Systems and methods for improving data handling in computed tomography systems

Abstract

SOLUTION: The CT imaging system comprises: 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 comprises: filtering, in the digital processor, the sampled digital detector data so as to generate filtered detector data; and resampling, in the digital processor, the filtered detector data so as to generate resampled detector data. The resampling comprises a reduction in data size of the filtered detector data.EFFECT: The invention realizes reduced data size, faster data communication and reduced processing time without a reduction in the spatial resolution and an increase in the noise level.SELECTED DRAWING: Figure 12

Description

[Technical Field]

[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), methods for operating such X-ray imaging systems, and corresponding computer program products for improving data handling. [Background technology]

[0002] Radiation imaging, such as computed tomography (CT) imaging systems and other conventional 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 multiple detector modules 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 and are received by the detector. The X-ray source and X-ray detector are typically arranged on a rotating member of 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 detector. The measured data is used to reconstruct 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 FIG. 1A. In this exemplary embodiment, x-ray imaging system 100 includes 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 record radiation from x-ray source 10, which is optionally focused by x-ray optics or a collimator and passes through an object, subject, or portion thereof. X-ray detector 20 can be connected to image processing system 30 through suitable readout electronics, at least partially contained within x-ray detector 20, which can then perform image processing and / or image reconstruction.

[0005] As an example, a conventional CT imaging system includes an x-ray source and an x-ray detector positioned to acquire projection images of a subject or object at different view angles covering at least 180 degrees. This is most commonly achieved by mounting the x-ray source and 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 on different detector elements for different view angles is called a sinogram. In the following, even though the detector is two-dimensional, the collection of projections recorded on different detector elements for different view angles will be referred to as a sinogram, and the sinogram will be considered a three-dimensional image.

[0006] FIG. 1B is a schematic diagram illustrating an example of a prior art X-ray imaging system configuration, 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, in which X-ray transmission is measured for several different energy levels. This can be achieved by rapidly switching the X-ray source between two different emission spectra, by using two or more X-ray sources emitting 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 recorded photon generates a current pulse that is compared to a set of thresholds, thereby counting the number of photons incident on each of several energy bins.

[0008] Spectral X-ray projection measurements generate projection images at each energy level, which can be weighted together to optimize the contrast-to-noise ratio (CNR) for a given imaging task, as described in "SNR and DQE analysis of broad spectrum X-ray imaging," Tapiovaara and Wagner, Phys. Med. Biol. 30, 519.

[0009] Another technique enabled by energy-resolved X-ray imaging is reference 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 well approximately described as a linear combination of two (or more) basis functions, i.e., 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, where i = 1,...,N, and N is the total number of basis functions. If one or more elements with high atomic numbers are present in the imaged volume and have sufficiently high K-edges that appear 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 may typically be iodine or gadolinium, substances used as contrast agents.

[0010] Reference material decomposition is described in "Energy-selective reconstructions in X-ray computerized tomography", Alvarez, Macovski, Phys. Med. Biol. 1976; 21(5):733-744. In reference material decomposition, the integral of each basis coefficient, expressed as follows, is estimated from each measurement data of each projection line l (el) from the source to the detector element:

number

number

[0011] Next, under the assumption that the count values ​​of each bin are random variables with a Poisson distribution, we use the maximum likelihood method to find the i can be estimated. 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).

number

[0012] The resulting estimated basis coefficient line integrals for each projection line

number

[0013] Standard data management procedures for X-ray imaging systems present various techniques to optimize data acquisition, but these may come at the cost of spatial resolution, noise level, and / or system complexity.

[0014] Therefore, there remains a general demand for improvements in data management for X-ray imaging systems, such as reducing data size, enabling faster data communication, reduced storage requirements, and shorter processing times, without reducing spatial resolution or increasing noise levels. Summary of the Invention

[0015] This summary introduces concepts of the subject matter that are more fully described 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.

[0016] The present invention aims to mitigate, alleviate or eliminate one or more of the above-identified deficiencies and disadvantages in the art singly or in any combination, or to at least solve the problems set forth above.

[0017] A particular object of the present invention is to provide a method for processing CT data of a CT imaging system.

[0018] It is also an object to provide an improved X-ray imaging system.

[0019] It is also an object to provide a computer program for carrying out the above-mentioned method.

[0020] These and other objects are achieved by one or more embodiments of the present invention, as defined by the claims.

[0021] According to a first aspect, there is provided a method of processing computed tomography (CT) data in a CT imaging system including 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 comprising: and 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, the resampling comprising reducing a data size of the filtered detector data, the filtering being performed on at least a portion of the sampled digital detector data in accordance with a filtering setting, and the resampling being performed on at least a portion of the filtered detector data in accordance with a resampling setting, the filtering setting and the resampling setting being decoupled.

[0022] According to a second aspect, there is provided a CT imaging system including 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. A data size of the resampled detector data is smaller than a data size of the filtered detector data. The filtering setting and the resampling setting are decoupled.

[0023] In other words, the proposed technique enables a novel approach to processing and data management in CT imaging systems. Furthermore, the present invention provides a decoupled approach to data filtering and resampling in CT imaging systems, thereby increasing flexibility in filter and resampling design and image quality selection, while simultaneously reducing the size and improving performance of data sets generated by X-ray scans. Improved performance relates, for example, to reduced data size, which can result in faster data communication, reduced memory requirements, and / or faster processing times, without excessively reducing spatial resolution and / or increasing noise levels. It should be noted that the proposed technique is suitable for all three-dimensional (3D) X-ray-based medical imaging systems, including digital mammography imaging systems, interventional imaging systems, fluoroscopic imaging systems, and other X-ray imaging systems. [Brief explanation of the drawings]

