X-ray CT scanner, data processing method, and program

Abstract

To improve the image quality.SOLUTION: The X-ray CT apparatus according to the embodiment comprises an X-ray irradiation part, a photon-counting detector, an acquisition part, an estimation part, and a calculation part. The X-ray irradiation part executes low flux scan with respect to a phantom formed of known materials and with known transmission length in each pixel of the photon-counting detector (PCD). The photon-counting detector detects a signal according to the low flux scan. The acquisition part acquires a count for each energy bin detected by the photon-counting detector. The estimation part estimates a first parameter dependent on energy on the basis of the count, and estimates a second parameter dependent on the total count of the all energy bins on the basis of the first parameter. The calculation part performs material discrimination on the basis of the first parameter and the second parameter.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The embodiments disclosed in this specification and the drawings relate to an X-ray CT apparatus, a data processing method, and a program.

Background Art

[0002] A CT system based on a photon counting detector (Photon-Counting-Detector Based CT System: PCCT) has great advantages in X-ray diagnosis such as high spatial resolution.

[0003] However, when the flux of X-rays incident on the detector is high, pile-up occurs in the detector, and the measured count may deviate from the true count. Therefore, it is desirable to perform correction processing on the detector response.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Patent Document 2

Patent Document 3

Non-Patent Documents

[0005]

Non-Patent Document 1

[0006] One of the problems that the embodiments disclosed in this specification and drawings aim to solve is to improve image quality. However, the problems that the embodiments disclosed in this specification and drawings aim to solve are not limited to the above problem. Problems corresponding to the effects of each configuration shown in the embodiments described later can also be positioned as other problems. [Means for solving the problem]

[0007] The X-ray CT apparatus according to this embodiment comprises an X-ray irradiation unit, a photon count detector, an acquisition unit, an estimation unit, and a calculation unit. The X-ray irradiation unit performs a low-flux scan on a phantom consisting of a known material and transmission length at each pixel of the photon count detector (PCD). The photon count detector detects the signal related to the low-flux scan. The acquisition unit acquires the count for each energy bin detected by the photon count detector. The estimation unit estimates a first parameter that depends on energy based on the count, and estimates a second parameter that depends on the total number of energy bins based on the first parameter. The calculation unit performs material discrimination based on the first parameter and the second parameter. [Brief explanation of the drawing]

[0008] [Figure 1] Figure 1 shows an example of an X-ray CT apparatus according to an embodiment. [Figure 2] Figure 2 shows an example of the PCD bin response function Sb(E) of a photon count detector. [Figure 3] Figure 3 is a flowchart showing the calibration and processing procedures for material discrimination according to the embodiment. [Figure 4] Figure 4 shows the normalized linear decay coefficients of various materials. [Figure 5] Figure 5 shows a schematic diagram illustrating the generation of the pile-up correction table Pb and the calibration used individually for each mA. [Figure 6] Figure 6 shows a schematic of another calibration in which a universal pile-up correction table Pb is generated for the entire mA range. [Modes for carrying out the invention]

[0009] The following describes in detail embodiments of the X-ray CT apparatus, data processing method, and program with reference to the drawings.

[0010] Throughout this specification, any reference to “one embodiment” or “embodiment” means that certain features, structures, materials, or properties described in relation to that embodiment are included in at least one embodiment of the Application, but not that they are present in all embodiments.

[0011] Therefore, the appearance of the phrase "in one embodiment" or "in one embodiment" in various places throughout this specification does not necessarily refer to the same embodiment of the Application. Furthermore, certain features, structures, materials, or properties may be combined in any suitable manner in one or more embodiments.

[0012] First, the background to this embodiment will be described.

[0013] Computed tomography (CT) systems and methods are commonly used for medical imaging and diagnosis. CT systems generally create projection images of a subject's body at a series of projection angles. A radiation source, such as an X-ray tube, illuminates the subject's body, generating projection images at different angles. These projection images can then be used to reconstruct an image of the subject's body.

[0014] Traditionally, energy-integrating detectors (EIDs) and / or photon-counting detectors (PCDs) have been used to measure CT projection data. PCDs offer many advantages, including the ability to perform spectral CT, where the PCD decomposes the count of incident X-rays into spectral components called energy bins, which collectively spread across the entire energy spectrum of the X-ray beam. Unlike non-spectral CT, spectral CT yields information attributable to various materials exhibiting different X-ray attenuations, as a function of X-ray energy. These differences allow for the decomposition of spectrally decomposed projection data into different material components; for example, two material components for material discrimination could be bone and water.

