Information processing device, information processing method, and information processing program

Abstract

The information processing device acquires one piece of projection data at each of at least two of a plurality of imaging positions corresponding to each of the plurality of imaging positions in which radiation irradiates an object to be examined in different directions, and generates a tomographic image of the object to be examined on the basis of the projection data and attenuation information of the object to be examined.

Description

Technical Field

[0001] The present invention relates to an information processing device, an information processing method and an information processing program. Background Art

[0002] As one of the non-destructive inspection techniques for detecting defects in inspection objects, such as mechanical parts or structures, without destroying them, there is a known technique for generating a tomographic image of an inspection object based on projection data obtained by irradiating the inspection object with radiation such as X-rays. For example, Japanese Patent Application Laid-Open No. 2014-61274 describes a technique that separates projection data into line integral data for each of at least two or more predetermined reference materials, and generates a tomographic image of each reference material based on the line integral data for each reference material. Summary of the Invention

[0003] Technical issues to be solved by the invention

[0004] However, in non-destructive inspection using radiation, it is desirable to detect defects in the inspection object at high speed, reduce the number of images of the inspection object, and generate tomographic images even from a relatively small amount of projection data. Furthermore, depending on the inspection object, the irradiation direction of the radiation, i.e., the projection direction, is sometimes restricted. Therefore, it is desirable to generate tomographic images even from projection data with such restricted projection directions.

[0005] The present invention provides an information processing device, an information processing method, and an information processing program capable of generating a high-precision tomographic image even from a small number of projection data or projection data with limited projection directions.

[0006] Means for solving technical problems

[0007] A first embodiment of the present invention is an information processing device comprising at least one processor, which acquires projection data at at least two of the multiple photographic positions corresponding to different directions of radiation irradiation of an object to be inspected, and generates a tomographic image of the object to be inspected based on the projection data and attenuation information of the object to be inspected.

[0008] An information processing apparatus according to a second aspect of the present invention is the information processing apparatus according to the first aspect, wherein the attenuation information may include attenuation information corresponding to at least two components included in the inspection object.

[0009] An information processing device according to a third aspect of the present invention is the information processing device according to the first aspect, wherein the attenuation information may include attenuation information of air.

[0010] An information processing device according to a fourth aspect of the present invention is the information processing device according to the first aspect, wherein the attenuation information may include attenuation information in at least two energy bands.

[0011] An information processing apparatus according to a fifth aspect of the present invention is the information processing apparatus according to the first aspect, wherein the processor may generate a tomographic image based on scattered rays generated by the inspection object.

[0012] An information processing apparatus according to a sixth aspect of the present invention is the information processing apparatus according to the first aspect, wherein the processor may generate the tomographic image based on the length of a path along which radiation passes through the inspection object.

[0013] In the information processing apparatus according to the seventh aspect of the present invention, the radiation may be irradiated as any one of a parallel beam, a fan beam, and a cone beam.

[0014] In addition, the eighth embodiment of the present invention is an information processing method, in which a processor performs the following processing: obtaining projection data at at least two of the multiple photographic positions corresponding to different photographic positions with respect to the irradiation direction of the radiation on the inspection object, and generating a tomographic image of the inspection object based on the projection data and the attenuation information of the inspection object.

[0015] In addition, the ninth method of the present invention is an information processing program, which enables the processor to perform the following processing: obtaining a projection data at each of at least two of the multiple photographic positions corresponding to the multiple photographic positions different from the irradiation direction of the radiation to the inspection object, and generating a tomographic image of the inspection object based on the projection data and the attenuation information of the inspection object.

[0016] Effects of the Invention

[0017] According to the above aspects, the information processing apparatus, information processing method, and information processing program of the present invention can generate a high-precision tomographic image even from a small number of projection data or projection data with limited projection directions. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a block diagram showing an example of the overall configuration of a tomographic image generating system according to an exemplary embodiment.

[0019] Figure 2 This is a block diagram showing an example of the structure of an information processing device.

[0020] Figure 3 This is a functional block diagram showing an example of the configuration of an information processing device.

[0021] Figure 4 This is a flowchart showing an example of table creation processing executed by the information processing device.

[0022] Figure 5A This is a diagram for explaining the setting of the radiation path from the radiation source to the detector.

[0023] Figure 5B yes Figure 5A Magnified view of the area around the radiation source.

[0024] Figure 5C It will constitute Figure 5A A diagram showing an enlarged image of each pixel of a tomographic image of an object to be inspected.

[0025] Figure 5D A diagram for explaining the path of radiation irradiated from a radiation source.

[0026] Figure 6 This is an example of the interpolated linear attenuation coefficient table.

[0027] Figure 7 This is a flowchart showing an example of tomographic image generation processing executed by the information processing apparatus according to the first exemplary embodiment.

[0028] Figure 8 This is a diagram for explaining the setting of subset numbers and projection data numbers.

[0029] Figure 9 This is a flowchart showing an example of tomographic image generation processing executed by the information processing apparatus according to the second exemplary embodiment.

[0030] Figure 10A This is a diagram for explaining the pencil beam method.

[0031] Figure 10B This is a diagram for explaining the cone beam method.

[0032] Figure 11 It is a diagram for explaining the scattering angle. DETAILED DESCRIPTION

[0033] Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings. However, these exemplary embodiments do not limit the present invention.

[0034] First, an example of the overall configuration of the tomographic image generation system 1 according to this exemplary embodiment will be described. Figure 1 1 is a block diagram showing an example of the overall configuration of the tomographic image generation system 1 according to this exemplary embodiment. Figure 1 As shown in FIG. 1 , the tomographic image generation system 1 of this exemplary embodiment includes an information processing device 10, an inspection object 60, a radiation source 64, and a detector 68. Figure 1Although one detector 68 is shown in FIG. 1 , the number of detectors 68 included in the tomographic image generation system 1 is not limited. For example, a detector 68 may be provided in each irradiation direction of the radiation R. Alternatively, a plurality of detectors 68 may be arranged in a row to obtain a single piece of projection data from the plurality of detectors 68.

[0035] The information processing device 10 of this exemplary embodiment generates a tomographic image of the inspection object 60 using one piece of projection data at each of at least two of the plurality of imaging positions, corresponding to each of the plurality of imaging positions in which the radiation R irradiates the inspection object 60 in different directions. The shape of the inspection object 60 is not particularly limited and can be any shape.

[0036] Specifically, the information processing apparatus 10 generates a tomographic image of the inspection object 60 based on projection data obtained by the detector 68 when the inspection object 60 is irradiated with radiation R from at least two directions and attenuation information of the inspection object 60 .

[0037] Here, attenuation information refers to information about the linear attenuation coefficient of components included in the inspection object 60. The linear attenuation coefficient refers to the probability of radiation R interacting with the inspection object 60 per unit distance traveled, and is the ratio of decrease in the intensity or number of photons of the radiation R. The linear attenuation coefficient varies depending on the energy of the radiation. The radiation R irradiated from the radiation source 64 has an energy distribution. Therefore, the attenuation information preferably includes information about the linear attenuation coefficients of at least two energy bands of the radiation R irradiated onto the inspection object 60.

[0038] Projection data is the sum of the energies of radiation R in multiple energy bands incident on each detector 68. In the information processing apparatus 10 of this exemplary embodiment, by effectively utilizing information on the linear attenuation coefficient of the inspection object 60 in the multiple energy bands of radiation R irradiated onto the inspection object 60, it is possible to address issues such as degradation of tomographic image quality and the generation of artifacts.

[0039] When the radiation R passes through the inspection object 60, it is attenuated due to interactions with the inspection object 60 such as the photoelectric effect, interferential scattering (Thomson scattering), non-interferential scattering (Compton scattering) and electron pair generation, and a portion of the attenuated radiation R is generated as new radiation (scattered radiation).

[0040] The detector 68 detects the radiation R (hereinafter referred to as direct radiation) irradiated from the radiation source 64 and passing through the inspection object 60, and also detects the newly generated (scattered) radiation due to the above-mentioned interaction. That is, in addition to the data based on the direct radiation, the projection data obtained by the detector 68 also includes data based on the scattered radiation. In other words, the projection data obtained by the detector 68 is affected by the scattered radiation. Therefore, in order to reduce the influence of the scattered radiation included in the projection data, it is preferred that the attenuation information includes the scattered radiation information. As for the probability and angle of occurrence of scattering, it can be calculated in theory. For example, for Compton scattering, it can be calculated by the Klein-Nishida formula (Klein-Nishida model). However, the probability and angle of occurrence of the scattering vary depending on the type of components included in the inspection object 60. Therefore, the attenuation information preferably includes the scattering information of each component included in the inspection object 60.

[0041] Furthermore, the probability and angle of scattering also vary depending on the energy of the irradiated radiation R. Therefore, the attenuation information preferably includes scattering information in at least two energy bands. In Compton scattering, the scattered energy varies depending on the scattering angle. To be able to calculate multiple scattering (secondary scattering, tertiary scattering, etc.) resulting from such energy changes, the attenuation information preferably includes scattering information in at least two energy bands. By effectively utilizing the scattering information of the inspection object 60, the information processing device 10 can generate tomographic images with higher accuracy.

[0042] Next, generation of a tomographic image of the inspection object 60 by the information processing apparatus 10 of the tomographic image generation system 1 according to this exemplary embodiment will be described in detail for each exemplary embodiment.

[0043] [First exemplary embodiment]

[0044] (Structure of Information Processing Device)

[0045] First, the hardware structure of the information processing device 10 of this exemplary embodiment will be described. Figure 2 1 is a block diagram showing an example of the hardware configuration of the information processing device 10 according to this exemplary embodiment. Figure 2 As shown, the information processing device 10 includes a processor 20 such as a CPU (Central Processing Unit), a memory 22, an interface (Interface) unit 23, a storage unit 24, a display 26, and an input device 28. The processor 20, the memory 22, the interface unit 23, the storage unit 24, the display 26, and the input device 28 are connected via a bus 29 such as a system bus or a control bus so that they can exchange various information with each other.

[0046] The processor 20 reads various programs stored in the storage unit 24, including the table generation program 30 and the tomographic image generation program 32, into the memory 22 and executes processing according to the read programs. Thus, the processor 20 controls the generation of tomographic images. The memory 22 is a working memory used by the processor 20 to execute processing.

[0047] The table generation program 30 and the tomographic image generation program 32 executed by the processor 20 are stored in the storage unit 24. Specific examples of the storage unit 24 include a HDD (Hard Disk Drive) and an SSD (Solid State Drive).

[0048] The I / F unit 23 communicates various information with the detector 68 through wireless communication or wired communication. The display 26 and the input device 28 function as a user interface. The display 26 provides the user with various information related to the analysis of the sample. The display 26 is not particularly limited, and examples thereof include a liquid crystal display and an LED (Light Emitting Diode) display. In addition, the input device 28 is operated by the user and is used to input various instructions related to the generation of tomographic images. The input device 28 is not particularly limited, and examples thereof include a keyboard, a stylus pen, and a mouse. In addition, in the information processing device 10, a touch panel display that integrates the display 26 and the input device 28 is used.

