X-ray position tracking

Abstract

A spectral X-ray imaging system (100) includes an X-ray source (110) and an X-ray detector (120) mounted to a support structure. The support structure (150) is configured to rotate the X-ray source (110) and the X-ray detector (120) about two or more orthogonal axes (A-A', B-B'). One or more processors (130) are configured to cause the system (100) to perform operations including generating a spectral image based on the spectral image data and identifying a location of a first fiducial marker (1801) including a first material in the spectral image based on a first X-ray absorption k-edge energy value (1901) of the first material.

Description

Technical Field

[0001] This disclosure relates to position tracking during X-ray imaging. It discloses energy-spectral X-ray imaging systems, computer-implemented methods, and computer-readable storage media. It also discloses related interventional instruments, kits including multiple interventional instruments, and implantable devices. Background Technology

[0002] Spectral X-ray computed tomography (CT) imaging systems generate tomographic images for medical research. Compared to X-ray CT imaging systems, spectral X-ray CT imaging systems measure X-ray attenuation across multiple energy ranges. By processing X-ray attenuation data from multiple energy levels, spectral X-ray CT imaging systems can distinguish media that have similar X-ray attenuation values ​​when measured within a single energy range and would be indistinguishable in X-ray CT images.

[0003] Various dual-energy and multi-energy X-ray CT imaging systems have been developed to generate spectral X-ray CT image data. Systems employing time-sequential scanning of X-rays with different energies, rapid kVp switching of X-ray tube potential, multilayer detectors, dual X-ray sources, and photon counting detectors have been developed.

[0004] Various material decomposition algorithms and image reconstruction algorithms have also been developed to process spectral X-ray CT image data and thereby generate spectral images in which different materials are distinguished. These include techniques disclosed in Brendel, B. et al. entitled “Empirical, projection-based basis-component decomposition method” (Medical Imaging 2009, Physics of Medical Imaging, edited by Ehsan Samei and Jiang Hsieh, Proc. of SPIE Vol. 7258, 72583Y); and techniques disclosed in Mory, C. et al. entitled “Comparison of five one-step reconstruction algorithms for spectral CT” (Physics in Medicine and Biology, IOP Publishing, 2018, 63(23), pp. 235001).

[0005] In a spectral X-ray CT imaging system, while the source and detector are rotated around the imaging area, spectral image data representing the attenuation of X-rays passing through the imaging region between the X-ray source and the X-ray detector is generated for multiple energy ranges of X-rays. The rotation frequency can be approximately 1 Hz or higher. The spectral image data is then reconstructed into image slices, or "tomographic" images, which can be stacked to provide volumetric or "three-dimensional" images. Spectral X-ray CT imaging has been used, for example, in diagnostic imaging procedures to provide volumetric images that distinguish between contrast agents and tissue, thereby allowing for accurate measurement of contrast agents within the tissue.

[0006] In contrast, interventional procedures such as catheter insertion and stent placement are typically performed using conventional X-ray imaging systems. Compared to X-ray CT imaging systems, conventional X-ray imaging systems used in interventional procedures typically employ a support structure that allows the X-ray source and detector to rotate about two or more orthogonal axes. The X-ray source and detector are mounted to the support structure in relative positions to image the area between them. The multiple degrees of freedom provided by the support structure allow image data to be generated from desired orientations relative to the patient's anatomy. During interventional X-ray imaging procedures, the support structure is typically held in a stationary position relative to the patient while generating single or live X-ray projection images. Tomographic images can be generated by rotating the support structure, and therefore the X-ray source and detector, around the patient while acquiring image data from multiple different orientations. The image data is then reconstructed to generate the tomographic images. Support structures of various shapes have been used, including, for example, C-arms, O-arms, and U-arms.

[0007] Position tracking is typically required during interventional X-ray imaging procedures. Position tracking can be used to locate portions of anatomical structures or objects that may be difficult to visualize on X-ray or distinguish from other image features (such as interventional instruments and implantable devices). For example, interventional instruments such as guidewires consist of dense materials that strongly attenuate X-rays and are clearly visible in X-ray images, but are often difficult to distinguish from overlapping image features produced by other strongly attenuating media such as bone. Interventional instruments that include less dense materials such as polymers are generally poorly visible on X-ray imaging. Implantable devices such as vascular stents can also be made of metals or polymers and suffer from similar problems.

[0008] Various technologies have been developed for tracking anatomical structures, interventional instruments, and implantable devices within the body. These include reference markers that help determine the location of interventional devices in three-dimensional space, electromagnetic "EM" tracking, and the use of fiber optic shape sensing systems.

[0009] However, there is still room for improvement in tracking parts of anatomical structures and objects, such as interventional instruments and implantable devices, when performing interventional X-ray imaging procedures. Summary of the Invention

[0010] According to a first aspect of this disclosure, a spectral X-ray imaging system is provided. The spectral X-ray imaging system includes an X-ray source, an X-ray detector, a support structure, and one or more processors. The X-ray source and the X-ray detector are mounted to the support structure and configured to generate spectral image data representing the attenuation of X-rays passing through an imaging region between the X-ray source and the X-ray detector for each of three or more energy ranges. The support structure is configured to rotate the X-ray source and the X-ray detector about two or more orthogonal axes. The one or more processors are configured to cause the system to perform operations including: generating a spectral image based on the spectral image data; and identifying the location of a first reference marker including the first material in the spectral image based on a first X-ray absorption k-edge energy value of a first material.

[0011] According to a second aspect of this disclosure, the location of a second reference marker including the second material is identified in the energy spectrum image based on the second X-ray absorption k-edge energy value of the second material.

[0012] According to a third aspect of this disclosure, generating an energy spectrum image includes: applying a material decomposition algorithm to the energy spectrum image data in the projection domain to provide a first projection image representing the first material and a second projection image representing the second material; and fusing the first projection image and the second projection image to provide the energy spectrum image.

[0013] According to a fourth aspect of this disclosure, generating an energy spectrum image includes: reconstructing a first volume image representing the first material; reconstructing a second volume image representing the second material; and fusing the first volume image and the second volume image to provide the energy spectrum image.

