X-ray CT apparatus, data processing method and program

Abstract

To improve image quality.SOLUTION: An X-ray CT device according to the embodiment includes: an X-ray irradiation unit; a photon count detector; an acquisition unit; an estimation unit; and a calculation unit. The X-ray irradiation unit executes a first scan and a second scan. The photon count detector detects a signal related to the first scan and the second scan. The acquisition unit acquires a count of an energy bin detected by the photon count detector. The estimation unit estimates an energy bin response function which depends on the energy based on the count of the first scan, and estimates a pileup correction term per current intensity of the second scan based on the count of the second scan and the energy bin response function. The calculation unit performs a substance discrimination.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 technology]

[0002] Photon-counting-detector based CT systems (PCCT) have great advantages in X-ray diagnosis, such as high spatial resolution.

[0003] However, if the flux of X-rays incident on the detector is high, pile-up may occur in the detector, causing the measured counts to deviate from the true counts. Therefore, it is desirable to perform a correction process for the detector response. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] US Patent Application Publication No. 2007 / 0076842 [Patent Document 2] US Patent Application Publication No. 2017 / 0224299 [Patent Document 3] US Patent Application Publication No. 2018 / 0252822 [Non-patent literature]

[0005] [Non-Patent Document 1] Emil idky et al., "A robust method of x-ray source spectrum estimation from transmission measurements: Demonstrated on computer simulated, scatter-free transmission data", Journal of Applied Physics, 2005, Vol.97, Issue.12, p124701 [Non-patent document 2] Duan et al., "CT scanner x-ray spectrum estimation from transmission measurements", Medical Physics, February 2011, Vol. 38, Issue 2, p993 [Non-patent document 3] Jannis Dickmann et al., "A count rate-dependent method for spectral distortion correction in photon counting CT", Proc. SPIE 10573, Medical Imaging 2018: Physics of Medical Imaging, March 9, 2018, p1057311 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 improve image quality. 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 below can also be positioned as other problems. [Means for solving the problem]

[0007] An X-ray CT apparatus according to an embodiment includes an X-ray irradiator, a photon counting detector, an acquisition unit, an estimation unit, and a calculation unit. The X-ray irradiator performs a first scan and a second scan, including multiple scans using a current intensity higher than the current intensity of the first scan, on a phantom consisting of a known material and penetration length at each pixel of the photon counting detector (PCD). The photon counting detector detects signals related to the first scan and the second scan. The acquisition unit acquires counts for each energy bin detected by the photon counting detector. The estimation unit estimates an energy-dependent energy bin response function based on the counts of the first scan, and estimates a pile-up correction term for each current intensity of the second scan based on the counts of the second scan and the energy bin response function. The calculation unit performs material decomposition in a scan of a subject using a model based on the energy bin response function and the pile-up correction term for each current intensity of the second scan. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is a diagram showing an example of an X-ray CT apparatus according to an embodiment. [Figure 2] FIG. 2 shows an example of the PCD bin response function Sb(E) of a photon-counting detector. [Figure 3] FIG. 3 is a flowchart showing the procedure of calibration and processing of material decomposition according to the embodiment. [Figure 4] FIG. 4 shows the normalized linear attenuation coefficients of various materials. [Figure 5] FIG. 5 is a diagram showing an outline of the calibration in which the pile-up correction table Pb is generated and used individually for each mA. [Figure 6] FIG. 6 shows an alternative calibration scheme in which a universal pile-up correction table Pb is generated for the entire mA range. DETAILED DESCRIPTION OF THE INVENTION

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

[0010] Throughout this specification, references to "one embodiment" or "embodiment" mean that a particular feature, structure, material, or characteristic described in connection with that embodiment is included in at least one embodiment of the application, but do not mean that it is present in all embodiments.

[0011] Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment of the present application. Furthermore, the particular features, structures, materials, or characteristics may be combined in any suitable manner in one or more embodiments.

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

[0013] Computed tomography (CT) systems and methods are commonly used in medical imaging and diagnosis. CT systems generally 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, producing projection images at different angles. From the projection images, an image of the subject's body can be reconstructed.

