Simulator, injection device or imaging system equipped with the simulator, and simulation program

Abstract

To achieve a prediction approximating a secular change of a pixel value in an actual tissue, with higher accuracy.SOLUTION: A simulator 20, which predicts a secular change of a pixel value in a tissue of a subject, includes: a subject information acquisition unit 11 to acquire information on the subject; a protocol acquisition unit 12 to acquire an injection protocol for a contrast medium; a tissue information acquisition unit 13 to acquire information on the tissue; and a prediction unit 16 to predict, based on the information on the subject, the injection protocol, and the information on the tissue, a secular change of a pixel value of each of a plurality of compartments obtained by dividing the tissue along a blood flow direction D.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to a simulator for predicting the temporal change of pixel values (CT values) in an image of a subject's tissue, an injection device or an imaging system including the simulator, and a simulation program.

Background Art

[0002] Conventionally, there has been a method of enhancing a patient's tissue as a subject with a contrast agent injected into a blood vessel and imaging it using a CT (Computed Tomography) device. Further, a prediction method for predicting the enhancement level (pixel value) of contrast intensity by a contrast agent based on the constitutional characteristics of a subject and the injection protocol of the contrast agent has been known. And this prediction method predicts the degree of enhancement of contrast intensity in a subject's tissue as a function of the passage of time from the start time of injection of the contrast agent (Patent Document 1).

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The method described in Patent Document 1 assumes that the heart and blood vessels constitute a single compartment. Furthermore, it assumes that other organs also constitute a single compartment comprising intravascular and extracellular spaces. Predictions are then made assuming that the contrast agent diffuses throughout the compartment upon arrival. However, because tissue volume differs in reality, the prediction results sometimes differ significantly from the actual changes in pixel values. In particular, when the amount of contrast agent injected is small, the injection time is short, or the contrast agent concentration is low, the actual diffusion rate within the tissue is slow. Therefore, the prediction results tend to differ significantly from the actual changes in pixel values. [Means for solving the problem]

[0005] To solve the above problems, a simulator as an example of the present invention predicts the change over time of the pixel values ​​of a tissue based on information about the subject, a contrast agent injection protocol, and information about the subject tissue, and displays each tissue on a display unit with a color density corresponding to the pixel value, comprising: a prediction unit that predicts the change over time of the pixel values; and a display control unit that displays a predicted image that mimics a horizontal cross-section of the human body in the head-to-tail direction on the display unit, wherein the display control unit controls the display unit so that multiple tissues are included in the predicted image and so as to change the density of each tissue according to the change over time of the pixel value.

[0006] Another example of the present invention is an injection device comprising an injection head for injecting a contrast agent according to an injection protocol, and the above-mentioned simulator.

[0007] Another example of the present invention is an imaging system comprising a medical imaging device for imaging a subject and the simulator described above.

[0008] Another example of the present invention is a simulation program that causes a computer to predict the change over time of pixel values ​​in the tissue of a subject, wherein the simulation program causes the computer to function as a subject information acquisition unit that acquires information about the subject, a protocol acquisition unit that acquires the injection protocol of a contrast agent, a tissue information acquisition unit that acquires information about the tissue, and a prediction unit that predicts the change over time of pixel values ​​in each of a plurality of compartments into which the tissue is divided along the blood flow direction, based on the subject information, the injection protocol, and the tissue information.

[0009] This allows for more accurate predictions that approximate the temporal changes in pixel values ​​in actual tissue. In particular, highly accurate predictions can be made even when the amount of contrast agent injected is small, the injection time is short, or the contrast agent concentration is low. Furthermore, the position of the contrast agent within each tissue can be predicted. Therefore, the optimal injection conditions or imaging conditions can be predicted before actual injection, thus preventing failures in tissue imaging.

[0010] Further features of the present invention will become apparent from the following description of the exemplary embodiments shown with reference to the accompanying drawings. [Brief explanation of the drawing]

[0011] [Figure 1] This is a schematic block diagram of the simulator. [Figure 2] This is a diagram illustrating multiple compartments according to the first embodiment. [Figure 3] This is a diagram illustrating a blood flow model. [Figure 4] This table shows parameters for the stomach, spleen, pancreas, and intestines. [Figure 5] This is the predicted image displayed on the simulator's display unit. [Figure 6] This is an example of an operation screen displayed on the display unit. [Figure 7]This is the predicted image displayed on the simulator's display when helical scan is selected. [Figure 8] This is a schematic diagram of the injection device and imaging system. [Figure 9] This is a diagram illustrating multiple compartments according to the second embodiment. [Figure 10] This is a flowchart explaining the addition process. [Figure 11] This is a diagram illustrating a blood flow model related to deformation patterns. [Figure 12] This graph shows the time-density curve related to deformation patterns. [Modes for carrying out the invention]

[0012] Hereinafter, exemplary embodiments for carrying out the present invention will be described in detail with reference to the drawings. However, the dimensions, materials, shapes, relative positions of components, etc., described in the following embodiments are arbitrary and can be changed according to the configuration of the device to which the present invention is applied or various conditions. Furthermore, unless otherwise specified, the scope of the present invention is not limited to the embodiments specifically described below. In this specification, up and down correspond to the upward and downward directions in the direction of gravity, respectively.

[0013] [First Embodiment] As shown in Figure 1, the simulator (perfusion simulator) 20 that predicts the time-dependent changes in pixel values ​​in the subject's tissue includes a prediction unit 16. Based on information about the subject, the injection protocol, and the tissue information, the prediction unit 16 predicts the time-dependent changes in the pixel values ​​of each of the multiple compartments obtained by dividing the subject's tissue along the blood flow direction, at least those caused by the contrast agent. Note that the pixel values ​​are affected by physiological saline and blood, in addition to the contrast agent.

[0014] In addition, the simulator 20 includes a subject information acquisition unit 11 that acquires subject information. The prediction unit 16 receives subject information regarding the subject, such as the hemoglobin level (g / dL) and the weight (kg) of the subject, from the subject information acquisition unit 11. Here, the subject information acquisition unit 11 acquires the subject information input by the operator via the input unit 27 of the simulator 20.

[0015] Alternatively, the subject information acquisition unit 11 may acquire subject information from the storage unit 24 of the simulator 20 or an external storage device (server). Examples of such a server include a RIS (Radiology Information System), a PACS (Picture Archiving and Communication Systems), a HIS (Hospital Information System), an imaging system, and an image creation workstation. Further, the subject information acquisition unit 11 may acquire subject information from the imaging device 3 (FIG. 8) or the injection device 2 (FIG. 8). The subject information may include the fat-free body weight, the circulating blood volume, the subject number (subject ID), the subject's name, gender, date of birth, age, height, blood volume, blood flow velocity, body surface area, the subject's disease, the history of side effects, the creatinine value, the heart rate, and the cardiac output.

