Medical information processing device, medical information processing method, method for acquiring calibration information, and program

Abstract

To efficiently and appropriately specify a biological index of a subject.SOLUTION: A medical information processing device according to an embodiment comprises a storage unit, an acquisition unit, and a specification unit. The storage unit stores a plurality of pieces of correspondence information indicative of a correspondence between a pixel value of a medical image created by scanning at least two types of phantoms different in body shape size and a predetermined biological index, for each body shape. The acquisition unit acquires body shape information on a subject to be scanned. The specification unit specifies correspondence information corresponding to the body shape information among the pieces of correspondence information stored in the storage unit.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] Embodiments of the present invention relate to a medical information processing apparatus, a medical information processing method, a method for acquiring calibration information, and a program.

Background Art

[0002] Conventionally, bone density measurement using an X-ray CT (Computed Tomography) apparatus has been known. Since the bone density varies depending on the beam hardening effect (BH effect) according to the subject size, conventionally, a beam hardening correction table is prepared in advance using a plurality of types of physical phantoms, and the above correction table is used for the measured value to obtain the bone density in consideration of the influence of the subject size. However, in the above method, it is necessary to prepare a large number of correction tables suitable for all body sizes (measured values) of users. In addition, in an actual living body, there are differences in the state of internal organs and the degree of intestinal gas, etc., so there is a problem of difference from the physical phantom. Therefore, there are cases where the biological index of the subject cannot be specified efficiently and appropriately.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Patent Document 2

Patent Document 3

Patent Document 4

Summary of the Invention

Problems to be Solved by the Invention

[0004] One of the problems that the present invention aims to solve is to identify biological indicators of a subject more efficiently and appropriately. However, the problems that the embodiments disclosed herein and in the drawings aim to solve are not limited to the above problem, and problems corresponding to the effects of each configuration shown in the embodiments described later can also be positioned as other problems. [Means for solving the problem]

[0005] The medical information processing device of this embodiment includes a storage unit, an acquisition unit, and an identification unit. The storage unit stores multiple sets of correspondence information for each body shape, which shows the correspondence between pixel values ​​of medical images created by scanning at least two different body shape sizes and predetermined biometric indicators. The acquisition unit acquires body shape information relating to the subject to be scanned. The identification unit identifies the correspondence information corresponding to the body shape information from among the multiple sets of correspondence information stored in the storage unit. [Brief explanation of the drawing]

[0006] [Figure 1] A diagram showing the configuration of the X-ray CT apparatus 1 according to this embodiment. [Figure 2] A diagram showing an example of data stored in memory 41. [Figure 3] A diagram showing an example of the functional configuration of the calibration function 57. [Figure 4] Figure (1) shows an example of images captured from phantoms of different sizes. [Figure 5] Figure (2) shows an example of images captured from phantoms of different sizes. [Figure 6] A diagram showing an example of the functional configuration of specific function 58. [Figure 7] A diagram showing an example of the first relationship depending on the size of the absorbent surrounding the HA. [Figure 8] A diagram illustrating the derivation of a second relationship depending on the size of the absorber. [Figure 9] A diagram illustrating how to derive slope information from the second relational equation. [Figure 10] A diagram showing the correspondence between absorber size and tilt information. [Figure 11] A figure showing an example of a CT image IM30 of the subject. [Figure 12] A flowchart showing the sequence of steps in the calibration process of processing circuit 50. [Figure 13] A flowchart showing the sequence of specific processing steps in processing circuit 50. [Modes for carrying out the invention]

[0007] The following describes the embodiment of the medical information processing device, medical information processing method, calibration information acquisition method, and program with reference to the drawings. The medical information processing device is, for example, a medical diagnostic device that processes medical images such as those from an X-ray CT scanner to diagnose a subject. In the following description, the case in which the medical information processing device is an X-ray computed tomography scanner (hereinafter referred to as an X-ray CT (Computed Tomography) scanner) will be used as an example. The X-ray CT scanner may also be a photon counting type X-ray CT scanner capable of performing photon counting CT.

[0008] Figure 1 is a configuration diagram of an X-ray CT apparatus 1 according to an embodiment. The X-ray CT apparatus 1 performs, for example, dual-energy X-ray scanning (dual-energy scanning). Dual-energy scanning can be performed by, for example, using a single X-ray tube and switching the tube voltage applied to the X-ray tube to individually irradiate the subject with low-energy X-rays and high-energy X-rays, or by rapidly switching the tube voltage of the X-ray tube to irradiate the subject with low-energy X-rays and high-energy X-rays almost simultaneously. The X-ray CT apparatus 1 may perform any method of dual-energy scanning. Furthermore, the X-ray CT apparatus 1 may be configured to perform data processing of three or more types of energy (multi-energy).

[0009] The X-ray CT apparatus 1 includes, for example, a gantry apparatus 10, a couch apparatus 30, and a console apparatus 40. In FIG. 1, for the sake of explanation, both a view of the gantry apparatus 10 seen from the Z-axis direction and a view seen from the X-axis direction are shown, but actually, there is only one gantry apparatus 10. In the present embodiment, the rotation axis of the rotating frame 17 or the longitudinal direction of the top plate 33 of the couch apparatus 30 in the non-tilt state is defined as the Z-axis direction, an axis that is orthogonal to the Z-axis direction and horizontal with respect to the floor surface is defined as the X-axis direction, and a direction that is orthogonal to the Z-axis direction and perpendicular to the floor surface is defined as the Y-axis direction. The X-ray CT apparatus 1 or the console apparatus 40 is an example of a "medical information processing apparatus".

