Radiation imaging processing device, method, and program

Abstract

The present invention provides a radiation image processing device, method, and program that can accurately derive the compositional ratio within a subject. [Solution] The processor acquires two radiation images based on radiation with different energy distributions that has passed through a subject containing multiple compositions. Using attenuation coefficients corresponding to the order in which the radiation for the multiple compositions passes through, the processor derives the thickness of the subject as the first and second thicknesses for each pixel in each of the two radiation images. Based on the first and second thicknesses, the processor derives the composition ratio of the subject for each pixel in the radiation image.

Description

Technical Field

[0001] The present disclosure relates to a radiation image processing apparatus, method, and program for deriving the composition ratio of a subject using radiation images.

Background Art

[0002] Conventionally, various methods for deriving the composition of the human body such as fat and muscle have been proposed. For example, in Patent Document 1, for each of two radiation images obtained by radiation having different energy distributions passing through a subject, the body thickness of the subject is derived as a first body thickness and a second body thickness, and based on the first body thickness and the second body thickness, a method for deriving the composition ratio of a subject such as muscle and fat has been proposed.

[0003] Here, the radiation emitted from a radiation source has an energy distribution. The attenuation coefficient of radiation in a subject depends on the energy of the radiation and has the property that the attenuation coefficient becomes smaller for higher energy components. For this reason, in the process of radiation passing through a substance, a phenomenon called beam hardening occurs in which relatively more low-energy components are lost and the proportion of high-energy components increases. The degree of beam hardening depends on the thickness of fat and the thickness of muscle in the subject. Therefore, in the method described in Patent Document 1, the attenuation coefficient μf of fat and the attenuation coefficient μm of muscle are represented as attenuation coefficients μf(tf,tm), μm(tf,tm) that are non-linear functions of the fat thickness tf and the muscle thickness tm, and the first body thickness and the second body thickness are derived.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] However, in reality, the effect of beam hardening is influenced by the order in which the radiation passes through the materials. For example, if there are two materials, the first material through which the radiation passes will be affected by beam hardening depending only on its own thickness, but the attenuation coefficient of the second material through which the radiation passes will be affected not only by the thickness of the second material itself, but also by the thickness of the first material through which the radiation passed first. For this reason, the method described in Patent Document 1 will result in an excessive effect of beam hardening, leading to errors in the accuracy of deriving the composition ratio of the subject.

[0006] This disclosure is made in view of the above circumstances and aims to enable more accurate derivation of the compositional ratios within the subject. [Means for solving the problem]

[0007] The radiation image processing device according to this disclosure comprises at least one processor, The processor acquires two radiation images based on radiation with different energy distributions that has penetrated a subject containing multiple compositions. Using attenuation coefficients corresponding to the transmission order of radiation for multiple compositions, the thickness of the subject is derived as the first and second body thicknesses for each pixel in each of the two radiation images. Based on the first and second body thicknesses, the composition ratio of the subject is derived for each pixel of the radiographic image.

[0008] Furthermore, in the radiation image processing apparatus according to this disclosure, the processor may derive the composition ratio by considering the subject as a model in which each of the multiple compositions is grouped together and divided such that each composition has one thickness.

[0009] Furthermore, in the radiation image processing apparatus according to this disclosure, the processor may derive the composition ratio based on the difference between the first body thickness and the second body thickness.

[0010] Furthermore, in the radiation image processing apparatus according to this disclosure, the processor may derive a first body thickness and a second body thickness using the changed composition thickness and attenuation coefficient for each composition while changing the composition thickness and attenuation coefficient for each composition, and derive a composition ratio based on the composition thickness at which the difference between the first body thickness and the second body thickness is less than or equal to a predetermined threshold.

[0011] Furthermore, in the radiation image processing apparatus according to this disclosure, the processor removes scattered radiation components contained in two radiation images. The composition ratio may also be derived based on two radiation images from which scattered radiation components have been removed.

[0012] Furthermore, in the radiation image processing apparatus according to this disclosure, the two radiation images may be acquired by two radiation detectors by simultaneously irradiating two radiation detectors that are superimposed on each other with radiation that has passed through the subject.

[0013] Furthermore, in the radiation image processing apparatus according to this disclosure, the processor may display the distribution of composition ratios superimposed on one of the two radiation images on a display.

[0014] Furthermore, in the radiation image processing apparatus according to this disclosure, the multiple compositions may be muscle and fat.

[0015] Furthermore, in the radiation image processing apparatus according to this disclosure, the multiple compositions may be bone and soft tissue.

[0016] Furthermore, in the radiation image processing apparatus according to this disclosure, the multiple compositions may be contrast agents injected into the subject and tissues other than the contrast agent.

[0017] Furthermore, in the radiation image processing apparatus according to this disclosure, the processor may derive a compositional image for at least one of a plurality of compositions based on two radiation images and compositional ratios.

[0018] Also, in the radiation image processing apparatus according to the present disclosure, the processor may quantify the composition represented by the composite image based on the composite image.

[0019] The radiation image processing method according to the present disclosure acquires two radiation images based on radiations having different energy distributions from each other, which have passed through a subject including a plurality of compositions, using attenuation coefficients according to the order in which the radiations for the plurality of compositions pass through, for each pixel of each of the two radiation images, derives the thicknesses of the subject as a first thickness and a second thickness, respectively, and based on the first thickness and the second thickness, derives the composition ratio of the subject for each pixel of the radiation image.

[0020] The radiation image processing program according to the present disclosure causes a computer to execute a procedure for acquiring two radiation images based on radiations having different energy distributions from each other, which have passed through a subject including a plurality of compositions, a procedure for using attenuation coefficients according to the order in which the radiations for the plurality of compositions pass through, for each pixel of each of the two radiation images, to derive the thicknesses of the subject as a first thickness and a second thickness, respectively, and a procedure for deriving the composition ratio of the subject for each pixel of the radiation image based on the first thickness and the second thickness.

Advantages of the Invention

[0021] According to the present disclosure, the composition ratio within the subject can be accurately derived.