[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 resolves the incident x-ray counts into spectral components, called energy bins, that collectively span the entire energy spectrum of the x-ray beam. Unlike non-spectral CT, spectral CT yields information attributable to various materials that exhibit different x-ray attenuation as a function of x-ray energy. These differences enable the decomposition of spectrally resolved projection data into different material components; for example, two material components in a material decomposition may be bone and water.

[0015] Although PCDs have a fast response time, at the high x-ray flux rates typical of clinical x-ray imaging, multiple x-ray detection events on a single detector can occur 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 and degrade the reconstructed image from the PCD. Correcting for these effects provides spectral CT with numerous 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 to more effectively using semiconductor-based PCDs for spectral CT is performing material decomposition of the projection data in a robust and efficient manner. For example, pileup correction in the detection process can be imperfect, and these imperfections degrade the material components resulting from material decomposition.

[0017] In photon-counting CT systems, semiconductor-based detectors using direct conversion are designed to resolve the energy of individual incident photons and generate measurements of multiple energy bin counts for each integration period. However, due to the detection physics 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 accumulation and charge induction processes, as well as electronic noise in the associated front-end electronics. As mentioned above, due to the finite signal counting time, pulse pileup also distorts the energy response at high count rates.

[0018] Due to the heterogeneity of sensor materials and the complexity of integrated detection systems, it is not possible to accurately model such detector responses of PCDs solely based on physical theory or Monte Carlo simulations using specific modeling of signal-inducing processes, which determines the accuracy of the forward model for each measurement. Also, due to uncertainties in incident X-ray tube spectral modeling, such modeling introduces additional errors into the forward model. All these factors ultimately reduce the accuracy of material decomposition from PCD measurements and therefore the generated spectral images.

[0019] In the prior art, calibration methods have been proposed to solve similar problems. The general idea is to use multiple transmission measurements with various known path lengths to modify the forward model to match the calibration measurements. 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), February 2011. These ideas were subsequently adopted for photon-counting 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, there may be many variations in the detailed design and implementation of the calibration method, especially considering the feasibility of application in full third-generation CT geometries, which has not yet been demonstrated or documented to date.

[0020] The embodiments presented herein relate to a two-stage calibration method for a PCD forward model for material decomposition, which consists of two parts: 1) estimating the flux-independent weighted bin response function S using the Expectation Maximization (EM) method; wb Estimation of (E), 2) pile-up correction term P b (E,N b ,N tot ) is estimated from the calibration at each tube voltage (kVp) setting for each detection pixel. wb Once (E) is estimated, it is saved as a software calibration table for the system. This is then used to calculate the pile-up correction term P at higher flux scans.b (E,N b , N tot ) are used as inputs to estimate the fundamental material path lengths. Both tables are then used in material decomposition in operational scans to estimate the fundamental material path lengths. The calibration tables are updated from time to time based on system / detector performance variations.

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

[0022] 1, the X-ray CT apparatus 1 according to the embodiment includes a gantry device 10, a bed device 30, and a console device 40. For the purpose of explanation, FIG. 1 illustrates the gantry device 10 from multiple directions, and shows a case where the X-ray CT apparatus 1 includes one gantry device 10.

[0023] 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 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 thermoelectrons and an anode (target) that generates X-rays upon impact of the thermoelectrons. The X-ray tube 11 generates X-rays to be irradiated onto the subject P by irradiating thermoelectrons from the cathode to the anode when a high voltage is applied from the X-ray high voltage device 14. For example, the X-ray tube 11 may be a rotating anode type X-ray tube that generates X-rays by irradiating a rotating anode with thermoelectrons.

[0025] The X-ray tube 11 and the control device 15 are an example of an X-ray irradiator. The X-ray irradiator performs a low-flux scan on a phantom consisting of a known material and penetration length at each pixel of a photon counting detector (PCD). Specifically, the X-ray irradiator performs a low-flux scan by performing an air scan and a scan on a phantom consisting of multiple different materials at each initial current intensity and tube voltage setting of 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 facing each other and rotates the X-ray tube 11 and the X-ray detector 12 using a control device 15. For example, the rotating frame 13 is an aluminum casting. Note that the rotating frame 13 can further support an X-ray high voltage device 14, a wedge 16, a collimator 17, a DAS 18, etc. in addition to the X-ray tube 11 and the X-ray detector 12. Furthermore, the rotating frame 13 can further support various components not shown in FIG. 1 .

