Radiation image processing device, method, and program

Abstract

To separate a plurality of components contained in a radiation image with a high degree of precision in radiation image processing device, method, and program.SOLUTION: A processor acquires first to (n-1)th radiation images obtained by imaging a subject containing a first component consisting of a plurality of compositions, and second to nth (n≥3) components each consisting of a single composition, by (n-1) kinds of radioactive rays with different energy distributions; derives characteristics of the first component in at least a region of the subject of the first to (n-1)th radiation images; acquires the body thickness of the subject; derives the thickness of the first to nth components using the body thickness, the characteristics of the first component, and the first to (n-1)th radiation images; and derives the first to nth component images in which each of the first to nth components is emphasized on the basis of the thickness of the first to nth components.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present disclosure relates to a radiation image processing device, method, and program. [Background technology]

[0002] Energy subtraction processing has been known for some time. This processing utilizes the fact that the amount of attenuation of transmitted radiation differs depending on the material constituting the subject, and uses two radiological images obtained by irradiating the subject with two types of radiation with different energy distributions. Energy subtraction processing is a method of matching the pixels of the two radiological images obtained in this manner, multiplying the pixels by weighting coefficients based on attenuation coefficients corresponding to the components, and then performing subtraction to obtain images in which specific components, such as bone and soft tissue, contained in the radiological images are separated. Techniques for separating the soft tissue of a subject into fat and muscle using energy subtraction processing have also been proposed (see Patent Documents 1 and 2). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2023-047911 [Patent Document 2] Japanese Patent Publication No. 2022-056084 Summary of the Invention [Problem to be solved by the invention]

[0004] In order to separate multiple components contained in a subject using energy subtraction processing, the attenuation coefficient of soft tissue is necessary. Soft tissue is not composed of a single component, but is a complex mixture of multiple components such as fat and muscle. Furthermore, the components of soft tissue vary greatly from person to person. Therefore, unless the attenuation coefficient of soft tissue is calculated according to the proportion of fat and muscle, multiple components such as bone and soft tissue cannot be separated accurately when processing is performed using characteristics related to radiation attenuation, as in energy subtraction processing.

[0005] In this case, it is conceivable to separate the soft tissue into fat and muscle. However, in energy subtraction processing, two radiographic images obtained using two types of radiation with different energy distributions are used, so only radiographic images of two components, for example, bone and soft tissue, can be obtained. In other words, if n radiographic images obtained using n types of radiographic images with different energy distributions are used, only n component images can be obtained.

[0006] The present disclosure has been made in consideration of the above circumstances, and aims to accurately separate n components of a subject contained in radiological images using n radiological images obtained from n-1 (n≧3) types of radiation with different energy distributions. [Means for solving the problem]

[0007] A radiation image processing device according to the present disclosure includes at least one processor, the processor acquires first to (n-1)th radiographic images obtained by imaging a subject including a first component consisting of a plurality of compositions and second to n-th (n≧3, n is a natural number) components each consisting of a single composition using n-1 types of radiation with different energy distributions; deriving a characteristic of a first component in at least a region of the subject in the first to (n-1)th radiographic images; Obtain the subject's body thickness, deriving thicknesses of the first to n-th components using the body thickness, the characteristics of the first component, and the first to n-1-th radiographic images; Based on the thicknesses of the first to n-th components, first to n-th component images in which the first to n-th components are enhanced, respectively, are derived.

[0008] In the radiological image processing device according to the present disclosure, the processor may obtain the body thickness by deriving the body thickness of the subject based on at least one of the first to n-1th radiological images.

[0009] In the radiation image processing device according to the present disclosure, the processor may derive the attenuation coefficient of the first component as the characteristic of the first component.

[0010] In addition, in the radiation image processing device according to the present disclosure, the processor may derive the attenuation coefficient of the first component as a characteristic of the first component based on information regarding the radiation attenuation coefficient of each of the multiple compositions contained in the first component and the thickness of each of the multiple compositions.

[0011] In addition, in the radiological image processing device according to the present disclosure, the processor acquires a first radiological image and a second radiological image obtained by photographing the subject with two types of radiation having different energy components; Deriving a characteristic of a first component in at least a region of the subject in the first radiographic image or the second radiographic image; deriving a body thickness of the subject based on at least one of the first radiographic image and the second radiographic image; deriving the first component, second component, and third component thicknesses of the subject using the body thickness, the characteristics of the first component, the first radiographic image, and the second radiographic image; A first component image, a second component image and a third component image in which the first component, the second component and the third component are enhanced, respectively, may be derived based on the thicknesses of the first component, the second component and the third component.

[0012] In the radiation image processing device according to the present disclosure, the first component, the second component, and the third component may be the soft part, the bone part, and the artificial object in the subject, respectively.

[0013] In the radiation image processing device according to the present disclosure, the first component may be composed of fat and muscle.

[0014] A radiological image processing method according to the present disclosure includes a computer acquiring first to (n-1)th radiological images by imaging a subject including a first component having a plurality of compositions and second to n-th (n≧3) components each having a single composition using n-1 types of radiation with different energy distributions; deriving a characteristic of a first component in at least a region of the subject in the first to (n-1)th radiographic images; Obtain the subject's body thickness, deriving thicknesses of the first to n-th components using the body thickness, the characteristics of the first component, and the first to n-1-th radiographic images; Based on the thicknesses of the first to n-th components, first to n-th component images in which the first to n-th components are enhanced, respectively, are derived.

[0015] The radiological image processing program according to the present disclosure includes a procedure for acquiring first to (n-1)th radiological images by imaging a subject including a first component consisting of a plurality of compositions and second to n-th (n≧3) components each consisting of a single composition using n-1 types of radiation with different energy distributions; deriving a characteristic of a first component in at least a region of the subject in the first to (n-1)th radiographic images; The procedure for obtaining the subject's body thickness; deriving thicknesses of the first to n-th components using the body thickness, the characteristics of the first component, and the first to n-1-th radiographic images; and deriving first to n-th component images in which the first to n-th components are enhanced, based on the thicknesses of the first to n-th components. [Effects of the Invention]

[0016] According to the present disclosure, n components of a subject included in a radiographic image can be accurately separated using n radiographic images obtained using n-1 (n≧3) types of radiation with different energy distributions. [Brief explanation of the drawings]