[0024] The present embodiments, together with further objects and advantages thereof, are best understood by referring to the accompanying drawings and the following description. [Figure 1A] FIG. 1 is a schematic diagram illustrating an exemplary X-ray imaging system. [Figure 1B] FIG. 1 is a schematic diagram illustrating an exemplary X-ray imaging system. [Figure 2] FIG. 1 is a schematic diagram illustrating another example of an X-ray imaging system (such as a CT imaging system). [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. 1 is a schematic diagram illustrating another example of relevant components of an X-ray imaging system (such as a CT imaging system). [Figure 5] 1 is a schematic diagram of a photon counting circuit and / or device according to an exemplary embodiment. [Figure 6] FIG. 1 is a schematic diagram illustrating an example of a solid-state detector sub-module in accordance with an example 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 showing an example of a set of arrayed detector submodules, each of which is a depth-segmented detector submodule, with an application specific integrated circuit (ASIC) or corresponding circuitry located below the detector submodule when viewed from the direction of incident X-rays. [Figure 9] FIG. 1 is a schematic configuration diagram illustrating an example of a CT imaging system. [Figure 10] FIG. 1 is a schematic diagram illustrating an example design of an X-ray source and X-ray detector system. [Figure 11] FIG. 1 is a schematic flow diagram illustrating an example of a data processing method for an X-ray imaging system. [Figure 12] FIG. 1 is a schematic flow diagram illustrating an example of a data processing method for an X-ray imaging system. [Figure 13] FIG. 1 is a schematic flow diagram illustrating an example of a data processing method for an X-ray imaging system. [Figure 14] FIG. 1 is a schematic diagram illustrating an example of a solid-state detector sub-module in accordance with an example embodiment. [Figure 15] FIG. 10 is a schematic diagram illustrating an example of a resampling pattern. [Figure 16] FIG. 1 is a schematic diagram illustrating an example of the relevant components of an X-ray imaging system in a CT configuration. [Figure 17] FIG. 1 is a schematic diagram illustrating an example of a CT imaging system in accordance with an exemplary embodiment. [Figure 18] FIG. 1 is a schematic diagram illustrating an example implementation of a computer according to one embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0025] Embodiments of the present disclosure will now be described, by way of example, with reference to the figures.

[0026] For a better understanding, it is 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 can be implemented.

[0027] 2 is a schematic diagram illustrating an example of an X-ray imaging system 100 (e.g., a CT imaging system). 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 an object, 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 other processing operations on the measured data (e.g., applying corrections, temporarily storing the data, 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 can include a digital processor. According to an exemplary embodiment, all or part of the analog processing circuit 25 can be implemented in the X-ray detector 20. The X-ray source and the X-ray detector can be coupled to a rotating member of a gantry 11 of the CT imaging system 100.

[0028] The entire X-ray detector may be considered an X-ray detector system 20, or may be considered an X-ray detector 20 in combination with associated analog processing circuitry 25.

[0029] Image processing system 30 is in communication with and electrically coupled to analog processing circuit 25, and may include digital processing circuit 40 and / or computer 50, and may be configured to perform image reconstruction based on image data from the X-ray detector. Thus, image processing system 30 may be viewed as computer 50, as a combined system of digital processing circuit 40 and computer 50, or as digital processing circuit 40 alone, where the digital processing circuit is further specialized for image processing and / or reconstruction.

[0030] 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 to be imaged.

[0031] 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 includes a computer 50. The computer 50 receives commands and scanning parameters from an operator through an operator console 60, which may have a display 62 and some form of operator interface (e.g., a keyboard, mouse, joystick, touch screen, 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 X-rays emitted 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 includes 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. Also included is a detector controller 44 configured to control and / or receive data from the x-ray detector 20 .

[0032] In one embodiment, computer 50 also performs post-processing and image reconstruction of image data output from x-ray detector 20. Computer 50 thus corresponds to image processing system 30 shown in Figures 1 and 2. An associated display 62 allows an operator to view the reconstructed image and other data from computer 50.

[0033] 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 may be formed, for example, by a plurality of pixels (also called sensors or detector elements) and associated processing circuitry (e.g., 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 processing circuitry (ASIC) digitizes the analog signals from the pixels. The processing circuitry (ASIC) may also include a digital processing unit that can perform further processing operations on the measurement data (e.g., applying corrections, temporarily storing and / or filtering the measurement data). During a scan to acquire X-ray projection data, the gantry and its mounted components rotate about an isocenter 13.

[0034] Current X-ray detectors usually require the conversion of incident X-rays into electrons, typically via the photoelectric effect or Compton interaction. The resulting electrons usually produce secondary visible light until they lose their energy, which is detected by a photosensitive material. Semiconductor-based detectors also exist, where the electrons generated by the X-rays create electron-hole pair charges that are collected by applying an electric field.

[0035] Some detectors operate in an energy-integrating mode, providing an integrated signal of multiple x-rays, with the output signal being proportional to the total energy deposited by the detected x-rays.

[0036] X-ray detectors with photon counting capability and energy resolution are becoming commonplace in medical applications of X-rays. Photon counting detectors have the advantage that, in principle, the energy of each X-ray can be measured, providing additional information about the composition of the object. This information can be used to improve image quality and / or reduce radiation dose.

[0037] Typically, photon-counting X-ray detectors determine the energy of a photon by comparing the height of the electrical pulse produced by the photon's interaction within the detector material to a set 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.

[0038] Photon-counting detectors count the number of photons that interact with the detector during a measurement period. New photons are typically identified by the fact that the height of an electrical pulse exceeds the comparator voltage of at least one comparator. Once a photon is identified, the event is recorded by incrementing a digital counter associated with the channel.

[0039] When multiple different thresholds are used, an energy-discriminating photon counting detector is obtained. In an energy-discriminating photon counting detector, detected photons can be classified into energy bins corresponding to various thresholds. This type of photon counting detector is sometimes called a multi-bin detector. Generally, energy information can be used to create a new type of image, 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 N programmable thresholds (T1-TN) of a comparator and classified according to the pulse height. This pulse height is proportional to the energy. In other words, a photon counting detector that includes two or more comparators is called a multi-bin photon counting detector. In a multi-bin photon counting detector, photon count values ​​are stored in a set of counters (typically one counter for each energy threshold). For example, one count value can be assigned to the highest energy threshold exceeded by a photon pulse. In another example, a counter records the number of times a photon pulse exceeds each energy threshold.

[0040] As an example, edge-on refers to a special non-limiting design for photon-counting detectors, where the edge of the X-ray sensor (e.g., X-ray detector element or pixel) faces the incident X-rays.

[0041] For example, such a photon counting detector can have pixels aligned in at least two directions, one of the at least two directions of the edge-on photon counting detector having a component in the direction of the x-rays. Such edge-on photon counting detectors are sometimes referred to as depth-segmented photon counting detectors, having two or more depth segments of pixels in the direction of the incident x-rays. 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.

[0042] Alternatively, the plurality of pixels may be arranged as an array in a direction substantially perpendicular to the direction of the incident X-rays (not segmented in the depth direction), and each pixel of the plurality of pixels may be arranged with an edge oriented toward the incident X-rays. In other words, the photon-counting detector may not be segmented in the depth direction, but may be arranged with an edge oriented toward the incident X-rays.

[0043] The absorption efficiency can be increased by placing the edge-on photon counting detector edge-on, in which case the absorption depth can be chosen to be any length, and the edge-on photon counting detector can be fully depleted without requiring very high voltages.