[0014] According to a fifth aspect of this disclosure, generating an energy spectrum image includes: generating first image data representing the first material, and generating second image data representing the second material. Identifying the location of the first reference marker and / or the location of the second reference marker in the energy spectrum image includes applying a feature detection algorithm to the first image data and / or the second image data, respectively.

[0015] According to other aspects of this disclosure, related computer-implemented methods, computer-readable storage media, and computer program products are also provided. Features of the system disclosed may be incorporated into each of these aspects in a corresponding manner, and for the sake of brevity, the described features are not repeated for each aspect. According to other aspects of this disclosure, interventional instruments, kits comprising multiple interventional instruments, and implantable devices are also provided.

[0016] Other features and advantages of this disclosure will become apparent from the following description of preferred embodiments given by way of example only with reference to the accompanying drawings. Attached Figure Description

[0017] Figure 1 The illustration shows an energy spectrum X-ray imaging system 100 including an X-ray source 110, an X-ray detector 120, and a support structure 150, according to some aspects of the present disclosure.

[0018] Figure 2 This is a graph illustrating the correlation between the mass attenuation coefficient (MAC) and X-ray energy for two example materials (gadolinium and gold).

[0019] Figure 3 An example of an interventional instrument 210 in the form of an IVUS catheter, including a first reference marker 1801, is illustrated.

[0020] Figure 4 An example of an implantable device 220 in the form of a stent, including a first reference marker 1801 and a second reference marker 1802, is illustrated. Detailed Implementation

[0021] Figure 1 The illustration shows an energy-spectral X-ray imaging system 100 according to some aspects of this disclosure, including an X-ray source 110, an X-ray detector 120, and a support structure 150. The X-ray source 110 and the X-ray detector 120 are mounted to the support structure 150. The X-ray source 110 and the X-ray detector 120 are separated to provide an imaging region 160 therebetween. X-rays emitted by the X-ray source 110 are detected by the X-ray detector 120, the range of which is [missing information]. Figure 1 The unidirectional arrow indicates this. X-ray detector 110 receives X-rays that have passed through the imaging region 160 and measures their intensity. Any X-ray attenuating medium within the imaging region 160 will affect the measured intensity. In doing so, X-ray detector 120 generates data representing the attenuation of X-rays passing through the imaging region 160.

[0022] Figure 1The support structure 150 shown is a so-called "C-arm". A C-arm is a C-shaped example of a support structure used to support an X-ray source and an X-ray detector. Support structures with alternative shapes can also be used instead of the shown C-arm, such as an O-arm and a U-arm. The X-ray source 110 and the X-ray detector 120 are mounted to the support structure 150. The support structure 150 is movable, allowing the X-ray source 110 and the X-ray detector 120 to rotate about two or more orthogonal axes. For example, the support structure 150 can allow the X-ray source 110 and the X-ray detector 120 to rotate about axis A-A' and about axis B', as by... Figure 1 The corresponding arrows A and B indicate this. Axis B' points vertically into the plane of the attached figure. The support structure 150 also allows the X-ray source 110 and the X-ray detector 120 to revolve around a third axis ( Figure 1 The C-C' axis rotates, but this is not necessary. Axis A-A', B', and C-C' are in... Figure 1 The axes are illustrated as intersecting, but this is not necessary, and in some examples, the axes do not intersect. The support structure 150 may be provided with various bearings and / or movable joints and / or hinges and / or other movable couplers to provide the desired movement.

[0023] The mobility provided by the support structure 150 allows the orientation of the X-ray source 110 and X-ray detector 120 to change relative to the imaging region 160. In particular, the ability to rotate the X-ray source 110 and X-ray detector 120 about two or more orthogonal axes facilitates their use in interventional imaging procedures. Image data can be acquired using the X-ray source 110 and X-ray detector 120 with the source and detector in a desired stationary orientation relative to the imaging region 160. A live or single-projection image representing X-ray attenuation in the imaging region 160 can be generated from the image data. Alternatively, image data can be acquired while rotating the X-ray source 110 and X-ray detector 120 about axis A-A' or axis B'. The rotation can be continuous or stepped. The image data acquired in this manner can then be reconstructed into a tomographic image representing X-ray attenuation in the imaging region 160.

[0024] Typically, the imaging region 160 is large enough to accommodate the object to be imaged. The object may be, for example, a part of a human or animal body. In some examples, the imaging region 160 may accommodate the torso of a human. Various factors influence the size of the imaging region 160, including the spacing between the X-ray source 110 and the X-ray detector 120, the profile of the X-ray beam emitted by the X-rays, the shape of the X-ray detector, and the range of motion of the support structure 150. By appropriately adjusting these factors, the size and shape of the imaging region 160 can be defined.

[0025] Figure 1 The X-ray source 110 and X-ray detector 120 are configured to generate energy spectrum image data. The energy spectrum image data represents the attenuation of X-rays passing through an imaging region 160 between the X-ray source 110 and the X-ray detector 120 for each of three or more energy ranges of X-rays. The energy spectrum image data can be provided by various configurations of the X-ray source 110 and the X-ray detector 120. Typically, the X-ray source 110 may include one or more monochromatic or multicolor sources, and the X-ray detector 120 may include: a common detector for all X-ray energy ranges, or a multilayer detector, or a photon-counting detector. Multilayer detectors and photon-counting detectors provide X-ray energy range discrimination as described below. The X-ray source 110 can be controlled to emit X-rays in different X-ray energy ranges in a time-sequential manner.

[0026] A linear or two-dimensional array of detector elements is envisioned in X-ray detector 120. A linear array of detector elements can be used to generate spectral image data representing a tomographic image by continuous or stepped rotation of X-ray source 110 and X-ray detector 120 around imaging region 160, thereby generating spectral image data from multiple orientations relative to imaging region 160. The spectral image data can then be reconstructed into a tomographic image. A volumetric image can be generated from tomographic images acquired at different axial locations stacked within imaging region 160. A two-dimensional array of detector elements can be rotated in a similar manner to generate spectral image data representing a tomographic image or a volumetric image. Alternatively, a two-dimensional array of detector elements can be maintained in a stationary position relative to imaging region 160 to generate spectral image data representing a projected image. For example, during a C-arm fluoroscopy procedure, a two-dimensional array of detector elements in a stationary position can be used to generate a real-time or single projected image.