Brief Description of the Drawings

[0022] [Figure 1] Schematic block diagram showing the configuration of a radiation image imaging system to which the radiation image processing apparatus according to the first embodiment of the present disclosure is applied [Figure 2] Diagram showing the schematic configuration of the radiation image processing apparatus according to the first embodiment [Figure 3] Diagram showing the functional configuration of the radiation image processing apparatus according to the first embodiment [Figure 4] A diagram illustrating the difference in body thickness derived from low-energy and high-energy images. [Figure 5] A diagram showing a table that defines the relationship between differences in body thickness and the proportion of fat composition. [Figure 6] A diagram illustrating that the energy distribution of radiation is not altered by the arrangement of muscle and fat. [Figure 7] Diagram showing the body fat percentage display screen. [Figure 8] A flowchart illustrating the process performed in the first embodiment. [Figure 9] A flowchart illustrating the process performed in the second embodiment. [Figure 10] This figure shows the functional configuration of a radiation image processing apparatus according to the third embodiment. [Figure 11] Figure showing soft tissue images [Figure 12] A diagram showing an example of the energy spectra of radiation after it has passed through muscle tissue and radiation after it has passed through adipose tissue. [Figure 13] Flowchart showing the process performed in the third embodiment [Modes for carrying out the invention]

[0023] Embodiments of this disclosure will be described below with reference to the drawings. Figure 1 is a schematic block diagram showing the configuration of a radiographic imaging system to which a radiographic imaging apparatus according to the first embodiment of this disclosure is applied. As shown in Figure 1, the radiographic imaging system according to this embodiment comprises an imaging apparatus 1 and a radiographic imaging apparatus 10 according to the first embodiment.

[0024] The imaging device 1 is an imaging device for performing energy subtraction using the so-called one-shot method, in which radiation such as X-rays emitted from the radiation source 3 and transmitted through the subject H is irradiated onto the first radiation detector 5 and the second radiation detector 6 with varying energies. During imaging, as shown in Figure 1, the first radiation detector 5, a radiation energy conversion filter 7 made of a copper plate or the like, and the second radiation detector 6 are arranged in 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 conversion filter 7 are in close contact.

[0025] As a result, the first radiation detector 5 acquires a first radiation image G1 of subject H using low-energy radiation, including so-called soft rays. The second radiation detector 6 acquires a second radiation image G2 of subject H using high-energy radiation, from which soft rays have been removed. The first and second radiation images G1 and G2 are input to the radiation image processing device 10.

[0026] In this embodiment, when photographing subject H, a scatter removal grid is not used to remove the scattered radiation component of the radiation that has passed through subject H. Therefore, the first radiation image G1 and the second radiation image G2 include both the primary radiation component and the scattered radiation component of the radiation that has passed through subject H.

[0027] Here, energy subtraction processing is a process that utilizes the fact that the attenuation rate of transmitted radiation differs depending on the material constituting the subject. It involves irradiating the subject with two types of radiation with different energy distributions, obtaining two radiation images, and using these images to generate an image in which different tissues (e.g., soft tissue and bone) within the subject are extracted. The imaging device 1 in the radiation imaging system according to this embodiment is capable of performing energy subtraction processing. However, since the first embodiment derives the composition ratio of the subject, a detailed explanation of energy subtraction processing is omitted.

[0028] The first and second radiation detectors 5 and 6 are capable of repeatedly recording and reading radiation images. They may be so-called direct-type radiation detectors that generate an electric charge by directly receiving radiation, or they may be so-called indirect-type radiation detectors that first convert radiation into visible light and then convert that visible light into an electric charge signal. Furthermore, as a method for reading out the radiation image signal, it is desirable to use a so-called TFT (thin film transistor) readout method, in which the radiation image signal is read out by turning a TFT switch on and off, or a so-called optical readout method, in which the radiation image signal is read out by irradiating it with reading light. However, other methods may also be used.

[0029] Next, a radiation image processing apparatus according to the first embodiment will be described. First, with reference to Figure 2, the hardware configuration of the radiation image processing apparatus according to the first embodiment will be described. As shown in Figure 2, the radiation image processing apparatus 10 is a computer such as a workstation, server computer, or personal computer, and includes a CPU (Central Processing Unit) 11, non-volatile storage 13, and memory 16 as a temporary storage area. The radiation image processing apparatus 10 also includes a display 14 such as a liquid crystal display, input devices 15 such as a keyboard and mouse, and a network I / F (Interface) 17 connected to a network (not shown). The CPU 11, storage 13, display 14, input devices 15, memory 16, and network I / F 17 are connected to a bus 18. Note that the CPU 11 is an example of a processor in this disclosure.

[0030] The storage 13 is implemented using an HDD (Hard Disk Drive), an SSD (Solid State Drive), and flash memory, etc. The storage 13, as a storage medium, stores the radiation image processing program 12 installed in the radiation image processing device 10. The CPU 11 reads the radiation image processing program 12 from the storage 13, expands it into memory 16, and executes the expanded radiation image processing program 12.

[0031] The radiation image 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 radiation image processing device 10 upon request. Alternatively, it is 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 on the computers comprising the radiation image processing device 10.

[0032] Next, the functional configuration of the radiation image processing apparatus according to the first embodiment will be described. Figure 3 is a diagram showing the functional configuration of the radiation image processing apparatus according to the first embodiment. As shown in Figure 3, the radiation image processing apparatus 10 comprises an image acquisition unit 21, a scattered radiation removal unit 22, a body thickness derivation unit 23, a composition ratio derivation unit 24, and a display control unit 25. The CPU 11 functions as the image acquisition unit 21, the scattered radiation removal unit 22, the body thickness derivation unit 23, the composition ratio derivation unit 24, and the display control unit 25 by executing the radiation image processing program 12. In the first embodiment, the composition ratio is the composition ratio of fat. For this reason, although the subject H includes bone, for the sake of explanation, the first and second radiation images G1 and G2 will be described as not including bone, but only soft tissue.

[0033] The image acquisition unit 21 causes the imaging device 1 to perform energy subtraction imaging of the subject H, thereby acquiring the first radiation image G1 and the second radiation image G2 of the subject H from the first and second radiation detectors 5 and 6. The imaging conditions are set as described above when acquiring the first radiation image G1 and the second radiation image G2.

[0034] The scattered radiation removal unit 22 removes scattered radiation components from the first and second radiation images G1 and G2, which are generated by the scattering of radiation within the subject H. Any method described in, for example, Japanese Patent Publication No. 2014-207958, can be used to remove the scattered radiation components. The method described in Japanese Patent Publication No. 2014-207958 involves acquiring the characteristics of a grid intended for use in removing scattered radiation during radiation image acquisition, deriving the scattered radiation components included in the radiation image based on these characteristics, and performing scattered radiation removal processing using the derived scattered radiation components. In subsequent processing, the first and second radiation images G1 and G2 are those from which the scattered radiation components have been removed.

