JP7066476B2 - Medical image processing equipment, medical image processing method and X-ray diagnostic equipment - Google Patents

Description

Embodiments of the present invention relate to a medical image processing apparatus, a medical image processing method, and an X-ray diagnostic apparatus.

Conventionally, in an X-ray diagnostic apparatus, for example, a technique called color parametric imaging for imaging parameters related to the inflow time of a contrast medium may be applied. In color parametric imaging, for example, a change in pixel value in an arbitrary pixel of a DSA image is regarded as a change in contrast medium concentration, and a time when a time-series change in pixel value becomes a peak or a specific value is calculated as an inflow time. Then, in color parametric imaging, color information is assigned to the calculated time and displayed as a color image or a color moving image.

Japanese Unexamined Patent Publication No. 2015-21536

An object to be solved by the present invention is to provide a medical image processing apparatus, a medical image processing method, and an X-ray diagnostic apparatus capable of applying parametric imaging to a moving object.

The medical image processing apparatus of the embodiment includes a processing unit, a generation unit, and a display control unit. The processing unit selects a reference image from a plurality of blood vessel images based on a plurality of contrast images acquired in time series, and the blood vessel shape in the reference image substantially matches the blood vessel shape in another blood vessel image. Deformation processing is performed on other blood vessel images. The generation unit generates a color image in which colors corresponding to changes in pixel values over time are assigned to each pixel based on the plurality of blood vessel images after the deformation process. The display control unit causes the display unit to display the color image.

FIG. 1 is a block diagram showing a configuration example of the X-ray diagnostic apparatus according to the first embodiment. FIG. 2 is a flowchart showing an example of a procedure of processing by the X-ray diagnostic apparatus according to the first embodiment. FIG. 3 is a flowchart showing a processing procedure of image acquisition processing by the X-ray diagnostic apparatus according to the first embodiment. FIG. 4 is a diagram (1) for explaining the first embodiment. FIG. 5 is a flowchart showing a processing procedure of a color image generation process by the generation function according to the first embodiment. FIG. 6 is a diagram (2) for explaining the first embodiment. FIG. 7 is a diagram (3) for explaining the first embodiment. FIG. 8 is a diagram (4) for explaining the first embodiment. FIG. 9 is a diagram (5) for explaining the first embodiment.

Hereinafter, the medical image processing apparatus, the medical image processing method, and the X-ray diagnostic apparatus according to the embodiment will be described with reference to the drawings. The embodiment is not limited to the following embodiments. Further, in principle, the contents described in one embodiment are similarly applied to other embodiments.

(First Embodiment)
FIG. 1 is a diagram showing an example of the configuration of the X-ray diagnostic apparatus 1 according to the first embodiment. As shown in FIG. 1, the X-ray diagnostic apparatus 1 according to the first embodiment includes an X-ray imaging mechanism 10 and a medical image processing apparatus 100. For example, the X-ray diagnostic apparatus 1 collects a contrast image using X-rays and generates a blood vessel image based on the collected contrast image. In this embodiment, as an example of a blood vessel image, a difference image between a contrast-enhanced image and a non-contrast-enhanced image will be described.

The X-ray imaging mechanism 10 has an X-ray tube 11, a detector (Flat Panel Detector: FPD) 12, a C-shaped arm 13, and a sleeper 14, and an injector 60 is connected to the X-ray imaging mechanism 10.

The injector 60 is a device for injecting a contrast medium from a catheter inserted into the subject P. Here, the start of injection of the contrast medium from the injector 60 may be executed according to the injection start instruction received via the medical image processing device 100 described later, or an operator such as an operator may directly start the injection of the contrast medium 60. It may be executed according to the injection start instruction input to. Alternatively, the injection of the contrast medium may be performed manually by the operator using a syringe.

The C-shaped arm 13 supports an X-ray tube 11 and a detector 12 that detects X-rays emitted from the X-ray tube 11. The C-shaped arm 13 rotates at high speed like a propeller around the subject P lying on the bed 14 by a motor (not shown). Here, the C-shaped arm 13 is rotatably supported with respect to the XYZ axes, which are three orthogonal axes, and is individually rotated on each axis by a drive circuit (not shown).

The X-ray tube 11 is an X-ray source that generates X-rays by using a high voltage supplied from a high voltage generator (not shown). The detector 12 is a device in which a plurality of X-ray detection elements for detecting X-rays transmitted through the subject P are arranged in a matrix. Each X-ray detection element included in the detector 12 outputs the X-rays transmitted through the subject P to the A / D converter 21 described later.

As shown in FIG. 1, the medical image processing apparatus 100 includes an A / D (Analog / Digital) converter 21, an image memory 22, a subtraction circuit 23, a filtering circuit 24, an affine conversion circuit 25, and a LUT (. It has a Look Up Table) 26, an image pickup control circuit 27, a three-dimensional reconstruction circuit 31, a three-dimensional image processing circuit 32, a processing circuit 33, a monitor 40, and an input interface 50. The subtraction circuit 23 is an example of a blood vessel image generation unit.

The monitor 40 displays various images processed by the medical image processing device 100 and various information such as a GUI (Graphical User Interface). For example, the monitor 40 is a CRT (Cathode Ray Tube) monitor, a liquid crystal monitor, or the like.

The input interface 50 corresponds to an input device such as a mouse, a keyboard, a button, a panel switch, a touch command screen, a foot switch, a trackball, and a joystick. The input interface 50 receives various instructions from the operator and appropriately transfers the received various instructions to each circuit of the medical image processing apparatus 100.

Further, for example, the input interface 50 includes an X-ray trigger button for instructing X-ray irradiation. When the X-ray trigger button is pressed by the operator, the X-ray diagnostic apparatus 1 starts imaging an X-ray image.

The A / D converter 21 is connected to the detector 12, converts the analog signal input from the detector 12 into a digital signal, and stores the converted digital signal as an X-ray acquisition image in the image memory 22.

