Information processing device, method, and program

Abstract

In an information processing device, method, and program, when CT images are acquired at multiple timings, changes over time in the CT images are made easier to see. [Solution] The processor acquires multiple CT images derived by reconstructing multiple projection data with different energy levels obtained by imaging the subject at multiple times at multiple predetermined energy levels, derives visibility information regarding the visibility of the target area contained in each of the multiple CT images, and determines an interpretation energy level from among the multiple energy levels for interpreting the CT image based on the visibility information.

Description

[Technical Field]

[0001] This disclosure relates to information processing devices, methods, and programs. [Background technology]

[0002] In dynamic contrast-enhanced liver imaging using a CT (Computed Tomography) or MRI (Magnetic Resonance Imaging) scanner, multiple CT images with different contrast phases (dynamic contrast-enhanced CT images) are acquired at multiple timings while injecting a contrast agent, allowing observation of changes in the degree of enhancement of the lesion over time.

[0003] On the other hand, dual energy (DE) CT systems and photon counting (PC) CT systems acquire X-ray energy information. These DECT and PCCT systems are equipped with spectral imaging technology, which performs image reconstruction by material decomposition. For example, a DECT system can reconstruct two projection data sets acquired at two different tube voltages at a single energy level to generate a virtual monochromatic X-ray image that appears to have been acquired using X-rays at a single energy level. A PCCT system can also generate a material-decomposed image that distinguishes and visualizes multiple materials with different X-ray attenuation coefficients by reconstructing multiple projection data sets acquired at different energy levels for each energy bin at a single energy level. A DECT system can also generate a material-decomposed image using projection data acquired at different tube voltages.

[0004] In dynamic contrast-enhanced CT images, the visibility of lesions varies depending on the combination of contrast phase and energy level. Furthermore, the optimal energy level for interpretation also differs depending on the patient's body type, etc. Therefore, generating virtual monochromatic X-ray images with various energy levels for each of the multiple contrast phases, allowing radiologists to comprehensively review the images, is ideal for improving diagnostic accuracy.

[0005] However, generating virtual monochromatic X-ray images with various energy levels for each of the multiple contrast phases results in an enormous image volume, and the number of images that a doctor must interpret becomes enormous, placing a heavy burden on the doctor.

[0006] For this reason, a method has been proposed to reduce the burden on doctors by generating a map image that includes, for example, spatial information about the region of interest and information representing changes in the analysis target value due to changes in effective X-ray energy (see Patent Document 1). [Prior art documents] [Patent documents]

[0007] [Patent Document 1] Japanese Patent Application Publication No. 2019-072082 Summary of the Invention [Problem to be solved by the invention]

[0008] However, the technique described in Patent Document 1 does not take into consideration changes over time that are important for differentiating lesions.

[0009] The present disclosure has been made in consideration of the above circumstances, and aims to make it easier to see changes over time in CT images when acquiring CT images at multiple timings. [Means for solving the problem]

[0010] An information processing device according to the present disclosure includes a processor, The processor a plurality of projection data sets each having a different energy level, which are acquired by imaging the subject at a plurality of timings, are reconstructed at a plurality of predetermined energy levels to acquire a plurality of CT images; deriving visibility information regarding the visibility of a target region included in each of the plurality of CT images; Based on the visibility information, an interpretation energy level for interpreting the CT image is determined from among a plurality of energy levels.

[0011] In the information processing device according to the present disclosure, a processor derives, as visibility information, an amount of change that represents a change over time in visibility of the target region at each of a plurality of energy levels; The interpretation energy level may be determined by comparing the visibility information for each of a plurality of energy levels.

[0012] In the information processing device according to the present disclosure, the processor may determine, from among the plurality of energy levels, the energy level with the greatest amount of change as the interpretation energy level.

[0013] In the information processing device according to the present disclosure, a processor derives a representative value representing the visibility of the target region as visibility information at each of a plurality of timings; The interpretation energy level may be determined by comparing representative values ​​for each of a plurality of timings.

[0014] In the information processing device according to the present disclosure, the processor may determine the interpretation energy level based on the energy level at which the representative value for each of the plurality of timings is optimal.

[0015] In the information processing device according to the present disclosure, a processor derives, as visibility information, an amount of change in visibility of the target region over time at each of a plurality of energy levels and a representative value representing the visibility of the target region at each of a plurality of timings; The interpretation energy level may be determined based on the amount of change and the representative value.

[0016] In the information processing device according to the present disclosure, the processor may weight at least one of the change amount and the representative value according to the change amount and the representative value, and determine the interpretation energy level based on the weighted change amount and the representative value.

[0017] In the information processing device according to the present disclosure, the processor may display a plurality of CT images at different interpretation energy levels in chronological order.

[0018] In the information processing device according to the present disclosure, the processor may further display, at each of multiple timings, an image of the target area included in the CT image with the highest visibility, in association with multiple CT images at the interpretation energy level.

[0019] In the information processing device according to the present disclosure, the processor may switch between displaying and hiding the image of the target area in response to an instruction from an operator.

[0020] In the information processing device according to the present disclosure, the CT image may be a virtual monochromatic X-ray image derived by reconstructing a plurality of projection data sets with different energy levels at a plurality of predetermined energy levels.

[0021] In the information processing device according to the present disclosure, the CT image may be a material decomposition image derived by reconstructing a plurality of projection data with different energy levels at a plurality of predetermined energy levels.

[0022] In the information processing apparatus according to this disclosure, the processor may derive visibility information based on the feature quantities of the region including the target region included in the CT image.

[0023] In the information processing device according to the present disclosure, the plurality of timings may be based on the contrast phase when a contrast agent is injected into the subject.