[0010] The gantry apparatus 10 includes, for example, an X-ray tube 11, a wedge 12, a collimator 13, an X-ray high-voltage apparatus 14, an X-ray detector 15, a data acquisition system (hereinafter, DAS: Data Acquisition System) 16, a rotating frame 17, and a control apparatus 18.

[0011] The X-ray tube 11 generates X-rays by irradiating thermoelectrons from the cathode (filament) toward the anode (target) by applying a high voltage from the X-ray high-voltage apparatus 14. The X-ray tube 11 includes a vacuum tube. For example, the X-ray tube 11 is a rotating anode type X-ray tube that generates X-rays by irradiating thermoelectrons onto a rotating anode.

[0012] The wedge 12 is a filter for adjusting the amount of X-rays irradiated from the X-ray tube 11 to the subject P. The wedge 12 attenuates the X-rays passing through itself so that the distribution of the amount of X-rays irradiated from the X-ray tube 11 to the subject P becomes a predetermined distribution. The wedge 12 is also called a wedge filter or a bow-tie filter. The wedge 12 is, for example, made by processing aluminum to have a predetermined target angle and a predetermined thickness.

[0013] The collimator 13 is a mechanism for narrowing the irradiation range of the X-rays that have passed through the wedge 12. The collimator 13 narrows the irradiation range of the X-rays, for example, by forming a slit with a combination of a plurality of lead plates. The collimator 13 may also be referred to as an X-ray aperture.

[0014] The X-ray high-voltage device 14 has, for example, a high-voltage generator and an X-ray control device. The high-voltage generator has an electric circuit including a transformer (a transformer) and a rectifier, etc. The high-voltage generator generates a high voltage to be applied to the X-ray tube 11. The X-ray control device controls the output voltage of the high-voltage generator according to the X-ray dose to be generated in the X-ray tube 11. The high-voltage generator may perform voltage boosting by the above-described transformer, or may perform voltage boosting by an inverter. The X-ray high-voltage device 14 may be provided on the rotating frame 17, or may be provided on the side of a fixed frame (not shown) of the gantry device 10.

[0015] The X-ray control device controls the output voltage of the high-voltage generator so that, for example, the voltage required to perform a dual-energy scan is applied to the X-ray tube 11. For example, the X-ray control device controls whether to output high-energy X-rays (High-kV) (X-rays of the first energy) or low-energy X-rays (Low-kV) (X-rays of the second energy whose energy magnitude is lower than the first energy) to the high-voltage generator according to the tube voltage control signal from the console device 40.

[0016] The X-ray detector 15 detects the intensity of the X-rays generated by the X-ray tube 11 and incident after passing through the subject P. The X-ray detector 15 outputs an electrical signal (which may also be an optical signal, etc.) corresponding to the detected intensity of the X-rays to the DAS 16. The X-ray detector 15 has, for example, a plurality of X-ray detection element arrays. Each of the plurality of X-ray detection element arrays has a plurality of X-ray detection elements arranged in the channel direction along an arc centered on the focal point of the X-ray tube 11. The plurality of X-ray detection element arrays are arranged in the slice direction (column direction, row direction).

[0017] The X-ray detector 15 is an indirect type detector having, for example, a grid, a scintillator array, and a photosensor array. The scintillator array has multiple scintillators. Each scintillator has a scintillator crystal. The scintillator crystal emits light in an amount corresponding to the intensity of the incident X-rays. The grid is positioned on the X-ray incident surface of the scintillator array and has an X-ray shielding plate that absorbs scattered X-rays. The grid is sometimes called a collimator (one-dimensional collimator or two-dimensional collimator). The photosensor array has, for example, a photomultiplier tube (PMT) or other photosensor. The photosensor array outputs an electrical signal corresponding to the amount of light emitted by the scintillators. The X-ray detector 15 may also be a direct conversion type detector having semiconductor elements that convert incident X-rays into electrical signals.

[0018] DAS16 includes, for example, an amplifier, an integrator, and an A / D converter. The amplifier performs amplification processing on the electrical signals output by each X-ray detection element of the X-ray detector 15. The integrator integrates the amplified electrical signals over a view period (described later). The A / D converter converts the electrical signals showing the integration result into a digital signal. DAS16 outputs detection data based on the digital signal to the console device 40. The detection data is a digital value of X-ray intensity identified by the channel number, column number of the generating X-ray detection element, and the view number indicating the collected view. The view number is a number that changes according to the rotation of the rotating frame 17, for example, a number that is incremented according to the rotation of the rotating frame 17. Therefore, the view number is information indicating the rotation angle of the X-ray tube 11. The view period is the period that falls between the rotation angle corresponding to a certain view number and the rotation angle corresponding to the next view number. The DAS16 may detect the view switch by timing signals input from the control device 18, by an internal timer, or by signals acquired from sensors (not shown). When performing a full scan and the X-ray tube 11 is continuously emitting X-rays, the DAS16 collects detection data for the entire circumference (360 degrees). When performing a half scan and the X-ray tube 11 is continuously emitting X-rays, the DAS16 collects detection data for half the circumference (180 degrees).