[0027] In some examples, the X-ray detector 120 is a scintillator detector. Scintillator detectors use a scintillator material such as gadolinium oxysulfide (GOS) to convert each received X-ray into a burst of light, which is then converted into an electrical signal using a photodetector. In other examples, the X-ray detector 120 is a so-called direct conversion detector. Compared to scintillator detectors, direct conversion detectors use materials such as CZT or CdTe to convert the received X-rays into a cloud of electron-hole pairs, thereby generating an electrical signal without the intermediate step of converting the X-rays into scintillating light. In some examples, scintillator detectors or direct conversion detectors are stacked along the direction in which the X-rays are received. In such stacked or “multilayer” detectors, the layer that detects each X-ray depends on its energy. Detector layers distinguish different energy ranges of X-rays, thus providing energy spectrum data about the received X-rays. Multilayer detectors are capable of simultaneously detecting X-rays from multiple X-ray energy ranges. In some examples, the X-ray detector 120 generates its electrical output by integrating the scintillating light or by integrating the electrical signal generated by the electron-hole pair cloud. In some examples, the X-ray detector 120 is a photon-counting detector. A photon-counting detector provides energy spectrum data about the received X-rays by binning each received X-ray photon into one of multiple energy ranges. The relevant energy range for each received X-ray photon is determined based on the pulse height caused by electron-hole pairs generated in response to its absorption in the direct-conversion material. Therefore, a photon-counting detector can detect X-rays from multiple X-ray energy ranges almost simultaneously.

[0028] In one example Figure 1 The X-ray source 110 is controlled by its X-ray tube potential to generate X-rays in each of three or more X-ray energy ranges. The X-ray tube potential is modulated between three different values ​​to generate X-rays in each X-ray energy range. Thus, X-rays in each X-ray energy range are generated in a time-sequential manner. This technique is called kVp switching. X-rays can be generated in different X-ray energy ranges by changing the tube potential and / or filtering the X-ray spectrum before it passes through the object. In this example, the corresponding X-ray detector 120 can be common to all X-ray energy ranges. The spectral image data for a specific X-ray energy range corresponds to the time at which X-rays are generated for that X-ray energy range. In this example, the corresponding detector could alternatively be a multilayer detector, or actually a photon-counting detector.

[0029] In another example, Figure 1The X-ray source 110 includes multiple X-ray sources controlled to emit X-rays sequentially within three or more X-ray energy ranges. The corresponding detector can be common to all X-ray energy ranges. The energy spectrum image data for a specific X-ray energy range corresponds to the time at which X-rays were generated for that energy range. In this example, the corresponding detector could alternatively be a multilayer detector, or in fact, a photon counting detector.

[0030] In another example, Figure 1 The X-ray source 110 includes one or more multicolor sources. These multicolor sources simultaneously generate X-rays with energies spanning three or more energy ranges. A single multicolor source can, for example, generate X-rays within three or more X-ray energy ranges distributed across a range of 30 keV to 120 keV. In this example, a multilayer detector or a photon counting detector is used to identify the energy spectrum image data for each X-ray energy range.

[0031] Other combinations of the aforementioned X-ray sources and detectors can obviously also be used to provide desired energy spectrum image data for three or more X-ray energy ranges.

[0032] Figure 1 The system 100 also includes one or more processors 130. Figure 1 Various items within the system communicate with each other, as indicated by the interconnecting arrows. Thus, one or more processors communicate with the X-ray source 110 and the X-ray detector 120. System 100 may also include one or more non-transient computer-readable storage media 140, a display 200, and devices such as a keyboard and / or mouse. Figure 1 A user input device (not shown in the figure). One or more non-transient computer-readable storage media 140 may jointly store instructions that, when executed by one or more processors 130, cause system 100 to perform various operations described in more detail below. In some examples, the user input device may be used to provide user input to system 100 in the form of instructions for performing operations. Display 200 may be used to provide images, display user input, etc.

[0033] In use, Figure 1The support structure 150 is moved to a desired orientation relative to the object within the imaging region 160 to execute the imaging procedure. The X-ray source 110 is controlled by one or more processors 130 to generate X-rays, as described in the example above. A corresponding X-ray detector 120 generates energy spectrum image data representing the attenuation of X-rays passing through the imaging region 160 between the X-ray source 110 and the X-ray detector 120 for each of three or more X-ray energy ranges. As described above, the energy spectrum image data can be acquired using the X-ray source 110 and X-ray detector 120 in a stationary orientation relative to the imaging region 160. With the X-ray source 110 and X-ray detector 120 in this position, a single-frame or live-projection image representing the X-ray attenuation in the imaging region 160 can be generated from the energy spectrum image data. Alternatively, the energy spectrum image data can be acquired while rotating the X-ray source 110 and X-ray detector 120 about axis A-A' or axis B'. The rotation can be continuous or stepped and is controlled by one or more processors 130. The energy spectrum image data acquired in this way can then be reconstructed into a tomographic image representing X-ray attenuation in the imaging region 160. The image (one or more images) can then be displayed on the display 200.

[0034] The inventor has determined, through the above regarding Figure 1 Appropriate processing of the energy spectrum image data generated by the described system 100 can identify the locations of reference markers in the energy spectrum image, including materials with X-ray absorption k-edge energy values. Figure 1 The processing performed by one or more processors 130 includes the following operations:

[0035] Generate energy spectrum images based on energy spectrum image data;

[0036] The location of the first reference marker 1801, including the first material, is identified in the energy spectrum image based on the first X-ray absorption k-edge energy value 1901 of the first material; and

[0037] The location of a second reference marker 1802, which includes the second material, is identified in the energy spectrum image based on a second X-ray absorption k-edge energy value 1902 of the second material, which is different from the first X-ray absorption k-edge energy value.

