Data processing apparatus, method, and program

Abstract

To provide a data processing apparatus, a method, and a program which can improve accuracy of image selection.SOLUTION: A data processing apparatus that processes data of a plurality of slices acquired by a spectral CT device includes a processor, in which the processor outputs a first image generated on the basis of the data to a display destination, receives setting of a region that is an analysis target, on the first image output to the display destination, analyzes a second image of the plurality of slices generated on the basis of the data to generate statistical information indicating a distribution of a material in the region for each slice, and outputs the statistical information to the display destination.SELECTED DRAWING: Figure 4

Description

[Technical Field]

[0001] The present invention relates to a data processing device, method, and program, and more particularly to a data processing device, method, and program for processing data obtained by a spectral CT (Computed Tomography) device. [Background technology]

[0002] Spectral CT devices (multi-energy CT devices) are known as X-ray CT devices that utilize X-ray energy information (see, for example, Patent Documents 1-4). Images containing various information, such as virtual monochromatic X-ray images, effective atomic number images, and material decomposition images, can be reconstructed from data obtained by spectral CT devices. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-065390 [Patent Document 2] Japanese Patent Application Publication No. 2018-139754 [Patent Document 3] Japanese Patent Application Laid-Open No. 2016-55164 [Patent Document 4] Japanese Patent Application Laid-Open No. 2015-144808 Summary of the Invention [Problem to be solved by the invention]

[0004] However, in an examination using an X-ray CT scanner, tens to thousands of images are taken in one examination, which makes it difficult to identify which slices contain the substance specific to the symptom.

[0005] One embodiment of the technique of the present disclosure provides a data processing device, method, and program that can improve the accuracy of image selection. [Means for solving the problem]

[0006] (1) A data processing device that processes data of multiple slices acquired by a spectral CT device, comprising a processor, the processor outputs a first image generated based on the data to a display destination, accepts settings of an area to be analyzed on the first image output to the display destination, analyzes a second image of multiple slices generated based on the data, generates statistical information showing the distribution of substances in the area for each slice, and outputs the statistical information to the display destination.

[0007] (2) A data processing device according to (1), wherein the second image is a material decomposition image, and the processor analyzes the material decomposition images of multiple slices generated based on the data to generate statistical information showing the distribution of material density values ​​in the region for each slice.

[0008] (3) The data processing device according to (2), wherein the processor generates a histogram showing the area of ​​each density value in the region for each slice as statistical information.

[0009] (4) A data processing device according to (2) or (3), wherein the processor accepts a setting for a range of density values ​​to be displayed in the histogram and generates a histogram within the set range of density values.

[0010] (5) A data processing device described in any one of (2) to (4), wherein the first image is a material decomposition image, and the processor outputs the material decomposition image generated based on the data to a display destination, and accepts the setting of an area on the material decomposition image output to the display destination.

[0011] (6) A data processing device as described in (1), wherein the second image is an effective atomic number image, and the processor analyzes the effective atomic number images of multiple slices generated based on the data to generate statistical information showing the distribution of effective atomic numbers in the region for each slice.

[0012] (7) The data processing device according to (6), wherein the processor generates a histogram showing the number of counts for each effective atomic number in the region for each slice as statistical information.

[0013] (8) The data processing device according to (6) or (7), wherein the processor receives a threshold setting and generates a histogram in which the parts of the count number equal to or greater than the threshold are highlighted.

[0014] (9) A data processing device described in any one of (6) to (8), wherein the first image is an effective atomic number image, and the processor outputs the effective atomic number image generated based on the data to a display destination, and accepts the setting of an area on the effective atomic number image output to the display destination.

[0015] (10) A data processing device described in any one of (1) to (9), wherein the processor accepts setting of the range of the second image to be analyzed, analyzes the second image within the set range, and generates statistical information.

[0016] (11) A data processing device described in any one of (1) to (10), wherein the processor outputs a screen to the display destination for setting the reconstruction conditions when changing the conditions to perform reconstruction, and displays the first image on the screen.

[0017] (12) A data processing device according to any one of (1) to (10), wherein the processor outputs a screen for setting conditions for imaging and reconstruction to a display destination and displays the first image on the screen.

[0018] (13) A data processing device according to any one of (1) to (12), wherein the spectral CT device is a photon-counting CT device.

[0019] (14) A data processing method for processing data of multiple slices acquired by a spectral CT device, comprising the steps of: outputting a first image generated based on the data to a display destination; accepting settings of an area to be analyzed on the first image output to the display destination; analyzing a second image of the multiple slices generated based on the data to generate statistical information showing the distribution of material in the area for each slice; and outputting the statistical information to the display destination.

[0020] (15) A data processing program for processing data of multiple slices acquired by a spectral CT device, the data processing program being implemented by a computer and including the following functions: a function for outputting a first image generated based on the data to a display destination; a function for accepting settings of an area to be analyzed on the first image output to the display destination; a function for analyzing a second image of multiple slices generated based on the data to generate statistical information showing the distribution of substances in the area for each slice; and a function for outputting the statistical information to the display destination. [Effects of the Invention]

[0021] According to the present invention, the accuracy of image selection can be improved. [Brief explanation of the drawings]

[0022] [Figure 1] PCCT device schematic diagram [Figure 2] A diagram showing an example of the console hardware configuration [Figure 3] Block diagram of the console's main functions for generating tomographic images [Figure 4] Block diagram of the console's main analytical functions [Figure 5] A diagram showing an example of a screen for accepting settings for an area to be analyzed. [Figure 6] A diagram showing an example of setting an analysis target area using the area setting frame. [Figure 7] A diagram showing an example of a histogram [Figure 8] An example of the analysis results display [Figure 9] 1 is a flowchart showing the operational procedure of a process for determining the distribution of density values ​​of a substance. [Figure 10] Figure 10 shows another example of displaying analysis results. [Figure 11] A diagram showing an example of generating a histogram by specifying the range of density values ​​to display. [Figure 12] FIG. 10 is a diagram showing another example of a histogram display. [Figure 13] FIG. 10 is a diagram showing an example of a post-reconstruction setting screen that allows checking the distribution of density values ​​for each slice. [Figure 14] Figure 10 shows another example of displaying analysis results. [Figure 15] FIG. 10 is a diagram showing an example of an imaging planning screen that allows checking the distribution of density values ​​for each slice. [Figure 16] Figure 10 shows another example of displaying analysis results. [Figure 17] Block diagram of the console's main analytical functions [Figure 18] A diagram showing an example of a histogram [Figure 19] Flowchart showing the operational procedure for processing to obtain the distribution of effective atomic numbers [Figure 20] A diagram showing an example of a highlighted histogram [Figure 21] FIG. 10 is a diagram showing an example of a method for setting a threshold value. DETAILED DESCRIPTION OF THE INVENTION

[0023] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings.