[0019] The rotating frame 17 is an annular member that supports the X-ray tube 11, wedge 12, and collimator 13, and the X-ray detector 15 in opposition to each other. The rotating frame 17 is supported by a fixed frame so as to be rotatable around the subject P introduced inside. The rotating frame 17 further supports the DAS 16. Detection data output by the DAS 16 is transmitted by optical communication from a transmitter having a light-emitting diode (LED) provided on the rotating frame 17 to a receiver having a photodiode provided on the non-rotating part (e.g., the fixed frame) of the stand device 10, and is then transferred by the receiver to the console device 40. Note that the method of transmitting detection data from the rotating frame 17 to the non-rotating part is not limited to the optical communication method described above, but any non-contact transmission method may be used. The rotating frame 17 is not limited to an annular member, but may be an arm-like member, as long as it can support and rotate the X-ray tube 11, etc.

[0020] The X-ray CT apparatus 1 is, for example, a Rotate / Rotate-Type X-ray CT apparatus (third-generation CT) in which both the X-ray tube 11 and the X-ray detector 15 are supported by a rotating frame 17 and rotate around the subject P. However, it is not limited to this, and may also be a Stationary / Rotate-Type X-ray CT apparatus (fourth-generation CT) in which a plurality of X-ray detection elements arranged in a ring shape are fixed to a fixed frame and the X-ray tube 11 rotates around the subject P.

[0021] The control device 18 includes, for example, a processing circuit having a processor such as a CPU (Central Processing Unit), and a drive mechanism including a motor and actuators. The control device 18 receives input signals from an input interface 43 attached to the console device 40 or the support device 10, and controls the operation of the support device 10 and the bed device 30.

[0022] The control device 18 can, for example, rotate the rotating frame 17, tilt the support unit 10, or move the top plate 33 of the bed unit 30. When tilting the support unit 10, the control device 18 rotates the rotating frame 17 around an axis parallel to the Z-axis direction based on the tilt angle input to the input interface 43. The control device 18 knows the rotation angle of the rotating frame 17 from the output of a sensor (not shown), etc. The control device 18 also provides the rotation angle of the rotating frame 17 to the processing circuit 50 as needed. The control device 18 may be installed on the support unit 10 or on the console unit 40.

[0023] The patient bed device 30 is a device that places and moves the subject P to be scanned and introduces it into the rotating frame 17 of the stand device 10. The patient bed device 30 includes, for example, a base 31, a patient bed drive device 32, a top plate 33, and a support frame 34. The base 31 includes a housing that supports the support frame 34 so as to be movable in the vertical direction (Y-axis direction). The patient bed drive device 32 includes a motor and an actuator. The patient bed drive device 32 moves the top plate 33 on which the subject P is placed along the support frame 34 in the longitudinal direction (Z-axis direction) of the top plate 33. The top plate 33 is a plate-shaped member on which the subject P is placed.

[0024] The bed drive device 32 may move not only the top plate 33 but also the support frame 34 in the longitudinal direction of the top plate 33. Conversely, the stand device 10 may be movable in the Z-axis direction, and the movement of the stand device 10 may be controlled so that the rotating frame 17 is positioned around the subject P. Alternatively, both the stand device 10 and the top plate 33 may be movable. Furthermore, the X-ray CT apparatus 1 may be a device in which the subject P is scanned in a standing or sitting position. In this case, the X-ray CT apparatus 1 has a subject support mechanism instead of the bed device 30, and the stand device 10 rotates the rotating frame 17 around an axis perpendicular to the floor surface.

[0025] The console device 40 includes, for example, a memory 41, a display 42, an input interface 43, a network connection circuit 44, and a processing circuit 50. In this embodiment, the console device 40 is described as separate from the mounting device 10, but the mounting device 10 may include some or all of the components of the console device 40. The memory 41 is an example of a "storage unit".

[0026] Memory 41 can be implemented using, for example, semiconductor memory elements such as RAM (Random Access Memory) or flash memory, a hard disk, or an optical disk. Memory 41 stores, for example, detection data, projection data, and reconstructed images (CT images). This data may be stored in an external memory that can communicate with the X-ray CT apparatus 1, rather than in memory 41 (or in addition to memory 41). The external memory is controlled by a cloud server that manages the external memory, for example, by accepting read and write requests from the cloud server. The external memory can be implemented using, for example, a system called PACS (Picture Archiving and Communication Systems). PACS is a system that systematically stores images taken by various diagnostic imaging devices.

[0027] Figure 2 shows an example of data stored in memory 41. As shown in Figure 2, memory 41 stores information such as imaging conditions 41-1, detection data 41-2 output by DAS 16, projection data 41-3 generated by processing circuit 50, reconstructed image 41-4, generated image 41-5, calibration information 41-6 generated by calibration function described later, and identification result 41-7 generated by identification function described later. Note that the data before preprocessing (detection data 41-2) and the data after preprocessing in processing circuit 50 are sometimes collectively referred to as projection data 41-3. The reconstructed image 41-4 is a medical image acquired based on medical data scanned with multiple types of energy. For example, in a dual-energy scan, the reconstructed image 41-4 includes a first image obtained by irradiating the subject with high-energy X-rays and a second image obtained by irradiating the subject with low-energy X-rays.

