X-ray CT device and data processing method

Abstract

To calibrate data.SOLUTION: An X-ray CT device includes a photon-counting detector, a memory, and processing circuitry. The photon-counting detector has a plurality of detector pixels in a channel direction. The memory stores a detector response forward model of the photon-counting detector to be used during image reconstruction of an imaging object. The processing circuitry estimates an attenuation profile of the imaging object, determine, with respect to each of the plurality of detector pixels, spatial weights on the basis of the estimated attenuation profile, and update on the basis of the determined spatial weights the detector response forward model stored in the memory.SELECTED DRAWING: Figure 6

Description

[Technical field]

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

[0002] Energy-Integrating Detectors (EIDs) and / or Photon-Counting Detectors (PCDs) have been used to measure CT projection data.

[0003] In PCDs, multiple X-ray detection events at a single detector may occur within the time response of the detector, resulting in multiple collisions, which can degrade the reconstructed image from the PCD.

[0004] Also, due to the detector physics in semiconductor materials (eg, CdTe / CZT), the detector energy response may be degraded due to charge sharing, k-escape, and scattering effects in the energy deposition and charge induction processes. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] US Patent Application Publication No. 2021 / 0113178 [Patent Document 2] US Patent Application Publication No. 2022 / 0296202 Summary of the Invention [Problem to be solved by the invention]

[0006] One of the problems to be solved by the embodiments disclosed in this specification and the drawings is to calibrate data. However, the problems to be solved by the embodiments disclosed in this specification and the drawings 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] An X-ray CT apparatus according to an embodiment includes a photon-counting detector, a storage unit, and a processing circuit. The photon-counting detector has a plurality of detector pixels in a channel direction. The storage unit stores a detector response forward model of the photon-counting detector to be used during image reconstruction of an imaging object. The processing circuit estimates an attenuation profile of the imaging object, determines a spatial weight for each of the plurality of detector pixels based on the estimated attenuation profile, and updates the detector response forward model stored in the storage unit based on the spatial weight. [Brief description of the drawings]

[0008] [Figure 1] FIG. 1 illustrates an example of a photon-counting CT scanner system that can incorporate the techniques disclosed herein. [Diagram 2] 2 shows an example of a PCD bin response function Sb(E) of a photon counting detector, where each curve shows an example function for an energy bin. [Figure 3A] FIG. 3A shows air and slab scans using different combinations of known materials and thicknesses. [Figure 3B] FIG. 3B shows air and slab scans using different combinations of known materials and thicknesses. [Figure 3C] FIG. 3C shows air and slab scans using different combinations of known materials and thicknesses. [Figure 4]FIG. 4 is a diagram illustrating the difference in path length at different fan angles when a scan is performed on an imaging object. [Diagram 5] FIG. 5 is a block diagram of a calibration data weight optimizer 500 according to an embodiment of the present disclosure. [Figure 6] FIG. 6 is a flow diagram of a calibration data weight optimization procedure 600 according to an embodiment of the present disclosure. [Figure 7A] FIG. 7A illustrates an example weight adjustment according to an embodiment of the present disclosure. [Figure 7B] FIG. 7B is a diagram illustrating an example weight adjustment according to an embodiment of the present disclosure. [Figure 7C] FIG. 7C illustrates an example weight adjustment according to an embodiment of the present disclosure. [Figure 7D] FIG. 7D is a diagram illustrating an example weight adjustment according to an embodiment of the present disclosure. [Figure 8A] FIG. 8A illustrates an example of a continuous spatial weight adjustment scheme, according to one embodiment of the present disclosure. [Figure 8B] FIG. 8B is a diagram illustrating an example of a continuous spatial weight adjustment scheme, according to one embodiment of the present disclosure. [Figure 8C] FIG. 8C illustrates an example of a continuous spatial weight adjustment scheme, according to one embodiment of the present disclosure. [Figure 9A] FIG. 9A is a diagram illustrating weights before and after spatial adjustment for an exemplary detector channel 400, according to one embodiment of the present disclosure. [Figure 9B] FIG. 9B is a diagram illustrating weights before and after spatial adjustment for an exemplary detector channel 200, according to one embodiment of the present disclosure. [Figure 9C] FIG. 9C is a diagram illustrating weights before and after spatial adjustment for three exemplary detector channels 30, according to one embodiment of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, embodiments of an X-ray CT apparatus and a data processing method will be described in detail with reference to the drawings.

[0010] Computed Tomography (CT) systems and methods are commonly used in medical imaging and diagnosis. CT systems typically create projection images through a subject's body at a series of projection angles. A radiation source, such as an x-ray tube, irradiates the subject's body and projection images are produced at various angles. An image of the subject's body may be reconstructed from the projection images.

[0011] Traditionally, Energy-Integrating Detectors (EIDs) and / or Photon-Counting Detectors (PCDs) have been used to measure CT projection data. PCDs have many advantages, including the ability when performing spectral CT, where PCDs resolve incident x-ray counts into spectral components, also called energy bins, such that the energy bins collectively cover the energy spectrum of the x-ray beam. Unlike non-spectral CT, spectral CT produces information that is attributable to different materials that exhibit different x-ray attenuation depending on the x-ray energy. These differences allow the spectrally resolved projection data to be resolved into different material components, for example, two material components in a material decomposition may be bone and water.

[0012] Although PCDs have fast response times at the high X-ray flux rates implied for clinical X-ray imaging, multiple X-ray detection events at a single detector may occur within the time response of the detector, a phenomenon also known as multiple collisions. If left uncorrected, multiple collision effects can distort the energy response of the PCD and degrade the reconstructed image from the PCD. When these effects are corrected, spectral CT has many advantages over conventional CT. Many clinical applications can benefit from spectral CT technology, including improved material identification, as spectral CT extracts complete tissue characterization information from scanned objects.

[0013] One challenge to more effectively using semiconductor-based PCDs in spectral CT is to perform material decomposition of the projection data in a robust and efficient manner. For example, the detection process has imperfect correction for multiple hits, and these imperfections can degrade the material components resulting from the material decomposition.