[0024] In the information processing device according to the present disclosure, the target region may be a lesion region.

[0025] The information processing method disclosed herein involves a computer acquiring multiple CT images derived by reconstructing multiple projection data with different energy levels, obtained by imaging a subject at multiple timings, at a predetermined number of energy levels. deriving visibility information regarding the visibility of a target region included in each of the plurality of CT images; Based on the visibility information, an interpretation energy level for interpreting the CT image is determined from among a plurality of energy levels.

[0026] The information processing program disclosed herein includes a procedure for acquiring multiple CT images derived by reconstructing multiple projection data with different energy levels, obtained by imaging a subject at multiple timings, at predetermined energy levels. deriving visibility information regarding the visibility of a target region included in each of a plurality of CT images; The computer is instructed to perform a procedure to determine the appropriate energy level for interpreting CT images from among multiple energy levels, based on visibility information.

[0027] The technology of the present disclosure may also be applied to a program product. [Effects of the Invention]

[0028] According to the present disclosure, when CT images are acquired at multiple timings, it is possible to make changes over time in the CT images more visible. [Brief explanation of the drawings]

[0029] [Figure 1] FIG. 1 is a diagram showing a schematic configuration of a medical information system to which an information processing device according to a first embodiment of the present disclosure is applied. [Figure 2] Diagram showing virtual monochromatic x-ray images of arterial, portal, and equilibrium phases at multiple energy levels [Figure 3] FIG. 1 is a diagram showing a hardware configuration of an information processing apparatus according to a first embodiment; [Figure 4] Functional configuration diagram of an information processing device according to a first embodiment [Figure 5] Diagram to explain the extraction of lesion areas. [Figure 6] 1 is a diagram showing feature amounts of a lesion region included in virtual monochromatic X-ray images of the arterial phase, portal vein phase, and equilibrium phase at a plurality of energy levels in the first embodiment; [Figure 7] FIG. 10 is a diagram showing a display screen of a virtual monochromatic X-ray image in the first embodiment. [Figure 8] 1 is a flowchart showing the processing performed in the first embodiment. [Figure 9] 10A and 10B are diagrams showing feature amounts of a lesion area included in virtual monochromatic X-ray images of the arterial phase, the portal vein phase, and the equilibrium phase at a plurality of energy levels in the second embodiment; [Figure 10] 10 is a flowchart showing the processing performed in the second embodiment. [Figure 11] 10A and 10B are diagrams showing feature amounts of a lesion area included in virtual monochromatic X-ray images of the arterial phase, the portal vein phase, and the equilibrium phase at a plurality of energy levels in the third embodiment; [Figure 12] FIG. 10 is a diagram showing a display screen of a virtual monochromatic X-ray image in the third embodiment. [Figure 13] 10 is a flowchart showing the processing performed in the third embodiment. [Figure 14] Diagram showing the mass attenuation coefficients of gold and iodine DETAILED DESCRIPTION OF THE INVENTION

[0030] The embodiments of this disclosure will be described below with reference to the drawings. First, the configuration of a medical information system to which the information processing device according to the first embodiment of this disclosure is applied will be described. Figure 1 is a diagram showing the schematic configuration of a medical information system. In the medical information system shown in Figure 1, a computer 1 containing the information processing device according to the first embodiment, a CT scanner 2, and an image storage server 3 are connected via a network 4 in a manner that enables communication.

[0031] Computer 1 contains an information processing device according to the first embodiment, and the information processing program according to the first embodiment is installed on it. Computer 1 may be a workstation or personal computer directly operated by the physician performing the diagnosis, or it may be a server computer connected to them via a network.

[0032] CT device 2 is, for example, a dual-energy X-ray CT device (DECT device), and by reconstructing the first projection data and the second projection data acquired at two different tube voltages for the same subject using various weightings, it is possible to obtain a virtual monochromatic X-ray image, which is a CT image as if it were acquired at any single energy.

[0033] The image storage server 3 is a computer that stores and manages various types of data, and is equipped with a large-capacity external storage device and database management software. The image storage server 3 communicates with other devices via a wired or wireless network 4 to send and receive image data, etc. Specifically, it acquires various types of data, including image data of CT images generated by the CT device 2, via the network, and stores and manages them on a recording medium such as a large-capacity external storage device. The storage format of the image data and communication between each device via the network 4 are based on protocols such as DICOM (Digital Imaging and Communication in Medicine).

[0034] In this embodiment, the CT device 2 generates a dynamic contrast-enhanced CT image. To this end, the CT device 2 images the subject at multiple timings while injecting a contrast agent into the subject's liver, thereby acquiring combinations of first and second projection data with different energy levels in multiple contrast phases. The contrast phase includes three time phases: the arterial phase, the portal vein phase, and the equilibrium phase, depending on the elapsed time from the start of the injection of the contrast agent. For example, the arterial phase is the time phase 40 seconds after the start of the injection of the contrast agent, the portal vein phase is the time phase 70 seconds after the start of the injection of the contrast agent, and the equilibrium phase is the time phase 180 seconds after the start of the injection of the contrast agent. The arterial phase, the portal vein phase, and the equilibrium phase are examples of multiple timings in the present disclosure.

[0035] In this embodiment, the CT apparatus 2 generates virtual monochromatic X-ray images at three energy levels by reconstructing the first projection data and the second projection data at three energy levels in each of the contrast phases. In this embodiment, virtual monochromatic X-ray images at energy levels of, for example, 40 keV, 50 keV, and 70 keV are generated in the arterial phase, portal vein phase, and equilibrium phase, respectively. 40 keV, 50 keV, and 70 keV are examples of predetermined energy levels in this disclosure.