[0049] In addition, Figure 3 FIG. 1 is a functional block diagram showing an example of the structure of the information processing device 10. Figure 3 As shown, the information processing apparatus 10 includes an acquisition unit 40 and a tomographic image generation unit 42. The processor 20 functions as the acquisition unit 40 and the tomographic image generation unit 42 by executing the table generation program 30 or the tomographic image generation program 32.

[0050] The acquisition unit 40 has a function of acquiring a plurality of projection data obtained by the detector 68 when the inspection object 60 is irradiated with radiation R from different directions by the radiation source 64. For example, in the information processing apparatus 10 of this exemplary embodiment, after imaging the inspection object 60, the acquisition unit 40 acquires a plurality of projection data corresponding to different irradiation directions of the radiation R from the detector 68 at arbitrary timings and stores the data in the storage unit 24. Furthermore, when performing tomographic image generation processing, the acquisition unit 40 acquires the plurality of projection data corresponding to different irradiation directions of the radiation R stored in the storage unit 24 and outputs the data to the tomographic image generator 42.

[0051] The tomographic image generator 42 has a function of generating a tomographic image of the inspection object 60 based on a plurality of projection data of radiation R in different irradiation directions acquired by the acquisition unit 40 and attenuation information of the inspection object 60. Furthermore, the tomographic image generator 42 of this exemplary embodiment has a function of generating various tables (described in detail later) necessary for generating tomographic images.

[0052] Next, the operation of the information processing device 10 according to this exemplary embodiment will be described in detail.

[0053] As described above, in the information processing apparatus 10 of the present exemplary embodiment, various tables necessary for generating a tomographic image are generated before generating a tomographic image. Figure 4 , a flowchart showing an example of table generation processing executed by the information processing device 10 of this exemplary embodiment is shown. The information processing device 10 executes the table generation program 30 stored in the storage unit 24 to perform Figure 4 The table generation process shown.

[0054] exist Figure 4 In step S100 shown, the tomographic image generating unit 42 generates a path length table of the radiation R based on the geometrical arrangement of the radiation source 64 and the detector 68 , and stores the generated table.

[0055] As an example of the scanning method, in this exemplary embodiment, the case of the fan beam method is described. Figure 5A As shown in FIG. 7 , a path 70 of radiation R from the radiation source 64 to the detector 68 is set. Figure 5B , an enlarged view of the vicinity of the radiation source 64 is shown. Figure 5B As shown in FIG. 1 , each path 70 is set at equal intervals of a small angle. The more paths 70 are set, in other words, the smaller the angle is set, the more accurate the tomographic image can be generated, but it will take more computing time. For each detector 68, the paths 70 incident on the detector 68 are extracted and the number of paths 70 is tabulated. Then, for each path 70, as shown in FIG. Figure 5C As shown, the pixels 74 passed through are extracted to tabulate the number of extracted pixels 74 and the numbers of the pixels 74, and the path length L in each pixel 74 is obtained and tabulated. Figure 5C It will Figure 5A 7 is an enlarged view of each pixel constituting the tomographic image of the inspection object 60, where the pixels through which the path 70 passes are indicated by hatching, and the path length in each pixel is shown. Figure 5C In the example shown, path 70 passes through four pixels. Pixel numbers are numbers for each pixel in a specific tomographic image. Originally, they were given in two dimensions (x and y). However, when the number of dimensions increases, table reference becomes complicated, so they are now one-dimensional.

[0056] The tomographic image generating unit 42 generates, for example, the following table.

[0057] Table num_path(no_s, no_d): This table lists the number of paths 70 for scan no_s that are incident on detector no_d. "no_s" represents the scan identification number, and a scan with identification number no_s is labeled "scan no_s." In the case of fan beam radiation, this table indicates the identification number for each direction of imaging when rotating the radiation source 64 and detector 68. "no_d" represents the identification number of the detector 68, and a detector 68 with identification number no_d is labeled "detector no_d."

[0058] Table num_pix(no_s, no_d, no_path): a table showing the number of pixels passed by the path no_path of the scan no_s incident on the detector no_d. "no_path" indicates the identification number of the path 70, and the path 70 with the identification number no_path is marked as "path no_path".

[0059] Table no_pix(no_s, no_d, no_path, no_pix_path): A table of identification numbers for the pixel no_pix_path traversed by path no_path from scan no_s to detector no_d. "no_pix_path" represents the identification numbers of the pixels along path no_path from scan no_s to detector no_d, numbered from smallest to largest.

[0060] Table plen_pix(no_s, no_d, no_path, no_pix_path): table of path lengths in the no_pix_path-th pixel traversed by the path no_path of the scan no_s incident to the detector no_d.

[0061] In addition, for each subset (described in detail later) obtained by dividing all scans into several groups (subsets), the total path length of all paths 70 incident on all detectors 68 and passing through each pixel of all scans belonging to the subset is calculated and tabulated.

[0062] For example, the following table is generated.

[0063] Table plen_pix_sub (no_sub, no_pix): a table showing the sum of the path lengths of all paths 70 incident on all detectors 68 passing through pixel no_pix for all scans belonging to subset no_sub. "no_sub" represents the identification number of the subset, and the subset with identification number no_sub is labeled "subset no_sub".

[0064] In fact, if Figure 5D As shown, the radiation source 64 is not a point but is spatially extended. That is, in order to ensure the dose of the radiation R, that is, to ensure the brightness or shading difference of the projection data, the radiation source 64 has a certain size. Moreover, due to the extension of the radiation source 64, blurring occurs in the projection data, affecting the spatial resolution. In order to generate an accurate tomographic image based on the projection data including such blurring, it is preferable to set the path 70 of the radiation R based on the spatial extension of the radiation source 64. For example, it can also be as follows Figure 5D As shown, a plurality of (radiation sources 64) are set at predetermined intervals in a predetermined area (radiation source 64) that is spatially extended. Figure 5D 14 radiation sources 64S are provided, and paths 70 of radiation R from each radiation source 64S to each detector 68 are set. Even when a plurality of radiation sources 64S are provided in this manner, it is not necessary to generate a table for each radiation source 64S separately; the paths 70 of all radiation sources 64S can be combined to generate the above table.

[0065] In the next step S102 , the tomographic image generator 42 interpolates the linear attenuation coefficients of the components included in the inspection object 60 , generates a linear attenuation coefficient table, and stores the generated table.

[0066] exist Figure 6 , an example of the interpolated linear attenuation coefficient table f(no_f, no_e) is shown. "no_f" represents the identification number of the linear attenuation coefficient, "no_e" represents the identification number of the energy corresponding to the wavelength of the radiation R, and f(no_f, no_e) represents a table of linear attenuation coefficients in energy no_e for each linear attenuation coefficient number no_f. The tomographic image generating unit 42 generates f(no_f, no_e) by interpolating the linear attenuation coefficients of the components included in the inspection object 60 at each energy. Figure 6There are three thick line graphs recorded in it, which represent the linear attenuation coefficients of air, aluminum and iron respectively, from the smallest linear attenuation coefficient. In addition, the value of the linear attenuation coefficient is based on "Tables of X-Ray Mass Attenuation Coefficients andMass Energy-Absorption Coefficients from 1keV to 20MeV for Elements Z=1to92and 48Additional Substances of Dosimetric Interest,NIST,JHHubbell andS.M.Seltzer.". Here, the area around the inspection object 60 is filled with a substance corresponding to the environment in which the inspection object 60 exists, and in the tomographic image of the inspection object 60, pixels corresponding to the substance filling the area around the inspection object 60 are also included together with pixels corresponding to components included in the inspection object 60. Therefore, in this exemplary embodiment, the entire area of ​​the tomographic image is regarded as the inspection object 60, and the substance filling the area around the original inspection object 60 is also regarded as a component included in the inspection object 60. In Figure 6 In the example shown, the components included in the original inspection object 60 are aluminum and iron, but the original inspection object 60 is filled with air, and the air is also regarded as the components included in the inspection object 60 (in the tomographic image of the inspection object 60, each pixel corresponds to either the aluminum and iron included in the inspection object 60, or the air filling the inspection object 60).

[0067] Figure 6 Each thin wavy line represents a virtual linear attenuation coefficient generated by interpolating the linear attenuation coefficients of air, aluminum, and iron. The virtual linear attenuation coefficient is obtained by linearly interpolating the linear attenuation coefficients of air, aluminum, and iron at each energy. Figure 6 In the example, the linear attenuation coefficients of aluminum and iron are divided into 80 equal parts at each energy, and the virtual linear attenuation coefficient between aluminum and iron is obtained. According to this method of obtaining, the sum of the total energy of each adjacent virtual linear attenuation coefficient between aluminum and iron, or the difference between the average values, is equal. The maximum value of the linear attenuation coefficient number no_f is set to num_f, and in Figure 6In the example, num_f=85, and this linear attenuation coefficient is the linear attenuation coefficient of iron. In addition, the sum of the total energy of the adjacent virtual linear attenuation coefficients between air and aluminum, or the difference in the average value, and the sum of the total energy of each adjacent virtual linear attenuation coefficient between aluminum and iron, or the difference in the average value, are made equal. The interval between the virtual linear attenuation coefficients between air and aluminum, and the interval between the virtual linear attenuation coefficients between aluminum and iron can be any interval. In addition, the narrower the interval, the more accurate the generation of the tomographic image, but it takes calculation time. In addition, as Figure 6 As shown, the sums or average differences of the total energy of adjacent virtual linear attenuation coefficients between air and aluminum, and between aluminum and iron, can be made equal. Alternatively, considering energy, the differences in the total energy of the energy × linear attenuation coefficient sums can be made equal. Furthermore, the linear attenuation coefficient value for air can be approximated as 0.0 within the total energy. In other words, components no_f with a linear attenuation coefficient value of 0.0 within the total energy can be considered air.

[0068] In this exemplary embodiment, the virtual linear attenuation coefficient increases as the linear attenuation coefficient number no_f increases at any energy, assuming that the magnitude relationship of the linear attenuation coefficients of the components included in the inspection object 60 is the same in the total energy, that is, does not reverse.

[0069] When the processing of step S102 is completed, Figure 4 The table generation process shown ends.

[0070] Therefore, when Figure 4 When the table generation process shown is completed, the information processing device 10 generates a tomographic image. Figure 7 , a flowchart showing an example of tomographic image generation processing executed by the information processing apparatus 10 of this exemplary embodiment is shown. The information processing apparatus 10 executes the tomographic image generation program 32 stored in the storage unit 24 to perform Figure 7 The tomographic image generation process shown.

