Method and system for coincidence detection in an x-ray detector

Abstract

An X-ray detector system (5) is provided, comprising a photon counting X-ray detector (20) for detecting X-ray radiation from an X-ray source, and a coincidence detection system (60) configured to determine and / or obtain information about the radiation incident on the X-ray detector based on information about times of photon interactions in the X-ray detector and information about a positioning of the X-ray source relative to the X-ray detector. An X-ray imaging system comprising such an X-ray detector system is also provided, as well as a corresponding coincidence detection system and a corresponding method.

Description

Technical Field

[0001] The proposed technologies relate to X-ray imaging and X-ray detectors, and more specifically to photon counting X-ray detectors and X-ray detector systems and coincidence detection systems, as well as corresponding methods and systems, X-ray imaging systems, computer programs and computer program products. Background Technology

[0002] Radiographic imaging (such as X-ray imaging) has been used for non-destructive testing in medical applications for many years.

[0003] Typically, an X-ray imaging system consists of an X-ray source and an X-ray detector system. The X-ray source emits X-rays, which pass through the object or structure to be imaged and are then recorded by the X-ray detector system. Because some materials absorb a larger portion of the X-rays than others, an image of the object or structure is formed.

[0004] refer to Figure 1 It may be helpful to first provide a brief, illustrative overview of the overall X-ray imaging system. In this non-limiting example, the X-ray imaging system 100 essentially includes an X-ray source 10, an X-ray detector 20 or an X-ray detector system, and an associated image processing device 30. Generally, the X-ray detector 20 is configured to record radiation from the X-ray source 10, which may have been focused by optional X-ray optics and has passed through an object, or part thereof. The X-ray detector 20 may be connected to the image processing device 30 via suitable analog processing and readout electronics (which may be integrated into the X-ray detector 20) to enable the image processing device 30 to perform image processing and / or image reconstruction.

[0005] There is a general need for improvements in the performance of X-ray imaging systems and X-ray detector systems.

[0006] For example, it might be desirable to improve the signal-to-noise ratio and spectral performance of a photon-counting X-ray detector.

[0007] For example, it is also desirable to obtain and / or determine useful information about the radiation incident on the X-ray detector. Summary of the Invention

[0008] One objective is to provide an improved X-ray detector system.

[0009] Another objective is to provide an improved X-ray imaging system.

[0010] Another objective is to provide a method for obtaining or determining information about radiation incident on an X-ray detector.

[0011] Another objective is to provide an improved compliance detection system.

[0012] These and other objectives can be achieved through one or more implementations of the proposed technology.

[0013] According to a first aspect, an X-ray detector system is provided, the X-ray detector system comprising:

[0014] - A photon-counting X-ray detector used to detect X-ray radiation from an X-ray source; and

[0015] - A coincidence detection system configured to determine and / or obtain information about the radiation incident on the X-ray detector based on information about the timing of photon interactions in the X-ray detector and information about the positioning of the X-ray source relative to the X-ray detector.

[0016] According to the second aspect, an X-ray imaging system including such an X-ray detector system is provided.

[0017] According to a third aspect, a method is provided for obtaining or determining information about radiation incident on an X-ray detector. The method includes the following steps:

[0018] • Use a photon-counting X-ray detector to detect X-ray radiation, wherein the photon-counting X-ray detector is configured to operate with a wide energy X-ray spectrum emitted from a local X-ray source with a maximum energy of less than 160 keV;

[0019] • Record the timing information of photon interactions in the photon counting X-ray detector;

[0020] as well as

[0021] Based on the timing information and information about the positioning of the X-ray source relative to the X-ray detector, obtain or determine information about the radiation incident on the X-ray detector, including at least one of the number of incident photons in a specific region, the spatial distribution of incident photons, and the energy distribution of incident photons.

[0022] According to a fourth aspect, a coincidence detection system is provided, configured to operate in conjunction with a photon-counting X-ray detector. The coincidence detection system is configured to determine and / or obtain information about radiation incident on the X-ray detector based on information about the timing of photon interactions within the X-ray detector and information about the positioning of the X-ray source relative to the X-ray detector.

[0023] In this way, useful improvements are provided regarding X-ray imaging and / or detector technologies.

[0024] For example, it can significantly improve the signal-to-noise ratio and spectral performance of photon-counting X-ray detectors. Attached Figure Description

[0025] The embodiments described herein, together with their further objectives and advantages, can be best understood by referring to the following description in conjunction with the accompanying drawings, wherein:

[0026] Figure 1 This is a schematic diagram illustrating an example of an overall X-ray imaging system.

[0027] Figure 2A This is a schematic diagram illustrating another example of an X-ray imaging system.

[0028] Figure 2B This is a schematic diagram illustrating an example of an X-ray detector system based on the proposed technology.

[0029] Figure 2C This is a schematic diagram illustrating an example of a specific, non-limiting implementation of a conformity detection system in a digital processing circuit system.

[0030] Figure 3 This is a schematic diagram illustrating examples of the energy spectra of three different X-ray tube voltages.

[0031] Figure 4 This is a schematic diagram illustrating an example of a conceptual structure for implementing an energy-discriminative photon counting detector.

[0032] Figure 5 This is a schematic diagram of an X-ray detector according to an exemplary embodiment.

[0033] Figure 6 This is a schematic diagram illustrating an example of a semiconductor detector module according to an exemplary embodiment.

[0034] Figure 7 This is a schematic diagram illustrating another example of an X-ray detector submodule according to an exemplary implementation.

[0035] Figure 8 This is a schematic diagram illustrating an example of a modular X-ray detector, which includes, for example, multiple detector sub-modules arranged side-by-side in a slightly curved overall geometry relative to an X-ray source located at the X-ray focal point.

[0036] Figure 9 This is a schematic diagram illustrating an example of a modular X-ray detector comprising multiple detector sub-modules arranged side-by-side and stacked sequentially.

[0037] Figure 10This is a schematic diagram illustrating an example of a photon-counting X-ray detector based on multiple X-ray detector sub-modules 21, referred to herein as wafers.

[0038] Figure 11 This is a schematic diagram illustrating the Compton effect.

[0039] Figure 12 This is a schematic diagram illustrating an example of the deposition energy spectrum of the Compton and photoelectric portions of the interaction spectrum.

[0040] Figure 13 This is a schematic diagram illustrating an example of interaction over a certain time interval. The black lines represent interactions belonging to the same incident photon.

[0041] Figure 14 This is a schematic diagram illustrating an example of the 1D scattering distance of the photoelectric interaction chain 1 Compton+1.

[0042] Figure 15 This is a schematic diagram illustrating an example of the incident photon energy spectrum of different interacting chains.

[0043] Figure 16 This is a schematic diagram showing an example of pixels of a specific wafer in the xz plane.

[0044] Figure 17 This is a schematic diagram illustrating an example of the charge cloud distribution in the x-direction.

[0045] Figure 18 This is a schematic diagram illustrating an example of the charge cloud distribution in the z-direction.

[0046] Figure 19 This is a schematic diagram illustrating an example of how the width of charge diffusion or cloud depends on the distance from the initial interaction point to the detection point along the thickness of the wafer of the detector submodule or X-ray detector under consideration.

[0047] Figure 20 This is a schematic diagram illustrating an example of an X-ray detector submodule according to an implementation scheme.

[0048] Figure 21 This is a schematic diagram illustrating another example of an X-ray detector submodule according to an implementation scheme.

[0049] Figure 22 This is a schematic diagram illustrating an example of an active integrated pixel according to an implementation scheme.

[0050] Figure 23 This is a schematic diagram illustrating another example of an active integrated pixel according to another implementation scheme.

[0051] Figure 24This is a schematic diagram illustrating yet another example of an active integrated pixel according to a different embodiment.

[0052] Figure 25 This is a schematic diagram illustrating yet another example of an active integrated pixel according to yet another implementation scheme.

[0053] Figure 26 This is a schematic diagram illustrating an example of a computer implementation according to an embodiment.

[0054] Figure 27 This is a schematic flowchart illustrating an example of a method for obtaining or determining information about radiation incident on an X-ray detector. Detailed Implementation

[0055] To better understand, it may be helpful to continue to describe, introductory, non-limiting examples of overall X-ray imaging systems.

[0056] Figure 2A This is a schematic diagram illustrating an example of an X-ray imaging system 100, which includes: an X-ray source 10 that emits X-rays; an X-ray detector 20 that detects X-rays after they have passed through an object; an analog processing circuitry system 25 that processes and digitizes the raw electrical signals from the detector; a digital processing circuitry system 40 that can perform further processing operations on the measurement data, such as applying corrections, temporary storage, or filtering; and a computer 50 that stores the processed data and can perform further post-processing and / or image reconstruction.

[0057] The overall detector can be considered as X-ray detector system 20, or a combination of X-ray detector system 20 and associated analog processing circuitry system 25.

[0058] The digital portion of the digital processing circuit system 40 and / or computer 50, including that in Figure 2, can be considered as... Figure 1 A digital image processing system 30 performs image reconstruction based on image data from an X-ray detector. Therefore, Figure 1 The image processing system 30 can be viewed as the computer 50 of FIG2, or alternatively as a combination of the digital processing circuit system 40 and the computer 50, or, if the digital processing circuit system 40 is further specifically used for image processing and / or reconstruction, it may be viewed as the digital processing circuit system itself.

[0059] A common example of an X-ray imaging system is a computed tomography (CT) system, which may include an X-ray source that generates a fan-shaped or cone-shaped beam of X-rays and a corresponding X-ray detector system for recording the fraction of X-rays that travel through the patient or object. The X-ray source and detector system are typically mounted in a gantry that rotates around the object being imaged.

[0060] therefore, Figure 1 The X-ray source 10 and X-ray detector 20 shown in Figure 2 can therefore be arranged as part of a CT system, for example, they can be installed in a CT gantry.

[0061] The X-ray imaging system 100 may also include a conformity detection system 60 for implementing the proposed technique. (Reference) Figure 2A For example, the conformity detection system 60 may be implemented at least partially in the digital processing circuit system 40 and / or at least partially in the analog processing circuit system 25 and / or at least partially as executable program code for execution by the computer 50.

[0062] Figure 2B This is a schematic diagram illustrating an example of an X-ray detector system according to the proposed technology. The X-ray detector system 5 includes an X-ray detector 20 and a coincidence detection system 60.

[0063] Figure 2C This is a schematic diagram illustrating an example of a specific, non-limiting implementation of the conformity detection system 60 in the digital processing circuit system 40.

[0064] Accordingly, an improved X-ray detector system 5 is provided, the X-ray detector system comprising:

[0065] - Photon-counting X-ray detector 20, which is used to detect X-ray radiation from an X-ray source; and

[0066] - A coincidence detection system 60 is configured to determine and / or obtain information about the radiation incident on the X-ray detector 20 based on information about the timing of photon interactions in the X-ray detector 20 and information about the positioning of the X-ray source relative to the X-ray detector.

[0067] For example, such a detector system can be integrated into an imaging system that includes a detector system, an X-ray source, and a computer for data processing.

[0068] For example, an X-ray detector system can be configured to operate with a broad energy X-ray spectrum having a maximum energy of less than 160 keV; said X-ray spectrum is emitted by an X-ray source that is a localized X-ray source with a range of less than 0.5 millispheric degrees, observable from a point on the X-ray detector. This non-limiting example is consistent with typical operating conditions in medical X-ray or CT systems. Such systems also typically operate with even smaller sources, thereby providing better confinement of the radiation incident direction.