[0014] In photon-counting CT systems, semiconductor-based detectors using direct conversion are designed to resolve the energy of individual incident photons and measure multiple energy bin counts per integration period. However, due to the detection physics in such semiconductor materials (e.g., CdTe / CZT), the detector energy response is significantly degraded / distorted by charge sharing, k-escape, and scattering effects in the energy accumulation and charge induction process, plus electronic noise in the associated front-end electronics. Due to the limited signal induction time, in high count rate conditions, multiple collisions of pulses also distort the energy response as mentioned above.

[0015] Due to the heterogeneity of sensor materials and the complexity of the integrated detection system, it is difficult to accurately model such detector responses of PCDs based solely on physical property simulations or Monte Carlo simulations, with specific modeling of the signal-inducing processes determining the accuracy of the forward model for each measurement. Also, due to uncertainties in the incident X-ray tube spectrum modeling, the modeling will induce additional errors in the forward model, and all these factors ultimately degrade the material decomposition from the PCD measurement and therefore the accuracy of the generated spectral image.

[0016] Calibration methods have been proposed in the literature to solve similar problems. The basic idea is to use multiple transmission measurements of various known path lengths to modify the forward model to match the calibration measurements. Some ideas have been applied to estimate the x-ray spectrum of conventional CT (see Sidky et al., Journal of Applied Physics 97(12), 124701 (2005) and Duan et al., Medical Physics 38(2), Feb, 2011) and then to photon-counting detectors to estimate the spectral response of the combined system (see Dickmann et al., Proc. SPIE 10573, Medical Imaging 2018: Physics of Medical Imaging, 1057311 (March 9, 2018)). However, there may be many variations in the detailed design and implementation of the calibration methods, especially considering the feasibility of application in full third generation CT geometry.

[0017] The following disclosure presents many different embodiments, or examples, for implementing various features of the presented subject matter. Specific examples of components and arrangements are described below to simplify the disclosure. These are, of course, merely examples and are not intended to be limiting.

[0018] For example, as described herein, the order of description of different procedures is presented for clarity. Generally, these steps can be performed in any suitable order. Also, although each of the different features, techniques, and configurations herein may be described in various places in this disclosure, it is intended that each of the concepts can be implemented independently of each other or in combination with each other. Thus, this disclosure can be embodied and viewed in many different ways.

[0019] The present disclosure relates to spatial adjustment of calibration data weights used in a photon-counting CT scanner system that includes one or more X-ray tubes that emit X-ray radiation and an array of detector pixels for receiving the X-ray radiation propagating through an image Field Of View (FOV) of the CT scanning system. The weight adjustment scheme can be applied to both PCD counting and spectral forward models.

[0020] The weight adjustment function can be based on the shape of the potential imaging object estimated based on known alternative objects or calculated based on a low-dose scout scan of the imaging object. The adjusted weights can be incorporated into a pre-created PCD detector calibration table, which is used to generate attenuation line integrals or sinograms of the path lengths of the principal components of the material for further counting and reconstructing spectral images. By adjusting the calibration data weights based on pixel location according to the path length profile of the potential imaging object, the quality of the PCD detector response forward model fitting can be improved using limited data samples.

[0021] The calibration data weighting scheme may be implemented in a photon-counting detector included in a photon-counting CT scanning system, as described below with reference to Figure 1. The X-ray CT apparatus 1 shown in Figure 1 includes a gantry 10, a bed 30, and a console 40 for performing processing for the medical imaging processing apparatus. For purposes of illustration, Figure 1 shows multiple gantries 10.

[0022] In this embodiment, the rotation axis of the rotating frame 13 when not tilted, or the longitudinal direction of the table top surface 33 of the bed 30 is defined as the "Z-axis direction", the axial direction perpendicular to the Z-axis direction and horizontal to the floor surface is defined as the "X-ray direction", and the axial direction perpendicular to the Z-axis direction and perpendicular to the floor surface is defined as the "Y-axis direction".

[0023] For example, the gantry 10 and the bed 30 are installed in a CT examination room, and the console 40 is installed in a control room adjacent to the CT examination room. The console 40 is not necessarily installed in the control room. For example, the console 40 may be installed in the same room as the gantry 10 and the bed 30. In any case, the gantry 10, the bed 30, and the console 40 are connected to be able to communicate with each other by wires or wirelessly.

[0024] The gantry 10 is a scanner having a configuration for performing X-ray CT imaging on a subject (or an imaging object) P. The gantry 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 filter 16, a collimator 17, and a data acquisition system (DAS) 18.

[0025] The X-ray tube 11 is a vacuum tube that generates X-rays by emitting thermal electrons from a cathode (filament) to an anode (target) in response to application of a high voltage and supply of a filament current from an X-ray high voltage device 14. Specifically, X-rays are generated by thermal electrons colliding with a target. An example of the X-ray tube 11 is a rotating anode type X-ray tube that generates X-rays by emitting thermal electrons to a rotating anode. The X-rays generated by the X-ray tube 11 are formed into a cone beam by a collimator 17, for example, and irradiated onto the target P.

[0026] The X-ray detector 12 detects X-rays emitted by the X-ray tube 11 and passing through the object P, and outputs an electrical signal according to the X-ray dose to the DAS 18. The X-ray detector 12 includes, for example, a plurality of X-ray detection element rows each including a plurality of X-ray detection elements aligned in a channel direction (X-axis direction or cylindrical direction) along an arc centered on the focal point of the X-ray tube 11. The X-ray detector 12 has an array structure in which a plurality of X-ray detection element rows each including a plurality of X-ray detection elements aligned in the channel direction are aligned in the slice direction (Z-axis direction or row direction).

[0027] Specifically, the X-ray detector 12 may be, for example, a direct conversion detector including a semiconductor element that converts incident X-rays into an electrical signal. The X-ray detector 12 is an example of a PCD according to the present embodiment and is also referred to as a "PCD 12."

