WO2018023960A1 - 数据处理方法和数据处理装置 - Google Patents

数据处理方法和数据处理装置 Download PDF

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WO2018023960A1
WO2018023960A1 PCT/CN2017/073206 CN2017073206W WO2018023960A1 WO 2018023960 A1 WO2018023960 A1 WO 2018023960A1 CN 2017073206 W CN2017073206 W CN 2017073206W WO 2018023960 A1 WO2018023960 A1 WO 2018023960A1
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detector
energy
photon
data
spectrum data
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French (fr)
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张丽
陈志强
王森
许晓飞
李元景
金鑫
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Tsinghua University
Nuctech Co Ltd
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Tsinghua University
Nuctech Co Ltd
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Priority to AU2017306969A priority patent/AU2017306969A1/en
Priority to EP17836147.3A priority patent/EP3495850B1/en
Publication of WO2018023960A1 publication Critical patent/WO2018023960A1/zh
Priority to US16/137,547 priority patent/US10955572B2/en
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01T—MEASUREMENT OF NUCLEAR OR X-RADIATION
    • G01T1/00—Measuring X-radiation, gamma radiation, corpuscular radiation, or cosmic radiation
    • G01T1/36—Measuring spectral distribution of X-rays or of nuclear radiation spectrometry
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01T—MEASUREMENT OF NUCLEAR OR X-RADIATION
    • G01T7/00—Details of radiation-measuring instruments
    • G01T7/005—Details of radiation-measuring instruments calibration techniques
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01T—MEASUREMENT OF NUCLEAR OR X-RADIATION
    • G01T1/00—Measuring X-radiation, gamma radiation, corpuscular radiation, or cosmic radiation
    • G01T1/16—Measuring radiation intensity

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  • the present invention belongs to the field of data processing technology, and more particularly to a data processing method and a data processing device.
  • the energy spectrum detector is a detector that can distinguish the incident photon energy by the photon counting mode, and can obtain the photon counting of different energy intervals in one scan. However, this counting mode is affected by the detector response.
  • the detector response varies depending on the crystal material of the detector, and generally includes K-layer electron escaping, charge sharing, and pulse stacking. In order to take advantage of the energy spectrum detector, it is necessary to model and correct the detector response. There is no mature method at present.
  • Embodiments of the present invention provide a data processing method, including: performing detector response calibration according to a detector response obtained from a known energy ray incident to obtain a detector response model; and obtaining detector incident energy spectrum data according to the detector response model a detector photon counting model between the measured spectral data and the detector; according to the detector photon counting model, the photon count of each energy region detector in the energy spectrum data of the detector is deconvolved and calculated to obtain The photon count of the true energy regions in the incident energy spectrum data of the detector.
  • the method further comprises performing a deconvolution process calculation on the photon count of each energy region in the energy spectrum data of each detector unit for each incident angle of the detector according to the detector photon counting model. Obtaining each incident angle detector unit in the incident energy spectrum data of the detector The photon counts of the real energy zones are combined, and each set of data obtained is combined to realize multi-energy reconstruction of the attenuation coefficient of the test substance measured by the detector.
  • the method further comprises deconvolving the photon count of each energy region detector in the energy spectrum data by directly solving and adding the constraint term to obtain the incident energy spectrum of the detector. The photon count of the real energy zones in the data.
  • the method further comprises deconvolving the photon counts of the respective energy region detectors in the energy spectrum data of the detector by an EM solution method to obtain true fluorescence in the incident energy spectrum data of the detector. Photon counts for each energy zone.
  • calibrating the detector response includes obtaining a detector response calibration based on the metal fluorescence data known to simulate the energy deposition process in the photon detector.
  • an embodiment of the present invention provides a data processing apparatus, including: a calibration module, a photon counting model acquisition module, and a photon counting acquisition module.
  • the calibration module performs detector response calibration based on the detector response of the known energy ray incidence to obtain a detector response model.
  • the photon counting model acquisition module obtains a detector photon counting model between the detector incident energy spectrum data and the detector measured energy spectrum data according to the detector response model.
  • the photon counting acquisition module deconvolutes the photon count of each energy region detector in the energy spectrum data of the detector according to the photon counting model of the detector to obtain real energy regions in the incident energy spectrum data of the detector. Photon counting.
  • the apparatus further includes a multi-energy region reconstruction module for measuring photons of each energy region in the energy spectrum data for each detector unit for each incident angle of the detector according to the detector photon counting model Counting is performed by deconvolution processing to obtain the photon count of each true energy region of each detector unit at each incident angle of the incident energy spectrum data of the detector, and each set of data obtained is combined to realize the measurement of the detector. The attenuation coefficient of the substance to be tested is reconstructed in the multi-energy region.
  • the photon counting acquisition module of the device is further configured to perform deconvolution processing on the photon counts of the energy detectors in the energy spectrum data of the detector by directly solving and adding a constraint term. A photon count of the true energy regions in the incident energy spectrum data of the detector is obtained.
  • the photon counting acquisition module of the device is further configured to perform deconvolution on the photon count of each energy region detector in the energy spectrum data measured by the detector by an EM solution method. To obtain the photon count of the real energy regions in the incident energy spectrum data of the detector.
  • the calibration module of the device is operative to perform detector response calibration by simulating an energy deposition process in the photon detector based on the metal fluorescence data.
  • embodiments of the present invention provide a data processing apparatus including a memory, a processor coupled to the memory, wherein the processor is configured to: perform a detector response based on a detector response of a known energy ray incident Calibrating to obtain a detector response model; obtaining a detector photon counting model between the detector incident energy spectrum data and the detector measured energy spectrum data according to the detector response model; the detector is based on the detector photon counting model The photon counts of the energy detectors in the energy spectrum data are deconvoluted to obtain the photon counts of the real energy regions in the incident energy spectrum data of the detector. .
  • the photon data of the real energy region in the energy spectrum data is obtained by deconvolution processing the energy spectrum data of the detector by establishing a detector response model, thereby eliminating the response of the energy spectrum detector to the photon. Count the effects and get the true attenuation coefficient of each substance.
  • FIG. 1 is a schematic diagram showing an application scenario of a data processing method according to some embodiments of the present invention
  • FIG. 2 is a flow chart showing the detection of object projection by an X-ray machine of an application scenario of a data processing method shown in FIG. 1 according to some embodiments of the present invention
  • FIG. 3 shows a flow chart of a data processing method in accordance with some embodiments of the present invention
  • FIG. 4 is a schematic diagram showing a spectrum of a probe detecting a fluorescence spectrum of a metal according to some embodiments of the present invention
  • Figure 5 illustrates a comparison of measured data from a set of known energy ray incident detectors versus data obtained using a corresponding model of the detector, in accordance with some embodiments of the present invention
  • 6a is a diagram showing the distribution ratio of photons measured by detectors in respective energy regions in a simulation of a data processing method according to some embodiments of the present invention
  • Figure 6b illustrates a simulation of a data processing method in accordance with some embodiments of the present invention. Schematic diagram of the true input energy spectrum of the detector;
  • 6c shows a schematic diagram of a response matrix of a detector in a simulation of a data processing method, in accordance with some embodiments of the present invention
  • 6d shows a schematic diagram of an incident energy spectrum of a detector in a simulation of a data processing method, in accordance with some embodiments of the present invention
  • FIG. 7 shows a schematic diagram of a data processing method in accordance with some embodiments of the present invention.