[0069] In a particular example, the coincidence detection system is configured to determine and / or obtain information about the radiation incident on the X-ray detector based on the information about the timing of photon interactions and the information about the positioning of the X-ray source relative to the X-ray detector, including at least one of the number of incident photons in a particular region, the spatial distribution of the incident photons, and the energy distribution of the incident photons.

[0070] As an example, a coincidence detection system can be configured to operate based on a photon scattering model by combining the photon scattering model with the information about the positioning of the X-ray source relative to the X-ray detector to determine and / or obtain the information about the radiation. In a non-limiting example, the information about the positioning of the X-ray source can be used together with measurements of the interaction location to measure the scattering angle of the incident radiation, and the photon scattering model can be used to estimate the likelihood that the scattering angle is observed along with one or more of the recorded photon energies.

[0071] The inventors have recognized that having a localized source (e.g., an X-ray tube) allows for the construction of improved coincidence detection systems. For example, if the incident direction of the radiation is known with high precision, such as if the X-ray source is confined to a point of approximately 1 mm in size, or more generally, if the source observed from the detector occupies a solid angle of less than 0.5 millieudecimals, this information can be combined with a model of X-ray photon scattering to produce improved coincidence detection.

[0072] For example, if the first interaction is a Compton interaction and the second interaction is a photoelectric interaction, the total incident photon energy can be estimated as the sum of the deposited energies in the two interactions, and the scattering angle can be calculated from the positions of the two interactions relative to the incident direction. This angle can then be compared with the estimated incident energy and the energy recorded in the Compton interaction using the Compton scattering formula or the Klein-Neckar cross section. In this way, the likelihood of generating two interactions from a single incident photon can be calculated.

[0073] It should be understood that this is a non-limiting example, and other numbers and combinations of interactions can be handled in a similar manner. It should also be understood that having a local source, thus providing information about the incident direction of the photon on the detector, is necessary for this type of coincidence detection.

[0074] For example, a coincidence detection system can be configured to combine the photon scattering model and prior knowledge about the location of the X-ray source with prior knowledge of the probabilities of different incident X-ray energy distributions to determine and / or obtain the information about the radiation. For example, such prior knowledge can take the form of a model of the X-ray source spectrum filtered through different materials, based on tabulated or simulated X-ray tube spectra. The prior knowledge may also include knowledge of the existence of incident radiation with energies below a specific energy (e.g., 20 keV) or above a specific energy (e.g., 160 keV). Additionally, such prior information may include a model of the probability that the X-ray beam has passed through different combinations of thicknesses of different base materials, combined with a model of the output spectrum from the X-ray tube. Furthermore, such prior information may include knowledge about the typical interaction energies of secondary photoelectric interactions, such as those limited to specific portions of the detected deposition energy spectrum.

[0075] In a specific example, the X-ray detector is a photon-counting multi-compartment X-ray detector capable of distinguishing different photon interaction energies, and the coincidence detection system is configured to use information about the photon interaction energy to determine the information about the radiation.

[0076] For example, a coincidence detection system can be configured to determine and / or obtain the information about radiation based on at least one representation of the time and / or timing of photon interactions. This information can be provided, for example, as a measurement of the time when an electrical pulse reaches its maximum amplitude, wherein the pulse is generated by the interaction of X-ray photons in a sensor material.

[0077] Optionally, the coincidence detection system may be configured to determine and / or obtain the information about radiation based on at least one of information about the location of the photon interaction and information about the deposited energy in the photon interaction.

[0078] For example, a coincidence detection system may be configured to determine and / or obtain the information about the radiation incident on the detector based on identifying at least one set of photon interactions generated by a single incident photon.

[0079] In a particular example, the coincidence detection system is configured to generate and / or obtain information about radiation incident on an X-ray detector based on identifying at least two sets of photon interactions that may have been generated by at least two different incident photons, wherein all photon interactions in each set may have been generated by a single incident photon, and wherein the coincidence detection system is configured to identify the at least two sets of photon interactions as possibly generated by at least two different incident photons based on comparing these sets of photon interactions with at least one other possible set of photon interactions.

[0080] For example, a coincidence detection system can be configured to generate and / or obtain information about the radiation incident on an X-ray detector based on the information about the timing of photon interactions, combined with at least one angle defined by at least two photon interaction positions and / or based on at least one angle defined by three photon interaction positions and / or based on the incident radiation direction and at least one angle defined by two photon interaction positions. For example, such angles can be correlated with the deposited energy in at least one of the interactions and used to calculate the likelihood of a particular interaction sequence or to group interactions into a set of interactions that can be generated by a single interaction using a photon scattering model. The interaction sequence refers to the order in which interactions can be generated consecutively by a single photon. A correct interaction sequence corresponds to the temporal order of interactions generated by a single incident photon.

[0081] In a specific example, the X-ray detector is a silicon detector.

[0082] Typically, X-ray detector systems are configured to distinguish between Compton interactions and photoelectric interactions based on energy thresholds. For example, interactions with deposited energies below a certain threshold can be identified as Compton interactions, and interactions with energies above a certain threshold can be identified as photoelectric interactions, wherein the threshold is exemplarily chosen when the spectrum of deposited energies reaches a local minimum, or when the amounts of Compton interactions and photoelectric interactions are approximately equal.

[0083] Optionally, the X-ray detector system has a high-attenuation blocker for reducing scattering within the X-ray detector. By reducing scattering, the number of detected interactions is reduced, which in turn reduces the total number of interactions over a given time interval. This can simplify coincidence detection methods, for example, by reducing the number of potential coincidences. However, reducing scattering also results in photons being absorbed without depositing their full energy into the detector, which may, on the other hand, increase the difficulty of coincidence detection.

[0084] For example, an X-ray detector system can be configured to use logic to estimate the location of an interaction based on an estimate of the amount of charge diffusion.

[0085] In a specific example, the conformity detection system can be configured to operate based on an X-ray detector model.

[0086] For example, the coincidence detection system can be configured for operation based on a photon scattering model, and the photon scattering model can be based on at least one of the Compton scattering formula, the Klein-Nishina formula, the Lambert-Beer law, the photoelectric effect, the X-ray interaction cross section of the Compton effect or Rayleigh scattering, and the simulation of photon transport.

[0087] In a specific example, the coincidence detection system can be configured for operation based on a photon scattering model, which includes Rayleigh scattering, or alternatively excludes Rayleigh scattering. Rayleigh scattering describes the elastic expansion of photons from bound electrons. This type of scattering results in the deflection of the incident photons, but without the release of electron-hole pairs due to the lack of energy deposition.

[0088] The coincidence detection system can be configured to process photon interactions detected throughout the detector volume or within a sub-volume of the detector, independently of at least one other sub-volume. Processing data within a sub-volume may be preferred, for example, because data from the entire detector then does not need to be aggregated, and because it is computationally easier to perform corrections using a smaller number of interactions within the sub-volume. For example, a sub-volume may consist of a single physical detector module, or it may consist of multiple physical detector modules. However, a sub-volume is not necessarily limited to physical detector modules, but may also involve one or more partial volumes from one or more physical detector modules.

[0089] For example, a coincidence detection system may be configured to obtain and / or determine the information about incident radiation based on at least one of the following methods: maximum likelihood method, maximum a posteriori method, neural network, support vector machine, or decision tree-based method.

[0090] Maximum likelihood may include the steps of calculating the likelihood of a particular incident photon configuration and selecting such a photon configuration by optimizing the likelihood. For example, prior information may be incorporated, for instance, by using a prior model that includes probabilities of different incident spectra or other prior information, to generate a maximum a posteriori algorithm and improve the estimation.

[0091] For example, a neural network estimator can take input data including recorded photon counts, energies, and positions, and process it using an artificial neural network to generate output data related to the estimated number of incident photons or the estimated incident energy. This network can be trained on simulated or measured data.

[0092] For example, decision tree-based methods can process input data in several successive comparison steps and produce output based on the results of such comparisons. Several decision trees can be aggregated, for example, through guided aggregation, to form a composite estimator.

[0093] As an example, a coincidence detection system may be configured to obtain and / or determine the information about radiation incident on an X-ray detector based on assigning at least one likelihood to at least one set of photon interactions, wherein the likelihood is based on the probability of observing these photon interactions.

[0094] Optionally, the coincidence detection system is configured to obtain and / or determine the information about the radiation incident on the X-ray detector based on an optimized likelihood, wherein the likelihood is based on the probability of observing these photon interactions.

[0095] For example, a coincidence detection system may be configured to obtain and / or determine the information about radiation incident on an X-ray detector based on assigning at least one likelihood to at least one set of photon interactions, wherein the likelihood is based on the probability of observing these photon interactions when all of them originate from a single incident photon.

[0096] In a particular example, the coincidence detection system is configured to assign an interaction to a set of photon interactions for each of a plurality of photon interactions, based on at least one likelihood of observing these photon interactions from a single incident photon.

[0097] For example, a coincidence detection system can be configured to assign the plurality of photon interactions to a set of photon interactions in such a manner that no interaction is assigned to more than one set.

[0098] For example, a coincidence detection system can be configured to assign an interaction order to photon interactions in at least one set of the set based on the likelihood of at least one interaction order. As an example, such an interaction order can be selected as the interaction order with the maximum likelihood of all possible interaction orders.

[0099] In a particular example, the coincidence detection system is configured to assign the estimated position of photon incidence to at least one set of photon interactions based on the position of the first photon interaction in a set specified by at least one interaction order.

[0100] As an example, an X-ray detector system is configured to estimate the energy of at least one incident photon based on the detected energies of photon interactions within at least one set of photon interactions that may originate from a single incident photon. This can be performed, for example, by summing the energies of the photon interactions within said set.

[0101] In an optional embodiment, the X-ray detector system is configured to estimate the number of photons incident on the X-ray detector or at least one sub-volume of the X-ray detector during at least one time interval based on the at least one likelihood.

[0102] For example, the likelihood can be calculated based on the prior probability distribution of the set of possible spectra incident on an X-ray detector.

[0103] Optionally, the coincidence detection system can be configured to sum the counts of measurements over time intervals and read out at least one of them from a photon-counting X-ray detector before applying data to the measurements.

[0104] For example, an X-ray detector system may be configured to output the information about radiation incident on the X-ray detector as input data to at least one of an image reconstruction algorithm, a basic material decomposition algorithm, a denoising algorithm, a deblurring algorithm, a stacking correction algorithm, or a spectral distortion correction algorithm.

[0105] For example, image reconstruction algorithms can take a representation of projected count data as input and output a reconstructed image. Basic material decomposition algorithms can take count data as input and output a basic image or basic sine curve. Denoising algorithms can take a noisy image or sine curve as input and output a denoised sine curve or image. Deblurring algorithms can take a low-resolution image as input and output a high-resolution image. Stacking algorithms can take count data distorted by stacking as input and output a corrected image. Spectral distortion correction algorithms can take count data distorted by a non-ideal detector response function as input and output corrected count data.

[0106] Image reconstruction algorithms, basic material decomposition algorithms, denoising algorithms, deblurring algorithms, stacking correction algorithms, or spectral distortion correction algorithms can be built on, for example, maximum a posteriori, block matching, bilateral filtering, or convolutional neural networks.

[0107] In a preferred embodiment, coincidence detection is implemented, for example, in a digital processing circuitry system connected to the detector via a microcode sequencer or an FPGA. In another embodiment, coincidence detection is implemented in an analog processing circuitry system or in a computer after data is read from the detector.

