Medical image processing device, medical image processing method, and program

Abstract

To generate a display mode that contributes to increase in speed of medical image diagnosis in a dynamic contrast-enhanced inspection.SOLUTION: A medical image processing device according to an embodiment includes an acquisition unit, a determination unit, and a display mode generation unit. The acquisition unit acquires contrast images of a plurality of time phases related to a subject. The determination unit determines whether contrast is dominant or liquid storage is dominant in a pixel or a region including the pixel on the basis of a data value of each pixel of the contrast images of the plurality of time phases. The display mode generation unit generates a display mode based on the determination.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The embodiments disclosed in this specification and the drawings relate to a medical image processing apparatus, a medical image processing method, and a program.

Background Art

[0002] Conventionally, in medical image diagnosis using an X-ray computed tomography (CT) apparatus or a magnetic resonance imaging (MRI) apparatus, a contrast dynamic examination may be performed. In the contrast dynamic examination, based on a plurality of images contrasted at a plurality of time phases, for example, the presence or absence of bleeding into the abdominal cavity, damage to arteries, veins, or the portal vein is determined.

[0003] However, there has been a problem that the time required for medical image diagnosis is long, such as when determining the presence or absence of bleeding or damage while comparing a plurality of images contrasted at a plurality of time phases.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] One of the problems to be solved by the embodiments disclosed in this specification and the like is to generate a display mode that contributes to the speedup of medical image diagnosis in a contrast dynamic examination. However, the problems to be solved by the embodiments disclosed in this specification and the drawings are not limited to the above problems. The problems corresponding to each configuration shown in the embodiments described later can also be regarded as other problems.

Means for Solving the Problems

[0006] The medical image processing apparatus according to the embodiment comprises an acquisition unit, a determination unit, and a display mode generation unit. The acquisition unit acquires contrast-enhanced images of a subject at multiple time phases. The determination unit determines, based on the data value of each pixel in the contrast-enhanced images at multiple time phases, whether a pixel or a region containing the pixel is predominantly contrast-enhanced or predominantly liquid-retaining. The display mode generation unit generates a display mode based on the determination. [Brief explanation of the drawing]

[0007] [Figure 1] Figure 1 shows an example of the configuration of an X-ray computed tomography (CT) system equipped with a medical image processing device according to the embodiment. [Figure 2] Figure 2 is a diagram illustrating the generation of display modes in medical image processing according to an embodiment. [Figure 3] Figure 3 is a flowchart showing an example of medical image processing according to this embodiment. [Figure 4] Figure 4 shows an example of a display mode generated in the medical image processing according to the embodiment. [Figure 5] Figure 5 shows another example of a display mode generated in the medical image processing according to the embodiment. [Modes for carrying out the invention]

[0008] The following describes the medical image processing apparatus, X-ray CT apparatus, medical image processing method, and program according to each embodiment, with reference to the drawings. In the following description, components having the same or substantially the same function as those previously described in the drawings are denoted by the same reference numerals, and are described redundantly only when necessary. Furthermore, even when representing the same part, the dimensions and proportions may differ between drawings. In addition, for example, from the viewpoint of ensuring the readability of the drawings, reference numerals are denoted only for major or representative components in the description of each drawing, and reference numerals may not be denoted for components having the same or substantially the same function.

[0009] In the embodiments described below, examples are given in which the medical image processing apparatus according to each embodiment is mounted on an X-ray computed tomography (CT) apparatus.

[0010] Furthermore, the medical image processing devices according to each embodiment are not limited to being mounted on an X-ray CT scanner, but may also be implemented as independent devices using a computer that has a processor such as a CPU (Central Processing Unit) and memory such as ROM (Read Only Memory) or RAM (Random Access Memory) as hardware resources. In this case, the processor mounted on the computer can realize the various functions according to each embodiment by executing a program read from ROM, etc., and loaded into RAM.

[0011] Furthermore, the medical image processing apparatus according to each embodiment may be implemented by mounting it on other medical imaging diagnostic devices besides the X-ray CT apparatus. In this case, the processor mounted on each medical imaging diagnostic device can implement the functions according to each embodiment by executing a program read from ROM or the like and loaded into RAM. Other medical imaging diagnostic devices may include various medical imaging diagnostic devices such as magnetic resonance imaging (MRI) apparatus, SPECT-CT apparatus which integrates a SPECT (Single Photon Emission Computed Tomography) apparatus and an X-ray CT apparatus, and PET-CT apparatus which integrates a PET (Positron Emission Computed Tomography) apparatus and an X-ray CT apparatus. In addition, X-ray diagnostic devices such as X-ray rotation angiography apparatus may be used as other medical imaging diagnostic devices. That is, for example, a cone-beam CT image reconstructed based on multiple projected X-ray images collected by rotating the C-arm in an X-ray diagnostic apparatus can also be used as at least part of multiple temporal images relating to the subject, similar to the CT images according to each embodiment.

[0012] For example, there are various types of X-ray CT scanners, such as third-generation CT and fourth-generation CT, but any type can be applied to each embodiment. Here, third-generation CT is a Rotate / Rotate-Type in which the X-ray tube and detector rotate together around the subject. Fourth-generation CT is a Stationary / Rotate-Type in which a large number of X-ray detection elements are fixed in a ring-shaped array, and only the X-ray tube rotates around the subject.

[0013] (First embodiment) Figure 1 shows an example of the configuration of an X-ray CT apparatus 1 equipped with a medical image processing apparatus 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. The X-ray CT apparatus 1 generates a CT image of the subject P based on the output from the X-ray detector 12.

[0014] As shown in Figure 1, the X-ray CT scanner 1 comprises a stand 10, a patient table 30, and a console 40. Note that, for illustrative purposes, multiple stands 10 are depicted in Figure 1. The stand 10 is a scanning device configured for X-ray CT imaging of a subject P. The patient table 30 is a transport device for positioning and placing the subject P to be X-ray CT imaging. The console 40 is a computer that controls the stand 10. For example, the stand 10 and patient table 30 are installed in the CT examination room, and the console 40 is installed in a control room adjacent to the CT examination room. The stand 10, patient table 30, and console 40 are connected to each other by wired or wireless connections, enabling communication between them.

[0015] 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 as the frame 10 and the bed 30. Alternatively, the console 40 may be incorporated into the frame 10.

[0016] In this embodiment, the rotation axis of the rotating frame 13 or the longitudinal direction of the top plate 33 of the bed 30 in the non-tilted state is defined as the Z-axis direction, the axial direction that is orthogonal to the Z-axis direction and horizontal with respect to the floor surface is defined as the X-axis direction, and the axial direction that is orthogonal to the Z-axis direction and perpendicular to the floor surface is defined as the Y-axis direction.

[0017] 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 circuit (Data Acquisition System: DAS) 18.

[0018] 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 receiving the collision of thermoelectrons. The X-ray tube 11 irradiates the subject P with X-rays by irradiating thermoelectrons from the cathode toward the anode using the high voltage supplied from the X-ray high-voltage device 14.

[0019] Note that the hardware for generating X-rays is not limited to the X-ray tube 11. For example, instead of the X-ray tube 11, the fifth-generation method may be used to generate X-rays. The fifth-generation method includes a focus coil that focuses the electron beam generated from the electron gun, a deflection coil that deflects the electron beam electromagnetically, and a target ring that generates X-rays when the deflected electron beam that surrounds and deflects half of the subject P collides.

[0020] The X-ray detector 12 detects the X-rays irradiated from the X-ray tube 11 and passed through the subject P, and outputs an electrical signal corresponding to the detected X-ray dose to the DAS 18. The X-ray detector 12 has, for example, an X-ray detector element array in which a plurality of X-ray detector elements are arranged in the channel direction along an arc centered 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 detector elements are arranged in the slice direction (column direction, row direction) in the channel direction. Further, the X-ray detector 12 is an indirect conversion type detector having, for example, a grid, a scintillator array, and an optical sensor array. The scintillator array has a plurality of scintillators. The scintillator has a scintillator crystal that outputs light with an amount corresponding to the incident X-ray dose. The grid has an X-ray shielding plate disposed on the surface 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 be called a collimator (one-dimensional collimator or two-dimensional collimator). The optical sensor array has a function of converting the light amount of the light from the scintillator into an electrical signal. As the optical sensor, for example, a photomultiplier tube (photomultiplier: PMT) or the like is used. Note that the X-ray detector 12 may be a direct conversion type detector having a semiconductor element that converts the incident X-rays into an electrical signal. Here, the X-ray detector 12 is an example of a detection unit.

