JP2022184801A - Medical information display unit, medical image processor, medical information display method and program - Google Patents

Abstract

To evaluate the distribution of a lesion amount with respect to an anatomy.SOLUTION: A display control part is provided. The display control part performs display based on volume data including at least a lung. The display control part displays a first index value based on an anatomy of the lung, and a second index value based on the volume data on the lung, in such a manner that they correspond to each other.SELECTED DRAWING: Figure 1

Description

The embodiments disclosed in this specification and drawings relate to a medical information display device, a medical image processing device, a medical information display method, and a program.

Conventionally, for diffuse lung disease, for example, a method is known in which lesion distribution is quantified for each small area of the lung and the amount of lesion for each lung area is displayed using a polar map. Thus, there is a demand for a technique for quantitatively visualizing the distribution of lesions in organs or tissues because, for example, diagnosis can be facilitated.

By the way, diseases may arise or progress along the anatomy of an organ or tissue. As an example, in lung diseases such as diffuse lung disease, lesions may spread along the course of the bronchi, and disease states may differ between the distal and proximal portions of the bronchi. In other words, in diagnosing and treating lesions, it is important to understand the relationship between the three-dimensional distribution of lesions and the anatomical structure of organs or tissues.

WO2012/151579

One of the problems to be solved by the embodiments disclosed herein is evaluating the distribution of lesion volume with respect to the anatomy. However, the problem to be solved by the embodiments disclosed in this specification and drawings is not limited to the above problem. Problems corresponding to each configuration shown in the embodiments described later can also be positioned as other problems.

A medical information display device according to an embodiment includes a display control unit. The display control unit performs display based on volume data including at least lungs. The display control unit associates and displays a first index value based on the anatomical structure of the lung and a second index value based on the volume data regarding the lung.

FIG. 1 is a diagram showing an example of the configuration of an X-ray computed tomography (CT) apparatus equipped with a medical information display device or a medical image processing device according to an embodiment. FIG. 2 is a diagram for explaining an example of calculation of the degree of peripherality according to the embodiment. FIG. 3 is a flowchart illustrating an example of processing for displaying a lesion amount distribution with respect to an anatomical structure according to the embodiment. FIG. 4 is a diagram illustrating an example of display of the lesion amount distribution with respect to the anatomical structure according to the embodiment; FIG. 5 is a diagram for explaining another example of calculation of the degree of peripherality according to the embodiment. FIG. 6 is a diagram for explaining another example of calculation of the degree of peripherality according to the embodiment. FIG. 7 is a diagram for explaining another example of calculation of the degree of peripherality according to the embodiment. FIG. 8 is a diagram illustrating another example of display of the lesion amount distribution with respect to the anatomical structure according to the embodiment; FIG. 9 is a diagram illustrating another example of display of the lesion amount distribution with respect to the anatomical structure according to the embodiment; FIG. 10 is a diagram illustrating another example of display of the lesion amount distribution with respect to the anatomical structure according to the embodiment; FIG. 11 is a diagram illustrating an example of display of a screen for specifying the number of divisions relating to the degree of peripherality according to the embodiment. FIG. 12 is a diagram illustrating another example of display of the lesion amount distribution with respect to the anatomical structure according to the embodiment; FIG. 13 is a diagram illustrating another example of display of the lesion amount distribution with respect to the anatomical structure according to the embodiment; FIG. 14 is a diagram illustrating another example of display of the lesion amount distribution with respect to the anatomical structure according to the embodiment; FIG. 15 is a diagram illustrating another example of display of the lesion amount distribution with respect to the anatomical structure according to the embodiment;

A medical information display device, a medical image processing device, a medical information display method, a medical image processing method, and a program according to each embodiment will be described below with reference to the drawings. In the following description, constituent elements having the same or substantially the same functions as those described above with respect to the previous drawings are denoted by the same reference numerals, and duplicate description will be given only when necessary. Moreover, even when the same parts are shown, the dimensions and ratios may be different depending on the drawing. In addition, for example, from the viewpoint of ensuring the visibility of the drawings, only main or representative constituent elements are given reference numerals in the explanation of each drawing, and constituent elements having the same or substantially the same function are not indicated with reference numerals. It may not be attached.

In each embodiment described below, a case in which a medical information display device or a medical image processing device according to each embodiment is installed in an X-ray computed tomography (CT) apparatus will be exemplified.

The medical information display device or medical image processing device according to each embodiment is not limited to being installed in an X-ray CT apparatus, and can be operated by a computer having a processor and memories such as ROM and RAM as hardware resources. It may also be implemented as an independent device. In this case, a processor installed in a computer can implement various functions according to each embodiment by executing a program read from a ROM or the like and loaded into a RAM.

Further, the medical information display device or the medical image processing device according to each embodiment may be implemented by being installed in a medical image diagnostic device other than the X-ray CT device. In this case, the processor installed in each medical image diagnostic apparatus can implement the functions according to each embodiment by executing the program read from the ROM or the like and loaded into the RAM. Various functions according to each embodiment can be realized. Other medical image diagnostic devices include X-ray diagnostic devices, MRI (Magnetic Resonance Imaging) devices, ultrasonic diagnostic devices, SPECT (Single Photon Emission Computed Tomography) devices, PET (Positron Emission Computed Tomography) devices, SPECT devices, and X Various medical image diagnostic apparatuses are possible, such as a SPECT-CT apparatus in which a ray CT apparatus is integrated and a PET-CT apparatus in which a PET apparatus and an X-ray CT apparatus are integrated.

For example, there are various types of X-ray CT apparatuses such as third-generation CT and fourth-generation CT, and any type can be applied to each embodiment. Here, the third-generation CT is a Rotate/Rotate-Type in which the X-ray tube and detector are rotated around the subject as a unit. The fourth-generation CT is a Stationary/Rotate-Type in which a large number of X-ray detection elements arrayed in a ring are fixed and only the X-ray tube rotates around the subject.

(First embodiment)
FIG. 1 is a diagram showing an example of the configuration of an X-ray CT apparatus 1 equipped with a medical information display device or a medical image processing device according to an embodiment. The X-ray CT apparatus 1 irradiates a subject P with X-rays from an X-ray tube 11 and detects the irradiated X-rays with an X-ray detector 12 . X-ray CT apparatus 1 generates a CT image of subject P based on the output from X-ray detector 12 .

As shown in FIG. 1, the X-ray CT apparatus 1 has a gantry 10, a bed 30 and a console 40. As shown in FIG. In addition, in FIG. 1, a plurality of mounts 10 are drawn for convenience of explanation. The gantry 10 is a scanning device having a configuration for X-ray CT imaging of the subject P. As shown in FIG. The bed 30 is a transport device for placing a subject P to be subjected to X-ray CT imaging and positioning the subject P. As shown in FIG. A console 40 is a computer that controls the gantry 10 . For example, the gantry 10 and the bed 30 are installed in a CT examination room, and the console 40 is installed in a control room adjacent to the CT examination room. The gantry 10, the bed 30, and the console 40 are connected by wire or wirelessly so as to be able to communicate with each other.

Note that the console 40 does not necessarily have to be installed in the control room. For example, the console 40 may be installed in the same room together with the gantry 10 and the bed 30 . Also, the console 40 may be incorporated into the gantry 10 .

In this embodiment, the longitudinal direction of the rotation axis of the rotating frame 13 or the top plate 33 of the bed 30 in the non-tilt state is the Z-axis direction, and the axial direction perpendicular to the Z-axis direction and horizontal to the floor surface is the X direction. The axial direction, which is orthogonal to the Z-axis direction and perpendicular to the floor surface, is defined as the Y-axis direction.

As shown in FIG. 1, the gantry 10 includes an X-ray tube 11, an X-ray detector 12, a rotating frame 13, an X-ray high voltage device 14, a control device 15, a wedge 16, a collimator 17 and a data acquisition system. : DAS) 18.