[0024] [First embodiment] Here, an example will be described in which the present invention is applied to an X-ray CT device (PCCT device) capable of photon counting computed tomography (PCCT).

[0025] [PCCT device] A PCCT system is an X-ray CT system that uses a photon counting type X-ray detector to detect X-rays. The detection data obtained by a PCCT system is capable of spectral imaging, and images containing various information can be generated (reconstructed), such as virtual monochromatic X-ray images, effective atomic number images, and material decomposition images.

[0026] Here, the virtual monochromatic X-ray image is an image that virtually represents an image obtained with a single energy.

[0027] An effective atomic number image is an image that shows the distribution of the effective atomic number (effective Z) of a substance. The effective atomic number is the atomic number that corresponds to the average of the constituent elements of a compound or mixture. In an effective atomic number image, the effective atomic number is displayed for each pixel.

[0028] A material decomposition image is an image that represents the distribution of density values ​​of a material. In the material decomposition image, the density value of a material is represented for each pixel. The material decomposition image is also called a material density image.

[0029] A PCCT device is an example of a spectral CT device.

[0030] Figure 1 is a schematic diagram of a PCCT device. In Figure 1, the X-axis, Y-axis, and Z-axis are three axes that are perpendicular to one another. The Y-axis and Z-axis directions are horizontal, and the X-axis direction is vertical (up-down). The Z-axis direction is the body axis direction.

[0031] 1, the PCCT apparatus 1 includes a scanner gantry 10, a bed 20, and a console 30. Each apparatus is connected to each other so as to be able to communicate with each other.

[0032] [Scanner Gantry] The scanner gantry 10 has an opening (bore) and performs PCCT scanning by irradiating X-rays onto a subject P inserted into the opening 10A. The scanner gantry 10 includes an X-ray tube device 11, an X-ray detection device 12, a data acquisition system (DAS) 13, a rotating frame 14, etc.

[0033] The X-ray tube device 11 irradiates the subject P with X-rays. The X-ray tube device 11 includes an X-ray tube, an X-ray high voltage device, a bowtie filter, a collimator, etc. The X-ray tube, which is an X-ray source, receives a high voltage from the X-ray high voltage device and outputs X-rays. The X-rays output from the X-ray tube are irradiated onto the subject P via the bowtie filter and the collimator.

[0034] The X-ray detection device 12 detects X-rays emitted from the X-ray tube device 11 and transmitted through the subject P. The X-ray detection device 12 is a photon counting type X-ray detection device. A photon counting type X-ray detection device outputs an electrical signal corresponding to the number of photons as an X-ray detection signal. The X-ray detection device 12 has a structure in which, for example, a plurality of detection elements are two-dimensionally arranged in the channel direction (circulation direction) and the column direction (body axis direction).

[0035] The data collection system 13 collects electrical signals output from each detection element of the X-ray detection device 12 and generates detection data. The detection data is data in which a count value (count number) of X-ray photons is assigned to each energy bin. An energy bin is a section that divides the X-ray spectrum by a certain energy bandwidth. The detection data generated by the data collection system 13 is output to the console 30.

[0036] The rotating frame 14 has a cylindrical shape and is driven by a rotation drive device (not shown) to rotate about its axis. The inner periphery of the rotating frame 14 forms the opening 10A of the scanner gantry 10. The X-ray tube assembly 11 and the X-ray detection assembly 12 are mounted on the rotating frame 14. The X-ray tube assembly 11 and the X-ray detection assembly 12 are disposed opposite each other with the opening 10A in between. By rotating the rotating frame 14, the X-ray tube assembly 11 and the X-ray detection assembly 12 rotate about the rotation axis of the rotating frame 14. The rotation axis of the rotating frame 14 forms the center of imaging.

[0037] [bed] The bed 20 has the subject P placed thereon and moves up and down and horizontally. The bed 20 is equipped with a top board 21 on which the subject P is placed. The top board 21 is driven by a vertical drive device (not shown) to move up and down in the vertical direction. The top board 21 is also driven by a horizontal drive device (not shown) to move horizontally in the body axis direction (Z-axis direction). By moving the top board 21 up and down, the vertical position (height) of the subject P is adjusted. By moving the top board 21 horizontally along the body axis direction, the subject P moves within the opening 10A of the scanner gantry 10 along the body axis direction.

[0038] [console] The console 30 functions as an operation desk and also as a data processing device that performs predetermined image processing, analysis processing, and the like.

[0039] FIG. 2 is a diagram illustrating an example of a hardware configuration of a console.

[0040] The console 30 is configured as a computer, and includes a processor 31, a main memory device 32, an auxiliary memory device 33, an input device 34, a display device 35, an input / output interface 36, and the like.

[0041] For example, a CPU (Central Processing Unit), which is a general-purpose processor that executes programs and functions as various processing units, is employed as the processor 31. The various programs and data executed by the processor 31 are stored in the main storage device 32 and / or the auxiliary storage device 33. The term "program" is synonymous with "software."

[0042] The main memory device 32 includes a RAM (Random Access Memory) and a ROM (Read Only Memory).

[0043] The auxiliary storage device 33 is configured, for example, by a hard disk drive (HDD), a solid state drive (SSD), or the like.

[0044] The input device 34 includes, for example, a keyboard, a mouse, a touch panel, and the like.

[0045] The display device 35 is configured by, for example, a liquid crystal display (LCD), an organic electroluminescence diode display (OLED display), etc. In the present embodiment, the display device 35 is an example of a display destination.

[0046] An input / output interface (I / F) 36 connects the console 30 to the scanner gantry 10 and the bed 20 so that they can communicate with each other.

[0047] [Function as an operation console] The console 30 controls the overall operation of the PCCT device 1 based on operational input from the user. The console 30 receives operational input from the user using an input device 34 and a display device 35 as user interfaces. Instructions for performing imaging, setting of imaging and reconstruction conditions, setting of reconstruction conditions for post-reconstruction, setting of analysis conditions, etc. are performed via the console 30. Note that post-reconstruction refers to the process of redoing reconstruction after the fact by setting conditions different from those used during imaging (post-reconstruction).

[0048] [Function as a data processing device] (a) Image generation function (image processing function) The console 30 has the function of processing the detection data of multiple slices obtained by imaging and generating predetermined tomographic images (slice images). The tomographic images that can be generated include ordinary tomographic images showing the distribution of linear attenuation coefficients, virtual monochromatic X-ray images, effective atomic number images, and material decomposition images.