[0021] The rotating frame 13 is an annular frame that supports the X-ray tube 11 and the X-ray detector 12 opposite to each other, and rotates the X-ray tube 11 and the X-ray detector 12 by a control device 15 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. Note that the rotating frame 13 can further support, in addition to the X-ray tube 11 and the X-ray detector 12, an X-ray high voltage device 14, a wedge 16, a collimator 17, a DAS 18, and the like. Further, the rotating frame 13 can further support various configurations not shown in FIG. 1.

[0022] The X-ray high-voltage device 14 includes a high-voltage generator and an X-ray control device. The high-voltage generator has an electrical circuit including a transformer and a rectifier, and generates the high voltage applied to the X-ray tube 11 and the filament current supplied to the X-ray tube 11. The X-ray control device controls the output voltage according to the X-rays irradiated by the X-ray tube 11. The high-voltage generator may be of the transformer type or the inverter type. The X-ray high-voltage device 14 may be installed on the rotating frame 13 within the stand 10, or on a fixed frame (not shown) within the stand 10. The fixed frame is a frame that rotatably supports the rotating frame 13.

[0023] The control device 15 includes a drive mechanism such as a motor and actuator, and a processing circuit having a processor and memory for controlling this drive mechanism. The control device 15 receives input signals from the input interface 43 and an input interface provided on the frame 10, and controls the operation of the frame 10 and the bed 30. Examples of operation control by the control device 15 include controlling the rotation of the rotating frame 13, controlling the tilt of the frame 10, and controlling the operation of the bed 30. The control of the frame 10 is achieved by the control device 15 rotating the rotating frame 13 around an axis parallel to the X-axis direction based on the tilt angle information input by the input interface attached to the frame 10. The control device 15 may be provided on the frame 10 or on the console 40.

[0024] The wedge 16 is a filter for adjusting the amount of X-rays irradiated from the X-ray tube 11. Specifically, the wedge 16 is a filter that 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. For example, the wedge 16 is a wedge filter or a bow-tie filter, and is constructed by processing aluminum or the like to have a predetermined target angle and thickness.

[0025] The collimator 17 limits the irradiation range of X-rays that have passed through the wedge 16. The collimator 17 slidably supports multiple lead plates that shield the X-rays and adjusts the shape of the slit formed by the multiple lead plates. The collimator 17 is sometimes called an X-ray diaphragm.

[0026] The DAS18 reads an electrical signal from the X-ray detector 12 corresponding to the X-ray dose detected by the X-ray detector 12. The DAS18 amplifies the read electrical signal and integrates (adds) the electrical signal over the viewing period to collect detection data having a digital value corresponding to the X-ray dose over the viewing period. The detection data is called projection data. The DAS18 is implemented, for example, by an application-specific integrated circuit (ASIC) equipped with circuit elements capable of generating projection data. The projection data is transmitted to the console 40 via a non-contact data transmission device or the like. Here, the DAS18 is an example of a detection unit.

[0027] The detection data generated by DAS18 is transmitted via optical communication from a transmitter equipped with a light-emitting diode (LED) on the rotating frame 13 to a receiver equipped with a photodiode on the non-rotating part of the base 10 (for example, the fixed frame; not shown in Figure 1), and then forwarded to the console 40. The method of transmitting the detection data from the rotating frame 13 of the rotating part to the non-rotating part of the base 10 is not limited to the aforementioned optical communication; any non-contact data transfer method may be used.

[0028] In this embodiment, an X-ray CT apparatus 1 equipped with an integrating type X-ray detector 12 is described as an example, but the technology according to this embodiment can also be realized as an X-ray CT apparatus 1 equipped with a photon counting type X-ray detector.

[0029] The patient bed 30 is a device for placing and moving the subject P to be scanned. The patient bed 30 has a base 31, a patient bed drive device 32, a top plate 33, and a support frame 34. The base 31 is a housing that supports the support frame 34 so that it can move in the vertical direction. The patient bed drive device 32 is a drive mechanism that moves the top plate 33 on which the subject P is placed in the longitudinal direction of the top plate 33. The patient bed drive device 32 includes a motor and actuators, etc. 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 patient bed 30 toward the frame 10 so that the entire body of the subject P can be photographed. The top plate 33 is made of, for example, carbon fiber reinforced plastic (CFRP) which has good X-ray transparency and physical properties such as rigidity and strength. Also, for example, the inside of the top plate 33 is hollow. The support frame 34 supports the top plate 33 so that it can move in the longitudinal direction of the top plate 33. In addition to the top plate 33, the bed drive device 32 may also move the support frame 34 in the longitudinal direction of the top plate 33.

[0030] The console 40 includes a memory 41, a display 42, an input interface 43, and a processing circuit 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 separately from the mounting base 10, the mounting base 10 may include the console 40 or some of its components.

[0031] Memory 41 can be implemented using, for example, semiconductor memory elements such as ROM, RAM, or flash memory, or a hard disk or optical disc. For example, memory 41 stores projection data and reconstructed image data. Also, for example, memory 41 stores various programs. Also, for example, memory 41 stores Model 100, which will be described later. The storage area of ​​memory 41 may be located within the X-ray CT apparatus 1 or in an external storage device connected via a network. Here, memory 41 is an example of a storage unit.

[0032] The display 42 displays various types of information. For example, the display 42 displays medical images (CT images) generated by the processing circuit 44, or a GUI (Graphical User Interface) for receiving various operations from the operator. The information displayed on the display 42 includes various display modes generated by the medical image processing apparatus according to the embodiment. As an example, the display 42 displays a color image or a color superimposed image in which at least one of the hue and brightness is determined for each pixel based on the category determination result according to the embodiment. Various arbitrary displays can be used as the display 42 as appropriate. For example, a liquid crystal display (LCD), a cathode ray tube (CRT) display, an organic electroluminescent display (OELD), or a plasma display can be used as the display 42.

[0033] The display 42 may be installed anywhere in the control room. Alternatively, the display 42 may be installed on the stand 10. Furthermore, the display 42 may be a desktop type, or it may consist of a tablet terminal or the like that can communicate wirelessly with the console 40. Also, one or more projectors may be used as the display 42. Here, the display 42 is just one example of a display unit.

[0034] The input interface 43 receives various input operations from the operator and converts the received input operations into electrical signals, which are then output to the processing circuit 44. For example, the input interface 43 receives from the operator the acquisition conditions when acquiring projection data, the reconstruction conditions when reconstructing CT images, and the image processing conditions when generating post-processed images from CT images. In addition, for example, the input interface 43 receives from the operator the settings and changes of various display modes generated by the medical image processing apparatus according to the embodiment.

[0035] The input interface 43 can be, for example, a mouse, keyboard, trackball, switch, button, joystick, touchpad, or touch panel display, as appropriate. However, in this embodiment, the input interface 43 is not limited to those equipped with these physical operating components. For example, 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 this electrical signal to a processing circuit 44 is also included as an example of the input interface 43. Furthermore, the input interface 43 may be provided on the stand 10. In addition, the input interface 43 may consist of a tablet terminal or the like that can communicate wirelessly with the console 40 main unit. Here, the input interface 43 is an example of an input unit.

[0036] The processing circuit 44 controls the operation of the entire X-ray CT apparatus 1. The processing circuit 44 has a processor and memory such as ROM or RAM as hardware resources. The processing circuit 44 executes system control functions 441, image generation functions 442, image processing functions 443, category determination functions 444, conversion functions 445, and display control functions 446, etc., using a processor that executes programs loaded into memory. Here, the processing circuit 44 is an example of a processing unit.

[0037] 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, the system control function 441 controls the CT scan performed on the gantry 10. The system control function 441 acquires detection data obtained from the CT scan. The detection data obtained from the CT scan includes multiple detection data related to multiple time phases. The system control function 441 may also acquire detection data related to the subject P from outside the X-ray CT apparatus 1. Furthermore, in generating the display mode according to the embodiment, the system control function 441 acquires multiple time phase CT images related to the subject from, for example, the memory 41. Here, the processing circuit 44 that realizes the system control function 441 is an example of an acquisition unit.