  • Figure 8a is a schematic view showing the distribution of a substance to be tested according to a data processing method according to some embodiments of the present invention.
  • Figure 8b illustrates a plot of material line attenuation coefficients prior to processing by a detector tested in accordance with a data processing method, in accordance with some embodiments of the present invention
  • Figure 8c illustrates a processed material line attenuation coefficient plot obtained by testing according to a data processing method, in accordance with some embodiments of the present invention
  • FIG. 9 is a block diagram showing the structure of a data processing apparatus according to further embodiments of the present invention.
  • FIG. 10 is a block diagram showing the structure of a data processing apparatus according to still another embodiment of the present invention.
  • FIG. 1 shows a schematic diagram of an application scenario of a data processing method according to some embodiments of the present invention.
  • a ray source such as an X-ray machine and a detector array are disposed on opposite sides of the object to be tested.
  • the detector array receives the X-ray spectrum attenuated by the object to be tested. Due to the detector response, the measured energy spectrum of the detector array will have a certain error with the energy spectrum actually attenuated by the object to be tested.
  • the energy spectrum here actually attenuated by the object to be tested can also be referred to as the incident energy spectrum of the detector.
  • FIG. 2 is a flow chart showing the detection of object projection by an X-ray machine of an application scenario of a data processing method shown in FIG. 1 in accordance with some embodiments of the present invention.
  • the X-ray machine emits a continuous spectrum of light sources, and the attenuation is obtained by attenuation of the object. After the attenuation, the energy spectrum is incident on the detector, and is converted into an electrical signal due to the interaction of the detector crystal with the incident photon, and then read by the subsequent electronic component, thereby obtaining the energy spectrum measured by the detector.
  • the data processing method 300 includes the following steps: Step S301, performing detector response calibration based on a detector response obtained from a known energy ray incident to obtain a detector response model; and step S302, obtaining detector incident energy according to the detector response model a detector photon counting model between the spectral data and the measured spectral data of the detector; and in step S303, deconvolving the photon count of each energy region detector in the energy spectrum data of the detector according to the photon counting model of the detector The calculation is processed to obtain a photon count of the true energy regions in the incident energy spectrum data of the detector.
  • the detector response calibration can be understood as determining the relationship between the input and output of the detector.
  • the detector response is calibrated to obtain the detector response model, which is the basis for subsequent deconvolution of the photon counts in the energy region.
  • the energy deposition process in the detector can be simulated based on a known energy ray incident detector to perform detector response calibration.
  • the detector response calibration may be performed by, for example, using metal fluorescence.
  • detector response calibration may also be performed by using other forms of known energy rays, such as a synchrotron radiation source, a source of radiation, and the like. Referring to FIG. 4 together, a schematic diagram of a spectrum obtained by detecting a fluorescence spectrum of a metal by a detector according to the present embodiment.
  • a detector response model can be constructed by comprehensively utilizing Monte Carlo simulation and metal fluorescence data for detector modeling.
  • metal fluorescence refers to the characteristic ray emitted when the outer layer of electrons de-excited to the inner layer when irradiated by X-rays. This characteristic ray is used to approximate the single-energy ray incident detector, and then the detector response calibration is performed. As shown in Figure 4, it is the response of a typical CdTe to the fluorescence spectrum of lead, where the abscissa is photon energy in units of 1000 eV, expressed in keV; the ordinate is photon counting.
  • Fluorescence refers to the process of characteristic X-ray emission of a substance under X-ray illumination, which releases a series of X-rays with a certain energy. Both k ⁇ and k ⁇ are characteristic X-rays that lead is released during exposure, and the proportion of k ⁇ rays is very low. The energy of these characteristic X-rays is known and can be used to calibrate the detector response. It can be seen from Fig. 4 that in the measured data of the detector, the photon count is highest in the 0-20 energy region, which is due to the detector response caused by the electronic noise, and the photon appears in the 20-40 energy region due to the electron. Caused by escape. The different energy characteristics of the fluorescence spectra of some commonly used materials are given in Table 1:
  • the Monte Carlo simulation can include the entire process from the start of the electron beam target to the deposition of energy by the photons in the detector.
  • a Monte Carlo simulation program based on C++ and GEANT4 can be used to split the whole process into three sub-processes, including three sub-steps: steps of electron beam target and energy spectrum generation; simulation of photon transport process and The step of establishing a transport matrix under isotropic conditions; and the step of energy deposition of the single energy ray in the energy spectrum detector, thereby obtaining an energy spectrum of the photons incident on the detector.
  • the energy distribution of X-rays generated at a certain voltage is a continuous band, and the single-energy ray has one band and one center value.
  • the task of detector modeling is mainly to determine the detector response of single-energy spectrum incident, denoted as ⁇ (E 0 ), which can be understood as the detector response of single-energy ray incident with energy E 0 , which may include establishing detector counter bias
  • ⁇ (E 0 ) the detector response of single-energy ray incident with energy E 0
  • a Gaussian diffusion model can be used to simulate the diffusion of electron-hole pairs, as shown in equation (1):
  • ⁇ is the standard deviation of the Gaussian model
  • ⁇ 0 is a preset constant
  • z and D are the positions where the photons interact with the detector crystal and the crystal depth, respectively. It can be seen that the position where the interaction occurs is Close to D, the narrower the width.
  • the linear approximation spectrum is broadened with energy and the mapping between detector voltage threshold and photon energy is shown in equations (2) and (3):
  • ⁇ s is the broadening of the single-energy ray
  • TH is the preset bias of the detector
  • E is the energy of the photon.
  • the Monte Carlo simulation can obtain the incident energy spectrum S in (E) of the detector, and then use the measured energy spectrum S det (E) of the detector to determine the specific parameters p s1 , p s2 , p 1 and p in the detector response model .
  • a differential evolution algorithm can be used to derive parameters in an optimized detector response model, where elements in Table 1 can be selected
  • Figure 5 shows a set of known energy ray incidences in accordance with an embodiment of the present invention.
  • the measured data of the detector is compared with the data obtained by the simulation of the detector response model.
  • La, Ce, Nd and Gd are taken as examples to simulate Monte Carlo simulation and optimized detector response model parameters, wherein the abscissa is photon energy, the unit is keV; the ordinate is photon counting.
  • the hollow points represent the measured data, and the curves represent the results of the Monte Carlo results and the optimized detector response model parameters.
