Information processing device, method, and program

Abstract

In information processing equipment, methods, and programs, accurate material discrimination is performed. [Solution] The processor acquires projection data for each of several energy bins obtained by detecting radiation transmitted through the subject using a photon counting detector, identifies a first suitable calibration data that matches the energy spectrum of the projection data from among several calibration data for material discrimination that represent the energy spectrum for a predetermined combination of calibration members, and determines the presence or absence of a singularity in the attenuation coefficient based on the projection data based on the difference between the attenuation coefficient based on the first suitable calibration data and the attenuation coefficient based on the projection data for each of the several energy bins. If the determination is affirmative, a second suitable calibration data that matches the projection data is identified from among the multiple calibration data based on the energy spectrum of the projection data on the high-energy side or low-energy side of the singularity.

Description

Technical Field

[0001] The present disclosure relates to an information processing apparatus, method, and program.

Background Art

[0002] In a radiation imaging apparatus such as a photon counting type X-ray CT (Computed Tomography) apparatus, a substance discrimination technique is known that discriminates substances contained in a subject using data corresponding to a plurality of energy bins by utilizing the fact that the radiation absorption characteristics vary for each substance. By using such a substance discrimination technique, in addition to a normal CT image, a substance discrimination image in which a specific substance contained in the subject is discriminated can be obtained (see, for example, Patent Document 1).

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] On the other hand, an index representing the absorption of photons by a substance (i.e., the attenuation coefficient) generally decreases continuously as the photon energy increases, but it is known that substances with a large atomic number (hereinafter referred to as high atomic number substances) have singularities such as K absorption edges (also referred to as K edges) where the attenuation coefficient changes discontinuously within the range of the X-ray energy to be measured. A substance containing a K absorption edge within the range of the X-ray energy to be measured is hereinafter referred to as a high atomic number substance. Examples of such high atomic number substances include, for example, a contrast agent injected into a subject, and artifacts contained in the body of the subject (such as gold teeth, bolts for fixing bones, and embolization coils for thrombi). Since substance discrimination is premised on the continuous change of the attenuation coefficient of a substance, if a substance with a discontinuous change in the attenuation coefficient is contained in the subject, substance discrimination cannot be performed accurately.

[0005] This disclosure is made in light of the above circumstances and aims to enable accurate material discrimination. [Means for solving the problem]

[0006] The information processing apparatus according to this disclosure includes a storage unit that stores multiple calibration data for material discrimination, which represent the energy spectrum of a combination of calibration members, obtained by measuring multiple types of calibration members consisting of combinations of two or more base materials with different compositions using a photon counting detector that converts incident radiation into the number of detected photons for three or more energy bins. Equipped with a processor, The processor is, By detecting the radiation that has passed through the subject using a photon counting detector, projection data for each of the multiple energy bins is obtained. Among multiple calibration data sets, identify the first fitted calibration data that matches the energy spectrum of the projection data. Based on the difference between the attenuation coefficient based on the first fitted calibration data and the attenuation coefficient based on the projection data for each of the multiple energy bins, the presence or absence of singularities in the attenuation coefficient based on the projection data is determined. If a singularity is detected, a second fitted calibration data set that matches the projected data is identified from among multiple calibration data sets based on the energy spectrum of the projected data on the high-energy or low-energy side of the singularity.

[0007] In the information processing device according to this disclosure, the processor may use a maximum likelihood estimation method to identify calibration data from among a plurality of calibration data that fits the energy spectrum of the projection data.

[0008] In the information processing device according to this disclosure, the processor may determine that there is a singularity when the difference between the attenuation coefficient based on identified calibration data and the attenuation coefficient based on projection data in each of the multiple energy bins is greater than or equal to a predetermined threshold.

[0009] In the information processing apparatus according to this disclosure, if the processor determines that there are no singularities, it derives a material discrimination image based on the first conformance calibration data. If a singularity is detected, a substance discrimination image may be derived based on the second fitting calibration data.

[0010] The information processing method according to this disclosure is an information processing device equipped with a storage unit that stores a plurality of calibration data for material discrimination, which represent the energy spectrum of a combination of calibration members, obtained by measuring a plurality of calibration members consisting of a combination of two or more base materials with different compositions using a photon counting detector that converts incident radiation into the number of detected photons for each of three or more energy bins, The computer acquires projection data for each of the multiple energy bins obtained by detecting the radiation that has passed through the subject using a photon counting detector. Among multiple calibration data sets, identify the first fitted calibration data that matches the energy spectrum of the projection data. Based on the difference between the attenuation coefficient based on the first fitted calibration data and the attenuation coefficient based on the projection data for each of the multiple energy bins, the presence or absence of singularities in the attenuation coefficient based on the projection data is determined. If a singularity is detected, a second fitted calibration data set that matches the projected data is identified from among multiple calibration data sets based on the energy spectrum of the projected data on the high-energy or low-energy side of the singularity.

