JP2021040991A - X-ray ct system and medical processor - Google Patents

Abstract

To improve accuracy of substance discrimination.SOLUTION: An X-ray CT system according to the embodiment includes a scan part and a processing part. The scan part irradiates an X-ray to an analyte while rotating a focal position of the X-ray around the analyte, and collects projection data set including projection data corresponding to first X-ray energy projection data and projection data corresponding to second X-ray energy. The processing part performs correction processing using projection data of opposite views with respect to the projection data set, and performs substance discrimination by a plurality of reference substances on the basis of the projection data set after correction.SELECTED DRAWING: Figure 1

Description

Embodiments of the present invention relate to X-ray CT systems and medical processing devices.

Based on the projection data corresponding to two or more types of X-ray energies collected by an X-ray CT (Computed Tomography) scanner, there is a technology to discriminate the object by multiple reference substances and display the result as an image. .. When using two types of X-ray energy, this technique is called Dual Energy (DE), and it is possible to discriminate substances using two types of reference substances.

For example, dual energy technology makes it possible to discriminate substances such as kidney stones, fats, soft tissues, and bones in the body of a subject. Further, according to the dual energy technique, it is possible to determine whether the kidney stone in the body of the subject is a calcium type stone or a uric acid type stone.

Here, the projection data collected by the X-ray CT scanner may include noise and artifacts due to various factors. Further, the projection data collected by the X-ray CT scanner may be partially lost due to various factors. For example, when a discharge phenomenon occurs in an X-ray tube, the projection data of the corresponding view may be lost. Further, when performing dual energy scanning by the kV switching method, the projection data of the view that was irradiated with low energy X-rays is lost in the projection data corresponding to high energy, and the projection data corresponding to low energy is lost. The projected data of the view that was irradiated with high-energy X-rays will be lost. In order to improve the accuracy of substance discrimination, it is desirable to appropriately correct these noises, artifacts, and missing parts of projection data.

JP-A-2010-142478 Japanese Unexamined Patent Publication No. 2008-154784

An object to be solved by the present invention is to improve the accuracy of substance discrimination.

The X-ray CT system of the embodiment includes a scanning unit and a processing unit. The scanning unit irradiates the subject with X-rays while rotating the focal position of the X-rays around the subject, and corresponds to the projection data corresponding to the first X-ray energy and the second X-ray energy. Collect a projection dataset that contains at least the projection data to be produced. The processing unit performs correction processing on the projection data set using the projection data of the opposite views, and discriminates substances with a plurality of reference substances based on the corrected projection data set.

FIG. 1 is a block diagram showing an example of the configuration of the X-ray CT system according to the first embodiment. FIG. 2 is a diagram showing an example of a projection data set according to the first embodiment. FIG. 3A is a diagram showing an example of interpolation processing according to the first embodiment. FIG. 3B is a diagram showing an example of interpolation processing according to the first embodiment. FIG. 3C is a diagram showing an example of interpolation processing according to the first embodiment. FIG. 4 is a diagram for explaining the facing view according to the first embodiment. FIG. 5A is a diagram for explaining Case 1 and Case 2 according to the first embodiment. FIG. 5B is a diagram showing an example of processing in Case 1 according to the first embodiment. FIG. 6A is a diagram for explaining Case 3 and Case 4 according to the first embodiment. FIG. 6B is a diagram showing an example of processing in Case 3 according to the first embodiment. FIG. 7A is a diagram for explaining Case 5 and Case 6 according to the first embodiment. FIG. 7B is a diagram showing an example of processing in Case 5 according to the first embodiment. FIG. 8A is a diagram for explaining Case 7 and Case 8 according to the first embodiment. FIG. 8B is a diagram showing an example of processing in Case 7 according to the first embodiment. FIG. 9A is a diagram for explaining the case 9, the case 10, the case 11, and the case 12 according to the first embodiment. FIG. 9B is a diagram showing an example of processing in Case 9 according to the first embodiment. FIG. 10A is a diagram showing an example of a trained model generation process according to the first embodiment. FIG. 10B is a diagram showing an example of a trained model generation process according to the first embodiment. FIG. 11A is a flowchart for explaining a series of processes of the X-ray CT system according to the first embodiment. FIG. 11B is a flowchart for explaining a series of processes of the X-ray CT system according to the first embodiment. FIG. 12 is a block diagram showing an example of the configuration of the medical information processing system 1 according to the second embodiment.

Hereinafter, embodiments of the X-ray CT system and the medical processing apparatus will be described in detail with reference to the accompanying drawings. The X-ray CT system and the medical processing apparatus according to the present application are not limited to the embodiments shown below.

(First Embodiment)
First, the configuration of the X-ray CT system 10 according to the first embodiment will be described with reference to FIG. FIG. 1 is a block diagram showing an example of the configuration of the X-ray CT system 10 according to the first embodiment. As shown in FIG. 1, the X-ray CT system 10 includes a gantry device 110, a sleeper device 130, and a console device 140. The X-ray CT system 10 is also called an X-ray CT apparatus or an X-ray CT scanner.

In FIG. 1, the rotation axis of the rotation frame 113 in the non-tilt state or the longitudinal direction of the top plate 133 of the sleeper device 130 is the Z-axis direction. Further, the axial direction orthogonal to the Z-axis direction and horizontal to the floor surface is defined as the X-axis direction. Further, the axial direction orthogonal to the Z-axis direction and perpendicular to the floor surface is defined as the Y-axis direction. Note that FIG. 1 is a drawing of the gantry device 110 from a plurality of directions for the sake of explanation, and shows a case where the X-ray CT system 10 has one gantry device 110.

The gantry device 110 includes an X-ray tube 111, an X-ray detector 112, a rotating frame 113, an X-ray high voltage device 114, a control device 115, a wedge 116, a collimator 117, and a DAS 118.

The X-ray tube 111 is a vacuum tube having a cathode (filament) that generates thermoelectrons and an anode (target) that generates X-rays upon collision of thermions. The X-ray tube 111 generates X-rays to irradiate the subject P1 by irradiating thermions from the cathode toward the anode by applying a high voltage from the X-ray high voltage device 114.

The X-ray detector 112 detects the X-rays irradiated from the X-ray tube 111 and passed through the subject P1, and outputs a signal corresponding to the detected X-ray dose to the DAS 118. The X-ray detector 112 has, for example, a plurality of detection element sequences in which a plurality of detection elements are arranged in a channel direction (channel direction) along one arc centered on the focal point of the X-ray tube 111. The X-ray detector 112 has, for example, a structure in which a plurality of detection element sequences in which a plurality of detection elements are arranged in the channel direction are arranged in a row direction. The column direction is also described as the slice direction, the row direction, or the segment direction. The column direction corresponds to the body axis direction (Z-axis direction) of the subject P1 shown in FIG.

For example, the X-ray detector 112 is an indirect conversion type detector having a grid, a scintillator array, and an optical sensor array. The scintillator array has a plurality of scintillators. The scintillator has a scintillator crystal that outputs a photon amount of light according to the incident X-ray dose. The grid is arranged on the surface of the scintillator array on the X-ray incident side and has an X-ray shield plate that absorbs scattered X-rays. The grid may also be called a collimator (one-dimensional collimator or two-dimensional collimator). The optical sensor array has a function of converting into an electric signal according to the amount of light from the scintillator, and has, for example, an optical sensor such as a photodiode. The X-ray detector 112 may be a direct conversion type detector having a semiconductor element that converts incident X-rays into an electric signal.

The rotating frame 113 is an annular frame that supports the X-ray tube 111 and the X-ray detector 112 so as to face each other, and rotates the X-ray tube 111 and the X-ray detector 112 by the control device 115. For example, the rotating frame 113 is a casting made of aluminum. In addition to the X-ray tube 111 and the X-ray detector 112, the rotating frame 113 can further support the X-ray high voltage device 114, the wedge 116, the collimator 117, the DAS 118, and the like. Further, the rotating frame 113 can further support various configurations (not shown in FIG. 1). In the following, in the gantry device 110, the rotating frame 113 and the portion that rotates and moves together with the rotating frame 113 are also referred to as a rotating portion.

The X-ray high-voltage device 114 has an electric circuit such as a transformer and a rectifier, and has a high-voltage generator that generates a high voltage applied to the X-ray tube 111 and an X-ray that is generated by the X-ray tube 111. It has an X-ray control device that controls the output voltage according to the above. The high voltage generator may be a transformer type or an inverter type. The X-ray high voltage device 114 may be provided on the rotating frame 113 or on a fixed frame (not shown).

The control device 115 includes a processing circuit having a CPU (Central Processing Unit) and the like, and a drive mechanism such as a motor and an actuator. The control device 115 receives the input signal from the input interface 143 and controls the operation of the gantry device 110 and the sleeper device 130. For example, the control device 115 controls the rotation of the rotating frame 113, the tilt of the gantry device 110, the operation of the sleeper device 130, and the like. As an example, the control device 115 rotates the rotating frame 113 around an axis parallel to the X-axis direction based on the input tilt angle (tilt angle) information as a control for tilting the gantry device 110. The control device 115 may be provided in the gantry device 110 or in the console device 140.

The wedge 116 is an X-ray filter for adjusting the X-ray dose emitted from the X-ray tube 111. Specifically, the wedge 116 is an X-ray filter that attenuates the X-rays emitted from the X-ray tube 111 so that the X-rays emitted from the X-ray tube 111 to the subject P1 have a predetermined distribution. Is. For example, the wedge 116 is a wedge filter or a bow-tie filter, and is manufactured by processing aluminum or the like so as to have a predetermined target angle and a predetermined thickness.

The collimator 117 is a lead plate or the like for narrowing the irradiation range of X-rays transmitted through the wedge 116, and a slit is formed by a combination of a plurality of lead plates or the like. The collimator 117 may be called an X-ray diaphragm. Further, in FIG. 1, the case where the wedge 116 is arranged between the X-ray tube 111 and the collimator 117 is shown, but the case where the collimator 117 is arranged between the X-ray tube 111 and the wedge 116. May be good. In this case, the wedge 116 is irradiated from the X-ray tube 111, and the X-ray whose irradiation range is limited by the collimator 117 is transmitted and attenuated.