[0108] According to another aspect, an overall X-ray imaging system including such an X-ray detector system is provided.

[0109] For example, an X-ray imaging system can be configured to estimate the energy of at least one incident photon based on the detected energy of photon interactions within at least one set of photon interactions that may originate from a single incident photon.

[0110] According to yet another aspect, a coincidence detection system 60 is provided, configured to operate in conjunction with a photon-counting X-ray detector 20. The coincidence detection system 60 is configured to determine and / or obtain information about radiation incident on the X-ray detector 20 based on information about the timing of photon interactions within the X-ray detector and information about the positioning of the X-ray source relative to the X-ray detector.

[0111] According to another aspect, a method for obtaining or determining information about radiation incident on an X-ray detector is provided, as will be described in more detail later.

[0112] To better understand, the proposed technique will now be described with reference to specific non-limiting examples.

[0113] It is usually helpful to begin with a brief introduction to X-ray detector technology, followed by a set of non-limiting examples of the invention.

[0114] Generally speaking, the challenge of X-ray imaging detectors is to extract the maximum information from the detected X-rays to provide input for an image of an object or structure, which is depicted based on its density, composition, and structure. While using film-type screens as detectors remains common, it is now most prevalent to have detectors that provide digital images.

[0115] Modern X-ray detectors typically require the conversion of incident X-rays into electrons, usually through light absorption or Compton interactions. The resulting electrons typically produce secondary visible light until their energy is lost and this light is subsequently detected by a photosensitive material. Semiconductor-based detectors also exist, in which electrons generated by X-rays acquire an electric charge based on electron-hole pairs collected by an applied electric field.

[0116] Conventional X-ray detectors are energy integrals, so the contribution from each detected photon to the detection signal is proportional to its energy, and in conventional CT, measurements are acquired for a single energy distribution. Therefore, images produced by conventional CT systems have a certain appearance, with different tissues and materials displaying typical values ​​within a certain range.

[0117] There are detectors that operate in integration mode, in which they provide an integrated signal from multiple X-rays, and this signal is only digitized later to retrieve the best guess at the number of incident X-rays in a pixel.

[0118] In some applications, photon counting detectors have become a viable alternative; currently, these detectors are primarily commercially available in mammography. Photon counting detectors have the advantage of measuring the energy of each X-ray in principle, which produces additional information about the composition of the object. This information can be used to improve image quality and / or reduce radiation dose.

[0119] The most promising materials for photon-counting X-ray detectors are cadmium telluride (CdTe), cadmium zinc telluride (CZT), and silicon. CdTe and CZT have been used in several photon-counting spectral CT projects for high absorption efficiency of high-energy X-rays used in clinical CT. However, these projects have progressed slowly due to several drawbacks of CdTe / CZT. CdTe / CZT exhibits low charge carrier mobility, which leads to severe pulse buildup at flux rates ten times lower than those encountered in clinical practice. One way to mitigate this problem is to reduce the pixel size; however, this results in increased spectral distortion due to charge sharing and K-escape. Additionally, CdTe / CZT suffers from charge trapping, which leads to polarization that causes a rapid drop in the output count rate when the photon flux exceeds a certain level.

[0120] Conversely, silicon exhibits higher charge carrier mobility and lacks polarization issues. Its mature manufacturing process and relatively low cost are also advantages. However, silicon has limitations that CdTe / CZT does not. Silicon sensors must therefore be considerably thicker to compensate for their lower blocking power. Typically, silicon sensors require several centimeters of thickness to absorb most of the incident photons, while CdTe / CZT only requires a few millimeters. On the other hand, silicon's long attenuation path also allows the detector to be divided into different depth segments, as explained below. This, in turn, improves detection efficiency and enables silicon-based photon counting detectors to appropriately handle the high throughput in CT.

[0121] When using simple semiconductor materials such as silicon or germanium, Compton scattering can occur, in which only a portion of the photon energy is deposited in the detector. This results in most X-ray photons (initially at higher energies) producing far fewer electron-hole pairs than expected, which in turn causes a significant portion of the photon flux to appear at the low end of the energy distribution. Therefore, to detect as many X-ray photons as possible, it is necessary to detect the lowest possible energy.

[0122] Figure 3 This is a schematic diagram illustrating examples of energy spectra for three different X-ray tube voltages. The energy spectra are constructed by mixing deposition energies from different types of interactions, including the Compton event in the lower energy range and the photoelectric absorption event in the higher energy range.

[0123] Figure 4This is a schematic diagram illustrating an example of a conceptual structure for implementing an energy-discriminative photon counting detector.

[0124] Further improvements involve the development of so-called energy-discriminate photon counting detectors, such as... Figure 4 This is illustrated schematically. In this type of X-ray detector, each recorded photon generates a current pulse, which is compared to a set of thresholds to count the number of photons incident on each of multiple so-called energy chambers. This can be very useful in image reconstruction processes.

[0125] Figure 5 This is a schematic diagram of an X-ray detector according to an exemplary embodiment. In this example, a schematic diagram of an X-ray detector (A) is shown, in which an X-ray source (B) emits X-rays (C). The elements of the detector (D) point back to the source and are therefore preferably arranged in a slightly curved overall configuration. Two possible scanning movements (E, F) of the detector are shown. In each scanning movement, the source can be stationary or moving. In the scanning movement indicated by (E), the X-ray source and detector can rotate about an object positioned therebetween. In the scanning movement indicated by (F), the detector and source can translate relative to the object, or the object can move. Furthermore, in scanning movement (E), the object can be translated during rotation, i.e., a so-called helical scan. For example, in a CT implementation, the X-ray source and detector can be mounted in a gantry that rotates around the object or subject to be imaged.

[0126] Figure 6 This is a schematic diagram illustrating an example of a semiconductor detector module according to an exemplary embodiment. This is an example of a semiconductor detector module (A), where the sensor portion is divided into detector elements or pixels (B), each of which is typically based on a diode. X-rays (C) enter through the edge (D) of the semiconductor sensor.

[0127] Figure 7 This is a schematic diagram illustrating another example of an X-ray detector submodule according to an exemplary embodiment. In this example, assuming the X-rays enter through an edge, the sensor portion of the X-ray sensor submodule 21 is divided into so-called depth segments in the depth direction. Each detector element 22 is typically based on a diode with a charge collection electrode as a key component.

[0128] Typically, a detector element is a single X-ray sensing element of the detector. Generally, photon interactions occur within the detector element, and the resulting charge is collected by the corresponding electrodes of the detector element. Each detector element typically measures the incident X-ray flux as a sequence of frames. A frame is data measured during a specified time interval (called the frame time).

[0129] Figure 8 This is a schematic diagram illustrating an example of a modular X-ray detector, which includes, for example, multiple detector sub-modules 21 arranged side-by-side in a slightly curved overall geometry relative to an X-ray source located at the X-ray focal point.

[0130] Figure 9 This is a schematic diagram illustrating an example of a modular X-ray detector comprising multiple detector sub-modules 21 arranged side-by-side and stacked sequentially. The X-ray detector sub-modules can be stacked sequentially to form a larger detector module, which can be assembled side-by-side to construct an overall X-ray detector system.

[0131] As mentioned, side orientation is a design of X-ray detectors in which X-ray sensors (such as X-ray detector elements or pixels) are oriented sideways to the incoming X-rays.

[0132] For example, the detector may have detector elements in at least two directions, wherein one of the directions facing the detector has a component in the direction of the X-ray. Such a side-facing detector is sometimes referred to as a depth-segmented X-ray detector, which has two or more depth segments of detector elements in the direction of the incoming X-ray.

[0133] Alternatively, the X-ray detector can be non-depth segmented while still being arranged sideways to the incoming X-rays.

[0134] Depending on the detector topology, detector elements may correspond to pixels, such as when the detector is a flat panel detector. However, a depth segmented detector can be viewed as having multiple detector strips, each strip having multiple depth segments. For such a depth segmented detector, each depth segment can be viewed as a separate detector element, especially if each depth segment is associated with its own separate charge collection electrode.

[0135] The detector stripes of a depth segment detector typically correspond to the pixels of a conventional flat panel detector. However, a depth segment detector can also be viewed as a three-dimensional pixel array, where each pixel (sometimes called a voxel) corresponds to a separate depth segment / detector element.

[0136] In some applications, photon counting detectors have become a viable alternative; currently, these detectors are primarily commercially available in mammography. Photon counting detectors have advantages because, in principle, they can measure the energy of each X-ray, which produces additional information about the composition of the object. This information can be used to improve image quality and / or reduce radiation dose.

[0137] Compared to energy integration systems, photon-counting CT offers several advantages. First, by setting a minimum energy threshold above the noise floor in the photon-counting detector, electronic noise integrated into the signal by the energy integration detector can be rejected. Second, energy information can be extracted from the detector, allowing for improved contrast-to-noise ratio through optimal energy weighting, and also enabling so-called material-based decomposition, through which different materials and / or components within the examined object or body can be identified and quantified for efficient processing. Third, more than two base materials can be used, which is beneficial for decomposition techniques such as K-edge imaging, thereby quantitatively determining the distribution of contrast agents (e.g., iodine or gadolinium). Fourth, there is no detector afterglow, meaning high angular resolution can be achieved. Last but not least, high spatial resolution can be achieved by using smaller pixel sizes.

[0138] A problem in any counting X-ray photon detector is the so-called stacking problem. When the X-ray photon flux is high, it can be difficult to distinguish between two subsequent charge pulses. As mentioned above, the pulse length after the filter depends on the shaping time. If this pulse length is longer than the time between two X-ray photon induced charge pulses, the pulses will grow together, and the two photons will be indistinguishable and counted as a single pulse. This is called stacking. Therefore, one way to avoid stacking at high photon fluxes is to use a small shaping time, or to use depth segmentation as suggested in the optional embodiments described herein.

[0139] To improve absorption efficiency, the detector can be configured to face sideways. In this case, the absorption depth can be selected to any length, and the detector can still be completely depleted without reaching very high voltage.

[0140] In particular, silicon has many advantages as a detector material, such as high purity and the low energy required to generate charge carriers (electron-hole pairs) and the high mobility of these charge carriers, which means that it will even work for high-velocity X-rays.

[0141] Semiconductor X-ray detector submodules are typically laid out together to form a complete detector of almost arbitrary size with near-perfect geometric efficiency. In addition to optional anti-scattering modules (e.g., foils or sheets made of tungsten), the anti-scattering modules can be integrated between at least some of the semiconductor detector modules.

[0142] More information about so-called photon-counting side-facing X-ray detectors can generally be found, for example, in U.S. Patent 8,183,535, which discloses an example of a photon-counting side-facing X-ray detector. In U.S. Patent 8,183,535, there are multiple semiconductor detector modules arranged together to form a single detector area, each semiconductor detector module including an X-ray sensor oriented side-facing on the edge of the incident X-ray and connected to an integrated circuit system for recording X-rays interacting within the X-ray sensor.

[0143] As discussed, an overall X-ray detector can be based, for example, on detector submodules or wafers, each having multiple depth segments in the direction of incident X-rays.

[0144] These detector submodules can then be arranged sequentially or stacked and / or arranged side-by-side in various configurations to form any effective detector area or volume. For example, a full detector for a CT application typically has a total area greater than 200 cm², which results in a large number of detector modules, such as 1500-2000 detector modules.

