CN105007818A - X-ray diagnostic device and image processing device - Google Patents

Abstract

An X-ray diagnostic device (1) of an embodiment is provided with a contour measurement unit (25), a correction coefficient determination unit (26), a DSA image correction unit (27), and a control unit (34). A contour measuring unit (25) measures contours relating to the concentration of a contrast medium in regions of interest set at substantially the same positions including blood vessels in two differential images obtained by imaging the head of a subject from substantially the same direction at different imaging times. A correction coefficient determination unit (26) determines a correction coefficient so that the two profiles measured by the profile measurement unit (25) substantially match each other. A DSA image correction unit (27) corrects at least one of the two differential images on the basis of the determined correction coefficient. A control unit (34) controls a display unit (40) to display information based on at least one of the two difference images corrected by the DSA image correction unit (27).

Description

X-ray diagnostic device and image processing device

technical field

Embodiments of the present invention relate to an X-ray diagnostic device and an image processing device.

Background technique

Conventionally, brain perfusion (Brain Perfusion) analysis is well known in the diagnosis of cerebral infarction based on X-ray CT (Computed Tomography) equipment. In the cerebral perfusion analysis, a cerebral perfusion image, which is a tomographic image of the brain obtained by injecting a contrast agent and photographing the blood circulation state, is used for the diagnosis of cerebral infarction.

In addition, in recent years, the boundary line that divides the left and right hemispheres of the head into two parts is manually or automatically set for cross-sectional images of the head obtained by X-ray CT equipment, and one image divided according to the boundary line is reversed. Methods of analyzing a differential image of the superimposed image, converted and superimposed on another image, are well known. According to this analysis method, since the lesion area appears in the difference image, comparison and reading of left and right images becomes easy, and blood flow abnormalities such as cerebral infarction can be examined. However, in the prior art described above, it is sometimes impossible to accurately compare the pre-operative image and the post-operative image.

Patent Document 1: Japanese Patent Laid-Open No. 2009-153870

Contents of the invention

The problem to be solved by the present invention is to provide an X-ray diagnostic apparatus and an image processing apparatus capable of accurately comparing preoperative images and postoperative images.

An X-ray diagnostic apparatus according to an embodiment includes a measurement unit, an identification unit, a correction unit, and a display control unit. The measurement unit measures contours related to contrast medium concentrations in regions of interest set in blood vessels included in two difference images at which the head of the subject is photographed from approximately the same direction and at different imaging times. roughly the same position. The determining unit determines the correction coefficient such that the two profiles respectively measured by the measuring unit substantially match. The correction unit corrects at least one of the two difference images based on the correction coefficient determined by the determination unit. The display control unit controls to display information based on the two difference images at least one of which has been corrected by the correcting unit on a predetermined display unit.

Description of drawings

FIG. 1 is a diagram showing an example of the configuration of an X-ray diagnostic apparatus according to a first embodiment.

FIG. 2 is a diagram showing an example of the configuration of an ROI setting unit according to the first embodiment.

FIG. 3 is a flowchart showing the procedure of processing by the X-ray diagnostic apparatus according to the first embodiment.

FIG. 4 is a diagram showing an example of an ROI set by the ROI setting unit according to the first embodiment.

FIG. 5 is a flowchart showing the procedure of processing by the ROI setting unit according to the first embodiment.

FIG. 6 is a diagram for explaining an example of processing by the blood vessel searching unit according to the first embodiment.

7A is a diagram for explaining calculation processing of an integral value for each partial region by the lowest peak region specifying unit according to the first embodiment.

FIG. 7B is a diagram for explaining calculation processing of integral values for each partial region by the lowest peak region specifying unit according to the first embodiment.

7C is a diagram for explaining calculation processing of integral values for each partial region by the lowest peak region specifying unit according to the first embodiment.

8 is a diagram for explaining the lowest peak extraction process by the lowest peak area specifying unit according to the first embodiment.

FIG. 9 is a diagram for explaining an example of processing by the contour measurement unit according to the first embodiment.

10 is a diagram showing an example of a blood flow test image displayed on the display unit according to the first embodiment.

11A is a diagram showing an example of analysis results displayed on the display unit according to the first embodiment.

11B is a diagram showing an example of analysis results displayed on the display unit according to the first embodiment.

11C is a diagram showing an example of analysis results displayed on the display unit according to the first embodiment.

12 is a diagram showing an example of display information displayed on the display unit according to the first embodiment.

Detailed ways

Hereinafter, embodiments of the X-ray diagnostic apparatus and the image processing apparatus according to the present invention will be described in detail with reference to the drawings. However, the embodiments shown below do not limit the present invention.

(first embodiment)

FIG. 1 is a diagram showing an example of the configuration of an X-ray diagnostic apparatus 1 according to the first embodiment. As shown in FIG. 1 , an X-ray diagnostic apparatus 1 according to the first embodiment includes an X-ray imaging mechanism 10 and an image processing apparatus 100 . The X-ray imaging mechanism 10 has an X-ray tube 11, a detector (FPD (Flat Panel Detector)) 12, a C-arm 13, and a bed 14. The C-arm 13 supports the X-ray tube 11 and the detector 12 and rotates around the subject P at high speed like a propeller by a motor provided on a base (not shown).

As shown in FIG. 1 , the image processing device 100 has an A/D (Analog/Digital) conversion unit 21, a positional deviation determination unit 22, a positional deviation correction unit 23, an ROI (Region of Interest: region of interest) setting unit 24, Profile (profile) measurement part 25, correction coefficient (factor) determination part 26, DSA (Digital Subtraction Angiography) image correction part 27, first subtraction (subtraction) part 28, second subtraction part 29, filter part 30, simulation A radiation conversion unit 31, a LUT (Look Up Table) 32, an image memory 33, a control unit 34, and a display unit 40. In addition, although not shown, the image processing apparatus 100 includes, for example, an input unit such as a mouse, a keyboard, a trackball, and a pointing device for receiving various operations performed by an operator on the X-ray diagnostic apparatus 1 .

The display unit 40 displays various information such as various images processed by the image processing device 100 and a GUI (Graphical User Interface). For example, the display unit 40 is a CRT (Cathode Ray Tube) display, a liquid crystal display, or the like. 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 in the image memory 33 as an X-ray collected image. The image memory 33 stores X-ray collected images.