[0027] The wedge 16 is a filter for adjusting 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 a predetermined thickness.

[0028] The collimator 17 is a lead plate or the like for narrowing the irradiation range of the X-rays that have passed through the wedge 16, and a slit is formed by combining a plurality of lead plates or the like. The collimator 17 is also sometimes called an X-ray aperture. Although FIG. 1 shows a case where the wedge 16 is disposed between the X-ray tube 11 and the collimator 17, the collimator 17 may also be disposed 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] X-ray high voltage device 14 has electrical circuits such as a transformer and a rectifier, and includes a high voltage generator that generates a high voltage to be applied to X-ray tube 11, and an X-ray control device that controls the output voltage according to the X-rays generated by X-ray tube 11. The high voltage generator may be of a transformer type or an inverter type. X-ray high voltage device 14 may be provided on rotating frame 13 or on a fixed frame (not shown).

[0030] The control device 15 has a processing circuit having a CPU (Central Processing Unit) and the like, and a driving mechanism such as a motor and an actuator. The control device 15 receives input signals from the input interface 43 and controls the operation of the gantry 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 gantry device 10, the operation of the bed device 30 and the tabletop 33, etc. The control device 15 may be provided in the gantry device 10 or in the console device 40.

[0031] The X-ray detector 12 is a photon-counting detector, and outputs a signal capable of measuring the energy value of an X-ray photon, which is a photon derived from an X-ray irradiated from the X-ray tube 11 and transmitted through the subject P, each time the detector is hit. The X-ray photon is, for example, a photon of an X-ray irradiated from the X-ray tube 11 and transmitted through the subject P. The X-ray detector 12 has a plurality of X-ray detection elements, which output one pulse of an electrical signal (analog signal) each time an X-ray photon is incident. 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 a predetermined arithmetic process on this signal, it is possible to measure the energy value of the X-ray photon that caused the output of the signal. For example, the X-ray detector 12 is an area detector in which X-ray detection elements are arranged in N rows in the channel direction and M rows in the slice direction. Note that, for example, the channel direction is the circumferential direction around the X-ray tube 11. Furthermore, for example, the slice direction is a direction along the Z-axis direction described above, that is, a direction parallel to the Z-axis direction. The slice direction is also called the column direction or row direction.

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

[0033] The X-ray detector 12 has a plurality of X-ray detection elements and ASICs (Application Specific Integrated Circuits), which are readout circuits connected to the X-ray detection elements and 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 arithmetic processing based on the magnitude of each charge. The ASIC also outputs the X-ray photon counting results to the DAS 18 as digital data.

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

[0035] The DAS 18 generates detection data based on the results of the counting process input from the X-ray detector 12. The detection data is, for example, a sinogram. The sinogram is data in which the results of the counting process of X-rays incident on each X-ray detection element at each position of the X-ray tube 11 are arranged. The sinogram is data in which the results of the counting process are arranged in a two-dimensional Cartesian coordinate system with the view direction and channel direction as axes. The DAS 18 generates a sinogram, for example, for each row in the slice direction of the X-ray detector 12. Here, the results of the counting process are data in which the number of X-ray photons is assigned to each energy bin. The DAS 18 transfers the generated detection data to the console device 40. The DAS 18 is realized, for example, by a processor.

[0036] The data generated by the DAS 18 is transmitted by optical communication from a transmitter having a light emitting diode (LED) provided on the rotating frame 13 to a receiver having a photodiode provided on a non-rotating part of the gantry 10 (for example, a fixed frame, etc., not shown in FIG. 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 gantry 10 is not limited to optical communication, and any non-contact data transmission method or a contact data transmission method may be employed.

[0037] The bed device 30 is a device on which the subject P to be imaged is placed and moved, and includes a base 31, a bed driving 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 bed driving device 32 is a drive mechanism that moves the top plate 33, on which the subject P is placed, in the longitudinal direction of the top plate 33, and includes a motor, an 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. Note that the bed driving device 32 may move the support frame 34 in addition to the top plate 33 in the longitudinal direction of the top plate 33.