  • step S303 the detector response model constructed in step S302 can be described by h(E; E'), h(E; E') is a concept similar to a probability distribution, which can be understood as energy E'
  • the photon is recorded as the probability of energy E. Since the attenuated energy spectrum is also the incident energy spectrum of the detector is denoted as S in (E'), and the detected photon is denoted as S det (E), the detector photon counting response model can be expressed by the following formula:
  • this is a form of convolution of the detector response model, and deconvolution is based on the detector model to obtain S in (E') through S det (E).
  • Energy spectrum deconvolution is a very ill-posed problem. Especially in energy spectrum CT, the number of energy regions in energy spectrum CT is small. It should be understood that the number of energy regions means that the energy region is narrow, so The smaller the photon count in each energy region under scanning conditions, the greater the influence of noise. The method of deconvolution with a limited energy region count to obtain the entire spectrum data is very unstable.
  • the deconvolution of the broad energy region yields a count of the energy regions rather than the energy spectrum count, that is, when the photon count of each energy region detector in the energy spectrum data of the detector is deconvolved.
  • the whole spectrum is obtained, but the real energy region is counted, the number of unknowns is reduced, the stability of the algorithm is improved, and the practicability of the data processing method is also taken into account.
  • the method 300 may perform a deconvolution process calculation on the photon count of each energy region in the energy spectrum data of each detector unit for each incident angle of the detector according to the detector photon counting model.
  • the photon count of each true energy region of each detector unit for each incident angle in the incident energy spectrum data of the detector is obtained.
  • Each set of data obtained can be combined to achieve multi-energy reconstruction of the attenuation coefficient of the test substance measured by the detector. It can be understood that the count of each energy region of each detector unit at each angle obtained during the scanning of the detector can be deconvolved to remove the detector response, and the method can basically obtain the spectrum through the substance.
  • the true decayed spectrum can be used to perform a deconvolution process calculation on the photon count of each energy region in the energy spectrum data of each detector unit for each incident angle of the detector according to the detector photon counting model. The photon count of each true energy region of each detector unit for each incident angle in the incident energy spectrum data of the detector is obtained.
  • Each set of data obtained can be combined to achieve
  • the energy spectrum detector simultaneously obtains photon counts of many energy regions
  • the X-ray machine can obtain energy data of different energy regions in one scan, for example, a sinogram reflecting the incident angle, the position of the detector unit, and the number of photons.
  • the count of specific detector unit positions on different sinograms actually constitutes the energy spectrum.
  • Deconvolution processing can be performed on each detector unit to obtain a deconvoluted sinogram sequence, which can be directly used for reconstruction of the multi-energy region of the detector to obtain quantitative CT (quantitative CT).
  • step S303 of the method may perform deconvolution processing on the photon counts of the energy detectors in the energy spectrum data of the detector by iteratively updating, and obtain true information in the incident energy spectrum data of the detector. Photon counts for each energy zone.
  • the detector response of the energy region should be the result of the total integrated average of the response of the single-energy detector contained in the energy region. For example, it can be expressed by the formula (5):
  • Equation (5) Representing a set of single energy points contained in the kth energy region, the number of energy elements contained in the kth energy region is recorded as N k , and This is a weighted average factor, which can be adjusted according to different a priori. For example, it can take an equivalent value of 1/N k .
  • equation (6) Discretize equation (4) to obtain equation (6):
  • x n is the photon count of the incident spectrum at each energy
  • H i [m] is the discrete expression of the detector response function corresponding to the energy
  • y is the photon count measured by the detector in each energy region.
  • the iterative update method may use, for example, an EM algorithm (also referred to as an Expectation Maximization Algorithm), where the iterative update method for finding x according to y and A is as shown in equation (7):
  • the EM algorithm is selected for the characteristics of the spectrum detector and the specific application scenarios.
  • the wide-energy region is deconvolved to obtain the energy region count instead of the energy spectrum.
  • the EM algorithm itself is an algorithm that is relatively robust to noise, which improves the stability of the data processing method.
  • the count of each energy region of each detector unit is deconvolved by the EM algorithm to remove the detector response, and the total number of photons in different energy regions is experimentally counted.
  • the relative standard deviation is given in Table 2:
  • the data width can only be guaranteed to be relatively stable under the energy width of 4 keV, and the photon counting model is deconvolved by the EM solution method. After the energy zone width reaches 10keV, the total count changes. 0.11%, can still achieve the data processing effect of other methods in the energy zone width of 4keV. It can be seen that the EM solution method is used to deconvolute the photon counting model to obtain the incident spectral data of the attenuated spectral detector containing the real material attenuation information, and the data result is very stable.
  • Figure 6a is a diagram showing the distribution of photons measured by detectors in respective energy regions in a simulation test of a data processing method in accordance with one embodiment of the present invention.
  • Figure 6b shows a schematic diagram of the true input energy spectrum of a detector in a simulation test of a data processing method, in accordance with one embodiment of the present invention.
  • Figure 6c shows a schematic diagram of a response matrix of a detector in a simulation of a data processing method, in accordance with one embodiment of the present invention.
  • Figure 6d shows a schematic diagram of an incident energy spectrum of a detector in a simulation of a data processing method in accordance with one embodiment of the present invention. As shown in Fig.
  • step S303 of the method may perform deconvolution processing on the photon count of each energy region detector in the energy spectrum data measured by the direct solution method to obtain the incident energy spectrum data of the detector.
  • the photon count of the real energy zone may be understood that the direct solution method is a method for solving an inverse matrix.
  • the least square method can be used to solve the real energy spectrum vector x as shown in equation (8):
  • equation (10) can be written as equation (11):
  • the method is simple in solving the method, ensuring continuity and ensuring a small error value.
  • the true attenuation of the spectrum through the substance can be basically obtained. Post spectrum.
  • a sinogram of a sinogram-by-detection unit of each energy region obtained by the data processing method according to an embodiment of the present invention can obtain a series of deconvolved sinograms.
  • the sinogram shown in Figure 7 is true attenuation information that eliminates the photon count of the detector response.
  • After obtaining the deconvolved sinogram it can be used for CT reconstruction.
  • the CT reconstruction here can be understood as multi-energy reconstruction of the attenuation coefficient of the test substance measured by the detector.
  • CT reconstruction may be performed using an analytical reconstruction method such as the FDK method, or an CT reconstruction using an iterative reconstruction method such as the ART method.
  • selecting plastic bottled water as the object to be tested for projection demonstrates the data processing effect of the data processing method.
  • a plastic bottled water phantom established by the measured energy spectrum data after the multi-energy region reconstruction is performed by the method.
  • the attenuation coefficient of water obtained by directly reconstructing the sinogram of each energy region, wherein the abscissa is photon energy, the unit is keV, and the ordinate It is the attenuation coefficient of each energy zone of the ray. Because of the presence of the detector response, the measured curve data deviates from the theoretical true value curve data.
  • the attenuation coefficient of each energy region reconstructed by the deconvolution process is substantially coincident with the theoretical true curve data value.
  • FIG. 9 shows a block diagram of a data processing apparatus 900 in accordance with further embodiments of the present invention.
  • the data processing apparatus 900 includes a calibration module 901, a photon counting model acquisition module 902, a photon counting acquisition module 903, and an incident energy spectrum data acquisition module 904.