[0038] In the image generation function 442, the processing circuit 44 generates data by applying preprocessing such as logarithmic transformation, offset correction, inter-channel sensitivity correction, and beam hardening correction to the detection data output from the DAS 18. The image generation function 442 stores the generated data in the memory 41. Note that the data before preprocessing (detection data) and the data after preprocessing are sometimes collectively referred to as projection data. The image generation function 442 generates CT image data by performing reconstruction processing using filtered back projection, iterative reconstruction, machine learning, etc., on the generated projection data (projection data after preprocessing). The image generation function 442 stores the generated CT image data in the memory 41.

[0039] In the image processing function 443, the processing circuit 44 converts the CT image data generated by the image generation function 442 into tomographic data of an arbitrary cross-section or 3D image data using a known method, based on input operations received from the operator via the input interface 43. For example, the image processing function 443 applies 3D image processing such as volume rendering, surface rendering, image value projection processing, MPR (Multi-Planar Reconstruction) processing, and CPR (Curved MPR) processing to the CT image data to generate rendered image data in an arbitrary viewpoint direction. Note that the generation of 3D image data, such as rendered image data in an arbitrary viewpoint direction, i.e., volume data, may be performed directly by the image generation function 442.

[0040] As described above, the system control function 441, the image generation function 442, and the image processing function 443 generate image data of multiple time phases relating to the subject P. In this embodiment, as an example of images acquired at different time points relating to the subject P, a case in which plain CT images, pre-contrast CT images, arterial-dominant phase CT images, and parenchymal phase CT images are used will be described. In other words, in this embodiment, the image data of multiple time phases relating to the subject P includes, for example, plain CT image data obtained before the start of contrast, i.e., plain CT, arterial-dominant phase CT image data obtained with contrast-enhanced CT, and parenchymal phase CT image data obtained with contrast-enhanced CT. In this embodiment, when the image data of multiple time phases relating to the subject P are not distinguished, they may simply be referred to as dynamic CT image data.

[0041] Furthermore, the image processing function 443 selects the target area for color image creation from the entire range of CT images read from memory 41 by the system control function 441. The image processing function 443 also performs motion correction and filtering on the contrast-enhanced images for each time phase. The image processing function 443 also generates a contrast intensity map, a contrast change map, a local uniformity map, a pre-contrast CT value map, and a mask image. The image processing function 443 also generates a base image, which is the maximum value image between the time phases between the motion-corrected plain CT image, the arterial-dominant phase CT image, and the parenchymal phase CT image. The image processing function 443 adds the color image to the base image to generate a color superimposed image. The image processing function 443 stores the generated various image data in memory 41. Here, the processing circuit 44 that realizes the image processing function 443 is an example of an image processing unit.

[0042] In the category determination function 444, the processing circuit 44 normalizes the pixel values ​​of the contrast intensity map, contrast change map, local homogeneity map, pre-contrast CT value map, and mask image. The category determination function 444 also determines which of two categories each pixel belongs to based on at least one value of each pixel in the contrast intensity map, contrast change map, local homogeneity map, pre-contrast CT value map, and mask image. For example, the two categories available are "local homogeneity dominant" and "contrast dominant". In this way, the category determination function 444 determines whether each pixel is contrast-dominant or fluid-retention dominant based on the data values ​​of each pixel in CT images at multiple time phases. Here, the processing circuit 44 that implements the category determination function 444 is an example of a determination unit. The categories of local homogeneity dominant and contrast-dominant are examples of the first and second states.

[0043] In the conversion function 445, the processing circuit 44 generates a display mode based on the determination made by the category determination function 444. Specifically, the conversion function 445 determines the hue and brightness of each pixel using a color conversion function associated with each category determined by the category determination function 444. The conversion function 445 also generates a color image having hue, brightness, and opacity values ​​for each pixel according to the determined color code. Here, the processing circuit 44 that implements the conversion function 445 is an example of a display mode generation unit.

[0044] In the display control function 446, the processing circuit 44 displays an image on the display 42 based on various image data generated by the image processing function 443. The image displayed on the display 42 includes a color image according to the embodiment. The image displayed on the display 42 also includes a color superimposed image generated by adding the color image to a base image. The image displayed on the display 42 also includes a CT image based on CT image data, a cross-sectional image based on cross-sectional image data of an arbitrary cross-section, a rendering image of an arbitrary viewpoint direction based on rendering image data of an arbitrary viewpoint direction, and the like. The image displayed on the display 42 also includes images for displaying the operation screen and images for displaying notifications and warnings to the operator. Here, the processing circuit 44 that realizes the display control function 446 is an example of a display control unit.

[0045] The color superimposed image generated by adding the color image according to the embodiment to the base image, and the image data of a display screen including the color superimposed image, may be generated by either the image processing function 443 or the display control function 446.

[0046] Furthermore, each of the functions 441 to 446 is not limited to being implemented in a single processing circuit. A processing circuit 44 may be constructed by combining multiple independent processors, with each processor executing its respective program to realize each of the functions 441 to 446. Here, each of the functions 441 to 446 may be implemented by being appropriately distributed or integrated across one or more processing circuits.

[0047] Although console 40 has been described as a single console that executes multiple functions, it is also acceptable for multiple functions to be executed by separate consoles. For example, the functions of processing circuits 44, such as the image generation function 442, image processing function 443, category determination function 444, and conversion function 445, may be distributed among multiple consoles.

[0048] Furthermore, part or all of the processing circuit 44 may not be included in the console 40, but may also be included in an integrated server that performs processing on detection data acquired by multiple medical imaging diagnostic devices in a unified manner.

[0049] Furthermore, at least one of the following processes—post-processing, generation, learning, identification, and display—may be performed on either the console 40 or an external workstation. Alternatively, both the console 40 and the workstation may perform the processing simultaneously. As a workstation, a computer with hardware resources such as a processor that implements the functions corresponding to each process, and memory such as ROM or RAM, can be used as appropriate.

[0050] In the reconstruction of 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 a full 360 degrees around the subject P when using the full-scan reconstruction method. When using the half-scan reconstruction method, the processing circuit 44 uses projection data for 180 degrees plus the fan angle. For the sake of simplicity, in the following explanation, the processing circuit 44 will be assumed to use the full-scan reconstruction method, which reconstructs the image using projection data for a full 360 degrees around the subject P.

[0051] Furthermore, the technology according to this embodiment can be applied to both single-tube X-ray computed tomography (CT) systems and so-called multi-tube X-ray computed tomography systems, which have multiple pairs of X-ray tubes and detectors mounted on a rotating ring.

[0052] Furthermore, the technology according to this embodiment is also applicable to an X-ray CT apparatus 1 configured to perform dual-energy imaging. In this case, the X-ray high-voltage device 14 can alternately switch the energy spectrum of the X-rays emitted from the X-ray tube 11 by, for example, high-speed switching of two voltage values. In other words, the X-ray CT apparatus 1 is configured to collect projection data in each acquisition view while modulating the tube voltage at timings according to the control signal for tube voltage modulation. By imaging the subject with different tube voltages, the contrast of the density in the CT image can be improved based on the energy permeability of the material for each X-ray energy spectrum.

[0053] In this embodiment, the X-ray CT apparatus 1 is configured to read electrical signals from the X-ray detector 12 using a sequential readout method.

[0054] Furthermore, the X-ray CT apparatus 1 according to this embodiment may be configured as an upright CT. In this case, instead of moving the tabletop 33, a patient support mechanism is provided that supports the upright subject P and is configured to move along the rotation axis of the rotating part of the stand 10. Also, the X-ray CT apparatus 1 according to this embodiment may be configured as a mobile CT in which the stand 10 and the patient table 30 are movable.

[0055] Here, the generation of display modes in the medical image processing according to the embodiment will be described in more detail with reference to the drawings. Figure 2 is a diagram illustrating the generation of display modes in the medical image processing according to the embodiment.