[0011] The information processing program according to this disclosure is an information processing program that causes a computer to function as an information processing device equipped with a storage unit that stores multiple calibration data for material discrimination, which represent the energy spectrum of a combination of calibration members, obtained by measuring multiple types of calibration members consisting of combinations of two or more base materials with different compositions using a photon counting detector that converts incident radiation into the number of detected photons for each of three or more energy bins, A procedure for obtaining projection data for each of a plurality of energy bins acquired by detecting radiation transmitted through a subject using a photon counting type detector, A procedure for identifying first adaptive calibration data that matches the energy spectrum of the projection data among a plurality of calibration data, A procedure for determining the presence or absence of singularities in the attenuation coefficient based on the projection data, based on the difference between the attenuation coefficient based on the first adaptive calibration data and the attenuation coefficient based on the projection data, for each of the plurality of energy bins, When it is determined that there is a singularity, causing a computer to execute a procedure for identifying second adaptive calibration data that matches the projection data among a plurality of calibration data, based on the energy spectrum of the projection data on the high energy side or the low energy side of the singularity.

[0012] Note that the technology of the present disclosure may be applied to a program product.

Advantages of the Invention

[0013] According to the present disclosure, substance discrimination can be performed with high accuracy.

Brief Description of the Drawings

[0014] [Figure 1] Schematic configuration diagram of a medical imaging system equipped with an information processing device according to an embodiment of the present disclosure [Figure 2] Diagram showing the hardware configuration of the information processing device according to the present embodiment [Figure 3] Diagram showing the functional configuration of the information processing device according to the present embodiment [Figure 4] Diagram for explaining a method of obtaining calibration data [Figure 5] Diagram for explaining the derivation of the difference between the attenuation coefficient based on the first adaptive calibration data and the attenuation coefficient based on the projection data [Figure 6] Diagram showing a table of calibration data for explaining the derivation of the attenuation coefficient based on the projection data [Figure 7] Diagram showing the attenuation coefficient of a high atomic number substance [Figure 8] A diagram for explaining the derivation of the difference between the attenuation coefficient based on the first adaptive calibration data and the attenuation coefficient based on the projection data when there are singularities [Figure 9] A diagram for explaining the identification of the second adaptive calibration data when there are singularities [Figure 10] A flowchart showing the processes performed in this embodiment

Mode for Carrying Out the Invention

[0015] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings. First, an example of the configuration of a medical imaging system including the information processing apparatus of this embodiment will be described. FIG. 1 is a schematic configuration diagram of a medical imaging system including the information processing apparatus of this embodiment.

[0016] As shown in FIG. 1, the medical imaging system 1 of this embodiment includes a CT apparatus 2 and a console 3. The CT apparatus 2 includes a gantry 4 and a couch 8. In the following description, the horizontal direction in FIG. 1 is the X-axis, the vertical direction is the Y-axis, and the direction orthogonal to the XY plane is the Z-axis.

[0017] The gantry 4 has an opening 4A, and the subject H to be imaged is placed in the opening 4A while being placed on the couch 8. The gantry 4 and the couch 8 are relatively movable in the Z-axis direction.

[0018] Inside the gantry 4, a radiation source 5 having a radiation tube 6 and a bowtie filter 7, and a detector 9 are arranged facing each other with the subject H in between. The bowtie filter 7 optimizes the radiation dose by increasing the dose near the center and decreasing the dose around the periphery to reduce the exposure dose in the peripheral area. The radiation emitted from the radiation tube 6 is shaped into a beam shape suitable for the size of the subject H by the bowtie filter 7 and irradiated onto the subject H. The detector 9 detects the radiation that has passed through the subject H and generates projection data corresponding to the count of photons of the detected radiation. As an example, the detector 9 in this embodiment is a photon counting type detector in which multiple detection elements 9P that detect the photon energy, which is the energy of the photons of the incident radiation, are arranged in an arc shape centered on the focal point of the radiation tube 6. In this embodiment, the detector 9 detects the photon energy of the incident radiation by dividing it into multiple energy bins.

[0019] In this embodiment, X-rays are used as an example of radiation, but the invention is not limited to this, and gamma rays or other types of radiation may also be used.

[0020] The radiation source 5 and the detector 9 are rotated around the subject H by the rotation drive unit (not shown) of the gantry 4. As radiation irradiation from the radiation source 5 and detection of radiation by the detector 9 are repeated along with the rotation of both, data about the subject H (hereinafter referred to as projection data) is acquired for each radiation projection path. The projection data acquired by the detector 9 is output to the console 3 and stored in the console 3's storage. The data value corresponding to each detection element 9P, which is the smallest unit of projection data, is the count of photons detected by the detection element 9P. Projection data is acquired individually for each energy bin.