  • the calibration module 901 performs a detector response calibration based on the detector response obtained from the incident of the known energy ray to obtain a detector response model.
  • the photon counting model acquisition module 902 obtains a detector photon counting model between the detector incident energy spectrum data and the detector measured energy spectrum data according to the detector response model.
  • the photon counting acquisition module 903 performs deconvolution processing on the photon count of each energy region detector in the energy spectrum data measured by the detector according to the detector photon counting model to obtain real energy regions in the incident energy spectrum data of the detector. Photon counting.
  • the apparatus further includes a multi-energy region reconstruction module for measuring photon counts of each energy region in the energy spectrum data for each detector unit for each incident angle of the detector according to the detector photon counting model Deconvolution processing is performed to obtain a photon count of each true energy region of each detector unit at each incident angle of the incident energy spectrum data of the detector, and each of the obtained data is combined to realize the measurement of the detector.
  • the attenuation coefficient of the substance to be tested is reconstructed in the multi-energy region.
  • the photon counting acquisition module 903 of the device may perform deconvolution processing on the photon count of each energy region detector in the energy spectrum data of the detector by directly solving and adding a constraint term to obtain a detector. The photon count of the true energy region in the incident energy spectrum data.
  • the photon counting acquisition module 903 of the device may perform deconvolution processing on the photon count of each energy region detector in the energy spectrum data measured by the detector by the EM solution method to obtain the incident energy spectrum data of the detector. The photon count of the real energy zone.
  • the calibration module 901 of the device can perform detector response calibration based on metal fluorescence data known to simulate the energy deposition process in the energy ray detector.
  • FIG. 10 is a schematic structural diagram of some embodiments of a data processing apparatus provided by the present invention.
  • the device can be a general purpose computer system, and the computer system can be specifically processor based computer.
  • the data processing apparatus can include an input and output I/O interface 1001, a memory 1002, at least one processor 1003, and at least one communication interface 1004.
  • the input/output I/O interface 1001, the memory 1002, the at least one processor 1003, and the at least one communication interface 1004 are connected by a communication bus 1005.
  • the I/O interface 1001 is configured to receive text data from the user device and transmit the text data to the processor 1003, wherein the text data is represented using a structured query language SQL form.
  • the processor 1003 may be a general purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits for controlling the execution of the program of the present invention.
  • Communication bus 1005 can include a path for communicating information between the components described above.
  • Communication interface 1004 uses devices such as any transceiver for communicating with other devices or communication networks, such as Ethernet, Radio Access Network (RAN), Wireless Local Area Networks (WLAN), and the like.