[0028] The display 42 displays various types of information. For example, the display 42 displays images generated by the processing circuit 50, or GUI (Graphical User Interface) images that accept various operations from the operator. The display 42 may be, for example, a liquid crystal display, a CRT (Cathode Ray Tube), or an organic EL (Electroluminescence) display. The display 42 may be mounted on the stand device 10. The display 42 may be a desktop type, or it may be a display device (for example, a tablet terminal) that can communicate wirelessly with the main body of the console device 40.

[0029] The input interface 43 accepts various input operations from the operator and outputs an electrical signal indicating the content of the accepted input operation to the processing circuit 50. For example, the input interface 43 accepts input operations such as collection conditions when collecting detection data 41-2 or projection data 41-3, reconstruction conditions when reconstructing CT images, and image processing conditions when generating post-processed images from CT images. Each of the above conditions may be stored in memory 41 as shooting conditions 41-1. For example, the input interface 43 can be implemented by a mouse, keyboard, touch panel, trackball, switch, button, joystick, camera, infrared sensor, microphone, etc. The input interface 43 may be provided on the rigging device 10. Alternatively, the input interface 43 may be implemented by a display device (e.g., a tablet terminal) that can communicate wirelessly with the main body of the console device 40. The input interface 43 may also accept instructions for acquiring calibration information or obtaining biological indicators of the subject.

[0030] The network connection circuit 44 includes, for example, a network card having a printed circuit board, or a wireless communication module. The network connection circuit 44 implements an information communication protocol according to the type of network to be connected. The network includes, for example, a LAN (Local Area Network), a WAN (Wide Area Network), the Internet, a cellular network, a dedicated line, etc.

[0031] The processing circuit 50 controls the overall operation of the X-ray CT apparatus 1. The processing circuit 50 performs, for example, system control functions 51, preprocessing functions 52, reconstruction processing functions 53, image processing functions 54, scan control functions 55, display control functions 56, calibration functions 57, and specific functions 58. The processing circuit 50 realizes these functions, for example, by having a hardware processor execute a program stored in memory 41. Calibration function 57 is an example of a "calibration unit". Specific function 58 is an example of a "specific unit".

[0032] A hardware processor refers to circuits such as a CPU, GPU (Graphics Processing Unit), Application Specific Integrated Circuit (ASIC), and programmable logic devices (e.g., Simple Programmable Logic Device (SPLD) or Complex Programmable Logic Device (CPLD), Field Programmable Gate Array (FPGA)). Instead of storing the program in memory 41, the hardware processor may be configured to directly embed the program within its circuitry. In this case, the hardware processor performs its functions by reading and executing the program embedded within the circuitry. A hardware processor is not limited to being configured as a single circuit; it may be composed of multiple independent circuits combined to perform each function. Alternatively, multiple components may be integrated into a single hardware processor to perform each function.

[0033] Each component of the console device 40 or the processing circuit 50 may be distributed and implemented by multiple hardware components. The processing circuit 50 may not be implemented in the configuration of the console device 40, but rather by a processing unit that can communicate with the console device 40. The processing unit is, for example, a workstation connected to one X-ray CT device, or a device (e.g., a cloud server) connected to multiple X-ray CT devices that performs processing equivalent to that of the processing circuit 50 described below in a batch. In other words, the configuration of this embodiment can also be implemented as an X-ray CT system (medical diagnostic system) in which an X-ray CT device and other processing units are connected via a network.

[0034] The system control function 51 controls various functions of the processing circuit 50 based on input operations received through the input interface 43, for example.

[0035] The preprocessing function 52 performs preprocessing on the detection data 41-2 output by the DAS 16 based on the imaging conditions 41-1, such as logarithmic transformation, offset correction, inter-channel sensitivity correction, and beam hardening correction, to generate projection data 41-3, and stores the generated projection data 41-3 in memory 41. Alternatively, the preprocessing function 52 may also store the detection data 41-2 before preprocessing in memory 41.

[0036] The reconstruction processing function 53 performs reconstruction processing on the projection data 41-3 generated by the preprocessing function 52, such as a filtered back projection method or an iterative reconstruction method, to generate a reconstructed image 41-4, and stores the generated reconstructed image 41-4 in the memory 41.

[0037] The image processing function 54 converts the reconstructed images 41-4 into three-dimensional images or cross-sectional image data of an arbitrary cross-section using known methods, based on the input operations received by the input interface 43. The conversion to three-dimensional images may be performed by the preprocessing function 52.

[0038] The scan control function 55 controls the data acquisition process of the pallet unit 10 by issuing instructions to the X-ray high-voltage device 14, DAS 16, control device 18, and patient table drive device 32 based on the imaging conditions 41-1 stored in the memory 41. The scan control function 55 controls the operation of each part when acquiring alignment images, main images, images used for diagnosis, images for acquiring calibration information, etc., based on the imaging conditions 41-1.

[0039] The display control function 56 controls the display mode of the display 42. For example, the display control function 56 controls the display 42 to display reconstructed images generated by the processing circuit 50, calibration information generated by the calibration function 57, biological indicator values ​​of the subject identified by the identification function 58, GUI images that accept various operations by the operator, etc.