[0056] Here, the generation of display modes in medical image processing according to the embodiment will be explained, using as an example a case in which contrast agent leakage and fluid accumulation in the abdominal cavity are depicted as color images IC based on a three-phase contrast-enhanced abdominal dynamic CT, i.e., a pre-contrast CT, a plain CT, a contrast-enhanced CT with predominantly arterial phase, and a contrast-enhanced CT with parenchymal phase.

[0057] <1. Loading multi-phase images> The system control function 441 reads image data of a plain CT image ICT1, an arterial-dominant phase abdominal contrast-enhanced dynamic CT image (hereinafter referred to as arterial-dominant phase CT image ICT2), and a parenchymal phase abdominal contrast-enhanced dynamic CT image (hereinafter referred to as parenchymal phase CT image ICT3) from, for example, the memory 41. Here, the plain CT image ICT1 is a CT image taken from the chest to the thigh before contrast enhancement. The arterial-dominant phase CT image ICT2 is a CT image taken after contrast agent injection. After contrast agent injection means, for example, about 30 seconds after the injection of the contrast agent. Alternatively, after contrast agent injection means, for example, a few seconds after the aorta is visualized. The parenchymal phase CT image ICT3 is a CT image taken several tens of seconds after the arterial-dominant phase.

[0058] <2. Specifying the scope> The image processing function 443 selects the area to be used for color image creation from the entire range of the loaded CT image, based on input operations received from the operator, for example, via the input interface 43. The area can be selected from, for example, the head, chest, abdomen, pelvic region, or abdomen and pelvis. For example, if "abdomen" is selected, the image processing function 443 identifies a slice position 2 cm above the upper part of the diaphragm and a slice position 10 cm below the lower part of the kidney from the CT image, and sets these as the upper and lower limits of the processing range for color image creation. Furthermore, the image processing function 443 identifies the dorsal position of the torso, the abdominal surface position, and the left and right lateral positions, and records them in memory 41 as rectangular parallelepipeds indicating the processing range. The image processing function 443 also determines the slice interval and pixel interval according to the selected area and records them in memory 41.

[0059] <3-1. Motion Correction> The image processing function 443 performs motion correction for contrast-enhanced images at each time phase. For motion correction, for example, a non-rigid body registration algorithm can be used. First, the image processing function 443 generates an image by extracting a rectangular prism portion indicating the processing range from the simple CT image ICT1. Image interpolation such as Cubic Spline can be used for image extraction, and the image processing function 443 generates an image that corresponds to the rectangular prism of the recorded processing range and has the recorded slice interval and pixel interval. Next, the image processing function 443 performs non-rigid body registration on the arterial-dominant phase CT image ICT2 using the extracted simple CT image ICT1 as a reference image. The image processing function 443 temporarily records the result of the rigid body registration as the arterial-dominant phase CT image ICT2 after motion correction, for example, in memory 41. The image processing function 443 also performs rigid body registration on the substantial phase CT image ICT3 using the extracted simple CT image ICT1 as a reference image. The image processing function 443 temporarily records the result of the rigid body alignment as a motion-corrected substantial phase CT image ICT3 in, for example, memory 41. The extracted simple CT image ICT1 is then temporarily recorded as a motion-corrected simple CT image ICT1 in, for example, memory 41.

[0060] <3-2. Applying Image Filters> The image processing function 443 applies image filters to each of the motion-corrected simple CT image ICT1, arterial-dominant phase CT image ICT2, and solid phase CT image ICT3. For example, the image processing function 443 applies a Gaussian filter or a diffusion filter to each image to generate the motion-corrected simple CT image ICT1, arterial-dominant phase CT image ICT2, and solid phase CT image ICT3. The image processing function 443 temporarily records the generated images as the motion-corrected and filtered simple CT image ICT1, arterial-dominant phase CT image ICT2, and solid phase CT image ICT3, for example, in memory 41.

[0061] <4. Creation of images relating to spatial or temporal changes in pixel values> The image processing function 443 calculates multiple variable values ​​related to the spatial or inter-phase changes of pixel values ​​from multiple time-phase CT images ICT1, ICT2, and ICT3, for example, for each pixel, as described below. The image processing function 443 may construct and store multiple images from the calculated variable values, or it may not store them as images but perform the processing up to conversion to a color-coded image on a pixel-by-pixel basis.

[0062] <4-1. Generating a contrast intensity map> Image processing function 443 generates a contrast intensity map E. The contrast intensity map E is an image in which each pixel has a contrast intensity value. The contrast intensity map E is calculated by the following equation (1).

[0063]

number

[0064] Here, S0, S1, and S2 are the CT values ​​of each pixel in the motion-corrected and filtered plain CT image ICT1, the arterial-dominant phase CT image ICT2, and the parenchymal phase CT image ICT3, respectively. If the CT values ​​of S0, S1, and S2 are outside the CT value range for parenchymal organs, E = -2048 is set for those pixels. Here, -2048 is considered to be a value representing an abnormal value.

[0065] <4-2. Generating a contrast enhancement change map> Image processing function 443 generates a contrast enhancement change map R. The contrast enhancement change map R is an image in which each pixel has a value for the amount of contrast enhancement change in the late phase. The contrast enhancement change map R is calculated by the following equation (2).

[0066]

number

[0067] Here, S0, S1, and S2 are the CT values ​​of each pixel in the simple CT image ICT1, the arterial-dominant phase CT image ICT2, and the parenchymal phase CT image ICT3, respectively, after motion correction and filtering. Note that if the CT values ​​of S0, S1, and S2 fall outside the CT value range for a parenchymal organ, R=0 is set for that pixel.

[0068] <4-3. Generating a Local Homogeneity Map> Image processing function 443 generates a local uniformity map L. The local uniformity map L is an image in which each pixel has a local uniformity value L. Image processing function 443 generates an image in which each pixel has the difference between the maximum and minimum values ​​of the simple CT image ICT1, arterial-dominant phase CT image ICT2, and solid phase CT image ICT3 after motion correction and filtering, and generates the local uniformity map L from the image showing the local standard deviation. A local standard deviation image is an image in which, for any given pixel, the pixel value is the standard deviation of the surrounding pixels (e.g., radius 5 mm). The local uniformity map L is calculated by applying a function such as the reciprocal or exponential function to the variance, i.e., a constant multiple of the square of the standard deviation. Regions with high local uniformity are regions that may contain fluid. When local uniformity is used to evaluate the likelihood of fluid containment, fluid containment dominance refers to local uniformity dominance. The following explanation describes an example in which local uniformity dominance is used as fluid containment dominance.

[0069] <4-4. Generating a pre-contrast CT value map> Image processing function 443 generates a pre-contrast CT value map V. The pre-contrast CT value map V is an image in which each pixel has pixel value-related features of the pre-contrast CT image ICT1. Image processing function 443 uses the simple CT image ICT1 after motion correction and filtering as the pre-contrast CT value map V.

[0070] <4-5. Generating the Mask Image> Image processing function 443 generates a mask image C. Mask image C is an image in which the pixel values ​​of the areas to be colorized are set to 1, and the pixel values ​​of the areas not to be imaged are set to 0. In simple terms, image processing function 443 generates a mask image C in which the pixel values ​​are set to 0 when S0, S1, and S2 are outside the CT value range of the parenchymal organ, and to 1 when they are within the CT value range of the parenchymal organ.

[0071] <5. Conversion to color-coded image> The conversion function 445 generates a color image IC using multiple color value functions based on four images relating to spatial or temporal changes in pixel values ​​generated by the image processing function 443, and the category to which each pixel in each image belongs, as determined by the category determination function 444.

[0072] <5-1. Normalization of Pixel Values> The category determination function 444 normalizes the pixel values ​​of the four images E, R, L, V, and C using their respective typical lower and upper limits, as shown in equations (3) to (7).

[0073]

number

[0074] Here, Emin, Emax, Rmin, Rmax, Lmin, Lmax, Vmin, Vmax, Cmin, and Cmax are the lower limit of contrast intensity map E, the upper limit of contrast intensity map E, the lower limit of contrast change map R, the upper limit of contrast change map R, the lower limit of local homogeneity map L, the upper limit of local homogeneity map L, the lower limit of pre-contrast CT value map V, the upper limit of pre-contrast CT value map V, the lower limit of mask image C, and the upper limit of mask image C, respectively.