[0017] [Figure 1] FIG. 1 is a schematic block diagram illustrating a configuration of a radiographic image capturing system to which a radiographic image processing apparatus according to an embodiment of the present disclosure is applied. [Figure 2] FIG. 1 is a diagram showing a schematic configuration of a radiation image processing apparatus according to an embodiment of the present invention; [Figure 3] FIG. 1 is a diagram showing the functional configuration of a radiation image processing apparatus according to an embodiment of the present invention; [Figure 4] FIG. 1 shows first and second radiographic images. [Figure 5] FIG. 1 is a diagram schematically illustrating processing performed in a radiation image processing apparatus. [Figure 6] Diagram showing the attenuation coefficients of fat and muscle [Figure 7] FIG. 10 shows the amount of attenuation depending on muscle thickness and fat thickness in high-energy and low-energy images. [Figure 8] Diagram showing the display screen [Figure 9] A flowchart showing the processing performed in this embodiment DETAILED DESCRIPTION OF THE INVENTION

[0018] Hereinafter, an embodiment of the present disclosure will be described with reference to the drawings. Fig. 1 is a schematic block diagram showing the configuration of a radiographic image capturing system to which a radiographic image processing device according to an embodiment of the present disclosure is applied. As shown in Fig. 1, the radiographic image capturing system according to this embodiment includes an imaging device 1 and a radiographic image processing device 10 according to this embodiment.

[0019] The imaging device 1 is an imaging device for performing energy subtraction imaging by a so-called one-shot method in which radiation such as X-rays emitted from a radiation source 3 and transmitted through a subject H is irradiated at different energies onto a first radiation detector 5 and a second radiation detector 6. During imaging, as shown in Fig. 1, the first radiation detector 5, a radiation energy converting filter 7 made of a copper plate or the like, and the second radiation detector 6 are arranged in this order from the side closest to the radiation source 3, and the radiation source 3 is driven. The first and second radiation detectors 5 and 6 and the radiation energy converting filter 7 are in close contact with each other.

[0020] As a result, the first radiation detector 5 acquires a first radiographic image G1 of the subject H using low-energy radiation that includes so-called soft rays. The second radiation detector 6 acquires a second radiographic image G2 of the subject H using high-energy radiation from which the soft rays have been removed. The first and second radiographic images G1 and G2 are input to the radiographic image processing device 10.

[0021] The first and second radiation detectors 5, 6 are capable of repeatedly recording and reading out radiation images, and may be so-called direct type radiation detectors that generate electric charges upon direct exposure to radiation, or so-called indirect type radiation detectors that convert radiation into visible light and then convert the visible light into electric charge signals. The radiation image signal readout method is preferably a TFT readout method in which the radiation image signal is read out by turning a TFT (thin film transistor) switch on and off, or an optical readout method in which the radiation image signal is read out by irradiating the detector with readout light, but is not limited to these, and other methods may also be used.

[0022] Next, a radiological image processing device according to this embodiment will be described. First, the hardware configuration of the radiological image processing device according to this embodiment will be described with reference to Fig. 2. As shown in Fig. 2, the radiological image processing device 10 is a computer such as a workstation, a server computer, or a personal computer, and includes a CPU (Central Processing Unit) 11, non-volatile storage 13, and memory 16 as a temporary storage area.

[0023] The radiation image processing device 10 also includes a display 14 such as a liquid crystal display, an input device 15 such as a keyboard and a mouse, and a network I / F (Interface) 17 connected to a network (not shown). The CPU 11, storage 13, display 14, input device 15, memory 16, and network I / F 17 are connected to a bus 18. The CPU 11 is an example of a processor in the present disclosure.

[0024] The storage 13 is realized by a hard disk drive (HDD), a solid state drive (SSD), a flash memory, etc. The storage 13 as a storage medium stores the radiographic image processing program 12 installed in the radiographic image processing device 10. The CPU 11 reads out the radiographic image processing program 12 from the storage 13, loads it into the memory 16, and executes the loaded radiographic image processing program 12.

[0025] The radiation image processing program 12 is stored in a state accessible from the outside in a storage device of a server computer connected to a network or in a network storage, and is downloaded and installed in response to a request into a computer constituting the radiation image processing apparatus 10. Alternatively, the program is recorded on a recording medium such as a DVD (Digital Versatile Disc) or a CD-ROM (Compact Disc Read Only Memory) and distributed, and is installed into a computer constituting the radiation image processing apparatus 10 from the recording medium.

[0026] Next, the functional configuration of the radiological image processing apparatus according to this embodiment will be described. Fig. 3 is a diagram showing the functional configuration of the radiological image processing apparatus according to this embodiment. As shown in Fig. 3, the radiological image processing apparatus 10 includes an image acquisition unit 21, a characteristic derivation unit 22, a body thickness acquisition unit 23, a component thickness derivation unit 24, an image derivation unit 25, and a display control unit 26. The CPU 11 executes the radiological image processing program 12 to function as the image acquisition unit 21, the characteristic derivation unit 22, the body thickness acquisition unit 23, the component thickness derivation unit 24, the image derivation unit 25, and the display control unit 26.

[0027] The image acquisition unit 21 acquires a first radiographic image G1 and a second radiographic image G2 of the subject H from the first and second radiation detectors 5 and 6 by causing the imaging device 1 to perform energy subtraction imaging of the subject H. At this time, imaging conditions such as imaging dose, energy distribution, tube voltage, and SID are set. The imaging conditions may be set by input by the user via the input device 15. The set imaging conditions are saved in the storage 13. Note that the first and second radiographic images G1 and G2 may be acquired using a program separate from the radiographic image processing program according to the first embodiment. In this case, the image acquisition unit 21 reads out the first and second radiographic images G1 and G2 saved in the storage 13 from the storage 13 for processing.

[0028] FIG. 4 shows the first and second radiographic images. As shown in FIG. 4, the first and second radiographic images G1 and G2 include a region of the subject H and a direct radiation region obtained by directly irradiating the radiation detectors 5 and 6 with radiation. The region of the subject H includes a soft tissue region and a bone region. FIG. 4 also includes a region A1 of the screw used to fix the fractured portion of the femur to the femur. Soft tissue components of the human body include muscle, fat, blood, and water. In this embodiment, non-fatty tissue, including blood and water, is considered to be muscle. The screw is an example of an artificial object of the present disclosure.