[0040] The calibration function 57 derives correspondence information that shows the correspondence for extracting biometric indicators from the pixel values ​​of medical images obtained by scanning a subject, for example, and stores the derived correspondence information in memory 41 as calibration information 41-6. For example, the calibration function 57 derives a first correspondence between biological components and biometric indicators from medical images scanned with different concentrations of biological components for each of at least two different body-size phantoms. The calibration function 57 also derives a second correspondence between energy and pixel values ​​from medical images scanned with multiple types of energy and pixel values. Furthermore, the calibration function 57 derives a third correspondence from the absorber size and the second correspondence, and acquires the first to third correspondences as calibration information. The correspondence information is created by scanning at least two different body-size phantoms and includes a conversion formula for converting predetermined biometric indicators from the pixel values ​​of medical images. Biometric indicators are, for example, index values ​​related to the amount or ratio of biological components contained in the subject. Biomarkers include, for example, bone density, body fat percentage, and obesity level. Biocomponents include, for example, hydroxyapatite (hereinafter referred to as "HA"), but other components may also be used. Calibration function 57 generates the above relationship (conversion formula) for each body shape. In the following, bone density will be used as an example of a biomarker, and HA will be used as an example of a biocomponent.

[0041] Figure 3 shows an example of the functional configuration of the calibration function 57. The calibration function 57 includes, for example, an acquisition function 57-1, an extraction function 57-2, and a derivation function 57-3. The acquisition function 57-1 is an example of an "acquisition unit". The extraction function 57-2 is an example of an "extraction unit". The derivation function 57-3 is an example of a "derivation unit". The acquisition function 57-1 acquires scan results of at least two different phantom shapes and sizes. The shape and size may be volume, cross-sectional area during CT scanning, or diameter. The shape and size may also include information about the shape, such as circular or elliptical.

[0042] Figures 4 and 5 are examples (part 1 and part 2) of imaging images of phantoms of different sizes. Figure 4 shows a cross-sectional image IM10 of phantom FT1, which is smaller in size (e.g., diameter or area), when imaged with X-ray CT scanner 1, and Figure 5 shows a cross-sectional image IM20 of phantom FT2, which is larger in size than phantom FT1, when imaged with X-ray CT scanner 1. Phantoms FT1 and FT2 include a first region (absorbent region) AR1 made of an absorbent such as water, and a second region (biological component region) AR2 containing biological components at a predetermined concentration. The first region AR1 corresponds to, for example, the soft tissue portion of the human body, and the second region AR2 corresponds to, for example, the bone tissue portion of the human body. The second region AR2 can be attached and detached by inserting and removing it into a cavity provided in phantoms FT1 and FT2, and biological components of different concentrations can be attached to the second region AR2. In the examples shown in Figures 4 and 5, the second region AR2 is located in the center of the cross-sections of phantoms FT1 and FT2, but the position, number, and size of the second region AR2 are not limited to this.

[0043] The acquisition function 57-1 acquires projection data 41-3 obtained by scanning phantoms FT1 and FT2 while switching between HA with different densities. The extraction function 57-2 extracts the region of interest (ROI) to be calibrated from the scanned image. The derivation function 57-3 derives the correspondence (first relationship) between the biocomponents (HA) of the extracted region and biometric indicators (bone density). The derivation function 57-3 also derives the correspondence (second relationship) between two different energies and the pixel values ​​(CT values) of the medical image. Furthermore, the derivation function 57-3 derives the correspondence (third relationship) between the absorber size and information obtained from the two different energies (e.g., tilt information). Details of the calibration function 57 will be described later.

[0044] The identification function 58 identifies a biological indicator (bone density) from the pixel values ​​of a predetermined area of ​​the subject's medical image based on the subject's measured data and the first to third relational expressions described above, and stores the identification result 41-7 in the memory 41. Figure 6 shows an example of the functional configuration of the identification function 58. The identification function 58 includes, for example, an acquisition function 58-1, an extraction function 58-2, and a biological indicator identification function 58-3. The acquisition function 58-1 is an example of "7". The extraction function 58-2 is an example of the "extraction unit". The biological indicator identification function 58-3 is an example of the "identification unit".

[0045] The acquisition function 58-1 acquires actual measurement data when the subject is irradiated with X-rays. The extraction function 58-2 sets high bone density bone areas and soft tissue areas from the reconstructed image of the actual measurement data. The extraction function 58-2 also acquires CT values ​​when the set high bone density bone areas and soft tissue areas are irradiated with X-rays of a first energy and X-rays of a second energy, respectively, and extracts a relationship formula between the corresponding HA value and bone density based on the acquired CT values ​​and the subject's body size. The biomarker identification function 58-3 derives the bone density corresponding to the HA value obtained from the pixel values ​​of the subject's medical image based on the above relationship formulas.

[0046] (Calibration function) Next, the calibration function 57 will be explained in detail. Figure 7 shows an example of the first relationship equation depending on the size of the absorbent surrounding HA. In the example in Figure 7, the first relationship equation shows the correspondence between HA value and bone mineral density (BMD). Also, in the example in Figure 7, the horizontal axis is HA value [cm 3 / cm 3 The vertical axis shows bone mineral density [mg / cm³]. 3 This indicates [...].