[0071] exist Figure 7 In step S200 shown in FIG. 1 , as described above, the acquisition unit 40 acquires a plurality of projection data from the storage unit 24 and outputs the plurality of projection data to the tomographic image generator 42 .

[0072] In the next step S202, the tomographic image generator 42 divides the projection data of all directions (all scans) into several groups (subsets). For example, when the number of projection data (number of scans, number of projection directions) is 8, Figure 8 The subset number and projection data number can be set as shown in the example. Figure 8As shown in the example, it is preferable to include projection data with projection directions as close to opposite directions as possible in the same subset. Furthermore, it is preferable to include projection data with projection directions as close to orthogonal as possible in the same subset. Furthermore, it is preferable to assign subset numbers in the order of subsets with projection directions as close to orthogonal as possible (updating tomographic images (described in detail later)).

[0073] exist Figure 7 In the tomographic image generation process shown, the tomographic image generator 42 updates the tomographic image using only the projection data belonging to the subset. This operation is repeated for each subset. After all subsets have been updated, the linear attenuation coefficient of the tomographic image is quantized to the value of the components included in the inspection object 60, which is considered a complete update. The tomographic image generator 42 completes tomographic image generation after performing a preset number of complete updates.

[0074] In step S204 , the tomographic image generator 42 generates an initial tomographic image (described in detail later). Then, in the next step S206 , the tomographic image generator 42 sets the total number of updates i to “1”.

[0075] In the next step S208, the tomographic image generator 42 initializes the subset number j to "1". The subset number j is initialized every update number i. In the next step S210, the tomographic image generator 42 initializes the projection data number k to "1". The projection data number k is initialized for each subset j.

[0076] In the next step S212 , the tomographic image generator 42 calculates a linear attenuation coefficient (described in detail later) for each pixel of the tomographic image based on the projection data of the scan number corresponding to the combination of the subset number j and the projection data number k.

[0077] In the next step S214, the tomographic image generator 42 increments the projection data number k (k = k + 1). In the next step S216, the tomographic image generator 42 determines whether the projection data number k exceeds the total number Nk of projection data belonging to subset j (k > Nk). If the projection data number k exceeds the total number Nk, the determination in step S216 becomes negative, and the process returns to step S212, repeating the calculation of the linear attenuation coefficient for the tomographic image. On the other hand, if the projection data number k exceeds the total number Nk, the determination in step S216 becomes positive, and the process proceeds to step S218.

[0078] In step S218 , the tomographic image generator 42 updates the tomographic image based on the linear attenuation coefficient calculated in step S212 .

[0079] In the next step S220, the tomographic image generator 42 increments the subset number j (j = j + 1). In the next step S222, the tomographic image generator 42 determines whether the subset number j exceeds the total number of subsets Nj (j > Nj). If the subset number j exceeds the total number Nj, ​​the determination in step S222 becomes negative, and the process returns to step S210, repeating the tomographic image update. On the other hand, if the subset number j exceeds the total number Nj, ​​the determination in step S222 becomes positive, and the process proceeds to step S224.

[0080] In step S224 , the tomographic image generating unit 42 quantizes the tomographic image updated in step S218 (described in detail later).

[0081] In the next step S226, the tomographic image generator 42 increments the update count i (i=i+1). In the next step S228, the tomographic image generator 42 determines whether the update count i exceeds the preset count Ni (i>Ni). If the update count i exceeds the preset count Ni, the determination in step S228 becomes negative, and the process returns to step S208, repeating the updating and quantization of the tomographic images. On the other hand, if the update count i exceeds the preset count Ni, the determination in step S228 becomes positive, and the process proceeds to step S230.

[0082] In the next step S230, the tomographic image generating unit 42 displays the tomographic image generated by the above-mentioned processing on the display 26. When the processing of step S230 is completed, Figure 7 The tomographic image generation processing shown ends.

[0083] Next, the details of steps S204, S212, S218, and S224 are described respectively.

[0084] The details of step S204 are explained. In step S204, as described above, the initial tomographic image no_f (no_pix) is generated. no_f (no_pix) represents the linear attenuation coefficient number no_f in the pixel no_pix, and the total number of pixels of the tomographic image is set to num_pix_t, and no_pix takes any value from 1 to hum_pix_t. That is, the tomographic image is an image in which a linear attenuation coefficient number is assigned to each pixel, and the linear attenuation coefficient in each energy corresponding to each linear attenuation coefficient number is assigned according to the linear attenuation coefficient table f (no_f, no_e) generated in advance. As the initial value of no_f (no_pix), it is sufficient to set the average value. For example, in Figure 6 In the case of the example, the linear attenuation coefficients of air, aluminum, and iron are interpolated to set virtual linear attenuation coefficients of linear attenuation coefficient numbers 1 to 85, and their average value is set to 43.

[0085] The details of step S212 will be described. The tomographic image generator 42 calculates the linear attenuation coefficient of the tomographic image based on the projection data g(no_s, 1) to g(no_s, num_d). The scan number no_s is determined according to the combination of the subset number j and the projection data number k (see Figure 8 ). “num_d” represents the number of detectors 68.

[0086] The calculation method of projection data g(no_s, no_d) based on detector no_d is described. First, virtual projection data g_v (hereinafter referred to as temporary projection data g_v) of scan no_s and detector no_d is obtained based on the current tomographic image using the following formula (1).

[0087] g_v=I(1)+I(2)+……+I(num_path(no_s,no_d))…(1)

[0088] In addition, num_path(no_s, no_d) in the above formula (1) is the number of paths 70 that enter the detector no_d in the scan no_s, which is obtained in advance.

[0089] In the above formula (1), I(no_path) represents the intensity of the radiation incident on the path no_path of the detector, and is obtained by the following formula (2).

[0090] I(no_path)=(e(1)×I_e(1))+(e(2)×I_e(2))+……+(e(num_e)×I_e(num_e))…(2)

[0091] In the above formula (2), e(1), e(2), ..., e(num_e) represent energies and are known. I(no_path) can be regarded as the total energy of num_e types of photons of energy incident on the path no_path of the detector 68.

[0092] In the above formula (2), the intensity I_e(no_e) of the radiation R at the energy e(no_e) is obtained by the following formula (3).

[0093] I_e(no_e)=I0_e(no_e)×exp(-{(f(no_f_ext(1),no_e)×plen_pix(no_s,no_d,no_path,1))+(f(no_f_ext(2),no_e)×plen_pix(no_s,no_d , no_path, 2))+……+(f(no_f_ext(num_pix(no_s, no_d, no_path)), no_e)×plen_pix(no_s, no_d, no_path, num_pix(no_s, no_d, no_path)))})

[0094] in

[0095] no_f_ext(no_pix_path)=no_f(no_pix(no_s, no_d, no_path, no_pix_path))…(3)

[0096] The above formula (3) represents the intensity I_e(no_e) of the radiation R of intensity I0_e(no_e) among the energy e(no_e) irradiated from the radiation source 64 , which passes through each pixel while attenuating and enters the detector 68 .

[0097] In the above formula (3), f(no_f_ext(no_pix_path), no_e)×plen_pix(no_s, no_d, no_path, no_pix_path)=f(no_f(no_pix(no_s, no_d, no_path, no_pix_path)), no_e)×plen_pix(no_s, no_d, no_path, no_pix_path)),no_e) represents the linear attenuation coefficient f in the energy no_e corresponding to the linear attenuation coefficient number no_f in the no_pix_path-th pixel no_pix passed by the path no_path of the scan no_s incident to the detector no_d. In addition, plen_pix(no_s, no_d, no_path, no_pix_path) represents the path length plen_pix in the no_pix_path-th pixel no_pix through which the path no_path of the scan no_s incident on the detector no_d passes.

[0098] The intensity I0_e(no_e) in the above formula (3) is expressed as Figure 5BThe total number of photons (of energy e(no_e)) irradiated per unit time within a small angle range, in other words, within the angle range surrounded by the dotted line with the path 70 as the center. Figure 5B The narrower the interval of the angle of the path is set, in other words, the more paths 70 are set, the smaller the value of I0_e(no_e) is set. In addition, in order to make the explanation easier to understand, Figure 5B In the figure, the dotted line only shows a small angular range within the plane formed by the arrangement direction of the radiation source 64 and the detector 68 (within the plane of the two-dimensional fan beam). In fact, the radiation R is irradiated three-dimensionally from the radiation source 64, and each detector detects the radiation within the two-dimensional plane incident on each detector. That is, to be precise, the intensity I0_e(no_e) in the above formula (3) is expressed in Figure 5B The total number of photons irradiated per unit time within the detector's small angular range (the angular range enclosed by the dotted line) and in the direction perpendicular to it (the direction perpendicular to the plane of the fan beam) is estimated. I0_e(0), I0_e(1), ..., I0_e(num_e) depend on the radiation source 64 and are known.

[0099] The provisional projection data g_v calculated using equation (1) is compared with the measured projection data g(no_s, no_d). If the provisional projection data g_v is larger, the linear attenuation coefficient numbers for all pixels passing through all paths 70 of scan no_s that are incident on detector no_d are incremented by 1, and provisional projection data g_v is calculated again and compared with the measured projection data g(no_s, no_d). This process of incrementing the linear attenuation coefficient number by 1, recalculating the provisional projection data g_v, and comparing it with the projection data g(no_s, no_d) is repeated until the provisional projection data g_v falls below the measured projection data g(no_s, no_d). The data that is closer to the measured projection data g(no_s, no_d) is then used: the provisional projection data g_v just before falling below the measured projection data g(no_s, no_d) or the provisional projection data g_v that falls below the measured projection data g(no_s, no_d). The increment of the linear attenuation coefficient number when calculating the temporary projection data g_v is saved as df(no_s, no_d). When the linear attenuation coefficient number is increased by α and the temporary projection data g_v is recalculated, in the above formula (3), no_f(no_pix(no_s, no_d, no_path, no_pix_path)) is replaced by no_f_ext(no_pix_path) as no_f(no_pix(no_s, no_d, no_path, no_pix_path))+α, and I_e(no_e) is recalculated. Based on the recalculated I_e(1), I_e(2), ..., I_e(num_e), the intensity I(no_path) is recalculated using the above formula (2). Based on the recalculated I(1), I(2), ..., I(num_path(no_s, no_d)), the temporary projection data g_v is recalculated using the above formula (1).