[0036] As a result, virtual monochromatic X-ray images G11, G21, and G31 in the arterial phase, virtual monochromatic X-ray images G12, G22, and G32 in the portal vein phase, and virtual monochromatic X-ray images G13, G23, and G33 in the equilibrium phase are generated at the energy levels of 40 keV, 50 keV, and 70 keV, respectively, as shown in Fig. 2. The generated virtual monochromatic X-ray images G11 to G13, G21 to G23, and G31 to G33 are transmitted to and stored in the image storage server 3.

[0037] In dynamic contrast-enhanced CT images, the appearance of lesions in the subject's organs changes depending on the contrast-enhanced phase. For example, in the case of malignant hepatocellular carcinoma, the density of the lesion is highest compared to the surrounding tissue in the arterial phase, followed by the portal venous phase and the equilibrium phase, in that order, where the density decreases compared to the surrounding tissue.

[0038] The appearance of lesions also changes depending on the energy level used to reconstruct virtual monochromatic X-ray images. For example, even in the same contrast-enhanced phase, the density of the lesion may become higher or lower than that of the surrounding tissue depending on the energy level used for reconstruction. The way density changes also depends on whether the lesion is malignant or benign.

[0039] Next, an information processing device according to a first embodiment will be described. Fig. 3 is a diagram showing the hardware configuration of the information processing device according to this embodiment. As shown in Fig. 3, the information processing device 10 includes a CPU (Central Processing Unit) 11, non-volatile storage 13, and memory 16 as a temporary storage area. The information processing device 10 also includes a display 14 such as a liquid crystal display, input devices 15 such as a keyboard and a mouse, and a network I / F (Interface) 17 connected to a network 4. The CPU 11, storage 13, display 14, input devices 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.

[0040] 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 an information processing program 12. The CPU 11 reads the information processing program 12 from the storage 13, loads it into the memory 16, and executes the loaded information processing program 12.

[0041] Next, a functional configuration of the information processing device according to the first embodiment will be described. Fig. 4 is a diagram showing the functional configuration of the information processing device according to the first embodiment. As shown in Fig. 4, the information processing device 10 includes an image acquisition unit 21, a derivation unit 22, a determination unit 23, and a display control unit 24. When the CPU 11 executes the information processing program 12, the CPU 11 functions as the image acquisition unit 21, the derivation unit 22, the determination unit 23, and the display control unit 24.

[0042] In response to an instruction from the operator via the input device 15, the image acquisition unit 21 acquires virtual monochromatic X-ray images G11 to G13, G21 to G23, and G31 to G33 to be processed from the image storage server 3. Note that if the virtual monochromatic X-ray images G11 to G13, G21 to G23, and G31 to G33 have been acquired from the image storage server 3 and stored in the storage 13, the image acquisition unit 21 acquires the virtual monochromatic X-ray images G11 to G13, G21 to G23, and G31 to G33 from the storage 13 for processing. The virtual monochromatic X-ray images G11 to G13, G21 to G23, and G31 to G33 are examples of the multiple CT images of the present disclosure. Note that in the following description, the multiple virtual monochromatic X-ray images G11 to G13, G21 to G23, and G31 to G33 may be represented by a single reference symbol G0.

[0043] The derivation unit 22 derives visibility information regarding the visibility of the target region included in each of the virtual monochromatic X-ray images G11 to G13, G21 to G23, and G31 to G33. To this end, the derivation unit 22 first extracts a lesion region as a target region from the virtual monochromatic X-ray image G0. FIG. 5 is a diagram for explaining the extraction of the lesion region. FIG. 5 shows a tomographic image S0 of one axial cross section of the virtual monochromatic X-ray image G0. For example, when the virtual monochromatic X-ray image G0 is input, the derivation unit 22 extracts a lesion region M0 from the virtual monochromatic X-ray image G0 by using an extraction model that has been trained to extract liver lesions.

[0044] The derivation unit 22 derives feature quantities for a region including the lesion region M0 for each of the virtual monochromatic X-ray images G11-G13, G21-G23, and G31-G33. In this embodiment, the region including the lesion region M0 may be only the lesion region M0, or may be the lesion region M0 and a predetermined range of the region surrounding the lesion region M0, or may be the lesion region M0 and the entire organ region including the lesion region M0. Furthermore, the feature quantities may include, for example, noise, contrast, and edges of the region including the lesion region M0.

[0045] When the feature amount is noise, the derivation unit 22 derives noise in the region including the lesion region M0 for each of the virtual monochromatic X-ray images G11-G13, G21-G23, and G31-G33. The noise can be represented by the variance of the CT values. Here, the smaller the noise, the better the visibility of the lesion region M0.

[0046] When the feature amount is contrast, the derivation unit 22 derives the contrast of the region including the lesion region M0 for each of the multiple virtual monochromatic X-ray images G11-G13, G21-G23, and G31-G33. Specifically, the contrast is derived as the density difference between the lesion region M0 and its surrounding region, or the density difference between the lesion region M0 and the entire region of the organ including the lesion region M0. The greater the contrast, the better the visibility of the lesion region M0.

[0047] When the feature amount is an edge, the derivation unit 22 derives the edge strength of the lesion region M0 for each of the multiple virtual monochromatic X-ray images G11-G13, G21-G23, and G31-G33. The edge strength is the magnitude of the differential value at the boundary between the lesion region M0 and its surrounding area. The greater the edge strength, the better the visibility of the lesion region M0.