The DAS 118 collects X-ray signals detected by each detection element included in the X-ray detector 112. For example, the DAS 118 has an amplifier that amplifies an electric signal output from each detection element and an A / D converter that converts the electric signal into a digital signal, and generates detection data. DAS118 is realized by, for example, a processor.

The data generated by the DAS 118 is transmitted from a transmitter having a light emitting diode (LED) provided on the rotating frame 113 by optical communication to a non-rotating portion (for example, a fixed frame, etc.) of the gantry device 110. Is transmitted to a receiver having a light diode provided in (not shown in the above) and transferred to the console device 140. Here, the non-rotating portion is, for example, a fixed frame that rotatably supports the rotating frame 113 or the like. The method of transmitting data from the rotating frame 113 to the non-rotating portion of the gantry device 110 is not limited to optical communication, and any non-contact data transmission method may be adopted, and the contact-type data transmission method may be used. You may adopt it.

The sleeper device 130 is a device for placing and moving the subject P1 to be scanned, and has a base 131, a sleeper drive device 132, a top plate 133, and a support frame 134. The base 131 is a housing that supports the support frame 134 so as to be movable in the vertical direction. The sleeper drive device 132 is a drive mechanism for moving the top plate 133 on which the subject P1 is placed in the long axis direction of the top plate 133, and includes a motor, an actuator, and the like. The top plate 133 provided on the upper surface of the support frame 134 is a plate on which the subject P1 is placed. In addition to the top plate 133, the sleeper drive device 132 may move the support frame 134 in the long axis direction of the top plate 133.

The console device 140 has a memory 141, a display 142, an input interface 143, and a processing circuit 144. Although the console device 140 will be described as a separate body from the gantry device 110, the gantry device 110 may include a part of each component of the console device 140 or the console device 140.

The memory 141 is realized by, for example, a RAM (Random Access Memory), a semiconductor memory element such as a flash memory, a hard disk, an optical disk, or the like. For example, the memory 141 stores various data collected by performing a scan on the subject P1. Further, for example, the memory 141 stores a program for the circuit included in the X-ray CT system 10 to realize its function. The memory 141 may be realized by a server group (cloud) connected to the X-ray CT system 10 via a network.

The display 142 displays various information. For example, the display 142 displays a CT image for display generated by the processing circuit 144, or displays an image showing the result of substance discrimination. Further, for example, the display 142 displays a GUI (Graphical User Interface) for receiving various operations from the user. For example, the display 142 is a liquid crystal display or a CRT (Cathode Ray Tube) display. The display 142 may be a desktop type, or may be composed of a tablet terminal or the like capable of wireless communication with the console device 140 main body.

The input interface 143 receives various input operations from the user, converts the received input operations into an electric signal, and outputs the received input operations to the processing circuit 144. For example, the input interface 143 receives from the user reconstruction conditions for reconstructing CT image data, image processing conditions for generating a CT image for display from CT image data, and the like. For example, the input interface 143 includes a mouse, a keyboard, a trackball, a switch, a button, a joystick, a touch pad for performing input operations by touching an operation surface, a touch screen in which a display screen and a touch pad are integrated, and an optical sensor. It is realized by the non-contact input circuit, voice input circuit, etc. used. The input interface 143 may be provided on the gantry device 110. Further, the input interface 143 may be composed of a tablet terminal or the like capable of wireless communication with the console device 140 main body. Further, the input interface 143 is not limited to the one provided with physical operating parts such as a mouse and a keyboard. For example, an electric signal processing circuit that receives an electric signal corresponding to an input operation from an external input device provided separately from the console device 140 and outputs the electric signal to the processing circuit 144 is also an example of the input interface 143. included.

The processing circuit 144 controls the operation of the entire X-ray CT system 10 by executing the scanning function 144a, the processing function 144b, and the control function 144c. The scanning function 144a is an example of a scanning unit. The processing function 144b is an example of a processing unit.

For example, the processing circuit 144 executes a scan on the subject P1 by reading a program corresponding to the scan function 144a from the memory 141 and executing the program. For example, the scan function 144a supplies a high voltage to the X-ray tube 111 by controlling the X-ray high voltage device 114. As a result, the X-ray tube 111 generates X-rays to irradiate the subject P1. Further, the scanning function 144a moves the subject P1 into the photographing port of the gantry device 110 by controlling the sleeper driving device 132. Further, the scanning function 144a controls the distribution of X-rays applied to the subject P1 by adjusting the position of the wedge 116 and the opening degree and position of the collimator 117. Further, the scan function 144a rotates the rotating portion by controlling the control device 115. Further, while the scan is executed by the scan function 144a, the DAS 118 collects an X-ray signal from each detection element in the X-ray detector 112 and generates detection data. Further, the scan function 144a performs preprocessing on the detection data output from DAS118. For example, the scan function 144a performs preprocessing such as logarithmic conversion processing, offset correction processing, sensitivity correction processing between channels, and beam hardening correction on the detection data output from DAS118. The data after pretreatment is also described as raw data. In addition, the detection data before the pretreatment and the raw data after the pretreatment are collectively referred to as projection data.

Further, the processing circuit 144 generates image data based on the projection data after the preprocessing by reading the program corresponding to the processing function 144b from the memory 141 and executing the program. For example, the processing function 144b performs CT image data (volume data) by performing reconstruction processing on the projected data using a filter correction back projection method, a successive approximation reconstruction method, a successive approximation applied reconstruction method, or the like. To generate. In addition, the processing function 144b can also perform reconstruction processing by AI (Artificial Intelligence) to generate CT image data. For example, the processing function 144b generates CT image data by a DLR (Deep Learning Reconnection) method. Further, the processing function 144b performs substance discrimination by a plurality of reference substances based on the projection data. The processing function 144b may perform substance discrimination at a stage before the reconstruction process (projection data stage), or at a stage after the reconstruction process (CT image data stage). May be done. The discrimination process by the processing function 144b will be described later.

Further, the processing circuit 144 controls the display on the display 142 by reading the program corresponding to the control function 144c from the memory 141 and executing the program. For example, the control function 144c displays the CT image data generated by the processing function 144b by a known method based on an input operation received from the user via the input interface 143 (a fault of an arbitrary cross section). Convert to image data, 3D image data, etc.). Then, the control function 144c causes the converted CT image for display to be displayed on the display 142. Further, for example, the control function 144c causes the display 142 to display an image showing the result of substance discrimination by the processing function 144b. Further, the control function 144c transmits various data via the network. As an example, the control function 144c transmits the CT image data generated by the processing function 144b and an image showing the result of substance discrimination to an image storage device (not shown) for storage.

In the X-ray CT system 10 shown in FIG. 1, each processing function is stored in the memory 141 in the form of a program that can be executed by a computer. The processing circuit 144 is a processor that realizes a function corresponding to each program by reading a program from the memory 141 and executing the program. In other words, the processing circuit 144 in the state where the program is read has a function corresponding to the read program.

Although it has been described in FIG. 1 that the scan function 144a, the processing function 144b, and the control function 144c are realized by a single processing circuit 144, the processing circuit 144 is configured by combining a plurality of independent processors. , Each processor may realize the function by executing the program. Further, each processing function included in the processing circuit 144 may be realized by being appropriately distributed or integrated into a single processing circuit or a plurality of processing circuits.

Further, the processing circuit 144 may realize the function by utilizing the processor of the external device connected via the network. For example, the processing circuit 144 reads a program corresponding to each function from the memory 141 and executes it, and also uses a server group (cloud) connected to the X-ray CT system 10 via a network as a computational resource. Each function shown in FIG. 1 is realized.

The configuration example of the X-ray CT system 10 has been described above. Hereinafter, the processing performed by the X-ray CT system 10 will be described in detail.

First, a series of processes from executing dual energy collection to performing substance discrimination will be described. In this embodiment, a case where dual energy collection by the kV switching method is performed will be described as an example.

For example, prior to the main scan for performing dual energy collection, a positioning scan for setting the scan range of the main scan is first executed. Here, the positioning scan may be performed in two dimensions or in three dimensions.

When performing a two-dimensional positioning scan, the scan function 144a does not rotate the X-ray focal position around the subject P1, but at least one of the X-ray focal position and the subject P1 is the body of the subject P1. Positioning imaging is performed while moving along the axial direction. For example, the scanning function 144a fixes the position of the X-ray tube 111 at a predetermined rotation angle, and irradiates the subject P1 with X-rays from the X-ray tube 111 while moving the top plate 133 in the Z-axis direction. Therefore, two-dimensional positioning imaging can be performed.

Further, when executing the three-dimensional positioning scan, the scan function 144a executes the positioning image while rotating the focal position of the X-ray around the subject P1. For example, the scan function 144a can perform three-dimensional positioning imaging by executing a scan of a method such as a conventional scan, a helical scan, or a step-and-shoot.

Next, the processing function 144b generates positioning image data based on the scan result of the positioning scan. The positioning image data may be referred to as a scanno image data or a scout image data. Next, the control function 144c generates a reference image based on the positioning image data, and displays the generated reference image on the display 142. For example, when a three-dimensional positioning scan is executed, the control function 144c generates a reference image by executing a rendering process on the positioning image data, and displays the generated reference image on the display 142. Further, the control function 144c sets the scan range of the main scan by accepting an input operation from the user who has referred to the reference image.

An example of the rendering process is a process of generating a two-dimensional image of an arbitrary cross section from three-dimensional image data by a cross-section reconstruction method (MPR: Multi Planar Reconnection). Further, as another example of the rendering process, a two-dimensional image reflecting three-dimensional information from three-dimensional image data by volume rendering (Volume Rendering) processing or maximum value projection method (MIP: Maximum Intelligence Projection). The process of generating is mentioned.

Further, although the description has been made assuming that the scan range of this scan is set by the user, the embodiment is not limited to this. For example, the control function 144c may automatically set the scan range of this scan by analyzing the positioning image data or the reference image generated based on the positioning image data and extracting the organ or the like to be diagnosed.