[0038] The console device 40 has a memory 41, a display 42, an input interface 43, and a processing circuit 44. Although the console device 40 will be described as being separate from the gantry device 10, the gantry device 10 may include the console device 40 or some of the components of the console device 40.

[0039] The memory 41 is realized by, for example, a semiconductor memory element such as a RAM (Random Access Memory), a flash memory, a hard disk, an optical disk, etc. The memory 41 stores, for example, projection data and CT image data. Furthermore, for example, the memory 41 stores programs that enable circuits included in the X-ray CT apparatus 1 to realize various functions. The memory 41 may also be realized by a group of servers (cloud) connected to the X-ray CT apparatus 1 via a network.

[0040] The display 42 displays various types of information. For example, the display 42 displays various images generated by the processing circuitry 44, or displays a GUI (Graphical User Interface) for receiving various operations from an operator. For example, the display 42 is a liquid crystal display or a CRT (Cathode Ray Tube) display. The display 42 may be a desktop type, or may be configured as a tablet terminal or the like capable of wireless communication with the console device 40 main body. The display 42 is also an example of a display unit.

[0041] The input interface 43 accepts various input operations from the operator, converts the accepted input operations into electrical signals, and outputs the electrical signals to the processing circuitry 44. In addition, for example, the input interface 43 accepts 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 may be realized by a mouse, keyboard, trackball, switch, button, joystick, touchpad that performs input operations by touching the operation surface, a touchscreen that integrates a display screen and a touchpad, a non-contact input circuit using an optical sensor, a voice input circuit, etc. The input interface 43 may be provided in the gantry device 10. The input interface 43 may also be configured as a tablet terminal or the like that is capable of wireless communication with the console device 40 main body. The input interface 43 is not limited to those that have physical operation components such as a mouse and keyboard. For example, an example of the input interface 43 includes an electrical signal processing circuit that receives an electrical signal corresponding to an input operation from an external input device provided separately from the console device 40 and outputs the electrical signal to the processing circuit 44.

[0043] The processing circuitry 44 controls the overall operation of the X-ray CT apparatus 1. For example, the processing circuitry 44 executes a control function 44a, a pre-processing function 44b, a reconstruction processing function 44c, an image processing function 44d, an acquisition function 44e, an estimation function 44f, and a calculation function 44g. Here, for example, the processing functions executed by the control function 44a, the pre-processing function 44b, the reconstruction processing function 44c, the image processing function 44d, the acquisition function 44e, the estimation function 44f, and the calculation function 44g, which are components of the processing circuitry 44 shown in FIG. 1, are recorded in the memory 41 in the form of a computer-executable program. The processing circuitry 44 is, for example, a processor, which reads and executes each program from the memory 41 to realize a function corresponding to the read program. In other words, the processing circuitry 44 in a state in which each program has been read has each function shown in the processing circuitry 44 of FIG. 1.

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

[0045] 1 illustrates a case in which the control function 44a, preprocessing function 44b, reconstruction processing function 44c, image processing function 44d, acquisition function 44e, estimation function 44f, and calculation function 44g are each realized by a single processing circuit 44, but the embodiment is not limited to this. For example, the processing circuit 44 may be configured by combining multiple independent processors, and each processor may execute a program to realize each processing function. Furthermore, each processing function of the processing circuit 44 may be realized by being appropriately distributed or integrated into a single or multiple processing circuits.

[0046] The control function 44a controls various processes based on input operations received from an operator via the input interface 43. Specifically, the control function 44a controls the CT scan performed by the gantry 10. For example, the control function 44a controls the operations of the X-ray high voltage device 14, the X-ray detector 12, the control device 15, the DAS 18, and the bed driving device 32, thereby controlling the collection process of counting results in the gantry 10. As an example, the control function 44a controls the collection process of projection data in a positioning scan for collecting positioning images (scanograms) and in imaging (main scan) for collecting images used for diagnosis.

[0047] The control function 44 a also causes the display 42 to display images based on various image data stored in the memory 41 .

