Supporting calibration and adjustment of operational settings of an x-ray imaging system

Abstract

To provide a system and a method for supporting calibration of an X-ray imaging system.SOLUTION: The x-ray imaging system comprises a multi-bin photon-counting x-ray detector having a plurality of energy bin thresholds. The method comprises performing a series of X-ray attenuation measurements or measurement scans of at least one object using different settings of energy bin thresholds to obtain information about a material composition associated with the object. The method further includes determining, for each X-ray attenuation measurement or measurement scan, a value of at least one performance metric associated with the X-ray imaging system, and selecting a custom set of energy bin thresholds based on the determined values of the performance metric across the set of X-ray attenuation measurements or measurement scans. The method also includes determining calibration data that couples the selected custom set of energy bin thresholds to information about at least the material composition.SELECTED DRAWING: Figure 11

Description

[Technical Field]

[0001] The present technology relates to X-ray technology and X-ray imaging, and in particular to technology for assisting in the calibration of X-ray imaging systems. More specifically, the proposed technology relates to a method and system for assisting in the calibration of an X-ray imaging system having a multi-bin photon counting X-ray detector, a corresponding computed tomography (CT) imaging system, and a method and corresponding computer program product for adjusting the operating settings of an X-ray imaging system, such as a CT imaging system, to improve image quality. [Background technology]

[0002] Radiological imaging, such as CT imaging systems and other common X-ray imaging systems, has been used for many years in medical applications such as medical diagnosis and treatment.

[0003] A typical X-ray imaging system, such as a CT imaging system, includes an X-ray source, an X-ray detector, and an associated image processing system. The X-ray detector includes multiple detector modules, each containing one or many detector elements, for independently measuring X-ray intensity. The X-ray source emits X-rays, which pass through a subject or object being imaged and are received by the X-ray detector. The X-ray source and X-ray detector are typically positioned on a rotating member in a gantry to rotate around the subject or object. The emitted X-rays are attenuated as they pass through the subject or object, and the resulting transmitted X-rays are measured by the X-ray detector. The X-ray detector is connected to a digital acquisition system (DAS), and the measured X-ray data is transferred to an image processing system, which reconstructs an image of the subject or object.

[0004] It may be useful to provide a brief overview of an exemplary general x-ray imaging system according to the prior art with reference to Figure 1A. In this exemplary embodiment, x-ray imaging system 100 comprises an x-ray source 10, an x-ray detector 20, and an associated image processing system 30. In general, x-ray detector 20 is configured to register radiation from x-ray source 10 that has passed through an object, specimen, or portion thereof, optionally collected by x-ray optics or a collimator. x-ray detector 20 is connectable to image processing system 30 via suitable readout electronics, at least a portion of which is incorporated within x-ray detector 20, to enable image processing and / or image reconstruction by image processing system 30.

[0005] Conventional CT imaging systems include an X-ray source and an X-ray detector positioned to acquire projection images of a subject or object at different viewing angles covering at least 180 degrees. This is most commonly achieved by mounting the X-ray source and detector on a support that can rotate around the subject or object, such as a rotating member of a gantry. An image containing projections registered on different detector elements for different viewing angles is called a sinogram. Hereinafter, even if the detector is two-dimensional (2D), the collection of projections registered on different detector elements for different viewing angles will be referred to as a sinogram, and the sinogram will be considered a three-dimensional (3D) image.

[0006] FIG. 1B is a schematic diagram illustrating an example of a prior art X-ray imaging system setup, showing a projection line from an X-ray source through an object to an X-ray detector.

[0007] A further development of X-ray imaging is energy-resolved X-ray imaging, also known as spectral X-ray imaging. This can be achieved by rapidly switching the X-ray source between two different emission spectra, by using two or more X-ray sources that emit different X-ray spectra, or by using an energy-discriminating detector that measures incident radiation at two or more energy levels. One example of such a detector is a multi-bin photon-counting detector, in which each registered photon generates a current pulse that is compared to a set of thresholds to count the number of photons incident on each of a number of energy bins.

[0008] Spectral X-ray projection measurements generate projection images at each energy level. By calculating a weighted sum of these projection images, it is possible to optimize the contrast-to-noise ratio (CNR) for a given imaging task. Details are given in "SNR and DQE analysis of broad spectrum X-ray imaging," Tapiovaara and Wagner, Phys. Med. Biol. 30, 519.

[0009] Another technique made possible by energy-resolved x-ray imaging is basis material decomposition, which exploits the fact that all materials composed of elements with low atomic numbers, such as human tissue, have linear attenuation coefficients whose energy dependence can be approximately expressed as a linear combination of two (or more) basis functions.

[0010] μ(E)=a1f1(E)+a2f2(E) where f1 and f2 are basis functions and a1 and a2 are the corresponding basis coefficients. More generally, f i is the basis function, a iare the corresponding basis coefficients, i = 1,...,N, where N is the total number of basis functions. If there are one or more elements in the imaging volume with sufficiently high atomic numbers that their K-edges lie in the energy range used for imaging, then one basis function must be added for each such element. In medical imaging, such K-edge elements might typically be iodine or gadolinium, substances used as contrast agents.

[0011] Basis material decomposition is described in "Energy-selective reconstructions in X-ray computerized tomography", Alvarez, Macovski, Phys Med Biol. 1976; 21(5):733-744. In basis material decomposition, the integral value Ai = ∫ of each basis coefficient is l a i dl,i=1,…,N, where N is the number of basis functions) is estimated from the measured data at each projection line l from the source to the detector element. In one implementation, this is achieved by first expressing the expected number of registration counts in each energy bin as a function of Ai.

[0012]

number

[0013] Next, Ai can be estimated using maximum likelihood under the assumption that the counts in each bin are Poisson-distributed random variables. This is achieved by minimizing the negative log-likelihood function. See, for example, "K-edge imaging in X-ray computed tomography using multi-bin photon counting detectors," Roessl and Proksa, Phys. Med. Biol. 52 (2007), 4679-4696.

[0014]

number

[0015] As a result, the estimated basis coefficient line integral for each projection line

number

[0016] Improving the image quality of X-ray imaging systems is undoubtedly an important area for ensuring the quality and safety of patient care, and is relevant to a variety of approaches, not least in the field of spectroscopic X-ray imaging. Therefore, there remains a general demand for improved image quality, such as reduced noise, increased contrast-to-noise ratio (CNR), and improved patient dose efficiency. [Prior art documents] [Patent documents]

[0017] [Patent Document 1] U.S. Patent No. 8,183,535 Summary of the Invention

[0018] This Summary introduces concepts that are described in more detail in the Detailed Description. This Summary is not intended to identify essential features of the claimed subject matter, nor should it be used to limit the scope of the claimed subject matter.

[0019] According to one aspect, a method for supporting calibration of an X-ray imaging system is provided, the X-ray imaging system comprising a multi-bin photon counting X-ray detector having multiple energy bin thresholds. The method includes performing a set of X-ray attenuation measurements or measurement scans of at least one object using different settings of the energy bin thresholds, and obtaining information about material composition related to the at least one object.The method further includes determining, for each X-ray attenuation measurement or measurement scan, a value of at least one performance metric related to the X-ray imaging system, and selecting a custom set of energy bin thresholds based on the determined values ​​of the at least one performance metric over the set of X-ray attenuation measurements or measurement scans. The method also includes determining calibration data coupling the selected custom set of energy bin thresholds to at least the information about material composition.

[0020] According to one aspect, there is provided a system for assisting in the calibration of an X-ray imaging system including a multi-bin photon-counting X-ray detector having multiple energy bin thresholds, the system being configured to perform the aforementioned method.

[0021] According to one aspect, there is provided a CT imaging system comprising the above-described system for assisting in the calibration of an X-ray imaging system.

[0022] According to one aspect, a method for adjusting operational settings for an X-ray imaging system comprising a multi-bin photon counting X-ray detector having multiple energy bin thresholds is provided. The method includes obtaining information representative of material composition related to at least one object and selecting a custom set of energy bin thresholds based on at least the information representative of material composition by using at least the information representative of material composition as input to a look-up data structure or function holding calibration data for retrieval of a set of energy bin thresholds that matches or corresponds to the information representative of material composition.The method further includes applying the selected custom set of energy bin thresholds as at least part of operational settings of the multi-bin photon counting X-ray detector of the X-ray imaging system.

[0023] According to one aspect, there is provided a system for adjusting operational settings of an X-ray imaging system including a multi-bin photon-counting X-ray detector having multiple energy bin thresholds, the system being configured to perform the aforementioned method.

[0024] According to one aspect, there is provided a CT imaging system comprising the above-described system for adjusting operational settings of an X-ray imaging system.

[0025] According to an aspect, there is provided a computer program product including a non-volatile computer readable storage medium having stored thereon a computer program, the computer program including instructions that, when executed by a processor, cause the processor to perform any of the methods described above.