Next, the scan function 144a executes the dual energy collection of the kV switching method as the main scan for the set scan range. Specifically, the scan function 144a changes the X-ray energy to be applied to the subject P1 between the first X-ray energy and the second X-ray energy for each one or a plurality of views. Let me. As a result, the scanning function 144a collects a projection data set including at least the projection data corresponding to the first X-ray energy and the projection data corresponding to the second X-ray energy.

Hereinafter, the first X-ray energy will be referred to as “High kVp” and the second X-ray energy will be referred to as “Low kVp”. That is, the scanning function 144a changes the energy of X-rays radiated to the subject P1 between “High kVp” and “Low kVp” for each one or a plurality of views. As a result, as shown in FIG. 2, the scan function 144a has the projection data corresponding to “High kVp”, the projection data corresponding to “Low kVp”, and the switching period between “High kVp” and “Low kVp”. A projection data set A11 including projection data corresponding to "Transition", which is the energy of X-rays applied to the subject P1, is collected. "Transition" is an example of the third X-ray energy. Usually, "Transition" is a value that fluctuates between "Low kVp" and "High kVp". Further, the width of the "Transition" shown in FIG. 2 depends on the switching speed in kV switching. Further, FIG. 2 is a diagram showing an example of a projection data set according to the first embodiment.

Although the description is omitted in FIG. 2, the projection data set A11 is usually three-dimensional data including the body axis direction of the subject P1. That is, although the projection data set A11 is shown as a two-dimensional synogram in FIG. 2, the projection data set A11 is three-dimensional data having a channel direction, a view direction, and a body axis direction. In the following, for the sake of simplicity, the projection data set A11 will be described as two-dimensional data in the channel direction and the view direction.

Next, the processing function 144b performs various processing for performing substance discrimination on the projection data set A11 collected by dual energy. For example, as shown in FIG. 2, the projection data set A11 is a mixture of projection data corresponding to “High kVp”, projection data corresponding to “Low kVp”, and projection data corresponding to “Transition”. , Speaking of each energy, it is sparse data. For example, for "High kVp", it can be said that the projection data of the views indicated by "Low kVp" and "Transition" are missing. Therefore, the processing function 144b performs interpolation processing on the projection data of each energy. Hereinafter, an example of the interpolation process will be described with reference to FIGS. 3A, 3B and 3C. 3A, 3B and 3C are diagrams showing an example of interpolation processing according to the first embodiment.

For example, as shown in FIG. 3A, the processing function 144b first starts with the projection data set A11, the projection data set B11 corresponding to “High kVp”, the projection data set B12 corresponding to “Low kVp”, and the “Transition”. The projection data set B13 corresponding to "" is extracted.

Here, the projection data set B11 and the projection data set B12 are both sparse data having a missing portion. For example, as shown in FIG. 3B, in the projection data set B11, the view of "Low kVp" or "Transition" is a missing part. Therefore, the processing function 144b performs interpolation processing on the projection data set B11 as shown in FIG. 3C.

Specifically, the processing function 144b can perform interpolation processing by estimating the projection data of the defective portion using the projection data in the vicinity of the defective portion in the projection data set B11. Further, the processing function 144b can perform interpolation processing by performing scaling processing on the data corresponding to the missing portion of the projection data set B11 included in the projection data set B12. That is, the processing function 144b performs scaling processing on the projection data set B12 corresponding to "Low kVp" and processes it into data corresponding to "High kVp", thereby processing the projection data set corresponding to "High kVp". B11 interpolation processing can be performed.

Hereinafter, the projection data set B11 after the interpolation processing will be referred to as a projection data set C11. As shown in FIGS. 3A and 3C, the projection data set C11 is full data in which the missing portion is interpolated. Further, the processing function 144b can perform interpolation processing on the projection data set B12 and generate the projection data set C12 which is full data, similarly to the projection data set B11.

In general, the projection data set B13 corresponding to "Transition" is not used in the interpolation processing of the projection data set B11 and the projection data set B12. This is because the X-ray energy and the number of photons irradiated to the subject P1 fluctuate during the X-ray energy switching period, so the noise is increased by using the projection data set B13 for the interpolation process. This is because there is a possibility that it will end up.

Then, the processing function 144b performs substance discrimination by a plurality of reference substances based on the projection data set C11 corresponding to "High kVp" and the projection data set C12 corresponding to "Low kVp".

As an example, the processing function 144b uses the projection data set C11 and the projection data set C12 to separate two reference materials present within the scan range. Specifically, the processing function 144b obtains the distribution of the line attenuation coefficient for each of the projection data set C11 and the projection data set C12, and for each position (each pixel) of the line attenuation coefficient, the simultaneous equations of the following equation (1) By solving, the mixing amount and mixing ratio of the two reference substances at each position are calculated.

Here, "μ (E1)" indicates the line attenuation coefficient of each position in the monochromatic X-ray energy "E1", and "μ (E2)" indicates the line attenuation coefficient of each position in the monochromatic X-ray energy "E2". .. Further, "μ _α (E)" indicates the linear attenuation coefficient of the reference substance α, and “μ _β (E)” indicates the linear attenuation coefficient of the reference substance β. Further, "c _α " indicates the mixed amount of the reference substance α, and "c _β " indicates the mixed amount of the reference substance β. The linear attenuation coefficient for each energy of each reference substance is known. For example, the processing function 144b substitutes the X-ray energy irradiated as "High kVp" into "E1" and substitutes the X-ray energy irradiated as "Low kVp" into "E2", and the simultaneous equations of the equation (1). By solving, the substance is discriminated by two kinds of reference substances "α, β".

Then, the processing function 144b generates an image showing the result of substance discrimination. For example, the processing function 144b generates a substance discrimination image for each reference substance. As an example, the processing function 144b generates a substance discrimination image in which the reference substance α is emphasized and a substance discrimination image in which the reference substance β is emphasized, respectively. Further, the processing function 144b performs a weighting calculation process based on the mixing ratio of each reference substance using a plurality of substance discrimination images generated for each reference substance, thereby performing a virtual monochromatic X-ray image (monochromematic) at a predetermined energy. It is also possible to generate various images such as (also referred to as an image), a density image, and an effective atomic number image. In addition, the control function 144c causes the display 142 to display an image showing the results of these substance discriminations.

Although it has been described that the substance is discriminated before the reconstitution treatment is performed, the treatment function 144b may perform the substance discrimination at the stage after the reconstitution treatment is performed. That is, the processing function 144b may perform material discrimination by solving the simultaneous equations of the equation (1) for each pixel of the projection data set C11 and the projection data set C12, or the CT image data based on the projection data set C11 and the CT image data. Material discrimination may be performed by solving the simultaneous equations of the equation (1) for each pixel of the CT image data based on the projection data set C12.

Further, although the above equation (1) has been described with "μ" as the linear attenuation coefficient and "c" as the mixing amount, the embodiment is not limited to this. For example, the processing function 144b may solve the equation (1) with "μ" in each substance as the mass attenuation coefficient and "c" as the density.

By the way, the interpolation process described in FIGS. 3A to 3C estimates the missing portion based on the projection data in the vicinity or the projection data collected by another energy. Here, it is preferable to use more information in order to improve the accuracy of estimation.

Therefore, the X-ray CT system 10 estimates the defective portion by further using not only the projection data in the vicinity and the projection data collected by another energy but also the projection data of the opposite view. As a result, the X-ray CT system 10 improves the accuracy of estimating the defective portion, and thus improves the accuracy of substance discrimination. Hereinafter, the processing using the projection data of the opposite view will be described in detail.

First, the facing view will be described with reference to FIG. FIG. 4 is a diagram for explaining the facing view according to the first embodiment. In FIG. 4, a view facing the view V11 will be described with reference to the view V11.

As shown in FIG. 4, in view V11, projection data for each coordinate in the channel direction is collected. For example, in the view V11, a plurality of projection data such as the projection data of the channel W11, the projection data of the channel W12, and the projection data of the channel W13 are collected substantially at the same time.

Similarly, for view V21, view V22, and view V23, projection data for each coordinate in the channel direction is collected. Here, the projection data of the channel W21 in the view V21 is the projection data having substantially the same X-ray path as the projection data of the channel W11 in the view V11, although the X-ray irradiation directions are opposite to each other. Further, the projection data of the channel W22 in the view V22 is the projection data having substantially the same X-ray path as the projection data of the channel W12 in the view V11, although the X-ray irradiation directions are opposite to each other. Further, the projection data of the channel W23 in the view V23 is the projection data having substantially the same X-ray path as the projection data of the channel W13 in the view V11, although the X-ray irradiation directions are opposite to each other.

As described above, the views V21, the view V22, and the view V23 include the projection data having substantially the same X-ray path as the projection data collected in the view V11. Hereinafter, such a view will be described as an opposite view. That is, the view V21, the view V22, and the view V23 are views facing the view V11. As an example, the view facing the view V11 is a view that is "180 °" different from the view V11 and is within a range corresponding to the fan angle of the X-rays emitted by the X-ray tube 111.

Although not described in FIG. 4, the projection data set normally collected by the X-ray CT system 10 has a body axis direction (Z axis direction) in addition to the channel direction and the view direction. It is dimensional data. In addition, there are various types of scans for collecting projection data sets, such as conventional scans, helical scans, and step-and-shoot.

When the scan is performed by the conventional scan or the step-and-shoot method, there is no change in the body axis direction during one rotation of the rotating portion of the X-ray CT system 10. Therefore, when the scan is executed by the conventional scan or the step-and-shoot method, the processing function 144b can easily identify the opposing views in the two-dimensional space as shown in FIG.

On the other hand, when scanning is executed by the helical scan method, a change in the body axis direction occurs during one rotation of the rotating portion. Then, the processing function 144b needs to specify the opposite view in consideration of the change in the body axis direction. For example, the processing function 144b identifies opposing views based on the shooting pitch. Here, the shooting pitch is, for example, a beam pitch or a helical pitch. The beam pitch is the ratio of the amount by which the subject P1 moves with respect to the gantry device 110 during one rotation of the rotating portion and the width of the scan range in the body axis direction. The helical pitch is the ratio of the amount of the subject P1 moving with respect to the gantry device 110 during one rotation of the rotating portion and the width (collimation width) corresponding to one row of detection elements in the scanning range. .. The shooting pitch can be adjusted by, for example, the moving speed of the top plate 133 during scanning.