[0048] The derivation unit 22 derives the amount of change in visibility over time for the lesion region M0 at each of a plurality of energy levels as visibility information. Specifically, the derivation unit 22 derives the amount of change in feature values ​​for the arterial phase, portal vein phase, and equilibrium phase at each of a plurality of energy levels, and derives the largest amount of change as visibility information. For example, as shown in FIG. 6, assume that the contrasts for the arterial phase, portal vein phase, and equilibrium phase are 0.1, 0.2, and 0.4, respectively, at an energy level of 70 keV. In this case, the amount of change for 70 keV, i.e., the visibility information, is 0.2. Furthermore, if the contrasts for the arterial phase, portal vein phase, and equilibrium phase are 0.8, 0.4, and 0.1, respectively, at an energy level of 50 keV, the amount of change for 50 keV, i.e., the visibility information, is 0.7. Furthermore, if the contrasts for the arterial phase, portal vein phase, and equilibrium phase are 0.4, 0.2, and 0.3, respectively, at an energy level of 40 keV, the amount of change for 40 keV, i.e., the visibility information, is 0.2.

[0049] In the first embodiment, the derivation unit 22 may derive the amount of change, i.e., visibility information, for each of a plurality of energy levels using a derivation model constructed to output the amount of change in visibility over time for the lesion region M0 when three virtual monochromatic X-ray images G0 of the arterial phase, portal vein phase, and equilibrium phase are input. Alternatively, the amount of change, i.e., visibility information, may be derived on a rule-based basis from the feature amount for the lesion region M0 using an index such as CNR (Contrast-to-Noise Ratio).

[0050] The determination unit 23 determines an interpretation energy level from among the energy levels for interpreting the virtual monochromatic X-ray image G0 based on the visibility information for each of the energy levels derived by the derivation unit 22. Specifically, the determination unit 23 determines the energy level for which the visibility information, i.e., the amount of change, is the largest as the interpretation energy level. In this embodiment, as described above, the visibility information for the energy levels of 70 keV, 50 keV, and 40 keV are 0.2, 0.7, and 0.2, respectively. Therefore, the determination unit 23 determines the energy level for 50 keV for which the visibility information, i.e., the amount of change, is the largest as the interpretation energy level.

[0051] The display control unit 24 displays the virtual monochromatic X-ray images G0 at the determined interpretation energy level in chronological order on the display 14. Fig. 7 is a diagram showing a display screen for virtual monochromatic X-ray images. As shown in Fig. 7, on the display screen 30, virtual monochromatic X-ray images G21 to G23 at an interpretation energy level of 50 keV are displayed in the order of arterial phase, portal vein phase, and equilibrium phase from left to right.

[0052] Next, the processing performed in the first embodiment will be described. Fig. 8 is a flowchart showing the processing performed in the first embodiment. First, the image acquisition unit 21 acquires a virtual monochromatic X-ray image G0 to be processed (step ST1). Next, the derivation unit 22 derives, as visibility information, an amount of change that represents a change over time in visibility of the lesion region M0 at each of a plurality of energy levels (step ST2). The determination unit 23 determines the interpretation energy level by comparing the visibility information for each of the plurality of energy levels (step ST3). The display control unit 24 displays the virtual monochromatic X-ray images G0 at the determined interpretation energy levels in chronological order on the display 14 (step ST4), and the processing ends.

[0053] Thus, in the first embodiment, the amount of change representing the temporal change in visibility of the lesion region M0 is derived as visibility information, and the reading energy level is determined by comparing the visibility information. Specifically, the energy level at which the amount of change is maximum is determined as the reading energy level. As a result, in the displayed virtual monochromatic X-ray image, the temporal change of the lesion appears more clearly than in the virtual monochromatic X-ray image G0 of other energy levels. Therefore, according to this embodiment, the temporal change of the lesion can be made more visible in the virtual monochromatic X-ray image acquired by dynamic contrast-enhanced examination.

[0054] Next, a second embodiment of the present disclosure will be described. In the second embodiment, the functional configuration of the information processing apparatus is the same as that of the first embodiment, and only the processing performed by the derivation unit 22 and the determination unit 23 differs; therefore, a detailed explanation of the configuration will be omitted here.

[0055] In the second embodiment, the derivation unit 22 derives representative values ​​representing the visibility of the lesion region M0 as visibility information at each of the multiple timings. Specifically, the derivation unit 22 first derives characteristic quantities of the region including the lesion region M0 for each of the multiple virtual monochromatic X-ray images G11-G13, G21-G23, and G31-G33, similar to the first embodiment. Then, in each of the arterial phase, portal venous phase, and equilibrium phase, it derives representative values ​​representing the characteristic quantities of the lesion region M0 for each of the multiple energy levels as visibility information.

[0056] For example, as shown in Figure 9, suppose the contrasts of the energy levels 70 keV, 50 keV, and 40 keV in the arterial phase are 0.1, 0.2, and 0.4, respectively. In this case, the energy level with the greatest contrast is 40 keV. Therefore, the derivation unit 22 derives 0.4, which is the characteristic quantity of the 40 keV energy level, as the representative value, i.e., the visibility information, for the arterial phase.

[0057] Also, assume that the contrasts at the energy levels of 70 keV, 50 keV, and 40 keV in the portal vein phase are 0.2, 0.8, and 0.2, respectively. In this case, the contrast is greatest at the energy level of 50 keV. Therefore, the derivation unit 22 derives 0.8, which is the feature amount at the energy level of 50 keV, as the representative value, i.e., visibility information, for the portal vein phase.

[0058] Furthermore, suppose that the contrasts at the energy levels of 70 keV, 50 keV, and 40 keV in the equilibrium phase are 0.4, 0.1, and 0.3, respectively. In this case, the contrast is greatest at the energy level of 70 keV. Therefore, the derivation unit 22 derives 0.4, which is the feature amount at the energy level of 70 keV, as the representative value, i.e., visibility information, for the equilibrium phase.