The X-ray tube 11 is a vacuum tube having a cathode (filament) that generates thermoelectrons and an anode (target) that generates X-rays upon collision with thermoelectrons. The X-ray tube 11 uses a high voltage supplied from the X-ray high voltage device 14 to irradiate the subject P with X-rays by irradiating thermal electrons from the cathode to the anode.

Note that hardware for generating X-rays is not limited to the X-ray tube 11 . For example, instead of using the X-ray tube 11, X-rays may be generated using a fifth generation system. The 5th generation system consists of a focus coil that focuses the electron beam generated from the electron gun, a deflection coil that electromagnetically deflects it, and a target ring that surrounds the half circumference of the subject P and generates X-rays by colliding the deflected electron beam. including.

The X-ray detector 12 detects X-rays emitted from the X-ray tube 11 and passing through the subject P, and outputs an electrical signal corresponding to the dose of the detected X-rays to the DAS 18 . The X-ray detector 12 has, for example, an X-ray detection element array in which a plurality of X-ray detection elements are arranged in the channel direction along one circular arc centering on the focal point of the X-ray tube 11 . The X-ray detector 12 has, for example, a structure in which a plurality of X-ray detection elements are arranged in the slice direction (column direction, row direction) in the channel direction. Also, the X-ray detector 12 is, for example, an indirect conversion type detector having a grid, a scintillator array and a photosensor array. The scintillator array has a plurality of scintillators. The scintillator has a scintillator crystal that outputs an amount of light corresponding to the amount of incident X-rays. The grid has an X-ray shielding plate arranged on the X-ray incident surface side of the scintillator array and having a function of absorbing scattered X-rays. Note that the grid may also be called a collimator (one-dimensional collimator or two-dimensional collimator). The photosensor array has a function of converting the amount of light from the scintillator into an electrical signal. As the optical sensor, for example, a photomultiplier tube (photomultiplier: PMT) or the like is used. The X-ray detector 12 may be a direct conversion type detector having a semiconductor element that converts incident X-rays into electrical signals.

The rotating frame 13 is an annular frame that supports the X-ray tube 11 and the X-ray detector 12 so as to face each other and rotates the X-ray tube 11 and the X-ray detector 12 by means of a control device 15, which will be described later. An image field of view (FOV) is set in the opening 19 of the rotating frame 13 . For example, the rotating frame 13 is a casting made of aluminum. In addition to the X-ray tube 11 and the X-ray detector 12, the rotating frame 13 can also support the X-ray high voltage device 14, the wedge 16, the collimator 17, the DAS 18, and the like. Also, the rotating frame 13 can further support various configurations not shown in FIG.

The X-ray high voltage device 14 has a high voltage generator and an X-ray controller. The high voltage generator has electric circuits such as a transformer and a rectifier, and generates a high voltage to be applied to the X-ray tube 11 and a filament current to be supplied to the X-ray tube 11 . The X-ray control device controls the output voltage according to the X-rays emitted by the X-ray tube 11 . The high voltage generator may be of a transformer type or an inverter type. The X-ray high-voltage device 14 may be provided on the rotating frame 13 within the gantry 10 or may be provided on a fixed frame (not shown) within the gantry 10 . Note that the fixed frame is a frame that rotatably supports the rotating frame 13 .

The control device 15 includes a drive mechanism such as a motor and an actuator, and a processing circuit having a processor and memory for controlling the drive mechanism. The control device 15 receives input signals from the input interface 43 , an input interface provided in the gantry 10 , and the like, and controls the operations of the gantry 10 and the bed 30 . The operation control by the control device 15 includes, for example, control for rotating the rotating frame 13, control for tilting the gantry 10, control for operating the bed 30, and the like. The control for tilting the gantry 10 is performed by the control device 15 rotating the rotating frame 13 about an axis parallel to the X-axis direction based on inclination angle (tilt angle) information input through an input interface attached to the gantry 10. It is realized by letting Note that the control device 15 may be provided on the gantry 10 or may be provided on the console 40 .

Wedge 16 is a filter for adjusting the dose of X-rays emitted from X-ray tube 11 . Specifically, the wedge 16 transmits and attenuates the X-rays irradiated from the X-ray tube 11 so that the X-rays irradiated from the X-ray tube 11 to the subject P have a predetermined distribution. It is a filter that For example, the wedge 16 is a wedge filter or a bow-tie filter, and is constructed by processing aluminum or the like so as to have a predetermined target angle and a predetermined thickness.

The collimator 17 limits the irradiation range of X-rays transmitted through the wedge 16 . The collimator 17 slidably supports a plurality of lead plates that shield X-rays, and adjusts the form of slits formed by the plurality of lead plates. Note that the collimator 17 may also be called an X-ray diaphragm.

The DAS 18 reads from the X-ray detector 12 an electrical signal corresponding to the dose of X-rays detected by the X-ray detector 12 . The DAS 18 amplifies the readout electrical signals and integrates (sums) the electrical signals over the view period to collect detection data having digital values corresponding to the x-ray dose over the view period. The detected data are called projection data. The DAS 18 is implemented, for example, by an Application Specific Integrated Circuit (ASIC) incorporating circuit elements capable of generating projection data. Projection data is transmitted to the console 40 via a non-contact data transmission device or the like.

The detection data generated by the DAS 18 is transmitted from a transmitter having a light emitting diode (LED) provided on the rotating frame 13 to a non-rotating portion (for example, a fixed frame) of the pedestal 10 by optical communication. are omitted.) is transmitted to a receiver having a photodiode and transferred to the console 40 . The method of transmitting the detection data from the rotating frame 13 of the rotating portion to the non-rotating portion of the gantry 10 is not limited to the optical communication described above, and any method of non-contact data transfer may be adopted. .

In this embodiment, the X-ray CT apparatus 1 equipped with the integrating X-ray detector 12 will be described as an example. It can also be realized as an X-ray CT apparatus 1.

The bed 30 is a device for placing and moving the subject P to be scanned. The bed 30 has a base 31 , a bed driving device 32 , a top plate 33 and a support frame 34 . The base 31 is a housing that supports the support frame 34 so as to be vertically movable. The bed driving device 32 is a driving mechanism that moves the tabletop 33 on which the subject P is placed in the longitudinal direction of the tabletop 33 . The bed driving device 32 includes a motor, an actuator, and the like. The top plate 33 is a plate on which the subject P is placed. The top plate 33 is provided on the upper surface of the support frame 34 . The top plate 33 can protrude from the bed 30 toward the gantry 10 so that the whole body of the subject P can be imaged. The top plate 33 is made of, for example, carbon fiber reinforced plastic (CFRP) having good X-ray transparency and physical properties such as rigidity and strength. Further, for example, the inside of the top plate 33 is hollow. The support frame 34 supports the top plate 33 so as to be movable in the longitudinal direction of the top plate 33 . Note that the bed driving device 32 may move the support frame 34 in the longitudinal direction of the top plate 33 in addition to the top plate 33 .

Console 40 has memory 41 , display 42 , input interface 43 and processing circuitry 44 . Data communication between the memory 41, the display 42, the input interface 43 and the processing circuit 44 is performed via a bus (BUS). Although the console 40 is described as being separate from the gantry 10, the gantry 10 may include the console 40 or a part of each component of the console 40. FIG.

The memory 41 is implemented by, for example, a RAM (Random Access Memory), a semiconductor memory device such as a flash memory, a hard disk, an optical disk, or the like. For example, the memory 41 stores projection data and reconstructed image data. Also, for example, the memory 41 stores various programs. Note that the storage area of the memory 41 may be in the X-ray CT apparatus 1 or in an external storage device connected via a network. Here, the memory 41 is an example of a storage unit.