[0016] The simulator 20 also includes a protocol acquisition unit 12 that acquires a contrast agent injection protocol. The prediction unit 16 acquires the injection protocol, such as the contrast agent injection rate (mL / sec) and the contrast agent injection time (sec), from the protocol acquisition unit 12. Here, the protocol acquisition unit 12 acquires the injection protocol input by the operator via the input unit 27. The injection protocol may include information regarding injection conditions such as the injection method, the contrast agent injection site, the injection volume, the injection timing, the contrast agent concentration, and the injection pressure. Further, the protocol acquisition unit 12 may acquire the injection protocol from the storage unit 24, an external storage device, or the injection device 2.

[0017] In particular, the injection site of the contrast agent can be input from the injection setting screen of the simulator 20. In the standard case, the upper limb vein is selected. As other injection sites, the hepatic artery (CT hepatic arteriography: CTHA), the superior mesenteric artery (CT portal venography: CTAP), the right ventricle, the ascending aorta, etc. can be selected. Also, the injection protocol may include information such as a constant contrast agent injection rate, the presence or absence of post-push injection of the contrast agent, the injection rate of physiological saline, the injection time of physiological saline, the increase or decrease of the injection rate, and the volume of the injection tube.

[0018] Also, the simulator 20 includes a tissue information acquisition unit 13 that acquires information on the tissue of the subject. Then, the prediction unit 16 acquires tissue information such as the number of compartments in the tissue (the number of divided compartments of blood vessels and organs), the volume of the tissue (the volume of the blood vessel lumen), the volume of capillaries, the volume of the extracellular fluid cavity, the blood flow rate per unit tissue (blood flow velocity), the contrast agent leakage rate in the tissue (capillary permeability surface area), the contrast agent washback rate in the tissue (capillary permeability surface area), and the native pixel value of the tissue from the tissue information acquisition unit 13.

[0019] Here, the tissue information acquisition unit 13 acquires the subject information input by the operator via the input unit 27. The tissue includes the heart (right ventricle and left ventricle), blood vessels, other organs, muscles, etc. When the prediction unit 16 acquires the native pixel value of the tissue, the degree of enhancement by the contrast agent is predicted based on the native pixel value of each tissue. Also, the tissue information acquisition unit 13 may acquire tissue information from the storage unit 24, an external storage device, or the injection device 2.

[0020] The simulator 20 also includes a drug solution information acquisition unit 14 that acquires drug solution information. The prediction unit 16 acquires drug solution information from the drug solution information acquisition unit 14, such as contrast agent concentration (mgI / mL), contrast agent volume (mL), total iodine amount (mgI), and contrast agent half-life (contrast agent efflux rate). Furthermore, the prediction unit 16 can calculate the iodine amount per kg of body weight (mgI / kg) from the total iodine amount and the subject's body weight. The drug solution information acquisition unit 14 acquires drug solution information entered by the operator via the input unit 27. The drug solution information may include product name, product ID, chemical classification, contained ingredients, concentration, viscosity, expiration date, syringe capacity, syringe pressure resistance, cylinder bore diameter, piston stroke, and lot number.

[0021] Furthermore, the drug solution information acquisition unit 14 may acquire drug solution information from the storage unit 24, an external storage device, or the injection device 2. In addition, the drug solution information acquisition unit 14 may acquire drug solution information from a reader built into the injection device 2. The reader then reads the data carrier attached to the syringe mounted on the injection head. This data carrier may be an RFID chip, IC tag, barcode, etc., and stores drug solution information related to the drug solution.

[0022] Furthermore, the prediction unit 16 can acquire inspection information such as tube voltage (kVp) via the input unit 27. This inspection information may include inspection number (inspection ID), inspection site, inspection date and time, type of chemical solution, name of chemical solution, and imaging conditions (imaging site, etc.).

[0023] Furthermore, the prediction unit 16 can acquire additional information such as whether or not local perfusion (bolus transmission) is considered and the analysis time (sec) via the input unit 27. Here, the analysis time is the length of time to be predicted and corresponds to the length of the X axis of the graph (Figure 6) showing the time concentration curve (TDC curve) 43. Also, if the operator chooses to consider local perfusion, the prediction unit 16 takes into account the leakage of contrast agent from capillaries into the extracellular fluid space within the tissue.

[0024] The prediction unit 16 then predicts the time-dependent changes in pixel values ​​for each of the multiple compartments into which the tissue is divided along the blood flow direction, based on the subject information, the injection protocol, and the tissue information. Subsequently, the prediction unit 16 stores the pixel values ​​for each compartment at each time step in the storage unit 24 of the simulator 20, associating them with each tissue.

[0025] Furthermore, the simulator 20 includes a control unit 25 such as a CPU, and a storage unit 24 that stores the prediction results from the prediction unit 16 stores control programs and the like. The control unit 25 controls the simulator 20 according to the control program stored in the storage unit 24. The control unit 25 also has a display control unit 15 that controls the subject information acquisition unit 11, the protocol acquisition unit 12, the tissue information acquisition unit 13, the drug solution information acquisition unit 14, and the display unit 26. Then, the control unit 25 executes various processes in accordance with the control program implemented in the storage unit 24, so that each unit is logically realized as various functions.

[0026] Furthermore, the memory unit 24 stores a simulation program that causes the computer (control unit) to predict the time-dependent changes in pixel values ​​in the tissue of the subject. This simulation program causes the computer to function as a subject information acquisition unit 11 that acquires information about the subject, a protocol acquisition unit 12 that acquires the injection protocol for the contrast agent, a tissue information acquisition unit 13 that acquires information about the tissue, and a prediction unit 16 that predicts the time-dependent changes in pixel values ​​for each of the multiple compartments into which the tissue is divided along the blood flow direction, based on the subject information, the injection protocol, and the tissue information. This simulation program can be stored on a computer-readable recording medium.

[0027] Furthermore, the memory unit 24 includes RAM (Random Access Memory), which is a system work memory for the operation of the control unit 25, ROM (Read Only Memory), which stores programs or system software, or a hard disk drive. The control unit 25 can also control various processes according to programs stored on portable recording media such as CDs (Compact Discs) and DVDs (Digital Versatile Discs), CF (Compact Flash) cards, or external storage media such as servers on the Internet.

[0028] Furthermore, the simulator 20 includes a display unit 26 that displays compartments of each tissue in colors with a density corresponding to the pixel value. The display control unit 15 then changes the density of the compartments of each tissue displayed on the display unit 26 in accordance with the change in pixel value over time. To do this, the display control unit 15 reads the pixel values ​​of the compartments at a predetermined time from the storage unit 24 and changes the density of the compartments. Operation screens such as input screens are displayed on the display unit 26. Various information such as injection protocols, device input status, setting status, and injection results may also be displayed.

[0029] Furthermore, the input unit 27 of the simulator 20 is connected to the subject information acquisition unit 11, the protocol acquisition unit 12, the tissue information acquisition unit 13, and the drug solution information acquisition unit 14. While a keyboard or the like can be used as the input unit 27, a touch panel can also be used to combine the input unit 27 and the display unit 26.