[0035] The body thickness derivation unit 23 derives the body thickness of the subject H as the first body thickness and the second body thickness, respectively, for each pixel of the first and second radiation images G1 and G2, from which the scattered radiation component has been removed. Specifically, with respect to the first radiation image G1, the body thickness derivation unit 23 assumes that the brightness distribution matches the distribution of the body thickness of the subject H, and derives the first body thickness t1 of the subject H by converting the pixel values ​​of the first radiation image G1 into thickness using the attenuation coefficient in the muscle of the subject H. Similarly, with respect to the second radiation image G2, the body thickness derivation unit 23 assumes that the brightness distribution matches the distribution of the body thickness of the subject H, and derives the second body thickness t2 of the subject H by converting the pixel values ​​of the second radiation image G2 into thickness using the attenuation coefficient in the muscle of the subject H.

[0036] Here, the radiation emitted from radiation source 3 has an energy distribution, and the attenuation coefficient of the radiation in subject H also depends on the energy of the radiation, with the attenuation coefficient decreasing as the energy component increases. As a result, a phenomenon called beam hardening occurs, in which the radiation loses a relatively large amount of low-energy components as it passes through matter, and the proportion of high-energy components increases. The degree of beam hardening depends on the thickness of fat tf and the thickness of muscle tm within subject H. It also depends on the order in which the radiation passes through the materials within subject H. That is, if the radiation passes through fat first, the attenuation coefficient of fat depends only on the thickness of fat tf, but the attenuation coefficient of muscle, which is the next material through which the radiation passes, depends not only on the thickness of muscle tm but also on the thickness of fat tf. For this reason, the attenuation coefficient μf of fat can be defined as μf(tf) as a nonlinear function of the thickness of fat tf, and the attenuation coefficient μm of muscle can be defined as μm(tf,tm) as a nonlinear function of the thickness of fat tf and the thickness of muscle tm.

[0037] Here, the soft tissue of subject H includes muscle, fat, blood, and water. In this embodiment, tissue other than fat in the soft tissue is considered to be muscle. That is, in this embodiment, muscle is treated as including non-fatty tissue, including blood and water.

[0038] As in this embodiment, the first and second radiation images G1 and G2, acquired by radiation with two different energy distributions, correspond to a low-energy image and a high-energy image, respectively. Therefore, in this embodiment, the fat attenuation coefficient for the first radiation image G1, which is a low-energy image, can be expressed as μlf(tf), and the muscle attenuation coefficient can be expressed as μlm(tf,tm). Similarly, the fat attenuation coefficient for the second radiation image G2, which is a high-energy image, can be expressed as μhf(tf), and the muscle attenuation coefficient can be expressed as μhm(tf,tm).

[0039] Furthermore, the pixel values ​​G1(x,y) of each pixel in the first low-energy image G1 and the pixel values ​​G2(x,y) of each pixel in the second high-energy image G2 are expressed by the following equations (1) and (2), using the fat thickness tf(x,y), muscle thickness tm(x,y), and attenuation coefficients μlf(x,y), μhf(x,y), μlm(x,y), and μhm(x,y) at the corresponding pixel positions. Note that the (x,y) is omitted in equations (1) and (2). G1 = μlf × tf + μlm × tm (1) G2 = μhf × tf + μhm × tm (2)

[0040] As described above, in this embodiment, when deriving the first body thickness t1 and the second body thickness t2, the pixel values ​​of the first radiation image G1 and the second radiation image G2 are converted to thickness using the muscle attenuation coefficient in the subject H. Therefore, in the first embodiment, the body thickness derivation unit 23 derives the first body thickness t1 and the second body thickness t2 using the following equations (3) and (4). Note that the first body thickness t1 and the second body thickness t2 are derived at each pixel (x,y) of the first and second radiation images G1 and G2, but the (x,y) is omitted in equations (3) and (4). t1 = G1 / μlm (3) t² = G² / μhm (4)

[0041] If the subject H contains only muscle at the pixel positions from which the first and second body thicknesses t1 and t2 are derived, then the first body thickness t1 and the second body thickness t2 will coincide. However, in the actual subject H, both muscle and fat are included at the same pixel positions in the first and second radiographic images G1 and G2. Therefore, the first and second body thicknesses t1 and t2 derived by equations (3) and (4) will not coincide with the actual body thickness of the subject H. Furthermore, the first body thickness t1 derived from the first radiographic image G1, which is a low-energy image, will be larger than the second body thickness t2 derived from the second radiographic image G2, which is a high-energy image. For example, as shown in Figure 4, suppose the actual body thickness is 100 mm, and the thickness of fat and muscle is 30 mm and 70 mm, respectively. In this case, the first body thickness t1 derived from the first radiation image G1 obtained using low-energy radiation is, for example, 80 mm, and the second body thickness t2 derived from the second radiation image G2 obtained using high-energy radiation is, for example, 70 mm. Furthermore, the difference between the first body thickness t1 and the second body thickness t2 increases as the proportion of fat composition increases.

[0042] Here, the difference between the first body thickness t1 and the second body thickness t2 changes depending on the composition ratio of fat and muscle in the subject H. For this reason, in this embodiment, subject models with varying fat composition ratios are photographed with radiation of different energy distributions, and the body thickness is derived from the two radiation images obtained. A table is created in advance that associates the difference in body thickness derived from the two radiation images with the fat composition ratio and is stored in storage 13.

[0043] Figure 5 shows a table that correlates the difference in body thickness derived from two radiographic images with the fat composition ratio. As shown in Figure 5, the horizontal axis of Table LUT1 represents the difference in body thickness derived from each of the two radiographic images, and the vertical axis represents the fat composition ratio. As shown in Figure 5, the larger the difference in body thickness derived from each of the two radiographic images, the larger the fat composition ratio. Note that a table correlating the difference in body thickness derived from each of the two radiographic images with the fat composition ratio is prepared for each energy distribution of radiation used during imaging and stored in storage 13.