[0038] Because these operations are provided in a system 100 including a support structure 150 that allows the X-ray source 110 and the X-ray detector 120 to rotate about two or more orthogonal axes, the system 100 can be used to track a reference marker 180 in interventional imaging procedures. 1,2 The location.

[0039] Objects (e.g., including reference marker 180)1,2 Interventional instruments and implantable devices can therefore be reliably tracked using System 100. Additional operations can also be performed by... Figure 1 One or more processors 130 in the process execute, as described in more detail below.

[0040] According to this disclosure, energy spectrum image data is generated, and the positions of a first reference marker and a second reference marker are identified in the energy spectrum image, including a first material having a first X-ray absorption k-edge energy value and a second material having a different second X-ray absorption k-edge energy value. In this context, an energy spectrum image refers to an image that distinguishes between at least two materials using X-ray attenuation data from multiple X-ray energy ranges. In some examples, the energy spectrum image distinguishes between more than two materials; for example, it can distinguish between three or more materials. According to this disclosure, the first material is provided by the first reference marker, and the second material is provided by the second reference marker.

[0041] In one example, another material may be distinguishable in the energy dispersive spectroscopy (EDS) image. This other material is a composite body material comprising multiple materials commonly found in the human body. These multiple materials may include one or more of the following: bone, (soft) tissue, water, air, metals, contrast agents, etc. Therefore, in this example, the materials of the first and second reference markers are distinguishable from the composite body material in the EDS image. In another example, the other material is a more specific material within the composite body material, such as (soft) tissue, bone, water, air, contrast agents, metals, etc. The other material may also be classified according to a specific pathological condition, such as (e.g., breast or lung) tumor tissue, vascular plaque, kidney stones, etc. Therefore, in these examples, the first material of the first reference marker and the second material of the second reference marker can be distinguished from, for example, breast tumor tissue in the EDS image.

[0042] In another example, the second material has a second X-ray absorption k-edge energy value 1902, and based on this second X-ray absorption k-edge energy value 1902, the location of a second reference marker 1802 comprising the second material is identified in the energy spectrum image. The second X-ray absorption k-edge energy value 1902 of the second material differs from the first X-ray absorption k-edge energy value 1901 of the first material. In this example, the first material of the first reference marker and the second material of the second reference marker are distinguished in the energy spectrum image.

[0043] In any of these examples, an energy dispersive spectral image can distinguish a third material and another material (such as the example material mentioned above) from the first and second materials. For example, an energy dispersive spectral image can distinguish between a first material and a second material, a third material such as bone, and a fourth material such as tissue. Typically, generating an energy dispersive spectral image may include shading, color coding, segmenting, or labeling portions of the energy dispersive spectral image according to the material represented. Other techniques for identifying different materials in an energy dispersive spectral image may also be used.

[0044] Various techniques can be used to generate energy spectrum images. Typically, the X-ray attenuation spectrum of a material includes contributions from Compton scattering and contributions from the photoelectric effect. While the attenuation caused by Compton scattering is relatively similar across different materials, the attenuation from the photoelectric effect is strongly material-dependent. Both Compton scattering and the photoelectric effect exhibit energy dependence; this is utilized in energy spectrum X-ray CT imaging systems to differentiate between different materials. Materials with k-edge energies exhibit a sharp increase in their X-ray attenuation spectrum at X-ray energies corresponding to those k-edge energies. The k-edge energy is defined as the minimum energy required for a photoelectric event to occur with k-shell electrons and occurs at the characteristic energies of each material. Materials with k-edge energies within the range of X-ray energies used in diagnostic X-ray imaging (i.e., approximately 30–120 keV) are suitable for use in system 100. For example, metals such as gadolinium, gold, platinum, tantalum, and holmium all have k-edge energies within this range. By including such materials in the reference markers, the presence of these materials, and therefore the location of the reference markers, can be distinguished from other materials in the energy spectrum image generated by system 100.

[0045] Figure 2 This is a graph illustrating the correlation between the mass attenuation coefficient (MAC) and X-ray energy for two example materials (gadolinium and gold). The X-ray energy at... Figure 2 The value is denoted by E and measured in kiloelectron volts (keV). These example materials have characteristic k-edge energies of 50.2 keV and 80.7 keV, respectively, which leads to a sharp increase in their mass decay coefficients at k-edge values ​​of 1901 and 1902. Platinum at 78.4 keV, tantalum at 67.4 keV, and holmium at 55.6 keV exhibit similar characteristics to... Figure 2 The k-edge energy values ​​shown are different from the k-edge energy values ​​shown, and they also exhibit a sharp increase in their mass decay coefficient.

[0046] Figure 2 The diagram also illustrates multiple X-ray energy ranges within which energy spectrum X-ray image data can be generated 170. 1..n .exist Figure 2The example shown illustrates five X-ray energy ranges. Typically, in examples according to this disclosure, energy spectrum X-ray image data can be generated within three or more X-ray energy ranges. Figure 2 As shown, in some examples, the X-ray energy range is 170. 1..n One or more of these values ​​can be higher than the X-ray absorption k-edge energy values ​​of 1901, 1902 of the material to be detected, and the X-ray energy range is 170. 1..n One or more of these can be below the X-ray absorption k-edge energy value. Therefore, note that the energy range can have the same energy as... Figure 2 The energy ranges shown are different energy ranges, and the energy ranges can be discontinuous and can overlap.

[0047] In one example technique, a material decomposition algorithm is applied to energy spectrum image data to generate a projection image. In this example, generating the energy spectrum image includes:

[0048] In the projection domain, a material decomposition algorithm is applied to energy spectrum image data to provide a first projection image representing a first material and a second projection image representing a second material; and

[0049] The first projection image and the second projection image are fused to provide an energy spectrum image.

