Systems and methods for improved data processing in computed tomography imaging systems

Abstract

Various systems and methods for processing computed tomography (CT) data in a CT imaging system are provided. In one embodiment, a CT imaging system includes an X-ray source configured to emit X-rays; an X-ray detector configured to generate sampled digital detector data; and a digital processor configured to process the sampled digital detector data. The method includes filtering, in a digital processor, sampled digital detector data to generate filtered detector data and resampling, in the digital processor, the filtered detector data to generate resampled detector data. The resampling includes reducing a data size of the filtered detector data. At least a portion of the sampled digital detector data is filtered according to a filtering setting, and at least a portion of the filtered digital detector data is resampled according to a resampling setting, where the filtering setting and the resampling setting are decoupled.

Description

Background Art

[0001] The proposed technology relates to X-ray technology and X-ray imaging and corresponding data processing tasks and data management. In particular, the proposed technology relates to X-ray imaging systems such as computed tomography (CT) imaging systems and methods of operating such X-ray imaging systems and corresponding computer program products to achieve improved data processing.

[0002] Radiographic imaging, such as computed tomography (CT) imaging systems and other more general X-ray imaging systems, has been used for many years in medical applications such as medical diagnosis and treatment.

[0003] Typically, an X-ray imaging system such as a CT imaging system includes an X-ray source and an X-ray detector, which consists of a plurality of detector modules including one or more detector elements for independently measuring the intensity of the X-rays. The X-ray source emits X-rays, which pass through the subject or object to be imaged and are then received by the detector. The X-ray source and the X-ray detector are typically arranged to rotate around the subject or object on a rotating member of a gantry. The emitted X-rays are attenuated by the subject or object as they pass through, and the resulting transmitted X-rays are measured by the detector. The measured data can then be used to reconstruct an image of the subject or object.

[0004] refer to Figure 1A It may be useful to briefly summarize an illustrative, general X-ray imaging system according to the prior art. In this illustrative example, the X-ray imaging system 100 includes an X-ray source 10, an X-ray detector 20, and an associated image processing system 30. Generally speaking, the X-ray detector 20 is configured to record radiation from the X-ray source 10, which radiation has optionally been focused by an optional X-ray optics or collimator and has passed through an object, subject, or portion thereof. The X-ray detector 20 may be connected to the image processing system 30 via suitable readout electronics that are at least partially integrated in the X-ray detector 20 to enable the image processing system 30 to perform image processing and / or image reconstruction.

[0005] By way of example, a conventional CT imaging system comprises an X-ray source and an X-ray detector arranged so that projection images of a subject or object can be acquired at different viewing angles covering at least 180 degrees. This is most commonly achieved by mounting the source and the detector on a support (e.g., a rotating member of a gantry) that can rotate around the subject or object. An image containing projections recorded in different detector elements for different viewing angles is called a sinogram. In the following, the collection of projections recorded in different detector elements for different viewing angles will be called a sinogram, even if the detector is two-dimensional, making the sinogram a three-dimensional image.

[0006] Figure 1B is a schematic diagram illustrating an example of an X-ray imaging system setup according to the prior art, showing projection lines 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 called spectral X-ray imaging, in which the X-ray transmission is measured for several different energy levels. This can be achieved by rapidly switching the source between two different emission spectra, by using two or more X-ray sources emitting different X-ray spectra, or by using an energy-resolving detector that measures the incident radiation at two or more energy levels. An example of such a detector is a multi-bin photon counting detector, in which each recorded photon generates a current pulse that is compared to a set of thresholds, thereby counting the number of photons incident in each of a plurality of energy bins.

[0008] Spectral X-ray projection measurement produces projection images for each energy level. A weighted sum of these projection images can be performed to optimize the contrast-to-noise ratio (CNR) for a given imaging task, as described in "SNR and DQE analysis of broadspectrum X-ray imaging", Tapiovaara and Wagner, Phys. Med. Biol. 30, 519.

[0009] Another technique enabled by energy-resolved X-ray imaging is basis material decomposition. This technique exploits the fact that all substances composed of low atomic number elements (such as human tissue) have a linear attenuation coefficient whose energy dependence can be well approximated as a linear combination of two (or more) basis functions:

[0010] μ(E)=a1f1(E)+a2f2(E)

[0011] where f1 and f2 are basis functions, and a1 and a2 are the corresponding basis coefficients. More generally, f i is a basis function, and a i are the corresponding basis coefficients, where i = 1, …, N, where N is the total number of basis functions. If one or more elements with high atomic numbers are present in the imaging volume, high enough to have a K absorption edge in the energy range used for imaging, one basis function must be added for each such element. In the field of medical imaging, such K-edge elements can typically be iodine or gadolinium, which are substances used as contrast agents.

[0012] Basis material decomposition has been 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 of each of the basis coefficients (A i =∫ l a i dl, where i = 1, ..., N, where N is the number of basis functions) is inferred from the measured data in each projection ray l from the source to the detector element. In one specific implementation, this is done by first expressing the expected number of recorded counts in each energy bin as A i This is achieved by the function:

[0013]

[0014] where λ i is the expected number of counts in energy bin i, E is the energy, S i is a response function that depends on the shape of the spectrum incident on the imaged object, the quantum efficiency of the detector, and the sensitivity of energy bin i to X-rays of energy E. Although the term "energy bin" is most commonly used for photon counting detectors, this equation can also describe other energy-resolving X-ray imaging systems, such as multi-slice detectors, kVp switching sources, or multiple source systems.

[0015] Then, under the assumption that the number of counts in each bin is a Poisson-distributed random variable, we can use maximum likelihood to estimate A i 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:

[0016]

[0017] Where m i is the number of counts measured in energy bin i, and M b is the number of energy bins.

[0018] When the estimated basis coefficients obtained from each projection line are integrated When arranged into an image matrix, the result is a material-specific projection image for each basis i, also called a basis image. This basis image can be viewed directly (e.g., in projection X-ray imaging) or used as input to a reconstruction algorithm to form the basis coefficients a inside the object i In either case, the result of the basis decomposition can be viewed as one or more basis image representations, such as basis coefficient line integrals or the basis coefficients themselves.

[0019] Standard data management procedures for X-ray imaging systems propose different approaches to optimize data acquisition, but possibly at the expense of, for example, spatial resolution, noise level and / or system complexity.

[0020] Therefore, there remains a general need for improvements in data management for X-ray imaging systems, such as reducing data size, thereby enabling, for example, faster data communication, reduced storage requirements and reduced processing time, without having to suffer from reduced spatial resolution and increased noise levels. Summary of the invention

[0021] This summary introduces concepts that are described in more detail in the detailed description. It should not be used to identify essential features of the claimed subject matter, nor should it be used to limit the scope of the claimed subject matter.

[0022] An object is to mitigate, alleviate or eliminate one or more of the above identified deficiencies and disadvantages in the art, singly or in any combination, and to address at least the above mentioned problems.

[0023] A specific object is to provide a method for processing CT data in a CT imaging system.

[0024] It is also an object to provide an improved X-ray imaging system.

[0025] Yet another object is to provide a computer program for executing the above method.

[0026] These and other objects are achieved by one or more embodiments of the present invention as defined in the claims.

[0027] According to a first aspect, a method for processing computed tomography (CT) data in a CT imaging system is provided. The CT imaging system includes an X-ray source configured to emit X-rays, an X-ray detector configured to generate sampled digital detector data, and a digital processor configured to process the sampled digital detector data. The method includes filtering the sampled digital detector data in the digital processor to generate filtered detector data, and resampling the filtered detector data in the digital processor to generate resampled detector data. Resampling includes reducing the data size of the filtered detector data. At least a portion of the sampled digital detector data is filtered according to a filtering setting, and at least a portion of the filtered digital detector data is resampled according to a resampling setting, and wherein the filtering setting and the resampling setting are decoupled.