[0026] The proposed technique enables multi-energy bin photon counting detectors with the ability to improve the diagnostic quality of X-ray images, while also realizing that energy bin thresholds must be carefully selected to improve performance metrics such as overall contrast-to-noise ratio. The placement of energy bin thresholds is critical to improving image quality. Therefore, we propose an improved adaptive energy threshold selection system and method. [Brief explanation of the drawings]

[0027] The various aspects of the present disclosure may be better understood by reference to the accompanying drawings and by reading the detailed description. [Figure 1A] FIG. 1 is a schematic diagram illustrating an example of an X-ray imaging system. [Figure 1B] FIG. 1 is a schematic diagram illustrating an example of an X-ray imaging system. [Figure 2] FIG. 10 is a schematic diagram showing another example of an X-ray imaging device such as a CT imaging device. [Figure 3] 1 is a schematic block diagram of a CT imaging system as an example of an X-ray imaging system. [Figure 4] FIG. 10 is a schematic diagram showing another example of related parts of an X-ray imaging device such as a CT imaging device. [Figure 5] 1 is a schematic diagram of a photon counting circuit and / or device according to an exemplary embodiment. [Figure 6] 1 is a schematic diagram illustrating an example of a solid-state detector sub-module according to an exemplary embodiment. [Figure 7] FIG. 10 is a schematic diagram illustrating an example of a solid-state detector sub-module according to another exemplary embodiment. [Figure 8A] FIG. 10 is a schematic diagram illustrating an example of a solid-state detector sub-module according to yet another exemplary embodiment. [Figure 8B] FIG. 1 is a schematic diagram illustrating an example of a set of tiled detector sub-modules, each of which is a depth-separated detector sub-module, with an application specific integrated circuit (ASIC) or corresponding circuitry located below the detector sub-module when viewed from the direction of incident X-rays. [Figure 9] FIG. 1 is a schematic diagram illustrating an example of a CT imaging device. [Figure 10] FIG. 1 is a schematic diagram illustrating an example design of an X-ray source and X-ray detector system. [Figure 11] 1 is a schematic flow diagram illustrating a method for assisting in the calibration of an X-ray imaging system. [Figure 12] 1 is a schematic flow diagram illustrating a method for assisting in the calibration of an X-ray imaging system. [Figure 13]1 is a schematic diagram illustrating various options for calibration data for an X-ray imaging system in accordance with an exemplary embodiment; [Figure 14] 1 is a schematic flow diagram illustrating a method for adjusting operational settings of an x-ray imaging system. [Figure 15] FIG. 2 is a schematic diagram illustrating an example of the function or structure of lookup data. [Figure 16] FIG. 1 is a schematic diagram illustrating an example of a method for obtaining information about material composition based on scan type. [Figure 17] 1 is a schematic block diagram illustrating a system for assisting in the calibration of an X-ray imaging system. [Figure 18] 1 is a schematic block diagram illustrating a system for adjusting operational settings of an x-ray imaging system. [Figure 19] 1 is a schematic flow diagram illustrating a method for adjusting operational settings of an X-ray imaging system in accordance with an exemplary embodiment. [Figure 20] FIG. 1 is a schematic diagram illustrating an example of an arrangement of detector sub-modules and an associated arrangement of detector sub-pixels, according to an exemplary embodiment. [Figure 21] FIG. 1 is a schematic flow diagram illustrating a method for assisting in the calibration of an X-ray imaging system, in accordance with an exemplary embodiment. [Figure 22] FIG. 1 is a schematic flow diagram illustrating a method for assisting in the calibration of an X-ray imaging system, in accordance with an exemplary embodiment. [Figure 23] 1 is a table illustrating example calibration data for assisting in the calibration of an X-ray imaging system in accordance with an exemplary embodiment. [Figure 24] 10 is a graph illustrating an example of the relationship between energy (keV) and noise standard deviation of monochromatic decay for different energy bin thresholds, according to an example embodiment. [Figure 25] 1 is a schematic flow diagram illustrating a method for adjusting operational settings of an X-ray imaging system in accordance with an exemplary embodiment. [Figure 26] FIG. 1 is a schematic diagram illustrating an example implementation of a computer according to an embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0028] Next, embodiments of the present disclosure will be described by way of example with reference to the drawings.

[0029] For a better understanding, it will be useful to follow with an introductory description of a non-limiting example of an overall X-ray imaging system in which data processing and transfer in accordance with the concepts of the present invention may be implemented.

[0030] 2 is a schematic diagram illustrating an example of an X-ray imaging device 100, such as a CT imaging device. The X-ray imaging system 100 includes an X-ray source 10 that emits X-rays, an X-ray detector 20 that detects the X-rays after they pass through a subject, an analog processing circuit 25 that processes and digitizes raw electrical signals from the X-ray detector, a digital processing circuit 40 that can perform further processing operations on the measured data, such as applying corrections, temporarily storing it, or filtering, and a computer 50 that can store the processed data and perform further post-processing and / or image reconstruction. The digital processing circuit 40 may be implemented as a digital processor. According to an exemplary embodiment, all or part of the analog processing circuit 25 may be implemented in the X-ray detector 20. The X-ray source and the X-ray detector may be coupled to a rotating member of a gantry 11 of the CT imaging system 100.

[0031] The entire X-ray detector may be considered an X-ray detector system 20, or an X-ray detector 20 combined with associated analog processing circuitry 25.

[0032] An image processing system 30, in communication with and electrically coupled to the analog processing circuit 25, may include a digital processing circuit 40 and / or a computer 50 and may be configured to perform image reconstruction based on image data from the x-ray detector. Thus, the image processing system 30 may be viewed as the computer 50, or a combined system of the digital processing circuit 40 and the computer 50, or as the digital processing circuit 40 alone, where the digital processing circuit is further specialized for image processing and / or reconstruction.

[0033] One example of a commonly used X-ray imaging system is a CT imaging system, which may include an X-ray source or tube that produces a fan or cone beam of X-rays and an opposing array of X-ray detectors that measure the percentage of the X-rays that pass through the patient or object. The X-ray source or tube and X-ray detectors are mounted on a gantry 11 that can rotate around the object.

[0034] FIG. 3 shows a schematic diagram of a CT imaging system 100 as an example of an X-ray imaging system. The CT imaging system comprises a computer 50 that receives commands and scanning parameters from an operator via an operator console 60, which may have a display 62 and some form of operator interface, such as a keyboard, mouse, joystick, touchscreen, or other input device. The commands and parameters provided by the operator are used by the computer 50 to provide control signals to an X-ray controller 41, a gantry controller 42, and a table controller 43. Specifically, the X-ray controller 41 provides power and timing signals to the X-ray source 10 to control the emission of X-rays to an object or patient residing on a table 12. The gantry controller 42 controls the rotational speed and position of the gantry 11, which comprises the X-ray source 10 and the X-ray detector 20. By way of example, the X-ray detector 20 may be a photon-counting X-ray detector. The table controller 43 controls and determines the position of the patient table 12 and the scan area of ​​the patient. There is also a detector controller 44 configured to control and / or receive data from the x-ray detector 20 .

[0035] In an embodiment, computer 50 also performs post-processing and image reconstruction of image data output from x-ray detector 20. Computer 50 thereby corresponds to image processing system 30 as shown in Figures 1 and 2. An associated display 62 allows an operator to observe reconstructed images and other data from computer 50.

[0036] An X-ray source 10 disposed on a gantry 11 emits X-rays. An X-ray detector 20, which may be in the form of a photon-counting X-ray detector, detects the X-rays after they pass through an object or patient. The X-ray detector 20 is formed, for example, by a plurality of pixels, also called sensors or detector elements, and associated image processing circuitry, such as an application-specific integrated circuit (ASIC), disposed on a detector module. Some of the analog processing is implemented in the pixels, while the remaining processing is implemented, for example, in the ASIC. In one embodiment, the image processing circuit (ASIC) digitizes the analog signals from the pixels. The image processing circuit (ASIC) may also comprise digital processing that can perform further processing operations on the measurement data, such as applying corrections, temporary storage, and / or filtering. During a scan to acquire X-ray projection data, the gantry and the components mounted thereon rotate about an isocenter 13.

[0037] Modern X-ray detectors typically require the conversion of incident X-rays into electrons. This is usually achieved by the photoelectric effect or Compton interaction, and the resulting electrons typically emit secondary visible light until they lose energy, which is detected by a photosensitive material. Semiconductor-based detectors also exist, in which the electrons generated by the X-rays create charge electron-hole pairs that are collected by applying an electric field.

[0038] Some detectors operate in energy-integrated mode, meaning that they provide a signal integrated from many x-rays: the output signal is proportional to the total energy deposited by the detected x-rays.

[0039] Photon-counting and energy-resolved X-ray detectors are becoming increasingly common in medical X-ray applications. Photon-counting detectors have the advantage that they can, in principle, measure the energy of each X-ray, providing additional information about the composition of the object being examined. This information can be used to improve image quality and reduce radiation dose.

[0040] Typically, photon-counting X-ray detectors determine the energy of a photon by comparing the height of the electrical pulse generated by the photon's interaction within the detector material to a series of comparator voltages. These comparator voltages are also called energy thresholds. The analog voltage of the comparator is typically set by a digital-to-analog converter (DAC). The DAC converts the digital setting sent from the controller into an analog voltage against which the height of the photon pulse can be compared.

[0041] Photon-counting detectors count the number of photons that interact within the detector during a measurement period. A new photon is typically identified by an electrical pulse whose height exceeds the comparator voltage of at least one comparator. Once a photon is identified, the event is stored by incrementing a digital counter associated with the channel.

