) spectral computed tomography (CT) data (110) representing a volumetric image of a region of interest (120), the spectral CT data defined at a plurality of different energy intervals (DE 1..m ) in the X-ray attenuation within the region of interest; Receive (S120) spectral X-ray projection data (130) representing a two-dimensional image of the region of interest (120), the spectral X-ray projection data defined in the plurality of energy intervals (DE 1..m ) and is generated using set values ​​of one or more imaging parameters (140); and extracting (S130) material projection data (150) representing X-ray attenuation of one or more materials in the region of interest (120) from the spectral X-ray projection data (130) to provide the projection data; as well as The one or more processors are configured to extract the material projection data (150) from the spectral X-ray projection data (130) or to set the values ​​of the one or more imaging parameters (140) using the received spectral CT data (110).

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