[0049] FIG. 3 is a block diagram of the main functions of the console regarding the generation of tomographic images.

[0050] 3, with regard to generating a tomographic image, the console 30 has functions such as a data acquisition unit 31A, an image processing unit 31B, a recording control unit 31C, and an output control unit 31D. The functions of each unit are realized by the processor 31 executing a predetermined program.

[0051] The data acquisition unit 31A acquires X-ray detection data from the scanner gantry 10. As described above, the detection data is data in which the count value of X-ray photons is assigned to each energy bin. The detection data includes information such as the channel number of the detection element, the column number, the view number indicating the acquired view, and the count value of the detected X-ray photons for each energy bin.

[0052] The image processing unit 31B generates a tomographic image by performing a predetermined reconstruction process on the detection data acquired by the data acquisition unit 31A. The tomographic images generated by the image processing unit 31B include not only normal tomographic images but also virtual monochromatic X-ray images, effective atomic number images, material decomposition images, and the like.

[0053] The recording control unit 31C records the images generated by the image processing unit 31B in the auxiliary storage device 33. Images are recorded on an examination-by-examination basis. The images of each examination are recorded in association with the detection data from which they were generated. This enables post-reconstruction. Each image is assigned a slice number in the order of capture and is recorded in an identifiable manner.

[0054] The output control unit 31D outputs the tomographic image generated by the image processing unit 31B to the display device 35. The output control unit 31D also outputs the recorded tomographic image to the display device 35.

[0055] (b) Analysis function The console 30 of this embodiment has a function (analysis function) of generating a graph showing the distribution of substances (frequency distribution of the amount of each substance present) in a specified region for each slice and presenting the graph to the user as a function of supporting image interpretation. More specifically, the console 30 generates a graph showing the distribution of density values ​​of substances (frequency distribution of the amount of each density value present) for each slice and presents the graph to the user.

[0056] FIG. 4 is a block diagram of the main functions of the console regarding the analysis function.

[0057] 4, with regard to the analysis function, the console 30 has functions such as an image acquisition unit 31E, an analysis condition reception unit 31F, an image analysis unit 31G, a statistical information generation unit 31H, and an output control unit 31D. The functions of each unit are realized by the processor 31 executing a predetermined program. The program is an example of a data processing program.

[0058] (a) Image acquisition unit The image acquisition unit 31E acquires an image (slice image) to be analyzed. In this embodiment, the image to be analyzed is a material decomposition image. The image acquisition unit 31E acquires a series of material decomposition images obtained in one examination as images to be analyzed. The image acquisition unit 31E reads and acquires a series of material decomposition images of an examination specified by the user from the auxiliary storage device 33. The examination (image group) to be analyzed is specified on a predetermined selection screen. The user selects the examination (image group) to be analyzed on the predetermined selection screen. In this embodiment, the material decomposition image is an example of a second image.

[0059] (b) Analysis condition reception section The analysis condition receiving unit 31F receives the setting of a region to be analyzed (analysis target region) as an analysis condition. The analysis target region is set on the material decomposition image displayed on the screen by outputting the material decomposition image to the display device 35. The analysis target region is essentially synonymous with a region of interest (ROI).

[0060] FIG. 5 is a diagram showing an example of a screen for accepting the setting of an analysis target region.

[0061] As shown in FIG. 5, a main display area Dm and a sub-display area Ds are set on the screen MS.

[0062] The main display area Dm displays material decomposition images I_n (n=1, 2, ...) to be analyzed. In the example shown in FIG. 5, four images are displayed at one time. The number of images displayed at one time is not limited to this. It is sufficient that at least one image is displayed. The image displayed in the main display area Dm can be switched by clicking the next image button Bf or the previous image button Bb. The images are displayed in order of slice number and are switched in order of slice number.

[0063] In the sub-display area Ds, an analysis menu display area Ds1, an image range designation area Ds2, and an analysis result display area Ds3 are set.

[0064] The analysis menu display area Ds1 displays buttons for analysis processes that can be performed on the image being displayed in the main display area Dm. Since the PCCT device 1 of this embodiment is capable of analyzing the distribution of density values, at least a button for this function (density value distribution analysis button) B1 is displayed in the analysis menu display area Ds1. To perform analysis processing of the distribution of density values, the user clicks the density value distribution analysis button B1 displayed in the analysis menu display area Ds1.

[0065] When the density value distribution analysis button B1 is clicked, a frame (region setting frame) F is displayed superimposed on the material decomposition image I_n being displayed in the main display region Dm. The user uses this region setting frame F to set a region to be analyzed.

[0066] FIG. 6 is a diagram showing an example of setting an analysis target region using a region setting frame.

[0067] The region setting frame F is initially displayed as a circle of a predetermined size and is displayed at the center of the image. The user adjusts the position, size, and shape (aspect ratio of the ellipse) of the region setting frame F displayed overlaid on the material decomposition image I_n to set the region to be analyzed at a desired position. The adjustment of the position, etc. is performed, for example, by operating the mouse.

[0068] The region setting frame F is displayed on all material decomposition images I_n displayed in the main display region Dm. Adjustments can be made to all images, and adjustments made to one image are reflected in the other images.

[0069] The image range designation area Ds2 is an area for designating the range of a slice image (a material decomposition image in this embodiment) to be analyzed. As shown in FIG. 5, the image range designation area Ds2 includes a box Ds21 for inputting the start point of the range of the slice image to be analyzed, and a box Ds22 for inputting the end point. When narrowing the range for analysis, the user inputs the start point of the range in one box Ds21 (on the left side of the figure) and the end point of the range in the other box Ds22 (on the right side of the figure). For example, when the number of slice images acquired by the image acquisition unit 31E is 60, and the 20th to 35th slice images are to be analyzed, the user inputs "20" in one box Ds21 (on the left side of the figure) and "35" in the other box Ds22 (on the right side of the figure). When the number of slice images to be analyzed is large, the analysis process takes time. When the range of slice images to be analyzed has already been narrowed to some extent, the analysis is performed by narrowing the range. This reduces the processing time required for the analysis.

[0070] The analysis result display area Ds3 is an area where the analysis results are displayed. The display of the analysis results will be described later. As shown in FIG. 5, an execute button B2 is displayed in the analysis result display area Ds3. The execute button B2 is a button for instructing the execution of the analysis. After setting the area to be analyzed, the user clicks the execute button B2 to instruct the execution of the analysis.