[0059] In the second embodiment, the derivation unit 22 may derive a representative value representing the visibility for each of the arterial phase, portal vein phase, and equilibrium phase using a derivation model constructed to output a representative value representing the visibility for each of the arterial phase, portal vein phase, and equilibrium phase when three virtual monochromatic X-ray images G0 of the arterial phase, portal vein phase, and equilibrium phase are input. Furthermore, the representative value may be derived from the feature amount of the lesion region M0 on a rule-based basis using an index such as CNR (Contrast-to-Noise Ratio).

[0060] The determination unit 23 determines an interpretation energy level for interpreting the virtual monochromatic X-ray image G0 from among a plurality of energy levels based on the visibility information for each of the arterial phase, portal venous phase, and equilibrium phase derived by the derivation unit 22. Specifically, the determination unit 23 determines the energy level at which the visibility information, i.e., the representative value, is optimal as the interpretation energy level. Note that the optimal representative value differs depending on the type of feature. For example, if the feature is contrast or edge, the representative value at which the visibility information is optimal is the maximum representative value. On the other hand, if the feature is noise, the representative value at which the visibility information is optimal is the minimum representative value.

[0061] As described above, when contrast is the feature, the representative value for the arterial phase is 0.4, the representative value for the portal vein phase is 0.8, and the representative value for the equilibrium phase is 0.4. Therefore, the determination unit 23 determines 50 keV, which is the energy level with the maximum visibility information, i.e., the maximum representative value, as the interpretation energy level.

[0062] In the second embodiment, the display control unit 24 displays virtual monochromatic X-ray images G0 of 50 keV, which is the determined interpretation energy level, in chronological order on the display 14. The display screen in the second embodiment is similar to the display screen in the first embodiment shown in Fig. 5, and therefore a detailed description thereof will be omitted here.

[0063] Next, the processing performed in the second embodiment will be described. FIG. 10 is a flowchart showing the processing performed in the second embodiment. First, the image acquisition unit 21 acquires a virtual monochromatic X-ray image G0 to be processed (step ST11). Next, the derivation unit 22 derives a representative value representing the feature amount of the lesion region M0 for each of a plurality of energy levels in each of the arterial phase, portal vein phase, and equilibrium phase as visibility information (step ST12). The determination unit 23 determines the interpretation energy level by comparing the visibility information for the arterial phase, portal vein phase, and equilibrium phase (step ST13). The display control unit 24 displays the virtual monochromatic X-ray images G0 of the determined interpretation energy levels in chronological order on the display 14 (step ST14), and the processing ends.

[0064] Thus, in the second embodiment, a representative value representing the visibility of the lesion region M0 is derived as visibility information at each of multiple timings, and the reading energy level is determined by comparing the visibility information. Specifically, the energy level at which the representative value is optimal is determined as the reading energy level. As a result, in the displayed virtual monochromatic X-ray image, the temporal changes of the lesion are more clearly visible than in the virtual monochromatic X-ray image G0 at other energy levels. Therefore, the temporal changes of the lesion can be made more visible in the virtual monochromatic X-ray image acquired by dynamic contrast-enhanced examination.

[0065] Next, a third embodiment of the present disclosure will be described. In the third embodiment, the functional configuration of the information processing device is the same as that of the first embodiment, and only the processing performed by the derivation unit 22, the determination unit 23, and the display control unit 24 differs; therefore, a detailed explanation of the configuration will be omitted here.

[0066] In the third embodiment, the derivation unit 22, similar to the first embodiment, derives feature quantities for the region containing the lesion area M0 for each of the multiple virtual monochromatic X-ray images G11-G13, G21-G23, and G31-G33. Then, similar to the first embodiment, the derivation unit 22 derives the amount of change over time in the visibility of the lesion area M0 for each of the multiple energy levels. Also, similar to the second embodiment, the derivation unit 22 derives representative values ​​that represent the feature quantities of the lesion area M0 for each of the multiple energy levels in the arterial phase, portal venous phase, and equilibrium phase. In the third embodiment, the amount of change and the representative value become visibility information.

[0067] 11 is a diagram illustrating visibility information in the third embodiment. As shown in FIG. 11, assume that the feature values ​​for the arterial phase, portal vein phase, and equilibrium phase at an energy level of 70 keV are 0.2, 0.2, and 0.5, respectively; the feature values ​​for the arterial phase, portal vein phase, and equilibrium phase at an energy level of 50 keV are 0.5, 0.8, and 0.4, respectively; and the feature values ​​for the arterial phase, portal vein phase, and equilibrium phase at an energy level of 40 keV are 0.7, 0.1, and 0.1, respectively. In this case, the amount of change derived at the energy level of 70 keV is 0.3, the amount of change derived at the energy level of 50 keV is 0.4, and the amount of change derived at the energy level of 40 keV is 0.6.

[0068] On the other hand, the representative value for the arterial phase is 0.7 at an energy level of 40 keV, the representative value for the portal venous phase is 0.8 at an energy level of 50 keV, and the representative value for the equilibrium phase is 0.5 at an energy level of 70 keV. In Figure 11, the representative virtual monochromatic X-ray images G11, G22, and G33 are surrounded by thick lines.

[0069] The determination unit 23 determines the interpretation energy level based on the amount of change and the representative value included in the visibility information derived by the derivation unit 22. In the third embodiment, when determining the interpretation energy level based on the amount of change as in the first embodiment, the amount of change of each of the multiple energy levels is weighted by the representative value, and the interpretation energy level is determined according to the weighted amount of change.