[0042] When multiple different thresholds are used, an energy-discriminating photon counting detector is obtained, and detected photons can be sorted into energy bins corresponding to the various thresholds. This type of photon counting detector is sometimes called a multi-bin detector. Generally, 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 eliminated. In other words, in an energy-discriminating photon counting detector, the pulse height is compared to a number N of programmable thresholds (T1-TN) in the comparator, and the photon counts are sorted according to their pulse height, which is proportional to the energy. In other words, a photon counting detector consisting of two or more comparators is called a multi-bin photon counting detector. In a multi-bin photon counting detector, photon counts are typically stored in a set of counters, one for each energy threshold. For example, one count can be assigned to the highest energy threshold exceeded by the photon pulse. In another example, a counter records the number of times the photon pulse exceeds each energy threshold.

[0043] As an example, edge-on is a special, non-limiting design for photon-counting detectors, where the X-ray sensors such as X-ray detector elements or pixels are oriented edge-on to incoming X-rays.

[0044] For example, such a photon counting detector can have pixels in at least two directions, one of which has a component in the direction of the X-ray. Such an edge-on photon counting detector may also be referred to as a depth-segment photon counting detector, having pixels in two or more depth segments in the direction of the incident X-ray. It should be noted that one detector element may correspond to one pixel, and / or multiple detector elements may correspond to one pixel, and / or data signals from multiple detector elements may be used for one pixel.

[0045] Alternatively, the pixels may be arranged as an array (non-depth-resolved) in a direction substantially perpendicular to the direction of the incident X-rays, and each of the pixels may be oriented edge-on with respect to the incident X-rays. In other words, the photon-counting detector may be non-depth-resolved while still being positioned edge-on with respect to the incident X-rays.

[0046] By placing the edge-on photon counting detector on edge, the absorption efficiency can be increased, in which case the absorption depth can be chosen to be any length, and the edge-on photon counting detector can still be fully depleted without going to very high voltages.

[0047] The conventional mechanism for detecting X-ray photons directly through semiconductor detectors basically works as follows: the energy of the X-ray interaction in the detector material is converted into electron-hole pairs inside the semiconductor detector, and the number of electron-hole pairs is generally proportional to the photon energy. The electrons and holes drift towards the electrodes and backside of the detector (or vice versa). During this drift, the electrons and holes induce a current in the electrodes, and this current can be measured.

[0048] As shown in FIG. 4 , signals are routed from the detector elements 22 of the x-ray detector via paths 26 to the input of an analog processing circuit (e.g., an ASIC) 25. It should be understood that the term Application Specific Integrated Circuit (ASIC) is to be broadly interpreted as a general circuit configured for use with a specific application. The ASIC processes the electrical charge generated from each x-ray and converts it into digital data that can be used to obtain measurement data such as photon count and / or estimated energy. The ASIC is configured to interface with the digital processing circuit so that the digital data is sent to the digital processing circuit 40 and / or one or more memory circuits or components 45, and ultimately the data is input to the image processing circuit 30 of FIG. 2 or a computer 50 to generate a reconstructed image.

[0049] Because the number of electrons and holes generated by an X-ray event is proportional to the energy of the X-ray photon, the total charge contained in an induced current pulse is proportional to this energy. After a filtering step in the ASIC, the pulse amplitude is proportional to the total charge of the current pulse and therefore to the X-ray energy. The pulse amplitude can be measured by comparing its value with one or more thresholds (THR) in one or more comparators (COMPs), and a counter is introduced to record the number of times the pulse is greater than the threshold. In this way, it is possible to count and / or record the number of X-ray photons detected within a certain time period with energies exceeding the respective thresholds (THR).

[0050] The ASIC typically samples the analog photon pulse once per clock cycle and registers the output of a comparator, which outputs a 1 or 0 depending on whether the analog signal is above or below the comparator voltage. The information available with each sample is, for example, a 1 or 0 representing whether the respective compact was triggered (whether the light pulse exceeded the threshold).

[0051] In photon counting detectors, there is typically photon counting logic that determines if a new photon has been registered and registers the photon in a counter. Multi-bin photon counting detectors typically have multiple counters, one for each comparator, and photon counts are registered in the counters according to an estimate of the photon energy. The logic can be implemented in several different ways. The two most common categories of photon counting logic are the non-paralyzable counting modes and the paralyzable counting modes. Other photon counting logic includes, for example, local maxima detection, which counts, and possibly also registers the pulse height of, detected local maxima in the voltage pulse.

[0052] Photon-counting detectors have many advantages, including but not limited to high spatial resolution, low sensitivity to electronic noise, excellent energy resolution, and material separation capabilities (spectral imaging capabilities). However, energy-integrating detectors have the advantage of high count-rate tolerance. Count-rate tolerance comes from the fact / recognition that because the total energy of the photon is measured, adding one photon will always increase the output signal (within reasonable limits), regardless of the amount of photons currently registered by the detector. This advantage is one of the main reasons why energy-integrating detectors have become the standard in medical CT today.

[0053] FIG. 5 is a schematic diagram of a photon counting circuit and / or device according to an exemplary embodiment.

[0054] When a photon interacts in the semiconductor material, a cloud of electron-hole pairs is generated. When an electric field is applied to the detector material, the charge carriers are collected at electrodes attached to the detector material. A signal is sent from the detector element to the input of a parallel processing circuit, such as an ASIC. In one example, the ASIC can process the charge in such a way that a voltage pulse is generated whose maximum height is proportional to the amount of photon energy deposited in the detector material.

[0055] The ASIC may include a set of comparators 302, each of which compares the magnitude of a voltage pulse with a reference voltage. The comparator output is typically either zero or one (0 / 1), depending on which of the two compared voltages is greater. For the purposes of this discussion, we assume that the comparator output is one if the voltage pulse is higher than the reference voltage, and zero if the reference voltage is higher than the voltage pulse. A digital-to-analog converter (DAC) 301 may be used to convert digital settings provided by a user or control program into reference voltages usable by the comparators 302. If the voltage pulse height exceeds a particular comparator's reference voltage, that comparator is said to be triggered. Each comparator is typically associated with a digital counter 303, which is incremented based on the comparator output according to photon-counting logic.

[0056] As mentioned above, the estimated basis coefficient line integrals for each projection line are

number

[0057] It will be understood that the features and arrangements described herein can be implemented, combined and rearranged in various ways.

[0058] For example, embodiments may be implemented in hardware, or at least partially in software for execution by suitable image processing circuitry, or a combination thereof.

[0059] The steps, functions, procedures, and / or blocks described herein may be implemented in hardware using conventional techniques, including both general-purpose electronic circuitry and application-specific circuitry, such as discrete or integrated circuit technology.

[0060] Alternatively, or as a complement, at least some of the steps, functions, procedures and / or blocks described herein may be implemented in software, such as a computer program, for execution by suitable image processing circuitry, such as one or more processors or processing units.

[0061] Non-limiting examples of specific detector modules are described below. More specifically, these examples refer to edge-on-oriented detector modules and depth-segmented detector modules. Other types of detectors and detector modules are also possible.

[0062] 6 is a schematic diagram illustrating an example of a semiconductor detector sub-module according to an exemplary embodiment. This is an example of a detector module 21 with a semiconductor sensor having a plurality of detector elements or pixels 22, each of which typically consists primarily of a diode with a charge collection electrode. X-rays enter the detector module from the end.

[0063] 7 is a schematic diagram showing an example of a semiconductor detector sub-module according to another exemplary embodiment, in which a detector module 21 having a semiconductor sensor is also divided into a plurality of depth segments or detector elements 22 in the depth direction, again assuming that X-rays are incident from the end of the detector module.

[0064] Typically, a detector element is an individual X-ray sensitive sub-element of the detector. Generally, photon interactions occur within the detector element, and the charge thus generated is collected on a corresponding electrode of the detector element.

[0065] Each detector element typically measures the incident X-ray flux as a series of frames, each frame being a specified time interval known as the frame time.

[0066] Depending on the detector topology, a detector element may correspond to a pixel, particularly if the detector is a flat panel detector. A depth-segmented detector can be considered to have multiple detector strips, each strip having multiple depth segments. In such a depth-segmented detector, each depth segment can be considered to be a separate detector element, particularly if each depth segment is associated with its own separate charge collection electrode.

[0067] The detector strips of a depth-segmented detector typically correspond to the pixels of a regular flat panel detector and are therefore sometimes called pixel strips, but a depth-segmented detector can also be viewed as a three-dimensional pixel array, where each pixel may correspond to an individual depth segment / detector element.

[0068] Semiconductor sensors can be implemented as so-called multi-chip modules (MCMs). In this sense, the semiconductor sensor serves as a base substrate for electrical wiring and for several ASICs, which are preferably attached using so-called flip-chip technology. The wiring includes the connection of signals from each pixel or detector element to the ASIC inputs and the connection from the ASIC to external memory and / or digital data processing. Power to the ASIC can be supplied through similar wiring, taking into account the increased cross-sectional area required for the high currents in these connections, but power can also be supplied through a separate connection. ASICs can be placed to the side of the active sensor; in this case, an absorbing cover can be placed on top to protect them from incident X-rays, and an absorber can be placed in this direction to protect them from scattered X-rays from the side.