[0071] (c) Image analysis unit The image analysis unit 31G individually analyzes the material decomposition image to be analyzed, and calculates the density values ​​of materials present in the region to be analyzed and their areas (areas occupied by the density values ​​of each material in the region to be analyzed). The areas are calculated based on the image resolution (pixels / mm). The analysis is performed on all material decomposition images acquired by the image acquisition unit 31E.

[0072] (d) Statistical information generation section The statistical information generating unit 31H generates statistical information based on the analysis results by the image analyzing unit 31G. More specifically, the statistical information is generated as a graph showing the distribution of density values ​​of the substance (frequency distribution of area for each density value) for each slice. As an example, in this embodiment, a bivariate histogram showing the distribution of density values ​​of the substance for each slice is generated.

[0073] FIG. 7 is a diagram illustrating an example of a histogram.

[0074] As shown in Figure 7, a bivariate histogram H is generated with the first horizontal axis H1 representing the slice number, the second horizontal axis H2 representing the density value of the substance, and the vertical axis V representing the area. This histogram H corresponds to a display in which the histograms of each slice (histograms showing the distribution of the abundance of the density values ​​of the substance in the analysis target region of each slice) are overlaid in order of slice number. As shown in Figure 7, the histogram H is generated as a three-dimensional graph.

[0075] (e) Output control section The output control unit 31D outputs the histogram H generated by the statistical information generating unit 31H as the analysis result to the display device 35. As described above, the analysis result is displayed in the analysis result display area Ds3.

[0076] FIG. 8 is a diagram showing an example of a display of the analysis results.

[0077] As shown in FIG. 8, a histogram H is displayed as the analysis result in a graph display area Ds3a set within the analysis result display area Ds3.

[0078] The histogram H displayed in the graph display area Ds3a can be enlarged or reduced by a user's zoom-in or zoom-out operation. The display position can be changed by a user's movement operation. The viewpoint can be changed by a user's rotation operation (the display orientation can be changed). Operations such as zoom-in and zoom-out can be performed with a mouse, for example.

[0079] [Analysis processing behavior] FIG. 9 is a flowchart showing the operational procedure of the process for obtaining the distribution of density values ​​of a substance.

[0080] First, an object to be analyzed is selected (step S1). As described above, the analysis is performed on a series of material decomposition images obtained in one examination. The user specifies the examination to be analyzed and selects a group of images to be analyzed. The selection of the examination to be analyzed is performed on a predetermined selection screen.

[0081] When the analysis target is selected, an image is displayed (step S2). Specifically, a material decomposition image obtained by inspecting the analysis target is output to the display device 35 and displayed in the main display region Dm within the screen MS (see FIG. 5).

[0082] Next, it is determined whether or not an analysis has been requested (step S3). The analysis is requested by clicking the density value distribution analysis button B1. The processor 31 determines whether or not the density value distribution analysis button B1 has been clicked, and determines whether or not an analysis has been requested.

[0083] When the request to perform the analysis is accepted, analysis conditions are set (step S4). The processor 31 superimposes a region setting frame F on the material decomposition image I_n displayed in the main display region Dm, and accepts the setting of the region to be analyzed from the user (see FIGS. 5 and 6). The user sets the region to be analyzed by adjusting the position, size, and shape (aspect ratio) of the region setting frame F superimposed on the material decomposition image I_n.

[0084] Furthermore, the user sets the range of the material decomposition image to be analyzed as necessary. When narrowing down the range for analysis, the user sets the range to be analyzed in the image range designation region Ds2.

[0085] After completing the setting of the analysis target area, the user clicks the execute button B2 displayed in the secondary display area Ds on the screen MS to instruct the execution of the analysis (see FIG. 5). The processor 31 determines whether the execute button B2 has been clicked, and then determines whether an instruction to execute the analysis has been issued (step S5).

[0086] When an instruction to execute analysis is given, a material decomposition image of the analysis target is acquired (step S6). More specifically, a series of material decomposition images of the analysis target are read from the auxiliary storage device 33. Then, an analysis process is performed on the acquired series of material decomposition images (step S7). Specifically, the density value and area of ​​the material present in the region to be analyzed are calculated for each image.

[0087] When the analysis of all images is completed, statistical information is generated based on the analysis results (step S8). Specifically, a graph showing the distribution of density values ​​of the substance (frequency distribution of area for each density value) for each slice is generated. In this embodiment, a bivariate histogram H showing the distribution of density values ​​of the substance for each slice is generated (see FIG. 7).

[0088] The generated histogram H is output as the analysis result to the display device 35 (step S9). In this embodiment, as shown in Fig. 8, the histogram H is displayed on the same screen as the screen on which the material decomposition image I_n to be analyzed is displayed.

[0089] As described above, the histogram H is a three-dimensional graph with the first horizontal axis H1 representing the slice number, the second horizontal axis H2 representing the density value of the substance, and the vertical axis V representing the area (see Figure 7). By checking this histogram H, the distribution of the density values ​​of the substance for each slice can be easily understood. This makes it easy to determine, for example, which slice contains a density value specific to a symptom. This also improves the accuracy of image selection during analysis.

[0090] [Variations] [Display Analysis Results] In the above embodiment, as shown in FIG. 8, the histogram H of the analysis result is displayed on the same screen as the screen on which the material decomposition image I_n to be analyzed is displayed. However, the display form of the analysis result is not limited to this.

[0091] FIG. 10 is a diagram showing another example of display of the analysis results.

[0092] FIG. 10 shows an example of a case where a histogram H of the analysis results is displayed as a popup on a separate screen (separate window).

[0093] Alternatively, for example, the screen may be switched to display only the histogram H.

[0094] [Specify display range] In a histogram displayed as statistical information, the range of density values ​​to be displayed may be arbitrarily specified by the user.

[0095] FIG. 11 is a diagram showing an example of a case where a histogram is generated by specifying a range of density values ​​to be displayed.

[0096] 11, the analysis result display area Ds3 is provided with a field C1 (display range setting field) for setting the display range of density values. The display range setting field C1 is provided with a box C11 for inputting the start point of the display range and a box C12 for inputting the end point.

[0097] When specifying a display range and displaying the histogram H, the user inputs the density value of the desired display range in the display range setting field C1 and clicks the execute button B2.

[0098] When a display range is specified, a histogram H is generated in the specified display range and displayed in the graph display area Ds3a. The histogram H shown in FIG. 11 is generated in the range (20 to 35 [g / cm 3 ]) specified by the user, compared to the histogram H of the example shown in FIG. 8. 2 ]) is shown as an example.

[0099] FIG. 12 is a diagram showing another example of a histogram display.