[0100] Furthermore, if there is a pixel among pixels no_pix(no_s, no_d, no_path, 1) to no_pix(no_s, no_d, no_path, num_pix(no_s, no_d, no_path)) where no_f(no_pix(no_s, no_d, no_path, no_pix_path))+α exceeds the maximum value of the linear attenuation coefficient number, the linear attenuation coefficient number for that pixel is fixed to the maximum value. For example, Figure 6In the example, the maximum linear attenuation coefficient number is 85. Therefore, if the result of increasing the linear attenuation coefficient number by α exceeds 85, the linear attenuation coefficient number for that pixel is fixed at 85. If the provisional projection data g_v is smaller than the actual projection data g(no_s, no_d), conversely, for all paths 70 incident on detector no_d during scan no_s, the provisional projection data g_v is repeatedly recalculated by subtracting 1 from the linear attenuation coefficient number for all pixels passing through the path 70 until the provisional projection data g_v reaches or exceeds the measured projection data g(no_s, no_d). The provisional projection data g_v that is closer to the measured projection data g(no_s, no_d) is then used, and the decrement in the linear attenuation coefficient number during this calculation is stored as df(no_s, no_d). In this case, df(no_s, no_d) is a negative value. When the linear attenuation coefficient number is reduced, the subtraction result is also smaller than the minimum linear attenuation coefficient number of 1. If such a pixel exists, the linear attenuation coefficient number is fixed to 1 for that pixel.

[0101] Through the above calculation, the increments df(no_s, 1) to df(no_s, num_d) of the linear attenuation coefficient numbers can be obtained based on the projection data g(no_s, 1) to g(no_s, num_d).

[0102] Step S218 will be described in detail. After completing the calculation of the linear attenuation coefficient number differences df(no_s, 1) to df(no_s, num_d) for all projection data belonging to the subset j, the tomographic image generator 42 updates the tomographic image based on the differences.

[0103] Focusing on a particular pixel no_pix, assume that path 70 of scan no_s incident on detector no_d passes through that pixel. In this case, the difference df(no_s, no_d) is an estimated value of the difference in linear attenuation coefficient numbers for paths 70 of scan no_s incident on detector no_d that pass through pixel no_pix. Therefore, it is necessary to average the estimated differences in linear attenuation coefficient numbers for all paths 70 that pass through pixel no_pix, weighted by the length of each path. First, a tomographic image of the average of these differences is set as df_ave(no_pix), and df_ave(no_pix) is initialized by setting all pixel values ​​to "0." Here, df_ave(no_pix) represents the average of the differences in linear attenuation coefficient numbers for pixel no_pix, and no_pix is ​​any value between 1 and the total number of pixels, num_pix_t. Then, for all projection data belonging to subset j, the differences in the linear attenuation coefficient numbers df(no_s, 1) to df(no_s, num_d) are weighted by the path length and added to each pixel of df_ave. Finally, the average value of the differences is obtained for each pixel by dividing the value of each pixel of df_ave by the sum of the path lengths of each pixel in the table generated in advance, plen_pix_sub, and the tomographic image is updated by adding it to the tomographic image before the update.

[0104] The addition process will be specifically described using the projection data for scan no_s belonging to subset j as an example. The number of pixels traversed by path no_path from scan no_s to detector no_d is num_pix(no_s, no_d, no_path). In the tomographic image df_ave, the average difference, the value of the pixel no_pix_path no_pix(nos, no_d, no_path, no_pix_path) traversed by path no_path from scan no_s to detector no_d is multiplied by the difference multiplied by the path length: df(no_s, no_d) × plen_pix(no_s, no_d, no_path, no_pix_path). Similarly, in the tomographic image df_ave of the differential average value, the values ​​of all pixels of num_pix(no_s, no_d, no_path) through which the path no_path passes are respectively differentially calculated by df(no_s, no_d)×path length (plen_pix(no_s, no_d, no_path,1) to plen_pix(no_s, no_d, no_path, num_pix(no_s, no_d, no_path))). Furthermore, among the pixels no_pix(no_s, no_d, no_path,1) to no_pix(no_s, no_d, no_path, num_pix(no_s, no_d, no_path)), there are pixels (in the case of pixels) for which the result obtained by differentially calculating the linear attenuation coefficient number before the update by df(no_s, no_d) exceeds the maximum value of the linear attenuation coefficient number. Figure 6In the example, for a pixel exceeding the maximum value of 85), for that pixel, the difference is corrected so that the addition result reaches the maximum value and then added to df_ave. Similarly, in the case where there is a pixel whose result obtained by adding the linear attenuation coefficient number before the update to the difference df(no_s, no_d) is less than the minimum value 1 of the linear attenuation coefficient number, for that pixel, the difference is corrected so that the addition result reaches 1 and then added to df_ave. The above addition operation is performed on all paths 1 to num_path(no_s, no_d) incident on detector no_d of scan no_s. Furthermore, the above addition operation is performed on all detectors 1 to num_d of scan no_s. The above is the addition operation for the projection data of scan no_s belonging to subset j. This addition operation is performed on all projection data belonging to subset j. Finally, the values ​​df_ave(1) to df_ave(num_pix_t) generated by the above addition process are divided by the sum of the path lengths of each pixel, plen_pix_sub(j, 1) to plen_pix_sub(j, num_pix_t), to obtain the average values ​​of the differences in the linear attenuation coefficient numbers, df_ave(1) to df_ave(num_pix_t). These values ​​are then added to the pre-update tomographic images no_f(1) to no_f(num_pix_t) to update the tomographic image. Furthermore, the average value of the differences in the tomographic image no_f, df_ave, is added to the tomographic image no_f and rounded to an integer.

[0105] The details of step S224 will be described. After updating the tomographic images of all subsets, the tomographic image generator 42 quantizes the linear attenuation coefficients of the tomographic images to the values ​​of the components included in the inspection object 60. Specifically, the linear attenuation coefficient number no_f(no_pix) of each pixel no_pix of the tomographic image is corrected to the linear attenuation coefficient number of the component included in the inspection object 60 that is closest to the linear attenuation coefficient number. For example, Figure 6 In the example of , the components included in the inspection object 60 are air, aluminum, and iron. The linear attenuation coefficient of air is numbered 1 (minimum value), the linear attenuation coefficient of aluminum is numbered 5, and the linear attenuation coefficient of iron is numbered 85 (maximum value). In this case, in the tomographic image no_f, pixels with a linear attenuation coefficient number of 3 or less, or less than 3, are corrected to the linear attenuation coefficient number of air. Pixels with a linear attenuation coefficient number of 4 or greater, or between 3 and 45 or less, or less than 45, are corrected to the linear attenuation coefficient number of aluminum. Pixels with a linear attenuation coefficient number of 46 or greater, or greater than 45, are corrected to the linear attenuation coefficient number of iron.

[0106] As described above, according to the present exemplary embodiment, a high-precision tomographic image of the inspection object 60 can be generated regardless of the amount of projection data.

[0107] [Second exemplary embodiment]

[0108] In this exemplary embodiment, a tomographic image generation process different from that of the first exemplary embodiment will be described. In addition, in this exemplary embodiment, as in the first exemplary embodiment, a table generation process (see Figure 4 ), generating various tables. Specifically, the tomographic image generator 42 generates a table num_path (no_s, no_d) for the number of paths 70, a table num_pix (no_s, no_d, no_path) for the number of pixels, a table no_pix (no_s, no_d, nopath, no_pix_path) for the pixel numbers, and a table plen_pix (no_s, no_d, no_path, no_pix_path) for path lengths. The tomographic image generator 42 also generates a table f (no_f, no_e) for linear attenuation coefficients. In this exemplary embodiment, the table plen_pix_sub (no_sub, no_pix) for the total path lengths belonging to the subset no_sub is not generated.

[0109] exist Figure 9 , a flowchart showing an example of tomographic image generation processing executed by the information processing apparatus 10 of this exemplary embodiment is shown. Figure 9 As shown, the tomographic image generation process of this exemplary embodiment replaces the tomographic image generation process of the first exemplary embodiment (see Figure 7 ) steps S210 to S216 and includes the processing of step S209. In addition, the specific content of the processing in S218 after step S209 is different from the first exemplary embodiment.

[0110] In this exemplary embodiment, an evaluation function is determined that evaluates the difference between the provisional projection data g_v obtained from the generated tomographic image no_f and the measured projection data g(no_s, no_d). The tomographic image is then updated so that the evaluation function decreases. Specifically, the change in the evaluation function when the tomographic image value (linear attenuation coefficient number) is slightly changed for each pixel 1 to num_pix_t in tomographic image no_f is calculated, and this value is used as the gradient. The tomographic image is then updated based on the gradient calculated for each pixel 1 to num_pix_t. The gradient is calculated for each subset j, and the tomographic image is updated. Steps S209 and S218 are described in detail below.

[0111] In step S209, the tomographic image generator 42 calculates the gradient based on the projection data belonging to the subset j. First, the evaluation function ef is determined in advance as follows.

[0112] ef=∑∑(g_v(no_s, no_d)-g(no_s, no_d)) 2

[0113] in,

[0114] The first ∑ is the sum associated with the scan no_s belonging to subset j.

[0115] The second Σ is the sum associated with detector no_d (the sum of 1 to num_d).

[0116] g_v(no_s, no_d) represents temporary projection data of scan no_s and detector no_d obtained from the tomographic image.

[0117] g(no_s, no_d) represents the measured projection data of the scanner no_s and the detector no_d.

[0118] …(4)

[0119] First, based on the current tomographic image no_f, each provisional projection data g_v(no_s, no_d) is calculated using equations (1), (2), and (3), and then the evaluation function ef is calculated using equation (4). Next, no_f(1), that is, the value of pixel 1 (linear attenuation coefficient number) of tomographic image no_f, is temporarily increased by a predetermined amount α or 1, and each provisional projection data g_v(no_s, no_d) is again calculated using equations (1), (2), and (3), and then the evaluation function ef is calculated using equation (4). Furthermore, when no_f(1) is temporarily increased by α, if the pixel no_pix(no_s, no_d, no_path, no_pix_path) in equation (3) above is equal to 1, no_f(no_pix(no_s, no_d, no_path, no_pix_path)) is replaced with no_f_ext(no_pix_path) as no_f(no_pix(no_s, no_d, no_path, no_pix_path)) + α, and the intensity I_e(no_e) is calculated. Thus, the evaluation function ef before the increase of the value of pixel 1 in the tomographic image no_f is subtracted from the evaluation function ef after the increase of the value by α, and the result is the gradient Δef(1) at pixel 1.

[0120] Furthermore, in gradient calculation, it is desirable to accurately calculate the gradient even when no_f(1)+a exceeds the maximum value num_f of the linear attenuation coefficient number. Therefore, the linear attenuation coefficient table f(no_f, no_e) used for gradient calculation is configured such that the number of the table increases by α compared to the maximum value num_f of the linear attenuation coefficient number. Specifically, the table includes linear attenuation coefficient numbers 1 to num_f+α. At each energy no_e, the interval obtained by subtracting the linear attenuation coefficient f at the linear attenuation coefficient number num_f from the linear attenuation coefficient f at the linear attenuation coefficient number num_f-1 is set to Δf. The values ​​obtained by adding 1×Δf, 2×Δf, ..., and α×Δf to the linear attenuation coefficient f at the linear attenuation coefficient number num_f are assigned to the values ​​in the table corresponding to the number of α.