The image memory 22 stores an X-ray collected image (projection data). Further, the image memory 22 stores the reconstruction data (volume data) reconstructed by the three-dimensional reconstruction circuit 31 described later and the three-dimensional image generated by the three-dimensional image processing circuit 32. The image memory 22 can store a program that can be executed by a computer.

The subtraction circuit 23 generates a difference image such as a DSA (Digital Subtraction Angiography) image. For example, the subtraction circuit 23 has a collected image generated from an X-ray signal collected by imaging a subject in the absence of a contrast medium and an X-ray signal collected by imaging the subject in the presence of a contrast medium. Generates a difference image from the collected image generated from. More specifically, the subtraction circuit 23 is generated from a mask image (an X-ray signal collected by imaging a subject in the absence of a contrast medium) stored in an image memory 22 and collected from substantially the same direction. A DSA image is generated using the projection data of the collected image) and the contrast image (collected image generated from the X-ray signal collected by imaging the subject in the presence of a contrast medium).

The filtering circuit 24 performs an image processing filter such as a high-pass filter and a low-pass filter. For example, the filtering circuit 24 performs high frequency emphasis filtering. The affine transformation circuit 25 enlarges, reduces, moves, and the like of the image. The LUT 26 performs gradation conversion.

The image pickup control circuit 27 controls various processes related to image pickup by the X-ray image pickup mechanism 10 under the control of the process circuit 33 described later. For example, the image pickup control circuit 27 controls rotary imaging in which an X-ray image is imaged at a predetermined frame rate while rotating the C-shaped arm 13. As an example, the image pickup control circuit 27 controls a plurality of rotation imaging after a single contrast medium injection, triggered by a signal output from the injector 60 at the start of contrast medium injection. Here, the image pickup control circuit 27 controls the start of a plurality of rotation imaging by the elapsed time starting from the injection start time of a single contrast medium, so that the timing at which the contrast medium reaches the target of each rotation imaging. Rotational imaging is performed according to. Further, the image pickup control circuit 27 controls the image pickup that collects the projection data at a predetermined frame rate without rotating the C-shaped arm 13.

Further, the image pickup control circuit 27 controls a high voltage generator (not shown) to continuously or intermittently generate X-rays from the X-ray tube 11 while controlling the rotation of the C-type arm 13, and the detector. 12 is controlled so as to detect the X-ray transmitted through the subject P. Here, the image pickup control circuit 27 generates X-rays from the X-ray tube 11 based on the X-ray generation conditions set for each rotation imaging by the processing circuit 33 described later.

The three-dimensional reconstruction circuit 31 reconstructs reconstruction data (hereinafter referred to as three-dimensional image data or volume data) from the projection data collected by the X-ray imaging mechanism 10. For example, in the three-dimensional reconstruction circuit 31, the mask image and the contrast image are differentiated by the subtraction circuit 23, the difference image after subtraction stored in the image memory 22 is used as projection data, and the volume data is reconstructed from this projection data. do. Alternatively, the three-dimensional reconstruction circuit 31 converts the mask image or contrast image stored in the image memory 22 into digital data converted into digital data by the A / D converter 21, and reconstructs the volume data from the projection data. .. Then, the three-dimensional reconstruction circuit 31 stores the reconstructed volume data in the image memory 22.

The three-dimensional image processing circuit 32 generates three-dimensional medical image data from the volume data stored in the image memory 22. For example, the three-dimensional image processing circuit 32 generates volume rendering image data and MPR (Multi Planar Reconstruction) image data from the volume data. Then, the three-dimensional image processing circuit 32 stores the generated three-dimensional image in the image memory 22.

The processing circuit 33 controls the entire X-ray diagnostic apparatus 1. Specifically, the processing circuit 33 controls various processes related to imaging of an X-ray image by the X-ray imaging mechanism 10, generation of a display image, display of a display image on the monitor 40, and the like. For example, the processing circuit 33 generates a three-dimensional image from the X-ray image captured by the rotation image pickup by the X-ray image pickup mechanism 10 or the rotation image pickup, and displays it on the monitor 40.

Further, as shown in FIG. 1, the processing circuit 33 executes the deformation processing function 33a, the generation function 33b, and the display control function 33c. Here, for example, each processing function executed by the deformation processing function 33a, the generation function 33b, and the display control function 33c, which are the components of the processing circuit 33 shown in FIG. 1, is in the form of a program that can be executed by a computer. It is recorded in the storage device (for example, the image memory 22) of the X-ray diagnostic device 1. The processing circuit 33 is a processor that realizes a function corresponding to each program by reading each program from a storage device and executing the program. In other words, the processing circuit 33 in the state where each program is read out has each function shown in the processing circuit 33 of FIG.

The image memory 22 is, for example, a semiconductor memory element such as a RAM (Random Access Memory) or a flash memory (Flash Memory), or a storage device such as a hard disk or an optical disk. Further, the subtraction circuit 23, the filtering circuit 24, the affine conversion circuit 25, the LUT 26, the image pickup control circuit 27, the three-dimensional reconstruction circuit 31, the three-dimensional image processing circuit 32, and the processing circuit 33 are, for example, a CPU (Central Processing Unit). Electronic circuits such as MPU (Micro Processing Unit) and integrated circuits such as ASIC (Application Specific Integrated Circuit) and FPGA (Field Programmable Gate Array).

The configuration of the X-ray diagnostic apparatus 1 according to the first embodiment has been described above. In the X-ray diagnostic apparatus 1 configured in this way, for example, a technique called parametric imaging for imaging parameters related to the inflow time of the contrast medium may be applied. In parametric imaging, for example, the change in the pixel value at each position of the DSA image is regarded as the change in the contrast medium concentration, and the time when the time-series change in the pixel value becomes a peak or a specific value is calculated as the inflow time. Then, in parametric imaging, parametric imaging image data (also referred to as "parametric image data") or parametric imaging moving image data is generated by mapping the color corresponding to the calculated inflow time to each position.