  • the computer system includes one or more memories 1002, which may be read-only memory (ROM) or other types of static storage devices that can store static information and instructions, random access memory (RAM) or Other types of dynamic storage devices that can store information and instructions, or can be electrically erasable programmable read-only memory (EEPROM), CD-ROM (Compact Disc Read-Only Memory, CD-ROM) ) or other disc storage, optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), disk storage media or other magnetic storage devices, or capable of carrying or storing in the form of instructions or data structures.
  • ROM read-only memory
  • RAM random access memory
  • EEPROM electrically erasable programmable read-only memory
  • CD-ROM Compact Disc Read-Only Memory
  • CD-ROM Compact Disc Read-Only Memory
  • optical disc storage including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.
  • the memories 1002 are coupled to the processor 1003 via a communication bus 1005.
  • the memory 1002 is used to store application code for executing the solution of the present invention.
  • the application code of the solution of the present invention is stored in a memory and controlled by the processor 1003 for execution.
  • the processor 1003 is configured to perform the following steps, including: performing detector response calibration according to a detector response obtained from a known energy ray incident to obtain a detector response model; and obtaining detector incident energy spectrum data and a detector according to the detector response model The detector photon counting model between the energy spectrum data is measured; the photon count of each energy region detector in the energy spectrum data is deconvolved and processed according to the detector photon counting model to obtain the detector incidence The photon count of the real energy zones in the energy spectrum data.

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Abstract

数据处理方法(300)和数据处理装置(900)。数据处理方法(300)包括：根据已知能量射线入射得到的探测器响应执行探测器响应标定以获得探测器响应模型(S301)；根据该探测器响应模型获得探测器入射能谱数据与探测器测得能谱数据之间的探测器光子计数模型(S302)；根据该探测器光子计数模型对该探测器测得能谱数据中各个能区探测器的光子计数进行反卷积处理计算以获得探测器入射能谱数据中真实的各能区的光子计数(S303)。

Description

数据处理方法和数据处理装置
本申请基于并要求于2016年8月3日提交的申请号为201610630202.3并且发明名称为“数据处理方法和数据处理装置”的中国发明专利申请的优先权。
技术领域
本发明属于数据处理技术领域，更具体地涉及数据处理方法和数据处理装置。
背景技术
能谱探测器是能够采用光子计数的模式区分入射光子能量，并且一次扫描可以获得不同能量区间的光子计数的探测器。但这种计数模式受到探测器响应的影响。探测器响应因探测器晶体材质不同而有所不同，一般包括K层电子逃逸、电荷共享及脉冲堆叠。为了发挥能谱探测器的优势，需要对探测器响应进行建模和校正，目前还没有比较成熟的方法。
发明内容
本发明实施例提供了一种数据处理方法，包括：根据已知能量射线入射得到的探测器响应执行探测器响应标定以获得探测器响应模型；根据该探测器响应模型获得探测器入射能谱数据与探测器测得能谱数据之间的探测器光子计数模型；根据该探测器光子计数模型对该探测器测得能谱数据中各个能区探测器的光子计数进行反卷积处理计算以获得探测器入射能谱数据中真实的各能区的光子计数。
在一些实施例中，该方法还包括根据该探测器光子计数模型对该探测器的每个入射角度每个探测器单元测得能谱数据中各个能区的光子计数进行反卷积处理计算以获得探测器入射能谱数据中每个入射角度探测器单元 真实的各能区的光子计数，对得到的每组数据进行组合从而实现对探测器测得的待测物质的衰减系数进行多能区重建。
在一些实施例中，该方法还包括通过直接求解并增加约束项的方法对该探测器测得能谱数据中各个能区探测器的光子计数进行反卷积处理，以获得探测器入射能谱数据中真实的各能区的光子计数。
在一些实施例中，该方法还包括通过EM求解法对所述探测器测得能谱数据中各个能区探测器的光子计数进行反卷积处理，以获得探测器入射能谱数据中真实的各能区的光子计数。
在一些实施例中，对探测器响应进行标定包括：根据金属荧光数据已知能量射线对光子探测器中的能量沉积过程模拟获得探测器响应标定。
在另一方面，本发明实施例提供了一种数据处理装置，包括：标定模块、光子计数模型获取模块和光子计数获取模块。标定模块根据已知能量射线入射得到的探测器响应执行探测器响应标定以获得探测器响应模型。光子计数模型获取模块根据该探测器响应模型获得探测器入射能谱数据与探测器测得能谱数据之间的探测器光子计数模型。光子计数获取模块根据该探测器光子计数模型对该探测器测得能谱数据中各个能区探测器的光子计数进行反卷积处理计算以获得探测器入射能谱数据中真实的各能区的光子计数。
在一些实施例中，该装置还包括多能区重建模块，用于根据该探测器光子计数模型对该探测器的每个入射角度每个探测器单元测得能谱数据中各个能区的光子计数进行反卷积处理计算以获得探测器入射能谱数据中每个入射角度每个探测器单元真实的各能区的光子计数，对得到的每组数据进行组合从而实现对探测器测得的待测物质的衰减系数进行多能区重建。
在一些实施例中，该装置的光子计数获取模块还用于通过直接求解并增加约束项的方法对所述探测器测得能谱数据中各个能区探测器的光子计数进行反卷积处理，以获得探测器入射能谱数据中真实的各能区的光子计数。
在一些实施例中，该装置的光子计数获取模块还用于通过EM求解法对所述探测器测得能谱数据中各个能区探测器的光子计数进行反卷积处 理，以获得探测器入射能谱数据中真实的各能区的光子计数。