[0121] Similarly, the gradients Δef(2), Δef(3), ..., Δef(num_pix_t) are calculated for pixels 2, 3, ..., num_pix_t, respectively. Furthermore, in order to quickly calculate the evaluation function ef after adding α, if, when calculating the above equation (3), none of the pixels no_pix(no_s, no_d, no_path, 1) to no_pix(no_s, no_d, no_path, num_pix(no_s, no_d, no_path)) that the path no_path of the scan no_s incident on the detector no_d passes through does not correspond to the pixel for which α has been added, the calculation of the above equations (2) and (3) may be omitted for that path 70, and the intensity I(nopath) of the radiation R in the path no_path may be set to the same value as I(no_path) before adding α.

[0122] The evaluation function ef may be any function as long as it can evaluate the difference between the projection data g_v obtained from the tomographic image no_f and the actually measured projection data g(no_s, no_d). For example, the following function may be used.

[0123] ef=∑∑abs(g_v(no_s, no_d)-g(no_s, no_d))

[0124] in,

[0125] The first ∑ is the sum associated with the scan no_s belonging to subset j.

[0126] The second Σ is the sum associated with detector no_d (the sum of 1 to num_d).

[0127] abs(x) represents the absolute value of x.

[0128] g_v(no_s, no_d) represents temporary projection data of scan no_s and detector no_d obtained from the tomographic image.

[0129] g(no_s, no_d) represents the measured projection data of the scanner no_s and the detector no_d.

[0130] …(5)

[0131] The process of step S218 in this exemplary embodiment will be described. The tomographic image generating unit 42 updates the tomographic image no_f according to the following equation.

[0132] no_f(1)←no_f(1)-β×Δef(1)

[0133] no_f(2)←no_f(2)-β×Δef(2)

[0134] …

[0135] no_f(num_pix_t)←no_f(num_pix_t)-β×Δef(num_pix_t)

[0136] Among them, β represents the update step size.

[0137] …(6)

[0138] Furthermore, if there is a pixel whose updated value of tomographic image no_f is less than the minimum value 1 of the linear attenuation coefficient number, the value of tomographic image no_f for that pixel is set to 1. Furthermore, if there is a pixel whose updated value of tomographic image no_f exceeds the maximum value num_f of the linear attenuation coefficient number, the value of tomographic image no_f for that pixel is set to the maximum value num_f.

[0139] As described above, through the processing described above, in this exemplary embodiment as well, a high-precision tomographic image of the inspection object 60 can be generated regardless of the amount of projection data.

[0140] The technology of the present invention is not limited to the above-described exemplary embodiments, and various modifications are possible. For example, the following modifications are possible.

[0141] In the above exemplary embodiments, the fan beam scanning method is described as an example, but the scanning method is not limited and any scanning method can be used. For example, the pencil beam method can also be applied. In the case of the pencil beam method, Figure 10AAs shown, a plurality of radiation sources 64S can be set at predetermined equal intervals within a predetermined region (radiation source 64) that is spatially extended, and a path 70 of radiation R can be set in parallel from each radiation source 64S. Then, after the data based on a series of parallel scans of the radiation source 64 and the detector 68 is set as one projection data and the data at different rotation angles is set as another projection data, each projection data is divided into subsets, and similarly to the case of the fan beam method, the tomographic image generation process described above (see Figure 7 or Figure 9 ) to generate a tomographic image. Figure 10A As shown, when the paths 70 of the radiation R are set in parallel, the intensity I0_e(no_e) in the above formula (3) represents the total number of photons (of energy e(no_e)) irradiated per unit time within the range centered on the path 70 and surrounded by the boundaries with adjacent paths 70. Alternatively, in the case of the pencil beam method, as in the fan beam method, Figure 5D As shown in FIG. 1 , each path 70 may be set at equal intervals of a small angle from each radiation source 64S. In this case, the intensity I0_e(no_e) in the above formula (3) is expressed in terms of Figure 5D The intensity I0_e(no_e) in equation (3) represents the sum of the number of photons irradiated per unit time within a small angular range centered on each path 70. Furthermore, as described above, in both the fan beam and pencil beam methods, radiation R is actually irradiated three-dimensionally from the radiation source 64. Therefore, to be precise, the intensity I0_e(no_e) in equation (3) above represents the sum of the number of photons irradiated per unit time within a small range centered on each path 70 within the beam plane and within a small range perpendicular to the beam plane and directed toward the detector. The pencil beam of this exemplary embodiment is an example of a parallel beam of the present invention.

[0142] In addition, in the case of cone beam method, as Figure 10B As shown, multiple radiation sources 64S can be set at predetermined three-dimensional intervals within a predetermined region (radiation source 64) extending in three-dimensional space, and paths 70 can be set at equal intervals of small angles in two straight directions from each radiation source 64S. Specifically, let the angles in the two directions be θ and φ, respectively, and the combination of these (θ, φ) represents an arbitrary direction. When the small angles are Δθ and Δφ, the paths 70 can be set in the following directions.

[0143] ..., (-Δθ, -2×Δφ / cos(Δθ)), (-Δθ, -Δφ / cos(Δθ)), (-Δθ, 0), (-Δθ, +Δφ / cos(Δθ)), (-Δθ, +2×Δφ / cos(Δθ)), ……

[0144] ……, (0,-2×Δφ), (0,-Δφ), (0,0), (0,+Δφ), (0,+2×Δφ),…

[0145] ..., (+Δθ, -2×Δφ / cos(Δθ)), (+Δθ, -Δφ / cos(Δθ)), (+Δθ, 0), (+Δθ, +Δφ / cos(Δθ)), (+Δθ, +2×Δφ / cos(Δθ)), ……

[0146] In addition, Figure 10B In the equation, the small angles Δθ and Δφ in the two directions are represented by solid arrows and wavy arrows, respectively. In addition, in order to make the range of the small solid angle centered on each path 70 equal, the small angle Δφ in the angle θ is corrected to Δφ / cos(θ) in the above equation (the range of θ is set to -90 degrees to +90 degrees, and the correction is made in such a way that Δφ increases as the absolute value of θ increases). Then, each projection data is divided into subsets, and similarly to the case of the fan beam method, the tomographic image generation process described above can be performed (see Figure 7 or Figure 9 ) generates a tomographic image. However, in the case of the cone beam method, the detector 68 is arranged two-dimensionally, and the detector number no_d represents the number in the two-dimensional arrangement. In addition, the tomographic image no_f (no_pix) is a three-dimensional image, and the pixel no_pix of the tomographic image represents the pixel in the three-dimensional image. The tomographic image of the fan beam method is composed of two-dimensional pixels, while the tomographic image of the cone beam method is composed of three-dimensional voxels. In this exemplary embodiment, not only the two-dimensional image but also the three-dimensional image is called a tomographic image, and the three-dimensional voxels are also called pixels. In the case of the cone beam method, the intensity I0_e (no_e) in the above formula (3) represents the intensity I0_e (no_e) for Figure 10B The total number of photons (of energy e(no_e)) emitted per unit time within the small angular ranges of -Δθ / 2 to +Δθ / 2 and -Δφ / cos(θ) / 2 to +Δφ / cos(θ) / 2 in two directions of each path 70 with the path 70 as the center. In addition, in the case of the pencil beam method and the fan beam method, it can also be as follows Figure 10BAs shown, a radiation source 64 is set in three-dimensional space, and paths 70 extending in the three-dimensional space are set (paths 70 may also be set at slight angular intervals in two directions). In this case, each detector is set as a two-dimensional surface, and a tomographic image is constructed from three-dimensional voxels. Various tables are calculated and generated, including the number num_path(no_s, no_d) of paths 70 incident on each detector, the number num_pix(no_s, no_d, no_path) of pixels passed through by each path 70, or the pixel identification number no_pix(no_s, no_d, no_path, no_pix_path), and the path length plen_pix(no_s, no_d, no_path, no_pix_path).

[0147] In each of the above exemplary embodiments, the paths 70 of the radiation R are set at equal intervals, but by changing the intervals for each path 70, it is possible to express the difference in the intensity of the radiation R for each path 70. For example, in the case where it is desired to set the intensity of the irradiated radiation R to increase closer to the center of the radiation source 64 and decrease closer to the end, the paths 70 can be set at narrower intervals (dense) closer to the center of the radiation source 64 and at wider intervals (sparser) closer to the end. Alternatively, the intensity I0_e(no_e) irradiated from the radiation source 64 can be changed for each path 70. In this case, in the above formula (3), the intensity I0_e(no_e) of the energy e(no_e) irradiated from the radiation source 64 is set to the intensity I0_e(no_s, no_d, no_path, no_e) in the path no_path of the scan no_s incident on the detector no_d, and the intensity I0_e can be changed according to the path 70. In this case, in the table generation process (refer to Figure 4 ), table I0_e (no_s, no_d, no_path, no_e) is also generated.

[0148] In addition, each of the above exemplary embodiments can also be implemented when the information of the linear attenuation coefficient of the inspection object 60 is not a value in two or more energy bands but a single value. When the information of the linear attenuation coefficient is a single value, Figure 6 The linear attenuation coefficient table f(no_f, no_e) shown has a value f(no_f) for each linear attenuation coefficient identification number no_f. In addition, the above formula (3) is expressed as the following formula (3_2).

[0149] I=I0×exp(-{(f(no_f_ext(1))×plen_pix(no_s, no_d, no_path, 1))+(f(no_f_ext(2))×plen_pix(no_s, no_d, no_path, 2) )+……+(f(no_f_ext(num_pix(no_s, no_d, no_path)))×plen_pix(no_s, no_d, no_path, num_pix(no_s, no_d, no_path)))})

[0150] in,

[0151] no_f_ext(no_pix_path)=no_f(no_pix(no_s, no_d, no_path, no_pix_path))......(3_2)

[0152] Then, by omitting the above formula (2), the intensities I obtained for the respective paths 70 by the above formula (3_2) are respectively designated as I(1), I(2), ..., and the provisional projection data g_v can be obtained by the above formula (1).

[0153] In each of the exemplary embodiments described above, the paths 70 incident on the respective detectors 68 can be approximately grouped into one. In this case, the table generated in advance is as follows.

[0154] Table num_path(no_s, no_d): indicates the number of paths 70 incident on detector no_d for scan no_s. Even when the paths 70 are aggregated into one, the first exemplary embodiment requires a table showing the number of paths 70 before aggregation.

[0155] Table num_pix(no_s, no_d): This table represents the number of pixels passed through by at least one path 70 from among paths 1 to num_path(no_s, no_d) incident on detector no_d during scan no_s before the paths 70 are combined into one. If paths 70 are not combined into one, table num_pix(no_s, no_d, no_path) is generated. However, if paths 70 are combined into one, no_path can be omitted.