[0021] The radiation dose emitted from the radiation source 5, the rotation speed of the gantry 4, and the relative movement speed between the gantry 4 and the patient bed 8 are all set by the console 3 based on the acquisition conditions entered by the user, such as a technician, when acquiring projection data.

[0022] The console 3 in this embodiment performs control related to the acquisition of projection data, the generation of medical images, and the control related to material discrimination. The console 3 is an example of an information processing device of the present disclosure.

[0023] Next, the information processing device according to this embodiment will be described. First, the hardware configuration of the information processing device according to this embodiment will be described with reference to Figure 2. As shown in Figure 2, the information processing device 10 is a computer such as a workstation, server computer, or personal computer, and is equipped with a CPU (Central Processing Unit) 11, non-volatile storage 13, and memory 16 as a temporary storage area.

[0024] The information processing device 10 also includes a display 14, an input device 15, and an I / F (Interface) 17. The CPU 11, storage 13, display 14, input device 15, memory 16, and I / F 17 are connected to a bus 18. The CPU 11 is an example of a processor in this disclosure.

[0025] The storage 13 is implemented by an HDD (Hard Disk Drive), an SSD (Solid State Drive), flash memory, etc. The information processing program 12 installed on the information processing device 10 is stored in the storage 13 as a storage medium. The CPU 11 reads the information processing program 12 from the storage 13, expands it into memory 16, and executes the expanded information processing program 12. Calibration data, which will be described later, is also stored in the storage 13. The storage 13 is an example of a storage unit in this disclosure.

[0026] The display 14 is a device that displays various types of screens, such as a liquid crystal display or an EL (Electro Luminescence) display.

[0027] The input device 15 is used by the user to input instructions and various information regarding scanning conditions for acquiring projection data, image generation and display, etc. Examples of input devices 15 include various switches, buttons, touch panels, styluses, keyboards, and mice. The display 14 and the input device 15 may be integrated to form a touch panel display.

[0028] I / F17 communicates various types of information with the rotational drive unit (not shown) of the gantry 4, the radiation source 5, and the detector 9 via wired or wireless communication.

[0029] The information processing program 12 is stored in a memory device of a server computer connected to the network, or in network storage, in a state that allows external access, and is downloaded and installed on the computers comprising the information processing device 10 upon request. Alternatively, it may be recorded on a recording medium such as a DVD (Digital Versatile Disc) or CD-ROM (Compact Disc Read Only Memory) and distributed, and then installed from that recording medium onto the computers comprising the information processing device 10.

[0030] Next, the functional configuration of the information processing device according to this embodiment will be described. Figure 3 is a diagram showing the functional configuration of the information processing device according to this embodiment. As shown in Figure 3, the information processing device 10 includes an information acquisition unit 21, a first identification unit 22, a determination unit 23, a second identification unit 24, and a reconstruction unit 25. The CPU 11 functions as the information acquisition unit 21, the first identification unit 22, the determination unit 23, the second identification unit 24, and the reconstruction unit 25 by executing the information processing program 12.

[0031] The information acquisition unit 21 receives projection data P0 and calibration data C0 from the CT device 2 via the I / F 17. The calibration data C0 is acquired by performing calibration of the detector 9 using a calibration member. The calibration process will be described below.

[0032] In the medical imaging system 1 equipped with a photon counting detector 9, projection data for each energy bin of the subject H (i.e., the energy spectrum for each projection path) can be acquired, enabling the generation of material discrimination images in which substances with different compositions are decomposed, and medical images separated into multiple energy components. In order to obtain such material discrimination images, it is necessary to acquire calibration data C0 that represents the relationship between the output and photon energy when multiple combinations of base materials with known compositions are measured by the detector 9. Calibration refers to the acquisition of such calibration data C0.

[0033] The following describes an example of a method for obtaining calibration data C0. Figure 4 is a diagram illustrating the method for obtaining calibration data. To obtain calibration data C0, a calibration member consisting of a combination of one or more base materials with known compositions is used. In Figure 4, the calibration member 30 consists of a combination of two types of base materials: a first base material 30A and a second base material 30B. The first base material 30A and the second base material 30B have different attenuation coefficients for radiation. In this embodiment, the second base material 30B has a larger attenuation coefficient than the first base material 30A. For example, the first base material 30A can be water (a soft tissue equivalent), and the second base material 30B can be bone, which has a larger attenuation coefficient than water. Since it is difficult to measure water and bone, a water equivalent substance is used as the first base material 30A, and a bone equivalent substance is used as the second base material 30B.

[0034] In the example shown in Figure 4, two units of the first base material 30A and two units of the second base material 30B are combined. In this way, calibration data C0 is obtained for each of the 32 combinations of thicknesses of the first base material 30A and the second base material 30B in the direction of radiation transmission. For example, if there are M types of thicknesses for the first base material 30A and N types of thicknesses for the second base material 30B, then M × N calibration data C0 can be obtained for the M × N types of base material combinations 32.