[0044] In this embodiment, an attenuation coefficient is used that takes into account the effect of beam hardening according to the order in which radiation passes through fat and muscle. On the other hand, since fat and muscle are mixed within subject H, fat and muscle of various thicknesses alternate along the radiation transmission path. However, even if the arrangement of muscle and fat is changed within subject H under the condition that the overall muscle and fat thicknesses are fixed, the attenuation coefficient for each energy is determined by the material, so the radiation spectrum transmitted through the entire object will be the same.

[0045] Figure 6 illustrates that the energy distribution of radiation is not altered by the arrangement of muscle and fat. As shown in Figure 6, consider subject 31 in which fat of thickness tf1 and muscle of thickness tm2 are arranged in this order, subject 32 in which muscle of thickness tm2 and fat of thickness tf1 are arranged in this order, and subject 33 in which fat of thickness tf11, muscle of thickness tm2 and fat of thickness tf12 are arranged in this order. Assume that tf1 = tf11 + tf12. Radiation with an energy distribution 30 is irradiated onto these three subjects 31 to 33. In the energy distribution 30, the horizontal axis is the energy of the radiation and the vertical axis is the number of photons of the radiation. The energy distribution 34 of the radiation after passing through subject 31, the energy distribution 35 of the radiation after passing through subject 32, and the energy distribution 36 of the radiation after passing through subject 33 are the same. This is because even if the arrangement of fat and muscle is changed, the attenuation coefficient for each energy is determined by the material.

[0046] Therefore, in this embodiment, the subject H is considered as a model divided into two parts such that fat and muscle are grouped together and each has a thickness of one, and the composition is determined accordingly.

[0047] The composition ratio derivation unit 24 derives the difference between the first body thickness t1 and the second body thickness t2 derived by the body thickness derivation unit 23, and derives the fat composition ratio by referring to the LUT1 stored in the storage 13. The muscle composition ratio can be derived by subtracting the derived fat composition ratio from 100%.

[0048] The display control unit 25 displays the fat composition distribution on the display 14 based on the fat composition ratio for each pixel of the first and second radiation images G1 and G2 derived by the composition ratio derivation unit 24. Figure 7 shows the display screen of the fat composition distribution shown on the display 14. As shown in Figure 7, the fat composition distribution is displayed on the display screen 40 superimposed on the first radiation image G1 as a body fat percentage distribution. Alternatively, the body fat percentage distribution may be superimposed on the second radiation image G2. In Figure 7, the body fat percentage distribution is displayed in three color-coded stages. In Figure 7, the color coding is represented by differences in density, with higher density indicating a higher body fat percentage. The display 14 also displays a reference 41 that shows the relationship between density and body fat percentage. By referring to the reference 41, the distribution of body fat percentage can be easily recognized.

[0049] Next, the processing performed in the first embodiment will be described. Figure 8 is a flowchart showing the processing performed in the first embodiment. The first and second radiation images G1 and G2 are assumed to have been acquired by imaging and stored in the storage 13. When an instruction to start processing is input from the input device 15, the image acquisition unit 21 acquires the first and second radiation images G1 and G2 from the storage 13 (step ST1). Next, the scattered radiation removal unit 22 removes the scattered radiation component from the first and second radiation images G1 and G2 (step ST2). Furthermore, the body thickness derivation unit 23 derives the body thickness of the subject H as the first body thickness t1 and the second body thickness t2, respectively, for each pixel of the first and second radiation images G1 and G2 from which the scattered radiation component has been removed, using attenuation coefficients corresponding to the order in which radiation is transmitted through fat and muscle (step ST3).

[0050] Next, the composition ratio derivation unit 24 derives the difference between the first body thickness t1 and the second body thickness t2 derived by the body thickness derivation unit 23, and derives the fat composition ratio by referring to the LUT1 stored in the storage 13 (step ST4). Then, the composition ratio derivation unit 24 determines whether or not it has derived the composition ratio for all pixels (step ST5), and if step ST5 is denied, it returns to step ST3. If step ST5 is affirmed, the display control unit 25 displays the fat composition distribution based on the fat composition ratio derived by the composition ratio derivation unit 24 on the display 14 (step ST6), and the process ends.

[0051] Thus, in the first embodiment, for each pixel of the first and second radiation images G1 and G2, the body thickness of the subject H is derived as the first body thickness t1 and the second body thickness t2, respectively, using attenuation coefficients corresponding to the order in which radiation penetrates fat and muscle. Based on the difference between the first body thickness t1 and the second body thickness t2, the composition ratio of the subject H is derived. Therefore, the first body thickness t1 and the second body thickness t2, as well as the proportion of fat, can be derived while considering the effect of beam hardening according to the order in which radiation penetrates the material within the subject H. Accordingly, according to this embodiment, the composition ratio within the subject can be derived with high accuracy.

[0052] Furthermore, since the composition is determined by considering the subject H as a model divided into two parts such that fat and muscle each have a single thickness, the composition can be derived through relatively simple calculations. Therefore, according to this embodiment, the composition ratio of subject H can be derived in a short processing time.

[0053] In the first embodiment described above, the body thickness derivation unit 23 derives the first and second body thicknesses t1 and t2 by converting the pixel values ​​of the first and second radiographic images G1 and G2 into thickness using a muscle attenuation coefficient, but it is not limited to this. The first and second body thicknesses t1 and t2 may also be derived by converting the pixel values ​​of the first and second radiographic images G1 and G2 into thickness using a fat attenuation coefficient. In this case, a table is created in advance that associates the difference in body thickness derived from the two radiographic images with the muscle composition ratio and is stored in the storage 13. The composition ratio derivation unit 24 then derives the muscle composition ratio by referring to the table that associates the difference in body thickness derived from the two radiographic images with the muscle composition ratio. In this case, the fat composition ratio can be derived by subtracting the derived muscle composition ratio from 100%.

[0054] Next, a second embodiment of the present disclosure will be described. The radiation image processing apparatus in the second embodiment has the same configuration as the radiation image processing apparatus in the first embodiment of the present disclosure shown in Figure 3, with only the processing performed being different. For this reason, a detailed description of the apparatus will be omitted here. The radiation image processing apparatus in the second embodiment differs from the first embodiment in that the body thickness derivation unit 23 derives the first body thickness t1 and the second body thickness t2 using attenuation coefficients corresponding to the order in which radiation from multiple compositions is transmitted, and the composition ratio derivation unit 24 changes the thickness of the composition and the attenuation coefficient for each composition, and causes the body thickness derivation unit 23 to derive the first body thickness t1 and the second body thickness t2 using the changed thickness of the composition and the attenuation coefficient for each composition, and derives the composition ratio based on the thickness of the composition in which the difference between the first body thickness t1 and the second body thickness t2 is less than or equal to a predetermined threshold Th1.