[0028] According to a second aspect, a CT imaging system is provided, the imaging system comprising an X-ray source configured to emit X-rays, an X-ray detector configured to generate sampled digital detector data; and a digital processor configured to process the sampled digital detector data. The digital processor is configured to filter the sampled digital detector data according to a filtering setting to generate filtered detector data, and to resample the filtered detector data according to a resampling setting to generate resampled detector data. The data size of the resampled detector data is smaller than the data size of the filtered detector data. The filtering setting and the resampling setting are decoupled.

[0029] In other words, the proposed technique can provide a novel processing and data management method for CT imaging systems. The present invention also provides a decoupled method for filtering and resampling data in a CT imaging system, thereby increasing the flexibility of filter and resampling design and image quality selection, while reducing the size of the data set generated by the X-ray scan, and improving performance at the same time. For example, the improved performance can be associated with a reduced data size, which can achieve faster data communication, reduced storage requirements and / or reduced processing time without suffering an intolerable reduction in spatial resolution and / or an intolerable increase in noise levels. It should be noted that the proposed technique may be applicable to all three-dimensional (3D) X-ray-based medical imaging systems, such as digital mammography imaging systems, interventional imaging systems, fluoroscopic imaging systems, and other radiographic imaging systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The present embodiment, together with further objects and advantages thereof, may best be understood by reference to the following description taken in conjunction with the accompanying drawings, in which:

[0031] Figure 1A and Figure 1B is a schematic diagram illustrating an example of an X-ray imaging system.

[0032] Figure 2 is a schematic diagram illustrating another example of an X-ray imaging system such as a CT imaging system.

[0033] Figure 3 is a schematic block diagram of a CT imaging system as an illustrative example of an X-ray imaging system.

[0034] Figure 4 is a schematic diagram showing another example of relevant parts of an X-ray imaging system, such as a CT imaging system.

[0035] Figure 5 is a schematic diagram of a photon counting circuit and / or device according to an exemplary embodiment.

[0036] Figure 6 is a schematic diagram illustrating an example of a semiconductor detector submodule according to an exemplary embodiment.

[0037] Figure 7 is a schematic diagram illustrating an example of a semiconductor detector submodule according to another exemplary embodiment.

[0038] Fig. 8A is a schematic diagram showing an example of a semiconductor detector submodule according to yet another exemplary embodiment.

[0039] Figure 8B is a schematic diagram showing an example of a set of tiled detector submodules, wherein each detector submodule is a depth-segmented detector submodule and an application-specific integrated circuit (ASIC) or corresponding circuit system is arranged below the detector submodule as can be seen from the direction of the incident X-ray.

[0040] Fig. 9 is a schematic diagram illustrating an example of a CT imaging system.

[0041] Fig.10 is a schematic diagram showing an example of the design of an X-ray source and an X-ray detector system.

[0042] Fig.11 is a schematic flow chart illustrating an example of a method for processing data of an X-ray imaging system.

[0043] Fig.12 is a schematic flow chart illustrating an example of a method for processing data of an X-ray imaging system.

[0044] Fig.13 is a schematic flow chart illustrating an example of a method for processing data of an X-ray imaging system.

[0045] Fig.14is a schematic diagram illustrating an example of a semiconductor detector submodule according to an exemplary embodiment.

[0046] Fig.15 is a schematic diagram showing an example of a resampling mode.

[0047] Fig.16 is a schematic diagram showing an example of relevant parts of an X-ray imaging system in a CT configuration.

[0048] Fig.17 is a schematic diagram illustrating an example of a CT imaging system according to an exemplary embodiment.

[0049] Fig.18 is a schematic diagram illustrating an example of a computer implementation according to one embodiment. DETAILED DESCRIPTION

[0050] Embodiments of the present disclosure will now be described by way of example with reference to the accompanying drawings.

[0051] For a better understanding, it may be useful to continue with the introductory description of a non-limiting example of an overall X-ray imaging system in which data processing and transmission according to the inventive concept may be implemented.

[0052] Figure 2 1 is a schematic diagram showing an example of an X-ray imaging system 100, such as a CT imaging system, which includes: an X-ray source 10 that emits X-rays; an X-ray detector 20 that has an X-ray detector that detects the X-rays after they have passed through an object; an analog processing circuit system 25 that processes and digitizes the raw electrical signals from the X-ray detector; a digital processing circuit system 40 that can perform further processing operations on the measurement data, such as applying corrections, temporary storage, or filtering; and a computer 50 that stores the processed data and can perform further post-processing and / or image reconstruction. The digital processing circuit system 40 may include a digital processor. According to an exemplary embodiment, all or part of the analog processing circuit system 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.

[0053] The entire X-ray detector may be considered an X-ray detector system 20 , or an X-ray detector 20 in combination with associated analog processing circuitry 25 .

[0054] The image processing system 30 communicates with and is electrically coupled to the analog processing circuit system 25, and may include a digital processing circuit system 40 and / or a computer 50, which 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 alternatively as a combination of the digital processing circuit system 40 and the computer 50, or, if the digital processing circuit system 40 is further specialized for image processing and / or reconstruction, it may be viewed as the digital processing circuit system itself.

[0055] An example of a commonly used X-ray imaging system is a CT imaging system, which may include an X-ray source or X-ray tube that generates a fan beam or cone beam of X-rays and an array of opposing X-ray detectors that measure the fraction of X-rays that pass through a patient or object. The X-ray source or X-ray tube and the X-ray detectors are mounted in a gantry 11 that can rotate around the imaging object.

[0056] Figure 3 A CT imaging system 100 is schematically shown as an illustrative example of an X-ray imaging system. The CT imaging system includes 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, touch screen, or other input device. The commands and parameters provided by the operator are then 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 located on the table 12. The gantry controller 42 controls the rotation speed and position of the gantry 11 including the X-ray source 10 and the X-ray detector 20. For 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 coverage of the patient. There is also a detector controller 44, which is configured to control and / or receive data from the X-ray detector 20.

[0057] In one embodiment, the computer 50 also performs post-processing and image reconstruction on the image data output from the X-ray detector 20. Thus, the computer 50 corresponds to FIG. 1 and FIG. Figure 2 The image processing system 30 is shown. An associated display 62 allows an operator to observe the reconstructed image and other data from the computer 50.

[0058] An X-ray source 10 arranged in 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 have passed through an object or patient. The X-ray detector 20 may be formed, for example, of a plurality of pixels (also referred to as sensors or detector elements) and associated processing circuitry (such as an application specific integrated circuit (ASIC)) arranged in a detector module. A portion of the analog processing may be implemented in the pixels, while any remaining processing is implemented, for example, in the ASIC. In one embodiment, the processing circuitry (ASIC) digitizes the analog signals from the pixels. The processing circuitry (ASIC) may also include digital processing that may perform further processing operations on the measurement data, such as applying corrections, temporary storage, and / or filtering. During scanning to acquire X-ray projection data, the gantry and components mounted thereon rotate about an isocenter 13.

[0059] Modern X-ray detectors generally require the conversion of incident X-rays into electrons, which often occurs via the photoelectric effect or Compton interaction, and the resulting electrons generally produce secondary visible light until their energy is lost and this light is in turn detected by a photosensitive material. Semiconductor-based detectors also exist, and in this case the electrons generated by the X-rays generate a charge from electron-hole pairs that are collected by an applied electric field.

[0060] There are detectors that work in energy integrating mode, in the sense that they provide an integrated signal from a large number of X-rays. The output signal is proportional to the total energy deposited by the detected X-rays.

[0061] X-ray detectors with photon counting and energy resolving capabilities are increasingly being used in medical X-ray applications. Photon counting detectors have the advantage that in principle the energy of each X-ray can be measured, which yields additional information about the composition of the object. This information can be used to improve image quality and / or reduce radiation dose.