[0069] FIG. 8A is a schematic diagram showing a detector module implemented as an MCM similar to the embodiment of U.S. Patent No. 8,183,535. This example illustrates that a semiconductor sensor 21 can also function as a substrate within the MCM. Signals are routed by wiring paths 23 from the detector elements 22 to the inputs of a parallel processing circuit 24 (e.g., an ASIC) located next to the active sensor area. The ASIC processes the electrical charge generated from each X-ray and converts it into digital data that can be used to detect photons and / or estimate their energy. The ASIC can have its own digital processing circuitry and memory for small tasks. The ASIC can then be configured to connect to digital processing and / or memory circuits or components located outside the MCM, and the data is ultimately used as input for reconstructing an image.

[0070] However, the adoption of depth segmentation poses two notable challenges for silicon-based photon-counting detectors. First, a large number of ASIC channels must be employed to process the data provided by the associated detector segments. In addition to the reduced pixel size and increased channel count due to depth segmentation, multi-energy bins further increase data size. Second, because a given X-ray input count is divided into smaller pixels, segments, and energy bins, each bin has a much smaller signal, and the detector calibration / correction requires more than several orders of magnitude more calibration data to minimize statistical uncertainty.

[0071] Naturally, increasing data size by several orders of magnitude requires more computing resources, such as hard disks, memory, central processing units (CPUs), or graphics processing units (GPUs), slowing both data processing and preprocessing. For example, if the data size increases from 10 megabytes to 10 gigabytes, the processing time for reading and writing data can increase by 1000 times.

[0072] A problem with counting-type X-ray photon detectors is the pile-up problem. When the X-ray photon flux rate is high, there can be problems distinguishing between two subsequent charge pulses. As mentioned above, the pulse length after filtering depends on the shaping time. If this pulse length is greater than the time between two X-ray photon-induced charge pulses, the pulses grow together and the two photons cannot be distinguished and may be counted as a single pulse. This is called pile-up. One way to avoid pile-up at high photon flux is to reduce the shaping time or use depth segmentation.

[0073] For pile-up calibration vector generation, the pile-up calibration data must be preprocessed for spit correction. For material decomposition vector generation, the material decomposition data is preferably preprocessed for both spit correction and pile-up correction. For patient scan data, preprocessing for spit, pile-up, and material decomposition is required before image reconstruction. These are simplified examples to illustrate preprocessing, as the actual preprocessing steps can include several other calibration steps, such as reference normalization and air calibration, as needed. The term "processing" can sometimes refer only to the final step in each calibration vector generation or patient scan, but is sometimes used interchangeably.

[0074] FIG. 8B is a schematic diagram showing an example of a set of tiled detector sub-modules, each of which is a depth-segmented detector sub-module, with the ASIC or corresponding circuitry 24 positioned below the detector elements 22 as viewed from the direction of incident x-rays, allowing paths 23 from the detector elements 22 to parallel processing circuits 24 (e.g., ASICs) in the spaces between the detector elements.

[0075] 9 is a schematic diagram illustrating an example of a CT imaging system. In this example, the overall CT imaging system 100 includes a gantry 111 and a patient table 112 that can be inserted into an opening 114 in the gantry 111 during patient and / or calibration scans. The direction of the axis of rotation of the gantry's rotating members around the object or patient being imaged is referred to as the z-direction. The angular direction of the CT imaging system is referred to as the x-direction, and the direction of incident x-rays is referred to as the y-direction.

[0076] However, it should be understood that the rotating and stationary members of the gantry need not be part of the CT imaging system and may be otherwise arranged and / or configured, for example, for linear and / or translational relative movement without rotation. As an example, the x-ray source and detector combination may be moved linearly and / or translationally relative to the stationary members of the entire gantry. For example, the x-ray source and detector may move together as a collective assembly unit along a table axis, commonly referred to as the z-axis. Alternatively, the patient table may be moved while the x-ray source and detector combination remains stationary. This includes, for example, a geometric system configuration in which the patient stands in a so-called phone booth type scanner.

[0077] Figure 10 is a schematic diagram showing an example of the overall design of an X-ray source-detector system. This example shows a schematic diagram of an X-ray detector including multiple detector modules and an X-ray source. Each detector module can have a set of detector elements that define corresponding pixels. For example, the detector modules can be arranged side-by-side, edge-on detector modules facing away from the X-ray source, or they can be arranged in a slightly curved configuration. As mentioned above, the direction of the incident X-rays is called the Y direction. Arranging multiple detector pixels in the gantry's rotation axis direction (called the z direction) enables the acquisition of multislice images. Arranging multiple detector pixels in the angular direction (called the x direction) also enables the simultaneous measurement of multiple projection images on the same plane, which is applicable to fan / cone-beam CT. The x direction is sometimes called the channel direction. Most detectors have detector pixels in both the slice z direction and the angular x direction.

[0078] 11 is a schematic flow diagram illustrating a method for assisting in the calibration of an X-ray imaging system. According to an embodiment, a method for assisting in the calibration of an X-ray imaging system is provided, the X-ray imaging system comprising a multi-bin photon-counting X-ray detector having multiple energy bin thresholds. The method comprises: performing a series of X-ray attenuation measurements or measurement scans of at least one object (S1) using different settings of energy bin thresholds; and obtaining information about material composition associated with the at least one object (S2). The method further comprises: determining, for each X-ray attenuation measurement or measurement scan (S3), a value of at least one performance metric associated with the X-ray imaging system; and selecting a custom set of energy bin thresholds (S4) based on the determined value of the at least one performance metric across the set of X-ray attenuation measurements or measurement scans. The method further comprises determining calibration data (S5) that combines the selected custom set of energy bin thresholds with at least the information about material composition.

[0079] In clinical applications, selecting an energy threshold is important to achieve optimal image quality, as the threshold is typically set before exposure. Therefore, predetermined energy thresholds are typically used in a one-size-fits-all approach that fits different materials, anatomies, patient sizes, phantom sizes, etc. In contrast, the present embodiment instead relates to adaptive, dynamic, or otherwise, energy bin thresholds. By determining S5 calibration data that includes a selected, custom set of energy bin thresholds, the present method aids in the calibration of an x-ray imaging system. As a result of aiding in the calibration of an x-ray imaging system, the system can facilitate improved image quality and improved patient dose efficiency. Furthermore, the present method provides a more versatile and adaptable x-ray imaging system with respect to needs, requirements, and / or desires.

[0080] As described above, the method includes performing S1 a set of X-ray attenuation measurements or measurement scans. Accordingly, this step may include performing only a set of X-ray attenuation measurements, only X-ray attenuation measurement scans, or a combination of both. These X-ray attenuation measurements and / or measurement scans may involve high levels of radiation exposure, low levels of radiation exposure, or a combination of both. Furthermore, the measurements and / or measurement scans are performed on at least one object. The at least one object may be, for example, a phantom. Also, step S1 of performing a set of X-ray attenuation measurements or measurement scans may be performed using different settings of energy bin thresholds. For example, a first measurement / measurement scan may be performed using a first set of energy bin thresholds, and a second measurement / measurement scan may be performed using a second set of energy bin thresholds, where the first set of energy bin thresholds is different from the second set of energy bin thresholds. "Different" here refers to, for example, different energy ranges, different numbers of bins, different thresholds, and / or the like.

[0081] The method further includes obtaining S2 information related to material composition associated with at least one object. The information related to material composition can be obtained, for example, by performing material decomposition of at least a portion of the object(s), by receiving previously determined material composition data of at least a portion of the object(s), or by a combination of both.

[0082] Furthermore, the method includes determining S3 for each X-ray attenuation measurement or metrology scan the value of at least one performance metric associated with the X-ray imaging system. The performance metric may be, for example, a signal-difference-to-noise ratio (SDNR) and / or a signal-to-noise ratio (SNR). Thus, each X-ray attenuation measurement / metrology scan is associated with a determined value of the performance metric(s).

[0083] A custom set of energy bin thresholds is selected in S4 across the set of x-ray attenuation measurements or measurement scans based on the determined value of at least one performance metric, e.g., the custom set of energy bin thresholds corresponds to the set of energy bin thresholds associated with the highest value of the performance metric among all determined values ​​of the performance metric.

[0084] Additionally, the method includes determining S5 calibration data that combines the selected custom set of energy bin thresholds with information about at least the material composition. For example, a particular material composition may correspond to a particular corresponding custom set of energy bin thresholds. The calibration data can be used to calibrate the x-ray imaging system.

[0085] Figure 12 is a flow diagram illustrating a method for assisting in the calibration of an x-ray imaging system, according to an exemplary embodiment. The schematic flow diagram of Figure 12 has some features in common with the schematic flow diagram of Figure 11, and reference is now made to Figure 11 and associated text for a better understanding of at least some of the features and / or functions in the flow diagram.