The display 42 displays various information. For example, the display 42 displays a medical image (CT image) generated by the processing circuit 44, a GUI (Graphical User Interface) for accepting various operations from the operator, and the like. Information displayed on display 42 includes spatial distribution of lesions relative to anatomy according to an embodiment. Various arbitrary displays can be used as the display 42 as appropriate. For example, the display 42 can be a liquid crystal display (LCD), a cathode ray tube (CRT) display, an organic electroluminescence display (OELD), or a plasma display.

Note that the display 42 may be provided anywhere in the control room. Also, the display 42 may be provided on the gantry 10 . The display 42 may be of a desktop type, or may be configured by a tablet terminal or the like capable of wireless communication with the main body of the console 40 . Also, one or more projectors may be used as the display 42 . Here, the display 42 is an example of a display section.

The input interface 43 receives various input operations from the operator, converts the received input operations into electrical signals, and outputs the electrical signals to the processing circuit 44 . For example, the input interface 43 receives acquisition conditions for acquiring projection data, reconstruction conditions for reconstructing CT images, image processing conditions for generating post-processed images from CT images, and the like from the operator. . Further, for example, the input interface 43 receives from the operator calculation conditions and the like when calculating the lesion amount distribution for the anatomical structure from the CT image data.

As the input interface 43, for example, a mouse, keyboard, trackball, switch, button, joystick, touch pad, touch panel display, etc. can be used as appropriate. In addition, in the present embodiment, the input interface 43 is not limited to having these physical operation components. For example, the input interface 43 includes an electrical signal processing circuit that receives an electrical signal corresponding to an input operation from an external input device provided separately from the device and outputs the electrical signal to the processing circuit 44. . Also, the input interface 43 may be provided on the gantry 10 . Also, the input interface 43 may be composed of a tablet terminal or the like capable of wireless communication with the main body of the console 40 . Here, the input interface 43 is an example of an input unit.

A processing circuit 44 controls the operation of the entire X-ray CT apparatus 1 . The processing circuit 44 has a processor and memories such as ROM and RAM as hardware resources. The processing circuit 44 has a system control function 441, an image generation function 442, an image processing function 443, a region extraction function 444, a peripherality calculation function 445, a lesion quantification function 446, and a display control function by means of a processor that executes programs developed in memory. Function 447 and the like are executed. Here, the processing circuit 44 is an example of a processing unit.

In the system control function 441 , the processing circuit 44 controls various functions of the processing circuit 44 based on input operations received from the operator via the input interface 43 . For example, processing circuitry 44 controls CT scans performed on gantry 10 . The processing circuit 44 acquires volume data about the subject P by an image generation function 442 and an image processing function 443, which will be described later, based on detection data obtained by CT scanning. In this embodiment, as an example, a case where volume data including at least lungs is acquired will be described.

Note that the processing circuit 44 may acquire volume data regarding the subject P from outside the X-ray CT apparatus 1 . Further, in the present embodiment, for example, a case where volume data obtained by the X-ray CT apparatus 1 is used is exemplified, but volume data obtained by another medical image diagnostic apparatus may be used. Here, the processing circuit 44 that implements the image generation function 442 is an example of an acquisition unit.

In the image generation function 442, the processing circuit 44 performs preprocessing such as logarithmic conversion processing, offset correction processing, inter-channel sensitivity correction processing, and beam hardening correction on the detection data output from the DAS 18 to generate data. . Processing circuitry 44 stores the generated data in memory 41 . Data before preprocessing (detection data) and data after preprocessing may be collectively referred to as projection data. The processing circuit 44 performs reconstruction processing using a filtered back projection method, an iterative reconstruction method, machine learning, etc. on the generated projection data (projection data after preprocessing) to obtain CT image data. Generate. The processing circuit 44 stores the generated CT image data in the memory 41 .

In the image processing function 443, the processing circuit 44 converts the CT image data generated by the image generating function 442 into tomographic image data of an arbitrary cross section by a known method based on the input operation received from the operator via the input interface 43. or 3D image data. For example, the processing circuit 44 performs three-dimensional image processing such as volume rendering, surface rendering, image value projection processing, MPR (Multi-Planar Reconstruction) processing, CPR (Curved MPR) processing, etc. on the CT image data, and performs arbitrary Generate rendering image data in the viewing direction. Note that the image generation function 442 may directly generate three-dimensional image data such as rendering image data in an arbitrary viewpoint direction, that is, volume data. The processing circuit 44 stores tomographic image data and three-dimensional image data in the memory 41 .

In the region extraction function 444, the processing circuitry 44 extracts bronchi included in the volume data. Processing circuitry 44 segments the bronchi based on the volume data. Here, segmentation of the bronchi means separating the area of the bronchi from the surrounding tissue of the bronchi, ie the area of the lung parenchyma. Bronchial segmentation may be performed by a known method. As for the bronchi region, if the bronchi region can be extracted by segmentation of other bronchi region such as lung parenchyma, the bronchi region may be extracted by segmentation of the other bronchi region. Processing circuitry 44 also identifies bifurcations of the bronchi based on the segmentation results. The processing circuitry 44 identifies the head side of the first bifurcation as the trachea, and identifies the distal side of the bifurcation as the bronchi. For example, processing circuitry 44 may identify the side on which the other bifurcation resides as the distal side. Here, the processing circuit 44 that implements the region extraction function 444 is an example of a region extraction unit. Also, the run of the extracted bronchi is an example of lung anatomy.

For example, in the region extraction function 444, the processing circuitry 44 segments the bronchi by Region Growing. In this case, the processing circuit 44 sets, for example, a data point that satisfies a predetermined CT value condition among the data points of the volume data as the starting point. The predetermined CT value condition is, for example, the CT value of air or a CT value of a size conforming to the CT value of air. The processing circuit 44 adds the data points satisfying the conditions of the CT value to the area among the data points near the set start point. Thereafter, the processing circuit 44 segments the region to be extracted as the bronchi region by enlarging the region until there are no data points satisfying the CT value condition in the vicinity of each data point contained in the region.

For example, in region extraction function 444, processing circuitry 44 may use a machine learning model to segment the bronchi. In this case, the machine learning model is a parameterized composite function obtained by synthesizing a plurality of functions for inputting volume data and outputting volume data indicating the bronchi region of the volume data. A parameterized composite function is defined by a combination of multiple adjustable functions and parameters. This machine learning model can be any parameterized composite function that satisfies the above requirements, but is assumed to be a multi-layered network model. A machine learning model is a deep neural network (DNN:Deep Neural Network) as an example. This DNN may be of any structure, such as ResNet (Residual Network), DenseNet (Dense Convolutional Network), U-Net, and the like. The configuration and parameters of the machine learning model may be determined in advance and stored in the memory 41 or the like, for example.

In the peripherality calculation function 445, the processing circuit 44 calculates the peripherality at each evaluation point in the lung parenchyma based on the extracted bronchi region. Here, each evaluation point in the lung parenchyma is an arbitrary value belonging to the region of the lung parenchyma among the values of the volume data. In addition, the degree of peripherality is an index that indicates how far the periphery of the lung exists along the course of the bronchi for an arbitrary evaluation point. Here, the processing circuit 44 that implements the degree-of-mindfulness calculation function 445 is an example of a calculator. Also, each evaluation point in the lung parenchyma is an example of arbitrary evaluation points in the volume data. Also, the degree of peripherality is an example of the first index value.

Here, the calculation of the degree of peripherality will be described with reference to the drawings. FIG. 2 is a diagram for explaining an example of calculation of the degree of peripherality according to the embodiment. FIG. 2 schematically illustrates the trachea area A0, the bronchi area A1 and the lung parenchyma area A2. Also, FIG. 2 schematically illustrates a plurality of evaluation points P1. Here, calculation of the degree of peripherality for an arbitrary evaluation point P1 indicated by an X in a white circle in FIG. 2 among the plurality of evaluation points P1 will be described.