[0029] Artificial objects are objects that do not actually exist within the subject H, such as screws for fixing a fractured part as shown in FIG. 4, stents placed in blood vessels, silicone placed in the breast, and contrast agents. The soft tissue component, bone component, and artificial object component of the subject H are examples of the first component, second component, and third component, respectively, of the present disclosure. Muscle and fat are examples of multiple compositions of the present disclosure.

[0030] The soft tissue regions of the first and second radiographic images G1 and G2 contain only soft tissue components of the subject H. The bone regions and artificial object regions of the first and second radiographic images G1 and G2 are actually regions where bone components, artificial object components, and soft tissue components are mixed. The soft tissue regions are an example of a first component region according to the present disclosure that contains only the first component, and the bone regions are an example of a second component region according to the present disclosure that contains the second component. The artificial object region is an example of a third component region according to the present disclosure that contains the third component.

[0031] The characteristic deriving unit 22 derives the characteristics of the soft tissue component in at least the region of the subject H in the first radiographic image G1 and the second radiographic image G2. In this embodiment, the characteristic deriving unit 22 derives the soft tissue attenuation coefficient, which is the attenuation coefficient of the soft tissue component of low-energy radiation and high-energy radiation, as the characteristic of the first component.

[0032] Fig. 5 is a diagram schematically illustrating the processing performed by the characteristic deriving unit 22 in this embodiment. For ease of explanation, Fig. 5 shows that the first radiographic image G1 and the second radiographic image G2 do not include a direct radiation region, and that the soft tissue region includes a rectangular bone region and a circular artificial region.

[0033] In the present embodiment, the property derivation unit 22 first identifies soft tissue regions, bone regions, and artificial object regions in the first radiographic image G1 or the second radiographic image G2. To this end, the property derivation unit 22 derives attenuation characteristics related to radiation attenuation in at least the region of the subject H in the first radiographic image G1 or the second radiographic image G2, and identifies the soft tissue regions, bone regions, and artificial object regions based on the attenuation characteristics in the region of the subject H. In the present embodiment, the property derivation unit 22 derives a first attenuation image CL and a second attenuation image CH representing the amount of radiation attenuation caused by the subject H from the first radiographic image G1 and the second radiographic image G2, respectively, and derives an attenuation ratio, which is the ratio between corresponding pixels in the first attenuation image CL and the second attenuation image CH, as the attenuation characteristic.

[0034] The pixel values ​​of the first attenuation image CL represent the amount of attenuation of low-energy radiation by the subject H, and the pixel values ​​of the second attenuation image CH represent the amount of attenuation of high-energy radiation by the subject H. The first attenuation image CL and the second attenuation image CH are derived from the first radiographic image G1 and the second radiographic image G2 using the following equations (1) and (2). In equation (1), Gd1 is the pixel value of the direct radiation region in the first radiographic image G1, and in equation (2), Gd2 is the pixel value of the direct radiation region in the second radiographic image G2. CL(x,y)=Gd1-G1(x,y) (1) CH(x,y)=Gd2-G2(x,y) (2)

[0035] Next, the characteristic deriving unit 22 derives an attenuation ratio map that indicates the radiation attenuation ratio between the first radiographic image G1 and the second radiographic image G2. Specifically, the attenuation ratio map M1 is derived by deriving the ratio between corresponding pixels in the first attenuation image CL and the second attenuation image CH using the following equation (3). The attenuation ratio is an example of a characteristic related to radiation attenuation disclosed herein. M1(x,y)=CL(x,y) / CH(x,y) (3)

[0036] In the first radiographic image G1 and the second radiographic image G2, the attenuation ratio of a region containing only soft tissue components is smaller than the attenuation ratio of a region containing bone components and artifact components. Therefore, the characteristic derivation unit 22 compares the attenuation ratio of each pixel in the attenuation ratio map M1, and identifies regions consisting of pixels whose attenuation ratio is greater than a predetermined threshold as bone regions and artifact regions, and identifies regions whose attenuation ratio is less than the threshold as soft tissue regions.

[0037] Furthermore, in this embodiment, the characteristic deriving unit 22 derives a soft-tissue attenuation coefficient μLs for low-energy radiation and a soft-tissue attenuation coefficient μHs for high-energy radiation using the first attenuation image CL and the second attenuation image CH. The derivation of the soft-tissue attenuation coefficients μLs and μHs will be described below.

[0038] In this embodiment, the soft-tissue attenuation coefficient is derived on the premise that, among the compositions constituting soft tissue, the highest density composition is muscle and the lowest density composition is fat, and a mixed composition of fat and muscle has an intermediate value between the attenuation coefficients of both. First, the characteristic deriving unit 22 calculates provisional soft-tissue attenuation coefficients μ0Ls and μ0Hs for low-energy radiation and high-energy radiation, respectively, by setting the fat percentage at each pixel position to N %, and weighting and adding the fat attenuation coefficient and the muscle attenuation coefficient in a ratio of N:100-N while sequentially increasing N from 0.

[0039] When fat and muscle overlap, the attenuation coefficient changes due to the effect of beam hardening of the component (usually fat) present on the side of the radiation source 3; however, in this embodiment, the effect of beam hardening is not taken into consideration. For this reason, the fat percentage N % used in this embodiment does not coincide with the actual body fat percentage of the subject H. Processing is based on the assumption that the actual soft tissue attenuation coefficient is a value between the attenuation coefficient of fat and the attenuation coefficient of muscle shown in FIG. 6. The horizontal axis in FIG. 6 represents the thickness (mm) of fat and muscle.

[0040] Next, the characteristic derivation unit 22 calculates the body thickness TN when the fat percentage is N% from the pixel values ​​of the first attenuation image CL and the provisional soft-tissue attenuation coefficient μ0Ls for the low-energy image using the following formula (4). At this time, the body thickness TN is calculated assuming that pixels including bones and artificial objects are also composed of only soft tissue. TN(x,y)=CL(x,y) / μ0Ls(x,y) (4)

[0041] Next, the characteristic deriving unit 22 calculates the attenuation amount CHN1 of the high-energy radiation using the body thickness TN calculated using equation (4) and the provisional soft tissue attenuation coefficient μHs for the high-energy radiation using the following equation (5). Then, the characteristic deriving unit 22 calculates the difference ΔCH by subtracting the second attenuation image CH from the attenuation amount CHN1 using the following equation (6). CHN1(x,y)=TN(x,y)×μ0Hs(x,y) (5) ΔCH(x,y)=CHN1(x,y)-CH(x,y) (6)