[0055] Here, the first body thickness t1 is the sum of the fat thickness tf and the muscle thickness tm, i.e., t1 = tf + tm. Since tm = t1 - tf, equation (1) above can be transformed into equation (5) below. Note that the (x,y) terms are omitted in equations (5) to (7) as well. G1 = μlf × tf + μlm × (t1 - tf) (5)

[0056] Solving equation (5) for t1, we get the following equation (5). t1={G1+(μlm-μlf)×tf} / μlm (6)

[0057] Furthermore, since the second body thickness t2 = tf + tm, if we rearrange equation (2) in the same way as equation (5) and solve for t2, we get the following equation (7). t2={G2+(μhm-μhf)×tf} / μhm (7)

[0058] The composition ratio of fat can be derived by deriving the fat thickness tf such that the difference between t1 and t2 is small, preferably t1 = t2. However, since the attenuation coefficients μlf and μhf are nonlinear functions of the fat thickness tf, and μlm and μhm are nonlinear functions of the fat thickness tf and the muscle thickness tm, the fat thickness tf cannot be derived algebraically from equations (6) and (7). For this reason, in the second embodiment, the composition ratio derivation unit 24 changes the fat thickness tf and the attenuation coefficients μlf, μhf, μlm, and μhm, and causes the body thickness derivation unit 23 to derive the first body thickness t1 and the second body thickness t2 using the changed composition thickness tf and attenuation coefficients μlf, μhf, μlm, and μhm. The composition ratio derivation unit 24 then derives a fat thickness tf such that the difference between the first body thickness t1 and the second body thickness t2 is less than or equal to a predetermined threshold Th1, i.e., |t1-t2|≦Th1, and derives the fat composition ratio based on the fat thickness tf. It is preferable that the threshold Th1 be as small as possible, and more preferably Th1=0.

[0059] Specifically, if tf=0 and t1=t2, then all pixels (x,y) are muscle. Also, if tf=0 and t1≠t2, the composition ratio derivation unit 24 derives the fat thickness tf by changing the fat thickness tf and searching for a fat thickness tf such that |t1-t2|≦Th1. Then, the composition ratio derivation unit 24 derives the fat composition ratio by dividing the derived fat thickness tf by the first body thickness t1 or the second body thickness t2. The muscle composition ratio can be derived by subtracting the derived fat composition ratio from 100%.

[0060] Next, the processing performed in the second embodiment will be described. Figure 9 is a flowchart showing the processing performed in the second embodiment. The first and second radiation images G1 and G2 are assumed to have been acquired by imaging and stored in the storage 13. When an instruction to start processing is input from the input device 15, the image acquisition unit 21 acquires the first and second radiation images G1 and G2 from the storage 13 (step ST11). Next, the scattered radiation removal unit 22 removes the scattered radiation component from the first and second radiation images G1 and G2 (step ST12). Furthermore, the body thickness derivation unit 23 sets an initial value for the fat thickness tf (step ST13), and for each pixel of the first and second radiation images G1 and G2 from which the scattered radiation component has been removed, it derives the body thickness of the subject H as the first body thickness t1 and the second body thickness t2, respectively, using an attenuation coefficient corresponding to the order in which radiation is transmitted through fat and muscle (step ST14). The initial value of the fat thickness tf may be set by the composition ratio derivation unit 24.

[0061] Next, the composition ratio derivation unit 24 determines whether |t1-t2|≦Th1 (step ST15). If step ST15 is rejected, the fat thickness tf is changed (step ST16), and the process returns to step ST14. If step ST15 is confirmed, the composition ratio derivation unit 24 derives the fat composition ratio based on the fat thickness tf at the time step ST15 was confirmed (step ST17). Then, the composition ratio derivation unit 24 determines whether it has derived the composition ratio for all pixels (step ST18). If step ST18 is rejected, the process returns to step ST13. If step ST18 is confirmed, the display control unit 25 displays the fat composition distribution based on the fat composition ratio derived by the composition ratio derivation unit 24 on the display 14 (step ST19), and the process ends.

[0062] Thus, in the second embodiment as well, the compositional ratio within the subject can be derived with high accuracy.

[0063] In particular, in the second embodiment, the composition is determined by considering the subject H as a two-part model in which fat and muscle each form a single thickness. Therefore, when deriving the composition by performing iterative calculations as in the second embodiment, it is possible to prevent the fat thickness tf to be determined from diverging, the error with the actual fat thickness from increasing, and the processing time from increasing, compared to the case where the thickness and arrangement of fat and muscle are determined simultaneously. In other words, by reducing unnecessary variables used in the calculation formula in the iterative process of deriving body thickness, the probability of converging to the optimal solution in a shorter time than in conventional methods can be increased.

[0064] In the second embodiment described above, the composition ratio of fat is derived based on the fat thickness tf, but the composition ratio of muscle may also be derived based on the muscle thickness tm. In this case, tf = t1 - tm, and t1 is derived from equation (1) to obtain equation (8) below. Solving equation (2) for t2 yields equation (9) below. Note that the (x,y) terms are omitted in equations (8) and (9). t1={G1+(μlf-μlm)×tm} / μlf (8) t2={G2+(μhf-μhm)×tm} / μhf (9)

[0065] In this case, the composition ratio derivation unit 24 derives the muscle thickness tm by searching for a tm such that the difference between the first body thickness t1 and the second body thickness t2 is less than or equal to a predetermined threshold Th2, i.e., |t1-t2|≦Th2, and then derives the muscle composition ratio by dividing the derived muscle thickness tm by the first body thickness t1 or the second body thickness t2.

[0066] Furthermore, while the above embodiments assume that radiation penetrates fat first, the method is not limited to this. The composition ratio may also be derived assuming that radiation penetrates muscle first. In this case, the muscle attenuation coefficient μm can be defined as μm(tm) as a nonlinear function of the muscle thickness tm, and the fat attenuation coefficient μf can be defined as μf(tf,tm) as a nonlinear function of the fat thickness tf and the muscle thickness tm. For this reason, the fat attenuation coefficient for the first radiation image G1, which is a low-energy image, can be expressed as μlf(tf,tm), and the muscle attenuation coefficient can be expressed as μlm(tm). Similarly, the fat attenuation coefficient for the second radiation image G2, which is a high-energy image, can be expressed as μhf(tf,tm), and the muscle attenuation coefficient can be expressed as μhm(tm).