[0047] The derivation function 57-3 derives the correspondence between HA value and bone density using at least two different sizes of absorbers. In this case, using the largest and smallest phantoms from a set of pre-prepared phantoms, multiple HA values ​​with different HA concentrations (HA1 and HA2 in the example in Figure 7) are plotted against the actual bone density derived from each phantom, and the points are connected to derive a linear equation (conversion formula). In the example in Figure 7, two relational equations (linear functions) are shown for phantoms FT1 and FT2, respectively. By deriving these two relational equations, relational equations for other phantom sizes can be derived. For example, for phantoms of sizes other than phantoms FT1 and FT2 (e.g., phantom FT3), interpolation is performed using the relational equations for phantoms FT1 and FT2, respectively. For example, the derivation function 57-3 estimates the possible values ​​that Phantom FT3 can take based on the ratio of the magnification or reduction ratio of Phantom FT3 to Phantom FT1 and FT2, and derives the first relational equation shown in Figure 7.

[0048] Furthermore, the derivation function 57-3 derives a relationship (second relationship) between the data obtained by irradiating the phantom with X-rays of the first energy and X-rays of the second energy, and the phantom size (absorber size). Figure 8 is a diagram illustrating the deriving of the second relationship according to the absorber size. In the example in Figure 8, a cross-sectional image IM20 of phantom FT2 is shown as an example of an absorber. The derivation function 57-3 sets point P1 within the region containing bio-components (HA) for identifying bio-indicators (bone density) and point P2 corresponding to the absorber as target regions (ROIs), acquires CT values ​​when irradiated with X-rays of the first energy and X-rays of the second energy at the two set points, and derives the second relationship based on the change in the acquired CT values ​​for each point P1 and P2. Then, the slope of the derived relationship (linear function) is derived as slope information (Low-High kVp slope).

[0049] Figure 9 is a diagram illustrating the deriving of slope information from the second relational equation. In Figure 9, the horizontal axis represents the X-ray energy (kVp), and the vertical axis represents the CT value represented by the CT image. Note that the CT value may be replaced with the linear attenuation coefficient. As shown in Figure 9, the derivation function 57-3 acquires the CT value when scanning at point P1 in the first region AR1 and point P2 in the second region AR2 using the first energy E1 (Low) and the second energy E2 (High), respectively, and derives two relational equations for points P1 and P2 by linearly connecting the two acquired CT value points for points P1 and P2. Furthermore, the derivation function 57-3 derives a straight line SL (Low-High kVp Slope), which corresponds to the average of the two relational equations, as the second relational equation (conversion formula). Note that the second relational equation may be a straight line showing the relationship between energy and CT value at point P1, or a straight line showing the relationship between energy and CT value at point P2. Furthermore, the second relation may include information about the slope of the straight line (linear function). By deriving the straight line SL from the relational equations for bone tissue and soft tissue and using it as the second relational equation, the relationship between energy and CT value can be derived with high accuracy.

[0050] In the example in Figure 9, two points are connected linearly, but three or more points with different energy values ​​may be connected nonlinearly. In this case, the nonlinear equation is derived as the second relation.

[0051] Furthermore, the derivation function 57-3 derives the correspondence between slope information (Low-High kVp Slope) and absorber size (third relationship) by deriving the second relationship for different phantom sizes. Figure 10 shows the correspondence between absorber size and slope information. In Figure 10, the horizontal axis shows the slope information (Low-High kVp Slope) of the second relationship, and the vertical axis shows the absorber size. By deriving the first to third relationship in this way, it is not necessary to prepare calibration tables corresponding to many subject sizes in advance, and the subject's biological indicators (e.g., bone density) can be identified more appropriately.

[0052] (Specific function) Next, we will explain the specific function 58 in detail. The acquisition function 58-1 acquires the reconstructed image (CT image) of the subject P taken by the X-ray CT device 1. Figure 11 shows an example of the subject's CT image IM30. Image IM30 is an image extracted from a slice image of the subject's torso, specifically the area near the spine BN.

[0053] The extraction function 58-2 extracts the bone density measurement target area AR10, the high bone density bone area AR11, and the soft tissue area AR12 from the acquired image IM30. The high bone density bone area AR11 is defined as a target area (ROI) in a bone area that is not cancellous bone, but has high bone density compared to other bone areas, where there is little difference between subjects due to age, etc. The soft tissue area AR12 is defined as a target area (ROI) in a part corresponding to the abdominal aorta or soft tissue such as muscle. The areas AR10 to AR12 may be set by the operator inputting them into the input interface 43, or they may be set automatically based on the characteristic information of the image IM30 (for example, location information, shape, size, and pixel value of a predetermined part included in the image).

[0054] The biomarker identification function 58-3 derives slope information (Low-High kVp Slope) from the CT values ​​when the set regions AR11 and AR12 are irradiated with X-rays of the first energy and X-rays of the second energy, respectively. The absorber size of the subject is then obtained from the derived slope information and the third relational equation shown in Figure 10. Next, the biomarker identification function 58-3 derives the first relational equation shown in Figure 7 based on the derived absorber size, and by substituting the HA value in region AR10 into the derived first relational equation, the bone density in region AR10 is determined.

[0055] Thus, according to this embodiment, by deriving a relational expression for identifying biometric indicators from a small number of phantoms, it becomes possible to measure biometric indicators (bone density) more efficiently and appropriately with less influence from the BH effect, without having to generate calibration information corresponding to a large number of subject sizes in advance or select calibration information that matches the subject size from multiple calibration information sources.