However, in the conventional technique, it is not possible to track the change of the pixel value at each position of the DSA image in the moving part such as abdominal blood vessels and the heart, and the color parametric imaging cannot be applied. Therefore, in the first embodiment, in order to apply color parametric imaging to a moving part such as the abdomen or the heart, the processing circuit 33 includes a deformation processing function 33a, a generation function 33b, and a display control function 33c. To execute.

Hereinafter, an example of color parametric imaging by the X-ray diagnostic apparatus 1 according to the present embodiment will be described with reference to FIG. FIG. 2 is a flowchart showing an example of a processing procedure by the X-ray diagnostic apparatus 1 according to the first embodiment. Note that FIG. 2 describes a case where the imaging target is a blood vessel having periodic movement such as an abdominal blood vessel or a heart. As shown in FIG. 2, the X-ray diagnostic apparatus 1 executes an image acquisition process (step S1). For example, in step S1, the X-ray diagnostic apparatus 1 receives the selection of an imaging program dedicated to parametric imaging from the operator via the input interface 50. More specifically, the operator selects an imaging program dedicated to parametric imaging of a moving object during an angio examination or intervention treatment.

The image acquisition process of step S1 will be described with reference to FIG. FIG. 3 is a flowchart showing a processing procedure of image acquisition processing by the X-ray diagnostic apparatus 1 according to the first embodiment. The image acquisition process shown in FIG. 3 corresponds to step S1 in FIG.

Step S101 shown in FIG. 3 is a step realized by the processing circuit 33. In step S101, the processing circuit 33 determines whether or not the X-ray imaging switch has been pressed. Here, when the processing circuit 33 does not determine that the X-ray imaging switch has been pressed (steps S101 and No), the processing circuit 33 repeats the determination process of step S101. On the other hand, when the processing circuit 33 determines that the X-ray image pickup switch has been pressed (steps S101, Yes), the processing circuit 33 causes the image pickup control circuit 27 to execute step S102. It is desirable that the contrast medium is prepared in the syringe and prepared to inject the contrast medium before the start of step S101.

Step S102 and step S103 are steps realized by the image pickup control circuit 27. In step S102, the image pickup control circuit 27 collects a mask image. For example, when the operator presses the X-ray image pickup switch, the image pickup control circuit 27 controls the C-type arm 13 to start collecting mask images. The image pickup control circuit 27 repeats imaging in which projection data is collected for a certain period of time at a predetermined frame rate without rotating the C-shaped arm 13. Here, the fixed time is determined by the moving time of the organ and the imaging interval. Specifically, for example, in the case of respiratory movement, one cycle takes about 5 seconds, and in the case of movement accompanying a heartbeat, one cycle takes about 1 second. Therefore, images for one cycle or more, usually two to three cycles, are collected.

In step S103, the image pickup control circuit 27 determines whether or not a predetermined number of mask images have been collected. This predetermined number of sheets can be set in advance in the imaging program. Here, when the image pickup control circuit 27 does not determine that a predetermined number of mask images have been collected (steps S103, No), the determination process of step S103 is repeated. On the other hand, when the image pickup control circuit 27 determines that a predetermined number of mask images have been collected (steps S103, Yes), the processing circuit 33 executes step S104.

Step S104 is a step realized by the processing circuit 33. In step S104, the processing circuit 33 instructs the injection of the contrast medium. For example, when a predetermined number of mask images are collected in step S103, the processing circuit 33 instructs an operator such as an operator to start contrast enhancement. This instruction may be instructed by displaying an icon indicating contrast on the monitor 40 or by voice. Alternatively, the monitor 40 may count down the contrast start timing. As a result, the operator starts injecting the contrast medium with a syringe when the timing is instructed.

Step S105 is a step realized by the image pickup control circuit 27. In step S105, the image pickup control circuit 27 collects a contrast image. Here, the image pickup control circuit 27 controls the image pickup that collects the projection data at a predetermined frame rate without rotating the C-shaped arm 13. The collected contrast image is displayed on the monitor 40 in near real time by the processing circuit 33.

Step S106 is a step realized by the processing circuit 33. In step S106, the processing circuit 33 determines whether or not the release of the X-ray imaging switch has been accepted. Here, if the processing circuit 106 does not determine that the release of the X-ray imaging switch has been accepted (steps S106, No), the process proceeds to step S105. On the other hand, when the processing circuit 106 determines that the release of the X-ray imaging switch has been accepted (step S107, Yes), the processing circuit 106 ends the imaging and proceeds to step S107. For example, an operator such as an operator releases the image pickup switch after confirming that the target portion has been imaged while observing the image displayed on the monitor 40.

Step S107 is a step realized by the A / D converter 21. In step S107, the A / D converter 21 transfers all the collected images to the image memory 22. The A / D converter 21 may transfer the X-ray image to the image memory 22 in parallel while collecting the X-ray image.

Return to FIG. Step S2 shown in FIG. 2 is a step realized by the subtraction circuit 23. In step S2, the subtraction circuit 23 executes the DSA image generation process. For example, the subtraction circuit 23 reads out the mask image and the contrast image stored in the image memory 22, and subtracts the contrast image and the mask image to generate a DSA image. Here, the subtraction circuit 23 subtracts the contrast image from the mask image of all the phases and selects the optimum DSA image. FIG. 4 is a diagram for explaining the first embodiment.

The upper part of FIG. 4 shows the contrast images C1 to C8 collected in step S105 shown in FIG. 3, and the middle part of FIG. 4 shows the mask images M1 to M8 collected in step S102. Here, the subtraction circuit 23, for example, subtracts all of the mask images M1 to M8 with respect to the contrast image C1 to generate DSA images C1M1 to C1M8, respectively. Then, the subtraction circuit 23 identifies the optimum DSA image for the contrast image C1 from the generated DSA images by image processing.