[0048] The pre-processing function 442 generates projection data by performing pre-processing such as logarithmic conversion processing, offset correction processing, inter-channel sensitivity correction processing, beam hardening correction, scattered radiation correction, and dark count correction on the detection data output from the DAS 18.

[0049] The reconstruction processing function 44c performs reconstruction processing using a filtered back projection method, an iterative reconstruction method, or the like on the projection data generated by the preprocessing function 44b to generate CT image data. 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 on the energy of X-rays attenuated by passing through the subject P. Therefore, the reconstruction processing function 44c can reconstruct CT image data of a specific energy component, for example. Furthermore, the reconstruction processing function 44c can reconstruct CT image data of each of a plurality of energy components, for example.

[0051] In addition, the reconstruction processing function 44c can, for example, assign a color tone corresponding to the energy component to each pixel of the CT image data of each energy component, and generate image data that superimposes multiple CT image data color-coded according to the energy component.

[0052] 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 obtained by rendering processing, using a known method, based on an input operation received from the operator via the input interface 43. 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 counts for each energy bin detected by the X-ray detector 12, which is a photon counting detector.

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

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

[0056] In transmission measurements using a photon-counting energy-resolving detector (PCD), the forward model can be formulated as follows:

[0057]

number

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

[0059]

number

[0060] where R(E,E') is the detector response function, and EbL and EbH are the low and high energy thresholds for each counting bin. Figure 2 shows the general S b shows a model example of the w(E) function. 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 due to the associated electronic noise. In practice, w(E) and S b (E) are not known exactly, and they are grouped into one term S wb (E)=w(E)S b (E), which is hereafter referred to as the weighted bin response function. wb If we can calibrate (E), the degradation problem under low flux conditions can be fully resolved.

[0061] For high flux scan conditions (e.g., a few percent of pulse pile-up), pulse pile-up introduces additional spectral distortions into the measurements. One way to correct for the pile-up effect is to introduce an additional correction term (see, for example, Dickmann, supra, which uses the measured count rate as input). This type of additional calibration also allows for the flux-independent weighted bin response S wb (E) is based on accurate estimation of Swb How to estimate (E) is the first problem solved in this application.

[0062] In typical CT clinical scanning conditions, it is common to encounter pulse pileup of several percent or more for some measurements. The resulting impact on material decomposition depends on the spectrum and flux being measured. 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 environment, it is important to have a feasible calibration procedure. Therefore, how to efficiently parameterize the model and optimize the calibration procedure is the second problem solved in this application.

[0063] Additional practical challenges in performing such a two-step calibration on a full-size CT system that have not been addressed or resolved in prior art techniques primarily targeted at small-scale benchtop systems include fan-angle-dependent weighted spectral response due to beam pre-filters (e.g., bowtie filters), limited minimum flux due to x-ray tube operating specifications and fixed system geometry, full detector ring calibration with varying detector response across pixels, limited space for in-system calibration phantom positioning, complexities when calibrating on a rotating system with an anti-scatter grid, systematic calibration errors and associated mechanical design tolerances, and non-ideal detectors with issues of uniformity of energy resolution, counting, and energy threshold setting drift.

[0064] All of the above non-idealities must be taken into account for photon-counting CT to reach image quality comparable to traditional Energy Integrating Detector (EID)-based systems, which have much simpler detector response modeling and associated calibration, while maintaining a similar calibration procedure / workflow that does not significantly increase system downtime.

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

[0066] The calibrated forward model can be expressed as follows:

[0067]

number

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

[0069] Here, instead of using only two materials as in the prior art (see, for example, reference Dickmann), we use two to five different materials, such as polypropylene, water, aluminum, titanium / copper, and k-edge materials, to generate weighted bin response functions S wb(E) is calibrated at low flux. In other words, the phantoms for the low flux scans performed by the X-ray irradiator include polypropylene, water, aluminum, titanium / copper, and k-edge materials. Using more selective materials in the calibration reduces the number of total path lengths, achieving comparable or better results.