[0056] [Processing flow] The following describes the processing flow of the processing circuit 50 in this embodiment. The processing of the processing circuit 50 is broadly divided into calibration processing in the calibration function 57 and specific processing in the specific function 58. These will be explained separately below.

[0057] Figure 12 is a flowchart showing the sequence of calibration processes in the processing circuit 50. The flowchart in Figure 12 is initiated, for example, when the operator of the X-ray CT apparatus 1 instructs the start of the calibration process by operating the input interface 43. In the flowchart in Figure 12, the calibration function 57 acquires phantom scan results for several (for example, about 3 to 5 types) pre-prepared HA concentrations (step S100). Next, the calibration function 57 extracts the ROI (target area) of HA (step S110). Next, the calibration function 57 derives the correspondence relationship (first relational expression) between HA value and BMD value (bone density) (step S120). Next, the calibration function 57 extracts the target area (ROI) of the absorber (step S130).

[0058] Next, the calibration function 57 derives the Low-High kVp slope (slope information, second relational expression) from the extracted ROI (step S140), and derives the correspondence between the absorber size and the above slope (third relational expression) (step S150).

[0059] Next, the calibration function 57 determines whether or not to measure with other phantom sizes (step S160). If it determines to measure with other phantom sizes, the calibration function 57 performs the processing from step S100 onward for the other phantom sizes that have not yet been measured. If it determines not to measure with other phantoms, the calibration function 57 stores the relationships up to that point in memory as calibration information (step S170). This completes this flowchart. Note that the processing in step S120 shown in Figure 12 may be executed after the processing in step S150, or it may be executed in parallel with the processing in steps S130 to S150.

[0060] Figure 13 is a flowchart showing the sequence of specific processing steps in the processing circuit 50. Note that the processing shown in Figure 13 illustrates the process of identifying the subject's bone density as an example of a biological indicator. The flowchart shown in Figure 13 is initiated, for example, when the operator of the X-ray CT scanner 1 instructs the start of the specific processing by operating the input interface 43.

[0061] In the flowchart shown in Figure 13, the specific function 58 acquires the scan results of the subject (step S200), extracts the ROI of bone tissue (step S210), and extracts the ROI of soft tissue (step S220). Next, the specific function 58 derives the Low-High kVp slope from the extracted ROIs (step S230) and derives the absorber size from the derive result (step S240). Next, the specific function 58 acquires the correspondence between the HA value and BMD value according to the absorber size (step S250) and identifies the BMD value based on the acquired correspondence (step S260). This completes the processing of this flowchart.

[0062] <Variation> Furthermore, the medical information processing device of the embodiment may perform the above-mentioned processing using information acquired from a medical image generation device such as an X-ray CT scanner, or image data acquired from a medical image server that stores medical images captured by an X-ray CT scanner. In addition, the embodiment is not limited to dual energy, and the derivation of the above-mentioned relational formula and identification of biological indicators may be performed using data processing of three or more types of energy (multi-energy). Also, in the embodiment, instead of identifying bone density, other biological indicators such as the amount of body fat or degree of obesity of the subject may be identified.

[0063] According to at least one embodiment described above, the medical information processing device of the embodiment includes a storage unit that stores multiple pieces of correspondence information for each body shape, which shows the correspondence between pixel values ​​of a medical image created by scanning at least two different body shape sizes and predetermined biometric indicators; an acquisition unit that acquires body shape information of a subject to be scanned; and an identification unit that identifies correspondence information corresponding to the body shape information from among the multiple pieces of correspondence information stored in the storage unit. This enables the identification of biometric indicators of a subject more efficiently and appropriately.

[0064] Specifically, according to this embodiment, for example, the bone density of a subject can be determined more efficiently without being affected by the size of the subject. Furthermore, according to this embodiment, with less calibration information than conventional methods, the measurement of biomarkers less affected by the BH effect can be performed without the user having to select an appropriate subject size. In addition, according to this embodiment, for example, in actual cases, biomarkers can be identified with greater accuracy while reducing the influence of differences in the structure of the absorber (e.g., the abdomen is completely filled, or the chest contains a lot of air).

[0065] The embodiments described above can be expressed as follows. Memory to store the program, Equipped with a processor, The processor executes the program, By scanning at least two different body shape phantoms, the system stores multiple pieces of correspondence information for each body shape, showing the relationship between the pixel values ​​of medical images and predetermined biometric indicators. Obtain body shape information about the subject being scanned, From the multiple stored corresponding pieces of information, identify the corresponding piece of information that corresponds to the body shape information. Medical information processing method.

[0066] While several embodiments have been described, these embodiments are presented as examples only and are not intended to limit the scope of the invention. These embodiments can be carried out in a variety of other forms, and various reductions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their variations are included in the scope and spirit of the invention, as well as in the claims and their equivalents. [Explanation of Symbols]

[0067] 1...X-ray CT scanner, 10...Stand system, 11...X-ray tube, 12...Wedge, 13...Collimator, 14...X-ray high-voltage device, 15...X-ray detector, 16...Data acquisition system, 17...Rotating frame, 18...Control device, 30...Patient table system, 40...Console system, 50...Processing circuit, 51...System control function, 52...Preprocessing function, 53...Reconstruction processing function, 54...Image processing function, 55...Scan control function, 56...Display control function, 57...Calibration function, 58...Specification function

Claims

1. A storage unit that stores multiple pieces of correspondence information for each body shape, showing the correspondence between pixel values ​​of medical images created by scanning at least two different body shape phantoms and predetermined biometric indicators, An acquisition unit that acquires body shape information about the subject to be scanned, A selection unit identifies the corresponding information that corresponds to the body shape information from among the multiple corresponding information stored in the memory unit, Equipped with, The aforementioned phantom includes an absorber region and a biocomponent region. The system further includes a calibration unit that obtains calibration information by deriving a first correspondence between biological components and biological indicators from medical images scanned with different concentrations of biological components for each of the at least two different phantom shapes and sizes, deriving a second correspondence between energy and pixel values ​​from medical images scanned with multiple types of energy and pixel values, and deriving a third correspondence between the absorber size and the second correspondence. Medical information processing device.