[0044] The conventional mechanism for detecting X-ray photons with direct semiconductor detectors basically works as follows: the energy of the X-ray interaction in the detector material is converted into electron-hole pairs within the semiconductor detector, with the number of electron-hole pairs 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, which can be measured.

[0045] As shown in FIG. 4 , signals are transmitted from the detector elements 22 of the X-ray detector to the input of an analog processing circuit (e.g., an ASIC) 25 via a path 26. It should be understood that the term application specific integrated circuit (ASIC) should be broadly interpreted as a general circuit configured for use in a specific application. The ASIC processes the electrical charge generated from each X-ray, converts the processed electrical charge to digital data, and uses the digital data to derive measurement data (e.g., photon counts and / or estimated energy). The ASIC is configured to interface with the digital processing circuit so that the digital data is sent to a 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, which generates a reconstructed image.

[0046] Since the number of electrons and holes generated from an X-ray event is proportional to the energy of the X-ray photon, the total charge of an induced current pulse is proportional to that 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 (COMP), and a counter can be used to record the number of times the pulse is greater than the threshold. In this way, the number of X-ray photons detected within a certain time frame that have energies greater than the respective thresholds (THR) can be counted and / or recorded.

[0047] The ASIC typically samples the analog photon pulse once for every clock cycle and records the outputs of the comparators. The comparators (thresholds) output a 1 or 0 depending on whether the analog signal was above or below the comparator voltage. The information available at each sample is, for example, a 1 or 0 for each comparator indicating whether the comparator was triggered (the photon pulse was greater than the threshold) or not.

[0048] Photon counting detectors typically have photon counting logic that determines whether a new photon has been recorded and records the photon in a counter. Multi-bin photon counting detectors typically have multiple counters, e.g., one counter for each comparator, and photon counts are recorded in the counters according to an estimate of the photon energy. Logic can be implemented in several different ways. Two of the most common categories of photon counting logic are non-paralyzed counting mode and paralyzed counting mode. Other photon counting logic, such as maximum detection, counts detected maximums in a voltage pulse and, in some cases, records the pulse height of the detected maximum.

[0049] 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 (spectral imaging). However, energy-integrating detectors have the advantage of high count-rate tolerance. Count-rate tolerance comes from the fact / realization that, since the total energy of the photons 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.

[0050] FIG. 5 shows a schematic diagram of a photon counting circuit and / or device according to an exemplary embodiment.

[0051] 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 by electrodes attached to the detector material. A signal is transmitted from the detector elements to the input of a parallel processing circuit (e.g., an ASIC). In one example, the ASIC can process the charge to generate a voltage pulse whose maximum height is proportional to the amount of energy deposited in the detector material by the photon.

[0052] The ASIC may include a set of comparators 302, each of which compares the magnitude of the voltage pulse with a reference voltage. The comparator output is typically (0 / 1) depending on which of the two compared voltages is greater. Here, the comparator output is 1 if the voltage pulse is higher than the reference voltage, and 0 if the reference voltage is higher than the voltage pulse. A digital-to-analog converter (DAC) 301 may be used to convert a digital setting, which may be provided by a user or a control program, into a reference voltage that can be used by the comparators 302. If the height of the voltage pulse exceeds the reference voltage for a particular comparator, 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.

[0053] As mentioned above, the estimated basis coefficient line integral values ​​obtained for each projection line are

number

[0054] It will be understood that the features and configurations described herein can be implemented, combined, and rearranged in various ways.

[0055] For example, embodiments may be implemented in hardware, or at least partially implemented in software and executed by suitable processing circuitry, or a combination thereof.

[0056] The steps, functions, procedures, and / or blocks described herein may be implemented in hardware using conventional techniques (such as discrete or integrated circuit technology, including both general-purpose electronic circuitry and application-specific circuitry).

[0057] Alternatively or complementary, at least some of the steps, functions, procedures and / or blocks described herein may be implemented in software (such as a computer program) and executed by suitable processing circuitry (such as one or more processors or processing units).

[0058] Non-limiting examples of specific detector module implementations are described below. More specifically, these examples represent edge-on oriented detector modules and depth-segmented detector modules. Other types of detectors and detector modules are also possible.

[0059] 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 multiple detector elements or pixels 22, each of which is typically based on a diode with a charge collection electrode as its main component. X-rays enter the detector module at its end.

[0060] 7 is a schematic diagram illustrating 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, assuming that X-rays are incident from the end of the detector module.

[0061] Typically, a detector element is an individual X-ray sensitive sub-element of the detector. Generally, photon interaction occurs with the detector element and the charge thus generated is collected by a corresponding electrode of the detector element.

[0062] Each detector element typically measures the incident x-ray flux as a series of frames, where a frame is a specified time interval of measurement data called the frame time.

[0063] Depending on the detector topology, one detector element may correspond to one pixel, particularly if the detector is a flat panel detector. A depth-segmented detector can be considered to have several detector strips, each having several 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 of the multiple depth segments is associated with its own separate charge collection electrode.

[0064] The detector strips of a depth-segmented detector usually 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 corresponds to an individual depth segment / detector element.

[0065] 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 for several ASICs, which are preferably attached by so-called flip-chip technology. The wiring includes connection lines for signals from each pixel or detector element to the input of the ASIC, as well as connection lines 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 large cross-sectional area of ​​these connection lines to allow for high currents, but this power can also be supplied through a separate connection line. The ASIC can be located next to the active sensor, which means that the ASIC can be protected from incident X-rays by placing an absorbent cover on top of it and also from scattered X-rays by placing an absorber in the direction of the scattered X-rays.

[0066] 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. In this example, it is shown that a semiconductor sensor 21 can also function as the substrate of the MCM. Signals are transmitted by paths 23 from the detector elements 22 to the inputs of parallel processing circuits 24 (e.g., ASICs) located next to the active sensor area. The ASICs process the electrical charges generated from each X-ray and convert the processed charges into digital data. The digital data can be used to detect photons and / or estimate their energy. The ASICs can have their own digital processing circuitry and memory for simple tasks. The ASICs 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.

[0067] However, the adoption of depth segmentation poses two significant 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. Both the small pixel size and the use of depth segmentation increase the channel count, while the multi-energy bins further increase data size. Second, because the counts for a given X-ray input are divided into small pixels, segments, and energy bins, the signal in each bin is very low, and detector calibration / correction requires calibration data spanning several orders of magnitude or more to minimize statistical uncertainty.

[0068] Naturally, orders of magnitude larger data sizes require larger computing resources, hard disks, memory, and central processing units (CPUs) or graphics processing units (GPUs), as well as slower data handling and preprocessing. For example, if the data size becomes 10 gigabytes instead of 10 megabytes, data handling times (reading and writing) can increase by 1000 times.