The positional shift specifying unit 22 specifies a positional shift of two DSA images captured at different timings. The position shift correcting unit 23 corrects the position shift of the two DSA images based on the position shift specified by the position shift specifying unit 22 . The ROI setting unit 24 sets an ROI on the DSA image. Specifically, the ROI setting unit 24 extracts the blood vessel with the largest diameter among the blood vessels included in the predetermined range from the lower end of the DSA image, and calculates the concentration value for each predetermined region among the extracted blood vessels. The peak value of the partial integral value is calculated, and the region where the calculated peak value is the lowest is set as the ROI.

FIG. 2 is a diagram showing an example of the configuration of the ROI setting unit 24 according to the first embodiment. As shown in FIG. 2 , the ROI setting unit 24 includes a blood vessel searching unit 24 a, a largest blood vessel specifying unit 24 b, and a lowest peak region specifying unit 24 c. The blood vessel searching unit 24a searches for blood vessels from the DSA image on the horizontal axis that is separated by a certain range from the lower end of the DSA image. The largest blood vessel specifying unit 24b specifies a blood vessel having the largest blood vessel diameter from the blood vessels searched by the blood vessel searching unit 24a. The lowest peak area specifying unit 24c traces downward the largest blood vessel specified by the largest blood vessel specifying unit 24b, and specifies the portion where the peak value of the partial integral value of the concentration value is the lowest.

Returning to FIG. 1 , the contour measuring unit 25 measures the contour on the ROI set in the DSA image. Specifically, the contour measurement unit 25 measures contours related to contrast medium concentrations in ROIs set at time phases (imaging periods) obtained by imaging the subject's head from approximately the same direction. The two different DSA images contain roughly the same location of the blood vessel. For example, the contour measurement unit 25 measures contours related to contrast agent concentrations in regions of interest including large arteries as blood vessels or capillaries that do not affect treatment, respectively. The area correction coefficient determination unit 26 determines the correction coefficients so that the two contours substantially coincide. Specifically, the correction coefficient determination unit 26 determines the correction coefficients so that the two contours respectively measured by the contour measurement unit 25 substantially match each other. For example, the correction coefficient determination unit 26 determines a shift in time until the arrival of the contrast medium, a gain related to the stroke volume of the subject, and a midpoint in the blood flow velocity so that the two contours in the two difference images substantially match each other. at least one of the .

The DSA image correcting section 27 corrects at least one of the two DSA images based on the correction coefficient determined by the correction coefficient determining section 26 . The first subtraction unit 28 subtracts the image before the administration of the contrast agent and the image after the administration of the contrast agent. The second subtraction unit 29 subtracts the DSA image corrected by the DSA image correction unit 27 and another DSA image. The filter unit 30 performs high-frequency emphasis filtering and the like. The affine conversion unit 31 performs enlargement, reduction, movement, and the like of an image. LUT32 for tone conversion.

The control unit 34 controls the entire X-ray diagnostic apparatus 1 . Specifically, the control unit 34 controls various processes related to collection of X-ray collected images, generation of display images, display of display images on the display unit 40 , and the like. For example, the control unit 34 controls so that the display unit 40 displays information based on two DSA images at least one of which has been corrected by the DSA image correcting unit 27 (for example, difference information, etc.).

The X-ray diagnostic apparatus 1 according to the first embodiment can accurately compare pre-operative images and post-operative images, which were difficult to compare in the prior art, according to the above-mentioned configuration.

For example, in the treatment of the head using an X-ray diagnostic apparatus, there is a treatment in which a catheter is inserted into the stenosis, and a balloon provided around the catheter is inflated to expand the stenosis. This treatment is called an intervention. During the intervention, when the balloon is inflated, a small piece of a part of the stenosis may flow as a wipe, and the small piece may cause infarction in the capillary of the brain.

In view of this, after the intervention, whether or not infarction has occurred is diagnosed by cerebral perfusion images. In addition, conventionally, blood flow abnormalities are examined by, for example, comparing and reading images of the blood flow states of the left and right brains. However, in the above-mentioned prior art, since the preoperative state is not reflected, infarction that occurred before the treatment, some tendency of staining, etc. may be misdiagnosed. For example, even in a patient who chronically develops infarction and does not require treatment for the infarction, the existing infarction is diagnosed as a new infarction due to treatment, and thrombolytic therapy is administered meaninglessly.

In view of this, the X-ray diagnostic apparatus 1 according to the present embodiment can accurately compare pre-operative images and post-operative images of operations such as interventional or thrombolytic therapy through processing described in detail below. FIG. 3 is a flowchart showing the procedure of processing performed by the X-ray diagnostic apparatus 1 according to the first embodiment.

As shown in FIG. 3 , in the X-ray diagnostic apparatus 1 , a preoperative DSA image is first collected before an operation such as intervention or thrombolytic therapy is started (step S101 ). Specifically, in the X-ray diagnostic apparatus 1, the C-arm 13 is set in an arbitrary direction, several frames of mask images are taken before the contrast, and then the contrast agent is continuously taken while the contrast agent flows in the blood vessel. (contrast) image. The mask image and a plurality of contrast images of several frames before radiography are converted into digital signals by the A/D converter 21 and stored in the image memory 33 .

The first subtraction unit 28 reads out several frames of mask images stored in the image memory 33 , and adds and averages the read mask images to generate an average mask image with less noise. Furthermore, the first subtraction unit 28 generates a preoperative DSA image by performing subtraction (Log addition) on the average mask image from a plurality of contrast images.

When the preoperative DSA image is generated by the first subtraction unit 28 , the control unit 34 displays the generated preoperative DSA image on the display unit 40 as a moving image. Wherein, the generated preoperative DSA image is stored by the image memory 33 .

Afterwards, operations such as intervention or thrombolytic therapy are performed, and when the treatment is completed, the X-ray diagnostic apparatus 1 performs the same data collection as before the operation by specifying the same data collection program as before the operation, and collects postoperative DSA images (step S102).

Then, the control unit 34 displays the generated postoperative DSA image on the display unit 40 as a moving image. Wherein, the generated postoperative DSA image is stored by the image memory 33 .

Then, when an instruction to compare the blood flow based on the preoperative DSA image and the postoperative DSA image is received from the user via the GUI (not shown), the position shift specifying unit 22 reads the preoperative DSA image from the image memory 33 . The average mask image of the image and the postoperative DSA image is used to determine the amount of positional offset (step S103). For example, the position shift determination unit 22 determines the mask image (M _pre (i, j)) of the preoperative DSA image and the mask image (M position offset of _post (i,j)). Here, CR(Δi, Δj) in the formula (1) represents the amount of positional shift between (M _pre (i, j)) and (M _post (i, j)). In addition, N in the formula (1) represents an image size.