[0028] The rotating frame 13 supports the X-ray generator and the X-ray detector 12 rotatably around a rotation axis. Specifically, the rotating frame 13 is an annular frame that supports the X-ray tube 11 and the X-ray detector 12 so that the X-ray tube 11 faces the X-ray detector 12 and the X-ray tube 11 and the X-ray detector 12 rotate under the control of a control device 15 described later. The rotating frame 13 is rotatably supported by a fixed frame (not shown) made of a metal such as aluminum. Specifically, the rotating frame 13 is connected to an end of the fixed frame via a bearing. The rotating frame 13 rotates at a predetermined angular velocity around the rotation axis Z while receiving power from a driver of the control device 15.

[0029] In addition to the X-ray tube 11 and the X-ray detector 12, the rotating frame 13 includes and supports the X-ray high voltage device 14 and the DAS 18. Such a rotating frame 13 is accommodated in a substantially cylindrical housing with a bore 19 that defines an imaging space. The bore corresponds approximately to the FOV. The central axis of the bore coincides with the rotation axis Z of the rotating frame 13. Detection data generated by the DAS 18 is transmitted, for example, from a transmitter (not shown) to a receiver (not shown) installed in a non-rotating part of the gantry (such as a fixed frame not shown in FIG. 1), and then transferred to the console 40.

[0030] The X-ray high voltage device 14 includes a high voltage generator including electric circuits such as a transformer and a rectifier, and having a function of generating a high voltage applied to the X-ray tube 11 and a filament current supplied to the X-ray tube 11, and an X-ray control device configured to control an output voltage due to X-rays emitted by the X-ray tube 11. The high voltage generator can be of a transformer type or an inverter type. The X-ray high voltage device 14 may be installed on the rotating frame 13 described below or on a fixed frame (not shown) of the gantry 10.

[0031] The control device 15 includes a processing circuit including a central processing unit (CPU) and the like, and a driver for a motor or an actuator and the like. The processing circuit includes a processor such as a CPU or a micro processing unit (MPU) and a memory such as a read only memory (ROM) or a random access memory (RAM) as hardware resources. The control device 15 can be realized by an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or another complex programmable logic device (CPLD) or simple programmable logic device (SPLD). The control device 15 controls the X-ray high voltage device 14 and the DAS 18 and the like according to instructions from the console 40. The processor performs the above control by reading and executing a program stored in the memory.

[0032] The CPU may execute a computer program including a set of computer readable instructions that perform the functions described herein, the program being stored in either the non-transitory electronic memory mentioned above and / or a hard disk drive, a CD, a DVD, a FLASH drive, or any other known storage medium. Furthermore, the computer readable instructions may be provided as a utility application, a background daemon, or a component of an operating system, or a combination thereof, and executed in conjunction with a processor and an operating system known to those skilled in the art. Furthermore, the CPU may be implemented as multiple processors working together in parallel to perform the instructions.

[0033] The control device 15 also has a function of controlling the operation of the gantry 10 and the bed 30 in response to an input signal from an input interface 43 (described later) connected to the console 40 or the gantry 10. For example, the control device 15 controls the rotation of the rotating frame 13, the tilt of the gantry 10, or the operation of the bed 30 and the table top surface 33 in response to the input signal. The control of tilting the gantry 10 is performed by the control device 15 rotating the rotating frame 13 around an axis parallel to the X-axis direction based on tilt angle information input through the input interface 43 connected to the gantry 10. The control device 15 may be installed in either the gantry 10 or the console 40. The control device 15 may be configured by directly incorporating a program into the circuit of the processor instead of storing the program in a memory. In this case, the processor performs the above-mentioned control by reading and executing the program incorporated in the circuit.

[0034] The wedge filter 16 is a filter for adjusting the dose of X-rays emitted from the X-ray tube 11. Specifically, the wedge filter 16 is a filter that transmits the X-rays emitted from the X-ray tube 11 and attenuates the X-rays so that the X-rays emitted from the X-ray tube 11 show a predetermined distribution with respect to the object P. For example, the wedge filter 16 (or a bowtie filter) is a filter obtained by processing aluminum so as to have a predetermined target angle and a predetermined thickness.

[0035] The collimator 17 is a lead plate or the like for narrowing the irradiation range of the X-rays transmitted through the wedge filter 16, and includes a slit formed in combination with the lead plate or the like. The collimator 17 is also called an "X-ray aperture."

[0036] The DAS 18 generates digital data (also referred to as "detection data") indicating the count of X-rays detected by the X-ray detector 12 for each of a plurality of energy bands (called "energy bins" or simply "bins"). The detection data is a set of count value data identified by the channel number and row number of the source X-ray detection element, the view number indicating the collected view (also referred to as the projection angle), and the energy bin number. The DAS 18 is executed, for example, by an application specific integrated circuit (ASIC) equipped with circuit elements capable of generating the detection data. The detection data is transferred to the console 40.

[0037] The bed 30 is a device on which an object P to be scanned is placed and which moves the object P, and includes a base 31, a bed actuator 32, a table top surface 33, and a support frame .

[0038] The base 31 is a housing that supports the support frame 34 so that the support frame 34 is vertically movably.

[0039] The bed actuator 32 is a motor or actuator that moves the table top 33 on which the subject P is placed in the longitudinal direction of the table top 33. The bed actuator 32 moves the table top 33 in response to control by the console 40 or control by the control device 15. For example, the bed actuator 32 moves the table top 33 in a direction perpendicular to the subject P so that the body axis of the subject P placed on the table top 33 coincides with the central axis of the bore of the rotating frame 13. The bed actuator 32 can also move the table top 33 in the body axis direction of the subject P in response to X-ray CT imaging performed using the gantry 10. The bed actuator 32 generates electricity by driving at a rotation speed according to the duty ratio of a drive signal from the control device 15. The bed actuator 32 is implemented by a motor such as a direct drive motor or a servo motor.

[0040] The table top surface 33, which is installed on the upper surface of the support frame 34, is a plate on which the object P is placed. The bed actuator 32 can move not only the table top surface 33 but also the support frame 34 in the longitudinal direction of the table top surface 33.

[0041] The console 40 includes a memory 41, a display 42, an input interface 43, and a processing circuit 44. Data communication between the memory 41, the display 42, the input interface 43, and the processing circuit 44 is performed via a bus. Although the console 40 will be described as being separate from the gantry 10, the gantry 10 may include the console 40 or some of the components of the console 40.