In the degree-of-periphery calculation function 445, the processing circuit 44 identifies the data point P2 within the region A1 of the bronchus closest to the evaluation point P1. The processing circuit 44 calculates the distance L from the tracheal bifurcation A01 on the central side to the data point P2 on the peripheral side in the extracted bronchi region A1 as the degree of peripherality. Here, it is assumed that the trachea bifurcation A01 is a connecting portion between the trachea region A0 and the bronchi region A1. Thus, the degree of peripherality according to the embodiment is information that defines the position within the region A2 of the lung parenchyma based on the running of the bronchi, and is a continuous value that increases as it passes through bifurcations, that is, as it progresses to the periphery.

Note that the degree of peripherality is not limited to the distance L itself. For example, a value that changes according to the distance L, such as a value obtained by normalizing the distance L with the length of the tracheal region A0 or the longest length of the bronchial region A1. may be used. Further, the degree of peripherality may be set to a smaller value toward the distal end, such as the reciprocal of the distance from the tracheal bifurcation A01, for example.

Note that the processing circuit 44 may calculate the distance including the region A0 of the trachea, not limited to the distance from the trachea bifurcation A01, as the peripheral degree.

The processing circuit 44 may use one data point as the nearest neighbor data point P2, or may use two or more data points. As the two or more data points, for example, the closest data point P2 and the second closest data point can be used.

When there are a plurality of data points P2 closest to one evaluation point P1, the processing circuit 44 may calculate the degree of clarity according to a predetermined rule. As an example, processing circuitry 44 employs the highest degree of rectilinearity of the plurality of nearest neighbor data points. As another example, processing circuitry 44 employs the lowest rectilinearity of the plurality of nearest neighbor data points. As another example, processing circuitry 44 employs an average value of the degree of acuteness for each of the plurality of nearest neighbor data points. It is assumed that these rules are determined in advance and stored in the memory 41 or the like.

In the lesion quantification function 446, the processing circuitry 44 derives an index value for the lung lesion volume based on the volume data. The lesion may be one whose spatial distribution is difficult to grasp, such as inflammation, fibrosis, alveolar hemorrhage, pulmonary edema, tumor, and the like. In the present embodiment, index values relating to lesions are exemplified, but index values relating to other properties and characteristics of lesions, such as state quantities correlated with lesions, may be used. Here, the index value relating to the amount of lung lesions is an index value based on volume data relating to lungs among volume data including at least lungs. For example, the index value related to the lung lesion amount is lung lesion information indicating the presence or absence or degree of any type of lesion at each evaluation point of the lung parenchyma. As an example, the index value may be any voxel value or pixel value belonging to the lung parenchyma region in the volume data or image data based on the volume data. A value based on a voxel value or a pixel value may be used as the index value. The value based on the voxel value or pixel value may be a value indicating whether the voxel value or pixel value satisfies a predetermined threshold range, a normalized value, or a deviation value. It may be a statistical value such as Voxel or pixel values may represent CT values. For example, if CT values are utilized, each CT value can be compared to a predetermined threshold. For example, if the CT value satisfies a threshold condition, that voxel or pixel can be treated as a derived score with a lesion. In the following description, the index value related to the amount of lung lesions may simply be referred to as lesion information.

Note that the technique according to the present embodiment can be applied to index values related to any type of lesion. In addition, in deriving the index value related to the amount of lesion according to the present embodiment, any extraction method may be used to extract the lesion or the state quantity correlated with the lesion. As an example, the processing circuit 44 classifies the lung parenchyma into a plurality of texture patterns based on volume data in the same manner as the technology disclosed in Japanese Patent Laid-Open No. 2019-010410. Each of the plurality of texture patterns is a texture pattern on a CT image and includes a texture pattern characteristic of any lesion. For example, texture patterns characteristic of lesions of diffuse lung disease include ground-glass-like structures.

In the lesion quantification function 446, the processing circuit 44 calculates the lesion amount for the anatomical structure based on the derived lesion information of each evaluation point and the peripherality of each evaluation point calculated by the peripherality calculation function 445. Generate distribution information for Here, the lesion volume distribution information with respect to the anatomical structure is information indicating the relationship between the degree of peripherality and the lesion volume. Here, the processing circuit 44 that implements the lesion quantification function 446 is an example of a derivation unit. Also, the index value related to the amount of lesion is an example of the second index value.

As an example, lesion volume is the number of scores derived to have a lesion. If the scores are uniformly distributed in space, the number of scores correlates with the volume of lesions in the lung parenchyma. As another example, the lesion amount is the ratio of the number of evaluation points that were derived to have a lesion among the evaluation points belonging to each degree of peripherality or each category of peripheral degree. If the scores are uniformly distributed in space, the score percentages indicate the volume of lesions occupying each peripheral degree region of the lung parenchyma. If the evaluation points are not spatially uniformly distributed, the number of evaluation points derived to indicate that there is a lesion may be normalized by the number of evaluation points per unit volume or the like. As a result, it is possible to grasp the lesion distribution with respect to the anatomical structure intensively for a part of the lung, and reduce the number of evaluation points for other parts of the lung to reduce the calculation cost.

The processing circuit 44 in the display control function 447 causes the display 42 to display an image based on various image data generated by the image processing function 443 . The images to be displayed on the display 42 include images showing distribution information of lesion amounts with respect to anatomical structures derived by the lesion quantification function 446 . In other words, the processing circuit 44 causes the display 42 to display the peripheral degree and the lesion information in association with each other. Images to be displayed on the display 42 include CT images based on CT image data, cross-sectional images based on cross-sectional image data of arbitrary cross-sections, rendering images in arbitrary viewpoint directions based on rendering image data in arbitrary viewpoint directions, and the like. Images displayed on the display 42 include images for displaying operation screens and images for displaying notifications and warnings to the operator. Here, the processing circuit 44 that implements the display control function 447 is an example of a display control unit.

Note that the image data indicating the distribution information of the lesion amount with respect to the anatomical structure may be generated by any one of the image processing function 443 , lesion quantification function 446 and display control function 447 .

Note that the functions 441 to 447 are not limited to being realized by a single processing circuit. A plurality of independent processors may be combined to configure the processing circuit 44, and each processor may implement each function 441 to 447 by executing each program. Here, each of the functions 441-447 may be appropriately distributed or integrated in a single or a plurality of processing circuits and implemented.

Although the console 40 has been described as a single console that executes a plurality of functions, separate consoles may execute a plurality of functions. For example, the functions of the processing circuit 44 such as the image generating function 442, the image processing function 443, the region extracting function 444, the peripheral degree calculating function 445, and the lesion quantifying function 446 may be distributed.

Note that part or all of the processing circuit 44 may be included in an integrated server that collectively processes detection data acquired by a plurality of medical image diagnostic apparatuses without being limited to being included in the console 40 . .

At least one of post-processing, region extraction processing, severity calculation processing, lesion quantification processing, and display processing may be performed by either the console 40 or an external workstation. Alternatively, both the console 40 and the work station may be processed at the same time. As the work station, a computer having, as hardware resources, a processor that implements functions corresponding to each process and memories such as ROM and RAM, etc., can be appropriately used.

In reconstructing the X-ray CT image data, either the full-scan reconstruction method or the half-scan reconstruction method may be applied. For example, in the image generation function 442, the processing circuit 44 uses projection data for 360 degrees around the object P in the full scan reconstruction method. Also, the processing circuit 44 uses projection data for 180 degrees plus the fan angle in the half-scan reconstruction method. For simplicity of explanation, the processing circuit 44 uses a full-scan reconstruction method for reconstruction using projection data for 360 degrees around the subject P. FIG.

It should be noted that the technology according to the present embodiment is also applicable to a single-tube type X-ray computed tomography apparatus, in which a plurality of pairs of X-ray tubes and detectors are mounted on a rotating ring, so-called multi-tube type X-ray It can also be applied to a computer tomography apparatus.