[0075] <5-2. Category Determination> The category determination function 444 determines which of two categories each pixel belongs to based on at least one of the pixel values ​​En, Rn, Ln, Vn, and Cn from the four images. Here, the contrast intensity map E and the contrast change map R are images based on the time changes in the data values ​​of each pixel in the contrast-enhanced images at multiple time phases.

[0076] In its simplest form, the category determination function 444 classifies each pixel into two categories: "dominant local homogeneity" and "dominant contrast enhancement." Pixels with dominant local homogeneity are those where fluid accumulation is dominant. Specifically, the category determination function 444 determines that a pixel belongs to the contrast enhancement category when En > Ln. On the other hand, the category determination function 444 determines that a pixel belongs to the dominant local homogeneity category when En ≤ Ln. It is also acceptable for a pixel to be determined to belong to the contrast enhancement category when En = Ln.

[0077] <5-3. Determination of Hue and Brightness of Each Pixel> The conversion function 445 determines the hue and brightness of each pixel using the color conversion function associated with each category determined by the category determination function 444.

[0078] For example, for pixels with contrast enhancement priority, the conversion function 445 calculates the hue h, luminance b, and opacity α, respectively, using equations (8) to (10).

[0079]

number

[0080] For example, for pixels with superior local uniformity, the conversion function 445 calculates the hue h, luminance b, and opacity α, respectively, using equations (11) to (13).

[0081]

number

[0082] Here, y=hueE(x) and y=hueL(x) are hue functions that determine the hue y from the pixel value x, respectively. The color code assigned to each pixel is obtained by the product of hue h and luminance b. This conversion from pixel value to color code can be implemented as a Look-Up Table (LUP) that finds the color code by looking it up in a table. The two color conversion functions, each associated with two categories, can be implemented as functions that generate a color image IC from a subspace of four variables. In the contrast-dominant case, the color code is determined by the three variables R, E, and C, as shown in equations (8) to (10). Similarly, in the local uniformity-dominant case, the color code is determined by the three variables V, L, and C, as shown in equations (11) to (13). Note that the opacity α is used when displaying the image using volume rendering.

[0083] The conversion function 445 generates a color image IC having hue, luminance, and opacity values ​​for each pixel according to the color code determined in this way. The color conversion function is defined such that the color is continuous at the transition between contrast-dominant and local uniformity-dominant states, and that the hue of each category is different. Figure 2 shows examples of two hue functions, hueE() and hueL().

[0084] According to the contrast-dominant hue function hueE(), as shown in region R11, when the contrast-enhancing change R is a negative value, that is, when the pixel values ​​of the parenchymal phase are smaller than those of the arterial-dominant phase, blue is assigned. On the other hand, as shown in region R12, when the contrast-enhancing change R is a positive value, that is, when the pixel values ​​of the parenchymal phase are larger than those of the arterial-dominant phase, red is assigned. Furthermore, as in the region between regions R11 and R12, when the contrast-enhancing change R is near 0, that is, when the pixel values ​​of the parenchymal phase are about the same as those of the arterial-dominant phase, an intermediate color, purple, is assigned.

[0085] In other words, in the example shown in Figure 2, pixels with high pixel values ​​in the arterial-dominant phase are displayed in blue if the contrast intensity decreases in the parenchymal phase, and in red if the contrast intensity is further enhanced in the parenchymal phase. Therefore, areas displayed in blue, purple, and red can be identified as blood vessels or contrast agent leakage images. Furthermore, in the case of contrast agent leakage images, it becomes possible to infer from the hue whether the leakage is due to bleeding from an artery, a portal vein, or a vein.

[0086] Furthermore, according to the hue function hueL(), which prioritizes local uniformity, when the pixel value of the simple CT is small, as shown in region R21, light blue is assigned. On the other hand, when the pixel value of the simple CT is large, as shown in region R22, yellow is assigned. Also, when the pixel value of the simple CT is near the middle, such as in the region between regions R21 and R22, the intermediate color green is assigned.

[0087] In cases of fluid accumulation, it is known that the CT value of a hematoma is higher than that of ascites. Therefore, in the example shown in Figure 2, if the displayed color is close to light blue, it is likely that the fluid accumulation is ascites, while if the displayed color is close to yellow, it is likely that the fluid accumulation is a hematoma.

[0088] The two color correlation numbers are defined so that there are no overlapping hues. Therefore, the user can easily determine from the hue of the generated image, based on the displayed color, whether the area indicates contrast agent leakage or liquid accumulation. Note that the color correlation number usually outputs highly saturated colors, but it may be configured to output less saturated colors in certain sections. In this case, the less saturated colors can be treated as having no defined saturation, so that they do not overlap with other highly saturated colors.

[0089] Even if there are no discontinuities in the pixel values ​​of the original image and the pixel values ​​are spatially smooth in the original image, the color image IC generated by the above method will, strictly speaking, have discontinuities in the color code at the En=Ln portion. However, as mentioned above, when the value of the contrast intensity map E is large, the values ​​of each variable are defined so that the value of local uniformity L is low. Therefore, in the generation of the display mode according to this embodiment, both E and L will not be high values. Specifically, in the En≒Ln region, the luminance value will be low in both the contrast-dominant and local uniformity-dominant regions, so in the generated color image IC, as shown in regions R13 and R23 in Figure 2, both regions will be close to black, and the color of the color image IC can be considered to be continuous with respect to black.

[0090] As described above, the calculation formulas, category determination methods, and color conversion functions for each variable according to this embodiment are defined such that, if the pixel values ​​of the original image are spatially continuous, there is substantially no spatial discontinuity in the color of the color image IC. In other words, the color conversion function corresponding to the contrast-dominant category and the color conversion function corresponding to the fluid-retention-dominant category are defined to convert the value of at least one variable to the same color code. With this configuration, in areas where the spatial change of the pixel values ​​of the original CT image is small, large spatial changes cannot be seen in the color image IC either. In other words, if a radiologist sees a discontinuity in color on the color image IC, they can determine that the pixel values ​​of the original image are discontinuous. Since a discontinuity in color on the color image IC can be judged to represent a boundary of any of the anatomical tissue types, structures, or properties, defining the color so that there is substantially no spatial discontinuity in the color of the color image IC has the effect of making image interpretation easier.

[0091] <Generating the base image> The image processing function 443 generates the maximum value image between time phases of the motion-corrected simple CT image ICT1, the arterial-dominant phase CT image ICT2, and the solid phase CT image ICT3 as the base image IB, and temporarily records it in memory 41, for example.

[0092] Furthermore, the image processing function 443 may generate a base image IB using any of the following as the base image IB: the motion-corrected simple CT image ICT1, the arterial-dominant phase CT image ICT2, or the solid phase CT image ICT3, not limited to the maximum value image between time phases.

[0093] <Image Output> The image processing function 443 adds the color image IC to the base image IB to generate a color superimposed image ID. The display control function 445 outputs the generated color superimposed image ID to the display 42, which then displays it. Alternatively, the image processing function 443 outputs the generated color superimposed image ID as an image record to, for example, memory 41. For example, the image processing function 443 outputs images of multiple cross-sections as multiple images at once. The image processing function 443 may also generate a multi-cross-sectional display image in which the cross-sections are arranged on a matrix within a single image.

[0094] Here, with reference to the drawings, the flow of generating the display mode in the medical image processing according to the embodiment will be described. Figure 3 is a flowchart of an example of medical image processing according to the embodiment.

[0095] First, the system control function 441 acquires contrast-enhanced images of multiple time phases, for example, from memory 41 (S101). As for the contrast-enhanced images of multiple time phases, for example, as described above, a plain CT image ICT1, an arterial-dominant phase CT image ICT2, and a parenchymal phase CT image ICT3 are acquired.

[0096] The image processing function 443 specifies the range for color image generation in the acquired multiple time-phase contrast-enhanced images based on input operations received from the operator via the input interface 43, for example (S102). The image processing function 443 also performs motion correction and filtering between time phases (S103).

[0097] The image processing function 443 calculates variable values ​​related to signal changes in space or between time phases for each pixel from contrast-enhanced images of multiple time phases (S104). Here, variable values ​​related to signal changes in space or between time phases refer to, for example, variable values ​​related to spatial or inter-time phase changes in pixel values. More specifically, the image processing function 443 generates a contrast intensity map E, a contrast change map R, a local uniformity map L, a pre-contrast CT value map V, and a mask image C.