[0062] Generally speaking, photon counting X-ray detectors determine the energy of a photon by comparing the height of an electrical pulse generated by the interaction of a photon in the detector material with a set of comparator voltages. These comparator voltages are also called energy thresholds. Generally speaking, the analog voltage in the comparator is set by a digital-to-analog converter (DAC). The DAC converts the digital setting sent by the controller into an analog voltage to which the height of the photon pulse can be compared.

[0063] Photon counting detectors count the number of photons that have interacted in the detector during the measurement time. New photons are generally identified by the fact that the height of the electrical pulse exceeds the comparator voltage of at least one comparator. When a photon is identified, the event is stored by incrementing a digital counter associated with the channel.

[0064] When several different thresholds are used, an energy-resolving photon counting detector is obtained, in which the detected photons can be classified into energy bins corresponding to various thresholds. Sometimes, this type of photon counting detector is also called a multi-bin detector. In general, energy information allows the creation of new kinds of images, in which new information is available and image artifacts inherent to conventional techniques can be removed. In other words, for an energy-resolving photon counting detector, the pulse height is compared with multiple programmable thresholds (T1-TN) in a comparator and classified according to the pulse height, which is in turn proportional to the energy. In other words, a photon counting detector comprising more than one comparator is referred to herein as a multi-bin photon counting detector. In the case of a multi-bin photon counting detector, the photon counts are stored in a set of counters, typically each counter corresponding to an energy threshold. For example, a count can be assigned to the highest energy threshold that a photon pulse has exceeded. In another example, a counter tracks the number of times a photon pulse crosses each energy threshold.

[0065] For example, "side-on facing" is a special non-limiting design of a photon counting detector in which the X-ray sensors (such as X-ray detector elements or pixels) are oriented sideways toward incoming X-rays.

[0066] For example, such a photon counting detector may have pixels in at least two directions, wherein one of the two directions facing the sideways photon counting detector has a component in the direction of the X-ray. Such a sideways photon counting detector is sometimes referred to as a depth segmented photon counting detector having two or more pixel depth segments in the direction of the incoming 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.

[0067] Alternatively, the pixels may be arranged in an array (non-depth segmented) in a direction substantially perpendicular to the incident X-rays, and each pixel may be oriented sideways toward the incident X-rays. In other words, the photon counting detector may be non-depth segmented while still being arranged sideways toward the incoming X-rays.

[0068] The absorption efficiency can be improved by arranging the side-facing photon counting detector to face sideways, in which case the absorption depth can be chosen to be any length and the side-facing photon counting detector can still be fully depleted without reaching very high voltages.

[0069] The conventional mechanism for detecting X-ray photons by direct semiconductor detectors works essentially as follows. The energy of the X-ray interaction in the detector material is converted into electron-hole pairs inside the semiconductor detector, where the number of electron-hole pairs is generally proportional to the photon energy. The electrons and holes drift toward the detector electrodes and back (or vice versa). During this drift, the electrons and holes induce a current in the electrodes, which can be measured.

[0070] like Figure 4 As shown in , the signals are routed via wiring paths 26 of the detector elements 22 of the X-ray detector to the input of an analog processing circuit system (e.g., an ASIC) 25. It should be understood that the term "application specific integrated circuit (ASIC)" should be broadly interpreted as any general purpose circuit used and configured for a particular application. The ASIC processes the charge generated from each X-ray and converts it into digital data, which can be used to obtain measurement data, such as photon counts and / or estimated energy. The ASIC is configured to be connected to the digital processing circuit system so that the digital data can be sent to the digital processing circuit system 40 and / or one or more memory circuits or components 45, and ultimately the data will be provided for Figure 2 The image processing circuitry 30 or computer 50 in generates input for reconstructing the image.

[0071] Since the number of electrons and holes from one X-ray event is proportional to the energy of the X-ray photon, the total charge in one induced current pulse is proportional to this energy. After the filtering step in the ASIC, the pulse amplitude is proportional to the total charge in the current pulse and therefore proportional to the X-ray energy. The pulse amplitude can then be measured by comparing the value of the pulse amplitude with one or more thresholds (THR) in one or more comparators (COMP) and introducing a counter by which the number of cases where the pulse is greater than the threshold can be recorded. In this way, the number of X-ray photons whose energy exceeds the energy corresponding to the corresponding threshold (THR) that has been detected within a certain time frame can be counted and / or recorded.

[0072] The ASIC typically samples the analog photon pulse once per clock cycle and records the output of the comparator. Depending on whether the analog signal is above or below the comparator voltage, the comparator (threshold) outputs a one or zero. The available information at each sample is, for example, a one or zero for each comparator, which indicates whether the comparator has been triggered (the photon pulse is above the threshold) or not triggered.

[0073] In a photon counting detector, there is typically a photon counting logic component that determines whether a new photon has been recorded and records the photon in a counter. In the case of a multi-bin photon counting detector, there are typically several counters, such as one counter for each comparator, and the photon counts are recorded in these counters based on an estimate of the photon energy. This logic component can be implemented in several different ways. Two of the most common categories of photon counting logic components are non-paralyzable counting mode and paralyzable counting mode. Other photon counting logic components include, for example, local maximum detection, which counts local maxima detected in a voltage pulse and may also record its pulse height.

[0074] Photon counting detectors have many benefits, including but not limited to: high spatial resolution, low sensitivity to electronic noise, good energy resolution; and material separation capabilities (spectral imaging capabilities). However, energy integrating detectors have the advantage of high count rate tolerance. The count rate tolerance comes from the fact / recognition that since the total energy of the photons is measured, adding one additional photon will always increase the output signal (within reasonable limits), regardless of the amount of photons currently being recorded by the detector. This advantage is one of the main reasons why energy integrating detectors have become the standard for medical CT today.

[0075] Figure 5 A schematic diagram of a photon counting circuit and / or device according to an exemplary embodiment is shown.

[0076] When photons interact in a semiconductor material, a cloud of electron-hole pairs is generated. The charge carriers are collected by electrodes attached to the detector material by applying an electric field across the detector material. The signal is routed from the detector element to the input of a parallel processing circuit (e.g., an ASIC). In one example, the ASIC can process the charge so that a voltage pulse is generated whose maximum height is proportional to the amount of energy deposited by the photon in the detector material.

[0077] The ASIC may include a set of comparators 302, each of which compares the magnitude of the voltage pulse to a reference voltage. The comparator output is typically zero or one (0 / 1), depending on which of the two voltages being compared is larger. Here, we assume that if the voltage pulse is higher than the reference voltage, the comparator output is one (1); if the reference voltage is higher than the voltage pulse, the comparator output is zero (0). A digital-to-analog converter (DAC) 301 can be used to convert a digital setting that may be provided by a user or a control program into a reference voltage that may be used by the comparators 302. If the height of the voltage pulse exceeds the reference voltage of a particular comparator, we will refer to that comparator as being "triggered." Each comparator is typically associated with a digital counter 303, which increments based on the comparator output according to the photon counting logic component.

[0078] As mentioned earlier, when the estimated basis coefficients obtained for each projection line are integrated When arranged into an image matrix, the result is a material-specific projection image for each basis i, also called a basis image. This basis image can be viewed directly (e.g., in projection X-ray imaging) or used as input to a reconstruction algorithm to form the basis coefficients a inside the object i In any case, the result of the basis decomposition can be viewed as one or more basis image representations, such as basis coefficient line integrals or the basis coefficients themselves.

[0079] It will be appreciated that the mechanisms and arrangements described herein can be implemented, combined and rearranged in various ways.

[0080] For example, embodiments may be implemented in hardware, or at least partly in software executed by appropriate processing circuitry, or a combination of the above.

[0081] The steps, functions, processes and / or blocks described herein may be implemented in hardware (including both general purpose electronic circuitry and application specific circuitry) using any conventional technology such as discrete circuitry or integrated circuit technology.