Note that the technique according to the present embodiment can also be applied to the X-ray CT apparatus 1 that is configured to perform imaging using the dual energy method. At this time, the X-ray high-voltage device 14 can alternately switch the energy spectrum of the X-rays emitted from the X-ray tube 11, for example, by high-speed switching between two voltage values. That is, the X-ray CT apparatus 1 is configured to acquire projection data in each acquisition view while modulating the tube voltage at the timing according to the tube voltage modulation control signal. By imaging the subject with different tube voltages, it is possible to improve the contrast of shades in the CT image based on the energy transparency of the substance for each X-ray energy spectrum.

It is assumed that the X-ray CT apparatus 1 according to this embodiment is configured to read electric signals from the X-ray detector 12 by a sequential reading method.

The X-ray CT apparatus 1 according to this embodiment may be configured as a standing CT. In this case, instead of moving the top plate 33, a patient support mechanism that supports the subject P in the standing position and is configured to be movable along the rotation axis of the rotating portion of the gantry 10 may be provided. Further, the X-ray CT apparatus 1 according to this embodiment may be configured as a mobile CT in which the gantry 10 and the bed 30 are movable.

FIG. 3 is a flowchart illustrating an example of processing for displaying a lesion amount distribution with respect to an anatomical structure according to the embodiment. It is assumed that the following flow is performed after volume data including at least lungs is acquired by the system control function 441 .

First, the region extraction function 444 segments the bronchi based on the volume data. (S101). In addition, the region extraction function 444 identifies bifurcations of bronchi based on the segmentation results (S102). Then, the peripherality calculation function 445 calculates the peripherality at each evaluation point in the lung parenchyma based on the extracted bronchi region (S103).

The lesion quantification function 446 detects lung lesions based on the volume data to derive an index value for the amount of lung lesions (S104). The process of this step may be executed prior to the process of S101, or may be executed in parallel with the processes of S101 to S103.

In addition, the lesion quantification function 446 quantifies lung lesions for each lung peripherality based on the lung peripherality and lung lesion information (S105). That is, the lesion quantification function 446 generates lesion volume distribution information for the anatomy. In addition, the display control function 447 causes the display 42 to display the generated lesion amount distribution information for the anatomical structure (S106). The flow of FIG. 3 then ends.

Here, the display of the lesion amount distribution information with respect to the anatomical structure will be described with reference to the drawings. FIG. 4 is a diagram illustrating an example of display of the lesion amount distribution with respect to the anatomical structure according to the embodiment; In the graph shown in FIG. 4, the vertical axis and the horizontal axis indicate the lesion amount and lung peripherality, respectively. As an example, the processing circuit 44 generates image data for displaying a graph showing the size of the lesion volume with respect to the degree of peripherality as the distribution information of the lesion volume with respect to the anatomical structure. The display control function 447 causes the display 42 to display an image G1 including a graph showing the size of the lesion amount with respect to the degree of lung peripherality based on the generated image data. According to the mode of displaying the graph shown in FIG. 4, it is possible to grasp how the lesions are three-dimensionally distributed along the bronchi.

As described above, in the X-ray CT apparatus 1 equipped with the medical image processing apparatus according to the embodiment, the processing circuit 44 can realize the system control function 441, the region extraction function 444, the peripherality calculation function 445, and the lesion quantification function 446. is configured to The processing circuit 44 in the system control function 441 acquires volume data including at least lungs. In the region extraction function 444, the processing circuitry 44 extracts bronchi included in the volume data. In the peripheral degree calculation function 445, the processing circuit 44 calculates the lung peripheral degree at an arbitrary evaluation point in the volume data based on the extracted bronchi. In the lesion quantification function 446, the processing circuitry 44 derives an index value for the lung lesion volume based on the volume data.

According to this configuration, the degree of lung peripherality can be given to each evaluation point of the lung parenchyma along the course of the bronchi. Also, lung regions can be segmented based on lung peripherality and lesions within each segmented region can be quantified. Here, the run of the bronchi is an example of lung anatomy. In other words, according to the medical image processing apparatus according to the embodiment, it is possible to evaluate the distribution of the lesion amount with respect to the anatomical structure.

In addition, in the X-ray CT apparatus 1 equipped with the medical image processing apparatus according to the embodiment, the processing circuit 44 is configured to be able to further implement the display control function 447 . Further, in the X-ray CT apparatus 1 equipped with the medical information display apparatus according to the embodiment, the processing circuit 44 is configured to be able to implement the display control function 447 . In the display control function 447, the processing circuit 44 performs display based on volume data including at least lungs. As an example, the processing circuitry 44 displays the degree of peripheralness based on the bronchi running in the lungs and the amount of lesions in the lungs in association with each other. The processing circuit 44 causes the display 42, for example, to display display information in which the peripheral degree and the lesion amount are associated with each other.

According to this configuration, the correspondence between the lung peripherality given to each evaluation point of the lung parenchyma along the bronchial run and the lesion amount in each divided region, which is the lung region divided based on the lung peripherality. can be displayed. According to this display mode, it is possible to easily grasp the spatial distribution of lesions. In other words, the medical information display device according to the embodiment can provide information useful for diagnosis of various diseases. Here, the run of the bronchi is an example of lung anatomy. That is, according to the medical information display device according to the embodiment, it is possible to evaluate the distribution of the lesion amount with respect to the anatomical structure.

For example, in novel coronavirus pneumonia, bilateral ground-glass opacities are known to occur frequently in the lung periphery. Under such circumstances, the medical information display device according to the embodiment can display information quantified along the degree of lung peripherality regarding ground-glass opacities as lesions. Therefore, the operator of the medical information display device according to the embodiment can easily determine whether or not the CT image is characteristic of novel coronavirus pneumonia.

(Second embodiment)
In this embodiment, differences from the first embodiment will be mainly described. FIG. 5 is a diagram for explaining another example of calculation of the degree of peripherality according to the embodiment. FIG. 5 schematically illustrates the trachea area A0, the bronchi area A1 and the lung parenchyma area A2. Moreover, FIG. 5 schematically illustrates a plurality of evaluation points P1. Here, calculation of the degree of peripherality for an arbitrary evaluation point P1 indicated by an X in a white circle in FIG. 5 among the plurality of evaluation points P1 will be described.

In the region extraction function 444, the processing circuit 44 sets the trachea region A0 to the order 0, and calculates the number of bifurcations that pass when proceeding from the trachea region A0 to the bronchus region A1 to the peripheral side, that is, the number of bifurcations, as the bronchus region. It is set as the order of each region in A1. For example, the processing circuit 44 sets the order of the bronchial region A1 connected to the tracheal region A0 via the tracheal bifurcation A01 to one. Further, for example, the processing circuit 44 sets the order of the bronchial region A1 to 2, which is connected to the peripheral side of the bronchial region A1 having the order of 1 through the bifurcation.

In the degree-of-periphery calculation function 445, the processing circuit 44 identifies the data point P2 within the region A1 of the bronchus closest to the evaluation point P1. The processing circuit 44 calculates the order of the region A1 of the bronchi in which the data point P2 is located as the peripheral degree.

In this way, the degree of distality according to the present embodiment is a discrete value that increases as it passes through bifurcations, that is, as it progresses to the periphery. Even with this configuration, effects similar to those of the above-described embodiment can be obtained.

(Third embodiment)
In this embodiment, differences from the second embodiment will be mainly described. FIG. 6 is a diagram for explaining another example of calculation of the degree of peripherality according to the embodiment. As shown in FIG. 6, the peripheral degree calculation function 445 according to the present embodiment, in the same manner as the bronchi segmentation by the region extraction function 444, divides a plurality of bronchi regions A1 of the same order into the lung parenchyma by the region expansion method. Expand in area A2.

In the area expansion RG by the area expansion method of FIG. 6, when each evaluation point P1 of the lung parenchyma area A2 satisfies a predetermined condition, the evaluation point P1 is added to the combined areas R12 and R13 that are sequentially expanded. A combined region R12 in FIG. 6 is formed by expanding a plurality of bronchus regions A1 having a degree of 2 by a region expansion method and combining the expanded regions on a plurality of lung parenchymal regions A2. Similarly, the joint region R13 in FIG. 6 is the region formed on the region A2 of the lung parenchyma by extension from the region A1 of the plurality of bronchi of order 3. FIG.