[0098] The category determination function 444 classifies each pixel into at least two categories based on the image relating to the calculated variable values, i.e., the spatial or temporal changes in pixel values ​​(S105).

[0099] The conversion function 445 generates a color image by determining the hue and brightness of each pixel based on the determined category (S106). Specifically, the conversion function 445 generates a color image by determining the color code of each pixel using a color conversion function corresponding to the category to which each pixel belongs.

[0100] The image processing function 443 generates, for example, the maximum value image between the time phases of the motion-corrected simple CT image ICT1, the arterial-dominant phase CT image ICT2, and the solid phase CT image ICT3 as the base image IB (S107).

[0101] The image processing function 443 generates a color superimposed image by superimposing a color image onto the base image (S108). The display control function 445 outputs the generated color superimposed image to, for example, the display 42, and displays a screen including the color superimposed image on the display 42 (S109).

[0102] Figure 4 is a diagram showing an example of a display mode generated in the medical image processing according to the embodiment. Figure 4 illustrates a display screen 501 including the generated color superimposed image. As shown in Figure 4, the display control function 445 displays the display screen 501, which includes multiple time-phase contrast-enhanced images and a color superimposed image, on the display 42. The display control function 445 may also display a slider or the like to instruct slice position operation, and can also display images related to slice positions according to user operation.

[0103] As described above, the display format generated by the medical image processing apparatus according to this embodiment includes a list display of information on multiple features such as CT values, contrast intensity, contrast signal changes, and fluid accumulation. This allows the distribution of contrast agent leakage due to bleeding and fluid accumulation to be displayed in color within a single image, enabling the radiologist to easily confirm the damaged blood vessels from which the contrast agent is leaking and whether or not there is leakage outside the organ. Furthermore, the radiologist can confirm whether the fluid accumulation is a hematoma and easily determine the presence and amount of bleeding.

[0104] Figure 5 is a diagram showing another example of a display mode generated in the medical image processing according to the embodiment. Figure 5 illustrates a display screen 503 including the generated color superimposed image. As shown in Figure 5, the display control function 445 displays the display screen 503 including the multi-plane color superimposed image on the display 42. The display control function 445 may also display an input window for indicating the slice interval or the number of images to display at once, and can display each image with a slice interval and number of images according to user operation. The display control function 445 can also switch between displaying a display screen including a multi-plane color superimposed image, a display screen including a multi-plane arterial-dominant phase CT image, and a display screen including a multi-plane solid phase CT image, according to user operation.

[0105] Thus, the display modes generated in the medical image processing apparatus according to this embodiment include multi-sectional display of color superimposed images. This allows the radiologist to more quickly confirm the damaged blood vessels from which contrast agent has leaked and whether or not there has been leakage outside the organ.

[0106] Generally, contrast-enhanced dynamic CT of the abdomen is performed in the initial diagnosis of patients who have been brought to the emergency room with abdominal trauma or other injuries, when there is no massive bleeding, the condition is relatively stable, there are no central nervous system disorders, etc., and damage to intra-abdominal organs is suspected, or when plain CT shows no massive bleeding in the abdominal cavity and no damage to tubular organs. Contrast-enhanced dynamic CT of the abdomen may be performed to determine whether there is a large amount of bleeding, severe damage to parenchymal organs, or damage to tubular organs. If a large amount of bleeding, severe damage to parenchymal organs, or damage to tubular organs is determined, open surgery is chosen. Furthermore, if the bleeding vessel is an artery, treatment with TAE (percutaneous arterial embolization) is considered, and the treatment approach is considered, such as determining the artery to embolize based on the contrast-enhanced CT image. On the other hand, if there is no bleeding or relatively little bleeding, non-surgical treatment is chosen.

[0107] When making the above judgments using contrast-enhanced CT, it is important to analyze the leakage of contrast agent into the extravascular space (contrast agent leakage image), evaluate the presence or absence of a hematoma in the abdominal cavity, and evaluate the amount of the hematoma in the abdominal cavity. In the contrast agent leakage image, important information is which blood vessel is damaged, for example, whether it is an artery, portal vein, or vein. It is also important to know whether the blood leaked from the blood vessel has reached outside the organs such as the abdominal cavity and is causing active bleeding, and whether the bleeding rate is high. Furthermore, whether there is fluid accumulation in the abdominal cavity, whether that accumulated fluid is a hematoma or ascites, and the size of the hematoma are also important information for determining the amount of bleeding. In addition, the morphology, location, and extent of organ damage are evaluated in order to determine the necessity and method of organ restoration.

[0108] As an example, for the above assessment, contrast-enhanced dynamic CT images are used, specifically the plain CT image (pre-contrast CT), the arterial-dominant phase CT image, and the parenchymal phase CT image. The radiologist displays these three multi-phase images in an image viewer and visually compares them to analyze contrast agent leakage and intraperitoneal fluid accumulation.

[0109] However, accurately understanding the signal changes caused by contrast agent leakage requires detailed comparison and observation of images from multiple time phases, making it difficult to make a quick and accurate judgment.

[0110] As another example, contrast agent leakage causes changes in pixel values ​​on contrast-enhanced dynamic CT, so it is conceivable to display these changes in pixel values ​​in a form such as a color image. For example, in a difference image between arterial-dominant phase and plain CT, arteries are mainly depicted with high signal intensity, and if there is bleeding from the arteries, the resulting contrast agent leakage is also expected to be depicted. This image is an initial contrast-enhanced image that reflects the initial degree of contrast enhancement. In actual calculations, the signal ratio between arterial-dominant phase and plain CT may be used instead of the difference. Also, in a difference image between parenchymal phase and arterial-dominant phase, veins are depicted with high signal intensity and arteries with low signal intensity (negative value), and it is expected that the spread of bleeding from arteries to distant areas and bleeding from veins will be depicted. This image is a signal change image that reflects later signal changes. These difference images and similar calculated parametric images are often displayed with color coding. Images of peak contrast intensity, obtained by subtracting the signal value of plain CT from the higher signal value of arterial-dominant phase and parenchymal phase, are also used. Furthermore, motion correction is essential because accurate parametric images cannot be obtained if the patient moves during scanning. Color-coded parametric images may be displayed superimposed on the original CT image.

[0111] For example, by superimposing an initial contrast-enhanced image as a color image onto a plain CT image, it is possible to observe contrast enhancement between the pre-contrast phase and the arterial-dominant phase. Furthermore, by superimposing a signal change image as a color image onto a plain CT image, it is possible to observe the contrast signal changes from the arterial-dominant phase to the parenchymal phase. Thus, with the superimposition of parametric images, necessary information can be obtained simply by comparing two images, making interpretation easier compared to comparing three phases of dynamic CT images. However, because it is still necessary to compare and observe the two images in detail, rapid and accurate judgment remains difficult.

[0112] Another example involves techniques for extracting vascular regions, leaked contrast agents, and fluid accumulation areas. For instance, vascular regions and fluid accumulation areas can be extracted in the simplest form using thresholding or region expansion methods. These extracted regions may be color-overlaid onto the original CT image. For example, there is a technique for displaying vascular regions, contrast agent leakage areas, and fluid accumulation areas semi-transparently on arterial-dominant phase CT images. Alpha blending is used for semi-transparent overlay. In volume rendering, multiple types of extracted regions can be treated as multi-objects and displayed in different colors.

[0113] However, in all cases—vascular regions, contrast agent leakage regions, and fluid accumulation regions—there is a risk of misidentification when using region extraction techniques. If the frequency of misidentification exceeds a certain level, image interpretation becomes difficult, even though it is not necessary to compare multiple images. Generally, blood vessels become thinner towards the distal end, and the signal change due to contrast becomes smaller due to the partial volume effect. For this reason, there is a high risk of missing areas in extraction at the periphery of blood vessels. Also, false contrast enhancement due to patient movement can be a factor in misidentification. Extracting reliable regions from contrast agent leakage regions is difficult because the leakage region is not as simple as that of blood vessels, resulting in greater variation, and the change in pixel value may be slight. Regions of intraperitoneal fluid accumulation can be extracted as regions where the signal value corresponds to blood or water and there is no signal change due to contrast. On the other hand, regions without signal change due to contrast are not limited to intraperitoneal fluid accumulation, making it difficult to accurately determine the region.