[0100] The example shown in FIG. 12 is a density range (20 to 35 g / cm) specified by the user. 2]) is used to generate and display histogram H. That is, this example shows a case where the range of the second horizontal axis showing the density values ​​of the substance is limited to a range specified by the user, and histogram H is generated and displayed.

[0101] By limiting the display range of density values ​​and generating and displaying the histogram H in this way, the distribution of density values ​​within a specific range can be easily confirmed. This makes it easier to confirm which slices contain symptom-specific density values.

[0102] In the above example, a histogram is generated that displays only the density value range specified by the user. However, for example, a histogram may be generated that displays the entire distribution while changing the display of the density value range specified by the user. That is, a histogram is generated that displays the density value range specified by the user in a form that can be distinguished from others, such as by highlighting the density value range specified by the user. For example, by displaying the specified density value range in a different color from others, a histogram in which the specified density value range can be distinguished from others can be generated.

[0103] [Providing analysis functions on the post-recon settings screen] When performing post-reconstruction, there are cases where post-reconstruction is limited to a certain image range. For example, there are cases where post-reconstruction is limited to an image range where a case exists. In this case, the user needs to specify the image range and perform post-reconstruction.

[0104] Generally, post-reconstruction processing involves setting various conditions on a dedicated settings screen (post-reconstruction settings screen). When post-reconstruction is performed only on an image range where a case exists, it is easy to specify the image range if the density value distribution for each slice can be confirmed on the post-reconstruction settings screen.

[0105] FIG. 13 is a diagram showing an example of a post-reconstruction setting screen that allows checking the distribution of density values ​​for each slice.

[0106] As shown in FIG. 13, the post-reconstruction setting screen 100 of this example includes a subject information display area 110, a series information display area 120, a post-reconstruction condition setting area 130, a reference image display area 140, an analysis condition setting area 150, and a post-reconstruction execution button 160.

[0107] Information on subjects who have been examined (photographed) is displayed in list form in the subject information display area 110. The information displayed in the subject information display area 110 includes subject ID (identification), subject name, reception number, examination start date and time, examination area, examination comments, etc. From the subjects displayed in the subject information display area 110, subjects to be the target of post-reconnaissance are selected.

[0108] The series information display area 120 displays information about the image group taken of the subject selected in the subject information display area 110 in list format as series information. Information that can be displayed in the series information display area 120 includes the series number (the series number assigned to a series of images), combination, type, FOV (Field of View), filter, measurement start time, examination area, series comment, number of images, etc. The image group of a series selected from the series displayed in the series information display area 120 is the target of post-reconstruction.

[0109] The post-reconstruction condition setting area 130 is an area for setting reconstruction conditions when performing post-reconstruction. The post-reconstruction condition setting area 130 is provided with input fields for inputting various reconstruction conditions (parameters). The settable conditions include FOV size, FOV center (FOV-X, FOV-Y), reconstruction option, BGC (Bowel Gas Correction: body motion correction), BHC (Beam Hardening Correction: beam hardening correction), MAR (Metal Artifact Reduction: metal artifact reduction processing), window value (WW (window wide), WL (window level)), image slice thickness, reconstruction interval, reconstruction range (first image position, end image position), number of images, series comment, etc. Each input field initially displays the settings at the time of acquisition (or, in the case of a post-reconstructed image group, the settings at the time of post-reconstruction). The user inputs post-reconstruction conditions by changing the displayed numerical values ​​of each parameter to any value. To limit reconstruction to a certain image range, specify the image range in the reconstruction range field. That is, the range to be reconstructed is designated by specifying the start image position and the end image position in the reconstruction range column.

[0110] In the reference image display area 140, one of the images (series) selected as the target of post-reconstruction is displayed as the reference image Im. For example, the image of the first slice is displayed as the reference image Im. It is preferable that the reference image Im be arbitrarily switched. Note that the image displayed as the reference image Im is not limited to a material decomposition image, and may be another image.

[0111] The reference image display area 140 is provided with input fields for parameters such as FOV size, FOV center (FOV-X, FOV-Y), window values ​​(WW, WL), etc. The input fields for each parameter initially display the conditions at the time of shooting. When the numerical value displayed in the input field for each parameter is changed, the change is reflected in the reference image being displayed. In other words, a preview of the reconstruction is performed. Therefore, by adjusting each parameter while checking the reference image, the conditions for post-reconstruction can be determined.

[0112] The analysis condition setting area 150 is an area for setting analysis conditions when analyzing the distribution of density values ​​of a substance for each slice. The analysis condition setting area 150 includes a check box for turning the ROI setting on and off, a box for specifying the display range of density values, a box for specifying the range of the slice image to be analyzed, and an analysis execution button for instructing the execution of the analysis. When the check box for turning the ROI setting on and off is checked, a frame (region setting frame) F is displayed on the reference image Im displayed in the reference image display area 140, allowing the ROI to be set. The region set as the ROI by the region setting frame F is set as the region to be analyzed. Furthermore, when the range (start point and end point) of the slice image is specified in the box for specifying the range of the slice image to be analyzed, the slice image within the specified range is set as the analysis target. Execution of the analysis is instructed by entering the analysis conditions and clicking the analysis execution button.

[0113] When an instruction to execute the analysis is given, a density distribution analysis is performed on the group of images selected as the target of post-reconstruction under the set conditions.

[0114] The post-reconstruction execution button 160 is a button for instructing the execution of post-reconstruction. When the post-reconstruction execution button 160 is clicked, reconstruction is performed under the conditions set in the post-reconstruction condition setting area 130.

[0115] FIG. 14 is a diagram showing another example of display of the analysis results.

[0116] 14, when a distribution analysis of density values ​​is performed, an analysis result screen 170 is displayed as a pop-up. The user checks the histogram H displayed on the analysis result screen 170 and determines the image range to be reconstructed by post-reconstruction. That is, the user checks which slices contain symptom-specific density values ​​and determines the image range.

[0117] In this way, by being able to check the distribution of density values ​​for each slice on the post-reconstruction setting screen 100, the accuracy of image selection is improved when performing post-reconstruction on only an image range where a case exists, for example.

[0118] In this example, the post-reconstruction setting screen 100 is an example of a screen for setting reconstruction conditions when reconstruction is performed under different conditions. Also, the reference image Im displayed in the reference image display area 140 is an example of the first image.

[0119] In the above example, the analysis results are displayed on a separate screen (separate window), but a display area for the analysis results may be provided within the post-reconstruction setting screen 100, and the analysis results may be displayed in that area.