[0086] The flow diagram of FIG. 12 illustrates an iterative process of a method for assisting in the calibration of an x-ray imaging system. Steps S1-S3 may correspond to a sweep of varying threshold settings to find optimal threshold settings associated with a performance metric for a given material composition of at least one target. According to exemplary embodiments, the method may be repeated for different material compositions. Thus, according to exemplary embodiments, the calibration data may consist of different information regarding different material compositions combined with corresponding selected custom sets of energy bin thresholds. The method may be repeated for additional targets, such as different settings of scan parameters, different target sizes, and / or different pixels or modules.

[0087] By way of example, at least one object may be a phantom, and information regarding material composition may be obtained from known material composition data associated with at least a portion of the phantom. Additionally, information regarding the shape and size of the phantom may be used as complementary information along with the information regarding material composition. The known material composition of at least a portion of the object may represent a particular x-ray imaging purpose, intent, etc. For example, the phantom may represent the material composition of a patient's head, chest, pelvis, etc.

[0088] According to exemplary embodiments, information about the material composition may be obtained from a material basis decomposition procedure performed on spectral X-ray image data acquired during at least one of the X-ray attenuation measurements or measurement scans. Thus, the X-ray attenuation measurement(s) or measurement scan(s) may include spectral X-ray image data.

[0089] In particular examples, the selected custom set of energy bin thresholds may correspond to setting energy bin thresholds that provide an optimum for at least one performance metric. For example, the selected custom set of energy bin thresholds may correspond to maximizing at least one performance metric. According to another example, the selected custom set of energy bin thresholds corresponds to minimizing at least one performance metric.

[0090] In a non-limiting example, the at least one performance metric may include at least one of an image quality metric, a metric related to the ability to detect image features, and a metric related to the ability to provide a quantitative measurement of composition associated with at least one object. For example, the image quality metric may relate to spatial resolution, noise, contrast, artifact level, etc. An example of a metric related to the ability to detect image features may be the signal-to-noise ratio (SDNR). Including a metric related to the ability to detect image features may reduce undesirable phenomena such as pile-up and charge sharing. Such a metric may also enable optimal weighting of detector elements and / or detector pixels. An example of a metric related to the ability to provide a quantitative measurement of composition associated with at least one object is the signal-to-noise ratio (SNR). Including a metric related to the ability to quantitatively measure the composition of an object(s) may evaluate the accuracy of the obtained information regarding the material composition associated with at least one object. Thus, the ability to visualize and characterize different materials may be improved. Inclusion of any of the aforementioned performance metrics may make the x-ray images obtained from the x-ray imaging system more informative and valuable.

[0091] In another non-limiting example, the information regarding material composition may include at least one of material type data and material thickness data associated with at least two different types of material. Thus, the information regarding material composition may include data associated with at least two different types of material present in at least a portion of the object(s). By way of a non-limiting example, the information regarding material composition may include material type data indicating that aluminum (Al) and polyethylene (PE) are present in at least a portion of the object(s). The information regarding material composition may further include data associated with material thicknesses associated with the at least two different types of material present in at least a portion of the object. For example, the information regarding material composition may include material thickness data indicating a first material thickness of a first material and a second material thickness of a different second material. The thickness of the first material and the thickness of the second material may be the same or different.

[0092] According to exemplary embodiments, the information related to material composition may include at least one of path length information and material basis information associated with at least two different types of materials. Thus, the information related to material composition may include information related to a first path length associated with a first material and information related to a second path length associated with a second material. The first path length and the second path length may be the same or different. Furthermore, the information related to material composition may include information related to material basis. For example, the material basis information may include density information, damping characteristics, etc. associated with the at least two different types of materials. Thus, the material basis information may include information related to a first material basis associated with a first material and a second material basis associated with a second material of at least one target.

[0093] As an example, each measurement or measurement scan in a series of X-ray attenuation measurements or scans can be performed based on a respective unique set of energy bin thresholds to provide a sweep of varying energy bin threshold settings. Thus, a unique set of energy bin thresholds is used for each measurement or scan of at least one object. "Unique set of energy bin thresholds" herein means that each set of energy bin thresholds is different compared to other energy bin thresholds associated with the corresponding measurement or scan. "Different" herein means, for example, different energy ranges, different numbers of bins, different thresholds, and / or the like. Thus, performing each measurement or scan of at least one object based on a respective unique set of energy bin thresholds provides a sweep of varying energy bin threshold settings. Thus, the sweep allows for the selection of a custom set of energy bin thresholds corresponding to energy bin threshold settings that provide an optimal value of at least one performance metric for a given material composition.

[0094] In certain embodiments, a method for assisting in the calibration of an x-ray imaging system may be implemented to provide a set of calibration data usable to customize energy bin thresholds for multiple different patient scans by accessing an individualized custom set of energy bin thresholds for each patient scan. Thus, a single set of calibration data may be used to calibrate the x-ray imaging system. Thus, patient scans may be performed based on the individualized custom sets of energy bin thresholds. For example, the patient scans may include a head scan, a chest scan, a pelvis scan, etc. The patient scans may correspond to specific material compositions associated with the custom sets of energy bin thresholds in the determined calibration data. Because the patient scans are based on the individualized custom sets of energy bin thresholds associated with the corresponding material compositions, the patient scans may result in improved image quality and efficient patient radiation doses.

[0095] Figure 13 is a schematic diagram illustrating various options for calibration data for an X-ray imaging system in accordance with an exemplary embodiment. The schematic diagram of Figure 13 has some features in common with the schematic flow diagrams of Figures 11 and 12, and reference is now made to Figures 11 and 12 and associated text for a better understanding of at least some of the features and / or functionality in the schematic diagrams.

[0096] Option 1 of Figure 13 illustrates calibration data that combines information about the material composition of at least a portion of an object with a selected custom set of energy bin thresholds. Options 2 through 5 illustrate calibration data comprising a multitude of selected custom sets of energy bin thresholds coupled to at least a corresponding specific material composition of at least a portion of an object. Accordingly, Options 2 through 5 may include calibration data comprising selected custom sets of energy bin thresholds of different portions of a same object and / or selected custom sets of energy bin thresholds of different objects.

[0097] As an example, a method for assisting in the calibration of an x-ray imaging system may be performed or repeated for a variety of material compositions, and calibration data may be determined for each particular material composition. The calibration data for each particular material composition may include a particular selected custom set of energy bin thresholds coupled to at least material composition information associated with the particular material composition. Thus, the calibration data may be comprised of information for a plurality of different material composition sets, each material composition set corresponding to a selected custom set of energy bin thresholds.

[0098] In certain embodiments, the method for assisting in calibration of an x-ray imaging system may be further performed or repeated for various settings of scan parameters, and calibration data may be determined for each specific setting of scan parameters. The calibration data for each specific setting of scan parameters may include a specific selected custom set of energy bin thresholds coupled to a combination of at least material composition information and the specific setting of scan parameters. Thus, the calibration data may include information for a plurality of different sets of material compositions, each set of material compositions corresponding to a selected custom set of energy bin thresholds and a scan parameter setting. The various scan parameter settings may, for example, comprise different settings of peak voltage, current flow, and / or rotation speed associated with the x-ray imaging system. Taking scan parameter settings into consideration when selecting a custom set of energy bin thresholds combined with information about material composition may further assist in optimizing patient image quality, patient radiation dose, and the trade-off therebetween. Additionally, optimal scan parameters for a custom set of energy bin thresholds coupled with at least information about material composition may differ for different x-ray imaging systems. Thus, the method provides an x-ray imaging system that is more versatile and adaptable with respect to needs, requirements, and / or technologies.

[0099] In another embodiment, the method for assisting in calibration of an X-ray imaging system may be further performed or repeated for various object sizes of the at least one object, and calibration data may be determined for each specific object size. The calibration data for each specific object size may include a specific selected custom set of energy bin thresholds combined with at least a combination of material composition information and object size. Thus, the calibration data may be comprised of information on a plurality of different sets of material compositions, each set of material compositions corresponding to a selected custom set of energy bin thresholds and a specific object size. The object size may be comprised of at least one of length, width, and thickness information for each object of the at least one object. Considering object size when selecting a custom set of energy bin thresholds combined with information on material composition may further help optimize patient image quality and patient radiation dose. For example, a pediatric head scan may benefit, e.g., in terms of patient image quality and patient radiation dose, by using a custom set of energy bin thresholds that is different from the custom set of energy bin thresholds used to perform an adult head scan.

[0100] In a non-limiting example, a method for assisting in calibration of an x-ray imaging system may be performed on a set of detector pixels or detector modules of a multi-bin photon-counting x-ray detector. Calibration data including a specific selected custom set of energy bin thresholds may be determined for each of the detector pixels or detector modules. In this manner, potentially varying characteristics and properties of the set of detector pixels and / or detector modules may be taken into account when selecting the custom set of energy bin thresholds combined with information about material composition. Accordingly, the energy bin thresholds may be customized on a pixel-by-pixel and / or module-by-module basis. This is advantageous, for example, in optimizing patient image quality and patient radiation dose, since each detector pixel may have different detection efficiency and performance. Customizing the energy bin thresholds on a module-by-module basis is also advantageous, since module geometric efficiency, module location, and / or module size may be taken into account. Thus, the method provides a more versatile and adaptable x-ray imaging system with respect to needs, requirements, and / or technologies.