[0050] In this example technology, Figure 1 The support structure 150 is held in a stationary position during the generation of energy spectrum image data. The energy spectrum image data can be acquired using a two-dimensional array of detector elements. Single or live X-ray projection images can be generated in this manner. An example material decomposition algorithm and energy range selection for this purpose are disclosed in the article “Empirical, projection-based basis-component decomposition method” by Brendel, B. et al. (Medical Imaging 2009, Physics of Medical Imaging, edited by Ehsan Samei and Jiang Hsieh, Proc. of SPIE Vol. 7258, 72583Y).

[0051] In one example, the projected image can be generated, for instance, using the aforementioned technique with five energy ranges, to decompose the energy spectrum image data into four separate materials. The four materials include soft tissue and water (i.e., two materials commonly found in the human body) and two materials (gadolinium and gold) with different k-edge values.

[0052] Material decomposition algorithms using fewer than five energy ranges can also be used. In practice, energy spectrum imaging requires three or more energy ranges to decompose the energy spectrum image into its photoelectric, Compton, and k-edge contributions, thereby distinguishing materials with k-edge energy values ​​from body materials (such as bone, (soft) tissue, water, air, metals, contrast agents, etc.) that are typically present in X-ray images of human anatomy.

[0053] Image fusion can be performed by combining spatially corresponding pixel values ​​in an image (e.g., by overlaying the image with controlled transparency).

[0054] In one example, a material decomposition algorithm is selectively applied to energy spectrum image data to generate a projected image. In this example, a live stream of projected images is generated by system 100, including a current projected image and subsequent projected images. The material decomposition algorithm is applied to the energy spectrum image data for the current projected image to identify the location of a first reference marker in the current projected image, and is selectively applied to the energy spectrum image data for subsequent projected images to provide subsequent projected images by processing the region surrounding the expected location of the reference marker in the subsequent projected image. This selective processing can be used to alleviate the processing burden, for example, during fluorescence fluoroscopy imaging.

[0055] In other example techniques, volumetric images are generated. Energy spectrum image data can be acquired using a two-dimensional array of detector elements. In these examples, generating energy spectrum images includes:

[0056] Reconstruct a first volume image representing the first material;

[0057] Reconstruct a second volume image representing the second material; and

[0058] The first volume image and the second volume image are fused to provide an energy spectrum image.

[0059] In these example techniques, by making the support structure 150 surround Figure 1 Rotation along axis A-A' or axis B' generates spectral image data from multiple orientations relative to the imaging region 160°. The rotation can be continuous or stepped. The spectral images generated in this way can be reconstructed as tomographic or volumetric images and aid in the differentiation between reference markers that might otherwise overlap on the projected image. Implantable devices such as biopsy markers or attached brachytherapy seeds, or interventional devices such as guidewires incorporating such reference markers, can be more easily distinguished in such images, for example. Image fusion can be performed by combining spatially corresponding voxel values ​​in the images (e.g., by overlaying the image with controlled transparency).

[0060] In these examples, the step of fusing volumetric images may also include:

[0061] The first and second volume images are projected forward to provide an energy spectrum image as the projection image.

[0062] Forward projection may include projecting an image forward onto a plane parallel to the X-ray detector 120 or another plane. Discrimination provided by the k-edge energy values ​​of one or more reference markers allows for differentiation between reference markers that may otherwise overlap in the projected image.

[0063] Various image reconstruction techniques for reconstructing volumetric images are envisioned.

[0064] In one example technique, generating an energy spectrum image includes:

[0065] In the projection domain, a material decomposition algorithm is applied to energy spectrum image data to provide first sine wave data representing a first material and second sine wave data representing a second material;

[0066] Reconstruct the first volume image based on the first sine wave data;

[0067] Reconstruct the second volume image based on the second sine wave data;

[0068] Furthermore, the reconstruction of the first volumetric image and the reconstruction of the second volumetric image include applying a filtered back projection algorithm to the first sine wave data and the second sine wave data, respectively.

[0069] The material decomposition algorithms mentioned above can be used in conjunction with any of these techniques to distinguish different materials. In one example implementation, the location of a platinum-coated scaffold is identified. In this implementation, a volumetric image is generated using five energy ranges of photon count data as input to a maximum likelihood material decomposition algorithm in the projection domain to identify, for each pixel, the attenuation length with the highest probability through the three materials (water, iodine, and platinum) under a Poisson noise model. Subsequently, the three material sine maps are reconstructed separately using a filtered back-projection algorithm. One of the resulting images is a material-selective platinum image, i.e., a k-edge image, where the platinum coating of the scaffold is separated from the materials water and iodine present in typical contrast-enhanced X-ray images of human anatomy.

[0070] In another example technique, generating an energy spectrum image includes:

[0071] For multiple energy ranges 170 1..n Reconstruct energy channel images for each energy range; and

[0072] The first volume image and the second volume image are generated based on the reconstructed energy channel image using a material decomposition algorithm;

[0073] Furthermore, the generation of the first volumetric image and the second volumetric image is based on first calibration data representing the attenuation of X-rays by the first object comprising the first material and second calibration data representing the attenuation of X-rays by the second object comprising the second material, and wherein the first object and the second object are positioned at known locations in the imaging region 160.

[0074] The material decomposition algorithm mentioned above can also be used here to distinguish between different materials. In one example, calibration data is provided by placing samples of the first and second materials on a patient support tray or the surface of the patient's body. Since the locations of the first and second objects are known, their corresponding energy dispersive spectral image data can be identified and used to provide calibration data.

[0075] In another example technique, generating an energy spectrum image includes:

[0076] An iterative one-step inversion algorithm is used to simultaneously reconstruct the first volume image and the second volume image.

[0077] Example reconstruction algorithms and energy range selections for this purpose are disclosed in Mory, C. et al.’s paper entitled “Comparison of five one-step reconstruction algorithms for spectral CT” (Physics in Medicine and Biology, IOP Publishing, 2018, 63(23), pp. 235001).

[0078] Whether generating a projected image or a volumetric image, the system 100 can also perform other operations, as described below.