Further, when the scan is executed by the helical scan method, it may not be possible to directly identify the opposite view. That is, it is assumed that X-rays are not irradiated in the opposite views due to the change in the body axis direction. In such a case, the processing function 144b can generate projection data of opposite views by performing interpolation processing using projection data adjacent to each other in the channel direction or the body axis direction.

Further, as in the view V11 shown in FIG. 4, the projection data actually collected by the scan is collected substantially simultaneously for a plurality of channels. That is, as shown in FIGS. 2, 3A, 3B, 3C, 4, etc., the projected data actually collected is shown as a straight line parallel to the channel direction in the plane having the channel direction and the view direction. be able to. On the other hand, the projection data of the opposite views is collected by shifting in the view direction for each channel. That is, the projection data of the opposing views can be expressed as diagonal lines inclined with respect to the channel direction, as shown in FIG. In the following, in order to simplify the explanation by aligning the representation formats of the projection data actually collected and the projection data of the opposite view, the projection data of the opposite view is also expressed as a straight line parallel to the channel direction. And.

Here, in the kV switching method, the energy of X-rays emitted to the subject P1 may differ between the view to be corrected and the view facing the correction target. For example, when the X-ray of "High kVp" is irradiated in the view V11 of FIG. 4, the energy of the X-ray irradiated in the opposite view may be "High kVp" or "Low kVp". In some cases, it may be "Transition".

That is, various cases can be considered for the combination of X-ray energies in the view to be corrected and the views facing each other. Therefore, the processing function 144b performs processing according to the case when performing correction processing on the projection data collected in the view to be corrected. Hereinafter, the processing performed by the processing function 144b will be described for each case.

First, the processing performed by the processing function 144b in the cases of Case 1 and Case 2 will be described with reference to FIGS. 5A and 5B. FIG. 5A is a diagram for explaining Case 1 and Case 2 according to the first embodiment. Further, FIG. 5B is a diagram showing an example of processing in Case 1 according to the first embodiment.

As shown in FIG. 5A, in case 1 and case 2, the X-ray energy applied to the subject P1 is the same in the view to be corrected and the view facing the correction target. More specifically, in Case 1, the X-ray energy applied to the subject P1 in the view to be corrected is "Low kVp", and the X-ray energy applied to the subject P1 in the opposite view is "Low". This is the case of "kVp". Further, in Case 2, the X-ray energy irradiated to the subject P1 in the view to be corrected is "High kVp", and the X-ray energy irradiated to the subject P1 in the opposite view is "High kVp". It is a case.

In FIG. 5A, for convenience of explanation, in addition to the view to be corrected, the views before and after the view are shown. For example, in case 1 of FIG. 5A, in addition to the correction target view of “Low kVp”, the views “Transition” and “High kVp” before and after the view are also shown.

It should be noted that which of Case 1, Case 2, and other cases described later corresponds to is different for each view. For example, even if the projection data collected in the view V11 shown in FIG. 4 corresponds to the case 1, the projection data collected in the next view of the view V11 does not necessarily correspond to the case 1. Therefore, the processing function 144b determines which case corresponds to the projection data of each view.

For example, the scan function 144a first performs dual energy collection of the kV switching method and collects the projection data set A21. Similar to the projection data set A11 of FIG. 2, the projection data set A21 is a mixture of the projection data corresponding to “High kVp”, the projection data corresponding to “Low kVp”, and the projection data corresponding to “Transition”. It is data. Next, the processing function 144b separates the projection data set B21 corresponding to “High kVp” and the projection data set B22 corresponding to “Low kVp” from the projection data set A21. The projection data set B21 is an example of the first projection data set. The projection data set B22 is an example of the second projection data set.

Here, the projection data set B21 and the projection data set B22 are both sparse data having a missing portion in the view direction. Therefore, the processing function 144b performs interpolation processing for each of the projection data set B21 and the projection data set B22. FIG. 5B describes a case where the projection data set B21 corresponding to “High kVp” is subjected to interpolation processing to generate full data corresponding to “High kVp”. More specifically, in FIG. 5B, the case of interpolating the projection data of the view to be corrected in the projection data set B21 will be described in Case 1.

As shown in FIG. 5A, in the case of Case 1, the projection data of the view to be corrected is “Low kVp”, and corresponds to the missing portion in the projection data set B21 corresponding to “High kVp”. Further, in the case of Case 1, the projection data of the opposite views is also “Low kVp”, and corresponds to the missing portion in the projection data set B21 corresponding to “High kVp”. Here, if the projection data of the opposite view, which is “Low kVp”, is used as it is for the interpolation processing of the projection data set B21, it may cause an increase in noise.

Therefore, the processing function 144b converts the projection data of the opposite views from "Low kVp" to "High kVp" and uses them for the interpolation processing. Specifically, the processing function 144b first performs scaling processing on the projection data of the opposite views, as shown in FIG. 5B. The scaling process is a process of generating data of different energies based on the change in the amount of X-ray transmission according to the energy of the X-ray. Then, the processing function 144b interpolates the projection data of the view to be corrected in the projection data set B21 by using the projection data after the scaling processing.

Here, the processing function 144b may further use the projection data of the view to be corrected in the projection data set B22 to interpolate the projection data of the view to be corrected in the projection data set B21. .. Here, in the case of Case 1, since the projection data of the view to be corrected is "Low kVp", the processing function 144b changes the projection data of the view to be corrected from "Low kVp" to "High kVp" by the scaling process. The converted and scaled projection data is used to interpolate the projection data of the view to be corrected in the projection data set B21.

Further, the processing function 144b further uses the projection data of the view in the vicinity of the view to be corrected (projection data corresponding to “High kVp”), and the projection data of the view to be corrected in the projection data set B21. May be interpolated. The view in the vicinity of the view to be corrected is, for example, at least one view including a view adjacent to the view to be corrected. Further, as shown in FIG. 5B, the processing function 144b uses the projection data (projection data corresponding to “High kVp”) of the view in the vicinity of the opposite view to be corrected in the projection data set B21. You may interpolate the projected data of the view. That is, the processing function 144b may further use the projection data in the vicinity of the defective portion for the interpolation processing. Further, although the case 1 has been described with reference to FIG. 5B, the processing function 144b can perform the interpolation processing in the same manner for the case 2 except that the “Low kVp” and the “High kVp” are interchanged.

Next, the processing performed by the processing function 144b in the case of Case 3 and Case 4 will be described with reference to FIGS. 6A and 6B. FIG. 6A is a diagram for explaining Case 3 and Case 4 according to the first embodiment. Further, FIG. 6B is a diagram showing an example of processing in Case 3 according to the first embodiment.

As shown in FIG. 6A, the cases 3 and 4 are cases in which the X-ray energy applied to the subject P1 is different between the view to be corrected and the view facing the correction target. More specifically, in Case 3, the X-ray energy irradiated to the subject P1 in the view to be corrected is "Low kVp", and the X-ray energy irradiated to the subject P1 in the opposite view is "High". This is the case of "kVp". Further, in the case 4, the X-ray energy irradiated to the subject P1 in the view to be corrected is "High kVp", and the X-ray energy irradiated to the subject P1 in the opposite view is "Low kVp". It is a case. Note that, in FIG. 6A, for convenience of explanation, in addition to the view to be corrected, the views before and after the view are shown.

FIG. 6B describes a case where the projection data set B21 corresponding to “High kVp” is subjected to interpolation processing to generate full data corresponding to “High kVp”. More specifically, in FIG. 6B, the case of interpolating the projection data of the view to be corrected in the projection data set B21 will be described in Case 3.

As shown in FIG. 6A, in the case of Case 3, the projection data of the view to be corrected is “Low kVp”, and corresponds to the missing portion in the projection data set B21 corresponding to “High kVp”. Here, in the case of Case 3, the projection data of the opposite views is “High kVp”. Therefore, in the case of Case 3, the processing function 144b can interpolate the projection data of the view to be corrected in the projection data set B21 by using the projection data of the opposite views as they are.

Here, the processing function 144b may further use the projection data of the view to be corrected in the projection data set B22 to interpolate the projection data of the view to be corrected in the projection data set B21. .. Here, in the case of Case 3, since the projection data of the view to be corrected is “Low kVp”, the processing function 144b changes the projection data of the view to be corrected from “Low kVp” to “High kVp” by the scaling process. The converted and scaled projection data is used to interpolate the projection data of the view to be corrected in the projection data set B21. Further, the processing function 144b further uses the projection data of the view in the vicinity of the view to be corrected (projection data corresponding to “High kVp”), and the projection data of the view to be corrected in the projection data set B21. May be interpolated. Further, although the case 3 has been described with reference to FIG. 6B, the processing function 144b can perform the interpolation processing in the same manner for the case 4 except that the “Low kVp” and the “High kVp” are interchanged.

Next, the processing performed by the processing function 144b in the cases of Case 5 and Case 6 will be described with reference to FIGS. 7A and 7B. FIG. 7A is a diagram for explaining Case 5 and Case 6 according to the first embodiment. Further, FIG. 7B is a diagram showing an example of processing in Case 5 according to the first embodiment.

Cases 5 and 6 shown in FIG. 7A are cases in which the X-ray energy applied to the subject P1 in the view to be corrected is “Transition”. More specifically, in Case 5, the X-ray energy irradiated to the subject P1 in the view to be corrected is the X-ray irradiated to the subject P1 during the switching period from “Low kVp” to “High kVp”. This is a case where the X-ray energy irradiated to the subject P1 in the opposite view is “High kVp”. Further, in case 6, the X-ray energy irradiated to the subject P1 in the view to be corrected is the energy of the X-rays irradiated to the subject P1 during the switching period from “High kVp” to “Low kVp”. In addition, the X-ray energy applied to the subject P1 in the opposite view is "Low kVp". Note that, in FIG. 7A, for convenience of explanation, a subsequent view is shown in addition to the view to be corrected.