【Formula 1】

$\mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; R</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; \&Delta;</span>}\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; \&Delta;</span>}\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}\underset{\mathrm{<span\; class="notranslate">\; h</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{<span\; class="notranslate">\; \{</span>{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}\mathrm{<span\; class="notranslate">\; o</span>}\mathrm{<span\; class="notranslate">\; the\; s</span>}\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; m</span>}}_{\mathrm{<span\; class="notranslate">\; p</span>}\mathrm{<span\; class="notranslate">\; r</span>}\mathrm{<span\; class="notranslate">\; e</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; \&Delta;</span>}\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; \&Delta;</span>}\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \}</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}\mathrm{<span\; class="notranslate">\; ...</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>$

The position shift specifying unit 22 searches for (Δi, Δj) in which CR (Δi, Δj) is minimized while changing (Δi, Δj) by an iterative approximation algorithm (Δi, Δj). Then, the positional shift specifying unit 22 sends the searched (Δi, Δj) to the positional shift correcting unit 23 . Here, for simplification of description, a case of detecting a positional shift in a two-dimensional direction will be described, but it is also desirable to detect a positional shift in a rotational direction. That is, it is desired that the position shift determination unit 22 determine (Δi, Δj, Δθ).

Next, when the positional shift data is received from the positional shift determining part 22, the positional shift correcting part 23 uses the received positional shift data "(Δi, Δj) or (Δi, Δj, Δθ)" to correct One of the preoperative DSA image or the postoperative DSA image (step S104).

Then, the ROI setting unit 24 sets an ROI on the DSA image (step S105). Specifically, the ROI setting unit 24 uses one of the preoperative DSA image and the postoperative DSA image after the position shift is corrected, and the preoperative DSA image and the postoperative DSA image are at approximately the same position. Set ROIs. For example, the ROI setting unit 24 calculates the position of the aorta in at least one of the two difference images, and sets a region including the calculated aorta as the ROI. FIG. 4 is a diagram showing an example of the ROI set by the ROI setting unit 24 according to the first embodiment.

As shown in FIG. 4 , the ROI setting unit 24 sets ROI50 and ROI51 at approximately the same positions on the preoperative DSA image and the postoperative DSA image, respectively. Here, details of the processing by the ROI setting unit 24 will be described below. FIG. 5 is a flowchart showing the procedure of processing by the ROI setting unit 24 according to the first embodiment. However, the processing shown in FIG. 5 corresponds to the processing in step S105 in FIG. 3 .

As shown in FIG. 5 , in the ROI setting unit 24, the blood vessel searching unit 24a uses one of the preoperative DSA image and the postoperative DSA image in which position shift has been corrected to search for a predetermined region from the lower end of the DSA image. Blood vessels included in the range (step S201). FIG. 6 is a diagram for explaining an example of processing by the blood vessel searching unit 24 a according to the first embodiment. Here, in (A) of FIG. 6 , a case where a blood vessel is searched in a DSA image of an image size “N×N” is shown.

For example, as shown in (A) of FIG. 6 , the blood vessel search unit 24 a sets a position N/4 from the lower end of the DSA image as the initial position of the blood vessel search. Then, the blood vessel search unit 24a measures the profile of the DSA value on the horizontal axis at the position N/4 of the DSA image, as shown in FIG. 6(B) . Thereafter, the blood vessel searching unit 24 a extracts, as blood vessels, peaks whose DSA values exceed a predetermined threshold "Th" from the measured contours. For example, as shown in FIG. 6 , the blood vessel searching unit 24 a extracts blood vessels 60 and 61 corresponding to peaks exceeding “Th”. Furthermore, the blood vessel search unit 24a sends the contour and the position information of the blood vessel 60 and the blood vessel 61 to the largest blood vessel specifying unit 24b.

The largest blood vessel specifying unit 24b specifies a vessel having the largest vessel diameter among the vessels extracted by the vessel searching unit 24a (step S202). For example, the largest blood vessel identifying unit 24b measures the blood vessel diameter (peak width) based on the result of the profile shown in FIG. 6(B), and identifies the blood vessel 61 with the largest blood vessel diameter as the largest blood vessel. Then, the largest blood vessel specifying unit 24b transmits the measured blood vessel diameter information and the position information of the blood vessel 61 to the lowest peak area specifying unit 24c. As described above, the blood vessel search unit 24a searches for a blood vessel at a position N/4 from the lower end of the DSA image as the initial position of the blood vessel search, and the largest blood vessel specifying unit 24b can extract a large artery as a set ROI by specifying the blood vessel with the largest vessel diameter. blood vessels.

The lowest peak area specifying unit 24c calculates the integral value of the concentration value for each time for each partial area of the blood vessel specified by the largest blood vessel specifying unit 24b (step S203). Specifically, the lowest peak area specifying unit 24c calculates the integral value of the concentration value of the partial area for each time while tracing down the blood vessel center from the N/4 position on the vertical axis of the DSA image. FIGS. 7A to 7C are diagrams for explaining calculation processing of integral values for each partial region by the lowest peak region identification unit 24c according to the first embodiment. However, in FIG. 7B , FIG. 1 is shown in which the region 53 in FIG. 7A is enlarged.

For example, as shown in FIG. 7A , the lowest peak area identification unit 24c calculates the concentration value of each partial area while tracing down the center of the blood vessel 61 from a position N/4 from the lower end of the vertical axis of the DSA image. points value. Here, for example, as shown in FIG. 7B , the lowest peak region specifying unit 24c sets a rectangle of “1.5L (=1.5×vascular diameter)”×3 pixels vertically as a partial region, and each partial region Calculate the integral value. In addition, the size of the partial region can be arbitrarily set by the user.

The lowest peak area specifying unit 24c calculates the integral value of the concentration value for each partial area for each DSA image while the contrast agent is flowing. That is, the lowest peak area specifying unit 24c calculates the time change of the integrated value as shown in FIG. 7C for each partial area.

Then, the lowest peak area specifying unit 24c extracts the calculated peak value of the integrated value for each time for each partial area (step S204). For example, as shown in FIG. 7C , the lowest peak area specifying unit 24 c extracts an integral value for each partial area to be a representative value of the peak.

After that, the lowest peak area specifying unit 24c sets a sub-area showing the lowest peak in the ROI (step S205). Specifically, the lowest peak region specifying unit 24c compares the representative values of the subregions, and sets the subregion showing the lowest value as the ROI. FIG. 8 is a diagram for explaining the lowest peak extraction process by the lowest peak area specifying unit 24c according to the first embodiment. In FIG. 8, the horizontal axis represents "0" to "N/4" shown in FIG. 7A, and the vertical axis represents the integrated value. That is, in FIG. 8 , a graph in which representative values of each partial region are plotted is shown.