[0114] Furthermore, when the extracted region was superimposed on the original image, there was a problem in that the signal value and the degree of signal change could not be determined. For example, when a contrast agent leakage region was superimposed in color on the original image, the extent of the leakage could be determined, but the magnitude of the signal value or signal change due to the leakage could not be determined. The magnitude of the signal change due to leakage is an important clue in determining whether the damaged blood vessel is an artery or a vein. Therefore, when the extracted region was displayed in color superimposed on the original image, accurate judgment was difficult due to the problem of mis-extraction and the problem of the loss of information on signal values ​​and signal changes.

[0115] In this context, in the X-ray CT apparatus 1 equipped with the medical image processing apparatus according to this embodiment, the processing circuit 44 is configured to implement a system control function 441, a category determination function 444, and a conversion function 445. The system control function 441 acquires contrast-enhanced images of multiple time phases related to the subject P. The category determination function 444 determines whether each pixel or group of pixels is predominantly contrast-enhanced or predominantly fluid-retaining based on the data value of each pixel in the contrast-enhanced images of multiple time phases. The conversion function 445 generates a display mode based on the determination result by the category determination function 444.

[0116] This configuration allows for the generation of a display mode for showing a color superimposed image equivalent to comparing contrast-enhanced images from multiple time phases. Therefore, it is possible to generate a display mode that contributes to accurate and rapid medical image diagnosis in contrast-enhanced dynamic examinations compared to comparing multiple images.

[0117] (Second embodiment) This embodiment will primarily describe the differences from the first embodiment. The display control function 445 may display a display screen including a color image, not limited to a color superimposed image, using the display 42.

[0118] For example, the display control function 445 can also display a display screen on the display 42 that includes multiple time-phase contrast images and a color image, similar to the display screen 501.

[0119] Furthermore, for example, the display control function 445 can also display a display screen including multi-plane color images on the display 42, similar to the display screen 503. The display control function 445 can also switch between displaying a display screen including multi-plane color images, a display screen including multi-plane arterial-dominant phase CT images, and a display screen including multi-plane solid phase CT images, depending on user operation. The display control function 445 may also switch to a display screen including multi-plane color superimposed images depending on user operation.

[0120] In addition, in generating the display mode according to this embodiment, the generation of the base image in step S107 and the generation of the color superimposed image in step S108 of Figure 3 may or may not be performed.

[0121] (Third embodiment) This embodiment will primarily describe the differences from the first embodiment. In generating the display modes according to this embodiment, when displaying a color image or a color superimposed image, the display for each of the multiple categories can be individually turned ON / OFF.

[0122] For example, if the category of local uniformity dominance is turned OFF, the conversion function 445 sets the brightness value to 0 for pixels determined to belong to the local uniformity dominance category. Alternatively, the conversion function 445 sets the opacity value to 1 for pixels determined to belong to the local uniformity dominance category. In this case, the color superimposed image is generated as an additive image of the grayscale image of the base image and the contrast-enhanced color image.

[0123] The ON / OFF status of each category may be determined, for example, by the user controlling the ON / OFF status of the color images for each category via a GUI. In this case, the conversion function 445 changes the brightness or opacity value of each pixel according to the ON / OFF operation of the color images for each category. The display control function 445 then updates the display screen according to the ON / OFF operation of the color images for each category.

[0124] The ON / OFF status of each category may be determined, for example, based on pre-set ON / OFF information. The ON / OFF information may be set according to the area being photographed, etc. In this case, the conversion function 445 generates color images for the categories set to ON. The image processing function 443 also generates a color superimposed image by superimposing the color images on the base image for the categories set to ON.

[0125] Furthermore, if the ON / OFF status of each category is determined based on pre-configured ON / OFF information, the image processing function 443 can also generate and output multiple color image sets corresponding to multiple combinations of ON / OFF status for each category set in the ON / OFF information.

[0126] (Fourth embodiment) This embodiment will primarily explain the differences from the first embodiment. In the first embodiment, the generation of a display mode was described as an example in which the category to which each pixel belongs is determined based on the calculated variable value, and the color code of each pixel is determined using a color conversion function associated with the determined category, but it is not limited to this. In other words, the example was given in which each pixel is determined to belong to one of two categories as a discrete value (nominal variable), but it is not limited to this.

[0127] The conversion function 445 according to this embodiment corresponds to the category determination function 444 and the conversion function 445 according to each embodiment described above.

[0128] The conversion function 445 converts the variable value of each pixel into a color code using a color conversion function that applies a class determination method to each pixel, where the strength of its belonging to each category is represented by a continuous value.

[0129] As an example, the conversion function 445 converts the variable value of each pixel into a color code using a contrast-preferential color conversion function, as shown in equations (14) to (16).

[0130]

number

[0131] Furthermore, the conversion function 445 converts the variable value of each pixel into a color code using a color conversion function that prioritizes local uniformity, as shown in equations (17) to (19).

[0132]

number

[0133] Then, the conversion function 445 is b of equation (15). E Let be the contrast-enhancing membership function, and equation (18) B L By using as a membership function that prioritizes local uniformity, a color image is generated by a weighted sum of color values ​​with each membership function as the weight, as shown in equation (20).

[0134]

number

[0135] Thus, in generating the display mode according to this embodiment, individual color conversion functions are applied to multiple categories in the same manner as in the first embodiment. On the other hand, in generating the display mode according to this embodiment, each pixel is not explicitly classified into each category. However, in generating the display mode according to this embodiment, the final color is determined by calculating a weighted sum of the colors obtained by the color conversion function for each category as a membership function. In other words, the process of generating a color image by weighted sum of color values ​​with each membership function as the weight in this embodiment can be said to correspond to the process of determining the category to which each pixel belongs in the first embodiment.

[0136] Furthermore, the color conversion functions for each category are configured, as in the first embodiment, so that there is no hue matching between categories. Also, as in the first embodiment, if the pixel values ​​of the original image are spatially continuous, the colors of the resulting color image will be spatially continuous.

[0137] (Fifth embodiment) This embodiment will primarily describe the differences from the first embodiment.

[0138] In generating the display mode according to this embodiment, the image processing function 443 performs volume rendering using the color image and opacity image generated in the same manner as in the first embodiment. Here, the opacity image is an image having an opacity value as a variable value calculated for each pixel.

[0139] In this way, by applying volume rendering to a color image, a three-dimensional image can be displayed, similar to the example shown in Figure 2, in which contrast intensity and the amount of contrast change in the later stages are displayed in blue to red, and the pixel values ​​of a simple CT in a locally uniform area are displayed in light blue to yellow. This has the effect of making it easier for radiologists to infer the location of damaged vessels by grasping the positional relationship between blood vessels and contrast agent leakage images in three dimensions. It also has the effect of making it easier for radiologists to confirm whether fluid accumulation is a hematoma and to determine the presence and amount of hematoma.

[0140] (Sixth embodiment) This embodiment will primarily describe the differences from the first embodiment. The color image in the first embodiment may be generated using multi-objects.

[0141] Specifically, a color image can also be generated using a processing procedure equivalent to that of the first embodiment shown below.

[0142] The category determination function 444 extracts contrast-enhanced regions and local homogeneous regions using the motion-corrected contrast-enhanced CT image and the generated local homogeneity map L. In the simplest method, the category determination function 444 extracts regions where the contrast intensity is above a certain value as contrast-enhanced regions, and regions where the local homogeneity is above a certain value as local homogeneous regions. The threshold values ​​for contrast intensity and local homogeneity are predetermined and recorded in memory 41, for example.

[0143] For example, the conversion function 445 uses the color conversion functions shown in equations (21) and (22) as color conversion functions corresponding to the contrast-enhanced area.

[0144]

number

[0145] Furthermore, for example, the conversion function 445 uses the color conversion functions shown in equations (23) and (24) as color conversion functions corresponding to the locally uniform region.