[0082] Alternatively, or in addition, at least some of the steps, functions, processes and / or blocks described herein can be implemented in software, such as a computer program executed by appropriate processing circuitry (such as one or more processors or processing units).

[0083] In the following, non-limiting examples of specific detector module implementations will be discussed. More specifically, these examples refer to edge-oriented detector modules and depth-segmented detector modules. Other types of detectors and detector modules are also possible.

[0084] Figure 6is a schematic diagram showing an example of a semiconductor detector submodule according to an exemplary embodiment. This is an example of a detector module 21 in which the semiconductor sensor has a plurality of detector elements or pixels 22, wherein each detector element (or pixel) is typically based on a diode having a charge collection electrode as a key component. X-rays enter through the edge of the detector module.

[0085] Figure 7 is a schematic diagram showing an example of a semiconductor detector submodule according to another exemplary embodiment. In this example, again assuming that X-rays enter through the edge of the detector module, the detector module 21 with semiconductor sensors is also divided into a plurality of depth segments or detector elements 22 in the depth direction.

[0086] Typically, a detector element is a single X-ray sensitive sub-element of a detector. Generally speaking, photon interactions occur in a detector element and the charge generated thereby is collected by corresponding electrodes of the detector element.

[0087] Each detector element typically measures the incident X-ray flux as a sequence of frames. A frame is the data measured during a specified time interval, called a frame time.

[0088] Depending on the detector topology, a detector element may correspond to a pixel, especially when the detector is a flat panel detector. A depth segmented detector may be viewed as having multiple detector strips, each strip having multiple depth segments. For such a depth segmented detector, each depth segment may be viewed as a separate detector element, especially if each of the depth segments is associated with its own separate charge collection electrode.

[0089] The detector strips of a depth segmented detector usually correspond to pixels of a common flat panel detector and are therefore sometimes also called pixel strips. However, a depth segmented detector can also be viewed as a three-dimensional pixel array, where each pixel corresponds to a separate depth segmenter / detector element.

[0090] The semiconductor sensor can be implemented as a so-called multi-chip module (MCM), in the sense that it serves as a base substrate for electrical wiring and multiple ASICs, which are preferably attached by so-called flip-chip technology. The wiring will include signal connections from each pixel or detector element to the ASIC input and connections from the ASIC to external memory and / or digital data processing. The power to the ASIC can be supplied by similar wiring, but also by separate connections, taking into account the increase in the cross-section required for the high currents in these connections. The ASIC can be positioned to the side of the active sensor, which means that it can be protected from incident X-rays if an absorber cover is placed on top, and it can also be protected from scattered X-rays coming from the side by positioning the absorber in this direction as well.

[0091] Fig. 8A 8,183,535 is a schematic diagram showing a detector module implemented as an MCM similar to the embodiment in U.S. Patent No. 8,183,535. In this example, it is shown how the semiconductor sensor 21 can also have the function of the substrate in the MCM. The signal is routed from the detector element 22 to the input of the parallel processing circuit 24 (e.g., ASIC) positioned next to the active sensor area via the wiring path 23. The ASIC processes the charge generated from each X-ray and converts it into digital data, which can be used to detect photons and / or estimate the energy of the photons. The ASIC can have their own digital processing circuit system and memory for small tasks. In addition, the ASIC can be configured to connect to the digital processing circuit system and / or memory circuit or component located outside the MCM, and ultimately use the data as input for reconstructing the image.

[0092] However, the adoption of deep segmentation also brings two notable challenges to silicon-based photon counting detectors. First, a large number of ASIC channels must be employed to process the data fed from the associated detector segments. In addition to the increased number of channels due to both smaller pixel size and deep segmentation, multiple energy bins also increase the data size. Second, since a given X-ray input count is divided into smaller pixels, segments, and energy bins, each with a much lower signal, detector calibration / correction requires several orders of magnitude more calibration data to minimize statistical uncertainty.

[0093] Naturally, in addition to requiring larger computing resources, hard drives, memory, and central processing units (CPUs) or graphics processing units (GPUs), the data size that is several orders of magnitude larger also slows down both data processing and preprocessing. For example, the processing time for reading and writing data may be 1000 times longer when the data size is 10GB instead of 10MB.

[0094] A problem in any detector that counts X-ray photons is the pile-up problem. When the flux rate of X-ray photons is high, problems may arise in distinguishing two subsequent charge pulses. As mentioned above, the pulse length after the filter depends on the shaping time. If this pulse length is greater than the time between two X-ray photon induced charge pulses, the pulses will grow together and the two photons will be indistinguishable and can be counted as one pulse. This is called pile-up. Therefore, one way to avoid pile-up at high photon fluxes is to use a small shaping time, or to use deep segmentation.

[0095] For pile-up calibration vector generation, the pile-up calibration data needs to be pre-processed for jet correction. For material decomposition vector generation, the material decomposition data should preferably be pre-processed for both jet correction and pile-up correction. For patient scan data, the data needs to be pre-processed for jet, pile-up, and material decomposition before image reconstruction ensue. These are simplified examples to explain "pre-processing" as actual pre-processing steps may include several other calibration steps such as reference normalization and air calibration as needed. The term "processing" may simply indicate the last step of each calibration vector generation or patient scan, but may be used interchangeably in some cases.

[0096] Figure 8B is a schematic diagram showing an example of a set of tiled detector submodules, wherein each detector submodule is a deeply segmented detector submodule and an ASIC or corresponding circuit system 24 is arranged below the detector elements 22 as can be seen from the direction of the incoming X-rays, thereby allowing wiring paths 23 from the detector elements 22 to parallel processing circuits 24 (e.g., ASICs) in the space between the detector elements.

[0097] Fig. 9 1 is a schematic diagram showing an outline example of a CT imaging system. In this schematic example, the overall CT imaging system 100 includes a gantry 111, a patient table 112, which can be inserted into an opening 114 of the gantry 111 during a patient scan and / or a calibration scan. The direction of the rotation axis of the rotating member of the gantry around the imaged object or patient is denoted as the z-direction. The angular direction of the CT imaging system is denoted as the x-direction, and the direction of the incident X-ray is referred to as the y-direction.

[0098] It should be understood, however, that the rotating and fixed components of the gantry need not be part of the CT system, but may be arranged and / or configured in other ways, such as for linear and / or translational relative movement without rotation. As an example, the X-ray source and detector combination may move in a linear and / or translational manner relative to the fixed components of the entire gantry. For example, the X-ray source and detector may move together as a combined assembly unit along a table axis (commonly referred to as the z-axis). Alternatively, the patient table moves while the X-ray source and detector combination is stationary; relative motion is key. This also includes geometric system configurations in which the patient can stand, such as in a so-called phone booth scanner.

[0099] Fig.10 is a schematic diagram showing an example of an overall design of an X-ray source-detector system. In this example, a schematic diagram of an X-ray detector including a plurality of detector modules and an X-ray source emitting X-rays is shown. Each detector module may have a set of detector elements defining corresponding pixels. For example, the detector modules may be arranged in parallel and oriented with the side facing the detector module pointing to the X-ray source, and they may be arranged in a slightly curved overall configuration. As described above, the direction of the incident X-rays is referred to as the y-direction. Multiple detector pixels in the direction of the rotation axis of the gantry (referred to as the z-direction) enable multi-plane image acquisition. Multiple detector pixels in the angular direction (referred to as the x-direction) enable simultaneous measurement of multiple projections in the same plane, and this is applied to fan / cone beam CT. The x-direction is sometimes also referred to as the channel direction. Most detectors have detector pixels in both the plane (z) direction and the angular (x) direction.

[0100] Fig.11 is a schematic flow chart illustrating an example of a method for processing data of an X-ray imaging system.

[0101] According to a first aspect, there is provided a method for processing CT data in a CT imaging system 100. The CT imaging system 100 comprises an X-ray source 110 configured to emit X-rays, an X-ray detector 120 configured to generate sampled digital detector data, and a digital processor 140 configured to process the sampled digital detector data.