[0101] According to an exemplary embodiment, the method for assisting in calibration of an X-ray imaging system may further include storing S6 the calibration data in a look-up data structure or function that may be used, for example, by the X-ray imaging system itself, saved for later use, and / or transferred to another entity, such as another X-ray imaging system.

[0102] 14 is a schematic flow diagram illustrating a method for adjusting operational settings for an X-ray imaging system. According to one aspect, a method for adjusting operational settings for an X-ray imaging system including a multi-bin photon-counting X-ray detector having multiple energy bin thresholds is provided. The method includes steps S11 of acquiring information representative of a material composition associated with at least one object, and S12 of selecting a custom set of energy bin thresholds based on at least the information representative of the material composition by using at least the information representative of the material composition as input to a lookup data structure or function holding calibration data for searching for a set of energy bin thresholds that matches or corresponds to the information representative of the material composition. The method further includes step S13 of applying the selected custom set of energy bin thresholds as at least part of the operational settings of the multi-bin photon-counting X-ray detector of the X-ray imaging system.

[0103] Thus, the method includes a step S11 of acquiring material composition information associated with at least one object. The at least one object may be, for example, a phantom or a patient. More specifically, the at least one object may be the patient's head, chest, pelvis, etc. Based on the material composition information, a custom set of energy bin thresholds may be selected S12 by using calibration data that couples the material composition information to the custom set of energy bin thresholds. The selected custom set of energy bin thresholds is applied S13 to the operational settings of a multi-bin photon-counting x-ray detector of the x-ray imaging system.

[0104] As an example, the calibration data in the lookup data structure or function may be determined according to the method described above for assisting in the calibration of an X-ray imaging system.

[0105] 15 is a schematic diagram illustrating an example of a lookup data function or structure. As shown in FIG. 15, the lookup data structure or function compares input data with calibration data of the lookup data structure of the function. The input data includes information about the material composition of at least one target. Furthermore, a matching or corresponding custom set of energy bin thresholds is extracted as output from the lookup data structure or function. The output custom set of energy bin thresholds can be applied in S13 to the operational settings of the multi-bin photon-counting X-ray detector of the X-ray imaging system.

[0106] According to an exemplary embodiment, selecting a custom set of energy bin thresholds may be further based on the scan parameter settings by using at least the scan parameter settings as input to a lookup data structure or function that holds calibration data for searching for a set of energy bin thresholds that matches or corresponds to a combination of at least the information representing the material composition and the scan parameter settings. Thus, both the information regarding the material composition and the scan parameter settings may be provided as input to the lookup data structure or function.

[0107] In certain examples, selecting the custom set of energy bin thresholds may be further based on the object size of at least one object by using at least the object size as input to a lookup data structure or function that holds calibration data for searching for a set of energy bin thresholds that matches or corresponds to a combination of at least information representing material composition and object size. Thus, both information regarding material composition and information regarding the object size of the object(s) may be provided as input to the lookup data structure or function. For example, the patient's abdominal dimensions, size, etc. may be used as input to the lookup data structure or function.

[0108] In a non-limiting example, when setting a custom set of energy bin thresholds, positional information of pixels or detector modules within the X-ray detector may be used to select an optimal set of energy bin thresholds by inputting the positional information into a lookup table or function containing calibration data. This positional information, in combination with information representing material composition, is utilized to search for a corresponding set of energy bin thresholds. The positional information associated with pixels or detector modules within the X-ray detector may indicate the position of the pixel or detector module within the X-ray detector. This may be advantageous, for example, in optimizing image quality and patient radiation dose. For example, each detector pixel may have different detection efficiency and performance. Furthermore, detector modules may have different geometric efficiencies based on the position of the detector module and / or the size of the detector module. As described, both information regarding material composition and positional information associated with pixels and / or detector modules may be provided as inputs to a lookup data structure or function.

[0109] It should be noted that the input data to the lookup data structure or function consists of information regarding material composition, and may further consist of any combination of scan parameter settings, object size, and position information related to pixels and / or detector modules.

[0110] FIG. 16 is a schematic diagram illustrating an example of a method for obtaining information about material composition based on scan type. As an example, a method for adjusting operational settings of an X-ray imaging system may be performed for a given scan according to at least one of pre-scan and / or during the scan. The operational settings of the X-ray imaging system may consist of, for example, scan parameters and energy bin thresholds. Thus, a selected custom set of energy bin thresholds retrieved from a lookup data structure or function may provide an initial guess for the energy bin thresholds. The operational settings of the X-ray imaging system may be further adjusted on the fly during the scan, i.e., in real time or near real time. For example, the method may consider count rates, charge-sharing, pile-up, and / or system noise during the scan to further adjust the operational settings of the X-ray imaging system.

[0111] According to an exemplary embodiment, information representative of material composition may be derived based on the particular type of scan performed by the X-ray imaging system. As shown in FIG. 16 , the scan type may be provided as an input to a conversion function or structure. The conversion function or structure converts the scan type into corresponding information representative of material composition. The corresponding information representative of material composition may be an approximation. The (approximate) information related to material composition may be provided as an input to a lookup data structure, function, table, etc.

[0112] In a non-limiting example, the particular type of scan may be selected from a set of different scan types including at least one of a head scan, a chest scan, an abdominal scan, a pelvis scan, a spinal scan, and a cardiac scan. Information representing material composition may be derived based on the selected scan type and used as input to a lookup data structure or function to retrieve a custom set of energy bin thresholds. Thus, the custom set of energy bin thresholds may be customized for the material composition corresponding to the selected scan type. The scan type may be selected, for example, by an operator of the X-ray imaging system. It should be noted that scan types other than those mentioned herein are equally feasible.

[0113] In another non-limiting example, information representative of material composition may be derived based on a material basis decomposition performed by an X-ray imaging system using spectral X-ray image data obtained by a multi-bin photon counting X-ray detector. According to a non-limiting example, a material basis decomposition is performed on at least one scanned object (e.g., a patient) to obtain information representative of the material composition of the object(s).

[0114] FIG. 17 is a schematic block diagram illustrating a system for assisting in the calibration of an X-ray imaging system. The X-ray imaging system of FIG. 17 shares several features with the X-ray imaging systems of FIGS. 1-10 , and reference is now made to FIGS. 1-10 and the associated text for a better understanding of at least some of the features and / or functionality of the X-ray imaging system. According to one aspect, a system 80 for assisting in the calibration of an X-ray imaging system including a multi-bin photon-counting X-ray detector 20 having multiple energy bin thresholds is provided, the system 80 being configured to perform the aforementioned method for assisting in the calibration of an X-ray imaging system. The X-ray imaging system 100 of FIG. 17 may be the same X-ray imaging system 100 previously described herein. As shown in FIG. 17 , the calibration assistance system 80 may be integrated into the system controller 70 of the X-ray imaging system 100. Alternatively, the calibration assistance system 80 may be externally located and relatively connectable to the system controller 70.

[0115] According to one aspect, a computed tomography (CT) system 100 is provided that includes a system 80 for assisting in the calibration of an X-ray imaging system. The CT system 100 may be the same as the CT system 100 previously described herein.

[0116] Figure 18 is a schematic block diagram illustrating a system 90 for adjusting operational settings of an x-ray imaging system. The x-ray imaging system 100 of Figure 18 has several features in common with the x-ray imaging system 100 of Figures 1-10 and 17, and reference is now made to Figures 1-10 and 17 and the associated text for a better understanding of at least some of the features and / or functionality of the x-ray imaging system 100.

[0117] According to one aspect, there is provided a system 90 for adjusting operational settings of an X-ray imaging system including a multi-bin photon-counting X-ray detector 20 having multiple energy bin thresholds, the system being configured to perform the above-described method for adjusting operational settings of an X-ray imaging system. The system 90 for adjusting operational settings may be integrated into the system controller 70 of the X-ray imaging system 100. Alternatively, the system 90 for adjusting operational settings of an X-ray imaging system may be located externally and connectable to the system controller 70.

[0118] According to one aspect, a computed tomography (CT) system 100 is provided that includes a system 90 for adjusting operational settings of an x-ray imaging system. The CT system 100 may be the same as the CT system 100 previously described herein.

[0119] For a better understanding of the proposed technology, reference is now made to Figures 19-25 for non-limiting examples of specific embodiments.

[0120] FIG. 19 is a schematic flow diagram illustrating a method for adjusting operational settings of an X-ray imaging system according to an exemplary embodiment. More specifically, FIG. 19 schematically illustrates step S4 of selecting a custom set of energy bin thresholds based on determined values ​​of at least one performance metric according to a method for assisting in calibration of an X-ray imaging system. According to an exemplary embodiment, an initial X-ray attenuation measurement or measurement scan of at least one object is performed using an initial set of energy bin thresholds. At least one performance metric is determined for the X-ray attenuation measurements or measurement scans. During optimization of the energy bin thresholds, a set of X-ray attenuation measurements or measurement scans of at least one object is performed using different, modified, adjusted, etc. settings of the energy bin thresholds to select a custom set of energy bin thresholds corresponding to the (optimal) performance metric. The optimization of the energy bin thresholds can be stopped when at least one stopping criterion is reached or exceeded. For example, the stopping criterion may consist of a predetermined threshold of the performance metric and / or a maximum number of iterations associated with the optimization. The custom set of selected energy bin thresholds is then combined with at least the obtained information regarding material composition to determine calibration data. Optionally, the calibration data may include a combination between the custom set of energy bin thresholds, information regarding material composition, and size / thickness of the object. Additionally, additional parameters, aspects, settings, etc. may be considered in determining the calibration data. For example, charge sharing, count rate, and / or pile-up may also be considered in evaluating performance metrics to select the custom set of energy bin thresholds.