[0079] In some examples, feature detection algorithms are used to identify the location of the first reference marker 1801 and / or the location of the second reference marker 1802. In these examples, generating the energy spectrum image includes:

[0080] Generate first image data representing a first material, and generate second image data representing a second material; and

[0081] The process of identifying the position of the first reference marker 1801 and / or the position of the second reference marker 1802 in the energy spectrum image includes applying a feature detection algorithm to the first image data and / or the second image data, respectively.

[0082] The first image data may represent a first projected image or a first volumetric image, and the second image data may represent a second projected image or a second volumetric image. Various feature detection algorithms are envisioned for use in these examples. In one example, applying a feature detection algorithm to the first image data and / or the second image data includes:

[0083] Analyze the first data and / or the second image data to determine the location in the respective energy spectrum image corresponding to the maximum image intensity in the first image data and / or the second image data.

[0084] Using maximum intensity in this way provides an accurate indication of the marked location.

[0085] In another example, applying a feature detection algorithm to the first image data and / or the second image data includes:

[0086] Analyze the first image data and / or the second image data to determine the positions in the energy spectrum image that correspond to predetermined image intensity patterns in the first image data and / or the second image data, respectively.

[0087] In this example, the predetermined image intensity pattern corresponds to the expected pattern of the reference marker. For example, if the reference marker(s) are provided from a first or second material in the form of a line or disc having a circular shape, the expected pattern would be circular. Reference markers with different shapes can be identified in a similar manner. Similarly, if the reference marker is provided in the form of multiple elements formed from a first or second material, the expected pattern of the multiple elements will be used.

[0088] In another example, applying a feature detection algorithm to the first image data and / or the second image data includes:

[0089] Analyze the first image data and / or the second image data to determine the position and / or orientation of the interventional instrument or implantable device in the energy spectrum image based on a model representing the X-ray attenuation of the interventional instrument or implantable device, which respectively includes the first reference marker 1801 and the second reference marker 1802.

[0090] In this example, the model can represent the shape of one or more reference markers. For example, the markers can be provided in the form of multiple platinum wires that together form part or all of a cardiovascular stent. In this case, the model can represent the expected X-ray attenuation in a platinum-specific energy spectrum image and optionally the attenuation that can be expected in other material-specific images. By analyzing the image data to determine a match with the model, the location and, optionally, the spatial orientation of the reference markers in the energy spectrum image can be determined.

[0091] The aforementioned reference markers 1801 and 1802 can be provided in various forms and attached to various objects. The reference markers can be provided in any shape. For example, they can be provided as cylindrical, spherical, spiral, disc-shaped, or another shape. The reference markers or portions thereof can be formed of or coated with a material having a relevant k-edge energy value. In one example, the reference markers can be plated with gold or platinum. The reference markers can be implantable or attachable to a body surface.

[0092] In some examples, one or more reference markers 1801, 1802 are provided on the interventional instrument. The interventional instrument can be connected to... Figure 1 It is used in conjunction with System 100 or a spectral X-ray CT imaging system. Typically, the location of interventional instruments can be difficult to determine under X-ray imaging. When formed of metal, the appearance of the interventional instrument may be obscured by other strongly X-ray attenuating media (such as bone). This problem is particularly acute during projection imaging. When formed of polymer, the interventional instrument may be invisible under X-ray imaging. Figure 3 An example of an interventional instrument 210 in the form of an IVUS catheter, including a first reference marker 1801, is illustrated. (Reference) Figure 3 The interventional instrument 210 includes at least one reference marker. The at least one reference marker includes a first reference marker 1801, which comprises a first material having a first X-ray absorption k-edge energy value 1901. The first reference marker 1801 may, for example, be a platinum coating applied to a portion of the axis of an IVUS catheter. The interventional instrument 210 may include one or more additional reference markers.

[0093] In a particular embodiment, the interventional instrument 210 includes a second reference marker 1802 comprising a second material having a second X-ray absorption k-edge energy value 1901. The second material and the second X-ray absorption k-edge energy value 1902 differ from a first material and a first X-ray absorption k-edge energy value 1901, thus allowing for differentiation between the marker and its position on the interventional instrument 210. Consequently, the instrument can be more easily located and / or its orientation can be determined.

[0094] In another example, the interventional instrument 210 may include a plurality of first reference markers 1801 and / or a plurality of second reference markers 1802. By providing one or more reference markers on the interventional instrument 210, its visibility in the spectral image can be improved. The reference markers may be attached to other interventional instruments besides the example IVUS catheter, such as catheters, guidewires, balloons (such as angioplasty balloons or cutting balloons), atherosclerotic devices, thrombectomy systems, atrial appendage closure devices, aortic valve placement systems, or to instruments used in fractional flow reserve (FFR) measurements, optical coherence tomography (OCT) imaging instruments, near-infrared spectroscopy (NIRS) imaging systems, etc.

[0095] Interventional instruments including one or more reference markers may also be provided as a kit. The kit may include a first interventional instrument 210 and a second interventional instrument. This kit can be used with... Figure 1 The system 100 or a spectral X-ray CT imaging system can be used together. In this kit, the first interventional instrument 210 includes a first reference marker 1801 comprising a first material having a first X-ray absorption k-edge energy value 1901, and the second interventional instrument includes a second reference marker 1802 comprising a second material having a second X-ray absorption k-edge energy value 1902. The interventional instruments from the kit can be used together during imaging procedures and can be distinguished from each other by means of their reference markers. The kit may, for example, include two or more intravascular catheters.

[0096] Reference markers 1801 and 1802 may alternatively be provided on the implantable device. The implantable device can be used with... Figure 1 It can be used with either System 100 or the spectral X-ray CT imaging system. Figure 4 An example of an implantable device 220 in the form of a stent, including a first reference marker 1801 and a second reference marker 1802, is illustrated. Typically, the implantable device 220 may include multiple reference markers: a first reference marker 1801 including a first material having a first X-ray absorption k-edge energy value 1901 and a second reference marker 1802 including a second material having a second X-ray absorption k-edge energy value 1902.