[0070] Here, when the interpretation energy level is determined based only on the amount of change, an energy level of 40 keV, at which the amount of change is 0.6, is determined as the interpretation energy level. On the other hand, when the interpretation energy level is determined based only on the representative values ​​of the arterial phase, portal venous phase, and equilibrium phase, an energy level of 50 keV, at which the representative value is 0.8, is determined as the interpretation energy level. In the third embodiment, the larger the representative value, the greater the weighting assigned to each amount of change in the multiple energy levels. For example, when the representative value is 0.95 to 1.0, a weighting of 0.3 is added to the amount of change in the energy level of the representative value; when the representative value is 0.85 to 0.95, a weighting of 0.2 is added; and when the representative value is 0.7 to 0.85, a weighting of 0.1 is added.

[0071] In the third embodiment, the representative value is 0.8 for the 50 keV energy level, so adding 0.1 to the 0.4 change in the 50 keV energy level results in a value of 0.5 for the 50 keV energy level. In this embodiment, when the change amounts for the multiple energy levels after weighting are compared, the largest change is still 0.6 for the 40 keV energy level. Therefore, the determination unit 23 determines the 40 keV energy level as the interpretation energy level.

[0072] On the other hand, for example, if the representative value of the 50 keV energy level is 0.98, 0.3 is added to the change in the 50 keV energy level, resulting in a change of 0.7. In this case, the change in the 50 keV energy level is greater than the change in the 40 keV energy level, which is 0.6. In this case, the determination unit 23 determines the 50 keV energy level as the interpretation energy level.

[0073] The display control unit 24 displays the virtual monochromatic X-ray images G0 of the determined reading energy levels on the display 14 in chronological order, similar to the first embodiment. Furthermore, in the third embodiment, images of lesion regions M0 included in the virtual monochromatic X-ray images G0 of the energy levels for which representative values ​​were derived in the arterial phase, portal venous phase, and equilibrium phase are displayed in addition to the virtual monochromatic X-ray images G0 of the determined reading energy levels.

[0074] Figure 12 shows the display screen of the virtual monochromatic X-ray image in the third embodiment. As shown in Figure 12, the display screen 35 shows virtual monochromatic X-ray images G11 to G13 at a reading energy level of 40 keV, in the order of arterial phase, portal venous phase, and equilibrium phase from left to right. Furthermore, the image of the lesion region M0 (referred to as lesion region image GM1) included in the virtual monochromatic X-ray image G11 at the energy level of 40 keV, which was the maximum representative value in the arterial phase, is superimposed on the virtual monochromatic X-ray image G11 of the arterial phase. Also, the lesion region image GM2 of the virtual monochromatic X-ray image G22 at the energy level of 50 keV, which was the maximum representative value in the portal venous phase, is superimposed on the virtual monochromatic X-ray image G12 of the portal venous phase. Furthermore, the lesion region image GM3 of the virtual monochromatic X-ray image G33 at the energy level of 70 keV, which was the maximum representative value in the equilibrium phase, is superimposed on the virtual monochromatic X-ray image G13 of the equilibrium phase.

[0075] The operator may give an instruction from input device 15 to switch between displaying and hiding lesion area images GM1 to GM3.

[0076] Next, the processing performed in the third embodiment will be described. Fig. 13 is a flowchart showing the processing performed in the third embodiment. First, the image acquisition unit 21 acquires a virtual monochromatic X-ray image G0 to be processed (step ST21). Next, the derivation unit 22 derives, as visibility information, a change amount that indicates a change over time in visibility of the lesion region M0 at each of a plurality of energy levels (step ST22). Furthermore, the derivation unit 22 derives, as visibility information, a representative value that represents the feature amount of the lesion region M0 at each of a plurality of energy levels in each of the arterial phase, portal vein phase, and equilibrium phase (step ST23). Note that the processing of step ST22 and the processing of step ST23 may be performed in reverse order or simultaneously.

[0077] Next, the determination unit 23 determines the interpretation energy level based on the amount of change and the representative value included in the visibility information derived by the derivation unit 22 (step ST24). The display control unit 24 displays the virtual monochromatic X-ray images G0 at the determined interpretation energy level together with the lesion area image in chronological order on the display 14 (step ST25), and the process ends.

[0078] In this way, in the third embodiment, the interpretation energy level is determined based on the amount of change that represents the change over time in the visibility of the lesion region M0 and the representative value that represents the visibility of the lesion region M0 at each of multiple timings. Therefore, while taking into consideration the energy level at which the representative value is optimal, it is possible to determine the interpretation energy level at which the change over time of the lesion appears more clearly than in the virtual monochromatic X-ray image G0 at other energy levels.

[0079] In the third embodiment, when virtual monochromatic X-ray images G0 of interpretation energy levels are displayed in chronological order, lesion area images of virtual monochromatic X-ray images G0 of energy levels with the largest representative values ​​in each of the arterial phase, portal vein phase, and equilibrium phase are displayed, which allows the user to clearly confirm the state of the lesion stained with contrast agent in each contrast phase while checking changes in the lesion over time.

[0080] There are various types of contrast agents, including iodine, iron, and gold. The energy level at which the contrast effect is high varies depending on the type of contrast agent. Figure 14 shows the mass attenuation coefficients of gold and iodine, which are used as contrast agents. As shown in Figure 14, gold has a K absorption edge near 80 keV, so it has a high contrast effect at energy levels near 80 keV. On the other hand, iodine has a K absorption edge near 30 keV, so it has a high contrast effect at energy levels near 30 keV.

[0081] In addition, the length of time that contrast agents remain in the body varies depending on the type. Furthermore, the ease with which contrast agents are taken up varies depending on the type of cell. For example, there may be lesions that first take up a lot of iodine, and then a lot of gold.