[0070] Step 1: S is determined by appropriate tube spectral modeling that captures the characteristic peaks of the incident spectrum, and a physical model that simulates the spectral response of the photon-counting detector. wb An initial guess for (E) can be generated using the EM method (see reference Sidky). wb (E) can be reliably estimated for this very ill-conditioned problem based on several transmission measurements. That is, processing circuitry 44 uses expectation maximization (EM) via estimator function 44f to estimate a first parameter S based on the coefficients. wb (E) is estimated. The first parameter S wb (E) is the bin response function S b Depends on (E).

[0071] where P b (E,N b ,N tot ) is assumed to be constant in step 1. Therefore, the calibrated forward model can be simplified to a system of linear equations expressed as follows:

[0072]

number

[0073] Typically, the number of data measurements (M) is proportional to the number of unknowns (E max ) is much less than the Poisson distribution assumption for data acquisition. An iterative EM algorithm is derived to find the unknown energy bin response function S, as described below. wb The best estimate of (E) can be found.

[0074] When low-flux data acquisition is used to estimate the bin response functions, the pile-up effect correction P b is assumed to be a known term (e.g., a constant). Therefore, the model is simplified to the following equation (5):

[0075]

number

[0076] Here, A represented by the following formula (6) j Let (E) denote the attenuation path length of the jth measurement.

[0077]

number

[0078] In this case, for each measurement value j, the following equation (7) is obtained:

[0079]

number

[0080] For 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: A S wb =N b

[0083] By applying the EM iterative algorithm, S wb can be estimated as follows:

[0084]

number

[0085] That is, the processing circuit 44 uses the estimation function 44f to calculate a first parameter S that depends on the energy E based on the counts for each energy bin acquired by the acquisition function 44e. wb Estimate (E).

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

[0087]

number

[0088] Step 2: Calibration of each detector pixel at each tube voltage (kVp) setting to S wb Once (E) is estimated, it is stored as a software calibration table for the system. 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 of the first parameter S estimated in step 1 by the processing circuitry 44f. wb Based on (E), a second parameter P depends on the total number of all energy bins. b The second parameter P b is related to the pile-up correction term.

[0089] Both tables are then used in material decomposition of the object / patient scan to estimate the path lengths of the elementary materials. That is, processing circuitry 44, via calculation function 44g, calculates a first parameter S wb (E) and the second parameter P b Material decomposition is performed based on the above.

[0090] The calibration table is updated from time to time based on system / detector performance variations. This can also be designed as an iterative procedure. If the image quality is not sufficient in the quality check phantom, the calibration process is repeated using the updated calibration table from the last iteration as an initial guess.

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

[0092] First, a series of low-flux scans of slabs of various materials are collected at each tube kVp setting, which is the peak potential applied to the X-ray tube. Typical CT systems support several kVp settings, from 70 to 140 kVp, which produce different energy spectra from the X-ray tube for different scan protocols. For CT scans, both mA and kVp must be preselected before turning on the tube. Next, a low-flux weighted bin response function, S, is calculated. wb is estimated, and this estimated S wb A high flux slab scan is used in conjunction with S to estimate additional parameters for the pile-up correction term. wb and P b Using the estimated calibration table, the quality of the calibration is checked on a quality phantom (e.g., a homogeneous water phantom or a phantom with multiple inserts of known homogeneous material). The image quality is evaluated against predefined criteria, and if passed, the current calibration table is saved and used for the next patient / subject scan data processing. Otherwise, the procedure continues with S as an initial guess. wb and P bUse the last iteration to execute the first three steps again. Here, the generally considered criteria are the accuracy, uniformity, spatial resolution, noise, and artifacts of the image CT number. To confirm the quality of this calibration, it is necessary to check all of these evaluation criteria. In particular, it is necessary to check the accuracy and artifacts such as rings and bands in the image, which indicate insufficient calibration.

[0093] In other words, the X-ray irradiation unit uses the first parameter S wb and the second parameter P b to perform a phantom scan, and the processing circuit 40 evaluates the image quality of the projection image obtained by the phantom scan by means of the estimation function 44f. Also, when the quality of the projection image meets the pre-defined criteria, the processing circuit 40 uses the first parameter S wb and the second parameter P b for material discrimination.