For example, as shown in FIG. 8 , the lowest peak area specifying unit 24 c plots the representative values of the respective partial areas on a graph, and specifies the lowest peak. Furthermore, the lowest peak area specifying unit 24c sets a partial area corresponding to the identified lowest peak as an ROI.

As described above, the lowest peak region specifying unit 24c sets the partial region showing the lowest value among the representative values of the respective partial regions as the ROI. That is, the lowest peak region specifying unit 24c sets the partial region in which the temporal change of the integrated value is the smallest as the ROI. Although the diameter of the aorta in which the ROI is set hardly changes, the actual concentration value in the aorta greatly changes. This is mainly caused by the course of blood vessels and hardening of the beam.

For example, when the course of the blood vessel is 90 degrees relative to the irradiation direction of X-rays, the amount of contrast agent irradiated with X-rays is only the thickness of the diameter of the blood vessel, and the concentration value is the lowest. However, when the course of blood vessels is parallel to the direction of X-ray irradiation, the amount of contrast agent irradiated with X-rays becomes the depth of the course of blood vessels, and the concentration value is maximum.

Therefore, since the blood flow cannot be accurately evaluated when the course of the blood vessel is parallel to the irradiation of the X-ray, it is desirable to set the region where the course of the blood vessel forms an angle close to 90 degrees with respect to the irradiation of the X-ray as the ROI. In view of this, the lowest peak region determination unit 24c extracts the region where the course of the blood vessel is closest to 90 degrees relative to the X-ray irradiation by setting the partial region with the lowest integrated value of the density value as the ROI.

In addition, for example, areas of blood vessels that overlap thick bone have reduced concentration values due to beam hardening. In view of this, the ROI setting unit 24 may also calculate the X-ray transmittance of each partial area based on the density value of the mask image used for generating the DSA image, and use the calculated area with low X-ray transmittance as the ROI. outside of the object.

The processing by the ROI setting unit 24 has been described above. Returning to FIG. 3 , when the ROI is set on the DSA image as described above, information on the ROI is sent to the contour measuring unit 25 . The contour measuring unit 25 measures the contour in the ROI set by the ROI setting unit 24 (step S106).

Specifically, the contour measurement unit 25 reads out the DSA image after the position shift correction and another DSA image from the image memory 33, and measures the contour of the average value or the total value of the DSA pixel values in the ROI with respect to the read DSA image. . FIG. 9 is a diagram for explaining an example of processing by the contour measurement unit 25 according to the first embodiment.

For example, as shown in FIG. 9 , the contour measuring section 25 measures the contour "f(t)" of the preoperative DSA image and the contour "g(t)" of the postoperative DSA image. Here, "ΔT" shown in FIG. 9 represents a delay time until the contrast agent reaches the ROI. Information on the contour measured by the contour measurement unit 25 is sent to the correction coefficient determination unit 26 .

When the information on the contour in the ROI is acquired, the correction coefficient determining unit 26 determines a correction coefficient for which the contour in the ROI of the preoperative DSA image and the postoperative DSA image substantially match (step S107 ). For example, the correction coefficient determining unit 26 determines a correction coefficient for substantially matching the contour "f(t)" of the preoperative DSA image and the contour "g(t)" of the postoperative DSA image by the following formula (2). .

【Formula 2】

E＝||f(t)-αg{T(t-Δt)}|| ² ... (2)

The correction coefficient determination unit 26 searches for "α" that minimizes "E" while changing the gain "α", the delay time "Δt" until the arrival of the contrast medium, and the blood flow velocity "T" using a successive approximation algorithm. , "Δt" and "T". That is, the correction coefficient determination unit 26 uses different gains "α" for correcting the stroke volume of the patient's heart before and after the operation, and for correcting the displacement of the catheter due to the contrast agent administered before and after the operation. The two contours are roughly aligned by the "Δt" of the delay time until the arrival of the contrast agent and the different blood flow velocity "T" for correcting the heart rate of the patient before and after the operation. Then, the correction coefficient determination unit 26 sends the searched “α”, “Δt”, and “T” to the DSA image correction unit 27 .

However, in the above example, the case where all the correction coefficients of "α", "Δt", and "T" are obtained has been described. However, embodiment is not limited to this. For example, when it is assumed that the patient's heartbeat is stable and the blood flow velocity is substantially constant, the case may be set to "T=1". Thereby, determination of the correction coefficient can be quickly performed.

The DSA image correction unit 27 corrects the preoperative DSA image or the postoperative DSA image using the correction coefficients "α", "Δt" and "T" received from the correction coefficient determination unit 26 (step S108 ). Here, the correction of the DSA image generally corrects the preoperative DSA image, but in this embodiment, any DSA image can be corrected. The DSA image corrected by the DSA image correction unit 27 is stored in the image memory 33 .

Among them, here, the correction coefficient is determined by using the DSA image for examining the blood flow state before and after the operation. However, it is also possible to determine the correction coefficient using a DSA image taken at approximately the same time period that does not affect treatment or the like. Specifically, for example, when intravascular treatment of the right internal carotid artery is performed, the preoperative and postoperative images of the left internal carotid artery are photographed before and after the timing of capturing the preoperative and postoperative DSA images of the right internal carotid artery. DSA image. Since it is considered that there is no influence of treatment or the like on this image, there is an advantage that the correction coefficient can be determined stably.

When the DSA image is corrected by the DSA image correction unit 27, the second subtraction unit 29 reads out the corrected DSA image and another DSA image from the image memory 33, and performs subtraction (step S109). For example, the second subtraction unit 29 subtracts the corrected DSA image and another DSA image by the following equation (3).

【Formula 3】

C(i, j) = αg _{(i, j)} {T(t-Δt)}-f _{(i, j)} (t) ... (3)

Here, in Equation (3), C(i, j) represents a blood flow test image. In addition, in formula (3), f _{(i, j)} (t) represents a preoperative DSA image, and g _{(i, j)} (t) represents a postoperative DSA image. In addition, Equation (3) represents an equation when the postoperative DSA image is corrected. As shown in equation (3), the second subtraction unit 29 generates a blood flow test image by differencing the preoperative DSA image from the corrected postoperative DSA image. The blood flow inspection image (subtraction image) generated by the second subtraction unit 29 is stored in the image memory 33 .