[0042] When the difference ΔCH is negative, the provisional soft-tissue attenuation coefficients μ0Ls and μ0Hs are smaller than the correct soft-tissue attenuation coefficients, i.e., closer to fat. When the difference ΔCH is positive, the provisional soft-tissue attenuation coefficients μ0Ls and μ0Hs are closer to muscle. The characteristic derivation unit 22 calculates the provisional soft-tissue attenuation coefficients μ0Ls and μ0Hs for all pixels of the first attenuation image CL and the second attenuation image CH while changing N so that the difference ΔCH approaches 0. Then, when the difference ΔCH becomes 0 or is equal to or smaller than a predetermined threshold, N is determined as the fat proportion for that pixel. Furthermore, the characteristic derivation unit 22 determines the provisional soft-tissue attenuation coefficients μ0Ls and μ0Hs used when calculating the determined fat proportion N as the soft-tissue attenuation coefficients μLs and μHs when the thickness of the soft tissue is body thickness TN. If the difference ΔCH is negative, the fat percentage N is increased, and if the difference ΔCH is positive, the fat percentage N is decreased.

[0043] In this embodiment, the characteristic deriving unit 22 derives the soft part attenuation coefficients μLs and μHs as the characteristics of the first component in the soft part region. Meanwhile, the characteristic deriving unit 22 derives the soft part attenuation coefficients μLs and μHs in the bone part region and the artificial part region by interpolating the soft part attenuation coefficients of the soft part regions around the bone part region and the artificial part region, for example, within a predetermined distance from the boundary between the bone part region and the artificial part region. Instead of interpolation, the soft part attenuation coefficients μLs and μHs for the bone part region and the artificial part region may be derived as the median, average, or a predetermined percentage of the soft part attenuation coefficients μLs and μHs in the soft part region.

[0044] The body thickness acquisition unit 23 acquires the body thickness of the subject H by deriving the body thickness of the subject H for each pixel of the first and second radiographic images G1, G2 based on at least one of the first and second radiographic images G1, G2. Here, the body thickness refers to the thickness of the subject H in the direction in which radiation penetrates, i.e., the thickness in a direction perpendicular to the first and second radiographic images G1, G2. Because the body thickness is derived for each pixel of the first and second radiographic images G1, G2, the body thickness acquisition unit 23 derives the body thickness distribution in at least one of the first and second radiographic images G1, G2. When deriving the body thickness, the body thickness acquisition unit 23 uses the first radiographic image G1 acquired by the radiation detector 5 closer to the subject H. Alternatively, the second radiographic image G2 may be used. Regardless of which image is used, a low-frequency image representing the low-frequency components of the image may be derived, and the body thickness may be derived using the low-frequency image.

[0045] In this embodiment, the body thickness acquisition unit 23 assumes that the luminance distribution in the first radiographic image G1 matches the distribution of the body thickness of the subject H, and derives the body thickness of the subject H by converting the pixel values ​​of the first radiographic image G1 into thickness using the attenuation coefficient of the soft tissue of the subject H. Alternatively, the body thickness acquisition unit 23 may measure the thickness of the subject H using a sensor or the like. Alternatively, the body thickness acquisition unit 23 may derive the body thickness of the subject H by approximating the body thickness with a model such as a cube or an elliptical cylinder. Alternatively, the body thickness acquisition unit 23 may derive the body thickness of the subject H using any method, such as the method described in JP 2015-043959 A.

[0046] The component thickness derivation unit 24 derives the thicknesses of the first to third components contained in the subject H, i.e., the thickness of the soft tissue, the thickness of the bone, and the thickness of the artificial object, using the body thickness derived by the body thickness acquisition unit 23, the characteristics of the first component derived by the characteristic derivation unit 22 (i.e., the soft tissue attenuation coefficients μLs, μHs), the first radiographic image G1, and the second radiographic image G2.

[0047] Here, the amount of radiation attenuation by the subject H is determined depending on the thickness of soft tissue, bone, and artifacts, and the radiation quality (high energy or low energy). Therefore, if the attenuation coefficient representing the attenuation rate per unit thickness is μ, the amount of radiation attenuation CL0, CH0 at each pixel position in the low-energy image and the high-energy image, respectively, can be expressed by the following equations (7) and (8). In equations (7) and (8), ts is the thickness of soft tissue, tb is the thickness of bone, ta is the thickness of artifacts, μLs is the soft-tissue attenuation coefficient of low-energy radiation, μLb is the bone attenuation coefficient of low-energy radiation, μLa is the artifact attenuation coefficient of low-energy radiation, μHs is the soft-tissue attenuation coefficient of high-energy radiation, μHb is the bone attenuation coefficient of high-energy radiation, and μHa is the artifact attenuation coefficient of high-energy radiation. The attenuation coefficients represent the rate of radiation attenuation per unit thickness. CL0=μLs(ts,tb,ta)×ts+μLb(ts,tb,ta)×tb+μLa(ts,tb,ta)×ta (7) CH0=μHs(ts,tb,ta)×ts+μHb(ts,tb,ta)×tb+μHa(ts,tb,ta)×ta (8)

[0048] In equations (7) and (8), the amount of attenuation CL0 of the low-energy image corresponds to the pixel value of the first attenuation image CL, and the amount of attenuation CH0 of the high-energy image corresponds to the pixel value of the second attenuation image CH. Note that equations (7) and (8) express the relationship between each pixel of both the first attenuation image CL and the second attenuation image CH, but omit the (x, y) representing the pixel position. In equations (9) to (13) shown below, the (x, y) representing the pixel position is also omitted.