[0015] While PCDs offer rapid response times, high X-ray flux rates, which are characteristic of clinical X-ray imaging, can lead to multiple X-ray detection events on a single detector within the detector's time response—a phenomenon known as pile-up. If left uncorrected, the pile-up effect can distort the PCD energy response, potentially degrading reconstructed images from the PCD. Correcting these effects gives spectral CT several advantages over conventional CT. Because spectral CT extracts complete tissue characterization information from the imaged subject, many clinical applications can benefit from spectral CT technology, including improved material discrimination.

[0016] One challenge in using semiconductor-based PCDs more effectively for spectral CT is performing material discrimination of projection data in a robust and efficient manner. For example, pile-up correction in the detection process can be imperfect, and these imperfections degrade the material components resulting from material discrimination.

[0017] In photon counting CT systems, semiconductor-based detectors using direct conversion are designed to resolve the energy of individual incident photons and generate multiple energy bin count measurements for each integration period. However, due to the detection physical properties of such semiconductor materials (e.g., CdTe / CZT), the detector's energy response is significantly degraded / distorted by charge sharing, k-escape, and scattering effects in the energy storage and charge induction processes, as well as electronic noise from associated front-end electronics. As mentioned above, since the signal counting time is finite, under high counting rate conditions, the energy response is also distorted by pulse pileup.

[0018] Due to the heterogeneity of the sensor material and the complexity of the integrated detection system, it is impossible to accurately model such detector responses of PCDs based solely on Monte Carlo simulations using physical theory or specific modeling of the signal induction process, and this modeling determines the accuracy of the forward model for each measurement. Furthermore, due to the uncertainty in the incident X-ray tube spectral modeling, this modeling introduces additional errors into the forward model, and all these factors ultimately degrade the material discrimination accuracy from PCD measurements and, consequently, the resulting spectral image.

[0019] Prior art literature has proposed calibration methods to solve similar problems. The general idea is to modify the forward model to match the calibration measurement using multiple transmission measurements of various known path lengths. Several ideas have been applied to estimate the X-ray spectrum in conventional CT. See Sidky et al., Journal of Applied Physics 97(12), 124701 (2005) and Duan et al., Medical Physics 38(2), Feb, 2011. These ideas were later adopted in photon count detectors to estimate the composite spectral response. See Dickmann et al., Proc. SPIE 10573, Medical Imaging 2018: Physics of Medical Imaging, 1057311 (March 9, 2018). However, given the feasibility of applications in complete third-generation CT geometries, which have not yet been demonstrated or documented, there may be many variations in the detailed design and implementation of calibration methods.

[0020] Embodiments presented herein relate to a two-step calibration method for a PCD forward model for material discrimination. This method consists of two parts: 1) a flux-independent weighted bin response function S using the Expectation Maximization (EM) method. wb (E) Estimation, 2) Pile-up correction term P b (E,N b ,N tot This is an estimation of S from the calibration at each tube voltage (kVp) setting for each detected pixel. wb Once (E) is estimated, it is saved as the system's software calibration table. This is then used as the pile-up correction term P for higher flux scans. b (E,N b , N tot ) is used as input to estimate. Next, both tables are used for material discrimination in operational scans to estimate the underlying material path length. The calibration table is updated periodically based on system / detector performance variations.

[0021] Next, the configuration of the X-ray CT apparatus according to this embodiment will be described.

[0022] As shown in Figure 1, the X-ray CT apparatus 1 according to this embodiment includes a gantry unit 10, a patient bed unit 30, and a console unit 40. Note that Figure 1 shows the gantry unit 10 from multiple directions for illustrative purposes, and illustrates the case where the X-ray CT apparatus 1 has only one gantry unit 10.

[0023] The mounting device 10 includes an X-ray tube 11, an X-ray detector 12, a rotating frame 13, an X-ray high-voltage device 14, a control device 15, a wedge 16, a collimator 17, and a DAS (Data Acquisition System) 18.

[0024] The X-ray tube 11 is a vacuum tube having a cathode (filament) that generates thermionic electrons and an anode (target) that generates X-rays upon collision with thermionic electrons. The X-ray tube 11 generates X-rays to irradiate the subject P by irradiating thermionic electrons from the cathode to the anode when a high voltage is applied from the X-ray high-voltage device 14. For example, there is a rotating anode type X-ray tube 11 that generates X-rays by irradiating a rotating anode with thermionic electrons.

[0025] Note that the X-ray tube 11 and control device 15 are examples of an X-ray irradiation unit. The X-ray irradiation unit performs a low-flux scan on a phantom consisting of known materials and transmission lengths at each pixel of the photon counting detector (PCD). Specifically, the X-ray irradiation unit performs a low-flux scan by performing an air scan and a scan on a phantom consisting of multiple different materials, based on the initial current intensity and the voltage settings of each tube in the X-ray tube.