[0035] Specifically, in the example shown in Figure 4, if we define a calibration member 30 that does not use the first base material 30A as a calibration member 30 in which the thickness of the first base material 30A is "0 (zero)", then there are M = 3 possible thicknesses for the first base material 30A. Similarly, if we define a calibration member 30 that does not use the second base material 30B as a calibration member 30 in which the thickness of the second base material 30B is "0 (zero)", then there are K = 3 possible thicknesses for the second base material 30B. Therefore, in this case, there are 3 × 3 = 9 possible combinations of base materials for the calibration member. Note that in Figure 4, "Air" corresponds to a calibration member 30 that does not use either the first base material 30A or the second base material 30B, that is, a calibration member 30 in which the thicknesses of both the first base material 30A and the second base material 30B are "0 (zero)".

[0036] In this embodiment, for each of the nine combinations 32, radiation is irradiated from the radiation source 5, and the detector 9 detects the radiation that has passed through the combination 32. As a result, the photon energy spectrum (i.e., the relationship between the energy of the radiation and the number of photons) for each combination 32 is acquired as calibration data C0. The nine types of calibration data C0 acquired in this way are output to the console 3.

[0037] In console 3, calibration data C0 acquired from CT device 2 is stored in storage 13, associated with the 32 types of combinations used to acquire the calibration data C0. The stored calibration data C0 is used for substance discrimination using the projection data P0 of subject H.

[0038] In this embodiment, the detector 9 has four energy bins. Therefore, for each of the nine combinations 32 of the base materials 30A and 30B described above, calibration data C0 may be stored as a table representing the number of photons for each of the four energy bins. Alternatively, calibration data C0 may be represented by a graph or mathematical formula and stored in storage 13.

[0039] The information processing device 10 according to this embodiment derives a material discrimination image by performing material discrimination using projection data P0 acquired by the CT device 2 when it photographs the subject H. To this end, the first identification unit 22 identifies a first fitted calibration data C1 from among a plurality of calibration data C0 that matches the energy spectrum of the projection data P0. Material discrimination will be described below.

[0040] Projection data P0 is acquired at various projection angles in the CT device 2 and has a radiation energy spectrum for each detection element 9P included in the detector 9. The first identification unit 22 identifies the first fitted calibration data C1 that is closest in shape to the energy spectrum of each detection element 9P in each projection data P0 from among the energy spectra represented by calibration data C0, and obtains the combination of base material thickness corresponding to the identified energy spectrum. Estimating the thickness of the base material by this method is equivalent to performing a fitting using the maximum likelihood estimation method between the energy spectrum represented by calibration data C0 and the energy spectrum of each detection element 9P.

[0041] The determination unit 23 derives the difference between the attenuation coefficient μc based on the first conformance calibration data C1 identified by the first identification unit 22 and the attenuation coefficient μp based on the projection data P0. Then, based on the difference, the determination unit 23 determines whether or not there is a K edge in the attenuation coefficient μp based on the projection data P0. Figure 5 is a diagram illustrating the derivation of the difference between the attenuation coefficient μc based on the first conformance calibration data C1 and the attenuation coefficient μp based on the projection data P0. In Figure 5, the horizontal axis represents the energy of the radiation (keV), and the vertical axis represents the attenuation coefficient. In Figure 5, the attenuation coefficient μc based on the first conformance calibration data C1 is shown by a solid line. The attenuation coefficient μc can be derived for each energy bin of the detection element 9P using the relationship (counts when the calibration member is present) / (counts of Air) = exp(-μc(E)x) (E is the energy of the radiation, x is the thickness of the calibration member), and the attenuation coefficient for each derived energy bin can be interpolated.

[0042] On the other hand, the attenuation coefficient μp based on projection data P0 is derived as follows. Figure 6 is a table of calibration data used to explain the derivation of the attenuation coefficient μp based on projection data P0. For the sake of explanation, it is assumed that the detection element 9P has three energy bins. The thickness of the first base material 30A is assumed to be 0 mm (Air), 10 mm, 20 mm, and 30 mm, and the thickness of the second base material 30B is assumed to be 0 mm (Air), 1 mm, 2 mm, and 3 mm. In each column of the table shown in Figure 6, the number of photons in each of the three energy bins is shown in order from the lowest energy side.

[0043] Here, assuming that the counts for each energy bin of projection data P0 are (58, 68, 78), the counts for the thickness combinations of the first base material 30A and the second base material 30B (0mm,0mm), (10mm,0mm), (20mm,0mm), and (0mm,1mm) are greater than those for projection data P0, while the counts for the other thickness combinations are smaller than those for projection data P0. Therefore, in the table shown in Figure 6, it can be seen that the combinations of the first base material 30A and the second base material 30B corresponding to the thickness of subject H from which projection data P0 was obtained are located at the circled boundary between the columns for (0mm,0mm), (10mm,0mm), (20mm,0mm), and (0mm,1mm) and the other columns.