[0042] The memory 41 is a storage device such as a hard disk drive (HDD) or a solid state drive (SSD), and stores various kinds of information. The memory 41 stores, for example, projection data and reconstructed image data. The memory 41 may be a driver that writes various kinds of information to and reads them from not only an HDD or SSD, but also a portable storage medium such as a CD, a DVD, or a flash memory, or a semiconductor memory such as a random access memory (RAM). The storage area of ​​the memory 41 may be in the X-ray CT apparatus 1 or in an external storage device connected via a network. For example, the memory 41 stores data of a CT image or a display image. The memory 41 also stores a control program according to this embodiment. The memory 41 stores a detector response forward model of the X-ray detector 12, which is a photon counting detector to be used during image reconstruction of an imaging object. The memory 41 is an example of a storage unit.

[0043] The display 42 displays various kinds of information. For example, the display 42 outputs medical images (CT images) generated by the processing circuit 44 and a graphical user interface (GUI) for receiving various kinds of operations from an operator. As the display 42, for example, a liquid crystal display (LCD), a cathode ray tube (CRT) display, an organic electroluminescence display (OELD), a plasma display, or any other display may be appropriately used. The display 42 may be installed in the gantry 10. The display 42 may be a desktop type or may be configured by a tablet device capable of wirelessly communicating with the console 40.

[0044] The input interface 43 receives various types of input operations from an operator, converts the received input operations into electrical signals, and outputs the electrical signals to the processing circuitry 44. For example, the input interface 43 receives from the operator acquisition conditions for acquiring projection data, reconstruction conditions for reconstructing CT images, and image processing conditions for generating post-processed images from CT images. As the input interface 43, for example, a mouse, a keyboard, a trackball, a switch, a button, a joystick, a touchpad, or a touch panel display may be appropriately used. In this embodiment, the input interface 43 does not necessarily include a physical operation component such as a mouse, a keyboard, a trackball, a switch, a button, a joystick, a touchpad, or a touch panel display. For example, the input interface 43 also includes an electrical signal processing circuit that receives an electrical signal corresponding to an input operation from an external input device installed separately from the apparatus and outputs the electrical signal to the processing circuitry 44. The input interface 43 may be installed in the gantry 10. The input interface 43 may be configured by a tablet device capable of wirelessly communicating with the console 40.

[0045] The processing circuit 44 controls the overall operation of the X-ray CT device 1 in response to an electrical signal of an input operation output from the input interface 43. For example, the processing circuit 44 includes a processor such as a CPU or an MPU, or a graphics processing unit (GPU), and a memory such as a ROM or a RAM as hardware resources. Together with the processor that executes a program loaded in the memory, the processing circuit 44 performs a system control function 441, a pre-processing function 442, a reconstruction function 443, and a display control function 444. Each of the functions (the system control function 441, the pre-processing function 442, the reconstruction function 443, and the display control function 444) is not necessarily implemented by a single processing circuit. The processing circuit can be configured by combining multiple independent processors, and the processor can execute a corresponding program to execute the function.

[0046] The system control function 441 controls each function of the processing circuit 44 based on an input operation received from an operator via the input interface 43. Specifically, the system control function 441 reads out a control program stored in the memory 41, loads it into the memory in the processing circuit 44, and controls each part of the X-ray CT device 1 according to the loaded control program. For example, the processing circuit 44 performs each function of the processing circuit 44 based on an input operation received from an operator via the input interface 43. For example, the system control function 441 obtains a two-dimensional positioning image of the object P and determines a scan range, imaging conditions, etc. The positioning image may also be called a "scanogram" or a "scout image."

[0047] The pre-processing function 442 generates acquired data by performing pre-processing such as logarithmic conversion, offset correction, inter-channel sensitivity correction, beam hardening correction, as well as detector calibration, detector nonlinearity, polarity effect, noise balance, and material decomposition correction on the detection data output from the DAS 18. The data before pre-processing (detection data) and the data after pre-processing may be collectively referred to as "projection data." The pre-processing function 442 is an example of a pre-processor.

[0048] The reconstruction function 443 generates CT image data by performing reconstruction processing on the projection data generated by the pre-processing function 442 using a filtered back projection method, an iterative reconstruction method, a stochastic image reconstruction method, or the like. The reconstruction function 443 is an example of a reconstruction processing. If necessary, image filtering, smoothing, volume rendering, or image subtraction processing can be applied to the CT image data. The display control function 444 converts the CT image data generated by the reconstruction function 443 into tomographic image data or three-dimensional image data of a given cross section by a known method based on an input operation received from an operator via the input interface 43. The generation of the three-dimensional image data may be performed directly by the reconstruction function 443. The display control function 444 is an example of a display control device.

[0049] In one implementation, the X-ray tube 11 is a single source that emits a broad spectrum of X-ray energy, and the PCD 12 can use a direct conversion X-ray radiation detector based on semiconductors such as cadmium telluride (CdTe), cadmium zinc telluride (CZT), silicon (Si), mercuric iodide (HgI2) and gallium arsenide (GaAs). As previously mentioned, semiconductor-based direct conversion X-ray detectors generally have a faster time response than indirect conversion detectors such as scintillator detectors. The fast time response of direct conversion detectors allows for the resolution of individual X-ray detection events, but several multiple collisions of detection events may occur at high X-ray fluxes typical of clinical X-ray applications. The energy of the detected X-rays is proportional to the signal generated by the direct conversion detector, and the detection events can be grouped into energy bins that result in the spectrally resolved X-ray data of the spectral CT.

[0050] An example of a PCD bin response function is shown in Figure 2. As illustrated in Figure 2, due to charge sharing, pulse multi-collision effects, etc., the bin response functions have a very broad distribution beyond the ideal bin energy window per counter. Typically, a calibration procedure is applied based on multiple transmission measurements of various known attenuation path lengths, and the forward model is modified to match the calibration measurements.

[0051] For example, when the number of energy bins is n, the PCD forward model is obtained by the following equations (1) and (2).