[0067] Next, a third embodiment of the present disclosure will be described. Figure 10 is a diagram showing the functional configuration of the radiation image processing apparatus according to the third embodiment. In Figure 10, the same reference numerals are used for components identical to those in Figure 3, and detailed descriptions are omitted. The third embodiment differs from the first and second embodiments in that it includes a quantification unit 26.

[0068] The quantification unit 26 derives a soft tissue image Gs from the first and second radiation images G1 and G2, from which the scattered radiation component has been removed by the scattered radiation removal unit 22, by which the soft tissue of the subject H is extracted. Specifically, the quantification unit 26 generates a soft tissue image Gs from which only the soft tissue of the subject H contained in each radiation image G1 and G2 is extracted by performing weighted subtraction between the corresponding pixels of the first and second radiation images G1 and G2, as shown in equation (10) below. In equation (10), β2 is the weighting coefficient. The soft tissue image Gs is shown in Figure 11. Gs(x, y)=G1(x, y)-β2×G2(x, y) (10)

[0069] The quantification unit 26 derives muscle mass based on the pixel value for each pixel in the soft tissue region of the soft tissue image Gs. The quantification unit 26 separates muscle and fat from the soft tissue image Gs by utilizing the difference in energy characteristics between muscle tissue and adipose tissue. As shown in Figure 12, the dose of radiation after passing through the subject H, which is the human body, is lower than the dose of radiation before it enters the subject H. Also, since muscle tissue and adipose tissue absorb different energies and have different attenuation coefficients, the energy spectra of the radiation after passing through muscle tissue and the radiation after passing through adipose tissue are different. As shown in Figure 12, the energy spectra of the radiation that passes through the subject H and irradiates the first radiation detector 5 and the second radiation detector 6, respectively, depend on the body composition of the subject H, specifically the ratio of muscle tissue to adipose tissue. Since adipose tissue is more permeable to radiation than muscle tissue, a higher proportion of muscle tissue compared to adipose tissue results in a lower dose of radiation after passing through the human body.

[0070] Therefore, the quantification unit 26 separates muscle and fat from the soft tissue image Gs by utilizing the difference in energy characteristics between muscle tissue and adipose tissue as described above. In other words, the quantification unit 26 generates muscle images and fat images from the soft tissue image Gs. The quantification unit 26 also derives the muscle mass of each pixel based on the pixel values ​​of the muscle image.

[0071] Specifically, the quantification unit 26 generates a muscle image from the soft tissue image Gs using the following equation (11). In equation (11), rm(x,y) is the muscle composition ratio derived by the composition ratio derivation unit 24. The quantification unit 26 also generates a fat image from the soft tissue image Gs using the following equation (12). In equation (12), rf(x,y) is the fat composition ratio derived by the composition ratio derivation unit 24. Gm(x,y) = rm(x,y) × Gs(x,y) (11) Gf(x,y) = rf(x,y) × Gs(x,y) (12)

[0072] Then, as shown in equation (13) below, the quantification unit 26 multiplies each pixel (x,y) of the muscle image Gm by a predetermined coefficient C1(x,y) that represents the relationship between the pixel value and muscle mass, thereby determining the muscle mass M(x,y)(g / cm³) for each pixel of the muscle image Gm. 2 The quantification unit 26 derives the fat amount F(x,y) (g / cm³) for each pixel (x,y) of the fat image Gf by multiplying it by a predetermined coefficient C2(x,y) that represents the relationship between the pixel value and the amount of fat, as shown in equation (14) below. 2 Derive ). M(x,y) = C1(x,y) × Gm(x,y) (13) F(x,y) = C²(x,y) × Gf(x,y) (14)

[0073] Next, the process performed in the third embodiment will be described. Figure 13 is a flowchart of the process performed in the third embodiment. In Figure 13, the process from step ST5 in the flowchart of the first embodiment shown in Figure 8, or from step ST18 onwards in the flowchart of the second embodiment shown in Figure 9, will be described.

[0074] Following step ST5 or step ST18, the quantification unit 26 derives a soft tissue image Gs from the first and second radiographic images (step ST21). Subsequently, the quantification unit 26 derives a muscle image Gm and a fat image Gf from the soft tissue image Gs (step ST22). Furthermore, the quantification unit 26 derives muscle mass from the muscle image Gm (step ST23) and fat mass from the fat image Gf (step ST24), and then terminates the process.

[0075] In this third embodiment, the display control unit 25 may display the derived muscle mass and fat mass distributions on the display 14. For example, since the fat mass distribution correlates with the body fat percentage distribution, the display screen will be similar to that of the body fat percentage shown in Figure 7.

[0076] In the third embodiment described above, the fat thickness tf and muscle thickness tm may be derived using the method of the second embodiment, and the fat image Gf and muscle image Gm may be derived using attenuation coefficients based only on the fat thickness tf and muscle thickness tm. Specifically, the fat image Gf and muscle image Gm may be derived using the following equations (15) and (16). Here, μf(tf) is the fat attenuation coefficient based only on the fat thickness tf, and μm(tm) is the muscle attenuation coefficient based only on the muscle thickness tm. Note that the (x,y) is omitted in equations (15) and (16) as well. Gf = I0 × exp(-μf(tf)·tf) (15) Gm = I0 × exp(-μm(tm)·tm) (16)

[0077] In equations (15) and (16), I0 is the dose I0 of radiation emitted from the radiation source 3 that reaches the radiation detector 5 when the radiation source 3 is driven to irradiate the radiation detector 5 with radiation in the absence of an object H. The dose I0 is expressed by the following equation (17). In equation (17), mAs is the dose and kV is the tube voltage. Here, F is a linear or nonlinear function that represents the amount of radiation that reaches the radiation detector 5 when a reference dose (e.g., 1 mAs) is irradiated to the radiation detector 5 at a reference SID (e.g., 100 cm) in the absence of an object H. F changes depending on the tube voltage. Also, since the dose I0 is derived for each pixel of the radiation image G0 acquired by the radiation detector 5, (x,y) represents the pixel position of each pixel. I0(x,y) = mAs × F(kV) / SID 2 (17)

[0078] Alternatively, a dose sensor may be provided in the radiation detector 5 to detect the received dose, and the received dose I0 may be acquired by the dose sensor. In this case, the dose sensor may be provided in the radiation detector 5 by replacing a part of the image sensor of the radiation detector 5, or the dose sensor may be provided outside the image detection surface of the radiation detector 5.