[0044] At these boundaries, the thicknesses of the first base material 30A and the second base material 30B are changed, and the counts in each energy bin corresponding to the changed thicknesses of the first base material 30A and the second base material 30B are derived by interpolation. Then, the combination of thicknesses of the first base material 30A and the second base material 30B that minimizes the error between the derived counts in each energy bin and the corresponding counts in the projection data P0 is derived. In this case, it is sufficient to derive the combination of thicknesses of the first base material 30A and the second base material 30B that minimizes the sum of the squares of the differences in the counts in the corresponding energy bins.

[0045] Based on the combination of thicknesses of the first base material 30A and the second base material 30B derived in this way, the thickness of the subject H at the position of the detection element 9P where the projection data P0 was acquired can be derived, and the attenuation coefficient μp for the projection data P0 can be derived for each energy bin of the detection element 9P using the relationship (counts of projection data P0) / (counts of Air) = exp(-μp(E)x) (where E is the energy of the radiation and x is the derived thickness of the subject H).

[0046] Here, if the subject H does not contain high-atomic-number substances, or if high-atomic-number substances are present but the radiation detected by the detection element 9P does not penetrate them, plotting the attenuation coefficient μp based on projection data P0 for each energy bin will result in a substantially agreement with the attenuation coefficient μc of the first fitted calibration data C1, as shown in Figure 5. In this embodiment, since the detection element 9P has four energy bins, plots are shown in Figure 5 at four locations corresponding to the attenuation coefficients μp1 to μp4.

[0047] In this case, the difference between the attenuation coefficient μc for the first calibration data C1 and the attenuation coefficient μp for the projection data P0 is smaller than a predetermined threshold Th1. The difference can be derived, for example, by the sum of the squares of the differences in the count numbers in the corresponding energy bins. Therefore, the determination unit 23 determines that there are no K edges in the attenuation coefficient μp based on the projection data P0. In such a case, the water thickness and bone thickness are obtained for each detection element 9P in the projection data P0. Furthermore, projection data for water and projection data for bone are obtained. Then, as will be described later, the reconstruction unit 25 can derive a material discrimination image by reconstructing a tomographic image for each base material from the multiple projection data obtained for each base material.

[0048] On the other hand, the attenuation coefficient of high atomic number materials shows a discontinuous change with a K-edge, as shown in Figure 7. For materials in which such a K-edge is observed in the attenuation coefficient, the energy spectrum of the projection data P0 does not easily match any of the energy spectra of the multiple calibration data C0. However, the first identification unit 22 identifies the first fitted calibration data C1 that matches the energy spectrum of the projection data P0, i.e., the one that best matches it.

[0049] In such cases, when the attenuation coefficients μp1 to μp4 based on projection data P0 are plotted against the attenuation coefficient μc based on the identified first fitting calibration data C1, as shown in Figure 8, the presence of a K edge increases the difference between the attenuation coefficient μc based on the first fitting calibration data C1 and the attenuation coefficients μp1 to μp4 based on projection data P0. As a result, the difference between the attenuation coefficient μc and the attenuation coefficients μp1 to μp4 becomes greater than or equal to the threshold Th1. In this case, the determination unit 23 determines that the attenuation coefficient μp based on projection data P0 has a K edge. Note that in Figure 8, the attenuation coefficient μp including the K edge, which is assumed from the four attenuation coefficients μp1 to μp4 based on projection data P0, is shown by a dashed line.

[0050] If the determination by the judgment unit 23 is affirmed, the second identification unit 24 identifies a second fitted calibration data C2 that fits the projected data P0 from among the multiple calibration data C0, based on the energy spectrum of the projected data P0 on the high-energy or low-energy side of the K-edge, which is a singularity. In this case, the second identification unit 24 estimates the K-edge from the attenuation coefficients μp1 to μp4 for four energy bins based on the projected data P0. Then, using the energy spectrum with a larger number of energy bins on the high-energy side and the low-energy side of the K-edge in the energy spectrum of the projected data P0, it identifies the second fitted calibration data C2.

[0051] For example, as shown in Figure 9, suppose that in the attenuation coefficient μp that includes the K edge, which is assumed from the four attenuation coefficients μp1 to μp4, there are three energy bins higher in energy than the K edge. In this case, the second identification unit 24 uses the energy spectrum higher in energy than the K edge in the energy spectrum of the projected data P0 to fit with multiple calibration data C0 and identifies the second suitable calibration data C2. Although not shown in the figure, if there are three energy bins lower in energy than the K edge, the second identification unit 24 uses the energy spectrum lower in energy than the K edge in the energy spectrum of the projected data P0 to fit with multiple calibration data C0 and identifies the second suitable calibration data C2.