[0069] A problem with counting-type X-ray photon detectors is pile-up. When the X-ray photon flux rate is high, there can be problems distinguishing between two consecutive charge pulses. As mentioned above, the pulse length after filtering depends on the shaping time. If this pulse length is longer than the time between the two charge pulses induced by the X-ray photons, the pulses will merge into one, and the two photons cannot be distinguished and will be counted as one pulse. This is called pile-up. Therefore, one way to avoid pile-up at high flux is to use a short shaping time or to use depth segmentation.

[0070] To generate a pileup calibration vector, the pileup calibration data must be preprocessed for spit correction. When generating a material decomposition vector, the material decomposition data is preferably preprocessed for both spit correction and pileup correction. In the case of patient scan data, this data must be preprocessed for spit, pileup, and material decomposition before image reconstruction. Note that these examples are simplified examples to explain "preprocessing," and actual preprocessing steps may include several other calibration steps (such as reference normalization and air calibration) as needed. The term "processing" may refer only to the final step in each calibration vector generation process or patient scan, but the terms are sometimes used interchangeably.

[0071] FIG. 8B is a schematic diagram showing an example of a set of arrayed detector submodules, each of which is a depth-segmented detector submodule, and the ASIC or corresponding circuit 24 is arranged below the detector element 22 as viewed from the direction of incident X-rays, and a path 23 can be provided from the detector element 22 through the space between the detector elements to reach the parallel processing circuit 24 (e.g., ASIC).

[0072] 9 is a schematic diagram illustrating an example of an overall CT imaging system. In this schematic example, the overall CT imaging system 100 includes a gantry 111 and a patient table 112. The patient table 112 can be inserted into an opening 114 in the gantry 111 during a patient scan and / or a calibration scan. The z-direction represents the direction of the axis of rotation of the gantry's rotating members around the object or patient being imaged. The x-direction represents the angular orientation of the CT imaging system, and the y-direction represents the direction of incident x-rays.

[0073] However, it should be understood that the rotating and stationary components of the gantry need not be part of the CT system and may be in other arrangements and / or configurations (e.g., linear and / or translational relative movement without rotation). As an example, the x-ray source and detector combination may move linearly and / or translationally relative to the stationary components of the entire gantry. For example, the x-ray source and detector may move together as an integral assembly unit along the table axis (commonly referred to as the z-axis). Alternatively, the patient table may move, but the x-ray source and detector combination may be stationary, where relative movement is important. This includes, for example, geometric system configurations where the patient may be standing in a so-called telephone booth scanner.

[0074] 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 as edge-on detector modules with their edges facing the X-ray source, or they can be arranged with a slight overall curve. As mentioned above, the direction of the incident X-rays is called the Y direction. Multiple detector pixels along the gantry axis (called the z direction) enable multislice images to be acquired. Multiple detector pixels along the angular direction (called the x direction) enable simultaneous measurement of multiple projections of the same plane, as applied in fan / cone-beam CT. The x direction is sometimes called the channel direction. Most detectors have detector pixels aligned in both the slice (z) and angular (x) directions.

[0075] FIG. 11 is a schematic flow diagram illustrating an example of a data processing method for an X-ray imaging system.

[0076] 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 includes 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.

[0077] The method includes filtering (S1) the sampled digital detector data in a digital processor to generate filtered detector data, and resampling (S2) the filtered detector data in the digital processor to generate resampled detector data, the resampling including reducing a data size of the filtered detector data.

[0078] Filtering is performed on at least a portion of the sampled digital detector data according to a filtering setting, and resampling is performed on at least a portion of the filtered detector data according to a resampling setting, the filtering setting and the resampling setting being decoupled from each other.

[0079] The resampling and filtering settings can be selected to balance / weight filtering and sampling to obtain an image of desired quality in a desired manner. The image can have a desired, customizable quality and be generated in a desired, customizable time by an X-ray scan. The X-ray scan is performed using a certain radiation dose. "Decoupled" here can mean that the filtering and resampling settings can be selected independently of each other and / or that the filtering / filtering settings and the resampling / resampling settings are not coupled, or at least not strictly coupled. In other words, the filtering and resampling settings can be selected independently of each other, but, for example, the resampling settings can be selected based at least in part on the filtering settings, or vice versa. Similarly, "decoupled" can mean that the resampling settings are set / selected to achieve a certain effect on the filtered detector data and still produce a suitable image, without being constrained by the selection of the filtering settings. The resampling and filtering settings can be set / selected to be selected independently of each other, but the CT imaging system still provides a suitable image with a desired quality.

[0080] The filtering and resampling settings can be optimized to achieve a certain image quality and / or DQE index. The filtering and resampling settings can be optimized / set based on the radiation dose delivered by the CT imaging system during an x-ray CT scan or the data handling scheme employed by the CT imaging system, such as the operating mode. Image quality can be related to spatial resolution and / or noise and / or aliasing. For example, the filtering settings can be selected to generate detector data with lower image quality / resolution, and the resampling settings can then be selected to optimize the production of the final image in terms of at least one of image quality, radiation dose, DQW index, and / or data handling. Data handling can refer to where and when the detector data is processed / filtered / resampled. As another example, the filtering and resampling settings can be selected relative to one another to achieve a desired data size reduction. Data size can be reduced while minimizing common challenges in data processing (such as aliasing and noise). Thus, a desired data size reduction can be achieved without reducing spatial resolution. In some cases, the choice of filtering and resampling settings may allow for some loss of spatial resolution in order to significantly reduce data size.

[0081] 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.

[0082] In one example, the data size reduction reduces the data size by a reduction factor, and at least one of the filtering settings and the resampling settings is associated with the reduction factor. The reduction factor corresponds to how much the data is reduced in the resampling step, and the filtered detector data is resampled to generate resampled detector data. The resampled detector data has a smaller data size compared to the filtered detector data.

[0083] In another example, the filtering and resampling settings are set to achieve a predetermined image quality. Image quality can include at least one of spatial resolution, noise aliasing level, and signal aliasing. In other words, the filtering and resampling settings are set / selected / determined / calculated to provide a certain image quality. The image quality can be set based on the mode of operation and / or based on selection by an operator of the CT imaging system.