[0156] Table no_pix(no_s, no_d, no_pix_path): This table represents the number of the pixel no_pix_path th pixel passed through by at least one of the paths 1 to num_path(no_s, no_d) incident on detector no_d during scan no_s, before paths 70 are combined into one. If paths 70 are not combined into one, table no_pix(no_s, no_d, no_path, no_pix_path) is generated. However, if paths 70 are combined into one, table no_path can be omitted.

[0157] Table plen_pix(no_s, no_d, no_pix_path): This table represents the path lengths of at least one path 70 from among paths 1 to num_path(no_s, no_d) incident on detector no_d during scan no_s before the paths 70 are combined into one, i.e., the average path lengths. plen_pix(no_s, no_d, no_pix_path) is calculated by extracting all pixels passed by at least one path 70 from among paths 1 to num_path(no_s, no_d) before the paths 70 are combined into one, calculating the sum of the path lengths passed by paths 1 to num_path(no_s, no_d) in each pixel, and dividing this sum by num_path(no_s, no_d). Furthermore, the pixel number no_pix_path in the table no_pix(no_s, no_d, no_pix_path) and the table plen_pix(no_s, no_d, no_pix_path) are made common. When the paths 70 are not combined into one, the table plen_pix(no_s, no_d, no_path, no_pix_path) is generated. However, when the paths 70 are combined into one, no_path can be omitted.

[0158] Table I0_e (no_s, no_d, no_e): This represents the sum of the intensities of the energies e(no_e) of the radiation R incident on detector no_d for all paths 70 of scan no_s before the paths 70 are combined into one. When the paths incident on each detector are combined into one, the number of paths 70 incident on each detector before the combination varies from detector to detector. In other words, in equation (3), the intensity I0_e(no_e) of the energy e(no_e) emitted from the radiation source 64 varies from detector to detector. Therefore, table I0_e(no_s, no_d, no_e) is generated in advance instead of the intensities I0_e(no_e).

[0159] Table plen_pix_sub(no_sub, no_pix): represents the sum of the path lengths of all paths 70 incident on all detectors and passing through pixel no_pix for all scans belonging to subset no_sub. Even when paths 70 are aggregated into one, in the first exemplary embodiment, a table summing the path lengths is still required.

[0160] When the paths 70 incident on the respective detectors 68 are approximately combined into one path, the above-mentioned equation (3) becomes the following equation (33).

[0161] I_e(no_e)=I0_e(no_s, no_d, no_e)×exp(-{(f(no_f_ext(1), no_e)×plen _pix(no_s,no_d,1))+(f(no_f_ext(2),no_e)×plen_pix(no_s,no_d,2))+…+(f(no_f_ext(num_pix(no_s,no_d)),no_e)×plen_pix(no_s,no_d,num_pix(no_s,no_d)))})

[0162] in,

[0163] no_f_ext(no_pix_path)=no_f(no_pix(no_s, no_d, no_pix_path))…(3_3)

[0164] Furthermore, the above-mentioned formula (2) can be replaced with the following formula (2_3) to obtain the provisional projection data g_v. In this case, the above-mentioned formula (1) can be omitted.

[0165] g_v=(e(1)×I_e(1))+(e(2)×I_e(2))+……+(e(num_e)×I_e(num_e))…(2_3)

[0166] In addition, Figure 7In step S218 of the tomographic image generation processing of the first exemplary embodiment shown, the estimated value df(no_s, no_d) of the difference in the linear attenuation coefficient number in the path 70 of the scan no_s incident to the detector no_d is weighted by the path length and added to the no_pix_path-th pixel no_pix(no_s, no_d, no_pix_path) of the tomographic image df_ave of the average value of the difference, and the formula of the difference obtained by weighting by the path length is df(no_s, no_d)×plen_pix(no_s, no_d, no_pix_path)×num_path(no_s, no_d).

[0167] When the paths 70 incident on each detector 68 are approximately combined into one, and when the information on the linear attenuation coefficient of the inspection object 60 is not a single value in two or more energy bands but a single value, provisional projection data g_v can be generated by replacing the above equation (3) with the following equation (3_4). In this case, the above equations (1) and (2) can be omitted.

[0168] g_v=I0_e(no_s, no_d)×exp(-{(f(no_f_ext(1))×plen_pix(no_s, no_d, 1))+(f(no_f_ext(2))×plen_pix( no_s, no_d, 2))+……+(f(no_f_ext(num_pix(no_s, no_d)))×plen_pix(no_s, no_d, num_pix(no_s, no-d)))})

[0169] in,

[0170] no_f_ext(no_pix_path)=no_f(no_pix(no_s, no_d, no_pix_path))…(3_4)

[0171] In addition, in each of the above-mentioned exemplary embodiments, the detection efficiency of the detector 68 at each energy can also be set to de(1), de(2)...de(num_e), and the above formula (2) can be set to the following formula (25) to reflect the detection efficiency of the detector 68 in the intensity I of the radiation R.

[0172] I(no_path)=(e(1)×I_e(1)×de(1))+(e(2)×I_e(2)×de(2))+……+(e(num_e)×I_e(num_e)×de(num_e))…(2_5)

[0173] In addition, in the tomographic image generation process of the first exemplary embodiment (see Figure 7 ), in step S212, instead of calculating the linear attenuation coefficient number difference df(no_s, no_d), rf(no_s, no_d) may be calculated as the ratio of the linear attenuation coefficient numbers. Specifically, the provisional projection data g_v is compared with the measured projection data g(no_s, no_d). If g_v is larger or smaller, the linear attenuation coefficient numbers of all pixels passing through all paths incident on the detector no_d by the scan no_s are increased or decreased by a predetermined amount. Provisional projection data g_v is then calculated again and compared with the measured projection data g(no_s, no_d). This process is repeated until g_v is equal to or greater than g(no_s, no_d). Furthermore, the following data is used: The ratio of the linear attenuation coefficient numbers used during calculation (g_v, whichever is closer to the actual measured projection data g(no_s, no_d) than the value g_v when the value is about to fall below or above g(no_s, no_d) and the value g_v when the value falls below or above g(no_s, no_d)) is used. Here, the ratio of increasing or decreasing the specified amount refers to determining a specified amount increment of the ratio and multiplying the ratio by the increment while increasing or subtracting the ratio. For example, the specified amount increment of the ratio may be determined as 0.01, and the ratio may be multiplied by 1.01, 1.02, etc., or by 0.99, 0.98, etc. Since the linear attenuation coefficient number no_f is an integer, it is rounded after multiplication by the ratio. When the ratio of the prescribed amount of the linear attenuation coefficient number is set to α and the ratio α is added or subtracted to calculate g_v, in the above formula (3), no_f_ext(no_pix_path) can be replaced with round(α×no_f(no_pix(no_s, no_d, no_path, no_pix_path))) instead of no_f(no_pix(no_s, no_d, no_path, no_pix_path))). In addition, round(x) refers to the value obtained by rounding x. Of course, in the case where there is a pixel for which round(α×no_f(no_pix(no_s, no_d, no_path, no_pix_path))) exceeds the maximum value of the linear attenuation coefficient number (or conversely is less than the minimum value 1), the linear attenuation coefficient number for that pixel is fixed to the maximum value (minimum value 1).

[0174] When calculating the ratio rf(no_s, no_d) of the linear attenuation coefficient numbers, in the first exemplary embodiment (refer to Figure 7), first, in step S218, the tomographic image of the average value of the ratio is set to rf_ave(no_pix), and all pixels are set to zero to initialize rf_ave(no_pix). Then, for all projection data belonging to subset j, the ratios of the linear attenuation coefficient numbers rf(no_s, 1) to rf(no_s, num_d) are weighted by the path length and added to each pixel of the tomographic image rf_ave of the average value of the ratio. Finally, the average value of the ratio is calculated for each pixel by dividing the value of each pixel of rf_ave by the sum of the path lengths of each pixel, plen_pix_sub, and the tomographic image is updated by multiplying the tomographic image no_f before updating by the ratio and rounding it off. Of course, when adding the ratio of the linear attenuation coefficient number rf(no_s, no_d) to each pixel of the tomographic image rf_ave, if there is a pixel in which the result of multiplying the tomographic image no_f before the update by the ratio exceeds the maximum value of the linear attenuation coefficient number or is less than the minimum value 1, for this pixel, the ratio is corrected in such a way that the multiplication result reaches the maximum value or the minimum value 1, and then added to rf_ave.

[0175] In the step of quantizing the tomographic image in each of the above exemplary embodiments, it is not necessary to quantize all pixels of the tomographic image. For example, only when the linear attenuation coefficient number falls within a predetermined range close to the value of a component included in the inspection object, it may be corrected to the linear attenuation coefficient number of the component. Figure 6 In the case of the example, pixels with linear attenuation coefficient numbers below 3 are corrected to the linear attenuation coefficient numbers of air, pixels with linear attenuation coefficient numbers between 4 and 20 are corrected to the linear attenuation coefficient numbers of aluminum, pixels with linear attenuation coefficient numbers above 70 are corrected to the linear attenuation coefficient numbers of iron, and pixels other than these may not be corrected. In this case, it is also necessary to correct to the values ​​of the components included in the inspection object only at the last update (when the total number of updates i reaches Ni). In the case where the inspection object also includes components other than those set in the linear attenuation coefficient table, at the last update, correction may be performed only when the linear attenuation coefficient number enters the specified range close to the set component. In addition, quantization may be performed not every time the tomographic images of all subsets are updated, but may be performed every time the tomographic images of all subsets are updated several times.

[0176] As described above, projection data includes not only direct radiation emitted from the radiation source and attenuated by passing through the inspection object, but also scattered radiation generated as new radiation from a portion of the attenuated direct radiation as the direct radiation attenuates. In particular, a large number of scattered radiation are included in the cone beam method. Therefore, in each of the exemplary embodiments described above, it is preferable to also consider scattered radiation when generating tomographic images. The probability of occurrence of scattered radiation at each scattering angle is represented by p_s(no_e_in, no_e_out, no_th). no_e_in represents the energy of the incident radiation, no_e_out represents the energy of the scattered radiation, and no_th represents the angle (hereinafter referred to as the scattering angle) of the direction of the scattered radiation relative to the direction of the incident radiation (hereinafter referred to as the incident direction). The occurrence probability p_s represents the probability that scattered radiation with energy no_e_out will be generated at an angle no_th relative to the direction of the radiation (hereinafter referred to as the incident direction) when radiation with energy no_e_in travels a unit distance. In addition, in this exemplary embodiment, since the energy no_e_in of the incident radiation, the energy no_e_out of the scattered radiation, and the scattering angle no_th are handled discretely, they are represented by numbers. Figure 11 In the figure, the direction of the incident light is indicated by a thick solid arrow, the direction of the scattered light is indicated by a thin solid arrow, the scattering angle is indicated by θ, the direction of the scattered light projected onto a plane (xy plane) perpendicular to the incident light is indicated by a thin wavy arrow, and the angle between the direction of the scattered light projected onto the xy plane perpendicular to the incident light and the x-axis (positive direction) is indicated by φ. Figure 11 As shown, the scattering angle θ is the polar angle (zenith angle) in three-dimensional polar coordinates with the incident direction being the z-axis (positive direction), and the angle φ is the azimuth angle (declination angle). The occurrence probability p_s is equiprobable with respect to the angle φ.