[0052] If the number of energy bins on the higher energy side and the lower energy side of the K edge are equal, the second identification unit 24 compares the total number of photons in the higher energy bin with the total number of photons in the higher energy bin. Then, the second identification unit 24 uses the energy spectrum with the larger total count to derive the second fitted calibration data C2.

[0053] If the determination by the determination unit 23 is affirmed and the second conformance calibration data C2 is identified by the second identification unit 24, the water thickness and bone thickness are obtained for each detection element 9P in the projection data P0, similar to the first conformance calibration data C1 described above. Furthermore, projection data for water and projection data for bone are obtained. Then, the reconstruction unit 25 can derive a material discrimination image by reconstructing a tomographic image for each base material from the multiple projection data obtained for each base material.

[0054] Next, the processing performed in this disclosure will be described. Figure 10 is a flowchart showing the processing performed in this embodiment. It is assumed that the calibration data C0 has been acquired in advance and stored in the storage 13. First, the information acquisition unit 21 acquires a plurality of projection data P0 derived by imaging the subject H with the CT device 2 (step ST1). Next, the first identification unit 22 identifies a first fitted calibration data C1 from among the plurality of calibration data C0 that matches the energy spectrum of the projection data P0 (step ST2).

[0055] The determination unit 23 derives the difference between the attenuation coefficient μc based on the first fitting calibration data C1 identified by the first identification unit 22 and the attenuation coefficient μp based on the projection data P0 (step ST3). Then, based on the difference, the determination unit 23 determines whether or not there is a K edge in the attenuation coefficient μp based on the projection data P0 (step ST4).

[0056] If step ST4 is rejected, the reconstruction unit 25 derives a material discrimination image (step ST5) by reconstructing a tomographic image for each base material from multiple projection data for each base material obtained based on the first fitting calibration data C1, and then terminates the process.

[0057] If step ST4 is confirmed, the second identification unit 24 identifies a second fitted calibration data C2 that matches the projection data P0 from among the multiple calibration data C0 based on the energy spectrum of the projection data P0 on the high-energy or low-energy side of the singularity (step ST6). The reconstruction unit 25 derives a material discrimination image by reconstructing a tomographic image for each base material from the multiple projection data for each base material obtained based on the second fitted calibration data C2 (step ST7), and the process ends.

[0058] Thus, in this embodiment, a first suitable calibration data C1 that matches the energy spectrum of the projection data P0 is identified from among a plurality of calibration data C0, and the presence or absence of a singularity such as a K edge in the attenuation coefficient μp based on the projection data P0 is determined based on the difference between the attenuation coefficient μc based on the identified first suitable calibration data C1 and the attenuation coefficient μp based on the projection data P0. If a singularity is determined to exist, a second suitable calibration data C2 that matches the projection data P0 is identified from among the plurality of calibration data C0 based on the energy spectrum of the projection data P0 on the high-energy side or low-energy side of the singularity.

[0059] Therefore, even if a high-atomic-number substance with a singularity in the attenuation coefficient is included in the sample, and the projection data is obtained by transmission through that substance, the calibration data can be appropriately identified, and as a result, accurate substance discrimination can be performed.

[0060] In the above embodiment, four energy bins are set for the detector 9, but the number of bins is not limited to this. It is possible to set any number of energy bins, three or more. However, if the number of energy bins is too small, material discrimination cannot be performed well, and if it is too large, the amount of computation required for material discrimination will be large, so it is preferable that the number of energy bins be between three and eight.

[0061] In this embodiment, each process of the information processing device 10 is executed on any computer. Alternatively, any computer may execute these processes using a processor as hardware, a program as software, or a combination thereof. In that case, the processor is configured to work in cooperation with the program to execute the various processes in the information processing device 10 of this embodiment, and can function as a unit or means in this embodiment. Furthermore, the execution order of the processes by the processor is not limited to the order described and may be changed as appropriate. Any computer may be a general-purpose computer, a computer designed for a specific purpose, a workstation, or any other system capable of executing each process.

[0062] A processor may consist of one or more hardware components, and the type of hardware is not limited. For example, a processor may consist of a CPU (Central Processing Unit), an MPU (Micro Processing Unit), a programmable logic device such as an FPGA (Field Programmable Gate Array), a dedicated circuit for executing a specific process such as an ASIC (Application Specific Integrated Circuit), a GPU (Graphic Processing Unit), or an NPU (Neural Processing Unit). Furthermore, the type of hardware may be a combination of different types of hardware. When multiple hardware components are configured to execute one or more processes of a processor, these components may reside in physically separate devices or in the same device. Also, in any embodiment, the order of each process performed by the processor is not limited to the order described above and may be changed as appropriate. Hardware is composed of electrical circuits (circuitry) that combine circuit elements such as semiconductor elements.