Image processing for identifying the optimum DSA image will be described. In the X-ray diagnostic apparatus 1, pixels on a path with a large amount of X-ray attenuation show a low pixel value, and pixels on a path with a small amount of X-ray attenuation show a high pixel value. Further, in the DSA image, the pixels in the portion where the pixel value is lowered due to the inflow of the contrast medium show negative polarity (−). Here, when a structure such as an organ or a diaphragm moves due to body movement such as breathing between the time of collecting the contrast image and the time of collecting the mask image, the pixel in which the organ overlaps between the contrast image and the mask image in the DSA image. In principle indicates a zero value or an approximate value of zero. On the other hand, in the DSA image, the pixels in which the organs do not overlap in the contrast image and the mask image show positive polarity (+) or negative polarity (-).

Therefore, it is possible to determine that the pixel showing the positive polarity (+) in the DSA image is the pixel in which the position of the organ or tissue is displaced between the time of collecting the contrast image and the time of collecting the mask image. Therefore, the subtraction circuit 23 identifies the DSA image having the smallest number of pixels showing positive polarity (+) among the generated DSA images as the optimum DSA image for the contrast image C1. In other words, the subtraction circuit 23 generates a blood vessel image by differentiating the contrast image and the non-contrast image having the smallest number of pixels having the opposite phase to the pixel value at each position in the blood vessel image. That is, a mask image having the same phase and a contrast image are identified by image processing.

The subtraction circuit 23 similarly generates a DSA image for all the collected contrast images. That is, the optimum DSA image for each contrast image C2 to C8 is identified by subtracting all of the mask images M1 to M8 for each of the contrast images C2 to C8.

Return to FIG. Step S3 shown in FIG. 2 is a step corresponding to the deformation processing function 33a. This is a step in which the transformation processing function 33a is realized by the processing circuit 33 calling and executing a predetermined program corresponding to the transformation processing function 33a from the image memory 22. In step S3, the transformation processing function 33a executes the transformation processing. The deformation processing function 33a is an example of a processing unit.

That is, the deformation processing function 33a selects a reference image from a plurality of difference images generated by differentiating a contrast image and a non-contrast image obtained by capturing a moving object in time series. For example, the deformation processing function 33a identifies subtraction images having the least phase phase. Here, the transformation processing function 33a selects a reference image based on the result of the correlation calculation between the plurality of difference images. For example, the deformation processing function 33a performs a correlation operation between subtraction images that are continuously connected in time, and selects the image having the highest correlation value as the image with less movement.

Then, the transformation processing function 33a performs transformation processing on the plurality of difference images to other difference images with the reference image as a reference. Specifically, the deformation processing function 33a performs the other difference with respect to the plurality of difference images so that the position of the portion in the difference image other than the reference image substantially coincides with the position of the portion in the reference image. Performs transformation processing on the image. That is, the deformation processing function 33a selects a reference image from a plurality of blood vessel images based on a plurality of contrast images acquired in time series, and the blood vessel shape in the selected reference image substantially matches the blood vessel shape in the other blood vessel images. As such, the deformation processing is performed on other blood vessel images. For example, the deformation processing function 33a performs warping processing on another blood vessel image so that the blood vessel shape in the reference image substantially matches the blood vessel shape in the other blood vessel image. Here, the warping process is a process of aligning a portion having a different shape between DSA images for a predetermined cycle by partially deforming (warping) the reference image in pixel units. More specifically, the deformation processing function 33a arranges a large number of regions of interest such that the reference image and a DSA image other than the reference image (also referred to as a processing target image) locally coincide with each other, and the region of interest The movement vector is derived based on. Then, the deformation processing function 33a derives, for example, a movement vector in pixel units based on the derived movement vector, and deforms (warps) a predetermined portion displayed on the image to be processed based on the movement vector.

Here, for example, when the DSA image having the Nth phase among the plurality of DSA images is the reference image, the deformation processing function 33a warps each of the plurality of subtraction images with the Nth DSA image as a reference.

Step S4 shown in FIG. 2 is a step corresponding to the generation function 33b. This is a step in which the generation function 33b is realized by the processing circuit 33 calling and executing a predetermined program corresponding to the generation function 33b from the image memory 22. In step S4, the generation function 33b executes a color image generation process. The generation function 33b is an example of a generation unit. For example, the generation function 33b generates a color image in which colors corresponding to changes in pixel values over time are assigned to each position of each difference image after warping processing. That is, the generation function 33b generates a color image in which colors corresponding to changes in pixel values with time are assigned to each pixel based on a plurality of blood vessel images after the deformation process. Here, the generation function 33b creates a color parametric imaging image as a color image. The color image generation process will be described with reference to FIG.

FIG. 5 is a flowchart showing a processing procedure of a color image generation process by the generation function 33b according to the first embodiment. The processing procedure of the color image generation processing shown in FIG. 5 corresponds to step S4 shown in FIG.

As shown in FIG. 5, the generation function 33b performs a warping process to identify the inflow time from the DSA image corrected for the positional deviation due to the influence of movement (step S301). Here, the inflow time is a parameter defined based on the time-series change of each pixel value by regarding the change of the pixel value at each position of the DSA image as the change of the contrast medium concentration. In other words, the generation function 33b identifies the inflow time of the contrast agent injected into the object based on the change over time of the pixel value at each position of the plurality of difference images. That is, the generation function 33b identifies the inflow time of the contrast medium to be injected into the object based on the time course of the pixel value at each position of the plurality of blood vessel images after the deformation process. Any method can be selected as the method for calculating the inflow time. For example, as a method for calculating the inflow time, TTP (Time-to-Peak), which is a method for identifying the time at which the time change of the pixel value becomes maximum as the inflow time, can be selected. Further, for example, as a method of calculating the inflow time, a method of identifying as an inflow time the time when the time change of the pixel value reaches a predetermined value or the time when the time change of the pixel value reaches a predetermined ratio with respect to the maximum value in the time change. TTA (Time-to-Arrival) is selectable.