[0030] The simulator 20 described above can be mounted on an imaging system 100 equipped with a medical imaging device 3, as shown in Figure 8 later, or on an injection device 2 for injecting contrast agent. The simulator 20 can also be mounted on an external computer connected to the imaging device 3 or injection device 2 by wire or wireless connection. The imaging device 3 can be various medical imaging devices such as an MRI (Magnetic Resonance Imaging) device, a CT (Computed Tomography) device, an angiography device, a PET (Positron Emission Tomography) device, a SPECT (Single Photon Emission Computed Tomography) device, a CT angiography device, an MR angiography device, an ultrasound diagnostic device, and an angiography device, but this specification describes a CT device.

[0031] Next, the prediction of the time-dependent change in pixel values ​​by the prediction unit 16 will be explained with reference to Figure 2. The upper part of Figure 2 shows a schematic diagram in which blood vessels A1, organ A2, blood vessel A3, and organ A4, which are connected in series in the blood flow direction D, each correspond to one compartment. In this case, the prediction unit 16 makes a prediction assuming that the pixel values ​​of the entire tissue change immediately after the contrast agent reaches each tissue. As a result, it predicts that the pixel values ​​of the entire tissue have changed, regardless of the actual diffusion rate and location.

[0032] In other words, in organ A4 in Figure 2, the contrast agent has just arrived, and in reality, there is no change in pixel values ​​in most parts of organ A4. However, the prediction is that the pixel values ​​of organ A4 as a whole have increased (white). Similarly, in blood vessel A3, although the contrast agent has hardly moved into organ A4, the prediction is that the pixel values ​​of blood vessel A3 as a whole have decreased (gray). Furthermore, in organ A2, although the contrast agent is still present, the prediction is that the pixel values ​​of organ A2 as a whole have decreased (black). Therefore, especially when the diffusion rate within the tissue is slow, it becomes impossible to accurately predict changes in the pixel values ​​of the tissue.

[0033] On the other hand, a schematic diagram of the first embodiment is shown at the bottom of Figure 2. In this diagram, blood vessels B1, organ B2, blood vessel B3, and organ B4, which are connected in series in the blood flow direction D, are each divided into 15 compartments along the blood flow direction D. That is, each tissue is divided into multiple compartments along the blood flow direction D according to the number of divided compartments of the tissue acquired from the tissue information acquisition unit 13.

[0034] In this case, the prediction unit 16 divides the volume of the tissue containing the compartment to be predicted, the volume of the capillaries in that tissue, and the volume of the extracellular fluid space in that tissue by the number of divided compartments, and makes a prediction for each compartment. For example, if the number of divided compartments is 15, the prediction unit 16 predicts the change in pixel values ​​over time based on the values ​​obtained by dividing the volume of the tissue, the volume of the capillaries, and the volume of the extracellular fluid space by 15.

[0035] Therefore, in organ B4, where in reality there is little change in pixel values, only the compartment located on the side of blood vessel B3 is predicted to have increased pixel values ​​(white). Similarly, in blood vessel B3, since the contrast agent has hardly moved to organ B4, only the compartment located on the side of organ B2 is predicted to have decreased pixel values ​​(gray). Furthermore, in organ B2, since the contrast agent remains, only the compartment located on the side of blood vessel B1 is predicted to have decreased pixel values ​​(black). This allows for accurate prediction of changes in tissue pixel values ​​even when the actual diffusion rate within the tissue is slow.

[0036] Next, we will explain the prediction of specific pixel value changes with reference to Figure 3. As shown in Figure 3, the tissue of the subject in the first embodiment includes the right ventricle, aorta, veins, arteries, brain (head), upper limbs, right coronary artery (myocardium dominated by the right coronary artery), anterior descending branch (myocardium dominated by the anterior descending branch), circumflex branch (myocardium dominated by the circumflex branch), lungs, liver, stomach, spleen, pancreas, intestines, kidneys, lower limbs, left ventricle, ascending aorta, descending aorta, and abdominal aorta. The contrast agent injected from the upper limb veins travels to each organ via the right ventricle, lungs, left ventricle, and aorta (ascending aorta, descending aorta), and then reaches the right ventricle via the veins. The contrast agent injected into the body is then excreted from the body via the kidneys.

[0037] The prediction unit 16 predicts the time-dependent changes in the pixel values ​​of each tissue, starting from the right ventricle, in both the upstream and downstream directions of blood flow. Specifically, the prediction unit 16 first makes a prediction for 1-right ventricle, then makes a prediction for the second group of tissues, which includes 2-greater vena cava and 2-vein, located upstream of the right ventricle in the direction of blood flow, and 2-artery, located downstream of the right ventricle in the direction of blood flow. Subsequently, the prediction unit 16 makes predictions in the following order: third group of tissues including veins, brain, upper limbs, right coronary artery, anterior descending branch, circumflex branch, and lungs; fourth group of tissues including arteries, veins, and liver; fifth group of tissues including left ventricle, arteries, ascending aorta, descending aorta, kidneys, and lower limbs; sixth group of tissues including abdominal aorta, stomach, spleen, pancreas, intestines, and arteries; and seventh group of tissues including arteries. In Figure 3, the order of prediction is indicated by the number preceded by a hyphen in front of the name of each tissue.

[0038] The prediction unit 16 may also predict the time-dependent changes in the pixel values ​​of each tissue, starting from the tissue closest to the injection site of the contrast agent, in both the upstream and downstream directions in the blood flow direction. For example, when the contrast agent is injected into the hepatic artery (CTHA), the prediction unit 16 may first predict the liver. Subsequently, the prediction unit 16 predicts a group of tissues including arteries located upstream of the liver in the blood flow direction and veins located downstream of the liver in the blood flow direction.

[0039] Furthermore, the prediction unit 16 uses a differential equation, such as Equation 1 below, to determine the change in pixel values ​​in each tissue (blood vessels and organs) as a function of time. Hereinafter, C1 is the concentration of contrast agent flowing into the compartment, C2 is the concentration of contrast agent flowing out of the compartment, V is the volume of the compartment, and Q is the blood flow rate (blood flow velocity) per unit tissue in the compartment.

[0040]

number

[0041] Furthermore, the prediction unit 16 considers the leakage rate when the contrast agent permeates from the capillaries to the extracellular fluid space and the leakage rate when it permeates from the extracellular fluid space to the capillaries in order to determine the change in pixel values ​​in the right ventricle, left ventricle, and tissues other than blood vessels. For this reason, the prediction unit 16 uses differential equations such as those shown in equations 2 and 3 below. Hereinafter, Vec is the volume of the extracellular fluid space, Cec is the concentration of the contrast agent in the extracellular fluid space, Viv is the volume of the capillaries, Civ is the concentration of the contrast agent in the capillaries, PS1 is the leakage rate, and PS2 is the leakage rate.