[0026] The rotating frame 13 is an annular frame that supports the X-ray tube 11 and the X-ray detector 12 opposite each other and rotates the X-ray tube 11 and the X-ray detector 12 by the control device 15. For example, the rotating frame 13 is a casting made of aluminum. In addition to the X-ray tube 11 and the X-ray detector 12, the rotating frame 13 can also support an X-ray high-voltage device 14, a wedge 16, a collimator 17, a DAS 18, etc. Furthermore, the rotating frame 13 can also support various other components not shown in Figure 1.

[0027] The wedge 16 is a filter used to adjust the amount of X-rays irradiated from the X-ray tube 11. Specifically, the wedge 16 is a filter that transmits and attenuates the X-rays irradiated from the X-ray tube 11 so that the distribution of X-rays irradiated from the X-ray tube 11 to the subject P becomes a predetermined distribution. For example, the wedge 16 is a wedge filter or a bow-tie filter, which is a filter made of aluminum or the like processed to have a predetermined target angle and thickness.

[0028] The collimator 17 is a lead plate or the like used to narrow the irradiation range of X-rays that have passed through the wedge 16, and a slit is formed by combining multiple lead plates or the like. The collimator 17 is sometimes called an X-ray diaphragm. In Figure 1, the wedge 16 is shown to be placed between the X-ray tube 11 and the collimator 17, but the collimator 17 may also be placed between the X-ray tube 11 and the wedge 16. In this case, the wedge 16 transmits and attenuates the X-rays that are irradiated from the X-ray tube 11 and whose irradiation range has been limited by the collimator 17.

[0029] The X-ray high-voltage device 14 includes an electrical circuit such as a transformer and a rectifier, a high-voltage generator that generates a high voltage to be applied to the X-ray tube 11, and an X-ray control device that controls the output voltage according to the X-rays generated by the X-ray tube 11. The high-voltage generator may be of the transformer type or the inverter type. The X-ray high-voltage device 14 may be mounted on the rotating frame 13 or on a fixed frame (not shown).

[0030] The control device 15 includes a processing circuit with a CPU (Central Processing Unit), etc., and a drive mechanism such as a motor and actuator. The control device 15 receives input signals from the input interface 43 and controls the operation of the frame device 10 and the bed device 30. For example, the control device 15 controls the rotation of the rotating frame 13, the tilt of the frame device 10, and the operation of the bed device 30 and the top plate 33. The control device 15 may be installed on the frame device 10 or on the console device 40.

[0031] The X-ray detector 12 is a photon counting type detector, and outputs a signal that allows for the measurement of the energy value of an X-ray photon each time an X-ray photon, which originates from X-rays irradiated from the X-ray tube 11 and transmitted through the subject P, is incident on it. An X-ray photon is, for example, a photon of X-rays irradiated from the X-ray tube 11 and transmitted through the subject P. The X-ray detector 12 has multiple X-ray detection elements that output one pulse of electrical signal (analog signal) each time an X-ray photon is incident on it. By counting the number of electrical signals (pulses), it is possible to count the number of X-ray photons incident on each X-ray detection element. Furthermore, by performing predetermined calculation processing on this signal, the energy value of the X-ray photon that caused the output of the signal can be measured. For example, the X-ray detector 12 is a surface detector in which the X-ray detection elements are arranged in N rows in the channel direction and M rows in the slice direction. For example, the channel direction is the circumferential direction centered on the X-ray tube 11. Furthermore, for example, the slice direction is the direction along the Z-axis direction as described above, that is, the direction parallel to the Z-axis direction. The slice direction is also called the column direction or row direction.

[0032] An X-ray detection element is, for example, a semiconductor element (semiconductor detection element) such as CdTe (cadmium telluride) or CdZnTe (cadmium zinc telluride) with an anode electrode and a cathode electrode arranged on it.

[0033] The X-ray detector 12 has multiple X-ray detection elements and multiple Application Specific Integrated Circuits (ASICs) connected to the X-ray detection elements, which are readout circuits that count the X-ray photons detected by the X-ray detection elements. The ASIC counts the number of X-ray photons incident on the detection elements by discriminating the individual charges output by the X-ray detection elements. The ASIC also measures the energy of the counted X-ray photons by performing calculations based on the magnitude of each charge. Furthermore, the ASIC outputs the X-ray photon counting result as digital data to the DAS 18.

[0034] The X-ray detector 12 detects signals related to the low-flux scan performed by the X-ray irradiation unit.

[0035] DAS18 generates detection data based on the counting results input from the X-ray detector 12. The detection data is, for example, a sinogram. The sinogram is data that arranges the counting results incident on each X-ray detection element at each position in the X-ray tube 11. The sinogram is data that arranges the counting results in a two-dimensional Cartesian coordinate system with the view direction and channel direction as axes. DAS18 generates a sinogram, for example, in column units in the slice direction of the X-ray detector 12. Here, the counting results are data that assigns the number of X-ray photons to each energy bin. DAS18 transfers the generated detection data to the console device 40. DAS18 is implemented, for example, by a processor.