[0102] The method includes filtering sampled digital detector data in a digital processor to generate filtered detector data (S1); and resampling the filtered detector data in the digital processor to generate resampled detector data, wherein the resampling includes reducing the data size of the filtered detector data (S2).

[0103] At least a portion of the sampled digital detector data is filtered according to a filtering setting, and at least a portion of the filtered detector data is resampled according to a resampling setting. The filtering setting and the resampling setting are decoupled.

[0104] Resampling settings and filter settings can be selected to balance / weigh filtering and resampling to provide images of desired quality in a desired manner. Images generated from X-ray scans can have a desired customizable quality and can be generated within a desired customizable time. X-ray scans can be performed using a certain radiation dose. "Decoupling" here can mean that filter settings and resampling settings can be selected independently of each other, and / or that filtering / filter settings are not coupled to resampling / resampling settings, or at least not strictly coupled. In other words, filter settings and resampling settings can be selected independently of each other, but for example, the selection of resampling settings can be based at least in part on filter settings, and vice versa. Similarly, decoupling can mean that resampling settings can be set / selected to achieve certain effects on filtered detector data without being restricted by the selection of filter settings, and still generating sufficient images. Resampling settings and filter settings can be set / selected so that they are selected independently of each other, and the CT imaging system still provides sufficient images with desired quality.

[0105] The filtering settings and resampling settings can be optimized to achieve a specific image quality and / or DQE indicator. The filtering and resampling settings can be optimized / based on the radiation dose provided by the CT imaging system during the X-ray CT scan or the data processing scheme (such as the operating mode) adopted by the CT imaging system. Image quality can be related to spatial resolution and / or noise and / or aliasing. For example, the filtering settings can be selected to generate lower quality / lower resolution detector data, and then the resampling settings can be selected to optimize the generation of the final image in at least one aspect of image quality, radiation dose, DQW indicator and / or data processing. Data processing can be where and when the detector data is processed / filtered / resampling. As another example, the filtering settings and the resampling settings can be selected relative to each other to achieve the desired data size reduction. The data size can be reduced while managing and minimizing common challenges in data processing, such as aliasing and noise. Therefore, the desired data size reduction can be obtained without suffering from a reduction in spatial resolution. In some examples of selecting filtering settings and resampling settings, a certain degree of reduction in spatial resolution is acceptable, which is conducive to obtaining a greater degree of data size reduction.

[0106] It should be noted that the digital processor may include digital processing circuitry 40. The digital processing circuitry may perform processing operations on the sampled digital detector data.

[0107] For example, the data size reduction reduces the data size by a reduction factor, and wherein at least one of the filtering settings and the resampling settings is associated with the reduction factor. The reduction factor corresponds to an amount of data reduction in a resampling step, wherein the filtered detector data is resampled to generate resampled detector data. The resampled detector data is smaller in data size than the filtered detector data.

[0108] In another example, the filter settings and the resampling settings are set to achieve a predetermined image quality. The image quality may include at least one of spatial resolution, noise aliasing level, and signal aliasing. In other words, the filter settings and the resampling settings are set / selected / determined / calculated to provide a certain image quality. The image quality may be a set value based on the operating mode and / or a value selected by the CT imaging system operator.

[0109] For example, filtering can be performed in the spatial domain and / or the frequency domain. In the spatial domain, the length of the filter can be longer than the filter used in the known method. The longer length of the filter can involve that the filter has a longer length than the filter used in the known method. For example, since resampling is performed in a manner decoupled from filtering, the length of the filter can be increased without losing spatial resolution and / or increasing the noise level. In the frequency domain, the filter can have a uniform amplitude before the cutoff frequency and zero above the cutoff frequency. The filter can also have a more gradual transition, such as a Hanning filter and a Hamming filter. Such a filter can be called a finite impulse response (FIR) filter. The filter can also be designed to achieve a desired passband and / or stopband.

[0110] In another example, the resampling can be performed in the spatial domain and / or the frequency domain. It should be noted that the length of the filter and the amount of resampling may be different / disconnected / decoupled. Specifically, if the bandwidth reduction of the filtered signal is greater than the reduction in the resampling frequency, the residual aliasing (if any) can be controlled. This may be the desired effect of different methods of preventing signal aliasing in CT imaging systems (especially quarter shift and focus swing methods). For example, the design of filtering and resampling can be modeled based on the relative importance of retaining spatial resolution relative to maximizing quantum detection efficiency (DQE) by avoiding noise aliasing. For a given amount of downsampling, even if some aliasing occurs, a filter with a higher bandwidth can be used because the CT imaging system manipulates signal aliasing in other ways. Then, the CT imaging system can exhibit higher spatial resolution compared to a system with the same amount of downsampling and a filter with a bandwidth low enough to avoid aliasing. On the other hand, if the efficiency ratio between noise and dose is more important, the filter can be designed to prevent aliasing to a greater extent than the simple binning that results. This approach can improve DQE for low to mid-range frequencies, but may result in reduced spatial resolution compared to binning with the same degree of downsampling.

[0111] In yet another example, the resampling includes a first resampling according to a first resampling setting and a second resampling according to a second resampling setting. The first resampling setting and the second resampling setting may be selected to improve image quality. After filtering and / or the first resampling stage, the image quality may be reduced. The first resampling and the second resampling may be set / selected to provide an image with a desired image quality. The first resampling may involve a reduction in the data size of the filtered detector data as input. In addition, the first resampling may cause a decrease in image quality. The second resampling may be set / selected to at least partially restore the image quality. In a non-limiting example, the filtering setting and the resampling setting may be associated with an operating mode. The operating mode may correspond to a reduced data size, which may further provide faster data communication, reduced storage requirements, and / or reduced processing time. Therefore, the CT imaging system may be configured to operate in one or more operating modes, wherein each operating mode may balance / trade-off filtering and resampling to provide an image of desired quality in a desired manner. Based on an X-ray scan with a desired radiation dose, the image may have a desired quality and may be generated within a desired time. The operating mode may, for example, optimize filtering and resampling settings to achieve a certain image quality, radiation dose, DQE metric, and / or data processing scheme.

[0112] In illustrative practical examples, the operating mode may be associated with image quality, DQE metrics, and / or data processing schemes.

[0113] The data processing scheme may include operational settings / instructions for where and how data is processed in the CT system. Thus, the data processing scheme is related to the speed at which the operator can receive the final diagnostic image. The data processing scheme involves where and when how much data is processed in the CT imaging system, and also how and when the data is transmitted. For example, the CT system may perform filtering and resampling on the rotating side of the gantry of the CT system, and then transmit the resampled data to the fixed side, where the remaining processing to generate the final image is performed. This can increase the speed at which sufficient images are generated for viewing by the operator or technician of the CT imaging system.

[0114] It should be understood that the CT imaging system may include any 3-DX ray-based medical imaging modality. For example, the CT imaging system may include a general tomography system that is suitable for taking images from different angles and combining the information therein to create an image volume in which each layer can be viewed separately. The CT imaging system may include a CT system that is suitable for using an angle range of a full 180 degree range and / or a limited angle range. In other words, the CT system may include a general computed tomography system that uses a full 180 degree range, or a breast tomosynthesis system (also known as a mammography system) that uses a limited angle range. In one example, the CT imaging system may be configured to operate in an operating mode, wherein the operating mode balances at least two parameters, depending on the configuration of the operating mode. The parameters that need to be balanced may be radiation dose, DQE, and parameters related to the data processing scheme. For example, the first operating mode may be optimized / configured to generate images quickly, that is, to provide faster processing for the final image. Therefore, the first operating mode may balance parameters to provide images faster, such as by reducing the data size in resampling and reducing the resolution, but still by performing filtering and resampling in a decoupled manner to adjust the image quality reduced due to data reduction, thereby achieving sufficient image quality. By adjusting filtering and resampling in a decoupled manner, the second operating mode may be optimized / configured to reduce radiation dose while still providing a resampled data set with sufficient image quality and / or DQE metrics while also reducing data size.