FIG. 6 is a diagram for explaining the first embodiment. The horizontal axis of FIG. 6 indicates time, and the vertical axis of FIG. 6 indicates pixel values. The example of FIG. 6 illustrates a time density profile analyzed for any pixel contained in a DSA image. In addition, in FIG. 6, the case where the identification method of the inflow time is TTA will be described.

As shown in FIG. 6, for example, the generation function 33b identifies the inflow time by analyzing the time density profile for each pixel included in the DSA image. In the example of FIG. 6, the generation function 33b specifies the time to reach 20% (0.2 × Cmax) with respect to the maximum pixel value (Cmax) as the inflow time.

The content of FIG. 6 is merely an example, and is not limited to the example of FIG. For example, in FIG. 6, the case where “20%” is set as the ratio for identifying the inflow time by TTA has been described, but the present invention is not limited to this, and any ratio (or value) may be set. Further, the method for identifying the inflow time is not limited to TTA, and may be TTP.

Subsequently, the generation function 33b generates a color code (step S302). For example, the generation function 33b generates one color code when generating parametric image data as a still image. Further, when the generation function 33b generates parametric image data as a moving image, the generation function 33b generates a number of color codes corresponding to the number of frames included in the moving image. Hereinafter, the color code generation process in still image generation and the color code generation process in moving image generation will be described in order. Although the default conditions are set in advance for the generation conditions for generating the color code, the operator may appropriately set them.

FIG. 7 is a diagram for explaining the first embodiment. In addition, in FIG. 7, the case where the identification method of the inflow time is TTA will be described. In TTA, "Auto" is set as the default for the cycle and initial value of the color code. Note that "Auto" indicates that the DSA image is automatically set according to the collection time (imaging period).

As shown in FIG. 7, for example, when the collection time of the DSA image is 0 to T seconds, the generation function 33b sets the period of the color code to "T" and the initial value of the color code to "0", respectively. Set. In this color code, when the inflow time t is "0", it becomes "red", gradually changes from "red" to "green" from "0" to "T / 2", and when it is "T / 2". Is stipulated to be "green". Further, in this color code, it is stipulated that the color gradually changes from "green" to "blue" from "T / 2" to "T", and becomes "blue" when "T".

In this way, the generation function 33b generates a color code for a still image. The content of FIG. 7 is merely an example, and is not limited to the example of FIG. 7. For example, in FIG. 7, the case where the inflow time identification method is TTA has been described, but the present invention is not limited to this, and TTP may be used. Further, the order of colors specified in the color code is not limited to the example of FIG. 7, and may be set arbitrarily. Further, since it takes a certain amount of time for the contrast medium to flow in for the first time, the initial value of the color code may be set to "D". This initial value "D" may be set by default, or may be automatically identified from the DSA image. As an identification method, the time during which a change of a certain level or more first occurs in a certain range or more in a DSA image may be set as "D".

FIG. 8 is a diagram for explaining the first embodiment. FIG. 8 describes a case where the generation method for generating parametric image data as a moving image is CCC (Circular Color Coding). The method for identifying the inflow time in CCC may be TTA or TTP.

As shown in FIG. 8, for example, when the collection time of the DSA image is 0 to T seconds, the generation function 33b sets the period of the color code to "L (L <T)" and sets the initial value of the color code to "L (L <T)". Set "0" and the color code step to "ΔT" respectively. Then, the generation function 33b generates a number of color codes corresponding to the number of frames included in the moving image. For example, assuming that the number of frames of the moving image is "N = L / ΔT", the generation function 33b generates N color codes from the first frame to the Nth frame, respectively. In addition, "N" is an integer.

For example, in the color code of the first frame, when the inflow time t is "0", it becomes "red", gradually changes from "red" to "green" from "0" to "L / 3", and then "L". It is stipulated that it becomes "green" when it is "/ 3". In addition, in the color code of the first frame, it gradually changes from "green" to "blue" from "L / 3" to "2L / 3", and becomes "blue" at "2L / 3". Is stipulated. Further, in the color code of the first frame, it is stipulated that the color code gradually changes from "blue" to "red" from "2L / 3" to "L" and returns to "red" when "L". As described above, in the color code of the first frame, the inflow time t changes from “0” to “L” while changing from “red → green → blue → red”. Further, after "L", the color change from "L" to "2L" is repeated. That is, in the color code of the first frame, the inflow time t changes from “L” to “2L” while changing from “red → green → blue → red”. Further, in the color code of the first frame, the inflow time t changes from "2L" to "3L" while changing from "red → green → blue → red". After that, similarly, in the color code of the first frame, the inflow time t changes in the order of “red → green → blue → red” until it reaches “T”.

The color code of the second frame is generated by shifting the color code of the first frame by "ΔT". For example, in the color code of the second frame, when the inflow time t is "Δt", it becomes "red", and gradually changes from "red" to "green" from "Δt" to "L / 3 + Δt", and then "L". It is specified that it becomes "green" when "/ 3 + Δt". Further, in the color code of the second frame, the color code gradually changes from "green" to "blue" from "L / 3 + Δt" to "2L / 3 + Δt", and becomes "blue" when "2L / 3 + Δt". Is stipulated. Further, in the color code of the second frame, it is stipulated that the color code gradually changes from "blue" to "red" from "2L / 3 + Δt" to "L + Δt" and returns to "red" when "L + Δt". As for the color code after "L + Δt", the color change from "L + ΔT" to "2L + ΔT" is repeated as in the color code of the first frame. Further, before "ΔT", the color change from "0" to "ΔT" is repeated.

That is, the color code of the Nth frame is generated by shifting the color code of the N-1th frame by "ΔT". In other words, the color code of the Nth frame is generated by shifting the color code of the first frame by "ΔT × (N-1)".

The content of FIG. 8 is merely an example, and is not limited to the example of FIG. For example, the order of colors specified in the color code is not limited to the example of FIG. 8, and may be set arbitrarily.