FIG. 7B describes a case where the projection data set B21 corresponding to “High kVp” is subjected to interpolation processing to generate full data corresponding to “High kVp”. More specifically, in FIG. 7B, the case of interpolating the projection data of the view to be corrected in the projection data set B21 will be described in Case 5.

As shown in FIG. 7A, in the case of Case 5, the projection data of the view to be corrected is “Transition”, and corresponds to the missing portion in the projection data set B21 corresponding to “High kVp”. Here, in the case of Case 5, the projection data of the opposite views is “High kVp”. Therefore, in the case of Case 5, as shown in FIG. 7B, the processing function 144b may interpolate the projection data of the view to be corrected in the projection data set B21 by using the projection data of the opposite views as they are. it can. Further, although the case 5 has been described with reference to FIG. 7B, the processing function 144b can perform the interpolation processing in the same manner for the case 6 except that the “Low kVp” and the “High kVp” are interchanged.

Next, the processing performed by the processing function 144b in the case of the case 7 and the case 8 will be described with reference to FIGS. 8A and 8B. FIG. 8A is a diagram for explaining Case 7 and Case 8 according to the first embodiment. Further, FIG. 8B is a diagram showing an example of processing in Case 7 according to the first embodiment.

Cases 7 and 8 shown in FIG. 8A are cases in which the X-ray energy applied to the subject P1 in the view to be corrected is “Transition”. More specifically, in case 7, the X-ray energy irradiated to the subject P1 in the view to be corrected is the X-ray irradiated to the subject P1 during the switching period from “Low kVp” to “High kVp”. This is a case where the X-ray energy irradiated to the subject P1 in the opposite view is “Low kVp”. Further, in the case 8, the X-ray energy irradiated to the subject P1 in the view to be corrected is the energy of the X-rays irradiated to the subject P1 during the switching period from “High kVp” to “Low kVp”. In addition, the X-ray energy applied to the subject P1 in the opposite view is "High kVp". Note that, in FIG. 8A, for convenience of explanation, a subsequent view is shown in addition to the view to be corrected.

Further, in FIG. 8B, a case where the projection data set B21 corresponding to “High kVp” is subjected to interpolation processing to generate full data corresponding to “High kVp” will be described. More specifically, in FIG. 8B, the case of interpolating the projection data of the view to be corrected in the projection data set B21 will be described in Case 7.

As shown in FIG. 8A, in case 7, the projection data of the view to be corrected is “Transition”, and corresponds to the missing portion in the projection data set B21 corresponding to “High kVp”. Further, in case 7, the projection data of the opposite views is “Low kVp”. Here, if the projection data of the opposite view, which is “Low kVp”, is used as it is for the interpolation processing of the projection data set B21, it may cause an increase in noise.

Therefore, the processing function 144b converts the projection data of the opposite views from "Low kVp" to "High kVp" and uses them for the interpolation processing. Specifically, the processing function 144b first performs scaling processing on the projection data of the opposite views, as shown in FIG. 8B. Then, the processing function 144b interpolates the projection data of the view to be corrected in the projection data set B21 by using the projection data after the scaling processing. Further, although the case 7 has been described with reference to FIG. 8B, the processing function 144b can perform the interpolation processing in the same manner for the case 8 except that the “Low kVp” and the “High kVp” are interchanged.

Next, the processing performed by the processing function 144b in the cases of the case 9, the case 10, the case 11 and the case 12 will be described with reference to FIGS. 9A and 9B. FIG. 9A is a diagram for explaining the case 9, the case 10, the case 11, and the case 12 according to the first embodiment. Further, FIG. 9B is a diagram showing an example of processing in Case 9 according to the first embodiment.

Case 9, Case 10, Case 11 and Case 12 shown in FIG. 8A are cases in which the X-ray energy applied to the subject P1 is “Transition” in both the view to be corrected and the opposite view. More specifically, in Case 9, the X-ray energy irradiated to the subject P1 in the view to be corrected is the X-ray irradiated to the subject P1 during the switching period from “Low kVp” to “High kVp”. The X-ray energy irradiated to the subject P1 in the opposite view is the energy of the X-ray irradiated to the subject P1 during the switching period from "High kVp" to "Low kVp". It is a case. Further, in the case 10, the X-ray energy irradiated to the subject P1 in the view to be corrected is the energy of the X-rays irradiated to the subject P1 during the switching period from “High kVp” to “Low kVp”. In addition, the X-ray energy irradiated to the subject P1 in the opposite view is the energy of the X-rays irradiated to the subject P1 during the switching period from "Low kVp" to "High kVp". Further, in the case 11, the X-ray energy irradiated to the subject P1 in the view to be corrected is the energy of the X-rays irradiated to the subject P1 during the switching period from “Low kVp” to “High kVp”. In addition, the X-ray energy irradiated to the subject P1 in the opposite view is the energy of the X-rays irradiated to the subject P1 during the switching period from "Low kVp" to "High kVp". Further, in the case 12, the X-ray energy irradiated to the subject P1 in the view to be corrected is the energy of the X-rays irradiated to the subject P1 during the switching period from “High kVp” to “Low kVp”. In addition, the X-ray energy irradiated to the subject P1 in the opposite view is the energy of the X-rays irradiated to the subject P1 during the switching period from "High kVp" to "Low kVp". Note that, in FIG. 9A, for convenience of explanation, in addition to the view to be corrected, the views before and after the view are shown.

Further, in FIG. 9B, a case where the projection data set B21 corresponding to “High kVp” is subjected to interpolation processing to generate full data corresponding to “High kVp” will be described. More specifically, in FIG. 9B, the case of interpolating the projection data of the view to be corrected in the projection data set B21 will be described in Case 9.

As shown in FIG. 9A, the projection data of the view to be corrected in Case 9 is “Transition”, and corresponds to the missing portion in the projection data set B21 corresponding to “High kVp”. Further, in the case 9, the projection data of the opposite views is also “Transition”, and if the projection data of the opposite views is used as it is for the interpolation processing of the projection data set B21, it may cause an increase in noise.

Therefore, the processing function 144b corrects the projection data of the opposite views by using the projection data in the vicinity, and uses the corrected projection data for the correction processing. Specifically, the processing function 144b first, as shown in FIG. 9B, with respect to at least one projection data on the side corresponding to "Low kVp" among the projection data adjacent to the projection data of the opposite views. The scaling process is performed and converted into projection data corresponding to "High kVp". Then, the processing function 144b includes the projection data corresponding to "High kVp" after the scaling process and at least one projection data on the side corresponding to "High kVp" among the projection data adjacent to the projection data of the opposite view. Corrects the projection data of the opposing views based on. For example, the processing function 144b uses the projection data in the vicinity to convert the projection data of the opposite view from "Transition" to "High kVp". Alternatively, the processing function 144b replaces the projection data corresponding to the “Transition” with the projection data corresponding to the “High kVp” generated by using the projection data in the vicinity.

Here, the processing function 144b may further correct the projection data of the view to be corrected by using the projection data in the vicinity of the projection data of the view to be corrected. Further, although the case 9 has been described in FIG. 9B, the processing function 144b performs the interpolation processing in the same manner for the case 10, the case 11 and the case 12 except that the “Low kVp” and the “High kVp” are interchanged. be able to.

As shown in FIG. 9B, the projection data corresponding to "Low kVp" adjacent to the projection data of the view to be corrected also corresponds to the missing portion in the projection data set B21 corresponding to "High kVp". This point is included in Case 3, and the interpolation process can be performed by the method described above.

The interpolation process described with reference to FIGS. 5A to 9B can be executed, for example, by using the trained model M1 functionalized to perform the interpolation process using the projection data of the opposite views. For example, the processing function 144b generates the trained model M1 in advance and stores it in the memory 141. Then, when the scan for the subject P1 is executed, the processing function 144b executes the interpolation processing by inputting the collected projection data to the learned model M1 read from the memory 141.

Hereinafter, the generation process of the trained model M1 will be described with reference to FIGS. 10A and 10B. 10A and 10B are diagrams showing an example of a trained model generation process according to the first embodiment.

First, the processing function 144b acquires a set of the projection data set A31 for which dual energy has been collected and the projection data set A32 for which single energy has been collected from the same object as training data. The projection data set A31 and the projection data set A32 are collected by performing two scans on the subject P1, the subject P2 different from the subject P1, the phantom imitating the human body, and the like. Further, the projection data set A31 and the projection data set A32 may be collected in the X-ray CT system 10 or may be collected in another system.

For example, the dual energy collected projection data set A31 has the projection data corresponding to “High kVp”, the projection data corresponding to “Low kVp”, and the projection data corresponding to “Low kVp”, similarly to the projection data set A11 and the projection data set A21 described above. The data is a mixture of the projection data corresponding to "Transition". Further, as an example, the projection data set A32 collected by single energy will be described below as a projection data set corresponding to “High kVp”.

For example, the processing function 144b first separates the projection data set B31 corresponding to “High kVp” from the projection data set A31. That is, the processing function 144b generates a sparse projection data set from the projection data set A31. Then, the processing function 144b uses the projection data of the view to be corrected in the sparse projection data set, the projection data of the opposite view and the projection data of the vicinity as input side data, and the projection data set A32 as output side data. The trained model M1 is generated by executing the machine learning to be performed.

Here, the trained model M1 can be configured by, for example, a neural network (Neural Network). A neural network is a network that has a structure in which adjacent layers arranged in layers are connected and information propagates from the input layer side to the output layer side. For example, the processing function 144b generates a trained model M1 by performing deep learning on a multi-layer neural network using the above-mentioned training data. The multi-layer neural network is composed of, for example, an input layer, a plurality of intermediate layers (hidden layers), and an output layer.

For example, when the processing function 144b inputs the input side data to the neural network, the information propagates in the neural network from the input layer side to the output layer side while being coupled only between adjacent layers in one direction. From the output layer, a projection data set that estimates the projection data set A32 is output. A neural network in which information propagates in one direction from the input layer side to the output layer side is also called a convolutional neural network (CNN).