[0097] By providing multiple reference markers with different k-edge energy values ​​in an implantable device such as a stent, the orientation of the implantable device can be determined in an energy spectrum image. The reference markers can be attached to the stent or, alternatively, formed part of the stent, as in... Figure 4As indicated by reference markers 1801 and 1802. For example, reference markers 1801 and 1802 may be provided in the form of, for example, platinum wire within the support structure or a platinum coating on the support. In one example, and as... Figure 4 As shown, reference marker 1801 can be attached to the proximal end of the vascular stent, and another reference marker 1802 can be attached to the distal end of the vascular stent. Attaching multiple reference markers with different k-edge energy values ​​to an implantable device such as a stent in this manner can help distinguish overlapping stents in a projected image and also helps identify each end of the stent. It can also help distinguish between a stent currently being implanted and a previously implanted stent.

[0098] Benchmark markers can be attached to other implantable devices besides the example stents given above. For example, they can be attached to biopsy markers, brachytherapy seeds, pacemaker leads, heart valve replacements, ventricular assist devices, wireless heart monitors, intravascular defibrillators, neurostimulators, brain-computer interfaces, drug delivery injectors, etc. Biopsy markers are typically the size of a sesame seed and are used to mark the location of a tissue sample, for example, in breast cancer diagnosis. By providing biopsy markers with different k-edge materials or different spectral attenuations, biopsy markers can be better distinguished from other biopsy markers that are spatially close but have different properties (such as placement time, radiation level, etc.). By providing brachytherapy seeds with such benchmark markers, their location, placement time, and radiation level can be better differentiated. Pacemaker leads are typically left in the body and are not removed during pacemaker removal or replacement because lead removal is a complex surgical procedure. Leads can remain permanently attached to the heart. Providing a benchmark for pacemaker leads improves the distinction between currently implanted leads and previously implanted leads.

[0099] In another example, a computer-implemented method is provided. This computer-implemented method can be used with the system 100 described above and therefore can include functions corresponding to those described above with respect to system 100. For the sake of brevity, not all details of system 100 are repeated here with respect to this method. The method can be provided as a non-transient computer-readable storage medium comprising a set of 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 above method can be implemented as a computer program product. This computer program product can be provided by dedicated hardware or hardware capable of running the software in association with appropriate software. When provided by a processor, these functions can be provided by a single dedicated processor, a single shared processor, or multiple separate processors that can be shared by some processors. Furthermore, the explicit use of the terms "processor" or "controller" should not be construed as exclusively referring to hardware capable of running software and may implicitly include, but is not limited to, digital signal processor "DSP" hardware, read-only memory "ROM" for storing software, random access memory "RAM", non-volatile storage devices, etc. Furthermore, examples of this disclosure may take the form of a computer program product accessible from a computer-usable storage medium or a computer-readable storage medium, which provides program code for use by or in conjunction with a computer or any instruction execution system. For the purposes of this description, a computer-usable storage medium or a computer-readable storage medium may be any means that may include, store, transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. The medium may be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system or device or propagation medium. Examples of computer-readable media include semiconductor or solid-state memory, magnetic tape, removable computer disk, random access memory “RAM”, read-only memory “ROM”, rigid disk, and optical disc. Current examples of optical discs include compressed disc-read-only memory “CD-ROM”, optical disc-read / write “CD-R / W”, Blu-ray™, and DVD.

[0100] Therefore, a method is provided for three or more energy ranges 170 1..n A computer-implemented method for processing energy spectrum image data representing the attenuation of X-rays passing through an imaging region 160 between X-ray source 110 and X-ray detector 120 in each energy range. This method can be used with system 100 and includes:

[0101] An energy spectrum image is generated based on the energy spectrum image data; and

[0102] Based on the first X-ray absorption k-edge energy value 1901 of the first material, the location of the first reference marker 1801 including the first material is identified in the energy spectrum image.

[0103] Other operations described with respect to system 100 can also be provided by this method. For example, the computer-implemented method may also include applying a material decomposition algorithm to the energy spectrum image data to provide a projected image, and the volumetric image reconstruction operation described above.

[0104] A non-transient computer-readable storage medium is also provided. This non-transient computer-readable storage medium is encoded to be executable by one or more processors 130 for use with three or more energy ranges 170. 1..n The instructions in the computer-readable storage medium, which process energy spectrum image data representing the attenuation of X-rays passing through the imaging region 160 between the X-ray source 110 and the X-ray detector 120, are for processing each energy range. The computer-readable storage medium can be used to process the energy spectrum image data generated by the system 100 and includes instructions for performing operations, including:

[0105] An energy spectrum image is generated based on the energy spectrum image data; and

[0106] Based on the first X-ray absorption k-edge energy value 1901 of the first material, the location of the first reference marker 1801 including the first material is identified in the energy spectrum image.

[0107] A computer program product is also provided. This computer program product includes instructions that, when executed by a processor (such as processor 130 of system 100), cause the processor to perform a method comprising:

[0108] For three or more energy ranges 170 1..n Each energy range receives energy spectrum image data representing the attenuation of X-rays passing through the imaging region 160 between the X-ray source 110 and the X-ray detector 120.

[0109] An energy spectrum image is generated based on the energy spectrum image data; and

[0110] Based on the first X-ray absorption k-edge energy value 1901 of the first material, the location of the first reference marker 1801 including the first material is identified in the energy spectrum image.

[0111] Other operations described with respect to system 100 may also be provided by instructions of a computer program product or by instructions of a non-transient computer-readable storage medium.

[0112] The examples described above should be understood as illustrative examples of this disclosure. Other examples are also contemplated. For example, the examples described with respect to system 100 may also be provided by a computer-implemented method or by a computer program product or by a computer-readable storage medium. Therefore, it should be understood that the features described with respect to any example may be used alone or in combination with other features described, and may also be used in combination with one or more features of another example, or a combination of other examples. Furthermore, equivalent schemes and modifications not described above may be employed without departing from the scope of this disclosure as defined in the appended claims. No reference numerals in the claims should be construed as limiting the scope of this disclosure.