[0049] In equations (7) and (8), the variables are the thickness of the soft parts ts, the thickness of the bone parts tb, and the thickness of the artificial parts ta. Because there are three variables, the thickness of the soft parts ts, the thickness of the bone parts tb, and the thickness of the artificial parts ta cannot be derived using only the two equations (7) and (8). In this embodiment, the body thickness T of the subject H is acquired by the body thickness acquisition unit 23. The relationship between the body thickness T and the thickness of the soft parts ts, the thickness of the bone parts tb, and the thickness of the artificial parts ta is expressed by the following equation (9). Transforming equation (9) for the thickness ta of the artificial parts yields equation (10). T=ts+tb+ta (9) ta=T-ts-tb (10)

[0050] Since the attenuation amount CL0 of the low-energy image corresponds to the pixel value of the first attenuation image CL, and the attenuation amount CH0 of the high-energy image corresponds to the pixel value of the second attenuation image CH, substituting equation (10) into equations (7) and (8) gives the following equations (11) and (12). CL=μLs(ts,tb,T-ts-tb)×ts+μLb(ts,tb,T-ts-tb)×tb +μLa(ts,tb,T-ts-tb)×(T-ts-tb) (11) CH=μHs(ts,tb,T-ts-tb)×ts+μHb(ts,tb,T-ts-tb)×tb +μHa(ts,tb,T-ts-tb)×(T-ts-tb) (12)

[0051] In this embodiment, the body thickness T is acquired by the body thickness acquisition unit 23, and therefore, in equations (11) and (12), there are two variables: the soft part thickness ts and the bone part thickness tb. Therefore, by solving equations (11) and (12) using the soft part thickness ts and the bone part thickness tb as variables, the soft part thickness ts and the bone part thickness tb can be derived. Furthermore, the thickness ta of the artificial object can be derived from the body thickness T and the derived soft part thickness ts and bone part thickness tb using equation (10).

[0052] To solve equations (11) and (12), the soft tissue attenuation coefficients μLs and μHs, the bone attenuation coefficients μLb and μHb, and the artifact attenuation coefficients μLa and μHa for low-energy radiation and high-energy radiation, respectively, are required.

[0053] The soft tissue attenuation coefficients μLs and μHs used are those derived by the characteristic derivation unit 22. On the other hand, since there is no difference in composition between bones and artificial objects depending on the subject H, the bone attenuation coefficients μLb and μHb and the artificial object attenuation coefficients μLa and μHa corresponding to the soft tissue thickness ts, the bone thickness tb, and the artificial object thickness ta can be prepared in advance.

[0054] Here, in the process of the radiation passing through the subject H, low-energy components of the radiation are absorbed by the subject H, causing beam hardening, in which the radiation becomes more energetic. For this reason, in this embodiment, taking beam hardening into consideration, the bone attenuation coefficients μLb, μHb and artifact attenuation coefficients μLa, μHa are derived in advance and stored in storage 13.

[0055] When radiation passes through a subject that includes artificial objects, it passes through soft tissue, bone, and artificial objects in a complex order inside the human body depending on the overlap of these components. On the other hand, when monochromatic radiation is considered, the spectrum of the monochromatic radiation does not change, so the soft tissue, bone, and artificial objects have constant attenuation coefficients. If the total thickness of the soft tissue, bone, and artificial objects is the same, the amount of radiation after passing through the subject will always be the same, regardless of the order in which the radiation passes through each component within the subject.

[0056] The radiation actually irradiated to the subject is continuous radiation with a wide spectrum, but it is a collection of monochromatic radiation. Therefore, even if it is continuous radiation, if the total thickness of soft tissue, bone, and artificial objects is the same, the amount of radiation that passes through the subject will always be the same regardless of the order in which the components pass through.

[0057] In this embodiment, to derive the component image described below, only the thickness ts of the soft tissue, the thickness tb of the bone tissue, and the thickness ta of the artificial object are required, but information on how the soft tissue, bone, and artificial object overlap is not required. Therefore, in this embodiment, attenuation coefficients are derived using three types of models: a model consisting only of soft tissue, a simple model in which radiation first passes through the soft tissue and then through the bone tissue, and a simple model in which radiation first passes through the soft tissue, then through the bone tissue, and then through the artificial object. In the models, the soft tissue, bone, and artificial object are objects with corresponding attenuation coefficients. Furthermore, the order of the components in the model is not limited to soft tissue, bone, and artificial object, and can be any order.

[0058] As described above, the soft part attenuation coefficients μLs and μHs used are those derived by the characteristic deriving unit 22. For this reason, a model consisting of only soft parts is not used.

[0059] For the bone attenuation coefficient, a model consisting of soft tissue and bone, i.e., a model consisting of objects with attenuation coefficients corresponding to soft tissue and bone, is used, and low-energy and high-energy radiation are irradiated onto the model while varying the thickness of the soft tissue and bone, respectively, to derive low-energy and high-energy images. Then, the ratio of the pixel values ​​of each pixel in the low-energy and high-energy images to images obtained using low-energy and high-energy radiation in the absence of soft tissue and bone (hereinafter referred to as direct images) is derived, and the ratio is further divided by the bone thickness to derive the bone attenuation coefficients μLb and μHb for various bone thicknesses. Since the thickness of the soft tissue is also taken into account when deriving the bone attenuation coefficients μLb and μHb, the bone attenuation coefficients μLb and μHb take into account beam hardening in the soft tissue.

[0060] Regarding the artifact attenuation coefficient, a model consisting of soft tissue, bone, and artifacts, i.e., a model consisting of objects with attenuation coefficients corresponding to the soft tissue, bone, and artifacts, is used, and low-energy and high-energy radiation are irradiated onto the model while varying the thickness of the soft tissue, bone, and artifact, respectively, to derive low-energy images and high-energy images. The ratio of the pixel value of each pixel in the low-energy image and the high-energy image to that of the direct image is then derived, and the ratio is further divided by the thickness of the artifact to derive the artifact attenuation coefficients μLa and μHa for various artifact thicknesses. Since the thicknesses of the soft tissue and bone are also taken into account when deriving the artifact attenuation coefficients μLa and μHa, the artifact attenuation coefficients μLa and μHa take into account beam hardening of the soft tissue and bone.

[0061] For example, when deriving the artifact attenuation coefficient μLa1(ts1, tb1, ta1) when ts=ts1, tb=tb1, and ta=ta1, if the pixel value of the acquired low-energy component image is CL1 and the pixel value of the direct image is CLd, the artifact attenuation coefficient μLa1(ts, tb1, ta1) is derived using the following equation (13). μLa1(ts1,tb1,ta1)=CL1 / CLd (13)

[0062] Here, the thicknesses of the three materials included in the model are discrete, so the relationship between thickness and the attenuation coefficient of each component can be derived by interpolating the attenuation coefficients derived for each thickness for the three materials.

[0063] The soft tissue attenuation coefficients μLs and μHs derived as described above are expressed by a one-dimensional lookup table that depends only on the thickness of the soft tissue. The bone attenuation coefficients μLb and μHb are expressed by a two-dimensional lookup table that depends only on the thickness of the soft tissue and bone. The artifact attenuation coefficients μLa and μHa are expressed by a three-dimensional lookup table that depends on the thickness of the soft tissue, bone, and artifact.