[0084] By way of example, filtering can be performed in the spatial and / or frequency domains. In the spatial domain, the filter length can be increased compared to filters used in known methods. Long filters may include filters that are longer than filters used in known methods. For example, resampling can be performed in a manner that is decoupled from filtering, allowing the filter length to be increased without loss of spatial resolution and / or an increase in noise level. In the frequency domain, the filter can have a unity amplitude up to a cutoff frequency and zero at frequencies greater than the cutoff frequency. The filter can also have a more gradual transition, such as a Hanning filter or Hamming filter. Such filters can be referred to as finite impulse response (FIR) filters. The filter can also be designed to achieve a desired passband and / or stopband.

[0085] In another example, resampling can be performed in the spatial and / or frequency domains. It should be noted that the filter length and the amount of resampling can be different, decoupled, or uncoupled. In particular, if the bandwidth of the filtered signal is reduced further than the resampling frequency, residual aliasing, if present, can be controlled. This is desirable in various techniques for preventing signal aliasing in CT imaging systems (particularly quarter-offset and focal wobble techniques). For example, filtering and resampling designs can be modeled according to the relative importance of preserving spatial resolution versus maximizing detective quantum efficiency (DQE) by avoiding noise aliasing. For a given amount of downsampling, a higher bandwidth filter can be used, even if some aliasing occurs due to how the CT imaging system handles signal aliasing in other ways. The CT imaging system can exhibit higher spatial resolution compared to a system using the same amount of downsampling and a filter with a bandwidth low enough to avoid aliasing. On the other hand, if the noise-to-dose efficiency ratio is more important, filters can be designed to prevent aliasing to a greater extent than would be achieved with simple binning. This approach can improve DQE at low to mid-frequencies, but it may result in reduced spatial resolution compared to binning with the same degree of downsampling.

[0086] In yet another example, the resampling includes a first resampling using a first resampling setting and a second resampling using a second resampling setting. The first resampling setting and the second resampling setting can be selected to improve image quality, which may be degraded after the filtering and / or first resampling steps. The first resampling and the second resampling can be set / selected to obtain an image of a desired image quality. The first resampling can include reducing the data size of the filtered detector data provided as input. Furthermore, the first resampling may degrade image quality. The second resampling can be set / selected to at least partially restore image quality. In a non-limiting example, the filtering and resampling settings can be associated with a type of operating mode. The operating mode can accommodate a reduced data size, which can further speed up data communication, reduce memory requirements, and / or reduce processing time. Thus, a CT imaging system can be configured to operate in one or more operating modes, each of which can balance / weight filtering and resampling in a desired manner to obtain images of a desired quality. Images can be generated based on x-ray scans of a desired dose, with a desired quality, and in a desired time. An operating mode can, for example, optimize filtering and resampling settings to achieve a certain image quality, radiation dose, DQE index, and / or data handling scheme.

[0087] In an example embodiment, the operating mode may be associated with an image quality, a DQE index, and / or a data handling scheme.

[0088] A data handling scheme can include operational settings / instructions for where and how data is processed in a CT system. Therefore, the data handling scheme relates to how quickly the final image can be received by the operator for diagnosis. The data handling scheme relates to where, when, and how much data is processed in a CT imaging system, as well as how and when the data is transferred. For example, a CT system can perform filtering and resampling on the rotating side of the CT system gantry and then transfer the resampled data to the stationary side, where the remaining processing to generate the final image can be performed. This can improve the speed at which the CT imaging system generates images suitable for viewing by the operator or technician.

[0089] It should be understood that the CT imaging system can include any 3D-ray-based medical imaging modality. For example, the CT imaging system can include a general tomography system adapted to capture images from different angles and combine the information to create an image volume in which all slices can be individually examined. The CT imaging system can include a CT system adapted to use a full 180-degree angular range and / or a limited angular range. In other words, the CT system can include a conventional computed tomography system using a full 180-degree range, or a breast tomosynthesis system (also known as a mammogram) using a limited angular range. In one embodiment, the CT imaging system can be configured to run in an operating mode, where at least two parameters are balanced depending on the operating mode configuration. The balanced parameters can be related to radiation dose, DQE, and data handling scheme. For example, a first operating mode can be optimized / configured for rapid image generation (i.e., to obtain a final image in a fast process). Thus, a first mode of operation can balance parameters to provide images at a faster rate, for example by reducing data size and reducing resolution in resampling, but still achieve adequate image quality by performing filtering and resampling in a decoupled manner to accommodate the loss in image quality due to data reduction. A second mode of operation can be optimized / configured to reduce radiation dose, provide a resampled data set with adequate image quality and / or DQE index, and also reduce data size, by adjusting filtering and resampling in a decoupled manner.

[0090] In one embodiment, the CT imaging system 100 can include a gantry 111 having a rotating member disposed on a rotating side 102 and a stationary member disposed on a stationary side 104. The rotating member and the stationary member can be communicatively coupled through a data communication system 160, and the rotating member can include an x-ray source 110, an x-ray detector 120, and a digital processor 140.

[0091] In a particular embodiment shown in FIG. 17 , the digital processor 140 can 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 and / or second digital processors can be configured to perform at least a portion of the filtering and / or resampling. In a non-limiting example, the first digital processor can 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, which can be transferred to the stationary side and used to create a final image. The resampled detector data can be smaller in size than the filtered detector data. This can result in faster data communication, reduced memory requirements, shorter processing times, and faster image generation. In another embodiment, the filtering can be performed by the first digital processor and the resampling can be performed by the second digital processor. This has the advantage of allowing some processing to be performed before transferring data between the rotating side and the stationary side. This can result in, for example, faster image acquisition and therefore faster diagnosis. Additionally, the components of the CT imaging system can be used more efficiently.

[0092] Figure 12 is a schematic flow diagram illustrating an example of a method for processing data from a CT imaging system. The schematic flow diagram of Figure 12 has several features in common with the schematic flow diagram of Figure 11, and reference is made to Figure 11 and its associated text in Figure 12 for a better understanding of at least some of the features and / or functions in the flow diagram. In Figure 12, the illustrated method takes sampled digital detector data, performs filtering on the sampled digital detector data according to a filtering setting, and then resamples the filtered detector data according to a resampling setting. By way of example, several steps of resampling and / or filtering may be performed serially.

[0093] By way of example, the filtering settings may include a bandwidth, an amplitude, and / or a set of detector elements. The set of detector elements may be selected detector elements. For example, the detector elements may be selected according to their position within the detector and / or according to a downsampling pattern. The downsampling pattern may be, for example, a pattern in which several adjacent detector elements, for example, two or three adjacent detector elements, are binned.

[0094] Figure 13 is a schematic flow diagram illustrating an example of a method for processing data from a CT imaging system. The schematic flow diagram of Figure 13 has some features in common with the schematic flow diagrams of Figures 11 and 12, and reference is made in Figure 13 to Figures 11, 12, and associated text to facilitate an understanding of at least some of the features and / or functions in the flow diagram.