As described above, the generation function 33b generates a number of color codes corresponding to the number of frames of the moving image as the color code for the moving image. It can be said that the color code for moving images is a periodic color code in which the periodic change of the color with respect to the change of the inflow time is defined. The processing conditions in the parametric image generation process are registered in advance in an imaging program dedicated to parametric imaging. The processing conditions registered here include, for example, an inflow time identification method, a color code cycle, a color code phase, an initial value of the color code, and information indicating whether the image is a still image or a moving image. Is included.

Return to FIG. The generation function 33b generates parametric image data (step S303). For example, the generation function 33b generates parametric image data by assigning a color to each pixel of the DSA image according to the change of the pixel value with time. That is, the generation function 33b assigns colors according to the identified inflow time to each position to generate a color image.

FIG. 9 is a diagram for explaining the first embodiment. FIG. 9 illustrates parametric image data in which the blood vessels of the heart of the subject P are visualized.

As shown in FIG. 9, for example, the generation function 33b refers to the color code generated in step S302, assigns a color corresponding to the inflow time of each pixel, and generates parametric image data.

Specifically, the generation function 33b generates parametric image data of a still image. For example, the generation function 33b refers to the color code for a still image shown in FIG. 7, assigns a color corresponding to the inflow time of each pixel, and generates parametric image data.

In addition, the generation function 33b generates parametric image data of moving images. For example, the generation function 33b refers to the color code for the moving image shown in FIG. 8 and generates a number of parametric image data corresponding to the number of frames included in the moving image. Specifically, the generation function 33b generates the parametric image data of the first frame by allocating the color corresponding to the inflow time of each pixel based on the color code of the first frame. Further, the generation function 33b generates the parametric image data of the second frame by allocating the color corresponding to the inflow time of each pixel based on the color code of the second frame. As described above, the generation function 33b generates the parametric image data of the Nth frame by allocating the color corresponding to the inflow time of each pixel based on the color code of the Nth frame.

Although the colors assigned according to the inflow time have been described in FIG. 9, it is more preferable to adjust the brightness (brightness) based on the maximum pixel value of each pixel. For example, the brightness of each pixel is preferably set to "D / Dmax", which is the ratio of the maximum pixel value "D" of each pixel to the maximum pixel value "Dmax" among all the pixels included in the DSA image. ..

In this way, the generation function 33b generates parametric image data of a still image or a moving image. Then, the generation function 33b outputs the generated parametric image data to the display control function 33c.

Return to FIG. Step S5 shown in FIG. 2 is a step corresponding to the display control function 33c. This is a step in which the display control function 33c is realized by the processing circuit 33 calling and executing a predetermined program corresponding to the display control function 33c from the image memory 22. In step S5, the display control function 33c causes the monitor 40 to display a color image. The display control function 33c is an example of the display control unit. Here, the display control function 33c displays a color image as a still image. Alternatively, the display control function 33c displays a color image as a moving image. When displaying the moving image, the parametric image data in the Nth frame may be displayed, and then the parametric image data in the first frame may be displayed again.

As described above, in the first embodiment, the X-ray diagnostic apparatus 1 is based on a plurality of difference images generated by differentiating a contrast image obtained by capturing a moving object in time series and a non-contrast image. An image is selected, and for a plurality of difference images, warping processing is performed on other difference images with the reference image as a reference. Subsequently, the X-ray diagnostic apparatus 1 generates a color image in which colors corresponding to changes in pixel values with time are assigned to each position of each difference image after the warping process. Then, the X-ray diagnostic apparatus 1 displays a color image on the monitor 40. This makes it possible to apply parametric imaging to moving objects, according to the first embodiment.

(Variation example of the first embodiment)
In the first embodiment described above, the subtraction circuit 23 has been described as identifying a mask image having the same phase and a contrast image by image processing when generating a DSA image, but the embodiment is limited to this. It's not something. For example, the subtraction circuit 23 may generate a DSA image by specifying a non-contrast image having the same phase as the contrast image based on a signal from the biological signal acquisition device. In other words, the subtraction circuit 23 generates a differential image by differentiating the contrast image and the non-contrast image having the same phase as the biological signal of the contrast image. In such a case, the X-ray diagnostic apparatus 1 is connected to a biological signal acquisition device that acquires a biological signal from an object accompanied by periodic movement. Here, the biological signal acquisition device may be, for example, a respiration detection device that measures the respiration phase. In such a case, the respiration detection device has a respiration sensor and acquires the respiration waveform of the subject P. Alternatively, the biological signal acquisition device may be an electrocardiograph for measuring the cardiac phase, such as ECG (Electrocardiogram) / EKG (Elektrokardiogramm). The biometric signal acquisition device such as a respiratory detection device and an electrocardiograph will be described as an external device as a non-component of the X-ray diagnostic device 1, but the X-ray is described as a component of the X-ray diagnostic device 1. It may be incorporated in the diagnostic apparatus 1.

Further, the subtraction circuit 23 identifies a mask image having a phase similar to that of the contrast image based on the signal from the biological signal acquisition device, and the identified mask image and the mask image having a phase close to the identified mask image are combined with the contrast image. The optimum mask image may be identified by performing image processing.

Further, in the first embodiment described above, the case where the deformation processing function 33a identifies the reference image by the correlation calculation has been described, but the embodiment is not limited to this. For example, when the X-ray diagnostic apparatus 1 is connected to the biological signal acquisition device, the deformation processing function 33a may select a reference image based on the biological signal. That is, the deformation processing function 33a may select a reference image based on a biological signal from the plurality of blood vessel images based on a plurality of contrast-enhanced images obtained by capturing an object accompanied by periodic movement. Here, the deformation processing function 33a uses a time phase that appears with good reproducibility in a biological signal as a reference time phase, and selects a DSA image of this reference time phase as a reference image. When there are a plurality of reference time phases in the biological signal, the time phase having less movement is selected as the reference time phase from the plurality of reference time phases.