[0145] For example, detector submodules can typically be arranged side-by-side and / or stacked, for example, in a planar or slightly curved overall configuration.

[0146] Generally, it is desirable to have as many detector elements and segments as possible, as this improves spatial resolution. If this also results in smaller electrodes, electronic noise is typically reduced, which increases dose efficiency and energy resolution.

[0147] Because X-ray interactions will be distributed along the depth (length) of the sensor and occur in different depth segments, the total count rate will be distributed along the depth between segments, for example, from... Figure 5 As can be seen, this figure is a schematic diagram illustrating an example of the count rate in each segment. In this example, the first segment is the one closest to the X-ray source.

[0148] For example, a sensor with a depth of 40 mm could potentially have 400 segments or more, and the count rate would be correspondingly reduced. Sensor depth is critical for dose efficiency, and segmentation protects against pulse buildup and maintains the spatial resolution of the system.

[0149] Current can be measured, for example, through an amplifier such as a charge-sensitive amplifier (CSA) followed by a filter such as a shaping filter (SF), as previously mentioned. Figure 4 It is shown schematically in the middle.

[0150] Since the number of electrons and holes from an X-ray event is proportional to the X-ray energy, the total charge in an induced current pulse is proportional to that energy. The current pulse is amplified in a (CSA) amplifier and then filtered by a (SF) filter. By selecting an appropriate shaping time for the SF filter, the amplitude of the filtered pulse is proportional to the total charge in the current pulse, and therefore proportional to the X-ray energy. After the (SF) filter, the value of the pulse amplitude can be compared with one or more thresholds (T1-T2) in one or more comparators COMP. N The pulse amplitude is measured by comparison, and a counter is introduced to record the number of pulses exceeding a threshold. In this way, it is possible to determine the number of pulses whose energy exceeds the corresponding threshold (T1-T) detected within a certain time frame. N The number of X-ray photons with energy of ) is counted and / or recorded.

[0151] When several different thresholds are used, a so-called energy-resolved photon counting detector is obtained, in which detected photons can be classified into energy bins corresponding to the various thresholds. Sometimes, this particular type of photon counting detector is also called a multi-bin detector.

[0152] Generally speaking, energy information allows for the creation of new types of images where new information is available and image artifacts inherent in conventional techniques can be removed.

[0153] In other words, for an energy-resolved photon counting detector, the pulse height is compared with multiple programmable thresholds (T1-T2) in the comparator. N They are compared and classified according to pulse height, which is proportional to energy.

[0154] However, an inherent problem with any charge-sensitive amplifier is that it adds electronic noise to the detected current. To avoid detecting noise instead of real X-ray photons, it is important to set the minimum threshold high enough that the number of times the noise value exceeds the threshold is low enough not to interfere with the detection of X-ray photons.

[0155] By setting the minimum threshold above the noise floor, electronic noise, a major obstacle to reducing radiation dose in X-ray imaging systems, can be significantly reduced.

[0156] A general characteristic of shaping filters is that a large shaping time results in long pulses of X-ray photons and reduces the noise amplitude after the filter. A small shaping time results in shorter pulses and larger noise amplitudes. Therefore, to count as many X-ray photons as possible, it is desirable to use the longest possible shaping time (without causing accumulation), as this will minimize noise and allow for the use of relatively small threshold levels.

[0157] The quality of the image data generated by the photon counting detector is affected by the set or table of thresholds by which pulse heights are compared in the comparator. Furthermore, these thresholds are temperature-dependent. Therefore, in an embodiment, the calibration data generated by the power consumption circuitry system is the threshold (T1-T2). N A collection or table of ).

[0158] However, it should be understood that an energy-discriminating photon counting detector is not necessary, although this does bring certain advantages.

[0159] Figure 10 This is a schematic diagram illustrating an example of a photon-counting X-ray detector based on multiple X-ray detector sub-modules 21, referred to herein as wafers. The wafers 21 are stacked sequentially. It can be seen that each wafer has a length (x) and a thickness (y), and each wafer is also segmented in the depth direction (z), a process known as depth segmentation. Purely as an example, the wafer length can be approximately 25mm-50mm, the wafer depth can be approximately 25mm-50mm, and the wafer thickness can be approximately 300um-900um.

[0160] For example, each wafer has detector elements distributed on the wafer in two directions including the direction (z) of the incident X-ray.

[0161] Each wafer has a thickness (y) with two opposite sides having different potentials, such as the front and back sides, so that charges can drift toward the sides where detector elements (also called pixels) are typically arranged.

[0162] To better understand the proposed technique, recalling the basic concepts of the Compton effect may be helpful.

[0163] The incoming X-ray photons can interact with the semiconductor material of the detector module through the photoelectric effect (here referred to as the optical effect) or the Compton interaction, see [link to relevant documentation]. Figure 11 .

[0164] Compton interaction (also known as Compton scattering) is the scattering of photons by charged particles (usually electrons). This results in a reduction in the photon's energy, known as the Compton effect. Some of the photon's energy is transferred to the recoil electron. Multiple Compton interactions may be involved in the photon's path through a semiconductor substrate. In short, in a Compton interaction, an incident X-ray photon is deflected from its original path by an interaction with an electron that exits from its initial orbital position to form a so-called secondary or "free" electron. Such a secondary electron can also be a result of a photo-effect, in which case all the energy of the incident X-ray photon is transferred to the electron.

[0165] More specifically, X-ray photons can generate secondary electrons through Compton interactions or photo-effects. The electrons gain kinetic energy from the X-ray photons and travel a short distance, e.g., 1-50 μm, exciting electron-hole pairs along their path. Each electron-hole pair requires approximately 3.6 eV to generate, meaning that a Compton interaction with a deposition energy of, for example, 15 keV to the electron will generate approximately 4200 electron-hole pairs, forming what is known as a charge cloud. The cloud will move or drift according to electric field lines, and if the back side of the detector submodule or wafer is positively biased, holes will move toward the readout electrodes arranged on the front side of the detector submodule or wafer, while electrons will move toward the back side. During this drift, the electron-hole pairs forming the charge cloud also undergo diffusion, essentially meaning the size of the charge cloud will increase.

[0166] The readout electrodes serve as detector elements or pixels. For example, the voltage on the rear side may be approximately 200V, and the front side is a virtual ground.

[0167] As should be understood, it may be desirable to orient the X-ray detector relative to the beam side (i.e., relative to the incident X-ray side) while subdividing the sensor area into relatively high resolutions, such as 5 μm to 100 μm, in order to be able to resolve charge clouds.

[0168] Generally, X-ray photons are converted into electron-hole pairs within the semiconductor material of an X-ray detector, with the number of electron-hole pairs typically proportional to the photon energy. The electrons and holes drift toward the detector element and then away from the photon counting detector. During this drift, the electrons and holes induce an electric current in the detector element.

[0169] The present invention will now be described with reference primarily to a photon-counting silicon X-ray detector as a non-limiting example, but it can also be applied to other types of X-ray detectors.

[0170] Compton interactions in silicon detectors can lead to multiple counts from a single photon. Without tungsten shielding, this reduces the signal-to-noise ratio and diminishes spectral information. On the other hand, the mismatched purity and crystal quality of silicon result in very high spatial and spectral resolution, and it is proposed to use information about the deposited energy at each interaction point to pair Compton interactions induced by the same incident photon using a probability-based approach.

[0171] Due to silicon's low atomic number, Compton interactions are frequent. In Compton interactions, only a fraction of the incident photon energy is deposited, and a single incident photon can lead to multiple counts. Silicon has proven to be a competitive material for photon-counting CT detectors, but to further improve performance, it is desirable to use coincidence techniques to combine Compton-scattered photons.

[0172] For example, tungsten shielding or similar anti-scattering modules can be used to remove scattered photons, leaving the Compton count, which contains little energy information but corresponds to a single photon and thus contributes to image contrast as a photon count. However, if a photon deposits its energy through a series of interactions that conclude in a photoelectric event, the total photon energy can be estimated by summing the deposited energies from the interactions in that series. Extracting this information is desirable because it improves the spectral performance of the detector.

[0173] Since Compton-scattered photons can be identified based on their energy and scattering angle, the inventors have realized that interactions belonging to the same photons can be identified based on the interaction location and the energy deposited. High spatial and energy resolution will increase the likelihood of finding the correct combination of interactions.

[0174] Furthermore, the inventors have recognized the feasibility of using coincidence techniques to identify and pair interactions belonging to the same incident photons in order to improve the signal-to-noise ratio and spectral performance of photon-counting X-ray detectors. This is particularly useful for silicon X-ray detectors.

[0175] In silicon detectors, a subset of incident photons interact via Compton interactions. In Compton interactions, only a portion of the incident photon energy is deposited, leading to multiple interactions from a single photon. To eliminate this possibility, tungsten shielding can be used to remove any secondary interactions. Since each resulting Compton count subsequently corresponds to a unique photon, Compton counting is not detrimental but rather contributes to imaging performance. Compton counting is particularly important for density imaging tasks, but it also improves contrast in spectral imaging, as described in "Photon-counting spectral computed tomography using silicon strip detectors: a feasibility study," H. Bornefalk and M. Danielsson, Physics in Medicine and Biology, Vol. 55, pp. 1999–2022, 2010.

[0176] To further improve the performance of silicon detectors, it is desirable to use coincidence techniques to detect Compton-scattered photons, rather than tungsten shielding (or a combination thereof). In detectors without tungsten shielding, many photons interact through a series of Compton interactions that end in a photoelectric event. If all photon energy has already been deposited within the detector, the incident photon energy can be obtained by adding the deposited energies from the interactions in the series. The identification and pairing of interactions belonging to the same photon is more efficient at high spatial and energy resolution.

[0177] A method for obtaining 1µm resolution in a photon-counting silicon detector has been previously proposed in our jointly pending patent applications US 16 / 653200 and PCT / SE2019 / 051011.

[0178] In this invention, the aim is to evaluate whether the achieved spatial resolution can be used, for example, to identify Compton-scattered photons based on timing information of photon interactions (optionally combined with information about deposition energy and interaction location).

[0179] The non-limiting objective of this work is to evaluate the feasibility of using conformal logic to identify and pair interactions belonging to the same incident photons in order to improve the signal-to-noise ratio and spectral performance of X-ray detectors such as silicon photon counting detectors.

[0180] Non-restrictive examples

[0181] The silicon detector was modeled using the well-known GATE simulation toolkit and irradiated with an X-ray beam having photon energies sampled from the spectrum of an X-ray source operating at 120 kVp, with a 30 cm soft tissue filter between the X-ray source and the detector.

[0182] For more information about the GATE simulation toolkit, please refer to “GATE: a simulation toolkit for PET and SPECT”, S. Jan, G. Santin, D. Strul et al., Physics in Medicine and Biology, Vol. 49, pp. 4543-4561, 2004.

[0183] For each interacting photon, the energy and position of the resulting interaction are recorded along with the interaction type (photoelectric or Compton).

[0184] The resulting data is organized into smaller subsets, each representing an interaction that occurs in the detector during a certain time window (snapshot). The interactions in each snapshot are then characterized with respect to their energy and location. A maximum likelihood method is then implemented and used to classify interactions based on their most likely chains.