[0052]

number

[0053]

number

[0054] In equations (1) and (2), E means incident energy, E' means measured energy, and Nb,j denotes the count measured at a given detector pixel j for energy bin b, and Φ b (E') denotes the binning function that models the function of DAS18 (or ASIC), T b and T b+1 are the low and high energy thresholds for energy bin b, and E min and E max are the low and high energy thresholds of the incident spectral energy range, and N 0,j is the incident beam spectrum that may be represented by the air flux measured at detector pixel j using an air scan, and S O、j (E)D(E,E') is the detector response calibration term ("DR"), and Σ k=1 K μ k (E)l k is the attenuation sample at detector pixel j.

[0055] To calibrate the forward model parameters, a set of slab scans of known material and thickness are collected. b,i,j Let u be the count measured at detector pixel j for energy bin b and slab i, i = 1, ..., m. The parameters in the PCD forward model can be determined by solving the minimization problem using Eq. (3).

[0056]

number

[0057] In the formula, y b,i is the specific air flux N based on equation (1) Oj DR j together with the counts calculated for energy bin b, slab i, and detector pixel j.

[0058] It should be noted that the above formula is designed for the spectral mode of the photon-counting CT. If the photon-counting CT operates in the counting mode, the calculation can be reduced to the following formula (4):

[0059]

number

[0060] Here, the following equations (5) and (6) hold.

[0061]

number

[0062]

number

[0063] If the PCD calibration scan data is acquired using rectangular slabs 1,…,m, this procedure obtains the attenuation samples att i =Σ k=1 K μ k l k (i) (i=1, .., m) to generate a set of measurements. Figures 3A-3C show air and slab scans using different combinations of known materials and thicknesses. In the example shown in Figures 3B-3C, the PCD calibration slab scan covers the attenuation phase space that would occur during an object scan using two principal materials (i.e., K=2), such as solid water / aluminum, or other similar combinations (e.g., iodine, calcium, etc.). These data points N b,i,j (Spectral mode, or N for counting mode) tot , i,j Each of these will be used in the cost function calculation.

[0064] When a rectangular slab is used in the slab scan, the PCD calibration scan data set has approximately the same attenuation samples across the entire detector fan angle. When weighted least squares fitting is applied to determine the forward model parameters of the pixel-by-pixel DR function, the cost function design may usually weight the measured data based on the statistical noise, i.e., as shown in Equation (7) below.

[0065]

number

[0066] If the forward model parameterization is sufficient, the above cost function design can work well and produce good calibration results. However, in practical situations, the PCD detector response is usually too complex, so the quality of the parameter fitting for all of the attenuation samples is not equal, and the natural statistical weighting based on Poisson noise is usually not sufficient, especially in the high flux region when the pulse-multiple collisions start to become more intense.

[0067] Furthermore, objects scanned by photon-counting CT usually have a cylindrical shape. The attenuation in the center of the FOV generally has the longest path length, but decreases toward the fan edge, and even shorter as the imaged object approaches its edge. Figure 4 shows the path length at different fan angles, where the rays in the central region usually have a longer path length (e.g., L2) through the scanned object, and the rays in the edge region have a significantly shorter path length (e.g., L1).

[0068] Therefore, applying the same weighting scheme across the entire detector fan range would not yield optimal fitting quality, which could result in suboptimal image quality. Instead, it is desirable to favor fitting quality at longer path lengths over shorter path lengths in the center of the fan range, while simultaneously favoring fitting quality at shorter path lengths over longer path lengths toward the fan edges.

[0069] 5 shows a block diagram of a calibration data weight optimizer 500 according to an embodiment of the present disclosure. The calibration data weight optimizer 500 includes a reference scan data acquisition circuit 510, a calibration table receiving circuit 520, a reference path length profile generating circuit 530, a calibration data weight determining circuit 540, and a calibration table updating circuit 550.

[0070] The reference scan data acquisition circuit 510 acquires the reference scan data from a detector of the CT system. The reference scan data can be acquired using various techniques, such as a 2D scout scan or a 3D scout scan of the imaging object. Alternatively, the reference scan data can be acquired using a suitable phantom.

[0071] The calibration table receive circuit 520 can receive a calibration table from a calibration table storage device (not shown) of the CT system. Typically, the calibration table can be created by performing slab scans using various combinations of known materials and thicknesses, and then stored in the calibration table storage device for use in calibrating the CT forward model.

[0072] Based on the calibration table and the reference scan data, the reference path length profile generation circuit 530 generates a reference path length profile that allows an estimation of the path lengths that would be caused by X-rays at different pixel locations during a scan of an imaging object.

[0073] Based on the generated reference pathlength profiles, the calibration data weight determination circuit 540 determines weights for the slab calibration data. For example, in one embodiment, the weighting scheme is designed to ensure that the calibration slab scan data is weighted differently across the detector fan angle during the forward model fitting process, with thicker slabs in the center of the detector being weighted more heavily and thinner slabs towards the periphery being weighted more heavily.

[0074] Based on the determined calibration data weights, the calibration table update circuit 550 updates the calibration table and stores them in the calibration table storage. The processing circuit 44 then reconstructs an image of the imaging object using the updated detector response forward model. By making appropriate spatial adjustments to the fitting weights, more relevant path length ranges can be prioritized for each detector pixel. This can result in a better fitting of the forward model to the target path length ranges, resulting in optimized image quality.

[0075] FIG. 6 illustrates a flow chart of a calibration data weight optimization procedure 600 according to an embodiment of the present disclosure.

[0076] In step S610, reference scan data is acquired. As previously mentioned, the reference scan data can be acquired through a scout scan of the imaging object, or alternatively by scanning a cylindrical phantom. For example, a 32 cm cylindrical water phantom may be used alternatively. Alternatively, a 2D or 3D scout scan of the imaging object may be performed to acquire the reference scan data.

[0077] In step S620, a calibration table is received. For example, this step may involve retrieving the calibration table, which may have been previously prepared through a series of slab scans and stored in a calibration table storage device.

[0078] In step S630, a reference path length profile is generated based on the calibration table and the reference scan data. That is, processing circuitry 44 generates the reference path length profile as an estimated attenuation profile based on the reference scan data. The reference path length profile represents potential attenuation path lengths that may occur at the plurality of detector pixels during a scan of the imaging object. In this manner, processing circuitry 44 estimates the attenuation profile of the imaging object.