[0064] As described above, there are a plurality of artifacts made of different materials, such as metal, silicon, contrast agent, etc. Therefore, it is preferable to derive the artifact attenuation coefficients μLa and μHa in advance according to the type of artifact.

[0065] As described above, the component thickness derivation unit 24 derives the soft tissue thickness ts and the bone thickness tb by solving equations (11) and (12) using the soft tissue thickness ts and the bone thickness tb as variables. Note that the derived soft tissue thickness ts and bone thickness tb are derived for each pixel of the first attenuation image CL and the second attenuation image CH, but in the following description, (x, y) representing the pixel position will be omitted.

[0066] The component thickness deriving unit 24 first calculates the thickness tb of the bone portion when the thickness of the soft portion is set to ts = 0 using equation (12). When ts = 0 and tb = tb0, CH = μHs(0, tb0, T - tb0-0) × 0 + μHb(0, tb0, T - tb0-0) × tb0 + μHa(0, tb0, T - tb0-0) × (T - tb0-0), so tb0 is calculated using the following equation (14). Furthermore, the component thickness deriving unit 24 calculates the thickness ts0 of the soft portion when the thickness of the bone portion is set to tb = 0 using equation (12). When ts=ts0 and tb=0, CH=μHs(ts0,0,T-0-ts0)×ts0+μHb(ts0,0,T-0-ts0)×0+μHa(ts0,0,T-0-ts0)×(T-0-ts0), so ts0 is calculated using the following equation (15). tb0=CH / (μHb(0,tb0,T-tb0-0)×tb0+μHa(0,tb0,T-tb0)×(T-tb0)) (14) ts0=CH / (μHs(ts0,0,T-0-ts0)×ts0+μHa(ts0,0,T-ts0)×(T-ts0)) (15)

[0067] FIG. 7 is a diagram showing the relationship between the amount of attenuation depending on the thickness of bone and the thickness of soft tissue. In FIG. 7, the amount of attenuation 33 indicates the amount of attenuation CL, which is the pixel value of a low-energy image derived by actually photographing a subject, and the amount of attenuation CH, which is the pixel value of a high-energy image. Here, the amount of attenuation in the low-energy image and the amount of attenuation in the high-energy image increase as the density of the composition increases. Therefore, the composition when tb = 0 and ts = ts0 has a lower density than the composition based on the actual thickness of bone and soft tissue. Therefore, when tb = 0 and ts = ts0, the pixel value of the first attenuation image (here, assumed to be a provisional first attenuation image CL') derived by Equation (11), i.e., the amount of attenuation, is smaller than the pixel value of the first attenuation image CL derived from the actual thickness of bone and soft tissue, as shown by the amount of attenuation 34 in FIG. 7 (i.e., CL > CL').

[0068] On the other hand, when tb=tb0 and ts=0, the composition becomes denser than the composition based on the actual thickness of the bone and the soft tissue. Therefore, when tb=tb0 and ts=0, the pixel value of the provisional first attenuation image CL′ derived by equation (11), i.e., the amount of attenuation, becomes larger than the pixel value of the first attenuation image CL derived from the actual thickness of the bone and the soft tissue, as shown in the attenuation amount 35 in FIG. 7 (i.e., CL <CL′)。

[0069] The pixel values ​​of the provisional first attenuation image CL′, i.e., the attenuation amount, derived by equation (11) using the actual bone thickness and soft tissue thickness, are the same as the pixel values ​​of the first attenuation image CL, as indicated by the attenuation amount 36 in Fig. 7. Using this, the image derivation unit 25 derives the bone thickness tb and the soft tissue thickness ts as follows.

[0070] (Step 1) First, in equation (12), a tentative soft tissue thickness tsk is calculated using the pixel values ​​of the second attenuation image CH and the soft tissue attenuation coefficient μHs, bone attenuation coefficient μHb, and artifact attenuation coefficient μHa derived for each pixel. The initial value of the tentative bone thickness tbk is set to 0.

[0071] (Step 2) Next, a provisional first attenuation image CL' is calculated using the calculated provisional soft-tissue thickness tsk, provisional bone thickness tbk, and the soft-tissue attenuation coefficient μLs, bone attenuation coefficient μLb, and artifact attenuation coefficient μLa for low-energy radiation according to the above formula (11). The soft-tissue attenuation coefficient μLs, bone attenuation coefficient μLb, and artifact attenuation coefficient μLa are used according to the provisional soft-tissue thickness tsk, provisional bone thickness tbk, and provisional artifact thickness (i.e., T-tsk-tbk).

[0072] (Step 3) Next, a difference value ΔCL between the provisional first attenuation image CL′ and the first attenuation image CL is calculated. The difference value ΔCL is assumed to be the pixel value of the radiation attenuated by the bone, and the thickness tbk of the bone is updated.

[0073] (Step 4) Next, a provisional second attenuation image CH' is calculated by the above equation (12) using the updated bone thickness tbk and soft tissue thickness tsk.

[0074] (Step 5) Next, a difference value ΔCH between the provisional second attenuation image CH′ and the second attenuation image CH is calculated. The difference value ΔCH is assumed to be the pixel value of the amount of radiation attenuated by the soft tissue, and the thickness tsk of the soft tissue is updated.

[0075] Then, the processes of steps 1 to 5 are repeated until the absolute values ​​of the difference values ​​ΔCL and ΔCH become less than a predetermined threshold value, thereby deriving the soft tissue thickness ts and the bone thickness tb. Note that the processes of steps 1 to 5 may be repeated a predetermined number of times to derive the soft tissue thickness ts and the bone thickness tb.

[0076] The image derivation unit 25 derives first to third component images, i.e., a soft tissue image Gs, a bone image Gb, and an artifact image Ga, based on the soft tissue thickness ts and bone thickness tb derived by the component thickness derivation unit 24 and the artifact thickness ta derived by equation (10). The soft tissue image Gs has pixel values ​​whose size corresponds to the soft tissue thickness ts. The bone image Gb has pixel values ​​whose size corresponds to the bone thickness tb. The artifact image Ga has pixel values ​​whose size corresponds to the artifact thickness ta (i.e., T-ts-tb).

[0077] The display control unit 26 displays the derived soft tissue image Gs, bone image Gb, and artifact image Ga. Fig. 8 is a diagram showing a display screen 40 for the soft tissue image Gs, bone image Gb, and artifact image Ga.