[0185] Exemplary results

[0186] The following table shows an example of the interaction chain probability in a silicon detector without tungsten shielding:

[0187] Interacting chains Interaction chain probability 1. Optoelectronics 31.70％ 1 Compton + 1 Optoelectronic 19.26％ 1 Compton 16.09％ 2 Comptons + 1 Optoelectronic 11.39％ 2 Compton 6.30％ 3 Comptons + 1 Optoelectronic 6.10％ 4 Comptons + 1 Optoelectronic 2.97％ 3 Compton 2.55％ 5 Comptons + 1 Optoelectronic 1.35％ 4 Compton 0.99％ 5 Compton 0.34％ 6 Compton 0.12％ Sum of probabilities 99.16％

[0188] Table 1: Interaction chain probabilities in silicon detectors without tungsten shielding.

[0189] Figure 12 This is a schematic diagram illustrating an example of the deposition energy spectrum of the Compton and photoelectric components of the interaction spectrum. The vertical dashed line indicates the Compton threshold, above which 99.75% of all photoelectric interactions are found, and below which 99.70% of all Compton interactions are found.

[0190] Figure 13 This is a schematic diagram illustrating an example of interaction within a snapshot (i.e., during a certain time interval). The black lines represent interactions belonging to the same incident photons.

[0191] Figure 14 This is a schematic diagram illustrating an example of the 1D scattering distance of the photoelectric interaction chain 1 Compton+1.

[0192] Figure 15 This is a schematic diagram illustrating an example of the incident photon energy spectrum of different interacting chains.

[0193] Therefore, the interaction dynamics in photon-counting X-ray (silicon) detectors, such as those used in spectral CT, have been studied and characterized. Furthermore, the feasibility of using Compton compliance logic, for example based on the maximum likelihood method, to identify and pair interactions belonging to the same incident photon has been evaluated, and the impact of this on the detector's spectral performance has been shown. The proposed technique will enable an ideal X-ray detector with very high energy and position resolution for each incident photon.

[0194] It has been shown that photoelectric interactions and Compton interactions can be distinguished in X-ray detectors such as silicon detectors, and different interaction chains can be characterized based on photon energy and scattering distance. The results indicate that interactions belonging to the same incident photon can be identified based on the energy of the deposition and the interaction location.

[0195] Generally speaking, conventional coincidence detection techniques can be found in the Compton camera used in nuclear medicine and astrophysics, as described in, for example, R. Todd, J. Nightingale, D. Everett, A proposed γ camera. Nature, Vol. 251, pp. 132-134 (1974), https: / / doi.org / 10.1038 / 251132a0, and V. A. Hirner, K. Schneider, A telescope for soft gamma ray astronomy, Nuclear Instruments and Methods, Vol. 107, No. 2, 1973, pp. 385-394, https: / / doi.org / 10.1016 / 0029-554X(73)90257-7. The Compton camera is used to detect incident gamma photons in order to locate the emission source. In nuclear medicine, the incident photon is monoenergetic, while in astrophysics, its application can involve a wide energy spectrum (ranging from keV to MeV).

[0196] This invention provides a solution for using and / or improving coincidence detection techniques such as Compton coincidence in X-ray detectors, which involves detecting photons of many different energies when the location of the emission source is known.

[0197] Compton gamma cameras, including coarse collimators to limit the acceptance angle of incident radiation, have previously been proposed in nuclear medicine, see, for example, U.S. Patent 7,291,841. That patent describes the use of highly localized sources in X-ray radiography and how to design focused X-ray optical collimators for specific X-ray tube focal distributions. In contrast, this paper describes coincidence detection methods in which the location of the source is used in coincidence techniques.

[0198] For applications in nuclear medicine, such as single-photon emission computed tomography (SPECT) and, to some extent, positron emission tomography (PET), a Compton camera typically consists of two distinct detectors: a scatterer and an absorber, such as Si+CdTe. See “Compton imaging with…” 99m "Tc for human imaging", M. Sakai, Y., Kubota, RK, Parajuli et al., Sci Rep, Vol. 9, p. 12906 (2019), doi: 10.1038 / s41598-019-49130-z.

[0199] Instead, this invention proposes a coincidence detection method that can be used with a single detector in silicon.

[0200] In a Compton camera, such as one based on Si+CdTe, incident photons are scattered by the Compton within the silicon portion and interact with each other via photoelectric interactions in the CdTe, meaning that all photon energy has been deposited in the detector. The direction of the incident photons can then be determined using the Compton scattering formula based on the interaction locations and the deposited energy.

[0201] Gamma-ray tracking has also been proposed in Ge detectors to identify interactions belonging to the same incident photons and to obtain the gamma-ray energy and the direction of the incident photons, as described, for example, in the following reference: IYLee, Gamma-raytracking detectors, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 422, No. 1-3, 1999, pp. 195-200, https: / / doi.org / 10.1016 / S0168-9002(98)01093-6.

[0202] However, in many applications such as computed tomography (CT), the interest lies in quantifying the number and energy of incident photons, rather than determining their orientation. Therefore, coincidence detection methods for this application have very different requirements. These involve identifying interactions belonging to the same incident photon group to avoid double-counting single photons and pairing interactions to obtain the incident photon energy. Computed tomography also involves higher incident photon fluxes, which increases the difficulty of using coincidence techniques.

[0203] Various detector systems involving CT and Compton cameras have been previously described, see, for example, U.S. Patents 10,088,580, 10,067,239, WO 2017015473A8, 10,274,610, US 2018 / 0172848A1, 10,365,383, and US 2020 / 0096656A1. These do not include local sources whose source locations can be used in coincidence detection methods as described herein.

[0204] Many conventional coincidence methods rely solely on time: two interactions with the same time stamp are automatically assigned to the same photon.

[0205] In many applications and / or situations of interest, there may be interactions between several photons belonging to the same time window. This requires more sophisticated coincidence methods, such as finding the optimal solution corresponding to the recorded interactions (the number of incident photons and their energies). The proposed technique provides such a sophisticated solution.

[0206] Examples of other design considerations

[0207] The system and / or logic used for conformal detection should be able to separate photoelectric and Compton scattered photons.

[0208] To simplify the implementation and use of conformation detection methods, it may sometimes be desirable to omit the identification of long interaction chains.

[0209] Typically, there is a trade-off between recognizing too many and too few coincidences. Ideally, there should be one recorded event for each incident photon. However, if too many coincidences are recognized, some events will be incorrectly removed. On the other hand, if too few are recognized, a single photon will result in multiple recorded events.

[0210] Even if not all of the photon energy is deposited in the detector, the chain of interactions will still be of interest for identification because this eliminates the double counting.

[0211] Without tungsten shielding or similar anti-scattering modules, many photons deposit their full energy into the detector. However, this also results in longer interaction chains, which overall increases the difficulty of correctly pairing interactions. With tungsten shielding, pairing interactions become easier as the long interaction chains are removed, but this also reduces the number of photons that deposit their full energy into the detector, thus reducing the total spectral information.

[0212] Concordance logic can be performed as a post-processing step or directly in the detector electronics during data acquisition. Post-processing requires data output of the deposited energy and interaction location for each interaction. Concordance logic in the detector influences the detector design and can involve electronics and / or software specifically designed to perform the coincidence method.

[0213] Conformity detection technology can be continuously applied to interactions that occur as events are recorded or within a specific time window or snapshot.

[0214] Typically, high spatial resolution is required to obtain the angles between interactions. Additionally, high energy resolution may be needed to accurately record the energy deposited in each event.

[0215] In other words, the basic idea is to quantify the number and energy of incident photons based on the timing of photon interactions, optionally combined with information about the interaction positions and deposited energy in the detector.

[0216] For example, this can be done by identifying and pairing interactions belonging to the same incident photon based on the interaction location and the energy of the deposit, or by bypassing the identification and pairing steps and obtaining the number of incident photons and their energy more directly from the interaction location and the energy of the deposit.

[0217] The following section provides non-limiting examples of novel conformance methods and / or procedures.

[0218] 1. Record the energy and location of each interaction.

[0219] 2. Interactions are classified as Compton or photoelectric based on interaction energy.

[0220] 3. Create possible chains of interactions from the recorded interactions. Each chain of interactions represents an interaction arising from a single incident photon. An example of an interaction chain could be, for instance, 1 Compton interaction + 1 photoelectric interaction.

[0221] 4. For each possible chain of interactions, calculate the distance and angle between interactions, as well as the total deposition energy and photon energy between consecutive interactions.

[0222] 5. Then, distance, angle, and energy are used to estimate the likelihood function for each chain of the interaction. The likelihood function is based on, for example, the Compton scattering formula, the Klein-Nishina formula, the Lambert-Beer law, and the interaction cross-section.

[0223] 6. Classify interactions based on the interaction chains that maximize the likelihood function.

[0224] Some steps can be optional, and these steps can be performed to pair a single interaction with a nearby interaction and / or to categorize a set of interactions to obtain multiple interaction chains.

[0225] This method can also be used to obtain the number and energy of incident photons directly from the set of interaction locations and deposition energies, for example, if the likelihood function is obtained by simulating incident photons with well-defined energies and collecting the resulting interactions in the detector with respect to interaction type, location, and deposition energy.

[0226] When assigning interaction chains to a large set of interactions, it might be desirable to initially assign each interaction to an interaction chain, either randomly or using a probability-based method. This would produce an initial set of interaction chains, which could then be iteratively modified to maximize the collective likelihood constituting the interaction chains, thereby producing the most probable set of interaction chains.

[0227] Alternatively, a method can be applied in which machine learning is used to determine the number of incident photons and their energies. This can be accomplished by using a deep neural network to identify and pair interactions belonging to the same incident photon based on the interaction location and deposition energy. Supervised and unsupervised learning, as well as reinforcement learning, can be applied.

[0228] According to another alternative approach, a method is provided in which the number of incident photons and their energies are determined using a decision tree approach. This can be accomplished by pairing interactions that satisfy specific criteria based on the interaction location and deposition energy. For example, two interactions are paired together if they are within a specific distance from each other and their total deposition energy exceeds a specific value.

[0229] Optionally, the present invention can be combined with techniques for estimating the initial point of interaction between X-ray photons in a photon-counting X-ray detector, as will be discussed below.

[0230] As a supplement, it may be desirable to achieve an improved estimate of the initial point of interaction of X-ray photons in a photon-counting X-ray detector, based on multiple X-ray detector submodules or wafers, each including detector elements, wherein the X-ray detector submodules are oriented with their sides facing the geometry, with the edges pointing toward the X-ray source, assuming that the X-rays enter through the edges.

[0231] Each detector submodule or wafer has a certain thickness and two opposite sides with different potentials, such as the front / main side and the rear side, so that the charge can drift toward the (front / main) side where the detector elements (also called pixels) are usually arranged.

[0232] An estimate of charge diffusion originating from the Compton interaction or through optical effects associated with X-ray photons in a (specific) detector submodule or wafer of the X-ray detector can be determined, and the initial point of the interaction along the thickness of the detector submodule can be estimated, at least in part, based on the determined estimate of charge diffusion.

[0233] For example, the shape of charge diffusion, especially its width, can be measured or estimated, and the distance between the detection point and the initial point of the interaction can be determined based on the shape or width of the charge diffusion or distribution.

[0234] For example, charge diffusion can be represented by a charge cloud, and detector submodules distributed on the main side or detector elements on the wafer can provide a pixel array, where the pixels are typically smaller than the charge cloud to be resolved.

[0235] As mentioned, assuming that the X-rays enter through the edge, the X-ray detector submodule can be oriented with the edge facing the side of the X-ray source in a geometric orientation.