[0079] For example, if a water phantom is used as an alternative, the reference pathlength profile can be either the water-based material pathlength after material decomposition or the total water equivalent pathlength at a particular keV. If more than solid water is used in a slab scan, a particular keV (e.g., 70 keV) is selected to determine the total water equivalent pathlength L using equation (8) below. tot can be calculated and compared to a reference water phantom profile.

[0080]

number

[0081] When a 2D scout scan (also called "scano") method is used, the reference path length can be, for example, 1 / Var(N b、i,j The reference water equivalent path length across the detector array can be from either the maximum or average value of a 2D scout scan. If a 3D scout scan is used, the reference water equivalent path length can be obtained from a view-averaged sinogram.

[0082] The scout scan approach can provide further advantages over the simpler cylindrical phantom approach. By acquiring more information, such as the Z-profile of the imaged object, variations in body composition and radiation attenuation across different regions can be taken into account. For example, if significant variations are observed across the scanned object, multiple calibration tables may be prepared with different weighting schemes based on the Z-profile. This may include applying a first weighting scheme to the neck, a second weighting scheme to the chest, and a third weighting scheme to the abdomen, etc. By adjusting the weighting for specific regions of interest, the detector can achieve more precise and reliable imaging results.

[0083] In step S640, spatially adjusted weights may be determined for the slab scan data based on the reference path length profile and the calibration table. That is, processing circuitry 44 determines spatial weights for each of a plurality of detector pixels based on the attenuation profile estimated in step S630. More specifically, in the forward model fitting process, the calibration slab scan data may have different data weights across the fan angle range of the detector. In general, in the central region of the detector, a larger weight may be assigned to thicker slabs, while in the peripheral region of the detector, a larger weight may be applied to thinner slabs.

[0084] For example, the spatial weight adjustment is performed on the measured slab data N b,i,j and pixel position x j That is, the method may be further configured to use a function to calculate, for each of the plurality of detector pixels, one of the spatial weights corresponding to the particular calibration data associated with each of the plurality of slabs, the function being determined according to the detector pixel position x i and depends on the specific calibration data associated with the slab.

[0085]

number

[0086] Therefore, with the adjusted slab data weights, the forward model fitting minimization cost function for detector pixel j becomes:

[0087]

number

[0088] As previously mentioned, the weighting adjustment function can be designed based on a predefined imaging object geometry, for example, a predefined cylindrical phantom. Alternatively, the weighting adjustment function can be designed in situ based on scout scan data of the imaging object. The design of the weighting adjustment function is described in more detail with reference to various embodiments and Figures 7A-7D, 8A-8C, and 9A-9C.

[0089] In step S650, the PCD forward model calibration table is updated by replacing the original weights with spatially adjusted weights for the imaging object. That is, the processing circuit 44 updates the detector response forward model stored in the memory 41 as a storage unit based on the spatial weights determined in step S640. These updated calibration tables can be used later in the image reconstruction process, for example, to generate counting line integrals or principal component material path lengths. By optimizing the calibration data weights based on the attenuation profile of the imaging object, better fitting quality can be achieved using the same limited number of slab data samples. This can improve image quality during the image reconstruction process.

[0090] According to one embodiment of the present disclosure, for a given detector pixel j, its path length and the reference path length L j Different weight values ​​may be assigned to the slab data samples based on a comparison with the reference path length. The reference path length may be estimated using a cylindrical phantom or through a scout scan. If the thickness of the slab is L, j For all data samples thinner than 1, a first relative weight can be assigned, as shown in Equation (11) below. That is, the processing circuitry can use a function that includes an adjustment term that depends on the statistics of the calibration data associated with the multiple slabs. do.

[0091]

number

[0092] The thickness of the slab is L j If the thickness is equal to or greater than , then the data sample may be given a second relative weight, as per equation (12) below:

[0093]

number

[0094] By adjusting the values ​​of a1 and a2, the relative weights across slab samples can be adjusted to obtain optimal calibration results. For example, by choosing appropriate values ​​of a1 and a2, the weights assigned to slab samples whose path lengths are much longer (or much shorter, depending on design requirements) than the reference path length can be reduced. This is because such samples are less relevant for that pixel, since their path lengths typically do not occur during the object scan.

[0095] Typically, the reference path length is longest at the center of the detector and gradually decreases toward the edge. For a detector pixel located at the center of the detector, the weights assigned to all of the slab samples can be determined using equation (11). As the detector pixel location moves toward the edge of the detector, more slab samples may be weighted based on equation (12). In this manner, spatial weight adjustment of the slab data can be achieved by using equations (11) or (12) based on the reference path length profile.

[0096] N b、i,j If we assume that follows a Poisson statistic approximately, then the variance will be equal to its expected value (i.e., Var(N b ,i,j)=E(N b , i, j), and the first and second relative weights are as shown in the following equations (13) and (14).

[0097]

number

[0098]

number

[0099] According to another embodiment of the present disclosure, the weight for a given one of the entire set of slab path length samples can be designed as follows:

[0100]

number

[0101] In equation (15), I is the path length of the reference path length L j means the highest slab index that is shorter than

[0102] The values ​​of a1 and a2 may vary, for example, between 0 and 5 depending on the details of the PCD forward model and the minimization method. As such, their selection is generally made heuristically on a case-by-case basis. In an exemplary scenario, a1 is selected to be less than a2.

[0103] 7A to 7D show an example weight adjustment w opt (b,i,j) are shown. The water principal component pathlength view average profile is shown in Figures 7A and 7C, where the horizontal axis represents the detector pixel position in the channel direction and the vertical axis represents the pathlength (cm) at the corresponding pixel position. The pathlength profile is generated using a 32 cm cylindrical water phantom placed near the isocenter.

[0104] 7B and 7D show three different slab data weights w1, w2, and w optare compared along the slab sample path length. These can be calculated using equations (13), (14) and (15) above with a1 = 1, a2 = 2. The optimal weights w opt changes from Equation (16) to Equation (17) below when the pathlength reaches the reference pathlength (i.e., 29 cm as shown by the water-dominant pathlength view average profile shown in FIG. 7A):

[0105]

number

[0106]

number

[0107] Similarly, for detector pixel 200, as observed by the water-dominant pathlength view-averaged profile shown in FIG. 7C, once the pathlength reaches the reference pathlength of 17 cm, the optimal weight w opt A switch occurs.