[0094] To select the most suitable materials and path lengths for this calibration, different materials 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 materials present in the human body.

[0095] In step 1 of the flowchart, to meet the low-flux conditions 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 material can be calculated, and the remaining path lengths can be selected either by equal intervals in the path length or by the resulting measured count rate.

[0096] In step 3, the pile-up correction term is calibrated using the same slab material and path length for scans at the high mA setting. This calibration data is grouped by mA to generate different correction tables for each mA setting (FIG. 5), or measurements over the entire flux range (low to high mA, high to low mA, or the most frequently used value first) can be included to generate a universal correction table for continuous mA settings (FIG. 6). That is, the x-ray irradiator performs multiple scans at different current intensities, and processing circuitry 40, via estimator function 44f, derives the second parameter P based on the multiple scans performed at the different current intensities. b may be estimated.

[0097] Calibration measurements should be performed with sufficient statistics to minimize the effects of statistical variation. One non-limiting example is to use statistics of >1000x as a typical integration period for the calibration data set to minimize the statistical error transferred in the calibration. Each energy bin b in the calibration measurement has a corresponding S wb (E) and P b (E,N b ,N tot ) is used to update the

[0098] The estimation is very ill-conditioned because only a limited number of measurements can be performed in some energy bins. In this case, it places additional constraints on the EM method, so a good initial guess is essential for accurate estimation. One design variation to accommodate non-ideal detectors is to use the initial guess S, especially with small variations in the actual energy threshold settings of the ASIC. b The aim is to allow a more flexible energy window for each bin of . By setting the low threshold x keV low and the high threshold y keV high, the initial S b is expressed as the following equation (11).

[0099]

number

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

[0101] That is, the initial estimate of the first parameter is based on the detector response function R and the low and high energy thresholds for each counting bin.

[0102] To capture spectral variations across the fan beam after the bowtie filter, as well as detector response variations across different detector pixels, this calibration process is performed pixel-by-pixel with each bowtie / filter configuration, i.e., 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, which improves the sensitivity for constraining the weighted bin response function estimation problem of photon counting detectors.

[0104] In addition, this method uses a high flux pile-up correction term, P b We use different parameterizations for E, N b and N tot is a function of the total count term N tot was introduced to better approximate the true pile-up phenomenon, which can significantly improve the model performance at higher flux conditions with fewer parameters.

[0105] Furthermore, various calibration path length ranges are used at various fan angles to improve calibration accuracy and efficiency. 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 non-ideal detector / ASIC performance, an alternative scheme is presented for expanding the energy threshold window and computing the initial estimate of the weighted bin response function.

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

[0108] Although several 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 within the scope and spirit of the invention, as well as within the scope of the invention and its equivalents as defined in the claims. [Explanation of symbols]

[0109] 44 Processing circuit 44a Control Functions 44b Pre-processing function 44c Reconstruction processing function 44d Image Processing Function 44e Acquisition Function 44f Estimation Function 44g calculation function

Claims

1. an X-ray irradiation unit that performs a first scan and a second scan including a plurality of scans using a current intensity higher than the current intensity of the first scan on a phantom consisting of a known material and penetration length at each pixel of a photon counting detector (PCD); the photon-counting detector detecting signals associated with the first scan and the second scan; an acquisition unit that acquires counts for each energy bin detected by the photon counting detector; an estimator that estimates an energy-dependent energy bin response function based on the counts of the first scan, and that estimates a pile-up correction term for each current intensity of the second scan based on the counts of the second scan and the energy bin response function; a calculation unit that performs material decomposition in a scan of a subject using a model based on the energy bin response function and a pile-up correction term for each current intensity of the second scan; An X-ray CT device comprising:

2. an X-ray irradiation unit that performs a first scan and a second scan including a plurality of scans using a current intensity higher than the current intensity of the first scan on a phantom consisting of a known material and penetration length at each pixel of a photon counting detector (PCD); the photon-counting detector detecting signals associated with the first scan and the second scan; an acquisition unit that acquires counts for each energy bin detected by the photon counting detector; an estimator that estimates an energy-dependent energy bin response function based on the counts of the first scan, and that estimates a pile-up correction term for the second scan based on the counts of the second scan and the energy bin response function; a calculation unit that performs material decomposition in a scan of a subject using a model based on the energy bin response function and the pile-up correction term of the second scan; An X-ray CT device comprising:

3. 3. The X-ray CT apparatus according to claim 1, wherein the X-ray irradiation unit executes the first scan by executing an air scan and a scan on the phantom made of a plurality of different materials at initial current intensity and tube voltage settings of the X-ray tube.