[0036] The data generated by DAS18 is transmitted via optical communication from a transmitter having a light-emitting diode (LED) on the rotating frame 13 to a receiver having a photodiode located on the non-rotating part of the mounting device 10 (e.g., a fixed frame, which is not shown in Figure 1), and then transferred to the console device 40. Here, the non-rotating part is, for example, a fixed frame that rotatably supports the rotating frame 13. Note that the method of transmitting data from the rotating frame 13 to the non-rotating part of the mounting device 10 is not limited to optical communication; any non-contact data transmission method or a contact-type data transmission method may be used.

[0037] The patient bed apparatus 30 is a device for placing and moving the subject P to be photographed, and comprises a base 31, a patient bed drive device 32, a top plate 33, and a support frame 34. The base 31 is a housing that supports the support frame 34 so that it can move in the vertical direction. The patient bed drive device 32 is a drive mechanism that moves the top plate 33 on which the subject P is placed in the direction of the long axis of the top plate 33, and includes a motor and actuator, etc. The top plate 33, which is provided on the upper surface of the support frame 34, is a plate on which the subject P is placed. In addition to moving the top plate 33, the patient bed drive device 32 may also move the support frame 34 in the direction of the long axis of the top plate 33.

[0038] The console device 40 includes a memory 41, a display 42, an input interface 43, and a processing circuit 44. Although the console device 40 is described separately from the mounting device 10, the mounting device 10 may include the console device 40 or some of its components.

[0039] Memory 41 can be implemented using, for example, semiconductor memory elements such as RAM (Random Access Memory) or flash memory, a hard disk, or an optical disc. Memory 41 stores, for example, projection data or CT image data. It can also store, for example, programs for circuits included in the X-ray CT scanner 1 to perform various functions. Memory 41 may also be implemented using a group of servers (cloud) connected to the X-ray CT scanner 1 via a network.

[0040] The display 42 displays various types of information. For example, the display 42 may display various images generated by the processing circuit 44, or it may display a GUI (Graphical User Interface) to receive various operations from the operator. For example, the display 42 may be a liquid crystal display or a CRT (Cathode Ray Tube) display. The display 42 may be a desktop type, or it may be composed of a tablet terminal or the like that which can communicate wirelessly with the console device 40. Also, the display 42 is just one example of a display unit.

[0041] The input interface 43 receives various input operations from the operator, converts the received input operations into electrical signals, and outputs them to the processing circuit 44. In addition, the input interface 43 receives input operations from the operator such as scan conditions, reconstruction conditions when reconstructing CT image data, and image processing conditions when generating post-processed images from CT image data.

[0042] For example, the input interface 43 can be implemented by a mouse, keyboard, trackball, switch, button, joystick, touchpad for input operations by touching the operating surface, touchscreen with an integrated display screen and touchpad, non-contact input circuit using an optical sensor, audio input circuit, etc. The input interface 43 may also be provided on the mounting device 10. Furthermore, the input interface 43 may consist of the console device 40 main unit and a tablet terminal or the like that can communicate wirelessly. Moreover, the input interface 43 is not limited to those equipped with physical operating components such as a mouse or keyboard. For example, an electrical signal processing circuit that receives electrical signals corresponding to input operations from an external input device provided separately from the console device 40 and outputs these electrical signals to a processing circuit 44 is also included as an example of the input interface 43.

[0043] The processing circuit 44 controls the operation of the entire X-ray CT apparatus 1. For example, the processing circuit 44 executes control function 44a, preprocessing function 44b, reconstruction processing function 44c, image processing function 44d, acquisition function 44e, estimation function 44f, and calculation function 44g. Here, for example, each processing function executed by the components of the processing circuit 44 shown in Figure 1, namely control function 44a, preprocessing function 44b, reconstruction processing function 44c, image processing function 44d, acquisition function 44e, estimation function 44f, and calculation function 44g, is recorded in memory 41 in the form of a program that can be executed by a computer. The processing circuit 44 is, for example, a processor, and by reading each program from memory 41 and executing it, it realizes the function corresponding to each program that has been read. In other words, the processing circuit 44 in the state in which each program has been read has the functions shown in the processing circuit 44 of Figure 1.

[0044] The control function 44a, preprocessing function 44b, reconstruction processing function 44c, image processing function 44c, acquisition function 44e, estimation function 44f, and calculation function 44g are examples of a control unit, preprocessing unit, reconstruction unit, processing unit, acquisition unit, estimation unit, and calculation unit, respectively.