In the peripheral degree calculation function 445, the processing circuit 44 calculates the order of the bronchus region A1, which is the starting point for forming each joint region, as the degree of peripherality of each evaluation point P1 in each joint region. In the example of FIG. 6 , the evaluation point P1 in the combined region R12 formed from a plurality of bronchus regions A1 having a degree of 2 is calculated to have a peripheral degree of 2 for all evaluation points.

Thus, the degree of peripherality according to the present embodiment has a constant size in each of the connecting regions R12 and R13, and is the order of the bronchial region A1 that is the starting point of region expansion. Even with this configuration, effects similar to those of the above-described embodiment can be obtained.

(Fourth embodiment)
In this embodiment, differences from the first embodiment will be mainly described. In each of the above-described embodiments, a case was exemplified in which the degree of peripherality is calculated so as to increase toward the peripheral side along the running of the bronchi, but the present invention is not limited to this.

FIG. 7 is a diagram for explaining another example of calculation of the degree of peripherality according to the embodiment. FIG. 7 illustrates any of the regions of the lung. In the present embodiment, each region of the lung is each region such as lung lobes and lung segments obtained by dividing the lung into a plurality of anatomical regions. Here, the outline of each region of the lung is an example of the anatomical structure of the lung. The outer shape of each area is defined by the boundary surface of each area. Also, the boundaries of each region include at least the bronchus region A1 and the lung parenchyma region A2. Also, FIG. 7 schematically illustrates a plurality of evaluation points P1. Here, calculation of the degree of peripherality for an arbitrary evaluation point P1 indicated by an X in a white circle in FIG. 7 among the plurality of evaluation points P1 will be described.

As an example, the processing circuit 44 in the degree-of-periphery calculation function 445 calculates a continuous value corresponding to the depth D2 from the boundary surface of each region to the boundary with respect to the evaluation point P1 as the degree of perceptibility of the evaluation point P1.

Note that the processing circuit 44 in the degree-of-periphery calculation function 445 calculates a discrete value indicating the order of the division corresponding to the depth from the boundary surface of each region to the boundary with respect to the evaluation point P1 as the degree of degree of pervasiveness of the evaluation point P1. may For example, the processing circuit 44 sets the order of the region R21 from the boundary surface of each region to the depth D1 to one. For example, the processing circuit 44 sets the order of the region R22 from the depth D1 to the depth D2 of each region to two. For example, the processing circuit 44 sets the degree of the region R23 on the distal side of the depth D2 of each region, ie, the inner region of each region, to three.

Thus, the degree of peripherality according to the present embodiment is calculated according to the depth from the boundary surface of each region to the boundary in each region of the lung. Even with this configuration, effects similar to those of the above-described embodiment can be obtained.

Note that the degree of peripherality may be given according to the depth from the outer surface of the entire lung to the inside.

It should be noted that the degrees of peripherality according to the above-described embodiments can also be used in combination. For example, the total value or average value of the degree of peripherality according to each embodiment can be given as the peripherality of each evaluation point P1. At this time, different weighting can be performed among a plurality of degrees of peripherality. For example, it is also possible to give greater weight to the degree of peripherality according to the first embodiment, the second embodiment, or the third embodiment than the degree of peripherality according to the fourth embodiment.

(Fifth embodiment)
In this embodiment, differences from the first embodiment will be mainly described. The display of the lesion amount distribution information for the anatomical structure is not limited to the image G1 described with reference to FIG. FIG. 8 is a diagram illustrating another example of display of the lesion amount distribution with respect to the anatomical structure according to the embodiment; The display control function 447 may cause the display 42 to display an image G2 including a graph showing the size of the lesion amount with respect to the degree of lung peripherality, as shown in FIG. That is, the display mode of the graph showing the size of the lesion amount with respect to the degree of lung peripherality may be a bar graph as shown in FIG. 2 or a line graph as shown in FIG. Even with such a display mode, the same effect as in the first embodiment can be obtained.

(Sixth embodiment)
In this embodiment, differences from the first embodiment will be mainly described. FIG. 9 is a diagram illustrating another example of display of the lesion amount distribution with respect to the anatomical structure according to the embodiment; The display of the distribution information of the lesion amount with respect to the anatomical structure is, as shown in FIG. good too. The graph of the image G3 illustrated in FIG. 9 is a bar graph like FIG. Even with such a display mode, the same effect as in the first embodiment can be obtained. Also, the total amount of lesions can be quantitatively grasped from the cumulative lesion amount.

(Seventh embodiment)
In this embodiment, differences from the sixth embodiment will be mainly described. FIG. 10 is a diagram illustrating another example of display of the lesion amount distribution with respect to the anatomical structure according to the embodiment; The graph of the image G4 illustrated in FIG. 10 is a line graph as in FIG. Even with such a display mode, the same effects as in the sixth embodiment can be obtained.

(Eighth embodiment)
In displaying the lesion amount distribution information according to each of the above-described embodiments, the display control function 447 can arbitrarily change the scale of the graph based on the input operation received from the operator via the input interface 43 . Here, the scale of the graph means the target area for generating the lesion amount distribution information displayed by the graph. In other words, the distribution information of the amount of lesion displayed as a graph can be generated and displayed for each area such as the entire left and right lungs, the left and right lungs, the lung lobes, the lung segments, and the like. Note that the lesion amount distribution information is not limited to being generated and displayed for each region, and may be generated and displayed as a graph that can be viewed for a plurality of arbitrary regions. Even with such a display mode, effects similar to those of the above-described embodiments can be obtained.

(Ninth embodiment)
In each of the above-described embodiments, the case where the lesion amount distribution is displayed as the lesion amount distribution information is displayed as an example, but the present invention is not limited to this. As the lesion amount distribution information, it is also possible to display the distribution of the degree of peripherality with respect to the lesion amount. For example, the display control function 447 can replace the axis of each graph based on the input operation received from the operator via the input interface 43 . Even with such a display mode, effects similar to those of the above-described embodiments can be obtained. In addition, the operator can arbitrarily switch between the distribution of the lesion amount along the running of the bronchi and the peripheral degree where the lesion amount is large, that is, the position along the running of the bronchi where the lesion amount is large according to the content to be confirmed. can be confirmed.

(Tenth embodiment)
In displaying the lesion amount distribution information according to each of the above-described embodiments, the display control function 447 may cause the display 42 to further display the corresponding CT image. For example, the operator designates an arbitrary point on the graph. At this time, the display control function 447 causes the display 42 to display a CT image representing points on the specified graph based on the input operation received from the operator via the input interface 43 . The CT image representing the specified point on the graph is, for example, a CT image showing an MPR cross section passing through a representative point of the region having the peripherality of the specified point and in which a lesion exists. More preferably, the CT image representing the designated point on the graph is, for example, an MPR cross-section passing through the representative point of the region having the peripheral degree of the designated point and having the largest number of lesions. is a CT image shown. The display control function 447 may display the CT image instead of displaying the graph, or may display the CT image together with the graph. Even with such a display mode, effects similar to those of the above-described embodiments can be obtained.

(Eleventh embodiment)
In the display of the lesion amount distribution information according to each of the above-described embodiments, when the lung peripherality or the range of the lung peripherality is designated on the graph, the display control function 447 A lesion area having a range of lung peripherality may be highlighted, for example, on a CT image showing an MPR section or a three-dimensional CT image. Even with such a display mode, effects similar to those of the above-described embodiments can be obtained.

(Twelfth embodiment)
In the display of lesion amount distribution information according to each of the above-described embodiments, when a point on the CT image displayed as described above is specified, the display control function 447 displays the position of the point on the graph. You may At this time, the peripherality calculation function 445 calculates the lung peripherality at the specified point. The display control function 447 then highlights the position or range on the graph corresponding to the calculated lung peripherality, or displays an icon at the position or range. Even with such a display mode, effects similar to those of the above-described embodiments can be obtained.