[0146]

number

[0147] Furthermore, as in the first embodiment, each pixel is determined to belong to a specific category. Also, as in the first embodiment, the color conversion function applied to each of the multiple categories is a separate color conversion function corresponding to each category. Additionally, as in the first embodiment, the color conversion functions for each category are configured so that there is no hue matching between categories. Furthermore, as in the first embodiment, if the pixel values ​​of the original image are spatially continuous, the colors of the resulting color image will also be spatially continuous.

[0148] Generally, most volume rendering software (libraries) have multi-object capabilities, allowing different LUTs to be applied to each divided region to determine color and opacity. Therefore, the generation of the display configuration according to this embodiment can be implemented using a standard volume rendering library.

[0149] (Seventh Embodiment) This embodiment will primarily describe the differences from the fifth embodiment. The generation of the display mode according to the fifth embodiment can also be applied to projected images.

[0150] For example, the image processing function 443 divides the entire 3D CT image into slabs with a thickness of, for example, about 1 cm, and obtains multiple cross-sectional slabs. The image processing function 443 generates a projected image by projecting each slab in the thickness direction.

[0151] For example, when performing average value projection, the conversion function 445 calculates the average color value in the thickness direction of each slab to obtain the color code of each pixel in the projected image.

[0152] For example, when performing maximum value projection, the conversion function 445 determines the maximum value of luminance or opacity in the thickness direction of each slab, and then calculates the color code of each pixel in the projected image by multiplying the average value of hue in the thickness direction by the maximum value of luminance.

[0153] The display control function 445 displays a display screen containing multiple projection images generated for each slab on the display 42. On the display screen, the multiple projection images are displayed in a list on one screen, similar to the multi-section display of the color superimposed image exemplified in Figure 5.

[0154] In the embodiments described above, examples were given of generating color images according to the categories of "local homogeneity dominance" and "contrast enhancement dominance," but the invention is not limited to these examples. For example, the assignment of colors in the color image may be performed on organs extracted by segmentation, or on the results of perfusion analysis in the myocardial region, such as myocardial blood flow (MBF) or myocardial reserve (CFR). The objects to which colors are assigned in these color images are examples of the first or second state.

[0155] In each of the embodiments described above, the color conversion function is defined such that the display mode, such as color indicated by the color code, changes continuously in response to changes in pixel values, and the hue assigned to the first state and the hue assigned to the second state do not overlap in their main parts. Here, the statement that the hue assigned to the first state and the hue assigned to the second state do not overlap in their main parts means that while the hues may be continuous in the transition area between the first and second states, for example, black is used as the luminance value decreases, in areas away from the transition area, the hues of each state are different in areas where either the first or second state is stronger. In addition, in each of the embodiments described above, the color conversion function is defined such that the display mode, such as color, changes continuously with respect to pixel values ​​overall, but there are areas where the display mode, such as color, does not change with respect to pixel values. In other words, the conversion function 445 uses the color conversion function defined above to generate a display mode that includes regions in which the display mode changes continuously with respect to spatial or temporal changes in the data value of each pixel of the multi-phase modeled image, and regions in which the display mode does not change with respect to spatial or temporal changes.

[0156] In the above description, the term "processor" refers to circuits such as CPUs, GPUs (Graphics Processing Units), ASICs, and Programmable Logic Devices (PLDs). PLDs include Simple Programmable Logic Devices (SPLDs), Complex Programmable Logic Devices (CPLDs), and Field Programmable Gate Arrays (FPGAs). A processor functions by reading and executing a program stored in a memory circuit. The memory circuit containing the program is a computer-readable, non-temporary recording medium. Alternatively, instead of storing the program in a memory circuit, the processor may be configured to directly incorporate the program into its circuitry. In this case, the processor functions by reading and executing the program incorporated into the circuitry. Furthermore, instead of executing the program, the processor may implement the function corresponding to the program through a combination of logic circuits. In this embodiment, each processor is not limited to being configured as a single circuit; multiple independent circuits may be combined to form a single processor, and its functions may be implemented in this way. Furthermore, the multiple components shown in Figure 1 may be integrated into a single processor to realize their functions.

[0157] According to at least one embodiment described above, it is possible to generate a display mode that contributes to accelerating medical image diagnosis in contrast-enhanced dynamic imaging.

[0158] While several embodiments of the present invention have been described, these embodiments are presented as examples only and are not intended to limit the scope of the invention. These novel embodiments can be carried out in a variety of other forms, and various omissions, 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]

[0159] 1 X-ray CT device 10 mounting bases 11 X-ray tube 12 X-ray detectors 13 rotation frames 14 X-ray high-voltage equipment 15 Control device 16 Wedge 17 Collimator 18 Data Collection Circuit 19 Opening 30 berths 31 base 32 Bed drive mechanism 33 Top plate 34 Support Frame 40 Console 41 memory 42 displays 43 Input Interfaces 44 Processing Circuits 441 System control function (acquisition unit) 442 Image generation function 443 Image processing function (image processing unit) 444 Category determination function (determination unit) 445 Conversion function (display mode generation unit) 446 Display control function (display control unit)

Claims

1. An acquisition unit that acquires contrast-enhanced images of the subject at multiple time phases, A determination unit that determines whether the category of a pixel or region containing a pixel is predominantly contrast-enhanced or predominantly liquid-retaining, based on the data values ​​of each pixel in the multiple time-phase contrast-enhanced images, A display mode generation unit generates a color image by converting variable values ​​relating to the spatial or temporal changes of the data values ​​of each pixel in the multiple time-phase contrast images into a color code based on the category of the region. A medical image processing device equipped with [a specific feature].

2. The medical image processing apparatus according to claim 1, wherein the determination unit determines whether a pixel or a region containing a pixel is predominantly contrast-enhanced or predominantly liquid-retaining, based on the time change of the data value of each pixel in the multiple time-phase contrast-enhanced images.

3. The medical image processing apparatus according to claim 1 or claim 2, wherein the display mode generation unit converts the variable value to the color code using a color conversion function that separately corresponds to contrast-enhancing dominance and liquid-retention dominance for each pixel or region containing the pixel.

4. The medical image processing apparatus according to claim 3, wherein the contrast-enhancing color conversion function and the liquid-retention-enhancing color conversion function convert the value of at least one of the variable values ​​to the same color code.

5. The medical image processing apparatus according to claim 3 or claim 4, further comprising a display control unit that displays a color superimposed image obtained by superimposing the color image onto an image based on the multiple time-phase contrast images, or a display screen including the color image, on a display.

6. The medical image processing apparatus according to claim 5, wherein the display screen further includes the multiple time-phase contrast images.

7. The medical image processing apparatus according to claim 5, wherein the display screen includes the color superimposed image or the color image for each of the multiple slice positions.

8. An acquisition unit that acquires contrast-enhanced images of the subject at multiple time phases, A display mode generation unit calculates a first color code corresponding to contrast enhancement dominance and a second color code corresponding to liquid retention dominance for each pixel or region containing the pixel from variable values ​​relating to the spatial or temporal changes of the data values ​​of each pixel in the multiple time-phase contrast-enhanced images, and generates a color image in which the color code of each pixel is determined based on the first and second color codes. A medical image processing device equipped with [a specific feature].

9. A method for operating a medical image processing apparatus comprising at least one processor and at least one memory, wherein the at least one processor, Acquiring multiple contrast-enhanced images obtained from different time points in time for the subject from at least one of the memory units, Based on the data values ​​of each pixel in the multiple time-phase contrast-enhanced images, it is determined whether the category of the pixel or the region containing the pixel is predominantly contrast-enhanced or predominantly fluid-retaining. The variable values ​​relating to the spatial or temporal changes in the data values ​​of each pixel of the multiple time-phase contrast-enhanced images are converted into a color code based on the category of the region to generate a color image. A medical image processing method including [a specific term].

10. Acquiring contrast-enhanced images of the subject at multiple time phases, Based on the data values ​​of each pixel in the multiple time-phase contrast-enhanced images, it is determined whether the category of the pixel or the region containing the pixel is predominantly contrast-enhanced or predominantly fluid-retaining. The variable values ​​relating to the spatial or temporal changes in the data values ​​of each pixel of the multiple time-phase contrast-enhanced images are converted into a color code based on the category of the region to generate a color image. A program that causes a computer to execute something.

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