[0045] In Figure 1, the control function 44a, preprocessing function 44b, reconstruction function 44c, image processing function 44d, acquisition function 44e, estimation function 44f, and calculation function 44g are shown to be implemented by a single processing circuit 44. However, the embodiments are not limited to this. For example, the processing circuit 44 may be composed of a combination of multiple independent processors, with each processor executing its own program to implement each processing function. Furthermore, each processing function of the processing circuit 44 may be implemented by appropriately distributing or integrating them across one or more processing circuits.

[0046] The control function 44a controls various processes based on input operations received from the operator via the input interface 43. Specifically, the control function 44a controls the CT scan performed by the gantry device 10. For example, the control function 44a controls the collection of counting results in the gantry device 10 by controlling the operation of the X-ray high-voltage device 14, the X-ray detector 12, the control device 15, the DAS 18, and the patient table drive device 32. To give one example, the control function 44a controls the collection of projection data in the positioning scan, which collects positioning images (scan images), and the main scan, which collects images used for diagnosis.

[0047] Furthermore, the control function 44a displays images and other images based on various image data stored in the memory 41 on the display 42.

[0048] The preprocessing function 442 generates projection data by applying preprocessing such as logarithmic transformation, offset correction, inter-channel sensitivity correction, beam hardening correction, scattered radiation correction, and dark count correction to the detection data output from DAS18.

[0049] The reconstruction processing function 44c generates CT image data by performing reconstruction processing on the projection data generated by the preprocessing function 44b, using methods such as filtered back projection and iterative reconstruction. The reconstruction processing function 44c stores the reconstructed CT image data in the memory 41.

[0050] Here, the projection data generated from the counting results obtained by photon counting CT contains information about the energy of X-rays attenuated by passing through the subject P. Therefore, the reconstruction processing function 44c can, for example, reconstruct CT image data of a specific energy component. Furthermore, the reconstruction processing function 44c can, for example, reconstruct CT image data for each of multiple energy components.

[0051] Further, the reconstruction processing function 44c can generate, for example, image data in which color tones corresponding to each energy component are assigned to each pixel of the CT image data of each energy component and a plurality of CT image data color-separated according to the energy component are superimposed.

[0052] Based on an input operation received from an operator via the input interface 43, the image processing function 44d converts the CT image data generated by the reconstruction processing function 44c into image data such as a tomographic image of an arbitrary cross-section or a three-dimensional image by rendering processing by a known method. The image processing function 44e stores the converted image data in the memory 41.

[0053] The processing circuit 44 acquires, by the acquisition function 44e, the count for each energy bin detected by the X-ray detector 12 which is a photon counting detector.

[0054] Note that the processing of the estimation function 44f and the calculation function 44g will be described later.

[0055] Next, the calibration processing performed by the X-ray CT apparatus 1 according to the embodiment will be described.

[0056] In transmission measurement using a photon counting energy resolving detector (PCD), the forward model can be formulated as in the following equation (1).

[0057]

Equation

[0058] Here, N0 is the total flux from the air scan, μ m and l m are the linear attenuation coefficient and path length of the m-th base material. w(E) is the normalized incident X-ray spectrum. Also, S b (E) is a bin response function defined by the following equation (2).

[0059]

Equation

[0060] Furthermore, R(E,E') here is the detector response function, and EbL and EbH are the low-energy threshold and high-energy threshold for each counting bin. Figure 2 shows a typical S of a PCD. b A model example of the (E) function is shown. Here, the long tail above the energy window is induced by charge sharing, k escape, and scattering effects. The low-energy tail is mainly due to the finite energy resolution caused by associated electronic noise. In practice, w(E) and S b (E) is not known exactly, and they are not combined into a single term S wb (E)=w(E)S b (E) can be combined and this will be referred to as the weighted bin response function below. wb If (E) can be calibrated, the resolution problem under low flux conditions can be adequately solved.

[0061] Under high-flux scan conditions (e.g., a few percent of pulse pile-up), pulse pile-up introduces additional spectral distortion to the measurement. One way to compensate for the pile-up effect is to introduce an additional correction term (see, for example, Dickmann's work above, which uses the measured count rate as input). Alternatively, this type of additional calibration can be applied to a flux-independent weighted bin response S wb Based on accurate estimates in (E). S wb The method for estimating (E) is the first problem to be resolved in this application.

[0062] Under typical CT clinical scan conditions, it is common to encounter pulse pileup of several percent or more for some measurements. The resulting impact on material discrimination depends on the measured spectrum and flux. Without knowing the actual detector response, only a limited number of transmission measurements can be performed to adjust the forward model. For a complete CT system in a clinical setting, it is crucial to have a viable calibration procedure. Therefore, the second problem addressed in this application is a method for efficiently parameterizing the model and optimizing the calibration procedure.