Then, the blood flow test image generated by the second subtraction unit 29 is stored in the image memory 33 and sent to the control unit 34, and is displayed in color on the display unit 40 by the control unit 34 (step S110). For example, the display unit 40 displays a perfusion image based on a result of difference of two DSA images corrected by at least one. FIG. 10 is a diagram showing an example of a blood flow test image displayed on the display unit 40 according to the first embodiment.

For example, as shown in FIG. 10 , the control unit 34 converts the pixel values into colors (for example, converts a region where the blood flow is sufficiently increased (region R1 in FIG. 10 , etc.) The area R2 etc.) is converted into blue, the intermediate area (region R3 etc. in FIG. 10 ) is converted into yellow), and the converted image is displayed on the display unit 40 . In addition, the displayed blood flow inspection image may also be a black and white image.

As described above, the X-ray diagnostic apparatus 1 according to the first embodiment displays a dynamic image in color or black and white by continuously displaying the blood flow inspection images corrected for preoperative and postoperative An image obtained by difference of at least one of the two DSA images. Here, the X-ray diagnostic apparatus 1 according to the first embodiment can display various analysis results in addition to the above-mentioned moving images. For example, the X-ray diagnostic apparatus 1 can also display a graph showing changes in the state of blood flow before and after the operation for each predetermined region of the DSA image.

11A to 11C are diagrams showing examples of analysis results displayed on the display unit according to the first embodiment. In FIGS. 11A to 11C , (A) in the figure shows the preoperative and postoperative contours in a predetermined region, and (B) in the figure shows difference information between the preoperative contour and the postoperative contour. 11A to 11C show difference information when the preoperative contour "f(t)" is differentiated from the postoperative contour "g(t)".

For example, the control unit 34 corrects at least one of the preoperative and postoperative DSA images for a predetermined region of the two DSA images that are determined based on the correction coefficient determined based on the region including the aorta or the region including the capillary that is not affected by the treatment, and converts the preoperative And the contour and difference information of the postoperative DSA image are displayed on the display unit 40 . For example, as shown in (A) of FIG. 11A , the control unit 34 makes the contour "f(t)" of a predetermined region in the preoperative DSA image and the contour "g(t)" of the same region in the postoperative DSA image t)" is displayed on the display unit 40. Then, as shown in (B) of FIG. 11A , the control unit 34 obtains the difference of the contour “f(t)” before the operation from the contour “g(t)” after the operation shown in (A) of FIG. 11A . The graph 71 of is displayed on the display unit 40 .

Here, the operator can arbitrarily designate a predetermined area where the illustrated outline and the graph are displayed. In addition, the entire display image may be covered by an area of a predetermined size. Thus, the operator can observe in detail how the region showing the outline or the graph changes before and after the operation. For example, when an outline or a graph as shown in FIG. 11A is displayed, the operator can judge that the state of blood flow in the corresponding region is improving after the operation.

Similarly, the operator can easily judge how the corresponding region changes before and after the operation by using the contours and graphs shown in FIG. 11B and FIG. 11C . For example, when the outline or the graph shown in FIG. 11B is displayed on the display unit 40 , the operator can judge that there is a slight delay in the postoperative blood flow, and suspect that mild stenosis may have occurred. Also, for example, when the outline or the graph shown in FIG. 11C is displayed on the display unit 40 , the operator can judge that the postoperative blood flow has deteriorated, and can suspect that infarction may have occurred.

11A to 11C have described the graphs obtained by subtracting the preoperative contour "f(t)" from the postoperative contour "g(t)", but the embodiment does not Not limited to this. That is, a graph obtained by subtracting the postoperative contour "g(t)" from the preoperative contour "f(t)" may be shown.

In addition, the X-ray diagnostic apparatus 1 according to the first embodiment can also display a blood flow test image, a contour corresponding to a region of the blood flow test image, and the like. 12 is a diagram showing an example of display information displayed on the display unit according to the first embodiment. As shown in FIG. 12 , when the operator designates a point or region on the blood flow test image via the input unit, the control unit 34 displays the preoperative and postoperative contours of the designated point or region. In addition, although only the outline is shown in FIG. 12 , it is also possible to display a graph of the difference.

In this way, in the X-ray diagnostic apparatus 1 according to the first embodiment, in addition to the blood flow test image, the outline of each predetermined region, a graph of a difference, or the like can be displayed. Furthermore, in the X-ray diagnostic apparatus 1 according to the first embodiment, it is also possible to display a blood flow difference indicating a difference in blood flow in a predetermined region, and a degree of difference indicating a difference between preoperative and postoperative blood flow. Hereinafter, the difference in blood flow and the degree of difference will be described using Expressions (4) to (7). Here, in formulas (4) to (7), the case where correction is performed based on "α" and "Δt" among the above-mentioned correction coefficients "α", "Δt" and "T" is cited as an example. illustrate.

That is, the correction coefficient determining unit 26 determines the contour “f(t)” in the ROI of the preoperative DSA image measured by the contour measuring unit 25 and the contour “g(t)” in the ROI of the postoperative DSA image. It becomes "α" and "Δt" like Formula (4). Among them, "α" is used to correct the different gains of the stroke volume of the patient's heart before and after the operation, and "Δt" is used to correct the position deviation of the catheter due to the injection of the contrast medium before and after the operation Induced delay time until contrast agent arrives.

【Formula 4】

Then, the second subtraction unit 29 subtracts the corrected DSA image and another DSA image by the following equation (5). Here, in Equation (5), COMP(t) represents the difference of the DSA image. In addition, in formula (5), f _{(i, j)} (t) represents a preoperative DSA image, and g _{(i, j)} (t) represents a postoperative DSA image. In addition, Equation (5) shows an equation when the postoperative DSA image is corrected.

【Formula 5】

COMP(t)=αg _(i,j) (t-Δt)-f _(i,j) (t)...(5)

As shown in equation (5), the second subtraction unit 29 subtracts the corrected postoperative DSA image and the preoperative DSA image for each pixel. Then, the control unit 34 uses COMP(t) calculated by the second subtraction unit 29 to calculate the difference in blood flow rate according to Equation (6) shown below. That is, the control unit 34 calculates the difference in blood flow and the degree of difference based on the contour for each site calculated based on the corrected difference image. Here, in Equation (6), DifV represents a blood flow difference. In addition, in Equation (6), N represents an image size. In addition, in Equation (6), COMP(n) represents a difference for each pixel.