[0177] When radiation passes through an object under inspection, the probability of each interaction, such as the photoelectric effect, interferometric scattering (Thomson scattering), non-interferometric scattering (Compton scattering), and electron pair formation, varies depending on the energy of the radiation. Furthermore, the angular distribution of scattered radiation, that is, the probability of scattering at each angle, varies depending on the type of interaction. Therefore, in this exemplary embodiment, the sum of the probabilities of scattered radiation at scattering angle no_th due to each interaction, which varies depending on the energy no_e_in, is expressed as p_s(no_e_in, no_e_out, no_th). In other words, the sum of the probabilities of scattered radiation due to each interaction at each energy no_e_in is expressed as a function p_s(no_e_in, no_e_out, no_th). Furthermore, depending on the type of interaction, the energy before and after scattering varies. For example, in the case of interferometric scattering, the energy of the incident radiation is the same as the energy of the scattered radiation, but in the case of non-interferometric scattering, the energy of the incident radiation is different from the energy of the scattered radiation, and the energy of the scattered radiation varies depending on the scattering angle. Therefore, in this exemplary embodiment, the probability of scattered radiation occurrence p_s is determined as a function of the energy no_e_in of the incident radiation and the energy no_e_out of the scattered radiation. Furthermore, since the probability of scattered radiation occurrence p_s varies depending on the component included in the inspection object, in this exemplary embodiment, each component has a specific probability p_s. The probability of scattered radiation occurrence p_s can be determined by actually measuring the intensity of scattered radiation at each angle no_th with each energy no_e_out for each component, or it can be determined theoretically. For example, in the case of non-interferometric scattering, the probability of scattering at each angle can be calculated using the Klein-Nishina formula. The probability of scattered radiation occurrence p_s can also be assigned using any method. For example, it can be assigned using a three-dimensional table (no_e_in, no_e_out, no_th) or a function with the three variables no_e_in, no_e_out, and no_th as arguments. In this exemplary embodiment, the sum of the probabilities of occurrence of scattered rays due to various interactions is expressed as a function p_s. However, it may be given by a combination of the probability of occurrence of each interaction at each energy no_e_in and the probability of scattering at each energy no_e_out and each angle no_th when each interaction occurs.

[0178] Scatter calculations are computationally intensive and time-consuming, so in the exemplary embodiments described above, they are preferably performed less frequently. For example, scatter calculations can be performed after the tomographic image is quantized. Alternatively, scatter calculations can be performed each time the tomographic image is quantized a predetermined number of times. Since quantization of the tomographic image assigns each pixel in the tomographic image to a component included in the inspection object, scatter calculations can be performed by assigning the probability of scattered radiation occurrence p_s for the corresponding component to each pixel. In the scatter calculations, scattered radiation projection data is generated based on the tomographic image as g_s(1, 1) to g_s(num_s, num_d). Here, num_s represents the number of scans. Then, the calculated scattered ray projection data g_s(1,1) to g_s(num_s, num_d) are subtracted from the measured projection data g(1,1) to g(num_s, num_d), and only the direct ray projection data are obtained as g_p(1,1) to g_p(num_s, num_d). Furthermore, in the next update, the tomographic image is updated based only on the direct ray projection data g_p(1,1) to g_p(num_s, num_d), instead of the measured projection data g(1,1) to g(num_s, num_d). Then, after quantizing the updated tomographic image, scattered ray projection data g_s(1, 1) to g_s(num_s, num_d) are generated based on the tomographic image, and these data are subtracted from the measured projection data g(1, 1) to g(num_s, num_d) to generate only direct ray projection data g_p(1, 1) to g_p(num_s, num_d). Furthermore, the operation of updating the tomographic image based only on the direct ray projection data g_p(1, 1) to g_p(num_s, num_d) is repeated for the next update.

[0179] A method for calculating scattering based on the scattered ray occurrence probability p_s(no_e_in, no_e_out, no_th) for each pixel and generating scattered ray projection data g_s(1, 1) to g_s(num_s, num_d) will be described. When only the first scattering (hereinafter referred to as primary scattering) is calculated as a scattering calculation method, and multiple scattering (second, third, and so on) is not calculated, the scattered ray projection data g_s can be generated as follows. Focusing on the no_pix_path-th pixel no_pix(no_s, no_d, no_path, no_pix_path) through which the path no_path incident on the detector no_d passes, the intensity of the scattered ray generated when radiation of energy no_e on the path no_path is attenuated in pixel no_pix(no_s, no_d, no_path, no_pix_path) is defined as I_pix_out(no_e_out, noth) and assigned using the following equation (7).

[0180] I_pix_out(no_e_out, no_th)=I_pix(no_e)×plen_pix(no_s, no_d, no_pat h, no_pix_path)×p_s(no_e, no_e_out, no_th)…(7)

[0181] In equation (7), I_pix(no_e) represents the intensity of radiation with energy no_e on path no_path when it enters pixel no_pix(no_s, no_d, no_path, no_pix_path). I_pix_out(no_e_out, no_th) in equation (7) represents the intensity of radiation with incident intensity I_pix(no_e) scattered at each scattering angle no_th and each energy no_e_out with probability p_s(no_e, no_e_out, no_th) relative to the incident direction of path no_path, as radiation with incident intensity I_pix(no_e) attenuates along path no_path over path length plen_pix(no_s, no_d, no_path, no_pix_path). In equation (7), it should be noted that the scattering angle no_th is the polar angle (zenith angle) relative to the incident direction of path no_path. In equation (7), the scattering probability p_s(no_e, no_e_out, no_th) is equiprobable with respect to azimuth (declination). As a result, the scattering intensity I_pix_out(no_e_out, no_th) is uniform with respect to azimuth (declination). Furthermore, when calculating the scattering intensity I_pix_out(no_e_out, no_tn) for incident radiation of energy no_e using equation (7), calculations can be omitted for combinations of no_e_out and no_th for which the scattering probability p_s(no_e, no_e_out, no_th) is zero. After obtaining the intensity I_pix_out (no_e_out, no_th) of the scattered rays scattered at each energy no_e_out and each scattering angle no_th by formula (7), the detector into which each scattered ray enters can be identified among detectors 1 to num_d through geometric calculation based on the path no_path in the pixel no_pix (no_s, no_d, no_path, no_pix_path) and the positional relationship between the detectors 1 to num_d that scan no_s (assuming that each scattered ray does not undergo multiple scattering (scattering after the second time)). Similarly, geometric calculations can be used to determine the pixels through which each scattered ray passes from the path no_path in pixel no_pix (no_s, no_d, no_path, no_pix_path) at the scattering angle no_th until it reaches the detector, as well as the path length in these pixels. Therefore, based on the linear attenuation coefficient and path length in each energy no_e_out of each pixel passed through, the intensity of the scattered ray generated at each energy no_e_out and each scattering angle no_th when it is attenuated and incident on the detector can be determined.Furthermore, scattered radiation generated along the path of a direct ray incident on detector no_d can be detected by all detectors 1 to num_d in fan-beam or cone-beam modes, but can only be detected by detector no_d in pencil-beam mode. Taking into account the fact that the detectors capable of detection are limited by the scanning method, the detector on which scattered radiation generated along the path of a direct ray incident on detector no_d is identified after the detector capable of detecting scattered radiation is limited based on detector number no_d. The calculation described above allows for the determination of the intensity of scattered radiation incident on each detector, generated when radiation of energy no_e on path no_path attenuates, at pixel no_pix_path (no_s, no_d, no_path, no_pix_path) passed through by path no_path incident on detector no_d in scan no_s. Similarly, calculations can be performed for all energies 1 to num_e, all pixels 1 to num_pix(no_s, no_d, no_path), all paths 1 to num_path(no_s, no_d), and all detectors 1 to num_d. Through the above calculations, the sum of the intensities of scattered rays incident on each detector 1 to num_d during the scan no_s at each energy 1 to num_e can be calculated. Furthermore, for each detector 1 to num_d scanning no_s, the detected intensities, i.e., projection data g_s(no_s, 1) to g_s(no_s, num_d), can be generated by multiplying the scattered ray intensities at each energy by the energy and then adding them up (see equation (2) above). Furthermore, when summing the scattered ray intensities at each energy, the detection efficiency of the detector can also be reflected (see equation (25) above). Similarly, projection data g_s(1, 1) to g_s(num_s, num_d) for all scans can be generated. Furthermore, variables that are repeatedly used in scatter calculations and can be calculated in advance, such as the detector into which each scattered ray generated on the path of the direct ray enters, the pixels through which each scattered ray passes before reaching the detector, and the path lengths in these pixels, are preferably calculated in advance and tabulated.The method described above can generate projection data g_s(1,1) to g_s(num_s, num_d) for only the primary scattered rays. However, projection data g_s(1,1) to g_s(1,num_d), g_s(2,1) to g_s(2,num_d), etc., for each scan can also be blurred for each scan to virtually generate projection data for multiple scattered rays (secondary, tertiary, etc. scattered rays). This data can then be added to the original projection data to convert g_s(1,1) to g_s(num_s, num_d) into projection data for a combination of primary scattered rays and multiple scattered rays. Furthermore, in the pencil beam method, while the data from detectors 1 to num_d in each scan represent data for primary scattered rays generated by different beams, data from beams that are particularly close to each other are treated as data for primary scattered rays generated by approximately the same beam and blurred in one dimension, similar to the fan beam method (however, the blurring is less severe than in the fan beam method).

[0182] The following method will be described. Based on the scattered ray occurrence probability p_s (no_e_in, no_e_out, no_th) for each pixel, calculations are performed to include not only primary scattering but also multiple scattering to generate projection data g_s(1, 1) to g_s(num_s, num_d). When multiple scattering is also included, projection data can be generated using Monte Carlo simulation. Specifically, a large number of photons of radiation of various energies no_e are emitted in various directions from a radiation source 64. For each photon, based on the linear attenuation coefficient and scattered ray occurrence probability p_s (no_e_in, no_e_out, no_th) for each pixel, a determination is made as to whether the photon is allowed to travel in a straight line, absorbed, or scattered, each time the photon is allowed to travel along its mean free path. If the photon is scattered, the determination of the direction of scattering is repeated until the photon reaches a detector 68. This generates projection data g_s(1, 1) to g_s(num_s, num_d). Note that, in the case of the pencil beam method, the detector that can detect scattered photons is limited to one detector corresponding to the radiation source 64 .