[0120] [Providing analysis functions on the imaging planning screen] During an examination, the same subject may be imaged multiple times with different imaging and / or reconstruction conditions. For example, in an abdominal examination, after imaging the entire abdomen, the area where the disease exists may be imaged again with different imaging and / or reconstruction conditions. In this case, the user must adjust the imaging range in the body axis direction and perform the image retake.

[0121] Generally, the settings of the scanning conditions and reconstruction conditions are performed on a dedicated setting screen (scanning planning screen). When scanning is limited to the range where the case exists, it is easy to specify the range if the density value distribution for each slice can be confirmed on the scanning planning screen.

[0122] FIG. 15 is a diagram showing an example of an imaging planning screen that allows checking the distribution of density values ​​for each slice.

[0123] As shown in FIG. 15, the imaging planning screen 200 of this example includes a scanogram display area 210, a subject information display area 220, an imaging condition setting area 230, an image display area 240, an analysis condition setting area 250, an OK button 260, and a close button 270.

[0124] A scanogram is displayed in the scanogram display area 210. A scanogram is an image that is captured in advance to determine the imaging range.

[0125] Information about the subject is displayed in the subject information display area 220. The information displayed in the subject information display area 220 includes the subject ID (identification), subject name, examination date and time, examination site, and the like.

[0126] The imaging condition setting area 230 is an area for setting imaging and reconstruction conditions. The imaging condition setting area 230 is provided with input fields for inputting various imaging and reconstruction conditions (parameters). The user sets imaging and reconstruction conditions by inputting appropriate numerical values ​​into each displayed input field. The imaging range is set by inputting numerical values ​​for the imaging range into the imaging range input field.

[0127] In the image display area 240, one of the images obtained by imaging (a group of already-imaged images) is displayed as a reference image Im. For example, the image of the first slice is displayed as the reference image Im. It is preferable that the reference image Im be arbitrarily switched. Note that the image displayed as the reference image Im is not limited to a material decomposition image, and may be another image.

[0128] The analysis condition setting area 250 is an area for setting analysis conditions when analyzing the distribution of density values ​​of a substance for each slice. The analysis condition setting area 250 includes a check box for turning the ROI setting on and off, a box for specifying the display range of density values, a box for specifying the range of the slice image to be analyzed, and an analysis execution button for instructing the execution of the analysis. When the check box for turning the ROI setting on and off is checked, a frame (region setting frame) F is displayed on the reference image Im displayed in the image display area 240, allowing the ROI to be set. The region set as the ROI by the region setting frame F is set as the region to be analyzed. Furthermore, when the range (start point and end point) of the slice image is specified in the box for specifying the range of the slice image to be analyzed, the slice image within the specified range is set as the analysis target. Execution of the analysis is instructed by entering the analysis conditions and clicking the analysis execution button.

[0129] When an instruction to execute the analysis is given, a density distribution analysis is performed on the group of captured images under the set conditions.

[0130] The OK button 260 is a button for instructing that the settings input as the conditions for imaging and reconstruction be reflected. The close button 270 is a button for instructing that the imaging plan screen 200 be closed.

[0131] FIG. 16 is a diagram showing another example of display of the analysis results.

[0132] 24, when a distribution analysis of density values ​​is performed, an analysis result screen 280 is displayed as a pop-up. The user checks the histogram H displayed on the analysis result screen 280 and determines the imaging range. That is, the user checks which slice contains the symptom-specific density value and determines the imaging range.

[0133] In this way, by being able to check the distribution of density values ​​for each slice on the scanning plan screen 200, the accuracy of image selection is improved when scanning is limited to a range where a case exists, for example.

[0134] In this example, the imaging planning screen 200 is an example of a screen for setting imaging and reconstruction conditions. Also, the reference image Im displayed in the image display area 240 is an example of a second image.

[0135] In the above example, the analysis results are displayed on a separate screen (separate window), but a display area for the analysis results may be provided within the scanning plan screen 200, and the analysis results may be displayed in that area.

[0136] [Second embodiment] In the first embodiment, an effective atomic number image obtained by imaging is analyzed, and the distribution of materials in a specific region is determined based on the density values ​​of the materials. In the present embodiment, an effective atomic number image obtained by imaging is analyzed, and the distribution of materials in a specific region is determined based on the effective atomic numbers.

[0137] It should be noted that, except for the difference in the analysis function, the PCCT device is the same as the PCCT device 1 of the first embodiment, and therefore only the analysis function will be explained here.

[0138] [Analysis function] FIG. 17 is a block diagram of the main functions of the console regarding the analysis function.

[0139] 17, with regard to the analysis function, the console 30 has functions such as an image acquisition unit 31Ez, an analysis condition reception unit 31Fz, an image analysis unit 31Gz, a statistical information generation unit 31Hz, and an output control unit 31Dz. The functions of each unit are realized by the processor 31 executing a predetermined program. The program is an example of a data processing program.

[0140] (a) Image acquisition unit The image acquisition unit 31Ez acquires an image to be analyzed. In this embodiment, the image to be analyzed is an effective atomic number image. The image acquisition unit 31Ez acquires a series of effective atomic number images obtained in one examination as the image to be analyzed. The image acquisition unit 31Ez reads and acquires a series of effective atomic number images of an examination (series) specified by the user from the auxiliary storage device 33. In this embodiment, the effective atomic number image is an example of a second image.

[0141] (b) Analysis condition reception section The analysis condition receiving unit 31Fz receives the setting of the analysis target region as the analysis condition. The analysis condition receiving unit 31Fz outputs an effective atomic number image to the display device 35 and receives the setting of the analysis target region on the effective atomic number image displayed on the screen. Except for the type of image displayed, this is the same as the first embodiment. That is, the analysis target region is set by adjusting the position, size, and shape of the frame (region setting frame) displayed on the image (see FIGS. 6 and 7).

[0142] (c) Image analysis unit The image analysis unit 31Gz individually analyzes the effective atomic number images to be analyzed and measures the abundance of each effective atomic number in the analysis target region. Specifically, it counts the number of pixels for each effective atomic number in the analysis target region of each image. The analysis is performed on all effective atomic number images acquired by the image acquisition unit 31Ez.

[0143] (d) Statistical information generation section The statistical information generating unit 31Hz generates statistical information based on the analysis results of the image analyzing unit 31Gz. More specifically, the statistical information is generated as a graph showing the distribution of effective atomic numbers (frequency distribution of abundance for each effective atomic number) for each slice. As an example, in this embodiment, a bivariate histogram showing the distribution of effective atomic numbers for each slice is generated.

[0144] FIG. 18 is a diagram illustrating an example of a histogram.