【Formula 6】

$\mathrm{<span\; class="notranslate">\; D.</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; f</span>}\mathrm{<span\; class="notranslate">\; V</span>}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; N</span>}}\underset{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}\mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; o</span>}\mathrm{<span\; class="notranslate">\; m</span>}\mathrm{<span\; class="notranslate">\; P</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>\mathrm{<span\; class="notranslate">\; ...</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 6</span>}<span\; class="notranslate">\; )</span>$

As shown in equation (6), the control unit 34 calculates the average of the differences for each pixel as the difference in blood flow, and displays it on the display unit 40 . In addition, the control unit 34 calculates the degree of difference by using the COMP(t) calculated by the second subtraction unit 29 by the following equation (7). Here, in the formula (7), DifE represents the degree of difference. In addition, in Equation (7), N represents an image size. In addition, in Equation (7), COMP(n) represents a difference for each pixel.

【Formula 7】

$\mathrm{<span\; class="notranslate">\; D.</span>}\mathrm{<span\; class="notranslate">\; i</span>}\mathrm{<span\; class="notranslate">\; f</span>}\mathrm{<span\; class="notranslate">\; E.</span>}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; N</span>}}\underset{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}\mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; o</span>}\mathrm{<span\; class="notranslate">\; m</span>}\mathrm{<span\; class="notranslate">\; P</span>}{<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}\mathrm{<span\; class="notranslate">\; ...</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 7</span>}<span\; class="notranslate">\; )</span>$

As shown in Equation (7), the control unit 34 calculates the average of the squares of the differences for each pixel as the degree of difference in blood flow, and displays it on the display unit 40 . In this manner, the X-ray diagnostic apparatus 1 according to the first embodiment can calculate and display the blood flow difference and degree of difference in addition to the blood flow test image, contour, and difference graph. Among them, the difference in blood flow and the degree of difference are used differently by the operator according to the situation.

Here, the above-mentioned formulas for the difference in blood flow and the degree of difference are merely examples, and may be calculated by other formulas. For example, normalization may be performed based on preoperative and postoperative contours. Hereinafter, the case where normalization is performed based on the preoperative and postoperative contours will be described using Equation (8) to Equation (13). However, in the expressions (8) to (13), only the right sides of the blood flow difference DifV and the degree of difference DifE are shown.

For example, the control unit 34 calculates the blood flow difference normalized according to the preoperative contour by the following formula (8), and calculates the normalized blood flow difference according to the preoperative contour by the following formula (9). degree of difference. Here, in Formulas (8) and (9), "f(n)" represents a preoperative contour. Here, "β" in Equations (8) and (9) is used to avoid a case where the denominator is 0, and is set to a value much smaller than the value of the blood vessel region of "f(n)".

[Formula 8]

$\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; N</span>}}\underset{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}\frac{\mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; o</span>}\mathrm{<span\; class="notranslate">\; m</span>}\mathrm{<span\; class="notranslate">\; P</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; \&beta;</span>}}\mathrm{<span\; class="notranslate">\; ...</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 8</span>}<span\; class="notranslate">\; )</span>$

【Formula 9】

$\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; N</span>}}\underset{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{<span\; class="notranslate">\; \{</span>\frac{\mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; o</span>}\mathrm{<span\; class="notranslate">\; m</span>}\mathrm{<span\; class="notranslate">\; P</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; \&beta;</span>}}<span\; class="notranslate">\; \}</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}\mathrm{<span\; class="notranslate">\; ...</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 9</span>}<span\; class="notranslate">\; )</span>$

In addition, for example, the control unit 34 calculates the blood flow difference normalized according to the postoperative contour by the following formula (10), and calculates the blood flow difference normalized according to the postoperative contour by the following formula (11). standardized difference. Here, in the expressions (10) and (11), "g(n)" represents the contour before the operation, and shows that the image after the operation is corrected by the correction coefficient. Here, "β" in Equations (10) and (11) is set to a value much smaller than the value of the blood vessel region of "f(n)" in order to avoid a denominator of 0.

【Formula 10】

$\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; N</span>}}\underset{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}\frac{\mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; o</span>}\mathrm{<span\; class="notranslate">\; m</span>}\mathrm{<span\; class="notranslate">\; P</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; \&Delta;</span>}\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; \&beta;</span>}}\mathrm{<span\; class="notranslate">\; ...</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 10</span>}<span\; class="notranslate">\; )</span>$

【Formula 11】

$\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; N</span>}}\underset{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{<span\; class="notranslate">\; \{</span>\frac{\mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; o</span>}\mathrm{<span\; class="notranslate">\; m</span>}\mathrm{<span\; class="notranslate">\; P</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; \&alpha;</span>}\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; \&Delta;</span>}\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; \&beta;</span>}}<span\; class="notranslate">\; \}</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}\mathrm{<span\; class="notranslate">\; ...</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 11</span>}<span\; class="notranslate">\; )</span>$

In addition, for example, the control unit 34 calculates the normalized blood flow difference based on the average of the preoperative and postoperative contours by the following equation (12), and calculates the blood flow difference based on the following equation (13) by the following equation (13). The mean of the pre- and post-operative contours was normalized for the degree of difference. Here, in formulas (12) and (13), "f(n)" represents the contour before operation, "g(n)" represents the contour before operation, and shows that the postoperative image is corrected according to the correction coefficient Condition. Here, "β" in Equations (12) and (13) is used to prevent the denominator from becoming 0, and is set to a value much smaller than the value of the blood vessel region of "f(n)".

【Formula 12】

$\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; N</span>}}\underset{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}\frac{\mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; o</span>}\mathrm{<span\; class="notranslate">\; m</span>}\mathrm{<span\; class="notranslate">\; P</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>}{\frac{\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; \&alpha;</span>}\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; \&Delta;</span>}\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; \&beta;</span>}}\mathrm{<span\; class="notranslate">\; ...</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 12</span>}<span\; class="notranslate">\; )</span>$

【Formula 13】

$\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; N</span>}}\underset{\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{<span\; class="notranslate">\; \{</span>\frac{\mathrm{<span\; class="notranslate">\; C</span>}\mathrm{<span\; class="notranslate">\; o</span>}\mathrm{<span\; class="notranslate">\; m</span>}\mathrm{<span\; class="notranslate">\; P</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span>}{\frac{\mathrm{<span\; class="notranslate">\; f</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; \&alpha;</span>}\mathrm{<span\; class="notranslate">\; g</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; \&Delta;</span>}\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>}{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; \&beta;</span>}}<span\; class="notranslate">\; \}</span>}^{\mathrm{<span\; class="notranslate">\; 2</span>}}\mathrm{<span\; class="notranslate">\; ...</span>}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 13</span>}<span\; class="notranslate">\; )</span>$

In the above-mentioned example, the case where correction is performed based on the correction coefficients “α” and “Δt” has been described as an example, but the embodiment is not limited thereto. That is, the blood flow difference and degree of difference may be calculated using the correction coefficients "α", "Δt", and "T". At this time, the correction coefficient determining unit 26 determines the contour “f(t)” in the ROI of the preoperative DSA image measured by the contour measuring unit 25 and the contour “g(t)” in the ROI of the postoperative DSA image. "" becomes "α", "Δt" and "T" as shown in the following formula (14). Among them, "α" is used to correct the different gains of the cardiac output of the patient before and after the operation, and "Δt" is used to correct the position deviation of the catheter when the contrast medium is injected before and after the operation. The delay time until the arrival of the contrast agent. In addition, "T" represents a different blood flow velocity for correcting the heart rate of the patient before and after the operation.