For example, in the neural network, it is determined which of the above cases 1 to 12 is applicable, and the interpolation processing according to the case is executed. For example, in the cases corresponding to Case 1, Case 2, Case 7, Case 8, Case 9, Case 10, Case 11, and Case 12, the neural network executes the scaling process while setting appropriate parameters. Further, for example, in the cases corresponding to Case 9, Case 10, Case 11 and Case 12, in the neural network, the range used as the projection data in the vicinity of the projection data of the opposite views and the projection data in the vicinity are each attached. Weight etc. are adjusted. Then, from the output layer of the neural network, a projection data set that estimates the projection data set A32, which is full data, is output by interpolating the sparse projection data set.

The processing function 144b generates the trained model M1 by adjusting the parameters of the neural network so that the neural network can output a preferable result when the input side data is input. For example, the processing function 144b adjusts the parameters of the neural network by using a function (error function) representing the proximity between the projection data sets.

As an example, the processing function 144b calculates a cost function between the projection data set A32 and the projection data set estimated by the neural network. The cost function is an error function indicating the proximity between the projection data set A32 and the projection data set estimated by the neural network, with a regularization term added to prevent overfitting. Further, the processing function 144b calculates the iterative cost function while adjusting the parameters of the neural network. Then, as shown in FIG. 10B, the processing function 144b generates the trained model M1 by adjusting the parameters of the neural network until the cost function falls below the threshold value (Threshold). The horizontal axis in FIG. 10B is the number of iterations (Iteration), and the vertical axis is the cost function (Cost function). Further, the processing function 144b stores the generated learned model M1 in the memory 141.

Although the trained model M1 has been described as being composed of a neural network, the processing function 144b may generate the trained model M1 by a machine learning method other than the neural network. Further, although the processing function 144b has been described as generating the trained model M1, the trained model M1 may be generated by another device.

For example, in testing the subject P1, the scan function 144a collects the projection data set A21 from the subject P1 by performing a kV switching dual energy collection. Next, the processing function 144b separates the projection data set B21 corresponding to “High kVp” and the projection data set B22 corresponding to “Low kVp” from the projection data set A21.

Next, the processing function 144b performs interpolation processing on each of the projection data set B21 and the projection data set B22 using the trained model M1 read from the memory 141. For example, the processing function 144b has learned the projection data of the view to be corrected in the projection data set B21, the projection data of the view facing the view to be corrected, and the projection data in the vicinity of the projection data of the opposite view. Enter in M1. As a result, the processing function 144b acquires the projection data set C21 which is the full data in which the missing portion in the projection data set B21 is interpolated. Similarly, the processing function 144b acquires the projection data set C22, which is the full data in which the missing portion in the projection data set B22 is interpolated.

Next, the processing function 144b performs substance discrimination by a plurality of reference substances based on the corrected projection data set. For example, the processing function 144b discriminates substances by a plurality of reference substances based on the projection data set C21 corresponding to “High kVp” and the projection data set C22 corresponding to “Low kVp”. This allows the processing function 144b to, for example, separate kidney stones from soft tissue. The processing function 144b may perform substance discrimination at a stage before the reconstitution treatment is performed, or may perform substance discrimination at a stage after the reconstitution treatment is performed.

Then, the processing function 144b generates an image showing the result of substance discrimination. For example, the processing function 144b generates a substance discrimination image for each reference substance. As an example, the processing function 144b generates a substance discrimination image emphasizing "calcium" and a substance discrimination image emphasizing "water", respectively. Further, the processing function 144b performs a weighting calculation process based on the mixing ratio of each reference substance using a plurality of substance discrimination images generated for each reference substance, thereby performing a monochrome matic image, a density image, and an effective atomic number image. It is also possible to generate various images such as. In addition, the control function 144c causes the display 142 to display an image showing the results of these substance discriminations.

Next, an example of the processing procedure by the X-ray CT system 10 will be described with reference to FIG. 11A. FIG. 11A is a flowchart for explaining a series of processes of the X-ray CT system 10 according to the first embodiment. Step S1 corresponds to the scan function 144a. Step S2, step S3 and step S4 correspond to the processing function 144b. Step S5 corresponds to the control function 144c.

First, the processing circuit 144 collects the projection data set A21 by executing the dual energy collection of the kV switching method for the subject P1 (step S1). Next, the processing circuit 144 separates the projection data set A21 from the projection data set B21 corresponding to “High kVp” and the projection data set B22 corresponding to “Low kVp” (step S2).

Next, the processing circuit 144 performs correction processing for each of the projection data set B21 and the projection data set B22 to generate the projection data set C21 and the projection data set C22 (step S3). The details of step S3 will be described later. Then, the processing circuit 144 executes the substance discrimination based on the projection data set C21 and the projection data set C22 after the correction processing (step S4), displays the result of the substance discrimination on the display 142 (step S5), and processes. To finish.

Next, the details of step S3 of FIG. 11A will be described with reference to FIG. 11B. FIG. 11B is a flowchart for explaining a series of processes of the X-ray CT system 10 according to the first embodiment. Step S301, step S302, step S303, step S304, step S305, step S306, step S307, step S308, step S309 and step S310 correspond to the processing function 144b.

The processing circuit 144 may execute each step shown in FIG. 11B using the trained model M1 or may execute the steps without using the trained model M1. For example, the processing circuit 144 may execute each step shown in FIG. 11B by using a table in which cases 1 to 12 and the contents of correction processing for each case are associated with each other.

First, the processing circuit 144 sets the view to be corrected (step S301). For example, the processing circuit 144 sets a view that is a missing portion in the projection data set B21 or the projection data set B22 as a view to be corrected.

Next, the processing circuit 144 determines whether or not the view set as the correction target and the opposite view correspond to cases 3 to 6 (step S302). For example, in the correction processing for the projection data set B21 corresponding to "High kVp", when the projection data of the opposite view corresponds to "High kVp", the processing circuit 144 determines that the cases 3 to 6 are applicable. Further, for example, in the correction processing for the projection data set B22 corresponding to "Low kVp", when the projection data of the opposite views corresponds to "Low kVp", the processing circuit 144 determines that the cases 3 to 6 are applicable. .. Then, when it is determined that the cases 3 to 6 are applicable (affirmation in step S302), the processing circuit 144 performs the correction processing using the projection data of the opposite views as they are (step S303).

On the other hand, when it is determined that the cases 3 to 6 do not apply (step S302 is denied), the processing circuit 144 determines whether or not the cases 1 to 2 or cases 7 to 8 are applicable (step S304). For example, in the correction processing for the projection data set B21 corresponding to "High kVp", when the projection data of the opposite view corresponds to "Low kVp", the processing circuit 144 corresponds to cases 1 and 2 or cases 7 to 8. Then it is determined. Further, for example, in the correction processing for the projection data set B22 corresponding to "Low kVp", when the projection data of the opposite views corresponds to "High kVp", the processing circuit 144 has cases 1 and 2 or cases 7 to 8. It is judged that it corresponds to. Then, when it is determined that the case 1 or 2 or cases 7 to 8 is applicable (step S304 affirmative), the processing circuit 144 performs scaling processing on the projection data of the opposite view (step S305), and the projection data after the scaling processing is performed. Is used to perform the correction process (step S306).

On the other hand, when it is determined that the case 1 or 2 or the cases 7 to 8 are not applicable (step S304 is denied), the processing circuit 144 is determined to correspond to the cases 9 to 12. In this case, the processing circuit 144 corrects the projection data of the opposite views by using the projection data in the vicinity (step S307), and performs the correction processing by using the corrected projection data (step S308). For example, in the correction processing for the projection data set B21 or the projection data set B22, when the projection data of the opposite views corresponds to "Transition", the processing circuit 144 can determine that it corresponds to cases 9 to 12.

After step S303, step S306 or step S308, the processing circuit 144 determines whether or not the correction processing for all the missing portions in the projection data set B21 and the projection data set B22 is completed (step S309). When there is a defective portion for which the correction process has not been completed (denial in step S309), the processing circuit 144 changes the view to be corrected (step S310), and proceeds to step S302 again. On the other hand, when the correction processing for all the defective portions is completed (affirmation in step S309), the processing circuit 144 ends the processing.

As described above, according to the first embodiment, the scanning function 144a irradiates the subject P1 with X-rays while rotating the focal position of the X-rays around the subject P1 to obtain “High kVp”. A projection data set A21 containing at least the projection data corresponding to and the projection data corresponding to “Low kVp” is collected. In addition, the scan function 144a performs correction processing on the projection data set A21 using the projection data of the opposite views, and discriminates substances with a plurality of reference substances based on the corrected projection data set.

Therefore, the X-ray CT system 10 according to the first embodiment can accurately correct the projection data set A21 as compared with the case where the correction processing is performed without using the projection data of the opposite views. For example, the X-ray CT system 10 can improve the spatial resolution and noise characteristics of the projection data set A21. Further, for example, the X-ray CT system 10 can appropriately remove various artifacts such as motion artifacts, fluctuations in the number of X-ray photons, and artifacts caused by transient voltage from the projection data set A21. As a result, the X-ray CT system 10 can improve the accuracy of substance discrimination based on the projection data set A21, and can also improve the diagnostic accuracy.

(Second embodiment)
By the way, although the first embodiment has been described so far, it may be implemented in various different forms other than the above-described embodiment.

For example, in the above-described embodiment, as the correction process performed on the projection data set A21, a process of interpolating the missing portion of the projection data in the projection data set B21 or the projection data set B22 separated from the projection data set A21 has been described. However, the embodiment is not limited to this. For example, the processing function 144b may perform correction processing for a portion of the projection data set B21 or the projection data set B22 that is not missing.

As an example, the processing function 144b blends the projection data of the view to be corrected in the projection data set B21 corresponding to “High kVp” with the projection data of the opposite view. The projection data of the view to be corrected in the projection data set B21 corresponds to "High kVp".