As an example, the biological signal acquisition device acquires the cardiac phase as a biological signal. Further, the deformation processing function 33a selects a blood vessel image having a phase near the middle stage of expansion as a reference image based on the heart phase. More specifically, the deformation processing function 33a identifies the DSA image in the middle diastolic phase in which the cardiac phase is most stable from the EKG signal, and selects the DSA image close to the mid diastolic phase as the reference image.

Alternatively, the biological signal acquisition device acquires the respiratory phase as a biological signal. Further, the deformation processing function 33a selects a blood vessel image having a phase near the maximum exhalation as a reference image based on the respiratory phase. More specifically, the deformation processing function 33a identifies the DSA image of the maximum exhalation with the most stable respiratory waveform, and selects the DSA image close to the maximum exhalation as the reference image. That is, the deformation processing function 33a selects a reference image based on a biological signal from a plurality of difference images generated by differentiating a contrast image obtained by capturing an object with periodic movement and a non-contrast image.

(Other embodiments)
The embodiment is not limited to the above-described embodiment.

In the above-described embodiment, the case where the imaging target is a blood vessel having periodic movement such as an abdominal blood vessel or a heart has been described, but the embodiment is not limited to this. For example, the movement of the imaged object does not have to be periodic. More specifically, the above-mentioned color parametric imaging can also be applied to the case where the peristalsis of the intestinal tract is targeted for imaging. In such a case, the deformation processing function 33a selects a time phase that appears with good reproducibility in the peristalsis of the intestinal tract as a reference time phase, and a DSA image of this reference time phase as a reference image. When there are a plurality of reference time phases in the peristalsis of the intestinal tract, the time phase with less movement is selected as the reference time phase from among the plurality of reference time phases.

Further, in the above-described embodiment, the difference image generated by the subtraction circuit 23 has been described as an example of the blood vessel image. However, the embodiments are not limited to this. For example, the medical image processing apparatus 100 may include a blood vessel image generation circuit in place of or in addition to the subtraction circuit 23, and the blood vessel image generation circuit may generate a blood vessel image based on a contrast image. The blood vessel image generation circuit is an example of a blood vessel image generation unit.

For example, the blood vessel image generation circuit may generate a blood vessel image using machine learning. As an example, the blood vessel image generation circuit first acquires a plurality of learning data including a combination of a difference image based on a contrast image and a mask image and a contrast image. Next, the blood vessel image generation circuit removes background components (bones, soft tissues, etc.) other than blood vessels on the contrast image by supervised learning using learning data that inputs a contrast image and outputs a difference image. Generate a trained model. Next, the blood vessel image generation circuit inputs a plurality of contrast images acquired in time series to the trained model. As a result, the blood vessel image generation circuit removes the background component from the contrast image to generate the blood vessel image.

To give another example, the blood vessel image generation circuit performs low frequency processing on a contrast image to generate an image corresponding to a low frequency component (soft tissue or the like) in each contrast image. Next, the blood vessel image generation circuit differentiates the contrast image from the image subjected to the low frequency processing, and removes the low frequency component other than the high frequency component (blood vessel or the like) from each contrast image. As a result, the blood vessel image generation circuit removes the background component from the contrast image to generate the blood vessel image. That is, the blood vessel image generation circuit generates a blood vessel image by differentiating the contrast image and the image obtained by subjecting the contrast image to low frequency processing.

In addition to blood vessels, the high-frequency component in the contrast image may include edge components such as bone tissue and organs. Then, when a blood vessel image is generated by differentiating a contrast image containing an edge component such as bone tissue and an image obtained by subjecting the contrast image to low frequency processing, the edge component such as bone tissue remains in the blood vessel image. In some cases. However, unlike blood vessels (contrast medium components), edge components such as bone tissue do not change in the time series direction. Therefore, even if edge components such as bone tissue remain in the blood vessel image, the effect on parametric imaging is small.

The word "processor" used in the above description is, for example, a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), an integrated circuit for a specific application (Application Specific Integrated Circuit: ASIC), or a programmable logic device (for example, a programmable logic device). It means a circuit such as a simple programmable logic device (SPLD), a complex programmable logic device (CPLD), and a field programmable gate array (FPGA). The processor realizes the function by reading and executing the program stored in the storage circuit. Instead of storing the program in the storage circuit, the program may be directly embedded in the circuit of the processor. In this case, the processor realizes the function by reading and executing the program embedded in the circuit. It should be noted that each processor of the present embodiment is not limited to the case where each processor is configured as a single circuit, and a plurality of independent circuits may be combined to form one processor to realize its function. good. Further, the plurality of components in FIG. 1 may be integrated into one processor to realize the function.

The content shown in FIG. 1 is only an example. For example, FIG. 1 shows a plurality of circuits of a subtraction circuit 23, a filtering circuit 24, an affine transformation circuit 25, a LUT 26, an image pickup control circuit 27, a three-dimensional reconstruction circuit 31, a three-dimensional image processing circuit 32, and a processing circuit 33. Although the processor) is illustrated, these circuits do not necessarily have to be configured independently. For example, any circuit among these circuits may be appropriately combined and configured.

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

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

(Image processing device)
In the above, the case where the color parametric imaging described in the first embodiment and the modification of the first embodiment is performed by the X-ray diagnostic apparatus 1 has been described, but the embodiment is limited to this. It's not a thing. For example, in the color parametric imaging described in the first embodiment and the modification of the first embodiment, an image processing device that acquires a contrast image from the X-ray diagnostic apparatus 1 or a blood vessel image from the X-ray diagnostic apparatus 1 is obtained. It may be executed in the acquired image processing device.

According to at least one embodiment described above, parametric imaging can be applied to a moving object.