【Formula 14】

As shown in equation (14), the X-ray diagnostic apparatus 1 according to the first embodiment can correct the different blood flow velocities for correcting the heart rate of the patient before and after the operation to calculate the blood flow before and after the operation. Flow difference and degree of difference. Thereby, even when there is a large difference between preoperative and postoperative heart rate, analysis can be performed after correcting them.

As described above, according to the first embodiment, the contour measuring unit 25 measures contours related to the contrast agent concentration in the ROIs respectively set at the regions obtained by photographing the head of the subject from substantially the same direction. In the two DSA images having different phases (imaging periods), approximately the same position including blood vessels is obtained. Furthermore, the correction coefficient determination unit 26 determines the correction coefficients so that the two contours respectively measured by the contour measurement unit 25 substantially coincide. The DSA image correction unit 27 corrects at least one of the two DSA images based on the correction coefficient determined by the correction coefficient determination unit 26 . Furthermore, the control unit 34 performs control so that information based on the two DSA images corrected by the DSA image correcting unit 27 is displayed on the display unit 40 . Therefore, the X-ray diagnostic apparatus 1 according to the first embodiment can generate and display a difference image in which the dyeing effect by the contrast agent in the preoperative DSA image and the postoperative DSA image are approximately the same, and can accurately compare Preoperative and postoperative images.

In addition, according to the first embodiment, the first subtraction unit 28 uses two X-ray dynamic images with different phases (imaging periods) obtained by imaging the subject's head while injecting a contrast agent from approximately the same direction, A DSA image is calculated by subtracting a plurality of images in which the influence of the contrast agent is present and images in which the influence of the contrast agent is hardly present. The positional shift specifying unit 22 determines the positional shift from an image that is hardly affected by the contrast medium among the two X-ray dynamic images. The positional offset correcting unit 23 performs positional offset correction on at least one of the two DSA images based on the identified positional offset information. The contour measuring unit 25 measures contours of ROIs set at substantially the same positions including blood vessels in the two DSA images corrected at least one of them. The correction coefficient determination unit 26 determines the correction coefficients so that the two contours respectively measured by the contour measurement unit 25 substantially coincide. The DSA image correction unit 27 corrects at least one of the two DSA images based on the correction coefficient determined by the correction coefficient determination unit 26 . The control unit 34 performs control so that information based on the two DSA images corrected by the DSA image correcting unit 27 is displayed on the display unit 40 . Therefore, the X-ray diagnostic apparatus 1 according to the first embodiment can generate and display a difference image in which the dyeing effect by the contrast agent in the preoperative DSA image and the postoperative DSA image are approximately the same, and can accurately compare Preoperative and postoperative images.

In addition, according to the first embodiment, the ROI is a region including the aorta, which is a blood vessel, or capillaries that do not affect treatment. Therefore, the X-ray diagnostic apparatus 1 according to the first embodiment can determine the correction coefficient using various regions in the image.

In addition, according to the first embodiment, the ROI setting unit 24 calculates the position of the aorta in at least one of the two DSA images, and sets the region including the calculated aorta as the ROI. Therefore, the X-ray diagnostic apparatus 1 according to the first embodiment can determine a more accurate correction coefficient using a region including the aorta that does not affect treatment and in which a change in contrast agent concentration is significantly displayed.

In addition, according to the first embodiment, the display unit 40 displays a perfusion image based on a result of difference between two DSA images corrected at least one of them. Therefore, the X-ray diagnostic apparatus 1 according to the first embodiment can display more accurate analysis results.

In addition, according to the first embodiment, the correction coefficient determination unit 26 determines at least one of the offset, the gain, and the blood flow velocity of the time until the arrival of the contrast medium so that the two contours in the two DSA images substantially coincide. . Therefore, the X-ray diagnostic apparatus 1 according to the first embodiment can correct the DSA image using the correction coefficient whose effect of the contrast agent greatly changes, and can more accurately compare the preoperative DSA image and the postoperative DSA image.

In addition, according to the first embodiment, the ROI setting unit 24 extracts the blood vessel with the largest blood vessel diameter among the blood vessels included in the predetermined range from the lower end of the DSA image, and calculates the concentration for each partial region of the extracted blood vessel. The peak of the integrated value of the value, and set the region where the calculated peak is the lowest as the ROI. Therefore, the X-ray diagnostic apparatus 1 according to the first embodiment can set the region on the aorta where the effect of the contrast medium is high as the ROI, and can perform accurate comparative reading.

In addition, according to the first embodiment, the ROI setting unit 24 calculates the X-ray transmittance for each partial region based on the density value of the mask image used for generating the DSA image, and assigns the calculated X-ray transmittance to be lower. Part of the region is outside the target of the ROI. Therefore, the X-ray diagnostic apparatus 1 according to the first embodiment can speed up the steps involved in setting the ROI.

Also, according to the first embodiment, the blood flow test image (subtraction image) is created as a moving image, but the maximum error may be determined for each pixel within the moving image, and the maximum error image may be displayed as a still image.

(second embodiment)

The first embodiment has been described above, but it can also be implemented in various forms other than the above-mentioned first embodiment.

(Modification)

In the above-mentioned first embodiment, the case where the postoperative DSA image is collected using the same data collection program as that used for collecting the preoperative DSA image has been described. However, the embodiment is not limited thereto, and for example, a coefficient for changing the staining effect of the contrast medium may be arbitrarily selected so that the selected coefficient is the same before and after the operation.

At this time, for example, the control unit 34 performs control so that the X-ray imaging conditions, imaging angle, FOV (Field Of View), SID (Source Image Distance), collimator position, compensation filter position, X-ray focus size At least one of the radiation quality adjustment filter, the time until the injection of the contrast agent, and the contrast condition is the same as that of the preoperative DSA image, and the postoperative DSA image is captured.