[0042]

number

[0043]

number

[0044] Then, by solving the differential equation above, the elapsed time from the start of injection and the change in pixel value (contrast agent concentration) can be obtained as a function of time. As an example of the parameters used in this prediction, Figure 4 shows a table of values ​​for each tissue: stomach, spleen, pancreas, and intestinal tract. In the first embodiment, the prediction unit 16 predicts the change in pixel value over time for the stomach, spleen, pancreas, and intestinal tract as separate tissues.

[0045] As shown in Figure 4, for the stomach, the following parameters are used: tissue volume of 120 mL to 160 mL, capillary volume of 2 mL to 5 mL, extracellular fluid volume of 12 mL to 18 mL, blood flow rate per unit tissue (arterial blood flow velocity) of 120 mL / min to 180 mL / min, exudation rate of 15 to 25, and infiltration rate of 15 to 25.

[0046] For the spleen, the following parameters are used: tissue volume of 120 mL to 160 mL, capillary volume of 10 mL to 15 mL, extracellular fluid volume of 45 mL to 65 mL, blood flow rate per unit tissue of 150 mL / min to 250 mL / min, leachate velocity of 15 to 25, and infiltration velocity of 15 to 25.

[0047] For the pancreas, the following parameters are used: tissue volume of 120 mL to 150 mL, capillary volume of 3 mL to 6 mL, extracellular fluid volume of 30 mL to 50 mL, blood flow rate per unit tissue of 120 mL / min to 180 mL / min, leachate velocity of 15 to 25, and infiltration velocity of 15 to 25.

[0048] Furthermore, for the intestinal tract, the following are used: tissue volume of 1800 mL to 2000 mL, capillary volume of 30 mL to 40 mL, extracellular fluid space volume of 500 mL to 600 mL, blood flow rate per unit tissue of 0.4 mL / min to 0.5 mL / min, seepage rate of 150 to 250, and seepage rate of 150 to 250.

[0049] Here, the seepage rate and the seepage-back rate can be calculated by the product of the capillary area and permeability. For example, if the total area of ​​capillaries in the human body is 800 m² 2 Assuming this, we can assign a capillary area to each organ according to its weight. Then, assuming the permeability of all organs is 1 ml / min / g, we can determine the leakage rate and the leakage rate.

[0050] The prediction unit 16 sequentially stores the prediction results in the storage unit 24 (Figure 1). These prediction results include information on pixel values ​​over time associated with the tissue. The display unit 26 (Figure 1) then schematically displays the predicted image of each tissue, which includes multiple compartments. Furthermore, the display control unit 15 reads the pixel values ​​from the storage unit 24 and controls the display unit 26 to change the intensity of each compartment according to the changes in pixel values ​​over time.

[0051] Figure 5 shows an example of the change in density in a predicted image, corresponding to a horizontal cross-section in the head-to-tail direction of the body. However, unlike an actual cross-section, all tissues are shown so that each tissue can be viewed at a glance. A window width of 350 and a window level of 40 are set. The window width corresponds to the contrast range of the pixel values, and the window level corresponds to the brightness of the screen. If the pixel value is smaller than the value obtained by subtracting half the window width value from the window level value, the display unit 26 displays in black. If the pixel value is larger than the value obtained by adding half the window width value to the window level value, the display unit 26 displays in white.

[0052] The upper part of Figure 5 shows image N1 of each tissue immediately after injection of contrast agent into the upper limb veins, with the upper limb veins shown in their original pixel values ​​(dark gray). At this point, the contrast agent has not yet reached all blood vessels, including the abdominal aorta and celiac artery, and all blood vessels are shown in their original pixel values ​​(dark gray). Next, the center of Figure 5 shows image N2 of each tissue approximately 25 seconds after the start of injection, with the abdominal aorta, celiac artery, and internal jugular vein being particularly stained white. On the other hand, the upper limb veins are shown in light gray because the pixel values ​​have decreased due to the early outflow of the contrast agent.

[0053] The lower part of Figure 5 shows image N3 of each tissue approximately 120 seconds after the start of injection. Compared to image N2, the pixel value has decreased and the entire image is shown in light gray because the contrast agent has diffused and been uniformly distributed throughout the blood vessels and organs of the body.

[0054] Next, with reference to Figure 6, an example of an operation screen displayed on the display unit will be described. As shown on the right side of Figure 6, once the prediction by the prediction unit 16 is complete, the display control unit 15 reads the pixel values ​​of each compartment at a predetermined time, for example, a time selected by the operator, from the storage unit 24. The display control unit 15 then reflects the read pixel values ​​in the prediction image 41 and displays it on the display unit 26. For example, in Figure 6, the time point of 9.90 seconds is selected, and the prediction image 41 at that time is displayed. In the initial settings, the prediction image 41 at the time of injection, i.e., at 0 seconds, is displayed.

[0055] In the predicted image 41 of Figure 6, a window width of 350 and a window level of 40 are set, and these values ​​are displayed in the upper right corner of the predicted image 41. Below the window width and window level, -1000HU is displayed as a pixel value. This displays the pixel value at the position corresponding to the area indicated by the pointer indicated by the arrow in the predicted image 41. The operator can input the window width (WW) and window level (WL) via the input unit 27.

[0056] Below the predicted image 41, operation buttons 42 are displayed. These operation buttons 42 include, from left to right in Figure 6, a stop button, a play button, a 2x speed play button, a 3x speed play button, and a 10x speed play button. When the operator selects a play button, the predicted image 41 is played back as a continuous video over time. This allows the operator to visually recognize the position of the contrast agent within each tissue at a desired time.

[0057] To the left of the predicted image 41, a time-concentration curve 43 is displayed. In this time-concentration curve 43, the X-axis (horizontal axis) corresponds to the elapsed time since the start of injection, and the Y-axis (vertical axis) corresponds to the pixel value. The Y-axis also has a first axis and a second axis, allowing multiple tissues to be displayed on a single graph. For example, in Figure 6, the time-concentration curves for the liver, portal vein, and hepatic artery are displayed.

[0058] The operator can select the tissues to display on the time-concentration curve 43 using the display options 44 located below the time-concentration curve 43. For example, in Figure 6, the liver, portal vein, and hepatic artery are selected from the brain, liver, portal vein, hepatic artery, and right ventricle. The amount of contrast agent expelled from the body can also be displayed. Other selectable tissues include the upper limbs, lungs, left ventricle, myocardium, right coronary artery, anterior descending branch, circumflex branch, bronchi, spleen, intestines, kidneys, lower limbs, pancreas, pulmonary artery, pulmonary vein, ascending aorta, descending aorta, abdominal aorta, celiac artery, superior mesenteric artery, inferior abdominal aorta, hepatic vein, renal artery, renal vein, cerebral artery, cerebral vein, upper limb arteries, upper limb veins, lower limb arteries, lower limb veins, superior vena cava, and inferior vena cava.