[0108] For both detector pixels 300 and 200, the weights w of the slab samples with path lengths shorter than the reference path length opt is the weight w1(=1 / Var(N b,i,j In contrast, the weights w of slab samples whose path length is equal to or longer than the reference path length opt is the weight w2(=1 / Var 2 (N b,i,j )). Furthermore, as the reference path length increases from detector pixels 200 closer to the edge to detector pixels 300 closer to the center, greater weights are assigned to slab samples with longer path lengths. That is, processing circuitry 44 reduces the spatial weight assigned to a particular calibration data associated with a slab when the attenuated path length of the slab is greater than the value of the reference path length profile corresponding to the detector pixel.

[0109] In the previous embodiment, the weight adjustment function is discrete along the detector fan angle direction (or channel direction) due to the non-continuous sampling of the attenuation path lengths used in the calibration. In another embodiment, the weight adjustment function can be designed to be continuous along the detector channel direction.

[0110] As an example, a per-channel window function may be applied on top of the original weights derived based on the statistics of the slab data. The window function can be designed according to both pixel location and slab path length. For example, for energy bin b, slab i, and pixel location x j Slab data points obtained for N b,i,j is given, the original weights w b,i,j =E -a (N b,i,j ) is transformed into w through continuous spatial adjustment. opt It is adjusted to (b,i,j).

[0111]

number

[0112] The adjustment function f in Eq. (13) b,i,j can be any function that satisfies various optimization criteria and accommodates different scanning conditions. For example, the adjustment function can be designed to increase the weighting for thick path lengths in detector channels close to the isocenter and decrease the weighting for thick path lengths in channels on the periphery. As in the previous embodiment, the adjustment function f b,i,j can be determined based on a reference path length profile generated from a phantom or through a 2D or 3D scout scan of the object being scanned.

[0113] An example of a continuous spatial weight adjustment scheme can be seen in Figures 8A to 8C. One way to perform spatial adjustment is, for example, to use a Gaussian kernel with various standard deviations σ for different slab path lengths. More specifically, the standard deviation σ of the Gaussian kernel i can be designed to be smaller with increasing slab path length, so that the weighting at thick path lengths can be gradually suppressed relative to the outer detector channels by the adjustment function given by Eq. (19) below.

[0114]

number

[0115] Figures 8A and 8C show the normalized weights before and after spatial adjustment for different detector channels and slabs, respectively. The original weights shown in Figure 8A are multiplied by the spatial weight adjustment function shown in Figure 8B to obtain the adjusted weights in Figure 8C. In each of Figures 8A to 8C, the central region of the horizontal axis corresponds to the location of the isocenter. As shown in Figure 8C, in most cases the weights are maintained for detector channels close to the isocenter, while for detector channels close to the periphery the weights for thick slab path lengths are strongly suppressed.

[0116] The effect of the spatial adjustment functions described above is illustrated more specifically in Figures 9A-9C, which show the weights before and after adjustment for three different detector channels 400, 200, and 30. Detector channel 400 is closest to the isocenter, while channel 200 is somewhat farther away and detector channel 30 is the furthest away. The original weight for detector channel 400 remains roughly the same, but detector channels 200 and 30, which are closer to the periphery, have had their weighting for the thick slab path length significantly reduced.

[0117] The above embodiments have focused primarily on the spectral mode, but are not limited to that mode. The spatial adjustment techniques described above can also be applied to the counting mode. For example, when response fitting is performed in the counting mode, N in the equation can be adjusted to represent the total counts above a first energy threshold (e.g., 20 keV) among other adjustments as needed. b,i,j The value of N tot,i,j can be replaced with.

[0118] The embodiments of the present disclosure can also be additionally described as follows.

[0119] (1) An X-ray scanner system comprising: a photon-counting detector having a plurality of detector pixels in a channel direction; a memory that stores a detector response forward model of the photon-counting detector to be used during image reconstruction of an imaging object; and a processing circuit configured to estimate an attenuation profile of the imaging object, determine for each of the plurality of detector pixels a set of spatial weights based on the estimated attenuation profile, and update the detector response forward model stored in the memory based on the determined set of spatial weights.

[0120] (2) The system of (1), wherein the processing circuitry is further configured to acquire reference scan data and generate a reference path length profile as an estimated attenuation profile based on the acquired reference scan data, the generated reference path length profile representing potential attenuation path lengths that may occur at a plurality of detector pixels during scanning of the imaging object.

[0121] (3) The system of (2), wherein the processing circuitry is further configured to receive calibration data including data generated through a calibration scan using a plurality of slabs, and to determine, for each of a plurality of detector pixels, a set of spatial weights based on the attenuation path length of the slab and a reference path length profile, each of the sets of spatial weights corresponding to particular calibration data associated with one of the plurality of slabs.

[0122] (4) The system described in (3), wherein the processing circuitry is further configured to use a function to calculate, for each of the plurality of detector pixels, one spatial weight of a set of spatial weights corresponding to specific calibration data associated with each slab of the plurality of slabs, the function being dependent on the position of the detector pixel and the specific calibration data associated with the slab.

[0123] (5) The system of (4), wherein the processing circuitry is further configured to use a function including an adjustment term that depends on statistics of the calibration data associated with the multiple slabs.

[0124] (6) The system described in (4), wherein the processing circuitry is further configured to reduce a spatial weight assigned to a particular calibration data associated with the slab when the attenuation path length of the slab is greater than a value of the reference path length profile corresponding to the detector pixel.

[0125] (7) The system described in (2), wherein the processing circuitry is further configured to acquire reference scan data by using an X-ray scanner system to scan the phantom.

[0126] (8) The system described in (2), wherein the processing circuitry is further configured to acquire reference scan data through a 2D or 3D scout scan of the imaging subject.

[0127] (9) The system of (8), wherein the processing circuitry is further configured to create a Z-directional profile of the imaging object and determine multiple sets of spatial weights based on the generated Z-directional profile, each set of spatial weights corresponding to a different Z-directional region of the imaging object.