[0079] In the embodiments described above, the composition ratios of fat and muscle in the subject H are derived, but the invention is not limited to these. The technology of this disclosure can also be applied to derive the composition ratios of bone and soft tissue other than bone in the subject H. In this case as well, when deriving the first body thickness t1 and the second body thickness t2, attenuation coefficients corresponding to the order in which radiation penetrates the bone and soft tissue may be used. Here, assuming that radiation penetrates the soft tissue first in the subject H, the attenuation coefficient μs for the soft tissue can be defined as μs(ts) as a nonlinear function of the thickness ts of the soft tissue, and the attenuation coefficient μb for the bone can be defined as μb(ts,tb) as a nonlinear function of the thickness ts of the soft tissue and the thickness tb of the bone.

[0080] Furthermore, the composition ratio is not limited to fat and muscle, or bone and soft tissue. The technology of this disclosure can also be applied when determining the composition ratio of artificial bone or silicone implants embedded in the human body to human tissue, or the composition ratio of fat and mammary gland in the breast. In addition, when a contrast agent is injected into subject H and imaging is performed, the technology of this disclosure can also be applied when determining the composition ratio of the contrast agent to tissues other than the contrast agent.

[0081] Furthermore, in each of the above embodiments, the scattered radiation component is removed from the first and second radiation images G1 and G2 by the scattered radiation removal unit 22, but the invention is not limited to this. For example, if a scattered radiation removal grid is used during imaging, the composition ratio may be derived without removing the scattered radiation component from the first and second radiation images G1 and G2. In this case, the scattered radiation removal unit 22 is unnecessary in the radiation image processing apparatus of this embodiment.

[0082] Furthermore, in each of the above embodiments, the first and second radiation images G1 and G2 are acquired by the one-shot method. However, the first and second radiation images G1 and G2 may also be acquired by the so-called two-shot method, which involves taking two images using only one radiation detector. In the case of the two-shot method, the position of the subject H included in the first radiation image G1 and the second radiation image G2 may shift due to the movement of the subject H. For this reason, it is preferable to perform the processing of this embodiment after aligning the position of the subject in the first radiation image G1 and the second radiation image G2.

[0083] Furthermore, in each of the above embodiments, the composition ratio is derived using the radiation images acquired in a system that captures radiation images G1 and G2 of a subject H using first and second radiation detectors 5 and 6. However, the technology of this disclosure can also be applied when acquiring the first and second radiation images G1 and G2 using a accumulative phosphor sheet instead of radiation detectors. In this case, two accumulative phosphor sheets are stacked and irradiated with radiation that has passed through the subject H to accumulate and record the radiation image information of the subject H on each accumulative phosphor sheet, and the first and second radiation images G1 and G2 can be acquired by photoelectrically reading the radiation image information from each accumulative phosphor sheet. Note that the two-shot method may also be used when acquiring the first and second radiation images G1 and G2 using a accumulative phosphor sheet.

[0084] Furthermore, the radiation in each of the above embodiments is not particularly limited, and in addition to X-rays, alpha rays or gamma rays, etc., can be applied.

[0085] Furthermore, in each of the above embodiments, the hardware structure of the Processing Unit that performs various processes such as the image acquisition unit 21, the scattered radiation removal unit 22, the body thickness derivation unit 23, the composition ratio derivation unit 24, and the display control unit 25 of the radiation image processing apparatus 10 can be the following types of processors. As mentioned above, these types of processors include a CPU, which is a general-purpose processor that executes software (programs) and functions as various processing units, as well as a Programmable Logic Device (PLD), which is a processor whose circuit configuration can be changed after manufacturing, such as an FPGA (Field Programmable Gate Array), and a dedicated electrical circuit, which is a processor with a circuit configuration specifically designed to perform a particular process, such as an ASIC (Application Specific Integrated Circuit).

[0086] A single processing unit may consist of one of these various processors, or it may consist of 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). Alternatively, multiple processing units may be composed of a single processor.

[0087] Examples of configuring multiple processing units with a single processor include, firstly, a configuration where one or more CPUs and software combine to form a single processor, as exemplified by client and server computers, and this processor functions as multiple processing units. Secondly, a configuration using a processor that realizes the functions of the entire system, including multiple processing units, on a single IC (Integrated Circuit) chip, as exemplified by System-on-a-Chip (SoC). Thus, various processing units are configured, in terms of hardware structure, using one or more of the above-mentioned processors.

[0088] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits (Circuitry) that combine circuit elements such as semiconductor elements.