[0236] Side orientation is a design of X-ray detectors in which X-ray sensors (such as X-ray detector elements or pixels) are oriented sideways to the incoming X-rays.

[0237] As an example, each X-ray detector in an X-ray detector submodule may include detector elements distributed in two directions on the detector submodule or wafer, including the direction of the incident X-rays. This typically corresponds to a so-called depth-segmented X-ray detector submodule. However, the proposed technique is also applicable to use with non-depth-segmented X-ray detector submodules. The detector elements may be arranged in an array in a direction substantially orthogonal to the incident X-rays, with each detector element oriented laterally toward the incident X-rays. In other words, the X-ray detector submodule can be non-depth-segmented while still being arranged laterally toward the incoming X-rays.

[0238] In a specific example, at least a portion of the detector element or pixel has a longer extension in the direction of the incident X-rays than in the direction orthogonal to the incident X-rays, with a ratio of at least 2:1. In other words, the detector element or pixel may be geometrically asymmetrical, and its extension (depth) in the direction of the incident X-rays is at least twice the extension in the direction orthogonal (perpendicular) to the direction of the incident X-rays.

[0239] Optionally, based on the measured width of the cloud and the integrated charge of the cloud, the initial point of interaction of the incident X-ray photons along the thickness of the detector submodule is estimated. As explained, a representation of the charge cloud can be provided by the induced current on the detector elements triggered by the detector submodule.

[0240] For example, an estimate of the distance between the detection point of the X-ray photon in the detector submodule and the initial point of interaction along the thickness of the detector submodule can be determined based on an estimate of charge diffusion, and then an estimate of the initial point of interaction can be determined based on the detection point and the determined estimate of the distance along the thickness of the detector submodule.

[0241] The interaction is the interaction between X-ray photons and a semiconductor substrate (usually made of silicon).

[0242] The thickness of a detector submodule or wafer typically extends between two opposite sides of the detector submodule (such as the rear and front sides).

[0243] For example, the shape of charge diffusion, especially its width, can be measured or estimated, and the distance between the detection point and the initial point of the interaction can be determined based on the shape or width of the charge diffusion or distribution.

[0244] For example, a system can be provided for estimating the initial point of interaction of X-ray photons in a photon-counting X-ray detector. The X-ray detector can be based on multiple X-ray detector submodules or wafers, each including a detector element. Assuming that the X-rays enter through the edge, the X-ray detector submodules can be oriented geometrically with the edge facing the side of the X-ray source (10).

[0245] Each detector submodule or wafer has a certain thickness and two opposite sides with different potentials, so that charges can drift toward the side where detector elements (also called pixels) are typically arranged.

[0246] The system can then be configured to determine an estimate of charge diffusion originating from the Compton interaction or through optical effects associated with X-ray photons in the detector submodule or wafer of the X-ray detector; and to estimate the initial point of the interaction along the thickness of the detector submodule based on the determined estimate of charge diffusion.

[0247] Figure 16 This is a schematic diagram illustrating an example of some pixels in a specific wafer in the xz plane. In this example, pixel 22 is typically smaller than the charge cloud to be resolved. For example, the charge cloud can have a width of approximately 100 μm, and therefore pixels are typically designed to be smaller or even significantly smaller than this. Consequently, X-ray photons traveling through the semiconductor substrate typically cause the charge cloud to cover multiple adjacent pixels in the detector module. This means that a single X-ray photon will likely trigger event detection in multiple pixels.

[0248] Although pixel 22 is shown as a square, it should be understood that a pixel can be rectangular or have other forms.

[0249] In a specific example, information about charge diffusion can be used to provide improved resolution in at least one of two directions in which the detector elements are distributed on the front side of the detector submodule or wafer. For example, increased resolution can be obtained based on information about the charge cloud distribution in one or both of these directions. The directions under consideration may include the length (x) direction and / or the depth (z) direction of the detector submodule or wafer.

[0250] For example, the method thus further includes the step of determining an estimate of the point of interaction of the incident X-ray photons in at least one of two directions (x, z) on the main side of the X-ray detector element distributed on the X-ray detector submodule or wafer.

[0251] For example, the step of estimating the point of interaction of incident X-ray photons in at least one of two directions (x, z) on the main side of the detector element can be performed based on information about the charge cloud distribution in one or both directions on the main side of the X-ray detector submodule or wafer.

[0252] Figure 17 This is a schematic diagram illustrating an example of the charge cloud distribution in the x-direction.

[0253] Figure 18 This is a schematic diagram illustrating an example of the charge cloud distribution in the z-direction.

[0254] As an example, this could involve determining one or more charge cloud distributions (e.g., see...). Figure 17 and Figure 18 And curve fitting can be performed using any standard curve fitting method, such as weighted averaging and / or least mean squares. For example, finding where the curve has its peak and identifying that peak as a point of interaction in a specific direction can significantly improve resolution, even down to subpixel resolution, such as 1µm. This can be compared to the spatial resolution of a conventional X-ray imaging system, which can have a resolution of about 1mm.

[0255] Alternatively, information about which pixel 22 has detected the highest charge can be used as the point of interaction. For example, the step of estimating the point of interaction of incident X-ray photons in at least one of two directions (x, z) on the main side of the detector element can be performed by identifying the pixel that has detected the highest charge as the point of interaction.

[0256] However, it should be understood that, as mentioned above, subpixel resolution can be obtained by using appropriate curve fitting.

[0257] As previously stated, the inventors have recognized that the photon detection point can be significantly different from the initial point of interaction along the thickness (y) of the detector submodule or wafer.

[0258] After careful analysis and experimentation, the inventors further realized that the shape (especially the width) of the charge diffusion or cloud depends on the distance from the initial interaction point to the detection point along the thickness of the wafer of the detector submodule or X-ray detector under consideration. This is in Figure 20 The diagram illustrates three different distances or depths (100 μm, 300 μm, and 600 μm).

[0259] For example, if the cross-section of the charge cloud is not circular, but elliptical or other shapes, and thus extends differently in different directions in the zx plane, it is recommended to use the minimum width of the charge cloud cross-section as a relevant measure of charge diffusion.

[0260] During the movement of the charge cloud, the charge diffuses, and this is accelerated by electrostatic repulsion. The induced current is dominated by the movement of charge that occurs closer to the front side. Since diffusion is a function of time, if the interaction occurs closer to the back side (over a longer time) than closer to the front side (where diffusion for contributing charge carriers is negligible), the charge cloud will be wider (when collected at the front side). Knowing the total energy (the combined charge of the electron-hole cloud) and the width of the cloud makes it possible to estimate the points of interaction along the sides towards the thickness of the wafer.

[0261] The area of ​​a photon-counting detector that detects concurrent or nearly simultaneous events in adjacent detector elements (in the xy plane) thus also provides depth information (in the z-direction) indicating the point of interaction between the incident X-ray photons and the semiconductor material. Therefore, a larger detection area results in wider charge diffusion, meaning that the interaction can occur at greater distances (e.g., 600 μm) compared to a smaller detection area and narrower charge diffusion (e.g., 100 μm). Figure 20 This is illustrated schematically. Experiments have shown that the resolution can be significantly improved, for example, down to 50 μm. This is a considerable improvement compared to simply knowing in which wafer the interaction occurs. It is now also possible to know, at a resolution of approximately 50 μm, where the initial point of the interaction occurs along the wafer thickness.

[0262] Figure 20 This is a schematic diagram of a detector module (also referred to as a chip or wafer) according to an embodiment. In this example, detector module 21 includes a semiconductor substrate or material comprising a plurality of active integrated pixels disposed in the semiconductor substrate. In a particular embodiment, the plurality of active integrated pixels are arranged in a grid or matrix or other pattern on the main side (front side) of the semiconductor substrate, as shown in the figure. The figure also shows the arrangement of pixels in different depth segments relative to an edge facing an X-ray source, where X-rays are incident on the detector module.

[0263] In the implementation, the detector module also includes additional processing circuitry, such as analog processing circuitry and / or digital processing circuitry, illustrated in the figures as readout circuitry, control circuitry, and analog-to-digital converter (ADC) circuitry. These additional processing circuitry may be implemented in or as one or more ASICs.

[0264] The additional processing circuitry is advantageously arranged in a semiconductor substrate on the same main side (front side) as the multiple active integrated pixels. In this case, the additional processing circuitry is preferably arranged on the main side at the edge opposite to the X-ray source and the incident X-rays, or at a portion or component connected thereto, as shown in the figure. This embodiment reduces any shielding area of ​​the detector module by reducing the portion of the detector module used for the additional processing circuitry. Furthermore, by arranging it at the location furthest from the incident edge, the additional processing circuitry is protected from the incident X-rays.

[0265] Figure 20 Active integrated pixels with so-called detector diodes (electrodes), readout electronics, and interconnects are also schematically shown. Each such active integrated pixel typically has a size in the μm range. In embodiments, the active integrated pixels are square, and typically all active integrated pixels in a detector module have the same shape and size. However, pixels of other shapes, such as rectangular, and / or in other shapes, can be used. Figure 21 The same detector module shown has active integrated pixels of different sizes and / or shapes. Figure 21 In this configuration, active integrated pixels have the same width but different depths. For example, the depth of the active integrated pixels can be for different depth segments and thus increase based on the distance to the edge of the X-ray incident on the detector module. This means that the active integrated pixel at the edge preferably has a smaller depth compared to the active integrated pixel closest to the opposite edge. In such an embodiment, the detector module may include active integrated pixels with two or more different depths.

[0266] Different pixel depths, especially pixel depths that are functions of depth segments or the distance to the edge where X-rays are incident on the detector module, can be used to customize the probability or likelihood of detecting events at active integrated pixels.

[0267] Depending on specific aspects of the proposed technology, analog signal processing (e.g., Figure 4 All or part of the analog processing shown can be integrated into the pixel to form a so-called active integrated pixel.

[0268] As mentioned, one aspect of the present invention relates to a side-facing photon counting detector. The side-facing photon counting detector includes at least one detector module having a corresponding edge facing the incident X-rays. The at least one detector module includes a semiconductor substrate.

[0269] In a particular example, the side-facing photon counting detector also includes multiple active integrated pixels arranged in a semiconductor substrate.

[0270] In the implementation, the edge photon counting detector includes multiple detector modules arranged side by side and / or stacked.

[0271] Edge photon counting detectors are typically made based on silicon, which is the semiconductor material used in the detector module.

[0272] To compensate for silicon's low blocking power, detector modules are typically oriented with their sides facing the X-ray source, thereby increasing absorption thickness. To handle the high photon flux in clinical CT, a segmented structure is preferably employed, dividing the active integrated pixels into depth segments. This is achieved by embedding individual active integrated pixels within these depth segments on a silicon substrate.

[0273] In a particular embodiment, the semiconductor substrate is made of floating zone (FZ) silicon. FZ silicon is very pure silicon obtained through vertical zone melting. In the vertical configuration, the molten silicon has sufficient surface tension to prevent charge separation. The absence of a sealed container prevents silicon contamination. Therefore, the concentration of light impurities in FZ silicon is extremely low. Due to the surface tension limitations during growth, the diameter of FZ silicon wafers is typically no greater than 200 mm. A polycrystalline ingot of ultrapure electronic-grade silicon is passed through an RF heating coil, which creates a locally molten zone from which the ingot grows. To initiate growth, a seed crystal is used at one end. The entire process is carried out in a vacuum chamber or purged with an inert gas. The molten zone carries away impurities, thus reducing the impurity concentration. Uniform concentrations of impurities can be incorporated using specialized doping techniques such as core doping, pellet doping, gas doping, and neutron transmutation doping.