(Thirteenth Embodiment)
The lesion volume distribution information according to each of the above-described embodiments can also be calculated and displayed by dividing the degree of peripherality into arbitrary segments. FIG. 11 is a diagram showing an example of display of the operation screen for the number of divisions related to the degree of peripherality. FIG. 11 illustrates an image G5 that is displayed when the operator performs an input operation to change the display division of the degree of peripheries from 5 divisions to 3 divisions in the image G1 of FIG. As shown in FIG. 11, the display control function 447 causes the display 42 to further display the thresholds related to the display classification of the degree of perceptibility in accordance with the input operation of the thresholds received from the operator via the input interface 43 . The input operation of the threshold value by the operator may be an operation of inputting the value of the degree of peripherality to be used as a threshold, an operation of inputting the number of divisions, or an operation of inputting a GUI for changing the position of the icon indicating the threshold on the image G5. can be operated.

The lesion quantification function 446 regenerates lesion amount distribution information based on the peripherality threshold according to the input operation. In addition, the display control function 447 causes the display 42 to display the lesion volume distribution information for the regenerated anatomical structure. FIG. 12 is a diagram illustrating another example of display of the lesion amount distribution with respect to the anatomical structure according to the embodiment; FIG. 12 exemplifies an image G6 showing lesion amount distribution information displayed in three display sections according to the two set thresholds.

In this way, the display control function 447 can arbitrarily change the number of divisions related to the degree of peripherality based on the input operation received from the operator via the input interface 43 . According to the technology according to the present embodiment, display divisions are divided according to an arbitrary degree of peripherality regardless of the number of divisions. It can be divided into sections and displayed. Even with such a display mode, effects similar to those of the above-described embodiments can be obtained.

In the example of FIG. 12, the peripheral degree is divided into three sections, the proximal portion, the intermediate portion, and the distal portion, but the number of divisions of the peripheral degree is not limited to this. The number of divisions of the degree of peripherality may be two divisions or four or more divisions. Here, the division number of the degree of peripherality can be arbitrarily set for each region in which lesion amount distribution information is generated and displayed, such as the entire left and right lungs, the left and right lungs, the lung lobes, and the lung segments.

Preferably, the degree of peripherality is divided into two sections, a peripheral portion and a central portion, for each lung lobe. That is, lesion volume can also be calculated for each of the 2 segments of each of the 5 lobes, including 3 lobes of the right lung and 2 lobes of the left lung, for a total of 10 segments. In this case, the lesion volume distribution information may be generated and displayed as a graph for each lung lobe divided by one threshold for each lung lobe, or as a graph of the entire left and right lungs that can list the lesion volume of 10 sections. and may be displayed. Alternatively, and preferably, instead of lobes, the peripheries are divided into two segments, peripheral and central, for each lung segment. That is, lesion volume can also be calculated for each of the 2 segments of each of the 18 lung segments, for a total of 36 segments. In this case, the lesion volume distribution information may be generated and displayed as a graph for each lung segment divided by one threshold for each lung segment, or a graph of the entire left and right lungs that can list the lesion volume of 36 segments. may be generated and displayed as

(14th embodiment)
The X-ray CT apparatus 1 equipped with the medical information display device or the medical image processing device according to each of the above-described embodiments can also calculate and display information indicating changes over time in the lesion volume distribution with respect to the anatomical structure. . FIG. 13 is a diagram illustrating another example of display of the lesion amount distribution with respect to the anatomical structure according to the embodiment; As shown in FIG. 13, the display control function 447 may display an image G7 showing changes over time in the lesion volume distribution with respect to the anatomical structure. The image G7 includes an image G71 that displays the distribution information of the amount of lesion related to "examination 1". The image G7 also includes an image G72 that displays the lesion amount distribution information related to "examination 2" different from "examination 1". Here, the lesion amount distribution information related to “examination 1” is the lesion amount distribution information generated based on the volume data obtained at the first time point. Similarly, the lesion amount distribution information related to “examination 2” is lesion amount distribution information generated based on volume data obtained at a second time point different from the first time point.

For example, the lesion amount distribution information display process illustrated in FIG. Each process relating to two volume data obtained at different points in time may be executed as a series of flows or as separate flows. In other words, the display control function 447 does not have to perform the processing of S101 to S105 of FIG. 3 again for volume data for which lesion amount distribution information has already been generated. Even with such a display mode, effects similar to those of the above-described embodiments can be obtained. In addition, the operator can easily grasp the time course of the lesion amount at each peripheral degree.

The image G71 and the image G72 may be displayed simultaneously like the image G7 illustrated in FIG. 13, or may be displayed sequentially based on the input operation received from the operator via the input interface 43.

(15th embodiment)
In this embodiment, differences from the fourteenth embodiment will be mainly described. FIG. 14 is a diagram illustrating another example of display of the lesion amount distribution with respect to the anatomical structure according to the embodiment; The display control function 447, as shown in the image G8 in FIG. 14, displays the distribution information of the lesion amount related to "examination 1" at a first time point and the lesion amount distribution information related to "examination 2" at a second time point different from the first time point. The distribution information of the amount of lesions may be displayed in one graph. Even with such a display mode, effects similar to those of the fourteenth embodiment can be obtained.

(16th embodiment)
In this embodiment, differences from the fourteenth embodiment will be mainly described. FIG. 15 is a diagram illustrating another example of display of the lesion amount distribution with respect to the anatomical structure according to the embodiment; The display control function 447, as shown in the image G9 of FIG. may be displayed in one graph. That is, the lesion quantification function 446 calculates the lung lesion amount for each lung peripheral degree from each volume data, and then further calculates the difference in the lung lesion amount for each lung peripheral degree. Even with such a display mode, effects similar to those of the fourteenth embodiment can be obtained.

Incidentally, referring to FIGS. 13 to 15, the case of calculating and displaying the information indicating the temporal change of the lesion amount distribution with respect to the anatomical structure based on the two volume data obtained at different time points has been exemplified. , but not limited to this. Information indicating changes over time in the lesion volume distribution with respect to the anatomical structure may be calculated and displayed based on three or more pieces of volume data.

It should be noted that the display of lesion amount distribution information according to each of the above-described embodiments can also be used in combination. For example, the display control function 447 can arbitrarily switch each display based on an input operation received from the operator via the input interface 43 . If additional processing occurs when switching the display, the medical information display device further acquires the information for display obtained in each of the above-described processing. In addition, the medical image processing apparatus further executes processing related to each display described above, and further generates information for display.

The term "processor" used in the above description means circuits such as CPU, GPU, ASIC, and programmable logic device (PLD). PLDs include Simple Programmable Logic Devices (SPLDs), Complex Programmable Logic Devices (CPLDs), and Field Programmable Gate Arrays (FPGAs). The processor realizes its functions by reading and executing the programs stored in the memory circuit. The memory circuit storing the program is a computer-readable non-temporary recording medium. It should be noted that instead of storing the program in the memory circuit, the program may be directly installed in the circuit of the processor. In this case, the processor implements its functions by reading and executing the program embedded in the circuit. Also, functions corresponding to the program may be realized by combining logic circuits instead of executing the program. Note that each processor of the present embodiment is not limited to being configured as a single circuit for each processor, and may be configured as one processor by combining a plurality of independent circuits to realize its function. good. Furthermore, a plurality of components in FIG. 1 may be integrated into one processor to realize its functions.

According to at least one embodiment described above, it is possible to evaluate the distribution of lesion volume with respect to the anatomy.

While several embodiments of the invention have been described, these embodiments have been presented by way of example and are not intended to limit the scope of the invention. These novel embodiments can be implemented in various other forms, and various omissions, replacements, and modifications can be made without departing from the scope of the invention. These embodiments and their modifications are included in the scope and gist of the invention, and are included in the scope of the invention described in the claims and equivalents thereof.