[0128] (10) The system described in (1), wherein the processing circuitry is further configured to scan the imaging object and reconstruct an image of the imaging object using the updated detector response forward model.

[0129] (11) A method for performing calibration data weight adjustment in an X-ray scanner system, the X-ray scanner system including a photon counting detector and a memory, the detector having a plurality of detector pixels in a channel direction, the memory storing a detector response forward model of the photon counting detector to be used during image reconstruction of an imaging object, the method including: estimating an attenuation profile of the imaging object; determining, for each of the plurality of detector pixels, a set of spatial weights based on the estimated attenuation profile; and updating the detector response forward model stored in the memory based on the determined set of spatial weights.

[0130] (12) The method of (11), wherein the estimating step further includes acquiring reference scan data and generating a reference path length profile as an estimated attenuation profile based on the acquired reference scan data, the generated reference path length profile representing potential attenuation path lengths that may occur at a plurality of detector pixels during scanning of the imaging object.

[0131] (13) The method of (12), further comprising receiving calibration data including data generated through a calibration scan using a plurality of slabs, wherein the determining step further comprises determining, for each of a plurality of detector pixels, a set of spatial weights based on the attenuation path length of the slab and a reference path length profile, each of the sets of spatial weights corresponding to a particular calibration data associated with one of the plurality of slabs.

[0132] (14) The method of (13), wherein the determining step further includes using a function to calculate, for each of the plurality of detector pixels, one spatial weight of a set of spatial weights corresponding to specific calibration data associated with each slab of the plurality of slabs, the function being dependent on the position of the detector pixel and the specific calibration data associated with the slab.

[0133] (15) The method according to (14), wherein the function includes an adjustment term that depends on statistics of calibration data associated with multiple slabs.

[0134] (16) The method of (14), wherein the determining step further includes reducing a spatial weight assigned to a particular calibration data associated with the slab when the attenuation path length of the slab is greater than a value of the reference path length profile corresponding to the detector pixel.

[0135] (17) The method according to (12), wherein the acquiring step further includes acquiring reference scan data by using an X-ray scanner system to scan the phantom.

[0136] (18) The method of (12), wherein the acquiring step further includes acquiring reference scan data through a 2D or 3D scout scan of the imaging object.

[0137] (19) The method of (18), wherein the acquiring step further includes creating a Z-directional profile of the imaging object, and the determining step further includes determining multiple sets of spatial weights based on the generated Z-directional profile, each set of spatial weights corresponding to a different Z-directional region of the imaging object.

[0138] (20) The method of (11), further comprising scanning the imaging object and reconstructing an image of the imaging object using the updated detector response forward model.

[0139] In accordance with at least one embodiment described above, the data may be calibrated.

[0140] Although some embodiments have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, substitutions, modifications, and combinations of embodiments can be made without departing from the spirit of the invention. These embodiments and their modifications are included in the scope of the invention and its equivalents as described in the claims, as well as in the scope and spirit of the invention. [Explanation of symbols]

[0141] 40 Console 41 Memory 42 Display 43 Input Interface 44 Processing circuit 441 System Control Functions 442 Pre-processing function 443 Reconfiguration function 444 Display Control Function

Claims

1. a photon counting detector having a plurality of detector pixels in a channel direction; a memory unit for storing a detector response forward model of said photon counting detector to be used during image reconstruction of an imaging object; and a processing circuit that estimates an attenuation profile of the imaging object, determines a spatial weight for each of the plurality of detector pixels based on the estimated attenuation profile, and updates the detector response forward model stored in the memory unit based on the spatial weight.

2. The processing circuitry includes: Obtain reference scan data, generating a reference path length profile as the estimated attenuation profile based on the reference scan data; The X-ray CT apparatus of claim 1 , wherein the reference path length profile represents potential attenuated path lengths that may occur at the plurality of detector pixels during a scan of the imaging object.

3. The processing circuitry includes: receiving calibration data including data generated through a calibration scan using a plurality of slabs; determining, for each of the plurality of detector pixels, the spatial weight based on the attenuation path length of the slab and the reference path length profile; 3. The X-ray CT apparatus of claim 2, wherein each of said spatial weights corresponds to a particular calibration data associated with one of said plurality of slabs.

4. The processing circuitry includes: and further configured to use a function to calculate, for each of the plurality of detector pixels, one of the spatial weights corresponding to particular calibration data associated with each slab of the plurality of slabs; 4. The X-ray CT apparatus of claim 3, wherein the function depends on the detector pixel positions and the particular calibration data associated with the slab.

5. 5. The X-ray CT apparatus of claim 4, wherein the processing circuitry uses the function that includes an adjustment term that depends on statistics of the calibration data associated with the plurality of slabs.

6. The processing circuitry includes: if the attenuated pathlength of the slab is greater than the value of the reference pathlength profile corresponding to the detector pixel, 5. The X-ray CT apparatus of claim 4, further comprising: a step of decreasing the spatial weight assigned to the particular calibration data associated with the slab.

7. The X-ray CT apparatus of claim 2 , wherein the processing circuitry acquires the reference scan data by using the X-ray CT apparatus to scan a phantom.

8. The X-ray CT apparatus of claim 2 , wherein the processing circuitry acquires the reference scan data through a 2D or 3D scout scan of the imaging object.

9. The processing circuitry includes: creating a Z-profile of the imaging object; determining a plurality of sets of spatial weights based on the generated Z-direction profile; 9. The X-ray CT apparatus of claim 8, wherein each set of spatial weights corresponds to a different Z-direction region of the imaged object.

10. The processing circuitry includes: Scanning the imaging object; The X-ray CT apparatus according to claim 1 , further comprising: a detector response forward model updated to reconstruct an image of the imaging object.

11. Estimating an attenuation profile of the imaging object; determining a spatial weight for each of a plurality of detector pixels of a photon-counting detector having a plurality of detector pixels in a channel direction based on the estimated attenuation profile; A data processing method for updating a detector response forward model of said photon counting detector to be used during image reconstruction of an imaging object based on said spatial weights.

Images