[0082] For this reason, in examinations using contrast agents, a mixture of multiple types of contrast agents may be used. For example, when using a contrast agent containing a mixture of gold and iodine, a virtual monochromatic X-ray image G0 is generated at energy levels of 30 keV and 80 keV. If the 30 keV energy level is determined as the interpretation energy level, the change over time in the degree of staining of the lesion due to iodine can be confirmed. Furthermore, if the 80 keV energy level is determined as the interpretation energy level, the change over time in the degree of staining of the lesion due to gold can be confirmed. In this case, the energy level to be determined as the interpretation energy level may be determined based on the visibility information derived from the virtual monochromatic X-ray image G0, i.e., the magnitude of the change or the magnitude of the representative value, in the first to third embodiments.

[0083] In the above embodiments, the CT device 2 is a dual-energy X-ray CT device (DECT device), but is not limited to this. The CT device 2 may also be a photon-counting CT device (PCCT device). In a PCCT device, multiple projection data sets with different energy levels acquired for each energy bin can be acquired, and a virtual monochromatic X-ray image G0 can be derived by reconstructing the multiple projection data sets at any single energy level.

[0084] On the other hand, material decomposition images can be derived from multiple projection data sets with different energy levels acquired by DECT and PCCT systems. A material decomposition image is a CT image obtained by material decomposition technology that uses projection data sets corresponding to multiple energy levels to distinguish between materials contained in the subject, taking advantage of the fact that each material has different radiation absorption characteristics.

[0085] The following describes the derivation of a material decomposition image when the CT device 2 is a PCCT device and projection data at three energy levels is acquired by the PCCT device. If the linear attenuation coefficients at an energy level E for fat, muscle, and contrast agent (iodine) contained in a subject during an examination using a contrast agent are μ1, μ2, and μ3, respectively, the projection data P at the energy level E can be expressed by the following equation (1). P(E)=-μ1(E)x1-μ2(E)x2-μ3(E)x3 (1) x1, x2, and x3 are the densities of fat, muscle, and contrast agent. Material decomposition involves determining x1, x2, and x3 in equation (1).

[0086] Here, in a PCCT device, the above equation (1) can be calculated at three energy levels by using the projection data acquired at three energies, so the unknown quantities x1, x2, and x3 can be calculated.

[0087] In this embodiment, for example, when iodine and gold are used together, projection data at multiple energy levels acquired by a PCCT device is used to generate a material fractionation image of iodine and a material fractionation image of gold. Because iodine and gold have different energy levels at which they have a high contrast effect, the material fractionation image of iodine and the material fractionation image of gold are examples of multiple CT images of the present disclosure.

[0088] Even when an iodine material fractionation image and a gold material fractionation image are generated in this way, the interpretation energy level may be determined in the first to third embodiments. In this case, the energy level at which the iodine contrast effect is high or the energy level at which the gold contrast effect is high is determined as the interpretation energy level.

[0089] In each of the above embodiments, the following various processors can be used as the hardware structure of a processing unit that executes various processes, such as the image acquisition unit 21, the derivation unit 22, the determination unit 23, and the display control unit 24. 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 that are processors having a circuit configuration specifically designed to execute specific processes, such as a programmable logic device (PLD), which is 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).

[0090] 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.

[0091] 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.

[0092] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements.

[0093] The following are appendices to the present disclosure. (Additional note 1) a processor; The processor: a plurality of projection data sets each having a different energy level, which are acquired by imaging the subject at a plurality of timings, are reconstructed at a plurality of predetermined energy levels to acquire a plurality of CT images; deriving visibility information regarding the visibility of a target region included in each of the plurality of CT images; an information processing device that determines an interpretation energy level for interpreting the CT image from among the plurality of energy levels based on the visibility information; (Additional note 2) The processor derives, as the visibility information, an amount of change representing a change over time in the visibility of the target region at each of the plurality of energy levels; 2. The information processing device according to claim 1, wherein the image interpretation energy level is determined by comparing the visibility information for each of the plurality of energy levels. (Additional note 3) 3. The information processing device according to claim 2, wherein the processor determines the energy level among the plurality of energy levels at which the amount of change is greatest as the interpretation energy level. (Additional note 4) the processor derives, as the visibility information, a representative value representing the visibility for the target region at each of the plurality of timings; 2. The information processing device according to claim 1, wherein the interpretation energy level is determined by comparing the representative values ​​for each of the plurality of timings. (Additional note 5) 5. The information processing device according to claim 4, wherein the processor determines the interpretation energy level based on the energy level at which the representative value for each of the plurality of timings is optimal. (Additional note 6) the processor derives, as the visibility information, a change in the visibility of the target region over time at each of the plurality of energy levels and a representative value representing the visibility of the target region at each of the plurality of timings; The information processing device according to Appendix 1, which determines the image interpretation energy level based on the amount of change and the representative value. (Additional note 7) The information processing device described in Appendix 6, wherein the processor weights at least one of the change amount and the representative value depending on the change amount and the representative value, and determines the interpretation energy level based on the weighted change amount and the representative value. (Additional note 8) 8. The information processing device according to any one of claims 1 to 7, wherein the processor displays the plurality of CT images at the interpretation energy levels in chronological order. (Additional note 9) The information processing device described in Appendix 8, wherein the processor further displays, at each of the multiple timings, an image of the target area included in the CT image with the highest visibility, in association with the multiple CT images at the interpretation energy level. (Additional note 10) The processor is an information processing device according to Appendix 9 that switches the display and hiding of the image of the target area in response to instructions from an operator. (Additional note 11) The information processing device according to any one of the appendices 1 to 10, wherein the CT image is a virtual monochromatic X-ray image derived by reconstructing multiple projection data at different energy levels at the predetermined multiple energy levels. (Additional note 12) The information processing device according to any one of the appendices 1 to 10, wherein the CT image is a material discrimination image derived by reconstructing multiple projection data at different energy levels at the predetermined multiple energy levels. (Additional note 13) The information processing apparatus according to any one of the appendices 1 to 12, wherein the processor derives the visibility information based on the feature quantities of the region including the target region included in the CT image. (Additional note 14) The information processing device described in any one of the appendix items 1 to 13, where the multiple timings are based on the contrast phase when the contrast agent is injected into the subject. (Additional note 15) The information processing device described in any one of the appendix items 1 to 14, wherein the target area is a lesion area. (Additional note 16) The computer acquires multiple CT images by reconstructing multiple projection data at different energy levels obtained by imaging the subject at multiple time points, using predetermined energy levels. deriving visibility information regarding the visibility of a target region included in each of the plurality of CT images; An information processing method for determining, based on the visibility information, the reading energy level for interpreting the CT image from among the plurality of energy levels. (Additional note 17) a step of acquiring a plurality of CT images derived by reconstructing a plurality of projection data with different energy levels acquired by imaging the subject at a plurality of timings, at a plurality of predetermined energy levels; A procedure for deriving visibility information regarding the visibility of a target region contained in each of the aforementioned multiple CT images, and a procedure for determining an interpretation energy level for interpreting the CT image from among the plurality of energy levels based on the visibility information. [Explanation of symbols]