Regarding the above embodiment, the following appendices are disclosed as one aspect and optional features of the invention.
(Appendix 1)
A display control unit that performs display based on volume data including at least organs,
The display control unit associates and displays a first index value based on the anatomical structure of the organ and a second index value based on the volume data related to the organ.
Medical information display device.
(Appendix 2)
The anatomical structure of the organ may be tubular tissue contained in the organ.
(Appendix 3)
The tubular tissue may have at least one branch.
(Appendix 4)
The first index value may be an index value based on the number of branches of the tubular tissue.
(Appendix 5)
The first index value may be an index value indicating the peripheral degree of the tubular tissue.
(Appendix 6)
The first index value may be tissue included in the organ, and may be information defining a position of the tubular tissue in surrounding tissue.
(Appendix 7)
The position of the tubular tissue in the surrounding tissue may be information defining a position based on the travel of the tubular tissue.
(Appendix 8)
The second index value may be an index value relating to the lesion amount of the organ.
(Appendix 9)
The second index value may be a voxel value or a pixel value satisfying a predetermined threshold condition among voxel values of the volume data or pixel values of image data based on the volume data.
(Appendix 10)
The voxel value or the pixel value may be a CT value or a value based on the CT value.
(Appendix 11)
an acquisition unit that acquires the volume data;
a region extraction unit that extracts the anatomical structure of the organ included in the volume data;
a calculation unit that calculates a first index value at an arbitrary evaluation point in the volume data based on the extracted anatomical structure of the organ;
The medical image processing apparatus may further include a deriving unit that derives the second index value based on the volume data.
(Appendix 12)
The score may be set on the surrounding tissue.
(Appendix 13)
The first index value may be calculated based on a point on the tubular tissue closest to the evaluation point.
(Appendix 14)
The first index value may be a length on the tubular tissue from a central reference point on the tubular tissue to a distal point on the tubular tissue closest to the evaluation point. .
(Appendix 15)
The first index value may be the number of branches on the tubular tissue from a central reference point on the tubular tissue to a distal point on the tubular tissue closest to the evaluation point. good.
(Appendix 16)
The first index value is formed on the surrounding tissue by a region expansion method from a plurality of regions on the tubular tissue having an equal number of branches on the tubular tissue from a central reference point on the tubular tissue. and the number of branches of the combined region in which the evaluation point is included.
(Appendix 17)
The reference point may be set on the most proximal bifurcation on the tubular tissue.
(Appendix 18)
The anatomical structure of the organ may be the contour of the organ or the contour of the anatomical region of the organ.
(Appendix 19)
The first index value may be the distance from the outer surface of the organ or the distance from the boundary surface of the anatomical region of the organ.
(Appendix 20)
The first index value may be a distance segment from the outer surface of the organ or a distance segment from the boundary surface of the anatomical region of the organ.
(Appendix 21)
When the organ is a lung, the anatomical structure of the organ may be a running bronchi contained in the lung.
(Appendix 22)
The tissue surrounding the tubular tissue may be lung parenchyma contained in the lung.
(Appendix 23)
When the organ is the brain, liver or kidney, the anatomical structure of the organ may be blood vessels contained in the organ.
(Appendix 24)
The apparatus may further include a display unit that displays a first index value based on the anatomical structure of the organ and a second index value related to the lesion amount of the organ in association with each other.
(Appendix 25)
A display in which the first index value and the second index value are associated is a graph in which one of the vertical axis and the horizontal axis indicates the first index value and the other indicates the second index value. may include an indication of
(Appendix 26)
The volume data may include two data obtained at different times.
(Appendix 27)
The display in which the first index value and the second index value are associated with each other may include display regarding each of the two data.
(Appendix 28)
The display in which the first index value and the second index value are associated is a display indicating a difference in the first index value or the second index value between the two data. good too.
(Appendix 29)
Associating the first index value with the second index value expresses how the second index value changes with respect to the first index value with a chart, picture, or image. may include doing
(Appendix 30)
A method for controlling each component of the above medical information display device.
(Appendix 31)
A method for controlling each component of the above medical image processing apparatus.
(Appendix 32)
A program that causes a computer to execute each component of the above medical information display device.
(Appendix 33)
A program that causes a computer to execute each component of the above medical image processing apparatus.
(Appendix 34)
A storage medium for storing the above program executed by a computer.

Claims (18)

A display control unit that performs display based on volume data including at least lungs,
The display control unit associates and displays a first index value based on the anatomical structure of the lung and a second index value based on the volume data related to the lung.
Medical information display device. 2. The medical information display device according to claim 1, wherein said first index value is an index value based on running of bronchi included in said lungs. 3. The medical information display device according to claim 1, further comprising a display unit that displays the first index value and the second index value in association with each other. 3. The medical information display device according to claim 2, wherein said first index value is an index value based on the number of bifurcations of said bronchi. 5. The medical information display device according to claim 2, wherein said first index value is information that defines a position within lung parenchyma included in said lung based on the movement of said bronchi. 2. The medical information display device according to claim 1, wherein said second index value is an index value relating to the lung lesion amount. 2. The medical information display device according to claim 1, wherein said second index value is a CT value that satisfies a predetermined threshold condition. A display in which the first index value and the second index value are associated is a graph in which one of the vertical axis and the horizontal axis indicates the first index value and the other indicates the second index value. 2. The medical information display device according to claim 1, comprising a display of . 2. The medical information display device according to claim 1, wherein said volume data includes two data obtained at different times. a medical information display device according to claim 1;
an acquisition unit that acquires the volume data;
a region extraction unit that extracts the anatomical structure of the lungs included in the volume data;
a calculation unit that calculates a first index value at an arbitrary evaluation point in the volume data based on the extracted anatomical structure of the lung;
and a derivation unit that derives the second index value based on the volume data. The first index value is an index value based on the running of bronchi contained in the lungs,
The calculation unit calculates the first index value based on the length on the bronchi from a reference point on the central side of the bronchi to a point on the peripheral side of the bronchi closest to the evaluation point. 11. The medical image processing apparatus of claim 10, which calculates. The first index value is an index value based on the running of bronchi contained in the lungs,
The calculation unit calculates the number of bifurcations on the bronchi from a reference point on the central side of the bronchi to a point on the peripheral side of the bronchi closest to the evaluation point as the first index value. The medical image processing apparatus according to claim 10, wherein If the evaluation point is included in a joint region formed on the lung parenchyma included in the lung by a region expanding method starting from the plurality of regions on the bronchi having the same number of branches, the calculation unit 13. The medical image processing apparatus according to claim 12, wherein the number of branches of the region on the bronchi, which is the starting point of region formation, is calculated as the first index value. 2. The medical information display apparatus according to claim 1, wherein said first index value is an index value based on a depth from a boundary surface that defines the contour of said lung or the contour of said anatomical region of said lung. A medical information display method for performing display based on volume data including at least lungs, wherein a first index value based on the anatomical structure of the lung and a second index value based on the volume data regarding the lung A medical information display method including displaying in association with . obtaining volumetric data including at least the lungs;
extracting the anatomy of the lung included in the volume data;
calculating a first index value at an arbitrary evaluation point in the volume data based on the extracted anatomical structure of the lung;
16. The method of displaying medical information according to claim 15, further comprising: deriving a second index value based on said volume data about said lung. A program for causing a computer to perform display based on volume data including at least lungs,
A program for displaying a first index value based on the anatomical structure of the lung and a second index value based on the volume data of the lung in association with each other. obtaining volumetric data including at least the lungs;
extracting bronchi of the lungs included in the volume data;
calculating the first index value at an arbitrary evaluation point in the volume data based on the extracted anatomical structure of the lung;
18. The program of claim 17, further comprising: deriving the second index value based on the volumetric data for the lungs.

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