[0059] Furthermore, a Y-axis input box 45 is displayed to the right of display option 44. The operator can select automatic setting or enter desired values ​​in the Y-axis input box 45. For example, in Figure 6, automatic setting is selected for the first axis, and the maximum and minimum values ​​for the first axis are set automatically. For the second axis, 100 is entered as the maximum value and 50 as the minimum value.

[0060] Furthermore, a current time box 46 is displayed below the Y-axis input box 45. When the operator selects the current time box 46, a current time bar 47 is displayed on the time-density curve 43. This current time bar 47 indicates the time corresponding to the predicted image 41 (9.90 seconds in Figure 6). As the predicted image 41 is played back continuously, the current time bar 47 moves along the X-axis in accordance with the elapsed time.

[0061] Furthermore, a helical scan box 48 is displayed below the current time box 46. The operator can then select the helical scan box 48 and input the bed movement speed (cm / sec). In Figure 6, a bed movement speed of 8.0 cm / sec is entered.

[0062] When the helical scan box 48 is selected, the display control unit 15 acquires the delay time due to the helical scan. Here, the delay time corresponds to the elapsed time from when the head is imaged until each tissue is imaged (time spent moving the patient's bed), and is determined based on the length from the top edge of the predicted image 41 to each tissue. The display control unit 15 then reads the pixel values ​​from the storage unit 24 at a time obtained by adding the delay time to a predetermined time. That is, the display control unit 15 reads the pixel values ​​of each tissue at a time obtained by adding the acquired delay time to a predetermined time (the current time). For example, in the predicted image 41 of Figure 6, the head (brain) shows the pixel value at the current time, which is 9.90 seconds later, and the right ventricle shows the pixel value at 14.90 seconds later.

[0063] This allows us to obtain a predictive image when a helical scan is performed. For example, if the current time is set to the time immediately after the start of infusion (0 seconds), the display control unit 15 may show the pixel values ​​for the brain immediately after the start of infusion, the display control unit 15 may show the pixel values ​​for the right ventricle 5 seconds after the start of infusion, and the display control unit 15 may show the pixel values ​​for the liver 7.5 seconds after the start of infusion. The display control unit 15 may also obtain the delay time by calculation. Alternatively, the delay time associated with the bed movement speed may be stored in the memory unit 24 in advance, and the display control unit 15 may obtain the delay time from the memory unit 24.

[0064] The predicted image obtained in this manner will be explained with reference to Figure 7. Figure 7 shows the change in density of a predicted image as an example during a helical scan. Note that the image in Figure 7 corresponds to a horizontal cross-section in the head-to-tail direction of the body, but unlike the actual cross-section, it shows all tissues. Also, a window width of 350 and a window level of 40 are set.

[0065] The upper part of Figure 7 shows image H1 of each tissue immediately after injection of contrast agent into the upper limb veins. However, unlike image N1 in Figure 5, the upper limb veins show the pixel values ​​at the point when the delay time has been added. Therefore, the upper limb veins are stained white by the contrast agent. The contrast agent has not yet reached the other blood vessels, and these other blood vessels are shown with their original pixel values ​​(dark gray).

[0066] Next, in the center of Figure 7, image H2 of each tissue is shown approximately 25 seconds after the start of injection. The internal jugular vein is stained white because the contrast agent has reached it. However, unlike image N2 in Figure 5, the pixel values ​​are shown at the point when the delay time has been added, so the abdominal aorta and celiac artery, where the contrast agent has already flowed out, are shown in light gray with reduced pixel values. Also, the portal vein is stained white because the contrast agent has circulated and reached it.

[0067] Furthermore, the lower part of Figure 7 shows image H3 of each tissue approximately 120 seconds after the start of injection. Compared to image H2, the pixel value has decreased and the entire area is shown in light gray because the contrast agent has diffused and been uniformly distributed throughout the blood vessels and organs of the body.

[0068] Returning to the explanation of Figure 6, an analysis button 49 is displayed on the lower side of the helical scan box 48. When the operator selects the analysis button 49, the prediction unit 16 starts predicting pixel values. The prediction unit 16 may also start predicting pixel values ​​when it has acquired subject information, injection protocol, and tissue information.

[0069] Next, with reference to Figure 8, the imaging system 100 equipped with the simulator 20 will be described. The imaging system 100 includes an injection device 2 for injecting contrast agent and a medical imaging device 3 connected to the injection device 2 by wire or wireless means for imaging the subject. The injection device 2 or the imaging device 3 is equipped with the simulator 20 described above.

[0070] The imaging device 3 includes an imaging unit 31 that performs imaging of the subject according to the imaging plan, a control device 32 that controls the entire imaging device 3, and a display 33 that serves as a display unit 26. The control device 32 and the display 33 can also be configured as an integrated unit. The imaging device 3 is connected to the injection device 2 by wire or wireless, for example, via a gateway device (not shown).

[0071] The imaging plan of the imaging device 3 can include information such as the imaging area, effective tube voltage, model name, manufacturer name, imaging time, tube voltage, imaging range, rotation speed, helical pitch, exposure time, dose, and imaging method. The control device 32 then controls the imaging unit 31 to image the subject according to the imaging plan. The control device 32 is connected to the display 33, which displays the device's input status, settings, imaging results, and various other information.

[0072] The imaging unit 31 includes a bed, an X-ray source for irradiating the subject with X-rays, and an X-ray detector for detecting the X-rays that have passed through the subject. The imaging unit 31 exposes the subject to X-rays and captures a fluoroscopic image of the subject by projecting the inside of the subject's body back onto the X-rays that have passed through the subject. The imaging unit 31 may also use radio waves or ultrasound instead of X-rays for imaging. The control device 32 can communicate with the imaging unit 31 and the injection device 2, etc., by wired or wireless means.

[0073] The injection device 2 for injecting contrast agents injects drug solutions, such as various contrast agents and physiological saline, filled in syringes, into the body of the subject. The injection device 2 also includes an injection head 21 for injecting contrast agents according to an injection protocol. Furthermore, the injection device 2 includes a stand 22 for holding the injection head 21 and a console 23 connected to the injection head 21 by wire or wirelessly.

[0074] The console 23 functions as a control device for controlling the injection head 21 and also functions as a simulator 20. The console 23 is also equipped with a touch panel that functions as an input unit 27 and a display unit 26, and can communicate with the injection head 21 and the imaging device 3, etc., via wired or wireless connection. The injection device 2 may, instead of the touch panel, be equipped with a display unit 26 and a user interface such as a keyboard as the input unit 27.

[0075] Furthermore, the simulator 20, input unit 27, and display unit 26 can each be configured as separate components. For example, instead of the console 23, the injection device 2 may have a control device connected to the injection head 21 and a display unit 26 (such as a touch panel display) connected to the control device that displays the injection status of the drug solution, etc. Such a control device can also function as the simulator 20. In addition, the injection head 21 and the control device can be configured integrally with the stand 22. Alternatively, instead of the stand 22, a ceiling suspension member can be provided, and the injection head 21 can be suspended from the ceiling via this ceiling suspension member.