Claims

1. A spectral X-ray imaging system (100), comprising: an X-ray source (110); an X-ray detector (120); a support structure (150); and one or more processors (130); wherein the support structure (150) is configured to rotate the X-ray source (110) and the X-ray detector (120) around two or more orthogonal axes (A-A', B-B'); and wherein the X-ray source (110) and the X-ray detector (120) are mounted to the support structure and configured to generate spectral image data representative of an attenuation of X-rays passing through an imaging region (160) between the X-ray source (110) and the X-ray detector (120) for each of three or more energy intervals (170 1…n ) of X-rays; wherein the one or more processors (130) are configured to cause the system (100) to perform operations comprising: generating a spectral image based on the spectral image data; identifying a location of a first fiducial marker (1801) comprising a first material in the spectral image based on a first X-ray absorption k-edge energy value (1901) of the first material, and identifying a location of a second fiducial marker (1802) comprising a second material in the spectral image based on a second X-ray absorption k-edge energy value (1902) of the second material, the second X-ray absorption k-edge energy value being different from the first X-ray absorption k-edge energy value. the spectral image distinguishes between the first material and the second material and at least one other material, and wherein the other material comprises: tissue, bone, water, air, contrast agent, or metal.

2. The spectral X-ray imaging system of claim 1, wherein, generating a spectral image comprises:

3. The spectral X-ray imaging system of claim 1 or 2, wherein, applying a material decomposition algorithm to the spectral image data in projection domain to provide a first projection image representing the first material and a second projection image representing a second material; and fusing the first projection image and the second projection image to provide the spectral image. generating a spectral image comprises:

4. The spectral X-ray imaging system of claim 1 or 2, wherein, reconstructing a first volume image representing the first material; reconstructing a second volume image representing a second material; and fusing the first volume image and the second volume image to provide the spectral image. the fusing comprises forward projecting the first volume image and the second volume image to provide the spectral image as a projection image.

5. The spectral X-ray imaging system of claim 4, wherein, generating a spectral image comprises:

6. The spectral X-ray imaging system of claim 4, wherein, applying a material decomposition algorithm to the spectral image data in projection domain to provide first sinogram data representing the first material and second sinogram data representing the second material; reconstructing the first volume image from the first sinogram data; reconstructing the second volume image from the second sinogram data; and wherein reconstructing the first volume image and reconstructing the second volume image comprise applying a filtered back-projection algorithm to the first sinogram data and the second sinogram data, respectively. generating a spectral image comprises:

7. The spectral X-ray imaging system of claim 4, wherein, generating the first volume image and the second volume image from the reconstructed energy channel images using a material decomposition algorithm; generating an energy channel image for each of the three or more energy intervals (170 1…n ) and ​ And wherein generating the first volume image and the second volume image is based on first calibration data and second calibration data, the first calibration data representing an attenuation of the X-rays by a first object comprising the first material, the second calibration data representing an attenuation of the X-rays by a second object comprising the second material, and wherein the first object and the second object are arranged in known positions in the imaging region (160).

8. The spectral X-ray imaging system of claim 4, wherein, Generating the spectral image comprises: simultaneously reconstructing the first volume image and the second volume image using an iterative one-step inversion algorithm.

9. The spectral X-ray imaging system of claim 1 or 2, wherein, Generating the spectral image comprises generating first image data representing the first material and generating second image data representing a second material; and wherein identifying the position of the first fiducial marker (1801) and the position of the second fiducial marker (1802) in the spectral image comprises applying a feature detection algorithm to the first image data and the second image data, respectively.

10. The spectral X-ray imaging system of claim 9, wherein, Applying a feature detection algorithm to the first image data and / or the second image data comprises at least one of: analyzing the first image data and / or the second image data to determine a position in the spectral image corresponding to a maximum image intensity in the first image data and / or the second image data, respectively; And analyzing the first image data and / or the second image data to determine a position in the spectral image corresponding to a predetermined image intensity pattern in the first image data and / or the second image data, respectively.

11. The spectral X-ray imaging system of claim 9, wherein, Applying a feature detection algorithm to the first image data and / or the second image data comprises analyzing the first image data and the second image data to determine a position and / or orientation of an interventional instrument or implantable device comprising the first fiducial marker (1801) and the second fiducial marker (1802), respectively, in the spectral image based on a model representing X-ray attenuation of the interventional instrument or the implantable device.

12. The spectral X-ray imaging system of claim 1 or 2, wherein, The one or more processors (130) are further configured to cause the system (100) to perform tracking of the positions of the first fiducial marker (1801) and the second fiducial marker (1802) in an interventional imaging procedure.

13. A computer-implemented method of processing spectral image data representing attenuation of X-rays through an imaging region (160) between an X-ray source (110) and an X-ray detector (120) for each of three or more energy intervals (170 1…n ) of X-ray energies; the method comprising: generating a spectral image based on the spectral image data; identifying a position of a first fiducial marker (1801) comprising a first material in the spectral image based on a first X-ray absorption k-edge energy value (1901) of the first material, and identifying a position of a second fiducial marker (1802) comprising a second material in the spectral image based on a second X-ray absorption k-edge energy value (1902) of the second material, the second X-ray absorption k-edge energy value being different from the first X-ray absorption k-edge energy value.

14. An interventional instrument (210) for use with the system of any of claims 1-11, the interventional instrument comprising at least two fiducial markers, at least one fiducial marker comprising a first fiducial marker (1801) comprising a first material having a first X-ray absorption k-edge energy value (1901) and a second fiducial marker (1802) comprising a second material having a second X-ray absorption k-edge energy value (1902) different from the first X-ray absorption k-edge energy value.

15. A kit comprising a first interventional instrument (210) and a second interventional instrument for use with the system of any of claims 1-11; wherein the first interventional instrument (210) comprising a first fiducial marker (1801) comprising a first material having a first X-ray absorption k-edge energy value (1901), and wherein the second interventional instrument comprises a second fiducial marker (1802) comprising a second material having a second X-ray absorption k-edge energy value (1902) different from the first X-ray absorption k-edge energy value.

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