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

1 X-ray diagnostic device 33 Processing circuit 33a Warping processing function 33b Generation function 33c Display control function 100 Image processing device

Claims (20)

A reference image is selected from a plurality of blood vessel images based on a plurality of contrast images acquired in time series, and the other blood vessel image is such that the blood vessel shape in the other blood vessel image substantially matches the blood vessel shape in the reference image. A processing unit that performs transformation processing on the image and
The pixel value assigned to each pixel is calculated based on the plurality of blood vessel images after the deformation processing, the parameter is identified based on the change over time of the pixel value assigned to each pixel, and the color corresponding to the parameter is obtained. Is identified for each pixel, and a generation unit that generates a color image in which the color is assigned to each pixel,
A medical image processing apparatus including a display control unit for displaying the color image on the display unit. The generation unit identifies and identifies the inflow time of the contrast medium injected into the object as the parameter based on the time course of the pixel value at each position of the plurality of blood vessel images after the deformation process. The medical image processing apparatus according to claim 1, wherein a color according to time is assigned to each position to generate the color image. The medical image processing apparatus according to claim 1 or 2, wherein the processing unit selects a reference image based on the result of a correlation calculation between the plurality of blood vessel images. It is connected to a biological signal acquisition device that acquires biological signals from objects with periodic movements.
The processing unit according to claim 1 or 2, wherein the processing unit selects a reference image based on a biological signal from the plurality of blood vessel images based on the plurality of contrast-enhanced images obtained by imaging the object with periodic movement. Medical image processing equipment. The biological signal acquisition device acquires the respiratory phase as the biological signal, and obtains the respiratory phase.
The medical image processing apparatus according to claim 4, wherein the processing unit selects a blood vessel image having a phase near the maximum exhalation as the reference image based on the respiratory phase. The biological signal acquisition device acquires the cardiac phase as the biological signal, and obtains the cardiac phase.
The medical image processing apparatus according to claim 4, wherein the processing unit selects a blood vessel image having a phase near the middle stage of expansion as the reference image based on the cardiac phase. A blood vessel image generation unit that generates the plurality of blood vessel images from a plurality of contrast images is further provided.
The blood vessel image generation unit performs difference processing with a plurality of non-contrast images for each contrast image included in the plurality of contrast images to generate a plurality of difference images, and shows positive polarity among the plurality of difference images. The medical image processing apparatus according to any one of claims 1 to 6 , wherein the difference image having the smallest number of pixels is identified as a blood vessel image for the contrast image . A blood vessel image generation unit that generates the plurality of blood vessel images from a plurality of contrast images is further provided.
The blood vessel image generation unit performs difference processing on each contrast image included in the plurality of contrast images with a non-contrast image having the same phase as the biological signal of the contrast image, and generates a blood vessel image for the contrast image. The medical image processing apparatus according to any one of claims 4 to 6. A blood vessel image generation unit that generates the plurality of blood vessel images from a plurality of contrast images is further provided.
The blood vessel image generation unit generates a blood vessel image for the contrast image by differentiating the contrast image and the image corresponding to the low frequency component in the contrast image for each contrast image included in the plurality of contrast images. The medical image processing apparatus according to any one of claims 1 to 6. The medical image processing device according to any one of claims 1 to 9, wherein the display control unit displays the color image as a still image. The medical image processing device according to any one of claims 1 to 9, wherein the display control unit displays the color image as a moving image. A reference image is selected from a plurality of blood vessel images based on a plurality of contrast images acquired in time series, and the other blood vessel image is such that the blood vessel shape in the other blood vessel image substantially matches the blood vessel shape in the reference image. Is transformed,
Based on the plurality of blood vessel images after the deformation process, the pixel value assigned to each pixel is calculated.
Identify the parameters based on the changes over time in the pixel values assigned to each pixel and
Identify the color corresponding to the above parameters for each pixel and
A color image in which the color is assigned to each pixel is generated.
A medical image processing method comprising displaying the color image on a display unit. Based on the time course of the pixel value at each position of the plurality of blood vessel images after the deformation process, the inflow time of the contrast medium injected into the object is identified as the parameter, and the color corresponding to the identified inflow time is identified. The medical image processing method according to claim 12, wherein the color image is generated by allocating the image to each position. The medical image processing method according to claim 12 or 13, wherein a reference image is selected based on the result of a correlation calculation between a plurality of blood vessel images. Claimed to select a reference image from the plurality of blood vessel images based on the plurality of contrast images obtained by imaging the object based on the biological signal acquired by the biological signal acquisition device from the object accompanied by periodic movement. 12 or 13, the medical image processing method according to 12. For each contrast image included in a plurality of contrast images, a difference process is performed with a plurality of non-contrast images to generate a plurality of difference images, and the difference image having the smallest number of pixels showing positive polarity among the plurality of difference images is selected. The medical image processing method according to any one of claims 12 to 15 , wherein the plurality of blood vessel images are generated by identifying the contrast image as a blood vessel image. Each of the contrast images included in the plurality of contrast images is subjected to difference processing with a non-contrast image having the same phase as the biological signal of the contrast image to generate a blood vessel image for the contrast image, thereby generating the plurality of blood vessel images. 15. The medical image processing method according to claim 15. For each contrast image included in the plurality of contrast images, the contrast image and the image corresponding to the low frequency component in the contrast image are differentiated to generate a blood vessel image for the contrast image, thereby generating the plurality of blood vessel images. The medical image processing method according to any one of claims 12 to 15, wherein the medical image processing method is generated. The medical image processing method according to any one of claims 12 to 18, wherein the color image is displayed as a still image or a moving image. A reference image is selected from a plurality of blood vessel images based on a plurality of contrast images acquired in time series, and the other blood vessel image is such that the blood vessel shape in the other blood vessel image substantially matches the blood vessel shape in the reference image. A processing unit that performs transformation processing on the image and
Based on the plurality of blood vessel images after the deformation processing , the pixel value assigned to each pixel was calculated, and the parameter was identified based on the change over time of the pixel value assigned to each pixel, and the parameter corresponded to the parameter. A generation unit that identifies a color for each pixel and generates a color image in which the color is assigned to each pixel.
An X-ray diagnostic apparatus including a display control unit for displaying the color image on the display unit.

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