[0076] Furthermore, the injection device 2 may include a power supply or battery, a hand switch connected to the console 23, and a remote control device for remotely operating the injection head 21. This remote control device can remotely operate the injection head 21 to start or stop the injection. The power supply or battery can be provided in either the injection head 21 or the control device (console 23), or it can be provided separately from these.

[0077] The injection head 21 has a first holding section 214 on which a syringe filled with contrast agent is mounted, and a second holding section 215 on which a syringe filled with physiological saline solution as a drug solution to push the contrast agent. The injection head 21 also has a drive mechanism (not shown) that pushes out the drug solution in the syringe mounted in the first holding section 214 according to the injection protocol, and a drive mechanism (not shown) that pushes out the drug solution in the syringe mounted in the second holding section 215 according to the injection protocol.

[0078] The injection head 21 also includes a head display 211 that displays injection conditions, injection status, device input status, setting status, and various injection results, and an operation unit 212 for inputting the operation of the drive mechanism. The head display 211 can be omitted. Alternatively, the head display 211 can be configured as a touch panel or the like and used as the operation unit 212.

[0079] The control unit 212 is equipped with a forward button for the drive mechanism, a reverse button for the drive mechanism, and a final confirmation button, etc. When injecting the drug solution, a mixing tube, etc., is connected to the tip of the syringe mounted on the injection head 21. The operator presses the final confirmation button when the injection preparations, such as connecting the mixing tube, are complete. This causes the injection head 21 to wait in a state where injection can begin.

[0080] Subsequently, the drug solution dispensed from the syringe is injected into the subject's body via a mixing tube or similar device. This mixing tube functions as a mixer for the contrast agent and the diluent. Examples of such mixers include "SPIRAL FLOW®" manufactured by Nemoto Kyorindo Co., Ltd.

[0081] The injection head 21 can be equipped with various syringes, such as pre-filled syringes that have data carriers such as RFID chips, IC tags, or barcodes. The injection head 21 also incorporates a reading unit (not shown) that reads the data carrier attached to the syringe. This data carrier stores drug solution information related to the drug solution.

[0082] The injection device 2 can receive information from a server (external storage device) (not shown) via an internal or external gateway device, and can also transmit information to the server. The imaging device 3 can also receive information from the server and transmit information to the server. This server has examination orders stored in advance. The examination order includes subject information about the subject and examination information about the examination content. The server can also store information related to imaging results, such as image data transmitted from the imaging device 3, and information related to injection results transmitted from the injection device 2.

[0083] With an imaging device 3 equipped with such a simulator 20, the operator can operate the imaging device 3 while checking the predicted image 41 on the display 33. The imaging device 3 may also change the imaging plan according to the prediction results from the prediction unit 16. For example, if the operator inputs a desired pixel value to the imaging device 3, and the desired pixel value differs from the pixel value predicted by the prediction unit 16 for the tissue to be imaged, the imaging device 3 may change the tube voltage or tube current, etc., so that the prediction result matches the desired pixel value.

[0084] Furthermore, with the injection device 2 equipped with the simulator 20, the operator can operate the injection device 2 while checking the predicted image 41 on the console 23. The injection device 2 may also change the injection protocol according to the prediction result from the prediction unit 16. For example, if the operator inputs a desired pixel value to the injection device 2, and the desired pixel value differs from the pixel value predicted by the prediction unit 16 for the tissue to be imaged, the injection device 2 may change the injection speed or injection time, etc., so that the prediction result matches the desired pixel value.

[0085] According to the invention of the first embodiment described above, it is possible to make predictions with higher accuracy that approximate the change in pixel values ​​over time in actual tissue. In particular, even when the amount of contrast agent injected is small, the injection time of the contrast agent is short, or the concentration of the contrast agent is low, it is possible to make predictions with higher accuracy. Furthermore, it is possible to predict the position of the contrast agent within each tissue. Moreover, the operator can visually recognize the position of the contrast agent within each tissue at a desired time.

[0086] Furthermore, according to the invention of the first embodiment, even if the injection protocol includes boost injection of contrast agent or increasing or decreasing the injection rate, the pixel values ​​of each tissue can be predicted. For example, even in the case of the so-called cross-injection method, in which the injection rate of contrast agent is gradually decreased while the injection rate of physiological saline is increased, the actual captured image can be predicted. Moreover, when a new injection protocol or imaging plan is created, the actual captured image can be predicted.

[0087] [Second Embodiment] A second embodiment will be described with reference to Figure 9. In the first embodiment, each tissue was divided into the same number of compartments. In contrast, in the second embodiment, a tissue with a large volume is divided into a larger number of compartments. That is, the prediction unit 16 according to the second embodiment predicts the change over time of pixel values ​​in each of the multiple compartments into which the tissue with a small volume (first tissue) is divided, and also predicts the change over time of pixel values ​​in each of the multiple compartments into which the tissue with a large volume (second tissue) is divided into a larger number of compartments than the tissue with a small volume.

[0088] In the description of the second embodiment, the differences from the first embodiment will be explained, and the same reference numerals will be used for the components described in the first embodiment, and their descriptions will be omitted. Unless otherwise specified, components with the same reference numerals will perform substantially the same operation and function, and their effects will also be substantially the same.

[0089] Actual organs each have different volumes. For example, the volume of organ A8 in Figure 9 is several times larger than the volume of organ A6. Therefore, the volume of each compartment in organ A8 is several times larger than the volume of each compartment in organ A6. When prediction is performed in the same manner as in the first embodiment, in the predicted image 41, even a change in the pixel value of one compartment is displayed as a small change (short distance traveled by the contrast agent) in organ A6. On the other hand, in organ A8, it is displayed as a large change (long distance traveled by the contrast agent) compared to organ A6. As a result, it becomes difficult for the operator to accurately recognize the position of the contrast agent within each tissue by visual means.

[0090] Therefore, in the second embodiment, tissues with a large volume are divided into more compartments than tissues with a small volume. Specifically, in Figure 9, organ B6 is divided into 3 compartments and organ B8 is divided into 15 compartments. As a result, the volumes of the compartments of organs B6 and B8 are approximate, allowing the operator to accurately recognize the location of the contrast agent within each tissue visually.

[0091] In this second embodiment, the optimal number of partitioned compartments corresponding to the volume of each tissue is determined in advance and stored in the storage unit 24. The tissue information acquisition unit 13 then acquires the number of partitioned compartments from the storage unit 24, and the prediction unit 16 predicts the change in pixel values ​​based on the acquired number of partitioned compartments.

[0092] The invention according to the second embodiment described above also enables more accurate predictions that approximate the time-dependent changes in pixel values ​​in actual tissue. In particular, highly accurate predictions can be made even when the amount of contrast agent injected is small, the injection time of the contrast agent is short, or the concentration of the contrast agent is low. Furthermore, the position of the contrast agent within each tissue can be predicted. Moreover, the operator can visually recognize the position of the contrast agent within each tissue at a desired time.