[0063] Furthermore, the program may be firmware or software such as microcode. Alternatively, the program may be, for example, a set of program modules, each function of which may be implemented by a processor configured to perform its respective function. The program may be program code or multiple code segments stored on one or more non-temporary computer-readable media (e.g., storage media or other storage). The program may be divided and stored on multiple non-temporary computer-readable media located in physically separate devices. Program code or code segments may represent any combination of procedures, functions, subprograms, routines, subroutines, modules, software packages, classes, or instructions, data structures, or program statements. Program code or code segments may be connected to other code segments or hardware circuits by sending and receiving information, data, arguments, parameters, or memory contents.

[0064] Furthermore, although the above embodiment describes a configuration in which the information processing program 12 is pre-stored (installed) in the storage 13, the invention is not limited to this configuration. The information processing program 12 may be provided in the form of a recording medium such as a CD-ROM (Compact Disc Read Only Memory), DVD-ROM (Digital Versatile Disc Read Only Memory), or USB (Universal Serial Bus) memory. Alternatively, the information processing program 12 may be provided in the form of a download from an external device via a network.

[0065] The technology disclosed herein extends to all program products. Program products include all forms of products for providing programs. For example, program products include programs provided via networks such as the Internet, and non-temporary computer-readable recording media such as CD-ROMs, DVDs, and USB memory sticks on which programs are stored.

[0066] The following are additional notes to this disclosure. (Additional note 1) A storage unit stores multiple calibration data for material discrimination, which represent the energy spectrum of a combination of calibration members, obtained by measuring multiple types of calibration members consisting of combinations of two or more base materials with different compositions using a photon counting detector that converts incident radiation into the number of detected photons for three or more energy bins. Equipped with a processor, The aforementioned processor, Projection data for each of the multiple energy bins obtained by detecting the radiation that has passed through the subject using the photon counting type detector is acquired. From the plurality of calibration data, a first suitable calibration data that matches the energy spectrum of the projection data is identified. Based on the difference between the attenuation coefficient based on the first fitted calibration data and the attenuation coefficient based on the projection data in each of the plurality of energy bins, the presence or absence of singularities in the attenuation coefficient based on the projection data is determined. If the presence of the singularity is determined, an information processing device identifies a second fitted calibration data from among the plurality of calibration data that is suitable for the projection data, based on the energy spectrum of the projection data on the high-energy or low-energy side of the singularity. (Additional note 2) The information processing device according to Appendix 1, wherein the processor identifies, by the maximum likelihood estimation method, calibration data from among the plurality of calibration data that fits the energy spectrum of the projection data. (Additional note 3) The information processing device according to appendix 1 or 2, wherein the processor determines that there is a singularity when the difference between the attenuation coefficient based on the identified calibration data and the attenuation coefficient based on the projection data in each of the multiple energy bins is greater than or equal to a predetermined threshold. (Additional note 4) If the processor determines that there are no singularities, it derives a material discrimination image based on the first conformance calibration data. If the aforementioned singularity is determined to exist, the information processing apparatus according to any one of the appendix items 1 to 3 derives a substance discrimination image based on the second conformance calibration data. (Additional note 5) An information processing method in an information processing device equipped with a storage unit that stores a plurality of calibration data for material discrimination, which represent the energy spectrum of a combination of calibration members, obtained by measuring multiple types of calibration members, each consisting of a combination of two or more base materials with different compositions, using a photon counting detector that converts incident radiation into the number of detectable photons for each of three or more energy bins, the calibration members being measured, The computer acquires projection data for each of the multiple energy bins obtained by detecting the radiation that has passed through the subject using the photon counting type detector. From the plurality of calibration data, a first suitable calibration data that matches the energy spectrum of the projection data is identified. Based on the difference between the attenuation coefficient based on the first fitted calibration data and the attenuation coefficient based on the projection data in each of the plurality of energy bins, the presence or absence of singularities in the attenuation coefficient based on the projection data is determined. An information processing method that, when it is determined that the singularity exists, identifies a second fitted calibration data from among the plurality of calibration data that is suitable for the projection data, based on the energy spectrum of the projection data on the high-energy side or low-energy side of the singularity. (Additional note 6) An information processing program that causes a computer to function as an information processing device equipped with a storage unit that stores multiple calibration data for material discrimination, which represent the energy spectrum of a combination of calibration members, obtained by measuring multiple types of calibration members, each consisting of a combination of two or more base materials with different compositions, using a photon counting detector that converts incident radiation into the number of detectable photons for each of three or more energy bins, A procedure for acquiring projection data for each of the multiple energy bins obtained by detecting radiation transmitted through the subject using the photon counting type detector, A procedure for identifying a first suitable calibration data from among the plurality of calibration data that matches the energy spectrum of the projection data, A procedure for determining the presence or absence of singularities in the attenuation coefficient based on projection data, based on the difference between the attenuation coefficient based on the first fitted calibration data and the attenuation coefficient based on projection data in each of the plurality of energy bins, An information processing program that, when it is determined that the singularity exists, causes a computer to perform a procedure to identify a second suitable calibration data from among the plurality of calibration data that is suitable for the projection data, based on the energy spectrum of the projection data on the high-energy side or low-energy side of the singularity. [Explanation of Symbols]