[0078] Next, the processing performed in this embodiment will be described. Fig. 9 is a flowchart showing the processing performed in this embodiment. First, the image acquisition unit 21 causes the imaging device 1 to perform energy subtraction imaging of the subject H, thereby acquiring first and second radiographic images G1 and G2 (radiographic image acquisition: step ST1). Next, the characteristic derivation unit 22 derives the characteristics of the soft tissue component related to attenuation of the radiographic image in the soft tissue region (step ST2). Subsequently, the body thickness acquisition unit 23 acquires the body thickness T of the subject H (step ST3). Note that the processing of step ST3 may be performed before step ST2, or may be performed in parallel with the processing of step ST2.

[0079] Next, the component thickness derivation unit 24 derives the soft tissue thickness ts, bone thickness tb, and artifact thickness ta using the body thickness T, soft tissue component characteristics, first radiographic image G1, and second radiographic image G2 (component thickness derivation: step ST4). Subsequently, the image derivation unit 25 derives the soft tissue image Gs, bone image Gb, and artifact image Ga based on the soft tissue thickness ts, bone thickness tb, and artifact thickness ta (component image derivation: step ST5). Then, the display control unit 26 displays the soft tissue image Gs, bone image Gb, and artifact image Ga (step ST6), and the process ends.

[0080] In this embodiment, the soft tissue thickness ts, bone thickness tb, and artifact thickness ta are derived using the body thickness T, soft tissue component characteristics, and the first and second radiographic images G1 and G2, and the soft tissue image Gs, bone image Gb, and artifact image Ga are derived based on the soft tissue thickness ts, bone thickness tb, and artifact thickness ta. Therefore, by simply acquiring the first and second radiographic images G1 and G2, which are two images with different energy distributions, it is possible to obtain the soft tissue image Gs, bone image Gb, and artifact image Ga of the subject H. Therefore, the soft tissue components, bone components, and artifact components within the subject H can be accurately separated using the first and second radiographic images G1 and G2, which are two images with different energy distributions.

[0081] In the above embodiment, images of three components, namely, soft tissue components, bone components, and artifact components, within the subject H are derived using the first and second radiographic images G1 and G2. However, this is not limiting. The first to n-1th radiographic images may be acquired using n-1 types of radiation with different energy distributions, and the first to n-th component images may be derived using the body thickness and the first to n-1th radiographic images. For example, three radiographic images with different energy distributions may be used to derive the thicknesses of the soft tissue components, bone components, and first and second artifact components with different compositions within the subject H from the body thickness and the three radiographic images, thereby deriving a soft tissue image, a bone image, a first artifact image, and a second artifact. Examples of artifacts with different compositions include a first artifact such as a screw used to fix a fracture, a metal such as a stent placed in a blood vessel, and a second artifact such as silicone placed in the breast. Different types of contrast agents may also be considered artifacts with different compositions.

[0082] Furthermore, in the above embodiment, the first and second radiographic images G1, G2 are acquired by a one-shot method, but this is not limiting. The first and second radiographic images G1, G2 may be acquired by a so-called two-shot method, in which imaging is performed twice using only one radiation detector. When using the two-shot method, the position of the subject H included in the first radiographic image G1 and the second radiographic image G2 may be shifted due to body movement of the subject H. For this reason, it is preferable to align the position of the subject in the first radiographic image G1 and the second radiographic image G2 before performing the processing of this embodiment.

[0083] Furthermore, in the above embodiment, radiographic images are used that are acquired in a system that uses the first and second radiation detectors 5 and 6 to capture the subject H. However, the technology of the present disclosure can also be applied to cases where the first and second radiographic images G1 and G2 are acquired using stimulable phosphor sheets instead of radiation detectors. In this case, two stimulable phosphor sheets are placed one on top of the other and irradiated with radiation that has passed through the subject H, and radiographic image information of the subject H is stored and recorded on each stimulable phosphor sheet. The radiographic image information is then photoelectrically read from each stimulable phosphor sheet to acquire the first and second radiographic images G1 and G2. Note that the two-shot method may also be used when acquiring the first and second radiographic images G1 and G2 using stimulable phosphor sheets.

[0084] Furthermore, the radiation in the above embodiment is not particularly limited, and in addition to X-rays, α rays, γ rays, etc. can be used.

[0085] In the above embodiment, the following various processors can be used as the hardware structure of the processing units that perform various processes, such as the image acquisition unit 21, the characteristic derivation unit 22, the body thickness acquisition unit 23, the component thickness derivation unit 24, the image derivation unit 25, and the display control unit 26. As described above, the various processors include a CPU, which is a general-purpose processor that executes software (programs) and functions as various processing units, as well as dedicated electrical circuits, such as a programmable logic device (PLD), a processor whose circuit configuration can be changed after manufacture, such as an FPGA (Field Programmable Gate Array), and an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically to perform specific processes.

[0086] A single processing unit may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs or a combination of a CPU and an FPGA). Also, multiple processing units may be configured with a single processor.

[0087] Examples of configuring multiple processing units with a single processor include, first, a form in which one processor is configured with a combination of one or more CPUs and software, and this processor functions as multiple processing units, as typified by computers such as client and server. Second, a form in which a processor is used to realize the functions of an entire system including multiple processing units with a single IC (Integrated Circuit) chip, as typified by systems on chips (SoCs). In this way, various processing units are configured using one or more of the above-mentioned various processors as a hardware structure.

[0088] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements.