[0115] In one embodiment, the CT imaging system 100 may include a gantry 111 including a rotating component on a rotating side 102 and a fixed component on a fixed side 104. The rotating component and the fixed component may be communicatively coupled via a data communication system 160, wherein the rotating component may include an X-ray source 110, an X-ray detector 120, and a digital processor 140.

[0116] In a specific example, Fig.17As shown, the digital processor 140 may include a first digital processor 140-1 arranged on the rotating member and a second digital processor 140-2 arranged on the fixed member. The first digital processor and / or the second digital processor may be configured to perform at least a portion of filtering and / or resampling. In a non-limiting example, the first digital processor may be configured to filter at least a portion of the detector data to generate filtered detector data, and resample the filtered detector data to generate resampled detector data, wherein the resampled detector data is transmitted to the fixed side, and the final image can be created based on the resampled detector data on the fixed side. Compared with the filtered detector data, the resampled detector data may be smaller in data size. This can achieve faster data communication, reduced storage requirements, reduced processing time, and faster image generation. In another example, filtering can be performed by the first digital processor and resampling can be performed by the second digital processor. The advantage of this is that part of the processing can be performed before the data is transmitted between the rotating side and the fixed side. For example, this can achieve faster image acquisition, so that the diagnosis can be determined more quickly. In addition, this can make the components of the CT imaging system more efficiently used.

[0117] Fig.12 is a schematic flow chart illustrating an example of a method for processing data of a CT imaging system. Fig.12 The schematic flow chart and Fig.11 The schematic flow charts in have several common features and are hereby referenced Fig.11 and related text to increase understanding of at least some of the features and / or functions in the flowchart. Fig.12 In the method shown, sampled digital detector data is obtained, filtered according to the filtering settings, and then the filtered detector data is resampled according to the resampling settings. In one example, several steps of resampling and / or filtering can be performed consecutively.

[0118] For example, the filter settings may include bandwidth, amplitude and / or a group of detector elements. The group of detector elements may be selected detector elements. For example, the detector elements may be selected according to their position in the detector and / or according to a downsampling mode. The downsampling mode may be, for example, binning a plurality of adjacent detector elements together, such as two or three adjacent detector elements.

[0119] Fig.13 is a schematic flow chart illustrating an example of a method for processing data of a CT imaging system. Fig.13 The schematic flow chart and Fig.11 and Fig.12 The schematic flow charts in have several common features and are hereby referenced Fig.11 , Fig.12 and related text to increase understanding of at least some of the features and / or functions in the flowchart.

[0120] For example, resampling may be performed along the view direction v. This method may be performed to form an image of an observed object in the CT imaging system 100, rather than binning different views together with equal weights. This is in contrast to performing resampling in the radial direction r and / or longitudinal direction l of the detector module.

[0121] In a specific example, a filter can be designed and applied in the azimuth direction and then resampled in the same direction. The purpose of this method can be to minimize, for example, noise aliasing, signal aliasing and / or azimuth blur. This scheme may be particularly useful when processing data originally collected by flying focus and / or kV switching between views. With respect to the flying focus, due to hardware limitations and / or data size considerations, the collected data may not be at the desired resampling position. Using the phase shifting method described later, the desired requirements can be achieved. With respect to kV switching between views, low energy and high energy data can be collected from different azimuths, so the data needs to be adjusted before further processing. This can be achieved by using the phase shifting method described later.

[0122] In certain examples, the resampling settings may include a resampling frequency and / or a phase shift setting.

[0123] In a non-limiting example, the resampling frequency may include a non-integer resampling frequency. In other words, the steps and / or actions described herein are not limited to integer resampling. The resampling frequency may be reduced by any factor (e.g., a fractional factor). Fig.15 Resampling frequency and phase shift are discussed further in and related text.

[0124] In another non-limiting example, the phase shift setting may include a non-integer phase shift. In other words, the steps and / or actions described herein are not limited to integer phase shifts. Applying a phase shift may be performed in the frequency domain by applying, for example, a linear phase shift. For example, after the amplitude of the frequency component is adjusted as desired, an inverse Fourier transform may be applied to a half-integer resampling frequency (1.5 or 2.5, etc.) to obtain a value at an integer sample position. In addition, a linear phase shift may be applied, followed by a second inverse Fourier transform to obtain a signal sample at a half-integer position. The sought / desired sample may then be selected from the two results.

[0125] Phase shifting can be performed in the spatial domain by having several different filters. The filter can be designed to perform a half resampled shift. For example, a filter without a phase shifting property can be applied to obtain the value at any original integer resampled position. In addition, a filter with a phase shifting property can be applied to obtain the resample at a half-integer position. As described herein, such a filter can be a FIR filter.

[0126] In an illustrative practical example, a filter with a bandwidth of a factor of 3 may be designed while resampling the signal by a factor of 1.5. This example may be useful to avoid aliasing. In another illustrative practical example, the same resampling factor of 1.5 may be combined with a filter with a bandwidth of 2.5 or 2. This example may be useful to achieve higher spatial resolution. This effect may be at the expense of DQE at higher frequencies, but may maintain DQE at low frequencies.

[0127] Additionally, the resampling described herein need not be uniform. In other words, the resampled output positions need not be equidistant compared to the original input positions. This may be advantageous if the resampled data is subsequently processed by a fan-beam to parallel-beam reconstruction algorithm. When adapting the data to a fan-beam to parallel-beam mode, the samples need to be converted from being equiangular in a fan-beam to being equidistant in a parallel-beam geometry. Notably, this method can be performed during resampling. This may be desirable because processing time may be reduced, and / or higher signal fidelity may be achieved.

[0128] Fig.13 is a schematic flow chart showing an example of resampling according to an embodiment of the present invention, and is hereby referenced Fig.12 and related text to increase understanding of at least some of the features and / or functions in the flowchart.

[0129] For example, a phase shift setting can be associated with a resampling mode. The resampling mode can set how to manage different data points received after the detector data is initially sampled. For example, certain data points can be resampled and / or phase shifted to, for example, reduce aliasing and / or noise issues. Fig.15 Further details of the resampling modes are shown in and related text.

[0130] In an illustrative practical example, the resampling pattern may include a radial quarter offset resampling pattern in the radial direction r1 and / or the longitudinal direction l1. In other words, relative to a completely symmetrical resampling pattern, the resampling may be offset to one side by a quarter of the resampling spacing. This may result in the conjugate projection lines being completely staggered after, for example, the gantry 111 rotates halfway. The pattern may be designed to implement a radial quarter offset in a digital and / or virtual manner. The advantage of this is that the aliasing of the image generated by the X-ray imaging system 100 may be further reduced.

[0131] In another illustrative practical example, the resampling pattern may include an equidistant resampling pattern in the radial direction r2 and / or the longitudinal direction l2.

[0132] In yet another illustrative practical example, the resampling pattern may include an interlaced resampling pattern in the radial direction r3 and / or the longitudinal direction l3. This can be achieved by selecting the resampling positions so that they shift back and forth between views. The resampling pattern may result in consecutive projections forming two interlaced radial patterns. The advantage of the resampling pattern is that signal aliasing can be suppressed while no conjugate projection is required. Therefore, resampling can be compatible with half-scan CT imaging.

[0133] It should be noted that the radial direction r1, the radial direction r2 and / or the radial direction r3 may coincide. It should also be noted that the longitudinal direction l1, the longitudinal direction l2 and / or the longitudinal direction l3 may also coincide. In addition, although the resampling is performed in the longitudinal direction l1, the longitudinal direction l2 and / or the longitudinal direction l3, the resampling position can be selected to obtain the effect of a z-axis flying focus. The advantage of this method is that a higher spatial resolution can be obtained and / or the noise level can be reduced.