在一些实施例中，该装置的标定模块用于通过根据金属荧光数据已知能量射线对光子探测器中的能量沉积过程模拟来执行探测器响应标定。
在另一方面，本发明实施例提供了一种数据处理装置，包括存储器、耦合到该存储器的处理器，其中处理器被配置为：根据已知能量射线入射得到的探测器响应执行探测器响应标定，以获得探测器响应模型；根据该探测器响应模型获得探测器入射能谱数据与探测器测得能谱数据之间的探测器光子计数模型；根据该探测器光子计数模型对该探测器测得能谱数据中各个能区探测器的光子计数进行反卷积处理计算以获得探测器入射能谱数据中真实的各能区的光子计数。。
根据本申请公开的实施例，通过建立探测器响应模型对探测器测得能谱数据进行反卷积处理来获得能谱数据中真实的能区光子计数，进而消除了能谱探测器响应对光子计数的影响，得到真实的各物质衰减系数。
附图说明
通过参考附图会更加清楚地理解本发明的特征和优点，附图是示意性的而不应理解为对本发明进行任何限制，在附图中：
图1示出根据本发明的一些实施例的一种数据处理方法的应用场景的示意图；
图2示出了在根据本发明的一些实施例的图1所示的一种数据处理方法的应用场景的X光机对物体投影进行检测的流程图；
图3示出根据本发明的一些实施例的一种数据处理方法的流程图；
图4示出根据本发明的一些实施例的探测器对金属的荧光谱进行探测测得的光谱的示意图；
图5示出根据本发明的一些实施例的一组已知能量射线入射探测器实测数据与利用探测器相应模型模拟得到数据的对比图；
图6a示出根据本发明的一些实施例的对一种数据处理方法进行仿真中探测器测得光子在各个能区分布比例的示意图；
图6b示出根据本发明的一些实施例的对一种数据处理方法进行仿真 中探测器的真实输入能谱的示意图；
图6c示出根据本发明的一些实施例的对一种数据处理方法进行仿真中探测器的响应矩阵的示意图；
图6d示出根据本发明的一些实施例的对一种数据处理方法进行仿真得到中探测器的入射能谱的示意图；
图7示出根据本发明的一些实施例的一种数据处理方法的示意图；
图8a示出根据本发明的一些实施例的根据一种数据处理方法进行试验的待测物物质分布示意图；
图8b示出根据本发明的一些实施例的根据一种数据处理方法进行试验的探测器测得处理前的物质线衰减系数图；
图8c示出根据本发明的一些实施例的根据一种数据处理方法进行试验得到的处理后的物质线衰减系数图；
图9示出根据本发明的另一些实施例的一种数据处理装置的结构示意图；以及
图10示出根据本发明的再一些实施例的一种数据处理装置的结构示意图。
具体实施方式
下面将详细描述本发明提供的数据处理方法及装置各个方面的特征和示例性实施例。在下面的详细描述中，提出了许多具体细节，以便提供对本发明的全面理解。但是，对于本领域技术人员来说很明显的是，本发明可以在不需要这些具体细节中的一些细节的情况下实施。下面对实施例的描述仅仅是为了通过示出本发明的示例来提供对本发明的更好的理解。本发明决不限于下面所提出的任何具体配置和方法，而是在不脱离本发明的精神的前提下覆盖了元素、部件和算法的任何修改、替换和改进。在附图和下面的描述中，没有示出公知的结构和技术，以便避免对本发明造成不必要的模糊。
下面结合附图，详细描述根据本发明实施例的数据处理方法和数据处理装置。
图1示出根据本发明的一些实施例的一种数据处理方法的应用场景的示意图。如图1所示，射线源例如X光机与探测器阵列对应设置在待测物体的两侧。X光机对待测物体进行投影后，探测器阵列接收经由待测物体衰减后的X光光谱。由于探测器响应，探测器阵列的测得能谱会与实际经由待测物体衰减后的能谱存在一定误差。这里的实际经由待测物体衰减后的能谱也可以称为探测器的入射能谱。
图2示出了在根据本发明的一些实施例的图1所示的一种数据处理方法的应用场景的X光机对物体投影进行检测的流程图。请参阅图2，X光机发出连续的光源光谱，经由物体衰减得到衰减后能谱。衰减后能谱入射探测器中，并且由于探测器晶体与入射光子发生相互作用而被转换为电信号，进而被后续电子学元件读出，从而得到探测器测得能谱。
请参阅图3，本发明的一些实施例提供的一种数据处理方法的流程图。该数据处理方法300包括以下步骤：步骤S301，根据已知能量射线入射得到的探测器响应执行探测器响应标定，以获得探测器响应模型；步骤S302，根据该测器响应模型获得探测器入射能谱数据与探测器测得能谱数据之间的探测器光子计数模型；步骤S303，根据该探测器光子计数模型对探测器测得能谱数据中各个能区探测器的光子计数进行反卷积处理计算，以获得探测器入射能谱数据中真实的各能区的光子计数。
在步骤S301中，探测器响应标定可以理解为确定探测器的输入和输出的关系。探测器响应标定是为了得到探测器响应模型，这也是之后进行能区光子计数反卷积的基础。应理解，这里可以根据已知能量射线入射探测器来对探测器中的能量沉积过程进行模拟，从而进行探测器响应标定。在一些示例实施例中，可以是通过例如采用金属荧光执行探测器响应标定。在一些示例实施例中，也可以通过采用同步辐射光源、放射源等其它形式的已知能量射线执行探测器响应标定。请一并参阅图4，根据本实施例的探测器对金属的荧光谱进行探测得到的光谱的示意图。
在步骤S302中，例如可以采用综合利用蒙特卡洛模拟与金属荧光数据进行探测器建模构建探测器响应模型。在采用综合利用蒙特卡洛模拟与金属荧光数据进行探测器建模过程中，金属荧光指的是物质在被X光照射 时外层电子退激到内层时发出的特征射线，金属荧光便是用这种特征射线来近似单能射线入射探测器，进而进行探测器响应标定。如图4所示，是一个典型的CdTe对铅的荧光谱的响应，其中，横坐标为光子能量，单位是1000电子伏特，用keV表示；纵坐标为光子计数。荧光指的是物质在X射线照射下产生的特征X射线释放的过程，它会释放出一系列具有一定能量的X射线。kα和kβ都是铅在被照射过程中释放的特征X射线，kβ射线所占比重很低。这些特征X射线的能量已知，可以用来标定探测器响应。根据图4可以看到，探测器测得数据中，在0-20能区光子计数最高，这是由于电子学噪声引起的探测器响应，在20-40能区光子出现峰值，这是由于电子逃逸所致。一些常用物质的荧光谱的不同能量特征X射线成分在表1中给出：
表1
Figure PCTCN2017073206-appb-000001
蒙特卡洛模拟可以包括自电子束打靶开始至光子在探测器中沉积能量为止的整个过程。例如可以采用基于C++与GEANT4编写蒙特卡洛模拟程序，把整个过程拆分成了3个子过程，包括3个子步骤：电子束打靶与能谱的产生的步骤；光子输运过程的模拟与在各向同性条件下输运矩阵的建立的步骤；以及，单能射线在能谱探测器中的能量沉积的步骤，进而得到入射到探测器上光子的能谱。可以理解，在某个电压下产生的X射线的能量分布是个连续的谱带，单能射线有一个谱带，一个中心值。探测器建模的任务主要是确定单能光谱入射的探测器响应，记作δ(E0)，可以理解为能量为E0的单能射线入射的探测器响应，可以包括建立探测器计数偏压与入射光子能量之间映射关系、能谱展宽的参数确定等。例如可以用高斯扩散模型模拟电子空穴对的扩散，如式(1)所示：
Figure PCTCN2017073206-appb-000002
式(1)中σ是高斯模型的标准差，σ0是预设的一个常量，z和D分别是光子与探测器晶体发生相互作用的位置和晶体深度，可以看出发生相互作用的位置越接近D，展宽越窄。线性近似能谱随能量的展宽以及探测器电压阈值与光子能量间的映射如式(2)和式(3)所示：
Figure PCTCN2017073206-appb-000003
E＝p1×TH+p2           (3)
式(2)和式(3)中σs是单能射线所在处的展宽，TH是探测器预设的偏压，E则是光子的能量。蒙特卡洛模拟可以得到探测器入射能谱Sin(E)，再利用探测器实测所得能谱Sdet(E)，确定探测器响应模型中具体的参数ps1、ps2、p1和p2，例如可以用差分进化的算法得到最优化的探测器响应模型的中的参数，这里可以选择表1中的元素，图5示出根据本发明的一个实施例的一组已知能量射线入射探测器实测数据与利用探测器响应模型模拟得到数据的对比图。如图5所示分别以La、Ce、Nd和Gd为例利用蒙特卡洛模拟与最优化的探测器响应模型参数模拟，其中，横坐标为光子能量，单位是keV；纵坐标为光子计数。空心点代表的是实测数据，曲线代表的是蒙特卡洛结果与最优化的探测器响应模型参数模拟得到的结果。
在步骤S303中，在步骤S302中构建的探测器响应模型可以用h(E；E')来描述，h(E；E')是一个类似于概率分布的概念，可以理解为能量为E’的光子被记录为能量E的概率。由于衰减后的能谱也就是探测器的入射能谱记作Sin(E’)，探测所得光子记作Sdet(E)，探测器光子计数响应模型可以由下式表示：