4. the X-ray irradiator 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, 4. The X-ray CT apparatus according to claim 3, wherein the calculation unit uses the energy bin response function and the pile-up correction term for the material decomposition when the quality of the projection image satisfies a predefined standard determined based on accuracy, uniformity, spatial resolution, noise, or artifacts of image CT numbers.

5. 5. The X-ray CT apparatus according to claim 4, wherein the calculation unit updates the model when the quality of the projection image does not satisfy a predefined criterion based on accuracy, uniformity, spatial resolution, noise, or artifacts of the image CT numbers.

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

7. 3. 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 a pixel count rate, and τ is an effective dead time of a PCD application specific integrated circuit (ASIC).

8. 3. The X-ray CT apparatus according to claim 1, wherein the phantom comprises at least two of the following materials: polypropylene, water, aluminum, titanium / copper, and k-edge material.

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

10. 3. 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.

11. the estimator further estimates a pile-up correction term in the current intensity of the first scan based on the counts of the first scan and the energy bin response function; 3. The X-ray CT apparatus according to claim 1, wherein the calculation unit performs the material decomposition using a model based on the energy bin response function, a pile-up correction term at the current intensity of the first scan, and a pile-up correction term for each current intensity of the second scan.

12. performing a first scan and a second scan, the second scan including a plurality of scans using a current intensity higher than the current intensity of the first scan, on a phantom of known material and transmission length at each pixel of a photon counting detector (PCD); Detecting signals associated with the first scan and the second scan; obtaining a count for each energy bin detected by the photon-counting detector; estimating an energy-dependent energy bin response function based on the counts of the first scan, and estimating a pile-up correction term for each current intensity of the second scan based on the counts of the second scan and the energy bin response function; performing material decomposition in a scan of the subject using a model based on the energy bin response function and a pile-up correction term for each current intensity of the second scan; Data processing methods.

13. performing a first scan and a second scan, the second scan including a plurality of scans using a current intensity higher than the current intensity of the first scan, on a phantom of known material and transmission length at each pixel of a photon counting detector (PCD); Detecting signals associated with the first scan and the second scan; obtaining a count for each energy bin detected by the photon-counting detector; estimating an energy-dependent energy bin response function based on the counts of the first scan, and estimating a pile-up correction term for the second scan based on the counts of the second scan and the energy bin response function; performing material decomposition in the scan of the object using a model based on the energy bin response function and the pile-up correction term of the second scan; Data processing methods.

14. performing a first scan and a second scan, the second scan including a plurality of scans using a current intensity higher than the current intensity of the first scan, on a phantom of known material and transmission length at each pixel of a photon counting detector (PCD); Detecting signals associated with the first scan and the second scan; obtaining a count for each energy bin detected by the photon-counting detector; estimating an energy-dependent energy bin response function based on the counts of the first scan, and estimating a pile-up correction term for each current intensity of the second scan based on the counts of the second scan and the energy bin response function; a program that causes a computer to execute a process of performing material decomposition in a scan of a subject using a model based on the energy bin response function and a pile-up correction term for each current intensity of the second scan;

15. performing a first scan and a second scan, the second scan including a plurality of scans using a current intensity higher than the current intensity of the first scan, on a phantom of known material and transmission length at each pixel of a photon counting detector (PCD); Detecting signals associated with the first scan and the second scan; obtaining a count for each energy bin detected by the photon-counting detector; estimating an energy-dependent energy bin response function based on the counts of the first scan, and estimating a pile-up correction term for the second scan based on the counts of the second scan and the energy bin response function; a program that causes a computer to execute a process of performing material decomposition in a scan of a subject using a model based on the energy bin response function and the pile-up correction term of the second scan;

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