In the first embodiment described above, the case where the ROI setting unit 24 automatically sets the ROI on the DSA image has been described. However, the embodiment is not limited to this, and for example, it may be set by the user.

At this time, for example, the ROI setting unit 24 requests the user to set the ROI. As an example, the ROI setting unit 24 causes the display unit 40 to display information urging setting of the ROI. The user sets the ROI via a GUI (not shown). At this time, the ROI setting unit 24 sets the ROI so that the user is close to the catheter into which the contrast medium is injected, and away from the affected part, and the blood vessel is parallel to the detector surface.

In the above-mentioned first embodiment, the case where the X-ray diagnostic apparatus 1 generates blood flow inspection images has been described, but the above-mentioned processing may be executed by an image processing apparatus such as a workstation. At this time, for example, a workstation connected to the X-ray diagnostic device or image storage device via a network acquires image data from the X-ray diagnostic device or image storage device. Then, the workstation executes the above-mentioned processing using the acquired image data.

As described above, according to the first to second embodiments, the X-ray diagnostic apparatus and the image processing apparatus of the present embodiment can accurately compare preoperative images and postoperative images.

Although some embodiments of the present invention have been described, these embodiments are shown as examples and are not intended to limit the scope of the present invention. These embodiments can be implemented in various other forms, and various omissions, substitutions, and changes can be made without departing from the spirit of the invention. These embodiments and modifications thereof are included in the invention described in the claims and their equivalents as included in the scope or spirit of the invention.

Claims (13)

1. An X-ray diagnostic device, comprising: The measuring unit measures contours related to contrast agent concentrations in regions of interest respectively set in two differential images obtained by imaging the subject's head from approximately the same direction and at different imaging times approximately the same location of the containing blood vessels; a determination unit that determines the correction coefficient in such a manner that the two contours respectively measured by the measurement unit substantially coincide; a correcting unit that corrects at least one of the two difference images based on the correction coefficient determined by the determining unit; and The control unit controls to display information based on the two difference images at least one of which has been corrected by the correcting unit on a predetermined display unit. 2. The X-ray diagnostic apparatus according to claim 1, wherein, The above-mentioned region of interest is a region including the aorta as the above-mentioned blood vessel or capillaries that do not affect the treatment. 3. The X-ray diagnostic apparatus according to claim 2, wherein, The X-ray diagnostic apparatus further includes a calculation unit that calculates the position of the aorta in at least one of the two difference images, The region of interest is a region including the aorta calculated by the calculation unit. 4. The X-ray diagnostic device according to claim 1, wherein, The display unit displays a perfusion image obtained by subtracting the at least one corrected two difference images. 5. The X-ray diagnostic apparatus according to claim 1, wherein, The specifying unit specifies a shift in time until arrival of the contrast medium, a gain related to a stroke volume of the subject, and a difference in blood flow velocity so that the two contours in the two difference images substantially coincide. at least one. 6. The X-ray diagnostic apparatus according to claim 1, wherein, The above-mentioned two difference images are images obtained before and after treatment at the time of photography. 7. The X-ray diagnostic apparatus according to claim 1, wherein, A setting unit is further provided that extracts a blood vessel having the largest blood vessel diameter among blood vessels included in a predetermined range from the lower end of the difference image, and calculates a concentration value for each predetermined region in the extracted blood vessels. The peak value of the partial integral value of , and the area where the calculated peak value is the lowest is set as the above-mentioned area of interest. 8. The X-ray diagnostic apparatus according to claim 7, wherein, The setting unit calculates the X-ray transmittance of each of the predetermined regions based on the density value of the mask image used for generating the differential image, and uses the calculated low X-ray transmittance region as the region of interest. Object outside. 9. The X-ray diagnostic apparatus according to claim 1, wherein, The control unit calculates the difference in blood flow and the degree of difference based on the contour for each site calculated based on the corrected difference image. 10. The X-ray diagnostic apparatus according to claim 1, wherein, The above-mentioned control unit controls the X-ray imaging conditions, imaging angle, FOV, SID, collimator position, compensation filter position, X-ray focus size, radiation quality adjustment filter, time until contrast agent injection And at least one of the imaging conditions is the same, and the above-mentioned two difference images with different imaging periods are captured. 11. An X-ray diagnostic device, comprising: The differential image calculation unit converts a plurality of images affected by the contrast agent into an almost Subtract the images without the influence of the contrast agent to calculate the difference image; The position offset determination unit determines the position offset according to the image that is hardly affected by the contrast agent among the above-mentioned two X-ray dynamic images; The position offset correction unit performs position offset correction on at least one of the two X-ray differential images according to the determined position offset information; The measurement unit measures contours related to the concentration of contrast medium in the regions of interest respectively, and the regions of interest are respectively set at approximately the same positions containing blood vessels included in the at least one corrected two X-ray differential images; The determination unit determines the correction coefficient in such a manner that the two contours respectively measured by the measurement unit substantially coincide; a correcting unit that corrects at least one of the two difference images based on the correction coefficient determined by the determining unit; and The control unit controls to display information based on the two difference images at least one of which has been corrected by the correcting unit on a predetermined display unit. 12. An image processing device, comprising: The measurement unit measures contours related to contrast agent concentrations in regions of interest respectively set in two difference images at different imaging times when the head of the subject is imaged from approximately the same direction. roughly the same location of the blood vessels; a determination unit that determines the correction coefficient in such a manner that the two contours respectively measured by the measurement unit substantially coincide; a correcting unit that corrects at least one of the two difference images based on the correction coefficient determined by the determining unit; and The control unit controls to display information based on the two difference images at least one of which has been corrected by the correcting unit on a predetermined display unit. 13. An image processing device, comprising: The differential image calculation unit converts a plurality of images affected by the contrast agent into an almost Subtract the images without the influence of the contrast agent to calculate the difference image; The position offset determination unit determines the position offset according to the image that is hardly affected by the contrast agent among the above-mentioned two X-ray dynamic images; The position offset correction unit performs position offset correction on at least one of the two X-ray differential images according to the determined position offset information; The measurement unit measures contours related to the concentration of contrast medium in the regions of interest respectively, and the regions of interest are respectively set at approximately the same positions containing blood vessels included in the at least one corrected two X-ray differential images; a determination unit that determines the correction coefficient in such a manner that the two contours respectively measured by the measurement unit substantially coincide; a correcting unit that corrects at least one of the two difference images based on the correction coefficient determined by the determining unit; and The control unit controls to display information based on the two difference images at least one of which has been corrected by the correcting unit on a predetermined display unit.