[0095] As an example, resampling can be performed in the view direction v. This approach can be performed to form an image of an object observed by the CT imaging system 100 without binning different views with equal weights. This is in contrast to resampling in the radial direction r and / or longitudinal direction l of the detector module.

[0096] In certain instances, a filter can be designed and applied in the azimuth direction before resampling in the same direction. This approach aims to minimize, for example, noise aliasing, signal aliasing, and / or azimuth blur. This method is particularly useful when dealing with data originally collected with flying focal points and / or kV switching between views. With flying focal points, the collected data may not be at the desired resampling location due to hardware limitations and / or data size issues. This can be achieved using the phase shifting method described below. With kV switching between views, low-energy and high-energy data may be collected at different azimuth angles, creating the need to align the data before further processing. This can be achieved using the phase shifting method described below.

[0097] In certain examples, the resampling settings may include a resampling frequency and / or a phase shift setting.

[0098] In a non-limiting example, the resampling frequency may include a non-integer resampling frequency. In other words, the steps and / or operations described herein are not limited to integer resampling. The resampling frequency may be reduced by any factor (e.g., a fractional factor). Resampling frequencies and phase shifts are further described in FIG. 15 and associated text.

[0099] In another non-limiting example, the phase shift setting can include a non-integer phase shift. In other words, the steps and / or operations described herein are not limited to integer phase shifts. Applying a phase shift can be performed in the frequency domain, for example, by applying a linear phase shift. As an example, after performing the desired adjustment to the amplitude of the frequency components, an inverse Fourier transform can be applied to a half-integer resampling frequency (e.g., 1.5 or 2.5) to obtain values ​​at integer sample positions. Furthermore, a linear phase shift can be followed by a second inverse Fourier transform to obtain signal sample values ​​at half-integer positions. The required / desired sample values ​​can then be selected from the two results.

[0100] By having multiple different filters, the phase shift can be performed in the spatial domain. The filters can be designed to achieve half resample shifts. As an example, a filter without phase shift properties can be applied to obtain values ​​at the original integer resample positions. Furthermore, a filter with phase shift properties can be applied to obtain resample values ​​at half integer positions. Such filters can be FIR filters, as described herein.

[0101] As an illustrative example, one can resample a signal by 1.5 times and simultaneously design a filter with a bandwidth that is three times larger. This example is beneficial for avoiding aliasing. In another example, the same resampling factor of 1.5 can be combined with a filter with a bandwidth of 2.5 or 2. This example is advantageous in that it allows for higher spatial resolution. This effect can preserve the DQE at low frequencies, at the expense of DQE at high frequencies.

[0102] Furthermore, the resampling described herein does not need to be uniform. In other words, the output positions after resampling do not need to be equally spaced compared to the original input positions. This can be advantageous when the resampled data is subsequently processed by a fan-parallel reconstruction algorithm. When adjusting the data to fit a fan-parallel pattern, the samples need to be transformed from equiangular in the fan beam to equidistant in the parallel beam geometry. It is worth noting that this approach can be performed during resampling, which can be desirable in that it can speed up processing time and / or increase signal fidelity.

[0103] FIG. 13 is a schematic flow diagram illustrating an example of resampling according to an embodiment of the present invention, and reference is made in FIG. 13 to FIG. 12 and its associated text for a better understanding of at least some of the features and / or functions in the flow diagram.

[0104] For example, the phase shift settings can be associated with a resampling pattern. The resampling pattern can configure how different data points received after the detector data is initially sampled are managed. For example, certain data points can be resampled and / or phase shifted to, for example, mitigate aliasing and / or noise issues. Further details of the resampling pattern are provided in FIG. 15 and its associated text.

[0105] In an exemplary implementation, the resampling pattern may include a quarter-radial offset resampling pattern in the radial direction r1 and / or the longitudinal direction l1. In other words, the resampling may be offset to one side by one-quarter of the resampling interval relative to a perfectly symmetric resampling pattern, such that conjugate projection lines are perfectly interleaved after, for example, half a rotation of the gantry 111. The pattern may be designed to achieve the quarter-radial offset digitally and / or virtually. This may be advantageous in that it may further reduce aliasing in images obtained by the X-ray imaging system 100.

[0106] In another illustrative example, the resampling pattern may include a radial r2 and / or longitudinal l2 equidistant resampling pattern.

[0107] In yet another exemplary embodiment, the resampling pattern may include an interlaced resampling pattern in the radial direction r3 and / or the longitudinal direction. This may be achieved by selecting the resampling positions such that the resampling positions shift back and forth between views. The resampling pattern may result in successive projections forming two interlaced radial patterns. This resampling pattern is advantageous in that it can suppress signal aliasing but does not require conjugate projections. Thus, the resampling may be compatible with half-scan CT imaging.

[0108] It should be noted that the radial directions r1, r2, and / or r3 may coincide. It should also be noted that the longitudinal directions l1, l2, and / or l3 may coincide as well. Furthermore, while resampling is performed in the longitudinal directions l1, l2, and / or l3, the resampling positions can be selected to achieve the effect of a z-flying focus. This approach is advantageous in that it provides a high spatial resolution and / or reduces the noise level.

[0109] Optionally, the interlaced resampling pattern may be formed from at least two consecutive projections.

[0110] Figure 14 is a schematic diagram illustrating an example of a solid-state detector sub-module according to an exemplary embodiment. The solid-state detector sub-module 21 of Figure 14 has some features in common with the solid-state detector sub-module 21 of Figure 7, and Figure 14 references Figure 7 and its associated text to further understand at least some of the features and / or functionality of the detector sub-module 21.

[0111] 14 shows coordinate systems (r1;l1), (r2;l2), and (r3;l3). As mentioned above, resampling can be performed in the radial direction r and / or the longitudinal direction l shown in FIG. 14. It should again be noted that the radial directions r1, r2, and / or r3 may coincide. It should further be noted that the longitudinal directions l1, l2, and / or l3 may coincide as well. The radial direction r can be recognized as the channel direction or column direction of the detector sub-modules 21. Furthermore, the longitudinal direction l can be recognized as the row direction of the detector sub-modules 21.

[0112] FIG. 15 is a schematic diagram showing example sampling and resampling patterns (A-E). The axis r represents the radial distance between sampling or resampling locations, and the dashed vertical lines are spaced at equal radial distances. A shows the original equiangular sampling pattern. B shows simple binned samples based on example resampling pattern A of adjacent detector channels. C shows resampling samples based on example resampling pattern B with an equidistant resampling pattern. D1-D2 show resampling samples based on example resampling pattern B with a radial quarter-offset resampling pattern. E1-E2 show resampling samples based on example resampling pattern B with a radially interlaced resampling pattern.