[0145] As shown in Figure 18, a bivariate histogram Hz is generated, with the first horizontal axis H1 representing the slice number, the second horizontal axis H2 representing the effective atomic number, and the vertical axis V representing the pixel count (abundance). This histogram Hz corresponds to a display in which the histograms of each slice (histograms showing the distribution of effective atomic numbers in the analysis target region of each slice) are overlaid in order of slice number. As shown in Figure 18, the histogram Hz is generated as a three-dimensional graph.

[0146] (e) Output control section The output control unit 31Dz outputs the histogram Hz generated by the statistical information generation unit 31Hz as the analysis result to the display device 35. The display format is the same as in the first embodiment. That is, as shown in FIG. 8, the histogram Hz is displayed in a graph display area Ds3a set in the analysis result display area Ds3 of the sub-display area Ds.

[0147] [Analysis processing behavior] FIG. 19 is a flowchart showing the operational procedure of the process for determining the distribution of effective atomic numbers.

[0148] First, an object to be analyzed is selected (step S11). The analysis is performed on a series of effective atomic number images obtained in one examination. The user specifies the examination to be analyzed and selects a group of images (series) to be analyzed. The selection of the examination to be analyzed is performed on a predetermined selection screen.

[0149] When an analysis target is selected, an image is displayed (step S12). Specifically, an effective atomic number image obtained in the examination designated as the analysis target is output to the display device 35 (see FIG. 5).

[0150] Next, it is determined whether or not there is a request to perform analysis (step S13). When the request to perform analysis is accepted, analysis conditions are set (step S14). The user sets the analysis target region by adjusting the position of the region setting frame superimposed on the effective atomic number image on the screen. The user also sets the range of the effective atomic number image to be analyzed as necessary. For example, when narrowing the range for analysis, the range of the effective atomic number image to be analyzed is set. After completing the settings, the user instructs the execution of analysis. Processor 31 determines whether or not there is a command to perform analysis from the user (step S15).

[0151] When an instruction to execute analysis is given, an effective atomic number image of the analysis target is acquired (step S16). More specifically, a series of effective atomic number images of the analysis target are read from the auxiliary storage device 33. Then, an analysis process is performed on the acquired series of effective atomic number images (step S17). Specifically, a process of counting the number of pixels for each effective atomic number is performed on the analysis target region for each image.

[0152] When the analysis of all images is completed, statistical information is generated based on the analysis results (step S18). Specifically, a graph showing the distribution of effective atomic numbers (frequency distribution of abundance for each effective atomic number) for each slice is generated. In this embodiment, a bivariate histogram Hz showing the distribution of effective atomic numbers for each slice is generated (see FIG. 18).

[0153] The generated histogram Hz is output as the analysis result to the display device 35 (step S19). In this embodiment, the histogram Hz is displayed on the same screen as the effective atomic number image to be analyzed (see FIG. 8).

[0154] As described above, the histogram Hz is a three-dimensional graph (see Figure 18) with the first horizontal axis H1 representing the slice number, the second horizontal axis H2 representing the effective atomic number, and the vertical axis V representing the pixel count (abundance). By checking this histogram Hz, the distribution of effective atomic numbers for each slice can be easily understood. This makes it easy to determine, for example, which slice contains an effective atomic number specific to a symptom. This also improves the accuracy of image selection during analysis.

[0155] [Variations] [Display Analysis Results] As in the first embodiment, the histogram Hz of the analysis results may be displayed on the same screen as the screen displaying the image for setting the analysis target area (see FIG. 8), or may be displayed on a separate screen (separate window) (see FIG. 10).

[0156] The histogram Hz may be generated to display the largest effective atomic number among the effective atomic numbers extracted from the analysis target region.

[0157] The histogram Hz may also be generated based only on the effective atomic numbers extracted from the analysis region. That is, the histogram Hz may be generated by displaying only the effective atomic numbers extracted from the analysis region on the second horizontal axis H2.

[0158] [Specify display range] (1) Specify the range of effective atomic numbers to display As in the first embodiment, the range of effective atomic numbers to be displayed in the histogram Hz displayed as statistical information may be arbitrarily specified by the user (see FIGS. 11 and 12). (2) Setting the threshold A threshold may be set and counts above the threshold may be highlighted to create a histogram Hz.

[0159] FIG. 20 is a diagram showing an example of a highlighted histogram.

[0160] FIG. 20 shows an example in which the color of the bar of the effective atomic number of the count value exceeding the threshold is changed to highlight it.

[0161] FIG. 21 is a diagram illustrating an example of a method for setting a threshold value.

[0162] 21, the analysis result display area Ds3 is provided with a field C2 for setting a threshold (threshold setting field) 21. The threshold setting field C2 is provided with a box C21 for inputting a threshold.

[0163] When setting a threshold and displaying a histogram Hz, the user inputs the threshold to be set in the threshold setting field C2 and clicks the execute button B2.

[0164] In this way, by highlighting the effective atomic numbers having counts equal to or greater than the threshold, the distribution of effective atomic numbers can be more easily confirmed.

[0165] [Providing analysis functions on the post-recon settings screen] As in the case of the distribution analysis of density values, it is preferable that the distribution analysis of effective atomic numbers can also be carried out on the post-reconstruction setting screen (see FIGS. 13 and 14).

[0166] [Providing analysis functions on the imaging planning screen] As in the case of the distribution analysis of density values, it is preferable that the distribution analysis of effective atomic numbers can also be performed on the imaging planning screen (see FIGS. 15 and 16).

[0167] [Other embodiments] [Distribution analysis of density values ​​and effective atomic number] It may be possible to perform both density value distribution analysis and effective atomic number distribution analysis in the same PCCT device 1. This allows the user to select the analysis target according to the purpose, application, etc., and further improves the accuracy of image selection.

[0168] [Statistics] In the above embodiment, a bivariate histogram using a so-called bar graph is generated as statistical information, but the form of the graph showing the statistical results is not limited to this.

[0169] For example, statistical information may be generated using a heat map. A heat map is a visualized graph that represents each value of two-dimensional data (matrix) as a color or a shade. For example, for the distribution of density values, a two-dimensional graph is generated by assigning a color or a shade to the area. Similarly, for the distribution of effective atomic numbers, a two-dimensional graph is generated by assigning a color or a shade to the count.

[0170] [Spectral CT device] In the above embodiment, the present invention has been described as being applied to a PCCT device, but the application of the present invention is not limited to this. The present invention can be applied to any X-ray CT device that is capable of reconstructing a material decomposition image and / or an effective atomic number image from detection data obtained by imaging. Because spectral CT involves the detection of transmitted X-rays at two or more energy levels, spectral CT generally includes dual-energy CT by definition.