[0093] Furthermore, according to the invention of the second embodiment, even when the predicted image 41 includes tissues with a large volume and tissues with a small volume, the operator can accurately recognize the location of the contrast agent within each tissue visually.

[0094] In Figure 9, blood vessels A5, A7, B5, and B7 are each divided into the same number of compartments, i.e., 15 compartments. However, blood vessels with a large volume may be divided into more compartments than blood vessels with a small volume. Alternatively, the number of compartments may be set so that the volume of each compartment of tissue B8 (which has a large volume) and tissue B6 (which has a small volume) is approximately equal.

[0095] Although the present invention has been described above with reference to the embodiments described, the present invention is not limited to the embodiments described above. Inventions modified within the scope that does not contradict the present invention, and inventions equivalent to the present invention are also included in the present invention. Furthermore, the above embodiments and each of the variations can be combined as appropriate within the scope that does not contradict the present invention.

[0096] For example, the display unit 26 can display not only horizontal cross-sections of the body but also predicted images 41 of the coronal cross-section. Furthermore, the number of tissue compartments is not limited to 15; any number of two or more can be selected.

[0097] Alternatively, the memory unit 24 may be pre-stored with noise information of pixel values, and the display control unit 15 may read the noise information from the memory unit 24 and add it to the predicted image 41 of each compartment. This noise information may include, for example, an image showing radial noise that occurs between tissues stained white by contrast agents. By superimposing an image showing noise onto the predicted image 41, a predicted image 41 that more closely resembles the actual captured image can be obtained.

[0098] In the embodiments described above, the display unit 26 arranged compartments so that multiple tissues formed a schematic diagram in the direction of blood flow, and displayed each compartment in a color with a density corresponding to the pixel value. However, the display unit 26 may also arrange compartments so that each tissue is displayed individually, and display each compartment in a color with a density corresponding to the pixel value. Furthermore, the display control unit 15 may control the display unit 26 so that the number of compartments differs for each tissue. In this case, the display control unit 15 causes each tissue to display a number of compartments set by the operator or a number of compartments pre-stored in the storage unit 24. The display unit 26 may also display each compartment in a color other than black and white.

[0099] Furthermore, the injection head 21 is not limited to a type that holds two syringes, but may have three or more syringe holders, or a type that has only one syringe holder. In addition, the display control unit 15 may display the read-out maximum pixel value on or near the image of a predetermined tissue when that tissue reaches its maximum pixel value in the predicted image 41 shown in Figure 6. For example, if the liver, portal vein, and hepatic artery are selected in the display option 44, the display control unit 15 may display the maximum pixel value of each selected tissue near the liver, portal vein, and hepatic artery in the predicted image 41. The maximum pixel value can be displayed in a color other than black and white, such as blue, green, red, or yellow.

[0100] Furthermore, the display control unit 15 may display each tissue (compartment) in the predicted image 41 shown in Figure 6 using colors other than grayscale, such as shades of blue, green, red, or yellow. In addition, the display control unit 15 may display a predetermined tissue using shades of colors other than grayscale. For example, if the liver, portal vein, and hepatic artery are selected in display option 44, the display control unit 15 can display the liver, portal vein, and hepatic artery in the predicted image 41 using shades of red, blue, and green, respectively.

[0101] [Transformed form] The prediction unit 16 may also consider the change in blood flow rate (blood flow velocity) per unit tissue due to the injection of the drug solution. That is, when a drug solution is injected, the blood flow velocity changes in tissues (compartments) downstream of the injection site in the blood flow direction due to the pressure from the injected drug solution. In particular, if the drug solution is injected at a speed faster than the normal blood flow velocity, the blood flow velocity increases in the downstream tissues. Therefore, if the injection speed of the drug solution is faster than the normal blood flow velocity, the prediction unit 16 can take into account the increase in blood flow velocity by adding the difference obtained by subtracting the blood flow velocity from the injection speed to the blood flow velocity.

[0102] Therefore, the prediction unit 16 predicts the change in pixel values ​​over time based on the blood flow velocity obtained by summing. That is, when the prediction unit 16 predicts the change in pixel values ​​over time using the formula described above, it adds the obtained difference to the blood flow velocity Q per unit tissue in the compartment. Specifically, as shown in the flowchart of the summing process in Figure 10, the prediction unit 16 obtains the normal blood flow velocity of the tissue corresponding to the injection site, i.e., the tissue to which the drug solution is injected, as subject information from the subject information acquisition unit 11 (S101). The prediction unit 16 also obtains the injection rate of the drug solution included in the injection protocol received from the protocol acquisition unit 12 (S102). The prediction unit 16 may also obtain the blood flow velocity after the injection rate. Alternatively, the prediction unit 16 may obtain the blood flow velocity by calculating it from the subject's weight instead of obtaining it from the subject information acquisition unit 11.

[0103] Next, the prediction unit 16 compares the blood flow velocity and the injection velocity to determine whether the injection velocity is faster than the blood flow velocity (S103). If the injection velocity is less than or equal to the blood flow velocity (NO in S103), the prediction unit 16 terminates the process without performing the addition. On the other hand, if the injection velocity is faster than the blood flow velocity (YES in S103), the prediction unit 16 calculates the difference by subtracting the blood flow velocity of the tissue corresponding to the injection site from the injection velocity (S104). Then, the prediction unit 16 adds the calculated difference to the blood flow velocity (S105) and terminates the process. For example, if the injection velocity is 2.0 mL / sec and the blood flow velocity is 1.5 mL / sec, the prediction unit 16 adds 0.5 mL / sec to the blood flow velocity as follows. Then, 2.0 mL / sec is applied as the blood flow velocity after the velocity increase. 1.5mL / sec+(injection rate 2.0mL / sec-1.5mL / sec)=2.0mL / sec

[0104] Here, the injection rate is the amount of drug solution injected per unit time. Therefore, when contrast agent and physiological saline are injected simultaneously, the prediction unit 16 adds them based on the total injection rate of both. Also, when physiological saline is injected at a rate faster than the normal blood flow rate, the prediction unit 16 adds based on the injection rate of the physiological saline. After the addition process is completed, the prediction unit 16 predicts the change in pixel values ​​over time for each of the multiple compartments into which the tissue is divided along the blood flow direction, based on the subject information including the blood flow rate obtained by addition, the injection protocol, and the tissue information. This makes it possible to bring the blood flow rate closer to the actual rate and improve the accuracy of the pixel value prediction.