[0063] In full-size CT systems, additional practical challenges in performing such two-step calibration include, but which have not been addressed or resolved by conventional techniques primarily aimed at smaller benchtop systems: sector-angle dependent weighted spectral response due to beam pre-filters (such as bowtie filters), minimum flux limited by the operating specifications of the X-ray tube and fixed system geometry, complete detector ring calibration with varying detector responses across pixels, limited space for in-system calibration phantom positioning, complexity during calibration on a rotating system with a scattering prevention grid, systematic errors in calibration and associated mechanical design tolerances, and non-ideal detectors with issues of uniformity of energy resolution, counting, and energy threshold setting drift.

[0064] For photon counting CT to achieve image quality comparable to conventional energy integrating detector (EID)-based systems, while maintaining similar calibration procedures / workflows that do not significantly increase system downtime, and with much simpler detector response modeling and associated calibrations, all of the above non-ideal factors must be taken into consideration.

[0065] In one non-limiting embodiment, a two-step calibration method for a PCD forward model for material discrimination is applied. This method consists of two parts: 1) a flux-independent weighted bin response function S using the Expectation Maximization (EM) method. wb (E) is the estimation, and (2) the pile-up correction term.

[0066] The calibrated forward model can be expressed as shown in equation (3) below.

[0067]

number

[0068] Here, P b (E,N b , N tot ) is the energy (E) and the number of bins measured (N) b , N tot ) is a function of where N b The number of individual bins is N. tot This is the total number of all energy bins.

[0069] Here, instead of using only two materials as in conventional techniques (see, for example, the reference Dickmann), we use 2 to 5 different materials such as polypropylene, water, aluminum, titanium / copper, and k-edge materials to obtain a weighted bin response function S wb (E) is calibrated at low flux. In other words, the phantoms involved in the low-flux scan performed by the X-ray irradiation unit include polypropylene, water, aluminum, titanium / copper, and k-edge material. Using a more selective material in calibration reduces the number of total path lengths and achieves equivalent or better results.

[0070] Step 1: By using appropriate tube spectral modeling to capture characteristic peaks in the incident spectrum, and a physical model to simulate the spectral response of the photon count detector, Swb Initial estimates of (E) can be generated. By using the EM method (see reference Sidky), S wb (E) can be reliably estimated for this problem under extremely adverse conditions based on several transmission measurements. That is, the processing circuit 44 uses the estimation function 44f to calculate the first parameter S based on the count using expectation maximization (EM). wb We estimate (E). The first parameter S wb (E) is the bin response function S b It depends on (E).

[0071] Here, P b (E,N b ,N tot It is assumed that ) is constant in step 1. Therefore, the calibrated forward model can be simplified to a system of linear equations represented by equation (4) below.

[0072]

number

[0073] Typically, the number of data measurements (M) is equal to the number of unknowns (E). max This is far less than the given value. Assuming a Poisson distribution for data acquisition, we derive an iterative EM algorithm and, as described below, the unknown energy bin response function S wb We can find the optimal estimate for (E).

[0074] When estimating the bin response function using low-flux data acquisition, pile-up effect correction P b It is assumed that is a known term (e.g., a constant). Therefore, the model is simplified as shown in equation (5) below.

[0075]

number

[0076] Here, A is expressed by the following equation (6). jLet (E) represent the decay path length of the j-th measurement.

[0077]

number

[0078] In such cases, the following equation (7) is obtained for each measured value j.

[0079]

number

[0080] In M measurements, the data collection can be written in matrix form as shown in equation (8) below.

[0081]

number

[0082] In other words, the following holds true: A·S wb =N b

[0083] By applying the EM iterative algorithm, S wb This can be estimated as shown in equation (9) below.

[0084]

number

[0085] In other words, the processing circuit 44 uses the estimation function 44f to determine a first parameter S that depends on energy E, based on the count for each energy bin obtained by the acquisition function 44e. wb Estimate (E).

[0086] S wb The update formula for (E) is given by equation (10) below.

[0087]

number

[0088] Step 2: Calibration of each detection pixel with each tube voltage (kVp) setting S wb Once (E) is estimated, it is saved as the system's software calibration table. This is the pile-up correction term P for higher flux scans. b (E, N b , N tot ) is used as input for further estimation. That is, the processing circuit 44 uses the estimation function 44f to estimate the first parameter S estimated in step 1. wb Based on (E), a second parameter P depends on the total number of energy bins. b We estimate the second parameter P. b This relates to the pile-up correction term.

[0089] Next, both tables are used for substance discrimination in the subject / patient scan to estimate the path length of the basic substance. That is, the processing circuit 44 calculates the first parameter S using the calculation function 44g. wb (E) and the second parameter P b Substance discrimination is performed based on this.

[0090] The calibration table is updated periodically based on variations in system / detector performance. This can also be designed as an iterative procedure. If the image quality is insufficient with the quality check phantom, this calibration process is repeated using the calibration table updated from the last iteration as the initial estimate.