[0134] Optionally, an interleaved resampling pattern may be formed from at least two consecutive projections.

[0135] Fig.14 is a schematic diagram illustrating an example of a semiconductor detector submodule according to an exemplary embodiment. Fig.14 The semiconductor detector submodule 21 in Figure 7 The semiconductor detector submodules 21 in the embodiment of the present invention have several common features and are hereby referred to as Figure 7 and related text to increase understanding of at least some features and / or functions of the detector submodule 21.

[0136] exist Fig.14 In , the coordinate systems (r1; l1), (r2; l2), and (r3; l3) are shown. As mentioned before, resampling can be done in Fig.14 The radial direction r and / or the longitudinal direction l are shown. It should be noted again that the radial direction r1, the radial direction r2 and / or the radial direction r3 may coincide. Similarly, it should also be noted that the longitudinal direction l1, the longitudinal direction l2 and / or the longitudinal direction l3 may also coincide. The radial direction r can be identified as the channel direction or the column direction of the detector submodule 21. In addition, the longitudinal direction l can be identified as the row direction of the detector submodule 21.

[0137] Fig.15Schematic diagrams showing examples (A to E) of sampling and resampling patterns. The axis r represents the radial distance between sampling positions or resampling positions, and the vertical dashed lines are equidistantly spaced over the radial distance. A shows the original equiangular sampling pattern. B shows a simple binned sample based on the sample pattern example A) of adjacent detector channels. C shows resampling of samples based on the resampling pattern example B according to the equidistant resampling pattern. D1 to D2 show resampling of samples based on the resampling pattern example B according to the radial quarter offset resampling pattern. E1 to E2 show resampling of samples based on the resampling pattern example B) according to the radial staggered resampling pattern.

[0138] It is noteworthy that the samples in Example A are positioned closer to each other, ie, away from the center of the axis r, when the fan angle is larger than when the fan angle is smaller.

[0139] The samples in example B are still equiangular after simple binning, but are not equidistant in radial distance. The arrows map the simple binning of adjacent detector channels.

[0140] The samples in Example C are all equiangular and equidistant in the radial direction, ie, follow parallel beam geometry.

[0141] The samples in examples D1 to D2 represent the resampling of direct and conjugate X-rays, respectively.

[0142] The samples in Examples E1 to E2 represent resampling of two consecutive views. Performing resampling using the resampling patterns shown in E1 to E2 can provide the same effect of focus deflection without moving the focus of the CT imaging system.

[0143] The present disclosure relates to a novel system architecture and corresponding process for improved data processing and management of CT imaging systems.

[0144] According to a second aspect, a CT imaging system 100 is provided, the imaging system comprising an X-ray source 110 configured to emit X-rays, an X-ray detector 120 configured to generate sampled digital detector data; and a digital processor 140 configured to process the sampled digital detector data. The digital processor 140 is configured to filter the sampled digital detector data according to a filter setting to generate filtered detector data, and to resample the filtered detector data according to a resampling setting to generate resampled detector data. The data size of the resampled detector data is smaller than the data size of the filtered detector data. The filter setting and the resampling setting are decoupled. In other words, the CT imaging system is configured to perform filtering and subsequent resampling in a decoupled manner. In other words, the digital processor is configured to perform filtering with a certain filter setting, and the digital processor is configured to perform resampling on the filtered detector data according to the resampling setting, wherein the filter setting and the resampling setting are decoupled. Thus, a certain filter setting may be used, and resampling may be performed on the filtered data according to a freely adjustable resampling setting to generate resampled detector data that may be optimized for a particular purpose, such as optimizing resolution / image quality versus speed, or resolution / image quality versus radiation dose. Furthermore, the filter setting and the resampling setting may be selected based on obtaining a desired data size reduction.

[0145] Fig.16 is a schematic diagram showing an example of a relevant part of an X-ray imaging system in such a CT imaging system.

[0146] For example, the digital processor may be configured to reduce the data size of the filtered detector data by resampling.The digital processor may be configured to reduce the data size by a reduction factor, and at least one of the filtering setting and the resampling setting may be associated with the reduction factor.

[0147] In another example, the digital processor is configured to filter and resample the sampled digital detector data to achieve a predetermined image quality. The filtering settings and resampling settings can be set to provide / achieve a certain image quality. The image quality can include at least one of spatial resolution, noise level, and aliasing level.

[0148] In yet another example, the digital processor is configured to resample the generated resampled detector data according to a second resampling setting to generate second resampled detector data. In other words, the digital processor is configured to perform a first resampling according to a first resampling setting and to perform a second resampling according to a second resampling setting. The first resampling setting and the second resampling setting may be selected to improve image quality. After filtering and / or the first resampling stage, the image quality may be reduced. The digital processor may be configured to set / select a resampling setting and a second resampling setting to provide an image with a desired image quality. The first resampling may involve a reduction in the data size of the filtered detector data as input. In addition, the first resampling may cause a decrease / deterioration in image quality. The second resampling may be set / selected to at least partially restore the image quality.

[0149] Optionally, digital processor 140 may be configured to operate in one or more operating modes, where the operating mode is associated with image quality, radiation dose, DQE metrics, and / or data processing schemes.

[0150] In a non-limiting example, the resampling setting may include a non-integer resampling frequency.

[0151] In another non-limiting example, the resampling setting may include a non-integer phase shift setting.

[0152] In a practical implementation, the resampling setting may include a phase shift setting associated with the resampling mode.

[0153] For example, the resampling pattern may include a radial quarter offset resampling pattern in the radial direction r1 and / or the longitudinal direction l1. The resampling pattern also includes an equidistant resampling pattern in the radial direction r2 and / or the longitudinal direction l2, and an interleaved resampling pattern in the radial direction r3 and / or the longitudinal direction l3.

[0154] It should be noted that the radial direction r1 , the radial direction r2 and / or the radial direction r3 may coincide. It should also be noted that the longitudinal direction l1 , the longitudinal direction l2 and / or the longitudinal direction l3 may also coincide.

[0155] In this example, the CT system includes an X-ray source 110 and an X-ray detector 120, which are arranged in the beam path of the X-rays so that projection images of the subject or object can be acquired from different viewing angles. This is most commonly achieved by mounting the X-ray source 110 and the X-ray detector 120 on a support (e.g., a rotating member of a gantry) that can rotate around the subject or object.

[0156] The proposed technique enables filtering and resampling of detector data in an improved and flexible manner, for example enabling efficient processing of large amounts of detector data and / or alleviating potential bottleneck effects in conventional CT imaging systems.

[0157] In this way, the proposed technique can more optimally exploit the superior imaging potential of modern X-ray detectors such as high resolution, photon counting, energy discriminating and / or multi-segmented detectors.

[0158] Therefore, the proposed techniques can effectively deal with the trade-offs between image quality related to, for example, resolution and / or aliasing, radiation dose, data size, data fidelity, and practical constraints related to computing resources, power, cooling, space and / or data transmission bandwidth.

[0159] By way of example, the CT imaging system 100 may include a gantry 111 including a rotating component on a rotating side 102 and a stationary component on a stationary side 104. The rotating component and the stationary component are communicatively coupled via a data communication system 160, wherein the rotating component includes an X-ray source 110, an X-ray detector 120, and a digital processor 140. Processed detector data may be transmitted from the rotating (moving) side of the gantry to the stationary side of the gantry.

[0160] The data communication system 160 may be a slip ring commonly used in CT imaging systems having a rotating member of a gantry. A "slip ring" herein refers to an electromechanical device that allows transmission of power and electrical signals, such as between a rotating structure and a stationary structure.

[0161] In a specific example, the digital processor 140 may include a first digital processor 140-1 disposed on the rotating member and a second digital processor 140-2 disposed on the stationary member. The first digital processor and / or the second digital processor are configured to perform at least a portion of filtering and / or resampling.