[0113] It is noteworthy that the samples in Example A are positioned closer to each other for larger fan angles, i.e., away from the center of axis r, than for smaller fan angles.

[0114] The samples in example B are still equiangular after simple binning, but the radial distances are not equally spaced. The arrows indicate the simple binning of adjacent detector channels.

[0115] The samples in Example C are both equiangular and radially equidistant, i.e., follow a parallel beam shape.

[0116] The samples in examples D1-D2 represent resampling of direct and conjugate X-rays, respectively.

[0117] The samples in examples E1-E2 show the resampling of two consecutive views. Performing resampling with the resampling pattern shown in E1-E2 can achieve the same effect as focal deflection without moving the focal point of the CT imaging system.

[0118] The present disclosure relates to a novel system architecture and corresponding procedures for improving data processing and management in CT imaging systems.

[0119] According to a second aspect, a CT imaging system 100 is provided. The CT imaging system 100 includes 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 filtering setting to generate filtered detector data and to resample the filtered detector data according to a resampling setting to generate resampled detector data. A data size of the resampled detector data is smaller than a data size of the filtered detector data. The filtering setting and the resampling setting are decoupled. In other words, the CT imaging system is configured to perform filtering and resampling following filtering in a decoupled manner. In other words, the digital processor is configured to perform filtering with a filter setting, and the digital processor is configured to perform resampling on the filtered detector data according to a resampling setting in which the filtering setting and the resampling setting are decoupled. Thus, certain filter settings can be used and resampling can be performed on the filtered data according to freely adjustable resampling settings to generate resampled detector data that can be optimized for a particular purpose (e.g., optimizing resolution / image quality versus speed, optimizing resolution / image quality versus radiation dose). Furthermore, the filter and resampling settings can be selected to achieve a desired data size reduction.

[0120] FIG. 16 is a schematic diagram showing an example of the relevant parts of an X-ray imaging system in such a CT imaging system.

[0121] As an example, the digital processor may be configured to reduce the data size of the filtered detector data by resampling, and the digital processor may be configured to reduce the data size using a reduction factor, and at least one of the filtering settings and the resampling settings may be associated with the reduction factor.

[0122] In another embodiment, the digital processor is configured to filter and resample the sampled digital detector data to achieve a predetermined image quality. The filtering 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.

[0123] In yet another embodiment, the digital processor is configured to resample the generated resampled detector data according to a second resampling setting to generate resampled second detector data. In other words, the digital processor is configured to perform 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 can be selected to improve image quality, which may be degraded after performing filtering and / or the first resampling. The digital processor can be configured to set / select the resampling setting and the second resampling setting to provide an image of desired image quality. The first resampling may involve reducing the data size of the filtered detector data provided as input. Furthermore, the first resampling may result in a reduction / degradation of image quality. The second resampling can be set / selected to at least partially restore image quality.

[0124] Optionally, digital processor 140 may be configured to operate in one or more operating modes, where the operating modes are associated with image quality, radiation dose, DQE metrics, and / or data handling schemes.

[0125] In a non-limiting example, the resampling settings can include a non-integer resampling frequency.

[0126] In another non-limiting example, the resampling setting may include a non-integer phase shift setting.

[0127] In a practical embodiment, the resampling settings may include a phase shift setting associated with the resampling pattern.

[0128] For example, the resampling pattern may include a quarter-radial offset resampling pattern in the radial direction r1 and / or the longitudinal direction l1, an equidistant resampling pattern in the radial direction r2 and / or the longitudinal direction l2, and an interlaced resampling pattern in the radial direction r3 and / or the longitudinal direction l3.

[0129] It should be noted that the radial directions r1, r2, and / or r3 may be coincident, and that the longitudinal directions l1, l2, and / or l3 may be coincident as well.

[0130] In this embodiment, the CT system includes an X-ray source 110 and an X-ray detector 120 positioned in the X-ray beam path so that projection images of the subject or object can be acquired at different view angles, most commonly achieved by mounting the X-ray source 110 and the X-ray detector 120 on a support that can rotate around the subject or object (e.g., the rotating member of a gantry).

[0131] The proposed technique can perform detector data filtering and resampling in an improved and versatile manner (e.g., be able to efficiently process large amounts of detector data and / or mitigate possible bottleneck effects in conventional CT imaging systems).

[0132] Thus, the proposed technique allows for more optimal utilization of the superior imaging capabilities of modern X-ray detectors, including high-resolution, photon-counting, energy-discriminating, multi-segment detectors.

[0133] The proposed technique can therefore effectively handle trade-offs between image quality related to, for example, resolution and / or aliasing, radiation dose, data size, and data fidelity, and practical constraints related to computational resources, power, cooling, space, and / or data transfer bandwidth.

[0134] By way of example, CT imaging system 100 may include a gantry 111. Gantry 111 includes a rotating member disposed on a rotating side 102 and a stationary member disposed on a stationary side 104. The rotating and stationary members are communicatively coupled through a data communication system 160, and the rotating member includes an x-ray source 110, an x-ray detector 120, and a digital processor 140. Processed detector data may be transferred from the rotating (moving) side of the gantry to the stationary side of the gantry.

[0135] The data communication system 160 may be a slip ring, as typically used in CT imaging systems having rotating members in a gantry. "Slip ring" refers herein to an electromechanical device that allows for the transmission of electrical power and electrical signals (e.g., power and data transfer between a rotating structure and a stationary structure).

[0136] In certain embodiments, digital processor 140 includes a first digital processor 140-1 disposed on the rotating member and a second digital processor 140-2 disposed on the stationary member, where the first and / or second digital processors are configured to perform at least a portion of the filtering and / or resampling.

[0137] 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 located on the rotating side 102 of the CT imaging system and a second data storage unit 150-2 located on the stationary side 104 of the CT imaging system, an example of which is shown in FIG. 17.

[0138] As mentioned above, at least some of the steps, functions, procedures, and / or blocks described herein may be implemented in software (e.g., a computer program) and executed by suitable processing circuitry (e.g., one or more processors or processing units).

[0139] FIG. 18 is a schematic diagram illustrating an example of a computer implementation according to one embodiment.

[0140] In this particular example, system 200 includes a processor 210 and a memory 220, where the memory includes instructions executable by the processor, thereby enabling the processor to perform the steps and / or operations described herein. The instructions are typically configured as computer programs 225; 235 and 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 can be connected to processor 210 and / or memory 220 to allow relevant data (such as input parameters and / or resulting output parameters) to be input and / or output.