Figure PCTCN2017073206-appb-000004
如式(4)所示，这是探测器响应模型的一个卷积的形式，反卷积则是基于探测器模型通过Sdet(E)得到Sin(E’)。能谱反卷积是一个病态性很强的问题，尤其是在能谱CT中，能谱CT中的能区数目不多，应理解，能区数目多就意味着能区宽度窄，那么同等扫描条件下每个能区的光子计数 越少，受到噪声的影响比较大。用有限的能区计数反卷积得到整个能谱数据的方法，数据很不稳定。这里对宽能区进行反卷积得到能区的计数而非能谱计数也就是在对所述探测器测得能谱数据中各个能区探测器的光子计数进行反卷积处理时不再要求得到整个能谱而是得到真实的能区计数，减少了未知数的个数，提升了算法的稳定性同时也兼顾了数据处理方法的实用性。
在一个实施例中，该方法300可以根据该探测器光子计数模型对该探测器的每个入射角度每个探测器单元测得能谱数据中各个能区的光子计数进行反卷积处理计算，以获得探测器入射能谱数据中每个入射角度每个探测器单元真实的各能区的光子计数。得到的每组数据可以进行组合从而实现对探测器测得的待测物质的衰减系数进行多能区重建。可以理解的是，在对探测器进行扫描的过程中得到的每个角度每个探测器单元各个能区的计数都可以进行反卷积去除探测器响应，通过该方法可以基本得到光谱经由物质的真实的衰减后光谱。
在一个示例中，能谱探测器同时获得很多能区的光子计数，X光机一次扫描便可获得不同能区的能量数据，例如：反映入射角度、探测器单元位置与光子数量关系的正弦图，特定探测器单元位置在不同正弦图上的计数，实际上组成了能谱。可以逐个探测器单元的进行反卷积处理，得到反卷积后的正弦图序列，可以直接用于探测器的多能区重建，得到量化CT(quantitative CT)。
在一个实施例中，该方法的步骤S303可以通过迭代更新对所述探测器测得能谱数据中各个能区探测器的光子计数进行反卷积处理，获得探测器入射能谱数据中真实的各能区的光子计数。这里由于需要在已经标定得到的单能探测器响应的基础上继续得到能区的探测器响应，能区的探测器响应应是能区包含的单能探测器响应的总的综合平均的结果，例如可以用式(5)表示：
Figure PCTCN2017073206-appb-000005
式(5)中
Figure PCTCN2017073206-appb-000006
表示的是第k个能区中包含的单能点的集合，第k个能区中包含的能量元素个数记为Nk，而且
Figure PCTCN2017073206-appb-000007
这是个加权平均因子，该因子可以根据不同的先验来进行调整，例如可以取等值1/Nk，这里可以理解为假设能区总的光子计数在各个点上均匀分布。将式(4)离散化得到式(6)：
Figure PCTCN2017073206-appb-000008
式(6)中，xn是入射光谱在各个能量上光子计数，Hi[m]是该能量对应的探测器响应函数的离散表达，y是探测器在各个能区测得的光子计数。可以将该表达式矩阵化，则x＝[x1,x2,…,xn]T是真实的各个能区的计数，A的每一列是对应能区的探测器响应，y依然是探测到的每个能区的计数。该迭代更新方法例如可以采用EM算法(也称为最大期望算法，Expectation Maximization Algorithm)，这里根据y和A求x的迭代更新方法如式(7)所示：
Figure PCTCN2017073206-appb-000009
为提升算法稳定性，针对能谱探测器计数的特性和具体的应用场景，选取EM算法，对宽能区进行反卷积得到能区的计数而非能谱计数。EM算法本身就是一个对噪声相对鲁棒性的算法，提升了该数据处理方法的稳定性。而在探测器进行扫描的过程中得到的每个角度每个探测器单元各个能区的计数都通过EM算法进行反卷积处理去除探测器响应，通过实验在不同能区宽度的光子总计数的相对标准差在表2中给出：
表2
能窗宽度 5keV 7keV 10keV
相对标准差 0.11％ 0.096％ 0.11％
参照表2，采用其他方法对光子计数模型进行反卷积处理后最宽只能在4keV的能区宽度下保证处理后数据的相对稳定，而采用EM求解法对光子计数模型进行反卷积处理后在能区宽度达到10keV时总计数的变化在 0.11％，仍能达到其他方法在能区宽度为4keV时的数据处理效果。可知采用EM求解法对光子计数模型进行反卷积处理可以得到包含真实物质衰减信息的衰减后光谱探测器入射光谱数据，且数据结果非常稳定。
图6a示出根据本发明的一个实施例的对一种数据处理方法进行仿真测试中探测器测得光子在各个能区分布比例的示意图。图6b示出根据本发明的一个实施例的对一种数据处理方法进行仿真测试中探测器的真实输入能谱的示意图。图6c示出根据本发明的一个实施例的对一种数据处理方法进行仿真中探测器的响应矩阵的示意图。图6d示出根据本发明的一个实施例的对一种数据处理方法进行仿真得到中探测器的入射能谱的示意图。如图6a所示，在一个试验中，模拟入射能量E’为70KeV的光子各个能区分布比例，图中，横坐标代表光子能量，单位为KeV，纵坐标表示采集到的光子在每个能区的分布比例，从中可以非常清楚的看出反卷积的效果。图6b是探测器的入射能谱，图6c是探测器响应矩阵A，它的每一列都是形如图6a的探测器响应函数。如图6a所示，由于光子在探测器晶体中产生的电子，在探测器晶体单元的边界处会分散到若干探测器晶体单元，使得每个探测器晶体单元只接收到部分电子，造成电子共享的情况，该情况造成的探测器响应在20-70能区范围所占比例0.02；其他探测器晶体单元的荧光造成光子能量被该探测器单元被记录在20-30能区的探测器响应比例0.03-0.04之间；电子逃逸造成光子能量被记录在20-30能区的探测器响应比例0.03-0.04之间。图6d中细实线和图6b完全一样是真实入射能谱，而星形线则是探测器实际测得的能谱，三角线是经过EM算法反卷积后得到的入射能谱，从图6d可以看出反卷积效果基本恢复了入射能谱的真实样貌。
在一个实施例中，该方法的步骤S303可以通过直接求解的方法对所述探测器测得能谱数据中各个能区探测器的光子计数进行反卷积处理，获得探测器入射能谱数据中真实的各能区的光子计数。应理解，直接求解法即求解逆矩阵的方法，该方法例如可以采用最小二乘方法求解真实能谱向量x如式(8)所示：
Figure PCTCN2017073206-appb-000010
于是x就有解析解如式(9)所示：
x＝(AT×A)-1×AT×y            (9)
由于A是个奇异性很强的矩阵，在有噪声的情况下这样的求解方法并不能求出真实能谱。在实际中，能谱一般都是连续的，所以可以增加连续性约束，如式(10)所示：
k1(x1-x2)2+k2(x2-x3)2+...+kn-1(xn-1-xn)2       (10)
在矩阵描述下，式(10)可以写为式(11)：
|C×x|2            (11)
其中C是连续性矩阵，增加连续项约束后，求解函数可以写为式(12)：
Figure PCTCN2017073206-appb-000011
能谱x的解析解可以写为式(13)：
x＝(AT×A+CT×C)-1×AT×y            (13)
从而得到探测器入射能谱数据中真实的各能区的光子计数，该方法求解方式简单，保证了连续性，又保证较小的误差值，通过该方法可以基本得到光谱经由物质的真实的衰减后光谱。
请参阅图7，根据本发明实施例提供的数据处理方法得到的对各能区的正弦图逐探测器单元的进行反卷积可以得到一系列反卷积后的正弦图。如图7所示的正弦图是消除了探测器响应的光子计数的具有真实的衰减信息。得到反卷积后的正弦图后，可以用于CT重建，这里的CT重建可以理解为对探测器测得的待测物质的衰减系数进行多能区重建。可以采用解析重建方法例如FDK方法进行CT重建，也可以采用迭代重建方法例如ART方法进行CT重建。
作为示例，选择塑料瓶装水作为待测物体进行投影示意验证该数据处理方法的数据处理效果。如图8a所示，在一个试验中，是通过该方法进行多能区重建后通过测得能谱数据建立的塑料瓶装水模体。如图8b所示， 如果对探测器测得能谱数据不进行针对各个能区进行反卷积处理，直接对各个能区正弦图重建获得的水的衰减系数，其中，横坐标为光子能量，单位为keV，纵坐标为射线各能区衰减系数。因为探测器响应的存在，测得曲线数据偏离理论真实值曲线数据。如图8c所示，经过反卷积处理重建得到的各个能区的衰减系数，测得数据基本上与理论真实曲线数据值完全重合。