[0094] 1. Computer 2. Imaging equipment 3. Image storage server 4 Network 11 CPU 12 Information Processing Program 13. Storage 14 Display 15 Input Devices 16 memory 17 Network I / F 18 Bus 20 Information processing equipment 21 Image acquisition unit 22 Derivation part 23 Decision Section 24 Display control unit 30, 35 display screen G11-G13, G21-G23, G31-G33 virtual monochromatic X-ray images GM1~GM3 lesion area images M0 lesion area S0 Tomographic image

Claims

1. a processor; The processor: A plurality of projection data sets with different energy levels obtained by imaging the subject at a plurality of timings are reconstructed at a plurality of predetermined energy levels to obtain a plurality of CT images; deriving visibility information regarding the visibility of a target region included in each of the plurality of CT images; An information processing device that determines an interpretation energy level for interpreting the CT image from among the plurality of energy levels based on the visibility information.

2. The processor derives, as the visibility information, an amount of change representing a change over time in the visibility of the target region at each of the plurality of energy levels; The information processing apparatus according to claim 1 , wherein the image interpretation energy level is determined by comparing the visibility information for each of the plurality of energy levels.

3. The information processing apparatus according to claim 2 , wherein the processor determines, as the interpretation energy level, the energy level at which the amount of change is greatest among the plurality of energy levels.

4. the processor derives, as the visibility information, a representative value representing the visibility for the target region at each of the plurality of timings; The information processing device according to claim 1 , wherein the interpretation energy level is determined by comparing the representative values ​​for the plurality of timings.

5. The information processing apparatus according to claim 4 , wherein the processor determines the interpretation energy level based on an energy level at which the representative value for each of the plurality of timings is optimal.

6. the processor derives, as the visibility information, a change in the visibility of the target region over time at each of the plurality of energy levels and a representative value representing the visibility of the target region at each of the plurality of timings; The information processing apparatus according to claim 1 , wherein the interpretation energy level is determined based on the amount of change and the representative value.

7. The information processing device according to claim 6 , wherein the processor weights at least one of the amount of change and the representative value according to the amount of change and the representative value, and determines the interpretation energy level based on the weighted amount of change and the representative value.

8. The information processing device according to claim 1 , wherein the processor displays the plurality of CT images at the interpretation energy levels in chronological order.

9. The information processing device according to claim 8 , wherein the processor further displays, at each of the plurality of timings, an image of the target area included in the CT image with the highest visibility in association with the plurality of CT images at the interpretation energy level.

10. The information processing device according to claim 9 , wherein the processor switches between displaying and hiding the image of the target area in response to an instruction from an operator.

11. 2. The information processing apparatus according to claim 1, wherein the CT image is a virtual monochromatic X-ray image derived by reconstructing a plurality of projection data sets at different energy levels at the predetermined plurality of energy levels.

12. 2. The information processing apparatus according to claim 1, wherein the CT image is a material decomposition image derived by reconstructing a plurality of projection data sets with different energy levels at the predetermined plurality of energy levels.

13. The information processing device according to claim 1 , wherein the processor derives the visibility information based on a feature amount of a region including the target region included in the CT image.

14. The information processing apparatus according to claim 1 , wherein the plurality of timings are based on a contrast phase when a contrast agent is injected into the subject.

15. The information processing device according to claim 1 , wherein the target region is a lesion region.

16. a computer acquires a plurality of CT images derived by reconstructing a plurality of projection data with different energy levels, which are acquired by imaging the subject at a plurality of timings, at a plurality of predetermined energy levels; deriving visibility information regarding the visibility of a target region included in each of the plurality of CT images; An information processing method for determining an interpretation energy level for interpreting the CT image from among the plurality of energy levels based on the visibility information.

17. a step of acquiring a plurality of CT images derived by reconstructing a plurality of projection data with different energy levels, which are acquired by imaging the subject at a plurality of timings, at a plurality of predetermined energy levels; deriving visibility information regarding the visibility of a target region included in each of the plurality of CT images; and a procedure for determining an interpretation energy level for interpreting the CT image from among the plurality of energy levels based on the visibility information.

Images