Here, when the projection data of the opposite view corresponds to "High kVp", the processing function 144b can directly blend the projection data of the opposite view and the projection data of the view to be corrected. When the projection data of the opposite view corresponds to "Low kVp", the processing function 144b performs scaling processing on the projection data of the opposite view, and the projection data after the scaling processing and the projection data of the view to be corrected Can be blended with. Further, when the projection data of the opposite view corresponds to "Transition", the processing function 144b corrects the projection data of the opposite view by using the projection data of the vicinity, and the corrected projection data and the correction target It can be blended with the projected data of the view.

Further, in the above-described embodiment, the case where the projection data set including at least the projection data corresponding to “High kVp” and the projection data corresponding to “Low kVp” is collected by the dual energy collection of the kV switching method has been described. However, the embodiment is not limited to this.

For example, the scan function 144a may perform dual energy collection of a stacked detector method (also referred to as a dual layer method) instead of the kV switching method. In this case, the X-ray CT system 10 includes a stacked detector as the X-ray detector 112. For example, the X-ray detector 112 is composed of a first layer 112a and a second layer 112b, and spectroscopically detects X-rays emitted from an X-ray tube 111. As an example, the scan function 144a has projection data corresponding to "High kVp" based on the detection result of the first layer 112a and projection data corresponding to "Low kVp" based on the detection result of the second layer 112b. Collect a projection dataset that includes and.

Then, the processing function 144b performs correction processing on the collected projection data set using the projection data of the opposite views. The projection data set collected by the dual layer method is usually not sparse data. However, for example, when a discharge phenomenon occurs in an X-ray tube, there is a possibility that a missing portion may occur in the projection data set collected by the dual layer method. Here, the X-ray CT system 10 can interpolate the missing portion of the projection data in the collected projection data set by using the projection data of the opposite views. Further, the X-ray CT system 10 can also blend the projection data of the view to be corrected in the collected projection data set with the projection data of the opposite view. Thereby, the processing function 144b can improve the spatial resolution and the photon statistics of the projection data set. As a result, the processing function 144b can improve the accuracy of substance discrimination based on the projection data set.

Further, for example, the scan function 144a may perform dual energy collection of the dual source system instead of the kV switching system. In this case, the X-ray CT system 10 includes an X-ray tube 1111 and an X-ray tube 1112 as the X-ray tube 111. Further, the X-ray CT system 10 uses the X-ray detector 112 as an X-ray detector 1121 for detecting X-rays emitted from the X-ray tube 1111 and an X-ray detector for detecting the X-rays emitted from the X-ray tube 1112. A line detector 1122 is provided. Then, the scan function 144a corresponds to "High kVp" by, for example, irradiating the X-ray tube 1111 with the X-ray of "High kVp" and irradiating the X-ray tube 1112 with the X-ray of "Low kVp". A projection data set containing projection data and projection data corresponding to "Low kVp" is collected.

Then, the processing function 144b performs correction processing on the collected projection data set using the projection data of the opposite views. Note that the projection dataset collected by the dual source method is usually not sparse data. However, for example, when a discharge phenomenon occurs in an X-ray tube, there is a possibility that a missing portion may occur in the projection data set collected by the dual source method. Further, in the dual source method, there is a case where the collection of 360 ° is not performed in each shooting system, and the view outside the shooting range becomes a defective portion. Here, the X-ray CT system 10 can interpolate the missing portion of the projection data in the collected projection data set by using the projection data of the opposite views. Further, the X-ray CT system 10 can also blend the projection data of the view to be corrected in the collected projection data set with the projection data of the opposite view. As a result, the processing function 144b can improve the temporal resolution of the projection data set. As a result, the processing function 144b can improve the accuracy of substance discrimination based on the projection data set. Further, since the fan angle differs between the X-rays emitted by the X-ray tube 1111 and the X-rays emitted by the X-ray tube 1112, the projection data set collected by the X-ray detector 1121 and the X-ray detector 1122 collect the X-rays. When the size of the FOV (Field Of View) is different from that of the projected data set to be performed, the processing function 144b corrects the other projected data set based on the projected data set having the larger FOV size. The size of the FOV used for discrimination or the like can be maintained large.

Further, for example, the scan function 144a may perform dual energy collection of the split method instead of the kV switching method. In this case, the X-ray CT system 10 includes a wedge 116 as a filter that divides the X-rays emitted from the X-ray tube 111 into a plurality of X-rays having different energies.

As an example, the X-ray CT system 10 includes an X-ray filter 116a and an X-ray filter 116b as wedges 116. The X-ray filter 116a and the X-ray filter 116b are different in material, thickness, and the like, and divide X-rays having the same energy into a plurality of X-rays having different energies. In this case, the scan function 144a can attenuate the X-rays emitted from the X-ray tube 111 to "High kVp" by the X-ray filter 116a and to "Low kVp" by the X-ray filter 116b. More specifically, the scanning function 144a refers to the X-ray filter 116a and the X-ray filter 116b in a state where the X-ray filter 116a and the X-ray filter 116b are arranged in the Z-axis direction (column direction) shown in FIG. X-rays are irradiated from the X-ray tube 111. As a result, the scan function 144a executes the scan in a state where the X-rays of "High kVp" and the X-rays of "Low kVp" are distributed in the column direction, and the projection data corresponding to "High kVp" and "Low" are executed. A projection data set containing projection data corresponding to "kVp" can be collected.

To give another example, the X-ray CT system 10 includes only the X-ray filter 116c as the wedge 116. For example, the scanning function 144a irradiates X-rays of "High kVp" from the X-ray tube 111, and attenuates a part of the X-rays to "Low kVp" by the X-ray filter 116c. More specifically, the scan function 144a attenuates X-rays in a predetermined range in the column direction from "High kVp" to "Low kVp" by the X-ray filter 116b, and irradiates the subject P1. The X-rays that have not been attenuated by the X-ray filter 116b are irradiated to the subject P1 as “High kVp”. As a result, the scan function 144a executes the scan in a state where the X-rays of "High kVp" and the X-rays of "Low kVp" are distributed in the column direction, and the projection data corresponding to "High kVp" and "Low" are executed. A projection data set containing projection data corresponding to "kVp" can be collected.

Then, the processing function 144b performs correction processing on the collected projection data set using the projection data of the opposite views. The projection data set collected by the split method is usually not sparse data. However, for example, when a discharge phenomenon occurs in an X-ray tube, there is a possibility that a missing portion may occur in the projection data set collected by the split method. Here, the X-ray CT system 10 can interpolate the missing portion of the projection data in the collected projection data set by using the projection data of the opposite views. Further, the X-ray CT system 10 can also blend the projection data of the view to be corrected in the collected projection data set with the projection data of the opposite view. As a result, the processing function 144b can accurately correct the projection data set and, by extension, improve the accuracy of substance discrimination based on the projection data set.

To give another example, the X-ray CT system 10 may perform photon counting CT as dual energy collection. In this case, the X-ray CT system 10 includes a photon counting type X-ray detector 1123 as the X-ray detector 112. For example, the X-ray CT system 10 counts the number of X-ray photons incident on each detection element by counting the number of electric signals (pulses) output from the X-ray detector 1123, and uses this signal as well. On the other hand, by performing a predetermined arithmetic process, the energy value of the X-ray photon that caused the output of the signal is measured. As a result, the X-ray CT system 10 can acquire the count values for each of "High kVp" and "Low kVp". In other words, the X-ray CT system 10 can collect a projection data set including projection data corresponding to "High kVp" and projection data corresponding to "Low kVp".

Then, the processing function 144b performs correction processing on the collected projection data set using the projection data of the opposite views. The projection data set collected by the photon counting CT is usually not sparse data. However, for example, when a discharge phenomenon occurs in an X-ray tube, there is a possibility that a missing portion may occur in the projection data set collected by the photon counting CT. Here, the X-ray CT system 10 can interpolate the missing portion of the projection data in the collected projection data set by using the projection data of the opposite views. Further, the X-ray CT system 10 can also blend the projection data of the view to be corrected in the collected projection data set with the projection data of the opposite view. As a result, the processing function 144b can accurately correct the projection data set and, by extension, improve the accuracy of substance discrimination based on the projection data set.

Further, in the above-described embodiment, the dual energy scan using the “High kVp” X-ray and the “Low kVp” X-ray has been described, but the scan function 144a uses X-rays having three or more kinds of energies. You may perform a multi-energy scan that was available. In this case, although other cases 1 to 12 described above are added depending on the number of X-ray energies, these additional cases can be processed in the same manner as any of cases 1 to 12. That is, the above-mentioned correction method can be similarly applied to the case where a multi-energy scan using X-rays having three or more kinds of energies is executed. Further, when a multi-energy scan using X-rays having three or more kinds of energies is executed, the processing function 144b can perform substance discrimination by three or more kinds of reference substances.

Further, although the X-ray CT system 10 has been described so far as performing the correction processing of the projection data set, the embodiment is not limited to this. For example, the correction process may be executed in another device different from the X-ray CT system 10. Hereinafter, this point will be described by taking the medical information processing system 1 shown in FIG. 12 as an example. FIG. 12 is a block diagram showing an example of the configuration of the medical information processing system 1 according to the second embodiment. The medical information processing system 1 includes an X-ray CT system 10 and a medical processing device 20 that performs substance discrimination.

As shown in FIG. 12, the X-ray CT system 10 and the medical processing device 20 are connected to each other via a network NW. Here, the place where the X-ray CT system 10 and the medical processing device 20 are installed is arbitrary as long as it can be connected via the network NW. For example, the medical processing device 20 may be installed in a hospital different from the X-ray CT system 10. That is, the network NW may be configured by a local network closed in the hospital, or may be a network via the Internet. Further, although one X-ray CT system 10 is shown in FIG. 12, the medical information processing system 1 may include a plurality of X-ray CT systems 10.

The medical processing device 20 is realized by a computer device such as a workstation. For example, the medical processing device 20 has a memory 21, a display 22, an input interface 23, and a processing circuit 24, as shown in FIG.

The memory 21 is realized by, for example, a semiconductor memory element such as a RAM or a flash memory, a hard disk, an optical disk, or the like. For example, the memory 21 stores various data transmitted from the X-ray CT system 10. Further, for example, the memory 21 stores a program for the circuit included in the medical processing device 20 to realize its function. The memory 21 may be realized by a server group (cloud) connected to the medical processing device 20 via the network NW.