[0089] The following are additional notes to this disclosure. (Additional note 1) Equipped with at least one processor, The aforementioned processor, By obtaining two radiation images based on radiation with different energy distributions that has passed through a subject containing multiple compositions, Using attenuation coefficients corresponding to the order in which the radiation penetrates for the aforementioned multiple compositions, the thickness of the subject is derived as the first body thickness and the second body thickness for each pixel in each of the two radiation images, respectively. A radiation image processing apparatus that derives the composition ratio of the subject for each pixel of the radiation image based on the first body thickness and the second body thickness. (Additional note 2) The radiographic image processing apparatus according to Appendix 1, wherein the processor considers the subject as a model in which each of the plurality of compositions is grouped together and divided such that each of the compositions has one thickness, and derives the composition ratio. (Additional note 3) The radiographic image processing apparatus according to appendix 1 or 2, wherein the processor derives the composition ratio based on the difference between the first body thickness and the second body thickness. (Additional note 4) The radiographic image processing apparatus according to any one of the appendices 1 to 3, wherein the processor derives the first body thickness and the second body thickness using the changed thickness of the composition and the attenuation coefficient for each composition while changing the thickness of the composition and the attenuation coefficient for each composition, and derives the composition ratio based on the thickness of the composition in which the difference between the first body thickness and the second body thickness is less than or equal to a predetermined threshold. (Additional note 5) The processor removes the scattered radiation components contained in the two radiation images. A radiation image processing apparatus according to any one of the appendices 1 to 4, which derives the composition ratio based on the two radiation images from which the scattered radiation components have been removed. (Additional note 6) The radiation image processing apparatus according to any one of the appendices 1 to 5, wherein the two radiation images are acquired by two radiation detectors by simultaneously irradiating the radiation that has passed through the subject with the radiation that has been superimposed on the two radiation detectors. (Additional note 7) The radiographic image processing apparatus according to any one of the appendices 1 to 6, wherein the processor displays the distribution of the composition ratio superimposed on one of the two radiographic images on a display. (Appendix 8) The radiographic imaging apparatus according to any one of the appendices 1 to 7, wherein the plurality of compositions are muscle and fat. (Additional note 9) The radiographic imaging apparatus according to any one of the appendices 1 to 7, wherein the plurality of compositions are bone and soft tissue. (Additional note 10) The radiographic imaging apparatus according to any one of the appendices 1 to 7, wherein the plurality of compositions are a contrast agent injected into the subject and tissue other than the contrast agent. (Additional note 11) The radiographic image processing apparatus according to any one of the appendices 1 to 10, wherein the processor derives a compositional image for at least one of the plurality of compositions based on the two radiographic images and the compositional ratios. (Additional note 12) The radiographic image processing apparatus according to Appendix 11, wherein the processor quantifies the composition represented by the composition image based on the composition image. (Additional note 13) By obtaining two radiation images based on radiation with different energy distributions that has passed through a subject containing multiple compositions, Using attenuation coefficients corresponding to the order in which the radiation penetrates for the aforementioned multiple compositions, the thickness of the subject is derived as the first body thickness and the second body thickness for each pixel in each of the two radiation images, respectively. A radiation image processing method for deriving the composition ratio of the subject for each pixel of the radiation image based on the first body thickness and the second body thickness. (Additional note 14) A procedure for obtaining two radiation images based on radiation with different energy distributions transmitted through a subject containing multiple compositions, and A procedure for deriving the thickness of the subject as the first body thickness and the second body thickness for each pixel of each of the two radiation images, using attenuation coefficients corresponding to the order in which the radiation passes through the plurality of compositions, A radiation image processing program that causes a computer to perform a procedure for deriving the composition ratio of the subject for each pixel of the radiation image based on the first body thickness and the second body thickness. [Explanation of symbols]

[0090] 1. Radiation imaging device 2 Computers 3 Radiation source 5, 6 Radiation detectors 7. Radiation energy conversion filter 10. Radiation image processing device 11 CPU 12. Radiation Image Processing Program 13 Storage 14 displays 15 Input Devices 16 memory 17 Network Interface 18 bus 21 Image acquisition unit 22 Scattered radiation removal section 23 Body thickness derivation part 24 Composition ratio derivation section 25 Display Control Unit 26 Quantification Department 30 Energy distribution of radiation before it passes through the subject 31-33 Subject 34-36 Energy distribution of radiation after passing through the subject 40 display screen 41 References Gs Fat Images LUT1 Table

Claims

1. Equipped with at least one processor, The aforementioned processor, By obtaining two radiation images based on radiation with different energy distributions that has passed through a subject containing multiple compositions, Using attenuation coefficients corresponding to the order in which the radiation penetrates for the aforementioned multiple compositions, the thickness of the subject is derived as the first thickness and the second thickness for each pixel in each of the two radiation images, respectively. A radiation image processing apparatus that derives the composition ratio of the subject for each pixel of the radiation image based on the first body thickness and the second body thickness.

2. The radiographic image processing apparatus according to claim 1, wherein the processor considers the subject as a model in which each of the plurality of compositions is grouped together and divided such that each of the compositions has one thickness, and derives the composition ratio.

3. The radiographic image processing apparatus according to claim 1, wherein the processor derives the composition ratio based on the difference between the first body thickness and the second body thickness.

4. The radiation image processing apparatus according to claim 1, wherein the processor derives the first body thickness and the second body thickness using the changed thickness of the composition and the attenuation coefficient for each composition while changing the thickness of the composition and the attenuation coefficient for each composition, and derives the composition ratio based on the thickness of the composition in which the difference between the first body thickness and the second body thickness is less than or equal to a predetermined threshold.

5. The processor removes the scattered radiation components contained in the two radiation images. The radiation image processing apparatus according to claim 1, which derives the composition ratio based on the two radiation images from which the scattered radiation components have been removed.

6. The radiation image processing apparatus according to claim 1, wherein the two radiation images are obtained by two radiation detectors by simultaneously irradiating the radiation that has passed through the subject with the radiation that has been superimposed on the two radiation detectors.

7. The radiographic image processing apparatus according to claim 1, wherein the processor superimposes the distribution of the composition ratios onto one of the two radiographic images and displays it on a display.

8. The radiation image processing apparatus according to claim 1, wherein the plurality of compositions are muscle and fat.

9. The radiographic image processing apparatus according to claim 1, wherein the plurality of compositions are bone and soft tissue.

10. The radiographic imaging apparatus according to claim 1, wherein the plurality of compositions are a contrast agent injected into the subject and tissue other than the contrast agent.

11. The radiographic image processing apparatus according to claim 1, wherein the processor derives a compositional image for at least one of the plurality of compositions based on the two radiographic images and the compositional ratios.

12. The radiographic image processing apparatus according to claim 11, wherein the processor quantifies the composition represented by the composition image based on the composition image.

13. By obtaining two radiation images based on radiation with different energy distributions that has passed through a subject containing multiple compositions, Using attenuation coefficients corresponding to the order in which the radiation penetrates for the aforementioned multiple compositions, the thickness of the subject is derived as the first thickness and the second thickness for each pixel in each of the two radiation images, respectively. A radiation image processing method for deriving the composition ratio of the subject for each pixel of the radiation image based on the first body thickness and the second body thickness.

14. A procedure for obtaining two radiation images based on radiation with different energy distributions transmitted through a subject containing multiple compositions, and A procedure for deriving the thickness of the subject as the first and second body thicknesses for each pixel of each of the two radiation images, using attenuation coefficients corresponding to the order in which the radiation passes through the plurality of compositions, A radiation image processing program that causes a computer to perform a procedure for deriving the composition ratio of the subject for each pixel of the radiation image based on the first body thickness and the second body thickness.

Images