[0274] In the implementation, the semiconductor substrate is made of high-resistivity silicon, such as high-resistivity FZ silicon. As used herein, high-resistivity silicon is defined as single-crystal silicon having a bulk resistivity greater than 1 kΩcm.

[0275] Multiple active integrated pixels can be realized as active integrated complementary metal-oxide-semiconductor (CMOS) pixels in a semiconductor substrate. Therefore, analog circuit systems with active integrated pixels can be produced using CMOS technology.

[0276] Figures 22 to 25 Various embodiments of this active integrated pixel with different analog readout electronics are shown. In these figures, the current generation component of the pixel is shown as a diode that outputs a current pulse or diode signal.

[0277] Figure 22 An embodiment of an active integrated pixel including an amplifier configured to generate an output signal based on current pulses generated by the active integrated pixel or a diode is shown. In this embodiment, the amplifier is a charge-sensitive amplifier (CSA) configured to integrate the current pulses into a voltage signal.

[0278] In this embodiment, the output signal (such as a voltage signal) from the amplifier (preferably a CSA) is routed to an external processing circuitry system disposed in the semiconductor substrate of the detector module, such as in the form of one or more ASICs, see [link to relevant documentation]. Figure 20 and Figure 21 The readout, control, and ADC in the process.

[0279] As the number of active integrated pixels in the detector module increases, the count rate per pixel decreases, and noise requirements are relaxed. This means that amplifiers with relatively low power consumption and low bandwidth can be used in active integrated pixels. Furthermore, single-ended amplifiers are preferred due to the nature of diodes. This further allows for less complex amplifiers. The lower the diode capacitance compared to using larger pixel sizes, the less dominant the input-related noise from the amplifier becomes.

[0280] Figure 23 Another implementation of an active integrated pixel is shown. In addition to the amplifier, this implementation includes a pulse shaper, also known as a shaping filter. The pulse shaper is configured to filter the output signal from the amplifier.

[0281] Preferably, a CSA is used to integrate the current pulse from the diode. Typically, this generates a slowly shifting voltage at the output of the CSA. To compensate for this behavior, a cancellation circuit (CC), such as a zero-pole cancellation circuit, is arranged to connect to the CSA and the pulse shaper. This zero-pole CC eliminates or at least suppresses the slow response of the CSA while maintaining the charge / current integration. Therefore, the time constant is instead determined by the shaping integration time of the pulse shaper.

[0282] In this embodiment, the output signal from the pulse shaper is routed to an external processing circuitry system disposed in the semiconductor substrate of the detector module, such as in the form of one or more ASICs, see [link to relevant documentation]. Figure 20 and Figure 21 The readout, control, and ADC in the process.

[0283] Figure 24 Another embodiment of the active integrated pixel is shown. This embodiment includes an analog storage device connected to and disposed downstream of a pulse shaper. The analog storage device can be implemented in the active integrated pixel to at least temporarily store and retain the output signal from the pulse shaper. This enables controlled reading of data from the active integrated pixel and the analog storage device, such as based on a control signal (ctrl) and / or at scheduled time instances (such as based on a clock signal (clk)).

[0284] like Figure 24 The analog storage device shown can also be used in, for example Figure 22In the illustrated implementation, there is no pulse shaper. In this case, the analog storage device is connected to the amplifier (CSA) or connected to the amplifier (CSA) via zero-pole CC.

[0285] In such Figure 25 In yet another embodiment shown, the pixel includes an event detector, represented as a comparator in the figure. This event detector is then configured to detect photon events by comparing the pulse amplitude of the output signal from the pulse shaper with a threshold, represented in the figure as a noise threshold.

[0286] In a particular implementation, the event detector is configured to generate a trigger signal based on a comparison of the pulse amplitude with a threshold, and preferably generates the trigger signal when the pulse amplitude is equal to or greater than, or exceeds, the threshold.

[0287] In this implementation, the readout of the analog storage device can be controlled by a trigger signal output by the event detector. Therefore, the readout of data in the analog storage device then preferably occurs only when the event detector confirms that the active integrated pixel has detected a photon event, as indicated by a pulse amplitude having (equal to or higher than) a noise floor represented by a noise threshold.

[0288] In other words, the comparator acting as an event detector can be used to signal the readout circuitry system, which is typically located externally relative to the active integrated pixel; see [link to relevant documentation]. Figure 20 and Figure 21 The readout circuitry reads the analog storage device based on a trigger signal from an event detector. The read data can then be further processed, such as with a threshold (T1-T). N For comparison, see [link / reference] Figure 4 , and / or digitized in the ADC, see Figure 20 and Figure 21 .

[0289] If no data is read from the analog storage device, it can be continuously refreshed, for example, by operating in a first-in, first-out (FIFO) manner. This allows data to be read asynchronously from the analog storage device, thereby reducing power consumption during the read process.

[0290] Trigger signals from the event detector can also be fed to adjacent active integrated pixels in the detector module to trigger them to store data, which can then be read out and further processed. This enables the detection of data characteristics even without passing a noise threshold.

[0291] In another implementation, the readout of the analog storage device is performed not only based on the trigger signal from the event detector in the active integrated pixel, but also based on the corresponding trigger signal from at least one adjacent active integrated pixel in the detector module.

[0292] Compared to existing solutions, the implementation of active integrated pixels enables a reduction in pixel size. This smaller size allows multiple active integrated pixels in a detector submodule to detect the charge cloud generated by a single X-ray photon. This, in turn, makes it possible to determine an estimate of charge diffusion originating from Compton interactions or interactions related to optical effects associated with X-ray photons in a specific detector submodule facing the photon counting detector, and to estimate, at least in part, the initial point of interaction of the X-ray photons along the thickness of the detector submodule, as described previously, based on the determined estimate of charge diffusion.

[0293] It should be understood that the methods and equipment described herein can be combined and rearranged in a variety of ways.

[0294] For example, a particular function may be implemented in hardware or in software for execution by a suitable processing circuitry system, or a combination thereof.

[0295] The steps, functions, processes, modules, and / or blocks described herein can be implemented in hardware using any conventional techniques, such as semiconductor technology, discrete circuitry, or integrated circuit technology, including general-purpose electronic circuits and application-specific circuits.

[0296] Specific examples include one or more appropriately configured digital signal processors and other known electronic circuits, such as discrete logic gates interconnected to perform specific functions, or application-specific integrated circuits (ASICs).

[0297] Alternatively, at least some of the steps, functions, processes, modules and / or blocks described herein may be implemented in software such as a computer program for execution by a suitable processing circuitry system such as one or more processors or processing units.

[0298] Examples of processing circuitry systems include, but are not limited to, one or more microprocessors, one or more digital signal processors (DSPs), one or more central processing units (CPUs), video acceleration hardware, and / or any suitable programmable logic circuitry, such as one or more field-programmable gate arrays (FPGAs) or one or more programmable logic controllers (PLCs).

[0299] It should also be understood that the general processing power of any conventional device or unit in which the proposed technology is implemented can be reused. Existing software can also be reused, for example, by reprogramming existing software or by adding new software components.

[0300] According to another aspect, an X-ray imaging system is provided, which includes an X-ray detector system and / or a coincidence detection system.

[0301] For example, an X-ray imaging system can be a computed tomography (CT) system.

[0302] In a particular example, the X-ray imaging system also includes an associated image processing device connected to the X-ray detector system for performing image reconstruction.

[0303] According to the fourth aspect, a corresponding computer program and computer program product are provided.

[0304] In particular, a computer program is provided that includes the following instructions: when executed by a processor, these instructions cause the processor to perform the methods described herein.

[0305] For example, a computer program product comprising a non-transitory computer-readable medium on which such computer program is stored may also be provided.

[0306] Figure 26 This is a schematic diagram illustrating an example of a computer implementation according to an embodiment. In this particular example, system 200 includes a processor 210 and a memory 220 containing instructions executable by the processor, thereby enabling the processor to perform the steps and / or actions described herein. The instructions are generally organized as computer programs 225, 235, which may be pre-configured in memory 220 or downloaded from external storage device 230. Optionally, system 200 includes an input / output interface 240 that may be interconnected to processor 210 and / or memory 220 to enable the input and / or output of relevant data, such as input parameters and / or resulting output parameters.

[0307] In a particular example, the memory includes a set of instructions that can be executed by a processor, thereby enabling the processor to determine an estimate or measurement of charge diffusion and, based on the determined estimate of charge diffusion, to estimate the starting point of the interaction along the thickness of the detector submodule.

[0308] The term “processor” should be interpreted in a general sense as any system or device capable of executing program code or computer program instructions to perform specific processing, determination, or computational tasks.

[0309] Therefore, a processing circuit system including one or more processors is configured to perform well-defined processing tasks (such as those described herein) when executing a computer program.

[0310] The processing circuitry system is not limited to performing the steps, functions, processes and / or blocks described above, but can also perform other tasks.

[0311] The proposed technology also provides a computer program product comprising computer-readable media 220 and 230 on which such computer program is stored.

[0312] For example, software or computer programs 225, 235 may be implemented as computer program products, which are typically carried or stored on computer-readable media 220, 230, particularly non-volatile media. Computer-readable media may include one or more removable or non-removable memory devices, including but not limited to: read-only memory (ROM), random access memory (RAM), compressed optical disc (CD), digital versatile optical disc (DVD), Blu-ray disc, universal serial bus (USB) storage, hard disk drive (HDD) storage devices, flash memory, magnetic tape, or any other conventional memory device. Thus, a computer program may be loaded into the operating memory of a computer or equivalent processing device for execution by its processing circuitry.

[0313] A method flow, when executed by one or more processors, can be viewed as a computer action flow. A corresponding device, system, and / or apparatus can be defined as a set of functional modules, where each step executed by the processor corresponds to a functional module. In this case, the functional module is implemented as a computer program running on the processor. Therefore, the device, system, and / or apparatus can alternatively be defined as a set of functional modules, where these functional modules are implemented as computer programs running on at least one processor.

[0314] Computer programs residing in memory can therefore be organized into appropriate functional modules that are configured to perform at least a portion of the steps and / or tasks described herein when the computer program is run by a processor.

[0315] Alternatively, modules can be implemented primarily through hardware modules or alternatively as pure hardware logic. The degree of software relative to hardware is purely a matter of implementation choice.

[0316] Figure 27 This is a schematic flowchart illustrating an example of a method for obtaining or determining information about radiation incident on an X-ray detector.

[0317] Basically, the method includes the following steps:

[0318] S1: X-ray radiation is detected using a photon-counting X-ray detector, wherein the photon-counting X-ray detector is configured to operate with a wide energy X-ray spectrum emitted from a local X-ray source with a maximum energy of less than 160 keV;

[0319] S2: Record the timing information of photon interactions in the photon counting X-ray detector;

[0320] S3: Based on the timing information and the information about the positioning of the X-ray source relative to the X-ray detector, obtain or determine information about the radiation incident on the X-ray detector, including at least one of the number of incident photons in a specific region, the spatial distribution of incident photons, and the energy distribution of incident photons.