[0183] In the exemplary embodiments described above, the overlapping region of each scan is used as the tomographic image region, and the attenuation of the radiation R in other regions, that is, regions outside the tomographic image, is not considered. The region outside the tomographic image is filled with a material corresponding to the environment in which the inspection object 60 exists, and its linear attenuation coefficient is known. Furthermore, for each path no_path along which the radiation R emitted from the radiation source 64 of each scan no_s enters each detector no_d, the path length from the radiation source 64 to the tomographic image region and the path length from the tomographic image region to the detector are known. Therefore, based on this information, provisional projection data g_v can be more accurately calculated. For example, if the region outside the tomographic image is filled with air, the attenuation in this region outside the tomographic image can be calculated based on the linear attenuation coefficient of air, thereby more accurately calculating the provisional projection data g_v. As a result, a more accurate tomographic image of the inspection object can be generated. However, the presence or absence of calculation of attenuation in the region outside the tomographic image is believed to have little impact on the quality of the tomographic image.

[0184] Furthermore, in the exemplary embodiments described above, the information processing device 10 uses a single piece of projection data at each imaging position to generate a tomographic image. However, the number of projection data acquired by the detector 68 at each imaging position need not necessarily be one. Specifically, in the exemplary embodiments described above, the acquisition unit 40 of the information processing device 10 acquires a single piece of projection data corresponding to each imaging position in order for the tomographic image generator 42 of the information processing device 10 to generate a tomographic image. However, the number of projection data acquired by the detector 68 at each imaging position is not limited to one and may be multiple. For example, at each imaging position, the inspection object 60 may be irradiated with radiation R of different energies from the radiation source 64, with the detector 68 acquiring multiple pieces of projection data corresponding to each energy. If the radiation R is X-rays, the energy of the irradiated X-rays can be varied, for example, by changing the tube voltage of the radiation source 64. Furthermore, the information processing device 10 may acquire only the projection data corresponding to each energy at each imaging position to generate a tomographic image corresponding to each energy. For example, when the inspection object 60 is irradiated with radiation R of two energies respectively and projection data corresponding to the two energies are obtained by the detector 68, the information processing device 10 can only obtain the projection data corresponding to the first energy among the projection data corresponding to the two energies at each imaging position to generate a tomographic image corresponding to the first energy. Similarly, it can also only obtain the projection data corresponding to the second energy to generate a tomographic image corresponding to the second energy.

[0185] Furthermore, in each of the exemplary embodiments described above, the tomographic image generator 42 of the information processing device 10 displays the generated tomographic image on the display 26. However, the tomographic image generator 42 may simply perform processing to generate the tomographic image without necessarily displaying the image on the display 26. For example, the tomographic image generator 42 may simply perform processing to generate tomographic images corresponding to the aforementioned multiple energies, without displaying the individual tomographic images on the display 26. Instead, the information processing device 10 may generate a tomographic image (hereinafter referred to as a composite tomographic image) by combining the tomographic images corresponding to the multiple energies and display the composite tomographic image on the display 26. A composite tomographic image can be generated from the tomographic images corresponding to the multiple energies, for example, as follows. For example, as the linear attenuation coefficient number for each pixel in the composite tomographic image, the linear attenuation coefficient numbers for the same pixel in the tomographic images corresponding to the multiple energies may be compared, and the largest linear attenuation coefficient number, i.e., the linear attenuation coefficient number of the component with the largest linear attenuation coefficient, may be assigned. Alternatively, as the linear attenuation coefficient number for each pixel in the synthetic tomographic image, the linear attenuation coefficient number occupying the largest area among the linear attenuation coefficient numbers for the same pixel in each tomographic image corresponding to a plurality of energies may be assigned. Specifically, the linear attenuation coefficient number having the largest spatial range (area or volume) of pixels connected (linked) to the same linear attenuation coefficient number including the pixel may be assigned. Furthermore, in order to generate the synthetic tomographic image as described above, the linear attenuation coefficient numbers for each tomographic image corresponding to a plurality of energies are set to be the same.

[0186] In addition, in each of the above exemplary embodiments, the number of projection data used when the information processing device 10 generates a tomographic image is one at each imaging position. However, as long as one is used at each of at least two imaging positions, the number of projection data used at other imaging positions need not be one. In other words, multiple projection data (for example, projection data corresponding to multiple energies) may be used depending on the imaging position. In this case, in each of the above exemplary embodiments, for imaging positions using multiple projection data, provisional projection data g_v is calculated for each of the multiple projection data using equation (1), and the average value of these values ​​is used as the provisional projection data g_v. Similarly, the average value of the multiple measured projection data g(no_s, no_d) may be used as the measured projection data g(no_s, no_d). In order to calculate the provisional projection data g_v as described above, the linear attenuation coefficient numbers used when calculating the provisional projection data g_v for each of the multiple projection data are made common.

[0187] Furthermore, in each of the exemplary embodiments described above, various processors described below can be used as the hardware configuration of processing units that perform various processes, such as the acquisition unit 40 and the tomographic image generation unit 42. These various processors include general-purpose processors (CPUs) that execute software (programs) to function as various processing units, as described above, as well as processors whose circuit configuration can be modified after manufacture, such as FPGAs (Field Programmable Gate Arrays), programmable logic devices (PLDs), and application-specific integrated circuits (ASICs), processors with circuit configurations specifically designed to perform specific processes, such as dedicated circuits.

[0188] A processing unit may be composed of one of these various processors, or a combination of two or more processors of the same or different types (e.g., a combination of multiple FPGAs, a combination of a CPU and an FPGA). Alternatively, multiple processing units may be composed of one processor.

[0189] As examples of multiple processing units composed of a single processor, there is a first approach, typified by computers such as clients and servers, where a single processor is composed of a combination of one or more CPUs and software, with this processor functioning as multiple processing units. A second approach, typified by systems on a chip (SoC), uses a processor that implements the functions of the entire system, including multiple processing units, on a single integrated circuit (IC). In this way, various processing units are constructed using one or more of the aforementioned processors as hardware structures.

[0190] Furthermore, as the hardware configuration of these various processors, more specifically, a circuit (circuitry) formed by combining circuit elements such as semiconductor elements can be used.

[0191] Furthermore, in the above exemplary embodiments, the table generation program 30 and the tomographic image generation program 32 are pre-stored (installed) in the storage unit 24 of the information processing device 10, but the present invention is not limited thereto. The table generation program 30 and the tomographic image generation program 32 may be provided by being recorded on a recording medium such as a CD-ROM (Compact Disc Read Only Memory), a DVD-ROM (Digital Versatile Disc Read Only Memory), or a USB (Universal Serial Bus) memory device. Furthermore, the table generation program 30 and the tomographic image generation program 32 may be downloaded from an external device via a network.

[0192] Based on the above description, the invention described in the following supplementary notes can be grasped.

[0193] [Note 1]

[0194] An information processing device, wherein:

[0195] Acquiring one projection data item at each of at least two imaging positions among a plurality of imaging positions, each corresponding to a different radiation irradiation direction of the inspection object;

[0196] A tomographic image of the inspection object is generated based on the projection data and the attenuation information of the inspection object.

[0197] [Note 2]

[0198] The information processing device according to Supplementary Note 1, wherein:

[0199] The attenuation information includes attenuation information corresponding to at least two components included in the inspection object.

[0200] [Note 3]

[0201] The information processing device according to Supplement 1 or 2, wherein:

[0202] The attenuation information includes air attenuation information.

[0203] [Note 4]

[0204] The information processing device according to any one of Supplementary Notes 1 to 3, wherein:

[0205] The attenuation information includes attenuation information in at least two energy bands.

[0206] [Note 5]

[0207] The information processing device according to any one of Supplementary Notes 1 to 4, wherein:

[0208] The processor

[0209] The tomographic image is generated based on scattered rays generated by the inspection object.

[0210] [Note 6]

[0211] The information processing device according to any one of Supplementary Notes 1 to 5, wherein:

[0212] The processor

[0213] The tomographic image is generated based on the length of a path along which the radiation passes through the inspection object.

[0214] [Note 7]

[0215] The information processing device according to any one of Supplementary Notes 1 to 6, wherein:

[0216] The radiation is irradiated as any one of a parallel beam, a fan beam, and a cone beam.

[0217] [Note 8]

[0218] An information processing method, wherein a processor is caused to perform the following processing:

[0219] Acquiring one projection data item at each of at least two imaging positions among a plurality of imaging positions, each corresponding to a different radiation irradiation direction of the inspection object;

[0220] A tomographic image of the inspection object is generated based on the projection data and the attenuation information of the inspection object.

[0221] [Note 9]

[0222] An information processing program configured to cause a processor to execute the following processing:

[0223] Acquiring one projection data item at each of at least two imaging positions among a plurality of imaging positions, each corresponding to a different radiation irradiation direction of the inspection object;

[0224] A tomographic image of the inspection object is generated based on the projection data and the attenuation information of the inspection object.

[0225] Application filed in Japan on January 23, 2023: The disclosure of Japanese Patent Application No. 2023-008365 is incorporated herein by reference in its entirety.

[0226] All documents, patent applications, and technical specifications described in this specification are incorporated herein by reference to the same extent as if each individual document, patent application, or technical specification was specifically and individually described as being incorporated by reference.

Claims

1. An information processing device, wherein: having at least one processor, The processor Acquiring one projection data item at each of at least two imaging positions among a plurality of imaging positions, each corresponding to a different radiation irradiation direction of the inspection object; A tomographic image of the inspection object is generated based on the projection data and the attenuation information of the inspection object.

2. The information processing device according to claim 1, wherein The attenuation information includes attenuation information corresponding to at least two components included in the inspection object.

3. The information processing device according to claim 1, wherein The attenuation information includes air attenuation information.

4. The information processing device according to claim 1, wherein: The attenuation information includes attenuation information in at least two energy bands. The information processing device according to claim 1 , wherein: The processor The tomographic image is generated based on scattered rays generated by the inspection object. The information processing apparatus according to claim 1 , wherein: The processor The tomographic image is generated based on the length of a path along which the radiation passes through the inspection object.

7. The information processing apparatus according to claim 1, wherein: The radiation is irradiated as any one of a parallel beam, a fan beam, and a cone beam.

8. An information processing method, wherein: The processor performs the following processing: Acquiring one projection data item at each of at least two imaging positions among a plurality of imaging positions, each corresponding to a different radiation irradiation direction of the inspection object; A tomographic image of the inspection object is generated based on the projection data and the attenuation information of the inspection object.

9. An information processing program, wherein: It is used to enable the processor to perform the following processing: Acquiring one projection data item at each of at least two imaging positions among a plurality of imaging positions, each corresponding to a different radiation irradiation direction of the inspection object; A tomographic image of the inspection object is generated based on the projection data and the attenuation information of the inspection object.

Images