2. A storage unit that stores multiple pieces of correspondence information for each body shape, showing the correspondence between pixel values ​​of medical images created by scanning at least two different body shape phantoms and predetermined biometric indicators, An acquisition unit that acquires body shape information about the subject to be scanned, A selection unit identifies the corresponding information that corresponds to the body shape information from among the multiple corresponding information stored in the memory unit, Equipped with, The identifying unit acquires bone regions and soft tissue regions from a medical image obtained by scanning the subject, identifies a correspondence relationship derived by scanning with multiple types of energy based on the pixel values ​​of the acquired bone regions and soft tissue regions and the absorber size of the subject, and identifies the bone density of the bone region included in the medical image of the subject based on the identified correspondence relationship. Medical information processing device.

3. The identification unit identifies predetermined biometric indicators from the pixel values ​​of a medical image created by scanning the subject, based on the correspondence information corresponding to the identified body shape information. A medical information processing device according to claim 1 or 2.

4. The aforementioned medical images were acquired based on medical data scanned with multiple types of energy. A medical information processing device according to any one of claims 1 to 3.

5. The aforementioned correspondence information includes a relational expression for identifying a predetermined biometric from the pixel values ​​of the medical image. A medical information processing device according to any one of claims 1 to 4.

6. The aforementioned biological component is hydroxyapatite, and the aforementioned biological indicator is bone density. The medical information processing device according to claim 1.

7. Computers Multiple pieces of correspondence information, showing the relationship between pixel values ​​of medical images created by scanning at least two different body shape phantoms and predetermined biometric indicators, are stored for each body shape. Obtain body shape information about the subject being scanned, From the multiple pieces of stored correspondence information, identify the correspondence information that corresponds to the body shape information. The aforementioned phantom includes an absorber region and a biocomponent region. For each of the two different phantom shapes and sizes, a first correspondence between biological components and biological indicators is derived from medical images scanned with varying concentrations of biological components; a second correspondence between energy and pixel values ​​is derived from medical images scanned with multiple types of energy and pixel values; and a third correspondence between the absorber size and the second correspondence is derived to obtain calibration information. Medical information processing method.

8. Computers Multiple pieces of correspondence information, showing the relationship between pixel values ​​of medical images created by scanning at least two different body shape phantoms and predetermined biometric indicators, are stored for each body shape. Obtain body shape information about the subject being scanned, From the multiple pieces of stored correspondence information, identify the correspondence information that corresponds to the body shape information. The system obtains bone and soft tissue regions from medical images obtained by scanning the subject, identifies correspondences derived from scans using multiple energy levels based on the pixel values ​​of the acquired bone and soft tissue regions and the absorber size of the subject, and identifies the bone density of the bone region included in the medical image of the subject based on the identified correspondences. Medical information processing method.

9. Computers For each of at least two different phantoms of different body shapes and sizes, a first correspondence between biological components and biological indicators is derived from medical images scanned with varying concentrations of biological components within the phantom. A second correspondence between energy and pixel value was derived from medical images scanned with multiple types of energy and their corresponding pixel values. Calibration information is obtained by deriving a third correspondence between the absorber size of the phantom and the second correspondence. How to obtain calibration information.

10. On the computer, By scanning at least two different body shape phantoms, the system stores multiple pieces of correspondence information for each body shape, showing the relationship between the pixel values ​​of the medical images created and predetermined biometric indicators. To obtain body shape information about the subject being scanned, From the multiple stored correspondence pieces of information, identify the correspondence piece that corresponds to the body shape information. The aforementioned phantom includes an absorber region and a biocomponent region. For each of the two different phantoms of varying body shapes and sizes, a first correspondence between biological components and biological indicators is derived from medical images scanned with different concentrations of biological components; a second correspondence between energy and pixel values ​​is derived from medical images scanned with multiple types of energy and pixel values; and a third correspondence between the absorber size and the second correspondence is derived to obtain calibration information. program.

11. On the computer, By scanning at least two different body shape phantoms, the system stores multiple pieces of correspondence information for each body shape, showing the relationship between the pixel values ​​of the medical images created and predetermined biometric indicators. To obtain body shape information about the subject being scanned, From the multiple stored correspondence pieces of information, identify the correspondence piece that corresponds to the body shape information. The system obtains bone and soft tissue regions from medical images obtained by scanning the subject, identifies correspondences derived from scans using multiple energy levels based on the pixel values ​​of the acquired bone and soft tissue regions and the absorber size of the subject, and identifies the bone density of the bone region included in the medical image of the subject based on the identified correspondences. program.

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