[0162] Preferably, the CT imaging system 100 may include a data storage unit 150 configured to store at least a portion of the sampled digital detector data, the filtered detector data, and / or the resampled detector data. The CT imaging system 100 may have a first data storage unit 150-1 disposed on the rotating side 102 of the CT imaging system, and a second data storage unit 150-2 disposed on the fixed side 104 of the CT imaging system. An example of this is shown in FIG. Fig.17 Shown in.

[0163] As mentioned, at least some of the steps, functions, processes and / or blocks described herein may be implemented in software, such as a computer program, for execution by appropriate processing circuitry, such as one or more processors or processing units.

[0164] Fig.18 is a schematic diagram illustrating an example of a computer implementation according to one embodiment.

[0165] In this particular example, the system 200 includes a processor 210 and a memory 220, the memory including instructions executable by the processor, whereby the processor is operable to perform the steps and / or actions described herein. The instructions are typically organized as a computer program 225, 235, which may be pre-configured in the memory 220 or downloaded from an external memory device 230. Optionally, the system 200 includes an input / output interface 240, which may be interconnected to the processor 210 and / or the memory 220 to enable input and / or output of relevant data (such as input parameters and / or resulting output parameters).

[0166] In a particular example, the memory 220 includes a set of instructions executable by the processor, whereby the processor is operable to perform the steps and / or actions described herein.

[0167] The term "processor" should be interpreted in a general sense as any system or device capable of executing program code or computer program instructions to perform specific processing, determination or computing tasks.

[0168] Thus, processing circuitry including one or more processors is configured to perform well-defined processing tasks (such as those described herein) when executing a computer program.

[0169] The processing circuitry need not be dedicated only to executing the steps, functions, processes and / or blocks described above, but may also perform other tasks.

[0170] The proposed technology also provides a computer program product comprising a computer readable medium 220, 230 having such a computer program stored thereon.

[0171] For example, the software or computer program 225, 235 may be implemented as a computer program product, which is typically carried 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 disc (CD), digital versatile disc (DVD), Blu-ray disc, universal serial bus (USB) memory, hard disk drive (HDD) storage device, 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 circuit system.

[0172] The method flow, when executed by one or more processors, can be regarded as a computer action flow. The corresponding device, system and / or apparatus can be defined as a set of functional modules, wherein each step executed by the processor corresponds to a functional module. In this case, the functional module is implemented as a computer program running on the processor. Therefore, the device, system and / or apparatus can alternatively be defined as a set of functional modules, wherein these functional modules are implemented as computer programs running on at least one processor.

[0173] The computer program resident in the memory may therefore be organized into appropriate functional modules that are configured to perform at least part of the steps and / or tasks described herein when the computer program is executed by a processor.

[0174] Alternatively, these modules may be implemented primarily through hardware modules, or alternatively through hardware. The degree of software versus hardware is purely a choice of the particular implementation.

[0175] The embodiments of the present disclosure shown in the drawings and described above are exemplary embodiments only and are not intended to limit the scope of the appended claims, including any equivalents included within the scope of the claims. Those skilled in the art will appreciate that various modifications, combinations and changes may be made to these embodiments without departing from the scope of the invention as defined by the appended claims. It is intended that any combination of non-mutually exclusive features described herein is within the scope of the invention. That is, the features of the embodiments may be combined with any appropriate aspect described above, and the optional features of any one aspect may be combined with any other appropriate aspect. Similarly, the features listed in the dependent claims may be combined with non-mutually exclusive features of other dependent claims, particularly where the dependent claims are dependent on the same independent claim. In some jurisdictions that require single claim dependencies, these dependencies may have been used as practice, but this should not be taken to mean that the features in the dependent claims are mutually exclusive.

[0176] It should also be noted that, unless explicitly stated otherwise, the concept of the invention relates to all possible feature combinations. In particular, different partial solutions in different embodiments can be combined in other configurations where technically possible.

Claims

1. A method for processing CT data in a CT imaging system, the CT imaging system comprising: An X-ray source configured to emit X-rays; an X-ray detector configured to generate sampled digital detector data; a digital processor configured to process the sampled digital detector data; the method comprising: filtering the sampled digital detector data in the digital processor to generate filtered detector data; resampling the filtered detector data in the digital processor to generate resampled detector data, wherein the resampling includes reducing a data size of the filtered detector data; wherein said filtering is performed on at least a portion of said sampled digital detector data according to a filtering setting, and said resampling is performed on at least a portion of said filtered detector data according to a resampling setting; and Wherein the filtering setting and the resampling setting are decoupled. 2 . The method of claim 1 , wherein the reducing the data size reduces the data size by a downscaling factor, and wherein at least one of the filtering settings and the resampling settings is associated with the downscaling factor. The method of claim 1 , wherein the filtering settings and the resampling settings are set to achieve a predetermined image quality. The method of claim 3 , wherein the image quality comprises at least one of spatial resolution, noise level, and aliasing level. The method of claim 1 , wherein the filtering is performed in at least one of the spatial domain and the frequency domain.

6. The method of claim 1, wherein the resampling is performed in at least one of the spatial domain and the frequency domain. 7 . The method of claim 1 , wherein the resampling comprises a first resampling performed according to a first resampling setting and a second resampling performed according to a second resampling setting.

8. The method of claim 1, wherein the filter settings include at least one of a bandwidth, an amplitude, and a set of detector elements.

9. The method of claim 1, wherein the resampling setting comprises at least one of a resampling frequency and a phase shift setting.

10. The method of claim 9, wherein the resampling frequency comprises at least one of a non-integer resampling frequency and a non-integer phase shift. The method of claim 9 , wherein the phase shift setting is associated with a resampling mode.

12. A CT imaging system, comprising: An X-ray source configured to emit X-rays; an X-ray detector configured to generate sampled digital detector data; and a digital processor configured to process the sampled digital detector data, wherein the digital processor is configured to: filtering the sampled digital detector data according to a filter setting to generate filtered detector data; and resampling the filtered detector data according to a resampling setting to generate resampled detector data; wherein the data size of the resampled detector data is smaller than the data size of the filtered detector data, and Wherein the filtering setting and the resampling setting are decoupled.

13. The CT imaging system of claim 12, further comprising a gantry, the gantry comprising a rotating member of the gantry on a rotating side of the gantry and a fixed member of the gantry on a fixed side of the gantry, the rotating member and the fixed member being communicatively coupled via a data communication system, wherein the rotating member comprises the X-ray source, the X-ray detector, and the digital processor.

14. The CT imaging system of claim 13, wherein the digital processor comprises a first digital processor disposed on the rotating component and a second digital processor disposed on the fixed component, wherein at least one of the first digital processor and the second digital processor is configured to perform at least a portion of the filtering and / or the resampling.

15. The CT imaging system of claim 12, wherein the CT imaging system comprises a data storage unit configured to store at least a portion of the sampled digital detector data, the filtered detector data and / or the resampled detector data.

16. The CT imaging system of claim 12, wherein the digital processor is configured to operate in one or more operating modes, wherein the operating mode is associated with at least one of image quality, radiation dose, DQE indicator, and data processing scheme.

17. The CT imaging system of claim 12, wherein the resampling setting comprises at least one of a non-integer resampling frequency and a non-integer phase shift setting.

18. The CT imaging system of claim 12, wherein the digital processor is configured to resample the generated resampled detector data according to a second resampling setting to generate second resampled detector data.

19. The CT imaging system of claim 12, wherein the resampling setting comprises a phase shift setting associated with a resampling mode.

20. The CT imaging system of claim 19, wherein the resampling mode comprises at least one of the following: a radial quarter-shift resampling pattern in at least one of the radial direction r1 and the longitudinal direction l1; an equidistant resampling pattern in at least one of a radial direction r2 and a longitudinal direction l2; and An interleaved resampling pattern in at least one of the radial direction r3 and the longitudinal direction l3.

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