[0091] Figure 3 shows a high-level workflow of the above process. Steps 1) to 4) represent the calibration workflow, and steps 5) to 8) show how the calibration table is used to generate spectral images in a patient / subject operational scan.

[0092] First, a series of low-flux scans of slabs of various materials are collected at the kVp setting of each tube, which is the peak potential applied to the X-ray tube. Typical CT systems support several kVp settings from 70 to 140 kVp, and these settings produce different energy spectra from the X-ray tube for different scanning protocols. For CT scans, both mA and kVp must be pre-selected before powering on the tube. Next, the low-flux weighted bin response function S wb This is estimated, and this estimated S wb Along with this, high-flux slab scanning is used to estimate additional parameters for the pile-up correction term. S for each detected pixel wb and P b Using the estimated calibration table, the calibration quality is checked for quality phantoms (e.g., a uniform water phantom, or a phantom with multiple inserts containing a uniform known substance). Image quality is evaluated against predefined criteria, and if successful, the current calibration table is saved and used for processing the next patient / subject scan data. Otherwise, the procedure is performed as the initial estimate. wb and P b The first three steps are repeated using the final iteration. Here, the criteria generally considered are image CT accuracy, uniformity, spatial resolution, noise, and artifacts. To verify the quality of this calibration, all of these evaluation criteria must be checked. In particular, accuracy and artifacts such as rings and bands in the images should be checked, as these indicate insufficient calibration.

[0093] In other words, the X-ray irradiation area is the first parameter S wb and the second parameter P b The phantom scan is performed using the processing circuit 40, and the processing circuit 40 evaluates the image quality of the projection image obtained by the phantom scan using the estimation function 44f. If the quality of the projection image meets a predefined standard, the processing circuit 40 calculates the first parameter S using the calculation function 44g.wb and a second parameter P b are used for substance discrimination.

[0094] To select the substances and path lengths optimal for this calibration, different substances can be selected using the normalized linear attenuation coefficient vs. energy curve (Figure 4). For example, polypropylene, water, aluminum, and titanium can be a good set of combinations for calibration that cover a wide range of common substances present in the human body.

[0095] In step 1 of the flow diagram, to satisfy the low flux condition through calibration measurements to minimize the pile-up effect, the low flux scan satisfies nτ < x, where x is approximately 0.005 to 0.01, n is the tube flux setting at the lowest pixel count rate (i.e., n is the pixel coefficient rate), and τ is the effective dead time of the PCD application-specific integrated circuit (ASIC). By satisfying this condition, the shortest path length of each selected calibration substance can be calculated, and the remaining path lengths can be selected either by equal intervals in the path length or by any of the resulting measured count rates.

[0096] In the calibration of the pile-up correction term in step 3, the same slab substance and path length are used for scans at high mA settings. This calibration data is grouped by mA, and to generate different correction tables for each mA setting (Figure 5) or a universal correction table for continuous mA settings (Figure 6), measurements in all flux ranges (from low mA to high mA, from high mA to low mA, or first the most frequently used values) can be included. That is, the X-ray irradiation unit performs a plurality of scans for different current intensities, and the processing circuit 40 may estimate the second parameter P b based on the plurality of scans performed for the different current intensities by the estimation function 44f.

[0097] Calibration measurements should be performed using sufficient statistics to minimize the effects of statistical variability. One non-restrictive example is to use statistics greater than 1000 times as the general integration period for the calibration dataset to minimize the statistical error transferred in the calibration. Each energy bin b of the calibration measurement corresponds to the S wb (E) and P b (E,N b ,N tot ) is used to update.

[0098] Because only a limited number of measurements can be performed in some energy bins, estimation is subject to very unfavorable conditions. In this case, it imposes additional constraints on the EM method, so a proper initial estimate is essential for accurate estimation. One variation of the design to accommodate non-ideal detectors is to use an initial estimate S, especially when there are slight variations in the actual energy threshold setting of the ASIC. b The goal is to allow for a more flexible energy window for each bin. By setting the low threshold x keV low and the high threshold y keV high, the initial S b The result is as shown in equation (11) below.

[0099]

number

[0100] Here, x and y can be selected between 5 and 10 keV, allowing for some variation in ASIC performance while imposing additional constraints on the EM problem.

[0101] In other words, the initial estimate of the first parameter is based on the detector response function R and the low-energy threshold and high-energy threshold of each counting bin.

[0102] To capture spectral variations across the entire fan beam after the bowtie filter, and detector response variations across different detection pixels, this calibration process is performed pixel by pixel using each bowtie / filter configuration. In other words, the calibration of the photon counting detector response is performed pixel by pixel.

[0103] The design described in this application employs three or more materials in the calibration, thereby improving the sensitivity for constraining the weighted bin response function estimation problem of the photon count detector.