[0105] Furthermore, in the addition process, the prediction unit 16 can add the difference to the blood flow velocity by adding all of the multiple tissues. However, the prediction unit 16 may add only a portion of the multiple tissues. Specifically, the difference to the blood flow velocity may be added only to the tissues from the tissue immediately downstream in the blood flow direction to the tissue corresponding to the injection site, up to the tissue corresponding to the injection site in the blood flow direction. For example, referring to Figure 11, an example of adding the difference to the blood flow velocity of only a portion of the multiple tissues when injecting a drug solution from an upper limb vein will be explained. In Figure 11, the right ventricle, arteries, lungs, veins, left ventricle, ascending aorta, arteries, myocardium (myocardium dominated by the right coronary artery, myocardium dominated by the anterior descending branch, and myocardium dominated by the circumflex branch), and veins constitute a closed circuit starting from the right ventricle. That is, these tissues constitute a closed circuit starting from the right ventricle, which is immediately downstream in the blood flow direction to the tissue corresponding to the injection site. Therefore, the prediction unit 16 adds only the tissues from the right ventricle to the veins included in the closed circuit, and does not add the difference to, for example, the blood flow velocity of the lower limb tissues. This keeps the effect of the addition within the closed circuit, simplifying the simulation and reducing calculation time.

[0106] Furthermore, the prediction unit 16 predicts the change in pixel values ​​over time by adding the difference to the blood flow velocity of all tissues to be added to, simultaneously with the start of drug injection. However, the prediction unit 16 may also predict the change in pixel values ​​over time by assuming that the difference is added to the blood flow velocity after a predetermined time has elapsed since the start of injection. That is, in tissues located downstream of the tissue corresponding to the injection site, the blood flow velocity may change after a predetermined time has elapsed since the start of injection. Therefore, for the tissue corresponding to the injection site, the prediction unit 16 predicts the change in pixel values ​​over time by assuming that the difference is added to the blood flow velocity simultaneously with the start of drug injection. On the other hand, for tissues that are far away from that tissue in the direction of blood flow, the prediction unit 16 predicts the change in pixel values ​​over time by assuming that the difference is added after a time that increases with distance has elapsed.

[0107] Figure 12 is a graph showing the time-density curve, which is the predicted result obtained by performing an additive processing related to the deformation morphology. Figure 12 shows the time-density curve of the hepatic artery, with the horizontal axis corresponding to the elapsed time from the start of injection and the vertical axis corresponding to the pixel value. The subject's height was set to 170 cm, the subject's weight to 70 kg, and the contrast agent concentration to 300 mgI / mL. Figure 12 also shows the time-density curve when 100 mL of contrast agent is injected at an injection rate of 2.0 mL / sec for 50 seconds, followed by a boost injection of 30 mL of physiological saline at an injection rate of 2.0 mL / sec for 15 seconds. The time-density curve with additive processing is shown as a solid line, and the time-density curve without additive processing is shown as a dashed line.

[0108] Without additive processing, the time it takes for the contrast agent to reach the tissue is delayed. Therefore, compared to when additive processing is performed, it is simulated as if the contrast agent arrived at a later time. As a result, in the time-density curve shown by the dashed line, a second peak in the pixel value occurs, as shown by arrow A in Figure 12. On the other hand, when additive processing is performed, the circulation of the contrast agent is simulated. Therefore, as shown in the time-density curve shown by the solid line, a second peak does not occur.

[0109] In this way, by performing an additive process, it is possible to predict pixel values ​​that are closer to the actual values. Furthermore, it is also possible to simulate the boost injection with physiological saline. Specifically, with boost injection, the contrast agent that was stagnant after the injection of the contrast agent is pushed out at an earlier timing. As a result of simulating the boost injection, the accuracy of predicting the pixel value at the peak has improved, and the pixel value is higher in the time density curve shown by the solid line in Figure 12.

[0110] Furthermore, the prediction unit 16 may also consider the diffusion of contrast agent between adjacent compartments. That is, if there is a difference in contrast agent concentration between adjacent compartments, diffusion of the contrast agent will occur from the compartment with higher concentration to the compartment with lower concentration. Therefore, the prediction unit 16 may consider the diffusion of the contrast agent by predicting pixel values ​​in such a way that it decreases the contrast agent concentration in the compartment with higher concentration and increases the contrast agent concentration in the compartment with lower concentration.

[0111] Here, the prediction unit 16 increases the decrease and increase in contrast agent concentration when the concentration difference is large. The prediction unit 16 also obtains the osmotic pressure of the contrast agent from the drug information acquisition unit 14, and increases the decrease and increase in contrast agent concentration when the osmotic pressure is large. Furthermore, the prediction unit 16 increases the decrease and increase in contrast agent concentration when the contact area between compartments is large. For example, when compartments of different tissues are adjacent to each other, the prediction unit 16 decreases the decrease and increase in contrast agent concentration because the contact area is small. However, when compartments of the same tissue are adjacent to each other, the contact area is large, so the prediction unit 16 increases the decrease and increase in contrast agent concentration.

[0112] Furthermore, the prediction unit 16 may calculate the contact area, and if the calculated contact area is large, it may increase the amount of decrease and increase in contrast agent concentration. In addition, each of the above-described modified forms can be appropriately combined with other embodiments or modified forms as long as it does not contradict the present invention.

[0113] This application claims priority from Japanese Patent Application No. 2014-240006, filed on 27 November 2014, and incorporates its entire contents as part of this application. [Explanation of Symbols]

[0114] 2: Injection device, 3: Imaging device, 20: Simulator, 11: Subject information acquisition unit, 12: Protocol acquisition unit, 13: Tissue information acquisition unit, 15: Display control unit, 16: Prediction unit, 21: Injection head, 26: Display unit, 100: Imaging system, D: Blood flow direction

Claims

1. A simulator for predicting the time-dependent changes in pixel values ​​of multiple tissues in a subject, A prediction unit that predicts the change in the aforementioned pixel value over time, It comprises a display control unit that displays a predictive image containing multiple organizations on the display unit, The display control unit controls the display unit to display the tissue in a color density corresponding to the predicted pixel value at a predetermined time, and is a simulator.

2. The simulator according to claim 1, wherein the display control unit controls the display unit to change the intensity of the predicted image in accordance with the change in the pixel value over time.

3. The simulator according to claim 2, wherein the display control unit controls the display unit so that a time point image showing the elapsed time since the start of injection is displayed, and the predicted image for the time point shown by the time point image is displayed.

4. The simulator according to claim 2, wherein the display control unit controls the display unit so that the predicted image is played back as a continuous video along the elapsed time from the start of injection.

5. A simulation program that predicts the time-dependent changes in pixel values ​​of multiple tissues in a subject, using a computer, A prediction unit that predicts the change in the aforementioned pixel value over time, It functions as a display control unit that includes multiple organizations and displays the predicted image on the display unit. The display control unit is a simulation program that controls the display unit to display the tissue in a color with a density corresponding to the predicted pixel value at a predetermined time.

6. A control method for a simulator that predicts the time-dependent changes in pixel values ​​of multiple tissues in a subject, wherein a computer controls To predict the change in the aforementioned pixel value over time, Multiple organizations are included and the predicted image is displayed on the display unit. A control method for controlling the display unit to display the tissue using the color of the density of the predicted pixel value at a predetermined time.

Images