[0121] FIG. 20 is a schematic diagram illustrating an example of an arrangement of detector sub-modules and an associated arrangement of detector sub-pixels according to an exemplary embodiment. Determining calibration data according to a method for assisting in calibration of an X-ray imaging system is based on step S1 of performing a set of X-ray attenuation measurements or measurement scans of at least one object using different settings of energy bin thresholds. Obtaining a set of X-ray attenuation measurements or measurement scans may further be performed for a set of detector pixels or detector modules of a multi-bin photon-counting X-ray detector of the X-ray imaging system. Accordingly, calibration data including a specific selected custom set of energy bin thresholds may be determined for each detector pixel and / or detector module. In other words, the custom set of energy bin thresholds may be adaptable on a pixel-by-pixel and / or module-by-module basis. For example, the custom set of energy bin thresholds may be selected based on the geometric efficiency of the module, the position of the module, and / or the size of the module. The custom set of energy bin thresholds may also be selected based on pixel efficiency. This may be advantageous because each detector pixel may have different detection efficiency and / or performance.

[0122] Additionally, step S12 of selecting a custom set of energy bin thresholds may be further based on location information associated with detector pixels and / or detector modules within the X-ray detector, which may be used as input to a lookup data structure, function, or the like, that holds calibration data to retrieve a set of energy bin thresholds that matches or corresponds to a combination of at least the material composition information and the location information.

[0123] 21 is a schematic flow diagram illustrating a method for assisting in the calibration of an X-ray imaging system according to an exemplary embodiment. As shown in FIG. 21 , an X-ray attenuation measurement or measurement scan of at least one object is performed using a predefined, predetermined, or similar setting of a set of energy bin thresholds. The material composition of the at least one object may be known. Optionally, the X-ray attenuation measurements or measurement scans may be performed for various materials, projection angles, rotation speeds, and / or count rates. For each measurement or measurement scan, a value of at least one performance metric related to X-ray imaging may be determined. As shown in FIG. 21 , the at least one performance metric may be a SDNR (Single-Spectral Random Number). 2 and / or SNR 2 The x-ray attenuation measurement or measurement scan may be repeated using different settings of energy bin thresholds to maximize at least one performance metric. An optimal, customized, etc. set of energy bin thresholds may be selected for each material composition. The custom set of energy bin thresholds associated with a material composition may be stored as calibration data. The selection of the custom set of energy bin thresholds may be further based on at least material path lengths, x-ray spectrum, count rate, detector pixel position, and / or detector module position.

[0124] FIG. 22 is a schematic flow diagram illustrating a method for assisting in the calibration of an X-ray imaging system according to an exemplary embodiment. As shown in FIG. 22 , the method for assisting in the calibration of an X-ray imaging system includes an iterative optimization step S4 for selecting a custom set of energy bin thresholds. Furthermore, the step S1 of performing a set of X-ray attenuation measurements or measurement scans of at least one object using different settings of energy bin thresholds may further include at least one dark scan, reference scan, noise scan, or the like. This embodiment may be advantageous because the at least one dark scan may further assist in the calibration of the X-ray imaging system by taking into account baseline noise and signal inherent in the X-ray imaging system itself. Therefore, considering the at least one dark scan in determining at least one performance measure of the X-ray attenuation measurements or measurement scans may improve the assistance in the calibration of the X-ray imaging system. The method further includes a step S6 of storing calibration data including the custom set of energy bin thresholds in a lookup data structure or function.

[0125] Additionally, as shown in Figure 22, there may be at least one stopping criterion in the method to evaluate whether the iterative optimization should continue. For example, the stopping criterion may include evaluating whether at least one performance metric reaches or exceeds a predetermined acceptable threshold. Figure 22 illustrates how an initial set of energy bin thresholds is used to perform an x-ray attenuation measurement or measurement scan. Thus, if the at least one stopping criterion is not met, the set of energy bin thresholds may be adjusted, modified, etc., to perform at least one additional x-ray attenuation measurement or measurement scan.

[0126] FIG. 23 is a table illustrating example calibration data for assisting in the calibration of an X-ray imaging system according to an exemplary embodiment. The table in FIG. 23 illustrates various example bindings between values ​​of operational settings corresponding to a custom set of energy bin thresholds. The operational settings may comprise, for example, information regarding material composition and / or scan parameters. The scan parameters may include, for example, the peak voltage (kVp) applied to the X-ray tube, current (mA), and / or rotation speed of an X-ray imaging system. The information regarding material composition may include the specific material composition of at least a portion of an object and the thickness of each material. When selecting a custom set of energy bin thresholds, the information representing the material composition may be used as input to a lookup data structure or function that holds the calibration data. Additional information, such as scan parameters, may also be used as input to the lookup data structure or function. For example, a lookup data structure or function may be configured with the table in FIG. 23. The lookup data structure or function may comprise a match function configured to match an input with corresponding calibration data. Thus, an input including at least information representative of material composition may be matched with the corresponding calibration data. Thus, the resulting output from the lookup data structure or function is a set of energy bin thresholds that match or correspond to at least the information representative of material composition.

[0127] FIG. 24 is a graph showing the relationship between mono-energy keV and noise standard deviation for different energy bin thresholds, according to an exemplary embodiment. According to the exemplary graph of FIG. 24, four different sets of energy bin thresholds are used, each corresponding to a respective subgraph. The target base materials resulting from the graph of FIG. 24 include at least water and iodine, in the exemplary embodiment. Threshold sets 1-3 (Thr1, Thr2, Thr3) are all optimized according to the method of any one of claims 1-14, but threshold set 4 (Thr4) is not. According to the exemplary graph, threshold set 1 (Thr1) yielded the most desirable performance in terms of mono-energy noise standard deviation, while threshold set 4 (Thr4) yielded the worst performance.

[0128] FIG. 25 is a schematic flow diagram illustrating a method for adjusting operational settings of an X-ray imaging system according to an exemplary embodiment. As an example, the method for adjusting operational settings of an X-ray imaging system may further include step S14 of iteratively adapting a custom set of energy bin thresholds during a patient scan using information representing material composition derived based on a material basis decomposition toward a maximum value of at least one performance metric associated with the X-ray imaging system. In other words, after performing a scan based on the custom set of energy bin thresholds retrieved from a lookup data structure or function, represented herein by a "LUT," the set of energy bin thresholds may be further adjusted, adapted, optimized, etc. In this manner, step S14 of iteratively adapting the set of energy bin thresholds may maximize the performance metric. Furthermore, step S14 may further include iteratively adapting, adjusting, optimizing, etc., scanning parameters of the associated X-ray imaging system toward a maximum value of at least one performance metric associated with the X-ray imaging system. Furthermore, charge sharing, count rate, and / or pileup may also be considered when selecting the custom set of energy bin thresholds. This is advantageous in that these phenomena may vary from X-ray imaging system to system. Also, the phenomenon may vary from scan to scan. Furthermore, a custom set of optimized energy bin thresholds can be applied as at least part of the operational settings of a multi-bin photon-counting X-ray detector in an X-ray imaging system.

[0129] According to an exemplary embodiment, a method for adjusting operational settings of an x-ray imaging system, including iteratively adapting a custom set of energy bin thresholds, may be performed if a performance metric for a set of energy bin thresholds initially retrieved from a lookup data structure or function is below a predetermined value. The iteratively adapting the custom set of energy bin thresholds may be repeated until an acceptable performance metric is obtained. The radiation dose for the scan performed in FIG. 25 may be adjusted based on, for example, the patient's physical condition. For example, the radiation dose may include a low radiation dose, a high radiation dose, or a radiation dose in between.

[0130] In certain embodiments, the proposed technique can be performed on the fly in real time or near real time to adaptively select energy bin thresholds for a particular patient scan.

[0131] As mentioned above, at least some of the steps, functions, procedures, and / or blocks described herein may be implemented in software, such as a computer program, for execution by suitable processing circuitry, such as one or more processors or processing units.

[0132] 26 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, where the memory includes instructions executable by the processor, thereby operable to perform the steps and / or actions described herein. The instructions, typically configured as computer programs 225, 235, may be pre-configured in memory 220 or downloaded from an external memory device 230. Optionally, system 200 includes an input / output interface 240 that may be interconnected to processor(s) 210 and / or memory 220 to allow input and / or output of relevant data, such as input parameters and / or resulting output parameters. In a particular example, memory 220 includes a set of instructions executable by the processor, thereby operable to perform the steps and / or actions described herein.

[0133] The term "processor" should be construed in a general sense as any system or device capable of executing program code or computer program instructions to perform specific processing, decision, or computational tasks. Thus, a processing circuit, including one or more processors, when executing a computer program, is configured to perform well-defined processing tasks as described herein. A processing circuit need not be dedicated to performing only the steps, functions, procedures, and / or blocks described above, but may also perform other tasks.