[0067] 1. Medical imaging system 2 CT device 3 Console 4 Gantry 4A opening 5 Radiation source 6 Radiation tubes 7 Bowtie Filter 8 berths 9 Detectors 9P detection element 10 Information Processing Devices 11 CPU 12. Information Processing Programs 13 Storage 14 displays 15 Input Devices 16 memory 17 I / F 18 bus 21 Information Acquisition Department 22 First Specific Part 23 Judgment section 24 Second Specific Part 25 Reconstruction part 30 Calibration Member 30A,30B Base material 32 combinations μp, μp1~μp4 Attenuation coefficients based on projection data Attenuation coefficient based on μc calibration data

Claims

1. A storage unit stores multiple calibration data for material discrimination, which represent the energy spectrum of a combination of calibration members, obtained by measuring multiple types of calibration members consisting of combinations of two or more base materials with different compositions using a photon counting detector that converts incident radiation into the number of detectable photons for each of three or more energy bins. Equipped with a processor, The aforementioned processor, Projection data for each of the multiple energy bins obtained by detecting the radiation that has passed through the subject using the photon counting type detector is acquired. From the plurality of calibration data, a first suitable calibration data that matches the energy spectrum of the projection data is identified. Based on the difference between the attenuation coefficient based on the first fitted calibration data and the attenuation coefficient based on the projection data in each of the plurality of energy bins, the presence or absence of singularities in the attenuation coefficient based on the projection data is determined. If the presence of the singularity is determined, an information processing device identifies a second fitted calibration data from among the plurality of calibration data that is suitable for the projection data, based on the energy spectrum of the projection data on the high-energy or low-energy side of the singularity.

2. The information processing apparatus according to claim 1, wherein the processor identifies calibration data from among the plurality of calibration data that fits the energy spectrum of the projection data by the maximum likelihood estimation method.

3. The information processing apparatus according to claim 1 or 2, wherein the processor determines that there is a singularity when the difference between the attenuation coefficient based on the identified calibration data and the attenuation coefficient based on the projection data in each of the plurality of energy bins is greater than or equal to a predetermined threshold.

4. If the processor determines that there are no singularities, it derives a material discrimination image based on the first conformance calibration data. If it is determined that there is a singularity, the information processing apparatus according to claim 1 derives a substance discrimination image based on the second conformance calibration data.

5. An information processing method in an information processing device equipped with a storage unit that stores a plurality of calibration data for material discrimination, which represent the energy spectrum of a combination of calibration members, obtained by measuring multiple types of calibration members, each consisting of a combination of two or more base materials with different compositions, using a photon counting detector that converts incident radiation into the number of detected photons for each of three or more energy bins, the calibration members being measured, The computer acquires projection data for each of the multiple energy bins obtained by detecting the radiation that has passed through the subject using the photon counting type detector. From the plurality of calibration data, a first suitable calibration data that matches the energy spectrum of the projection data is identified. Based on the difference between the attenuation coefficient based on the first fitted calibration data and the attenuation coefficient based on the projection data in each of the plurality of energy bins, the presence or absence of singularities in the attenuation coefficient based on the projection data is determined. An information processing method that, when it is determined that a singularity exists, identifies a second suitable calibration data from among the plurality of calibration data that is suitable for the projection data, based on the energy spectrum of the projection data on the high-energy side or low-energy side of the singularity.

6. An information processing program that causes a computer to function as an information processing device equipped with a storage unit that stores multiple calibration data for material discrimination, which represent the energy spectrum of multiple combinations of calibration members, each consisting of a combination of two or more base materials with different compositions, obtained by measuring the incident radiation with a photon counting detector that converts the number of detected photons for each of three or more energy bins, the calibration members, A procedure for acquiring projection data for each of the multiple energy bins obtained by detecting radiation transmitted through the subject using the photon counting type detector, A procedure for identifying a first suitable calibration data from among the plurality of calibration data that matches the energy spectrum of the projection data, A procedure for determining the presence or absence of singularities in the attenuation coefficient based on projection data, based on the difference between the attenuation coefficient based on the first fitted calibration data and the attenuation coefficient based on projection data in each of the plurality of energy bins, An information processing program that, when it is determined that the singularity exists, causes a computer to perform a procedure to identify a second suitable calibration data from among the plurality of calibration data that is suitable for the projection data, based on the energy spectrum of the projection data on the high-energy side or low-energy side of the singularity.

Images