[0321] In a specific, non-limiting example, the steps for obtaining or determining information about the radiation incident on the X-ray detector include:

[0322] The set of photon interactions is identified based on the likelihood of at least one set of photon interactions generated by a single photon incident on an X-ray detector, wherein timing information recorded about the photon interactions in the set is consistent with all photon interactions in the set originating from a single incident photon, wherein the likelihood is based on the positioning of the X-ray source relative to the X-ray detector and at least one of the following: the Compton scattering formula, the Klein-Nenko formula, the Lambert-Beer law, the photoelectric effect, the Compton effect, or Rayleigh scattering X-ray interaction cross-section, and simulations of photon transport; and

[0323] Based on the set of photon interactions or based on the likelihood, information is obtained or determined regarding at least one of the number of incident photons in a specific region, the spatial distribution of incident photons, and the energy distribution of incident photons.

[0324] Other illustrative and optional method steps have previously been described in conjunction with the system description as corresponding functions, i.e., steps and / or actions that will be performed by various systems and / or system components.

[0325] The above embodiments are given as examples only, and it should be understood that the proposed technology is not limited thereto. Those skilled in the art will understand that various modifications, combinations, and changes can be made to these embodiments without departing from the scope of the invention as defined by the appended claims. In particular, where technically possible, different portions of solutions from different embodiments can be combined in other configurations.

Claims

1. An X-ray detector system (5), the X-ray detector system comprising: - Photon-counting X-ray detector (20), which is used to detect X-ray radiation from an X-ray source and record the energy and position of each photon interaction in multiple photon interactions to provide the resulting dataset; and - A coincidence detection system (60) is configured to i) organize the resulting dataset into smaller subsets, each subset representing an interaction occurring in the detector during a time window, also referred to as a snapshot, and ii) determine information about the radiation incident on the X-ray detector for each snapshot based on at least one of the following: the number of incident photons in a particular region, the spatial distribution of the incident photons, and the energy distribution of the incident photons: For each of a plurality of incident photons, information regarding the time or timing of detected photon interactions within the set of photon interactions generated by the incident photons scattered by at least one Compton in the X-ray detector, corresponding to a chain of one or more Compton interactions and possible photoelectric interactions, is combined with information regarding the location of the photon interactions, information regarding the deposited energy in the photon interactions within the X-ray detector, and information regarding the positioning of the X-ray source relative to the X-ray detector. The coincidence detection system (60) is configured to identify and pair interactions belonging to the same incident photon for each of the plurality of incident photons.

2. The X-ray detector system of claim 1, wherein the X-ray detector system is configured to operate with a broad energy X-ray spectrum having a maximum energy of less than 160 keV; the X-ray spectrum is emitted by the X-ray source, the X-ray source being a local X-ray source with a range of less than 0.5 millispheroids observed from a point on the X-ray detector.

3. The X-ray detector system of claim 1, wherein the coincidence detection system is configured to operate based on the photon scattering model by combining a photon scattering model with information about the positioning of the X-ray source relative to the X-ray detector to determine and / or obtain information about the radiation.

4. The X-ray detector system of claim 3, wherein the coincidence detection system is configured to combine the photon scattering model and prior knowledge of the location of the X-ray source with prior knowledge of the probability of different incident X-ray energy distributions to determine and / or obtain information about the radiation.

5. The X-ray detector system according to any one of claims 1 to 4, wherein the X-ray detector is a photon-counting multi-compartment X-ray detector capable of distinguishing different photon interaction energies, and the coincidence detection system is configured to use information about the photon interaction energy to determine information about the radiation.

6. The X-ray detector system according to any one of claims 1 to 4, wherein the coincidence detection system is configured to determine and / or obtain information about the radiation based on measurements of the time and / or timing of photon interactions.

7. The X-ray detector system according to any one of claims 1 to 4, wherein the coincidence detection system is configured to determine and / or obtain information about the radiation incident on the detector based on identifying at least one set of photon interactions generated by a single incident photon.

8. The X-ray detector system of claim 7, wherein the coincidence detection system is configured to generate and / or obtain information about the radiation incident on the X-ray detector based on identifying at least two sets of photon interactions that have been generated by at least two different incident photons, wherein all photon interactions in each set have been generated by a single incident photon, and wherein the coincidence detection system is configured to identify the at least two sets of photon interactions as having been generated by at least two different incident photons based on comparing these sets of photon interactions with at least one other set of photon interactions.

9. The X-ray detector system according to any one of claims 1 to 4, wherein the coincidence detection system is configured to generate and / or obtain information about the radiation incident on the X-ray detector based on information about the timing of photon interactions combined with at least one angle defined by at least two photon interaction positions, and / or based on at least one angle defined by the incident radiation direction and the two photon interaction positions.

10. The X-ray detector system according to any one of claims 1 to 4, wherein the X-ray detector is a silicon detector.

11. The X-ray detector system of claim 10, wherein the X-ray detector system is configured to distinguish between Compton interactions and photoelectric interactions based on an energy threshold.

12. The X-ray detector system according to any one of claims 1 to 4, wherein the X-ray detector system has a high attenuation blocker for reducing scattering within the X-ray detector.

13. The X-ray detector system according to any one of claims 1 to 4, wherein the X-ray detector system is configured to employ logic for estimating the location of the interaction based on an estimate of the amount of charge diffusion.

14. The X-ray detector system according to any one of claims 1 to 4, wherein the coincidence detection system is configured to operate based on a model of the X-ray detector.

15. The X-ray detector system according to any one of claims 1 to 4, wherein the coincidence detection system is configured for operation based on a photon scattering model, and the photon scattering model is based on at least one of the Compton scattering formula, the Klein-Nenko formula, the Lambert-Beer law, the photoelectric effect, the X-ray interaction cross section of the Compton effect or Rayleigh scattering, and the simulation of photon transport.

16. The X-ray detector system according to any one of claims 1 to 4, wherein the coincidence detection system is configured for operation based on a photon scattering model, and the photon scattering model includes Rayleigh scattering, or alternatively excludes Rayleigh scattering.

17. The X-ray detector system according to any one of claims 1 to 4, wherein the coincidence detection system is configured to process the photon interactions detected in the entire volume of the detector or in a sub-volume of the detector independently of at least one other sub-volume.

18. The X-ray detector system according to any one of claims 1 to 4, wherein the coincidence detection system is configured to obtain and / or determine information about the incident radiation based on at least one of the maximum likelihood method, maximum a posteriori method, neural network, support vector machine or decision tree-based method.

19. The X-ray detector system according to any one of claims 1 to 4, wherein the coincidence detection system is configured to obtain and / or determine information about radiation incident on the X-ray detector based on assigning at least one likelihood to at least one set of photon interactions, wherein the likelihood is based on the probability of observing these photon interactions.

20. The X-ray detector system according to any one of claims 1 to 4, wherein the coincidence detection system is configured to obtain and / or determine information about radiation incident on the X-ray detector based on optimized likelihood, wherein the likelihood is based on the probability of observing these photon interactions.

21. The X-ray detector system according to any one of claims 1 to 4, wherein the coincidence detection system is configured to obtain and / or determine information about radiation incident on the X-ray detector based on assigning at least one likelihood to at least one set of photon interactions, wherein the likelihood is based on the probability of observing these photon interactions when all of them originate from a single incident photon.

22. The X-ray detector system according to any one of claims 1 to 4, wherein the coincidence detection system is configured to assign the interaction to a set of photon interactions for each of a plurality of photon interactions based on at least one likelihood of observing these photon interactions from a single incident photon.

23. The X-ray detector system of claim 22, wherein the coincidence detection system is configured to assign the plurality of photon interactions to a set of photon interactions in such a manner that no interaction is assigned to more than one set.

24. The X-ray detector system of claim 23, wherein the coincidence detection system is configured to assign the interaction order to the photon interactions in at least one set of the set based on the likelihood of at least one interaction order.

25. The X-ray detector system of claim 24, wherein the coincidence detection system is configured to assign an estimated position of photon incidence to at least one set of photon interactions based on the position of a first photon interaction in the set as specified by at least one interaction sequence.

26. The X-ray detector system of claim 25, wherein the X-ray detector system is configured to estimate the energy of at least one incident photon based on the detected energy of photon interactions within at least one set of photon interactions originating from a single incident photon.

27. The X-ray detector system of claim 21, wherein the X-ray detector system is configured to estimate the number of photons incident on the X-ray detector or at least one sub-volume of the X-ray detector in at least one time interval based on the at least one likelihood.

28. The X-ray detector system of claim 27, wherein the likelihood is calculated based on a prior probability distribution of the set of spectra incident on the X-ray detector.

29. The X-ray detector system according to any one of claims 1 to 4, wherein the coincidence detection system is configured to sum the counts of measurements over time intervals and read out at least one of them from the photon counting X-ray detector before applying data to the measurements.

30. The X-ray detector system according to any one of claims 1 to 4, wherein the X-ray detector system is configured to output information about the radiation incident on the X-ray detector as input data to at least one of an image reconstruction algorithm, a basic material decomposition algorithm, a denoising algorithm, a deblurring algorithm, a stacking correction algorithm, or a spectral distortion correction algorithm.

31. An X-ray imaging system comprising an X-ray detector system according to any one of claims 1 to 25 and 27 to 28.

32. The X-ray imaging system of claim 31, wherein the X-ray imaging system is configured to estimate the energy of at least one incident photon based on the detected energy of photon interactions within at least one set of photon interactions originating from a single incident photon.

33. A method for determining information about radiation incident on an X-ray detector, the method comprising the steps of: - Use a photon-counting X-ray detector to detect X-ray radiation, wherein the photon-counting X-ray detector is configured to operate with a wide energy X-ray spectrum emitted from a local X-ray source with a maximum energy of less than 160 keV; - Record the timing information of photon interactions in the photon counting X-ray detector, the location information of the photon interactions, and the information of the deposited energy in the photon interactions to provide the resulting dataset; - The resulting dataset is organized into smaller subsets, each representing an interaction occurring in the X-ray detector during a time window, also known as a snapshot, and information about the radiation incident on the X-ray detector is determined for each snapshot based on at least one of the following: the number of incident photons in a specific region, the spatial distribution of the incident photons, and the energy distribution of the incident photons: For each of a plurality of incident photons, information regarding the time or timing of detected photon interactions within the set of photon interactions generated by the incident photons scattered by at least one Compton in the X-ray detector, corresponding to a chain of one or more Compton interactions and possible photoelectric interactions, is combined with information regarding the location of the photon interaction, information regarding the deposited energy in the photon interaction, and information regarding the positioning of the X-ray source relative to the X-ray detector, and The step of determining information about the radiation incident on the X-ray detector for each snapshot is based on identifying and pairing photon interactions belonging to the same incident photon for each of the plurality of incident photons.

34. The method of claim 33, wherein the step of obtaining or determining information about the radiation incident on the X-ray detector comprises: - The set of photon interactions is identified based on the likelihood of at least one set of photon interactions generated by a single photon incident on the X-ray detector, wherein the timing information recorded with respect to the photon interactions in the set is consistent with all photon interactions in the set originating from a single incident photon, wherein the likelihood is based on the positioning of the X-ray source relative to the X-ray detector and at least one of the following: the Compton scattering formula, the Klein-Nenko formula, the Lambert-Beer law, the photoelectric effect, the Compton effect, or the simulation of Rayleigh scattering X-ray interaction cross-sections and photon transport. as well as - Based on the set of photon interactions or based on the likelihood, obtain or determine information about at least one of the number of incident photons in a specific region, the spatial distribution of incident photons, and the energy distribution of incident photons.

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