[0089] The following are appendices to the present disclosure. (Additional note 1) at least one processor; The processor: acquiring first to n-1th radiographic images by imaging a subject including a first component consisting of a plurality of compositions and second to n-th (n≧3) components each consisting of a single composition using n-1 types of radiation with different energy distributions; deriving a characteristic of the first component in at least the region of the subject in the first to (n-1)th radiographic images; Obtaining the body thickness of the subject; deriving thicknesses of the first to n-th components using the body thickness, the characteristics of the first component, and the first to n-1th radiographic images; a radiation image processing device that derives first to n-th component images in which the first to n-th components are enhanced, respectively, based on the thicknesses of the first to n-th components; (Additional note 2) Item 1. The radiographic image processing device according to item 1, wherein the processor acquires the body thickness by deriving the body thickness of the subject based on at least one of the first to n-1th radiographic images. (Additional note 3) 3. The radiological image processing device according to claim 1, wherein the processor derives an attenuation coefficient of the first component as a characteristic of the first component. (Additional note 4) The radiological image processing device according to claim 3, wherein the processor derives the attenuation coefficient of the first component as a characteristic of the first component based on information regarding the attenuation coefficient of the radiation of each of the plurality of compositions contained in the first component and the thickness of each of the plurality of compositions. (Additional note 5) the processor acquires a first radiographic image and a second radiographic image obtained by imaging the subject with two types of radiation having different energy components; deriving a characteristic of the first component in at least a region of the subject in the first radiographic image or the second radiographic image; deriving a body thickness of the subject based on at least one of the first radiographic image and the second radiographic image; deriving thicknesses of the first component, the second component, and the third component of the subject using the body thickness, the characteristic of the first component, the first radiographic image, and the second radiographic image; 5. The radiological image processing device according to any one of claims 1 to 4, wherein a first component image, a second component image, and a third component image in which the first component, the second component, and the third component are enhanced, respectively, based on the thicknesses of the first component, the second component, and the third component. (Additional note 6) 6. The radiological image processing device according to any one of claims 1 to 5, wherein the first component, the second component, and the third component are a soft part of the subject, a bone part, and an artificial object within the subject, respectively. (Additional note 7) 7. The radiation image processing device according to claim 6, wherein the first component is composed of fat and muscle. (Additional note 8) a computer acquires first to (n-1)th radiographic images by imaging a subject including a first component having a plurality of compositions and second to n-th (n≧3) components each having a single composition using (n-1) types of radiation with different energy distributions; deriving a characteristic of the first component in at least the region of the subject in the first to (n-1)th radiographic images; Obtaining the body thickness of the subject; deriving thicknesses of the first to n-th components using the body thickness, the characteristics of the first component, and the first to n-1th radiographic images; A radiation image processing method for deriving first to n-th component images in which the first to n-th components are enhanced, respectively, based on the thicknesses of the first to n-th components. (Additional note 9) acquiring first to (n-1)th radiographic images by imaging a subject including a first component having a plurality of compositions and second to n-th (n≧3) components, each of which is made up of a single composition, using n-1 types of radiation having different energy distributions; deriving a characteristic of the first component in at least the region of the subject in the first to n-1th radiographic images; obtaining a body thickness of the subject; deriving thicknesses of the first to n-th components using the body thickness, the characteristics of the first component, and the first to n-1th radiographic images; and deriving first to n-th component images in which the first to n-th components are enhanced, respectively, based on the thicknesses of the first to n-th components. [Explanation of symbols]

[0090] 1. Imaging device 3 Radiation source 5, 6 Radiation detector 7 Radiation Energy Conversion Filter 10 Image processing device 11 CPU 12 Radiation Image Processing Program 13. Storage 14 Display 15 Input Devices 16 memory 17 Network I / F 18 Bus 21 Image acquisition unit 22 Characteristics Derivation Unit 23 Body thickness acquisition part 24 Component thickness derivation section 25 Image derivation unit 26 Display control unit 33~36 Attenuation amount 40 display screen Ga Artifact Image Gb Bone image Gs Soft tissue images H Subject

Claims

1. at least one processor; The processor: acquiring first to (n-1)th radiographic images by imaging a subject including a first component having a plurality of compositions and second to n-th (n≧3) components each having a single composition using n-1 types of radiation having different energy distributions; deriving a characteristic of the first component in at least a region of the subject in the first to (n-1)th radiographic images; Obtaining the body thickness of the subject; deriving thicknesses of the first to n-th components using the body thickness, the characteristics of the first component, and the first to n-1th radiographic images; a radiation image processing apparatus that derives first to n-th component images in which the first to n-th components are enhanced, respectively, based on the thicknesses of the first to n-th components;

2. The radiological image processing apparatus according to claim 1 , wherein the processor acquires the body thickness by deriving the body thickness of the subject based on at least one of the first to (n−1)th radiological images.

3. The radiation image processing apparatus according to claim 1 , wherein the processor derives an attenuation coefficient of the first component as a characteristic of the first component.

4. The radiological image processing device according to claim 3 , wherein the processor derives the attenuation coefficient of the first component as a characteristic of the first component based on information regarding the attenuation coefficient of the radiation of each of the plurality of compositions contained in the first component and the thickness of each of the plurality of compositions.

5. the processor acquires a first radiographic image and a second radiographic image obtained by imaging the subject with two types of radiation having different energy components; deriving a characteristic of the first component in at least a region of the subject in the first radiographic image or the second radiographic image; deriving a body thickness of the subject based on at least one of the first radiographic image and the second radiographic image; deriving thicknesses of the first component, the second component, and the third component of the subject using the body thickness, the characteristic of the first component, the first radiographic image, and the second radiographic image; 2. The radiation image processing device according to claim 1, further comprising: a first component image, a second component image, and a third component image in which the first component, the second component, and the third component are enhanced, respectively, based on the thicknesses of the first component, the second component, and the third component.

6. The radiation image processing apparatus according to claim 1 , wherein the first component, the second component, and the third component are a soft part, a bone part, and an artificial object in the subject, respectively.

7. The radiation image processing apparatus according to claim 6 , wherein the first component is composed of fat and muscle.

8. a computer acquires first to (n-1)th radiographic images by imaging a subject including a first component having a plurality of compositions and second to n-th (n≧3) components each having a single composition using (n-1) types of radiation with different energy distributions; deriving a characteristic of the first component in at least a region of the subject in the first to (n-1)th radiographic images; Obtaining the body thickness of the subject; deriving thicknesses of the first to n-th components using the body thickness, the characteristics of the first component, and the first to n-1th radiographic images; A radiation image processing method for deriving first to n-th component images in which the first to n-th components are enhanced, respectively, based on the thicknesses of the first to n-th components.

9. acquiring first to (n-1)th radiographic images by imaging a subject including a first component having a plurality of compositions and second to n-th (n≧3) components, each of which is made up of a single composition, using n-1 types of radiation having different energy distributions; deriving a characteristic of the first component in at least a region of the subject in the first to (n-1)th radiographic images; obtaining a body thickness of the subject; deriving thicknesses of the first to nth components using the body thickness, the characteristics of the first component, and the first to n-1th radiographic images; and deriving first to n-th component images in which the first to n-th components are enhanced, respectively, based on the thicknesses of the first to n-th components.

Images