[0104] In addition, this method includes a high-flax pile-up correction term P b Different parameterizations are used for this purpose, which are E, N b and N tot It is a function of the total count term N. tot This was introduced to better approximate the true pile-up phenomenon, and it can significantly improve model performance under higher flux conditions with fewer parameters.

[0105] Furthermore, various calibration path length ranges are used at different fan angles to improve the accuracy and efficiency of calibration. The slab scan used for forward model calibration can be selected based on the imaging task to produce the best image quality.

[0106] Finally, to accommodate the performance of non-ideal detectors / ASICs, an alternative scheme is presented for expanding the energy threshold window and computing an initial estimate of the weighted bin response function.

[0107] According to at least one embodiment described above, image quality can be improved.

[0108] While several embodiments have been described, these embodiments are presented as examples only and are not intended to limit the scope of the invention. These embodiments can be implemented in a variety of other forms, and various omissions, substitutions, modifications, and combinations of embodiments are possible without departing from the spirit of the invention. These embodiments and their variations are included in the scope and spirit of the invention, as well as in the claims and their equivalents. [Explanation of symbols]

[0109] 44 Processing Circuits 44a Control function 44b Preprocessing function 44c Reconstruction Processing Function 44d Image Processing Function 44e Acquisition function 44f Estimation Function 44g calculation function

Claims

1. Each pixel of a photon counting detector (PCD) includes an X-ray irradiation unit that performs a first scan on a phantom consisting of a known material and transmission length, The photon count detector that detects the signal related to the first scan, An acquisition unit that acquires counts for each energy bin detected by the aforementioned photon count detector, An estimation unit that estimates an energy-dependent energy bin response function based on the aforementioned counts, and estimates a pile-up correction term that depends on the counts of each bin and the total counts of all energy bins based on the energy bin response function and data obtained from a second scan using a tube current higher than the tube current of the first scan. A calculation unit that performs material discrimination based on the energy bin response function and the pile-up correction term. An X-ray CT scanner equipped with [a specific feature].

2. The X-ray CT apparatus according to claim 1, wherein the X-ray irradiation unit performs the first scan by performing an air scan and a scan of the phantom, which is made up of a plurality of different materials, with an initial current intensity and a setting of the voltage of each tube of the X-ray tube.

3. The X-ray irradiation unit performs a plurality of the second scans for different current intensities. The X-ray CT apparatus according to claim 1, wherein the estimation unit estimates the pile-up correction term for the different current intensities.

4. The X-ray irradiation unit performs a phantom scan using the energy bin response function and the pile-up correction term. The estimation unit evaluates the image quality of the projection image obtained by the phantom scan, The X-ray CT apparatus according to claim 2, wherein the calculation unit uses the energy bin response function and the pile-up correction term for material discrimination when the quality of the projected image satisfies predefined criteria determined based on the accuracy, uniformity, spatial resolution, noise, or artifacts of the image CT count.

5. The X-ray CT apparatus according to claim 1, wherein the estimation unit estimates the energy bin response function based on the count using expectation maximization (EM).

6. The X-ray CT apparatus according to claim 1, wherein the first scan satisfies nτ < x, where x is approximately 0.005 to 0.01, n is the pixel counting rate, and τ is the effective dead time of the PCD application-specific integrated circuit (ASIC).

7. The X-ray CT apparatus according to claim 1, wherein the phantom comprises polypropylene, water, aluminum, titanium / copper, and a k-edge material.

8. The X-ray CT apparatus according to claim 1, wherein the initial estimate of the energy bin response function is based on the detector response function and the low-energy threshold and high-energy threshold of each counting bin.

9. The X-ray CT apparatus according to claim 1, wherein the calibration of the response of the photon counting detector is performed for each pixel.

10. A data processing method performed by an X-ray CT scanner, At each pixel of the photon counting detector (PCD), a first scan is performed on a phantom consisting of a known material and transmission length. The signal related to the first scan is detected, The photon count detector obtains the count for each energy bin it detects, Based on the aforementioned counts, an energy-dependent energy bin response function is estimated, and based on the energy bin response function and the data obtained from a second scan using a tube current higher than the tube current of the first scan, a pile-up correction term that depends on the counts of each bin and the total counts of all energy bins is estimated. Substance discrimination is performed based on the energy bin response function and the pile-up correction term. Data processing method.

11. In each pixel of a photon counting detector (PCD), an energy-dependent energy bin response function is estimated based on the counts for each energy bin detected by the photon counting detector in a first scan performed on a phantom consisting of a known material and transmission length. Based on the energy bin response function and data obtained from a second scan using a tube current higher than that of the first scan, a pile-up correction term dependent on the counts of individual bins and the total counts of all energy bins is estimated. A program that causes a computer to perform a process of discriminating materials based on the energy bin response function and the pile-up correction term.

Images