[0141] In particular embodiments, memory 220 includes a set of processor-executable instructions, such that the processor is operable to perform the steps and / or actions described herein.

[0142] The term "processor" should be construed in a general sense as a system or device capable of executing program code or computer program instructions to perform specific processing, decision-making, or computational tasks.

[0143] Thus, processing circuitry, including one or more processors, when executing computer programs is configured to perform well-defined processing tasks as described herein.

[0144] The processing circuitry need not be specialized solely to perform the steps, functions, procedures and / or blocks described above, but may perform other tasks.

[0145] The present technology also provides a computer program product comprising a computer readable medium 220; 230 having such a computer program stored thereon.

[0146] By way of example, the software or computer program 225; 235 may be embodied 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, but is not limited to, one or more removable or non-removable memory devices, including 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 and executed by its processing circuitry.

[0147] The flow of a method can be considered as a flow of computer operations when executed by one or more processors. The corresponding device, system, and / or machine can be defined as a group of functional modules, and each step performed by a processor corresponds to a functional module. In this case, the functional module is implemented as a computer program executed by a processor. Therefore, the device, system, and / or machine may alternatively be defined as a group of functional modules, and the functional module is implemented as a computer program executed by at least one processor.

[0148] 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.

[0149] Alternatively, most of the modules may be implemented as hardware modules or may be substituted with hardware - software or hardware is purely a matter of implementation choice.

[0150] The embodiments of the present disclosure shown in the drawings and described above are merely exemplary embodiments and are not intended to limit the scope of the claims (including equivalents thereto). 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 claims. Any combination of non-mutually exclusive features described herein is intended to be within the scope of the present invention. That is, features of the described embodiments may be combined with any appropriate aspect described above, and any feature of one aspect may be combined with other appropriate aspects. Similarly, features recited in multiple dependent claims may be combined with non-mutually exclusive features of other dependent claims, particularly when multiple dependent claims depend on the same independent claim. Although single claim dependency has been used in practice and some jurisdictions require single claim dependency, this does not mean that the features of multiple dependent claims are mutually exclusive.

[0151] Furthermore, it should be noted that the inventive concept relates to all possible combinations of features unless expressly stated otherwise, and in particular different part solutions in the different embodiments can be combined in other configurations, where technically possible. [Explanation of symbols]

[0152] 10 X-ray source 11 Gantry 12 patient tables 13 Isocenter 20 X-ray detector system 23 Routes 24 Parallel Processing Circuit 26 Routes 40 Digital Processing Circuit 41 X-ray controller 42 Gantry Controller 43 Table Controller 44 Detector Controller 45 Components 50 Computers 60 Operator Console 62 Display 102 Rotating side 104 Stationary side 110 X-ray source 111 Gantry 112 Patient Table 114 Opening 120 X-ray detector 160 Data Communication Systems 200 systems 210 processors 220 memory 225 Computer Programs 230 External Memory Device 240 output interface 301 Digital-to-Analog Converter (DAC) 303 Digital Counter

Claims

1. 1. A method for processing computed tomography (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 comprises: 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, the resampling including reducing a data size of the filtered detector data; Including, the filtering is performed on at least a portion of the sampled digital detector data according to a filtering setting, and the resampling is performed on at least a portion of the filtered detector data according to a resampling setting; the filtering setting and the resampling setting are decoupled so that the resampling setting can be selected independently of the selection of the filtering setting; The method, wherein the filtering and resampling settings are set to balance filtering and resampling to achieve a predetermined image quality and / or detective quantum efficiency (DQE) index.

2. The method of claim 1 , wherein the data size reduction reduces the data size by a reduction factor, and at least one of the filtering setting and the resampling setting is associated with the reduction factor.

3. The method of claim 1 , wherein the filtering and resampling settings are set to achieve a predetermined image quality.

4. The method of claim 3 , wherein the image quality includes at least one of a spatial resolution, a noise level, and an aliasing level.

5. The method of claim 1 , wherein the filtering is performed in at least one of the spatial and frequency domains.

6. The method of claim 1 , wherein the resampling is performed in at least one of the spatial and frequency domains.

7. The method of claim 1 , wherein the resampling comprises a first resampling according to a first resampling setting and a second resampling according to a second resampling setting.

8. The method of claim 1 , wherein the filtering 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 settings include 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.

11. The resampling setting includes the phase shift setting, The method of claim 9 , wherein the phase shift setting is associated with a resampling pattern.

12. 2. The method of claim 1, wherein the CT imaging system includes a gantry, the gantry including a rotating gantry member on a rotating side of the gantry and a stationary gantry member on a stationary side of the gantry, the rotating member and the stationary member being communicatively coupled through a data communication system, and the rotating member including the X-ray source, the X-ray detector, and the digital processor.

13. 1. 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 comprises: filtering the sampled digital detector data according to a filtering setting to generate filtered detector data; resampling the filtered detector data according to a resampling setting to generate resampled detector data. configured to run a data size of the resampled detector data is smaller than a data size of the filtered detector data; the filtering setting and the resampling setting are decoupled so that the resampling setting can be selected independently of the selection of the filtering setting; The CT imaging system, wherein the filtering and resampling settings are set to balance filtering and resampling to achieve a predetermined image quality and / or detective quantum efficiency (DQE) index.

14. 14. The CT imaging system of claim 13, further comprising a gantry, the gantry including a rotating gantry member provided on a rotating side of the gantry and a stationary gantry member provided on a stationary side of the gantry, the rotating member and the stationary member being communicatively coupled through a data communication system, the rotating member including the X-ray source, the X-ray detector, and the digital processor.

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

16. 14. The CT imaging system of claim 13, wherein the CT imaging system includes 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.

17. 14. The CT imaging system of claim 13, wherein the digital processor is configured to operate in one or more modes of operation, the modes of operation being associated with at least one of image quality, radiation dose, DQE index, and data handling scheme.

18. 14. The CT imaging system of claim 13, wherein the resampling settings include at least one of a non-integer resampling frequency and a non-integer phase shift setting.

19. 14. The CT imaging system of claim 13, 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.

20. The CT imaging system of claim 13 , wherein the resampling settings include a phase shift setting associated with a resampling pattern.

21. The resampling pattern is radial direction r １ and longitudinal direction l １ a radial quarter-offset resampling pattern in at least one of radial direction r ２ and longitudinal direction l ２ an equidistant resampling pattern in at least one of radial direction r ３ and longitudinal direction l ３ an interlaced resampling pattern in at least one of 21. The CT imaging system of claim 20, comprising at least one pattern of:

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