[0171] [others] In the above embodiment, the console has the image processing and analysis functions, but the image processing and analysis functions may be provided by a device separate from the console. Also, the device providing the image processing function and the device providing the analysis function may be configured separately.

[0172] The processing unit that provides the functions of a data processing device can be composed of various types of processors. These include general-purpose processors such as CPUs and GPUs (Graphic Processing Units), as well as programmable logic devices (PLDs) such as FPGAs (Field Programmable Gate Arrays), whose circuit configuration can be changed after manufacture, and dedicated electrical circuits such as ASICs (Application Specific Integrated Circuits), which are processors with circuit configurations specifically designed to perform specific processes. A single processing unit may be composed of one of the various types of processors, or two or more processors of the same or different types. For example, a single processing unit may be composed of multiple FPGAs or a combination of a CPU and an FPGA. Alternatively, multiple processing units may be composed of a single processor. A first example of multiple processing units composed of a single processor is a configuration in which a single processor is composed of one or more CPUs and software, and this processor functions as multiple processing units, as typified by computers used as clients, servers, etc. Secondly, there is a form using a processor that realizes the functions of an entire system including multiple processing units on a single IC (Integrated Circuit) chip, as typified by a System on Chip (SoC), etc. In this way, various processing units are configured as a hardware structure using one or more of the above-mentioned various processors. [Explanation of symbols]

[0173] 1. PCCT device 10...Scanner gantry 10A…Opening 11...X-ray tube device 12...X-ray detection device 13...Data acquisition system 14...Rotating frame 20...Bed 21...Tabletop 30...Console 31...Processor 31A...Data acquisition section 31B...Image processing unit 31C...Recording control section 31D...Output control section 31Dz...Output control section 31E...Image acquisition unit 31Ez...Image acquisition unit 31F: Analysis conditions reception section 31Fz...Analysis condition reception section 31G...Image analysis section 31Gz...Image analysis section 31H…Statistical information generation section 31Hz…Statistical information generation section 32…Main memory 33…Auxiliary storage device 34...Input device 35…Display device 36...Input / output interface 100...Post-recon setting screen 110...Subject information display area 120...Series information display area 130...Post-reconstruction condition setting area 140...Image display area 150…Analysis condition setting area 160...Post-recon execution button 170...Analysis results screen 200...Shooting plan screen 210...Scanogram display area 220...Subject information display area 230...Shooting condition setting area 240...Image display area 250…Analysis condition setting area 260...OK button 270...Close button 280...Analysis results screen B1…Density distribution analysis button B2: Execute button Bb...Previous image button Bf...Image forward button C1: Display range setting field C11: Box for entering the start point of the display range C12: Box for entering the end point of the display range C2: Threshold setting field C21: Threshold value input box Dm…Main display area Ds…Sub display area Ds1: Analysis menu display area Ds3…Analysis result display area Ds3a: Graph display area F...Area setting frame H...Histogram Hz...Histogram I_n...Material decomposition image Im…Reference image MS…Screen P...Subject H1: First horizontal axis H2: Second horizontal axis V: vertical axis S1 to S9: Operation procedure for determining the distribution of density values ​​of a substance S11~S11...Operation procedure for calculating the distribution of effective atomic numbers

Claims

1. A data processing device for processing data of multiple slices acquired by a spectral CT device, comprising: a processor; The processor: outputting a first image generated based on the data to a display destination; Accepting a setting of an area to be analyzed on the first image output to the display destination; analyzing a second image of a plurality of slices generated based on the data to generate statistical information indicating a distribution of material in the region for each slice; outputting the statistical information to the display destination; Data processing device.

2. the second image is a material decomposition image, The processor: analyzing material decomposition images of a plurality of slices generated based on the data to generate statistical information indicating a distribution of density values ​​of materials in the region for each slice; 2. The data processing device according to claim 1.

3. The processor: generating a histogram showing the area of ​​each density value in the region for each slice as the statistical information; 3. The data processing device according to claim 2.

4. The processor: Accepting a setting for a range of density values ​​to be displayed on the histogram; generating the histogram within the set range of density values; 4. The data processing device according to claim 3.

5. the first image is a material decomposition image; The processor: outputting a material decomposition image generated based on the data to the display device; accepting the setting of the region on the material decomposition image output to the display device; 3. The data processing device according to claim 2.

6. the second image is an effective atomic number image; The processor: analyzing the effective atomic number images of multiple slices generated based on the data to generate statistical information indicating the distribution of effective atomic numbers in the region for each slice; 2. The data processing device according to claim 1.

7. The processor: generating a histogram showing the number of counts for each effective atomic number in the region for each slice as the statistical information; 7. The data processing device according to claim 6.

8. The processor: Accepts threshold settings, generating the histogram in which the portion of the count number equal to or greater than the threshold is highlighted; 8. A data processing device according to claim 7.

9. the first image is an effective atomic number image; The processor: outputting an effective atomic number image generated based on the data to the display destination; Accepting the setting of the region on the effective atomic number image output to the display destination; 7. The data processing device according to claim 6.

10. The processor: Accepting a setting of a range of the second image to be analyzed; analyzing the second image within a set range to generate the statistical information; 10. A data processing device according to any one of claims 1 to 9.

11. The processor: outputting a screen for setting the conditions for reconstruction when the conditions are changed and reconstruction is performed to the display destination; displaying the first image on the screen; 10. A data processing device according to any one of claims 1 to 9.

12. The processor: A screen for setting the conditions for imaging and reconstruction is output to the display destination. displaying the first image on the screen; 10. A data processing device according to any one of claims 1 to 9.

13. The spectral CT device is a photon-counting CT device.

2. The data processing device according to claim 1.

14. A data processing method for processing data of multiple slices acquired by a spectral CT apparatus, comprising: outputting a first image generated based on the data to a display device; receiving a setting of an area to be analyzed on the first image output to the display device; analyzing a second image of multiple slices generated based on the data to generate statistical information indicative of a distribution of material in the region for each slice; outputting the statistical information to the display destination; Data processing methods, including:

15. A data processing program for processing data of multiple slices acquired by a spectral CT device, comprising: a function of outputting a first image generated based on the data to a display destination; a function of receiving a setting of an area to be analyzed on the first image output to the display device; a function of analyzing a second image of a plurality of slices generated based on the data and generating statistical information indicating the distribution of substances in the region for each slice; a function of outputting the statistical information to the display destination; A data processing program that is implemented by a computer.

Images