图9示出根据本发明的另一些实施例的数据处理装置900的结构示意图。如图9所示，数据处理装置900包括标定模块901、光子计数模型获取模块902、光子计数获取模块903和入射能谱数据获取模块904。标定模块901根据已知能量射线入射得到的探测器响应执行探测器响应标定，以获得探测器响应模型。光子计数模型获取模块902根据探测器响应模型获得探测器入射能谱数据与探测器测得能谱数据之间的探测器光子计数模型。光子计数获取模块903根据探测器光子计数模型对探测器测得能谱数据中各个能区探测器的光子计数进行反卷积处理计算，以获得探测器入射能谱数据中真实的各能区的光子计数。
在一个实施例中，该装置还包括多能区重建模块，用于根据该探测器光子计数模型对探测器的每个入射角度每个探测器单元测得能谱数据中各个能区的光子计数进行反卷积处理计算，以获得探测器入射能谱数据中每个入射角度每个探测器单元真实的各能区的光子计数，对得到的每组数据进行组合从而实现对探测器测得的待测物质的衰减系数进行多能区重建。
在一个实施例中，该装置的光子计数获取模块903可以通过直接求解并增加约束项的方法对探测器测得能谱数据中各个能区探测器的光子计数进行反卷积处理，获得探测器入射能谱数据中真实的各能区的光子计数。
在一个实施例中，该装置的光子计数获取模块903可以通过EM求解法对探测器测得能谱数据中各个能区探测器的光子计数进行反卷积处理，获得探测器入射能谱数据中真实的各能区的光子计数。
在一个实施例中，该装置的标定模块901可根据金属荧光数据已知能量射线对测器中的能量沉积过程模拟来进行探测器响应标定。
请参阅图10，本发明提供的数据处理装置的一些实施例的结构示意图。该装置可以采用通用计算机系统，计算机系统可具体是基于处理器的 计算机。所述数据处理装置可以包括输入输出I/O接口1001、存储器1002、至少一个处理器1003和至少一个通信接口1004。输入输出I/O接口1001、存储器1002、至少一个处理器1003和至少一个通信接口1004之间通过通信总线1005连接。I/O接口1001用于接收来自用户设备的文本数据，并将所述文本数据传输给所述处理器1003，其中，所述文本数据使用结构化查询语言SQL形式表示。处理器1003可以是通用中央处理器(CPU)、微处理器、专用集成电路(application-specific integrated circuit，ASIC)，或一个或多个用于控制本发明方案程序执行的集成电路。通信总线1005可包括一通路，在上述组件之间传送信息。通信接口1004使用任何收发器一类的装置，用于与其他设备或通信网络通信，如以太网、无线接入网(RAN)、无线局域网(Wireless Local Area Networks，WLAN)等。计算机系统包括一个或多个存储器1002，可以是只读存储器(read-only memory，ROM)或可存储静态信息和指令的其他类型的静态存储设备，随机存取存储器(random access memory，RAM)或者可存储信息和指令的其他类型的动态存储设备，也可以是电可擦可编程只读存储器(Electrically Erasable Programmable Read-Only Memory，EEPROM)、只读光盘(Compact Disc Read-Only Memory，CD-ROM)或其他光盘存储、光碟存储(包括压缩光碟、激光碟、光碟、数字通用光碟、蓝光光碟等)、磁盘存储介质或者其他磁存储设备、或者能够用于携带或存储具有指令或数据结构形式的期望的程序代码并能够由计算机存取的任何其他介质，但不限于此。这些存储器1002通过通信总线1005与处理器1003相连接。其中，存储器1002用于存储执行本发明方案的应用程序代码，本发明方案的应用程序代码保存在存储器中，并由处理器1003来控制执行。处理器1003用于执行如下步骤，包括：根据已知能量射线入射得到的探测器响应执行探测器响应标定，以获得探测器响应模型；根据探测器响应模型获得探测器入射能谱数据与探测器测得能谱数据之间的探测器光子计数模型；根据探测器光子计数模型对该探测器测得能谱数据中各个能区探测器的光子计数进行反卷积处理计算，以获得探测器入射能谱数据中真实的各能区的光子计数。
应当注意，在权利要求中，单词“包含”或“包括”并不排除存在未 列在权利要求中的元件或组件。位于元件或组件之前的冠词“一”或“一个”也并不排除存在多个这样的元件或组件的情况。
此外，还应当注意，本说明书中使用的语言主要是为了可读性和教导的目的而选择的，而不是为了解释或者限定本发明的主题而选择的。因此，在不偏离所附权利要求书的范围和精神的情况下，对于本技术领域的普通技术人员来说许多修改和变更都是显而易见的。关于本发明的范围，说明书中所做的描述都是说明性的，而非限制性的，本发明的范围由所附权利要求书限定。

Claims (11)

  1. 一种数据处理方法，包括以下步骤：
    根据已知能量射线入射得到的探测器响应执行探测器响应标定，以获得探测器响应模型；
    根据所述探测器响应模型获得探测器入射能谱数据与探测器测得能谱数据之间的探测器光子计数模型；以及
    根据所述探测器光子计数模型对所述探测器测得能谱数据中各个能区探测器的光子计数进行反卷积处理计算，以获得探测器入射能谱数据中真实的各能区的光子计数。
  2. 如权利要求1所述的数据处理方法，其中，根据所述探测器光子计数模型对所述探测器的每个入射角度每个探测器单元测得能谱数据中各个能区的光子计数进行反卷积处理计算以获得探测器入射能谱数据中每个入射角度探测器单元真实的各能区的光子计数，对得到的每组数据进行组合从而实现对探测器测得的待测物质的衰减系数进行多能区重建。
  3. 如权利要求1或2所述的数据处理方法，其中，通过直接求解并增加约束项的方法对所述探测器测得能谱数据中各个能区探测器的光子计数进行反卷积处理，以获得探测器入射能谱数据中真实的各能区的光子计数。
  4. 如权利要求1或2所述的数据处理方法，其中，通过EM求解法对所述探测器测得能谱数据中各个能区探测器的光子计数进行反卷积处理，以获得探测器入射能谱数据中真实的各能区的光子计数。
  5. 如权利要求1所述的数据处理方法，其中，执行探测器响应标定包括通过根据金属荧光数据已知能量射线对光子探测器中的能量沉积过程模拟来执行探测器响应标定。
  6. 一种数据处理装置，包括：
    标定模块，根据已知能量射线入射得到的探测器响应执行探测器响应标定以获得探测器响应模型；
    光子计数模型获取模块，用于根据所述探测器响应模型获得探测器入射能谱数据与探测器测得能谱数据之间的探测器光子计数模型；以及
    光子计数获取模块，根据所述探测器光子计数模型对所述探测器测得能谱数据中各个能区探测器的光子计数进行反卷积处理计算以获得探测器入射能谱数据中真实的各能区的光子计数。
  7. 如权利要求6所述的数据处理装置，还包括多能区重建模块，还用于根据所述探测器光子计数模型对所述探测器的每个入射角度每个探测器单元测得能谱数据中各个能区的光子计数进行反卷积处理计算以获得探测器入射能谱数据中每个入射角度每个探测器单元真实的各能区的光子计数，对得到的每组数据进行组合从而实现对探测器测得的待测物质的衰减系数进行多能区重建。
  8. 如权利要求6或7所述的数据处理装置，其中，所述光子计数获取模块还用于通过直接求解并增加约束项的方法对所述探测器测得能谱数据中各个能区探测器的光子计数进行反卷积处理，以获得探测器入射能谱数据中真实的各能区的光子计数。
  9. 如权利要求6或7所述的数据处理装置，其中，所述光子计数获取模块还用于通过EM求解法对所述探测器测得能谱数据中各个能区探测器的光子计数进行反卷积处理，以获得探测器入射能谱数据中真实的各能区的光子计数。
  10. 如权利要求6所述的数据处理装置，其中，所述标定模块用于通过根据金属荧光数据已知能量射线对光子探测器中的能量沉积过程模拟来 执行探测器响应标定。
  11. 一种数据处理装置，包括
    存储器；
    耦合到所述存储器的处理器；
    所述处理器被配置为：根据已知能量射线入射得到的探测器响应执行探测器响应标定以获得探测器响应模型；根据所述探测器响应模型获得探测器入射能谱数据与探测器测得能谱数据之间的探测器光子计数模型；根据所述探测器光子计数模型对所述探测器测得能谱数据中各个能区探测器的光子计数进行反卷积处理计算以获得探测器入射能谱数据中真实的各能区的光子计数。
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