The display 22 displays various information. For example, the display 22 displays an image showing the result of substance discrimination by the processing circuit 24, or displays a GUI or the like for accepting various operations from the user. For example, the display 22 is a liquid crystal display or a CRT display. The display 22 may be a desktop type, or may be composed of a tablet terminal or the like capable of wireless communication with the main body of the medical processing device 20.

The input interface 23 receives various input operations from the user, converts the received input operations into electric signals, and outputs the received input operations to the processing circuit 24. For example, the input interface 23 receives from the user reconstruction conditions for reconstructing CT image data, image processing conditions for generating a CT image for display from CT image data, and the like. For example, the input interface 23 includes a mouse, a keyboard, a trackball, a switch, a button, a joystick, a touch pad for performing input operations by touching an operation surface, a touch screen in which a display screen and a touch pad are integrated, and an optical sensor. It is realized by the non-contact input circuit, voice input circuit, etc. used. The input interface 23 may be composed of a tablet terminal or the like capable of wireless communication with the main body of the medical processing device 20. Further, the input interface 23 is not limited to one provided with physical operation parts such as a mouse and a keyboard. For example, an example of an input interface 23 is an electric signal processing circuit that receives an electric signal corresponding to an input operation from an external input device provided separately from the medical processing device 20 and outputs the electric signal to the processing circuit 24. include.

The processing circuit 24 controls the operation of the entire medical processing device 20 by executing the processing function 24a and the control function 24b. The processing function 24a is an example of a processing unit. The processing by the processing circuit 24 will be described later.

In the medical processing device 20 shown in FIG. 12, each processing function is stored in the memory 21 in the form of a program that can be executed by a computer. The processing circuit 24 is a processor that realizes a function corresponding to each program by reading a program from the memory 21 and executing the program. In other words, the processing circuit 24 in the state where the program is read has a function corresponding to the read program.

Although it has been described in FIG. 12 that the processing function 24a and the control function 24b are realized by a single processing circuit 24, a plurality of independent processors are combined to form the processing circuit 24, and each processor is programmed. The function may be realized by executing. Further, each processing function of the processing circuit 24 may be appropriately distributed or integrated into a single or a plurality of processing circuits.

Further, the processing circuit 24 may realize the function by utilizing the processor of the external device connected via the network NW. For example, the processing circuit 24 reads a program corresponding to each function from the memory 21 and executes it, and also uses a server group (cloud) connected to the medical processing device 20 via a network NW as a computational resource. Each function shown in FIG. 12 is realized.

For example, first, the scan function 144a in the X-ray CT system 10 irradiates the subject P1 with X-rays while rotating the focal position of the X-rays around the subject P1, and projects corresponding to "High kVp". Collect a projection dataset that contains at least the data and the projection data corresponding to "Low kVp". Further, the control function 144c transmits the collected projection data set to the medical processing device 20 via the network NW.

Next, the processing function 24a in the medical processing apparatus 20 performs correction processing using the projection data of the opposite views on the projection data set transmitted from the X-ray CT system 10. For example, when the projection data set is collected by the kV switching method, the processing function 24a first obtains the projection data set corresponding to "High kVp" and the projection data set corresponding to "Low kVp" from the projection data set. Is separated, and correction processing is performed for each of the separated projection data sets using the projection data of the opposite views. As an example, the processing function 24a blends the projection data of the view to be corrected in the projection data set corresponding to "High kVp" or the projection data set corresponding to "Low kVp" with the projection data of the opposite view. To do. To give another example, the processing function 24a uses the projection data of the opposite view to remove the missing portion of the projection data in the projection data set corresponding to “High kVp” or the projection data set corresponding to “Low kVp”. Interpolate.

Then, the processing function 24a performs substance discrimination based on the corrected projection data set, and the control function 24b causes the display 22 to display an image showing the result of the substance discrimination by the processing function 24a. Alternatively, the control function 24b transmits an image showing the result of substance discrimination to the X-ray CT system 10. In this case, the control function 144c in the X-ray CT system 10 causes the display 142 to display an image showing the result of substance discrimination.

Alternatively, the control function 24b transmits the projection data set corrected by the processing function 24a to the X-ray CT system 10. In this case, the processing function 144b performs substance discrimination based on the corrected projection data set, and the control function 144c causes the display 142 to display an image showing the result of the substance discrimination.

Further, the case where the first X-ray energy is “High kVp” and the second X-ray energy is “Low kVp” has been described so far, but the embodiment is not limited to this. That is, the first X-ray energy may be "Low kVp" and the second X-ray energy may be "High kVp".

The term "processor" used in the above description refers to, for example, a CPU, a GPU (Graphics Processing Unit), an integrated circuit for a specific application (Application Specific Integrated Circuit: ASIC), a programmable logic device (for example, a simple programmable logic device (for example, a simple programmable logic device)). It means a circuit such as a Simple Programmable Logical Device (SPLD), a compound programmable logic device (Complex Programmable Logic Device: CPLD), and a field programmable gate array (Field Programmable Gate Array: FPGA). The processor realizes the function by reading and executing the program stored in the memory 141 or the memory 21.

In FIG. 1, a single memory 141 has been described as storing a program corresponding to each processing function of the processing circuit 144. Further, in FIG. 12, it has been described that a single memory 21 stores a program corresponding to each processing function of the processing circuit 24. However, the embodiment is not limited to this. For example, a plurality of memories 141 may be distributed and arranged, and the processing circuit 144 may be configured to read a corresponding program from the individual memories 141. Similarly, a plurality of memories 21 may be distributed and arranged, and the processing circuit 24 may be configured to read a corresponding program from the individual memories 21. Further, instead of storing the program in the memory 141 or the memory 21, the program may be directly incorporated in the circuit of the processor. In this case, the processor realizes the function by reading and executing the program embedded in the circuit.

Each component of each device according to the above-described embodiment is a functional concept, and does not necessarily have to be physically configured as shown in the figure. That is, the specific form of distribution / integration of each device is not limited to the one shown in the figure, and all or part of the device is functionally or physically distributed in arbitrary units according to various loads and usage conditions. Can be integrated and configured. Further, each processing function performed by each device may be realized by a CPU and a program analyzed and executed by the CPU, or may be realized as hardware by wired logic.

Further, the processing method described in the above-described embodiment can be realized by executing a processing program prepared in advance on a computer such as a personal computer or a workstation. This processing program can be distributed via a network such as the Internet. Further, this processing program is recorded on a non-transient recording medium such as a hard disk, a flexible disk (FD), a CD-ROM, an MO, or a DVD that can be read by a computer, and is executed by being read from the recording medium by the computer. You can also do it.

According to at least one embodiment described above, the accuracy of substance discrimination can be improved.

Although some embodiments of the present invention have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, replacements, and changes can be made without departing from the gist of the invention. These embodiments and modifications thereof are included in the scope and gist of the invention, as well as in the scope of the invention described in the claims and the equivalent scope thereof.

1 Medical information processing system 10 X-ray CT system 140 Console device 144 Processing circuit 144a Scan function 144b Processing function 144c Control function 20 Medical processing device 24 Processing circuit 24a Processing function 24b Control function

Claims (9)

The subject is irradiated with X-rays while rotating the focal position of the X-rays around the subject, and the projection data corresponding to the first X-ray energy and the projection data corresponding to the second X-ray energy are obtained. A scan unit that collects at least the projected data set that it contains,
An X-ray CT system including a processing unit that performs correction processing using projection data of opposite views on the projection data set and discriminates substances with a plurality of reference substances based on the corrected projection data set. .. The scanning unit changes the energy of the X-ray irradiating the subject between the first X-ray energy and the second X-ray energy for each one or a plurality of views. The X-ray CT system according to claim 1, wherein the projection data set is collected. The processing unit separates the first projection data set corresponding to the first X-ray energy and the second projection data set corresponding to the second X-ray energy from the projection data set, and the first projection The X-ray CT system according to claim 2, wherein the correction processing is performed on each of the data set and the second projection data set. The third aspect of the present invention, wherein the processing unit blends the projection data of the view to be corrected in the first projection data set or the second projection data set with the projection data of the opposite view as the correction processing. X-ray CT system. The processing unit according to claim 3 or 4, wherein, as the correction process, the missing portion of the projection data in the first projection data set or the second projection data set is interpolated by using the projection data of the opposite views. X-ray CT system. The processing unit faces the case where the projection data of the views facing each other in the correction processing for the first projection data set corresponds to the second X-ray energy, or in the correction processing for the second projection data set. When the projection data of the view corresponds to the first X-ray energy, the projection data of the opposite view is scaled, and the projection data after the scaling processing is used to use the first projection data set or the second projection data. The X-ray CT system according to any one of claims 3 to 5, wherein the correction processing is performed on the projected data set. The processing unit faces the case where the projection data of the views facing each other in the correction processing for the first projection data set corresponds to the first X-ray energy, or in the correction processing for the second projection data set. When the projection data of the view corresponds to the second X-ray energy, the projection data of the opposite view is used as it is to perform the correction processing on the first projection data set or the second projection data set. Item 6. The X-ray CT system according to any one of Items 3 to 6. In the correction processing for the first projection data set or the second projection data set, the processing unit performs a switching period between the first X-ray energy and the second X-ray energy for the projection data of the opposite views. When corresponding to the third X-ray energy irradiated to, the projection data of the opposite view is corrected by using the projection data in the vicinity of the projection data, and the corrected projection data is used to correct the first projection. The X-ray CT system according to any one of claims 3 to 7, wherein the correction processing is performed on the data set or the second projection data set. Corresponds to the projection data corresponding to the first X-ray energy and the second X-ray energy collected by irradiating the subject with X-rays while rotating the X-ray focal position around the subject. It is provided with a processing unit that performs correction processing using the projection data of the opposite views for the projection data set including at least the projection data to be performed, and discriminates substances with a plurality of reference substances based on the corrected projection data set. , Medical processing equipment.

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