[0134] The present technology also provides a computer program product comprising a computer readable medium 220, 230 having such a computer program stored thereon.

[0135] By way of example, the software or computer program 225, 235 may be embodied as a computer program product, typically embodied or stored on a computer-readable medium 220, 230, particularly a non-volatile medium. The computer-readable medium may include one or more removable or non-removable memory devices, including, but not limited to, read-only memory (ROM), random-access memory (RAM), compact discs (CDs), digital versatile discs (DVDs), Blu-ray discs, universal serial bus (USB) memory, hard disk drive (HDD) storage, flash memory, magnetic tape, or any other conventional memory device. Thus, the computer program may be loaded into the operating memory of a computer or equivalent processing device for execution by its processing circuitry.

[0136] A method flow can be considered as a computer action flow when executed by one or more processors. The corresponding device, system, and / or apparatus can be defined as a group of functional modules, where each step executed by a processor corresponds to a functional module. In this case, the functional module is implemented as a computer program executed on a processor. Therefore, the device, system, and / or apparatus may alternatively be defined as a group of functional modules, where the functional module is implemented as a computer program executed on at least one processor.

[0137] Thus, the computer programs resident in the memory can be organized as appropriate functional modules configured to perform at least some of the steps and / or tasks described herein when executed by the processor.

[0138] Alternatively, most of the modules can be implemented as hardware modules or alternatively implemented in hardware - software or hardware is purely an implementation choice.

[0139] The embodiments of the present disclosure illustrated in the drawings and described above are merely exemplary embodiments and are not intended to limit the scope of the appended claims, including equivalents as may be included within the scope of the claims. Those skilled in the art will understand that various modifications, combinations, and variations can be made to the embodiments without departing from the scope defined by the appended claims. Any combination of non-mutually exclusive features described herein is intended to be within the scope of the present disclosure. That is, features of the described embodiments may be combined with any appropriate aspect described above, and any feature of any one aspect may be combined with any other appropriate aspect. Similarly, features defined in a dependent claim may be combined with non-mutually exclusive features of other dependent claims, particularly if the dependent claims depend on the same independent claim. Single-claim dependent claims may be used as required by practice in some jurisdictions, but this does not imply that the features in the dependent claims are mutually exclusive.

[0140] Furthermore, it should be noted that the inventive concept relates to all possible combinations of features unless expressly stated otherwise, and in particular different component solutions in the different embodiments can be combined in other configurations, if technically possible. [Explanation of symbols]

[0141] 10: X-ray source 11: Gantry 12: Table / Patient table 13: Isocenter 20: X-ray detector 21: Detector module 22: Pixel 23: Path / wiring path 24: Parallel processing circuit 25: Analog processing circuit 26: Routing path 30: Image processing system 40: Digital processing circuit 41: X-ray controller 42: Gantry controller 43: Table controller 44: Detector controller 45: Memory 50: Computer 60: Operator console 62: Display 70: System controller 80: Calibration support system 90: System 100: X-ray imaging system 111: Gantry 112: Patient table 114: Opening 200: Computer implementation / system 210: Processor 220: Memory 225: Computer program 230: External memory device 235: Computer program 240: Input / output interface / I / O 301: DA converter / digital-analog converter 302: Comparator 303: Digital counter

Claims

1. 1. A method for assisting in calibration of an X-ray imaging system, the X-ray imaging system including a multi-bin photon counting X-ray detector having multiple energy bin thresholds, the method comprising: performing a series of X-ray attenuation measurements or measurement scans of at least one object using different settings of the energy bin thresholds (S1); obtaining information about the material composition associated with said at least one object (S2); - determining (S3) for each X-ray attenuation measurement or measurement scan the value of at least one performance metric associated with said X-ray imaging system; selecting (S4) a custom set of energy bin thresholds based on the determined value of the at least one performance metric across the set of x-ray attenuation measurements or measurement scans; and (S5) determining calibration data that combines the selected custom set of energy bin thresholds and at least information regarding material composition.

2. 10. The method of claim 1, wherein the method is performed or repeated for various material compositions, and the calibration data is determined for each specific material composition, and the calibration data for each specific material composition includes a specific selected custom set of energy bin thresholds coupled to at least material composition information associated with the specific material composition.

3. 10. The method of claim 1, wherein the method is further performed or repeated for various settings of scanning parameters, and calibration data is determined for each particular setting of scanning parameters, the calibration data for each particular setting of scanning parameters including a particular selected custom set of energy bin thresholds coupled to a combination of at least material composition information and the particular setting of scanning parameters.

4. 10. The method of claim 1, wherein the method is further performed or repeated for various object sizes of the at least one object, and calibration data is determined for each particular object size, the calibration data for each particular object size including a particular selected custom set of energy bin thresholds coupled to a combination of at least material composition information and object size.

5. 10. The method of claim 1, wherein the method is further performed for a set of detector pixels or detector modules of a multi-bin photon counting X-ray detector, and calibration data including a particular selected custom set of energy bin thresholds is determined for each of the detector pixels or detector modules.

6. The method of claim 1 , wherein the at least one object is a phantom, and the information regarding material composition is obtained from known material composition data associated with at least a portion of the phantom.

7. The method of claim 1 , wherein the information regarding material composition is obtained from a material basis decomposition procedure performed on spectral X-ray image data acquired during at least one of an X-ray attenuation measurement or a measurement scan.

8. The method of claim 1 , wherein the selected custom set of energy bin thresholds corresponds to a setting of energy bin thresholds that provides an optimal value of the at least one performance metric.

9. 2. The method of claim 1, wherein the at least one performance metric comprises at least one of an image quality metric, a metric related to the ability to detect image features, and a metric related to the ability to provide a quantitative measure of composition related to the at least one subject.

10. The method of claim 1 , wherein the information regarding material composition includes at least one of material type data and material thickness data associated with at least two different types of material.

11. The method of claim 1 , wherein the information regarding material composition includes at least one of path length information and material basis information associated with at least two different types of materials.

12. The method of claim 1 , further comprising the step (S6) of storing the calibration data in a look-up data structure or function.

13. The method of claim 1 , wherein each measurement or measurement scan of the set of X-ray attenuation measurements or measurement scans is performed based on a respective unique set of energy bin thresholds to provide a sweep of varying energy bin threshold settings.

14. 10. The method of claim 1, wherein the method is performed to provide a set of calibration data that can be used to customize energy bin thresholds for multiple different patient scans by accessing an individual custom set of energy bin thresholds for each patient scan.

15. 1. A method for adjusting operational settings of an x-ray imaging system including a multi-bin photon-counting x-ray detector having multiple energy bin thresholds, comprising: Obtaining information representative of a material composition associated with at least one object (S11); selecting (S12) a custom set of energy bin thresholds based on at least the information representative of the material composition by using the information representative of at least the material composition as input to a look-up data structure or function that holds calibration data to retrieve a set of energy bin thresholds that matches or corresponds to the information representative of the material composition; and applying (S13) the selected custom set of energy bin thresholds as at least part of an operational setting of the multi-bin photon counting X-ray detector of the X-ray imaging system.

16. 16. The method of claim 15, wherein selecting the custom set of energy bin thresholds is performed based on the scanning parameter settings by using at least the scanning parameter settings as input to a lookup data structure or function that holds calibration data to retrieve a set of energy bin thresholds that matches or corresponds to a combination of at least the information representing the material composition and the scanning parameter settings.

17. 16. The method of claim 15, wherein selecting the custom set of energy bin thresholds is based on an object size of at least one object by using at least the object size as input to a lookup data structure or function that holds calibration data to search for a set of energy bin thresholds that matches or corresponds to a combination of at least information representing material composition and object size.

18. 16. The method of claim 15, wherein selecting the custom set of energy bin thresholds is based on location information associated with pixels or detector modules within the X-ray detector by using the location information as input to a lookup data structure or function that holds calibration data to search for a set of energy bin thresholds that matches or corresponds to a combination of at least the information representing the material composition and the location information.

19. The method of claim 15 , performed for a given scan at least one of before and during the scan.

20. The method of claim 15 , wherein the information representative of material composition is derived based on a particular type of scan performed by the X-ray imaging system.

21. The particular type of scan is selected from a set of different scan types including at least one of the following: head scan, Chest scan, abdominal scan, pelvic scan, spinal cord scan, cardiac scan, 21. The method of claim 20, wherein information representative of material composition is derived based on a selected scan type and used as input to a lookup data structure or function to retrieve a custom set of energy bin thresholds.

22. 16. The method of claim 15, wherein information representative of material composition is derived based on a material basis decomposition performed by the X-ray imaging system using spectral X-ray image data obtained by the multi-bin photon counting X-ray detector.

23. 23. The method of claim 22, further comprising the step (S14) of iteratively adapting, during a patient scan, a custom set of energy bin thresholds toward a maximum value of at least one performance metric associated with the X-ray imaging system using information representative of material composition derived based on the material basis decomposition.

24. 23. The method of claim 22, wherein the step of iteratively adapting the custom set of energy bin thresholds is performed if a performance metric of the set of energy bin thresholds initially retrieved from the lookup data structure or function is less than or equal to a predetermined value.

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