Simulator, injection device or imaging system provided with 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 that predicts changes over time in pixel values ​​(CT values) of an image captured in tissue of a subject, an injection device or an imaging system that includes the simulator, and a simulation program. [Background technology]

[0002] Conventionally, there has been a method of imaging a patient's tissue as a subject using a CT (Computed Tomography) device, enhancing the tissue with a contrast agent injected into the blood vessels. A prediction method has also been known that predicts the level of contrast enhancement (pixel value) caused by a contrast agent based on the subject's physical characteristics and the contrast agent injection protocol. This prediction method predicts the degree of contrast enhancement in the subject's tissue as a function of the elapsed time from the start of the contrast agent injection (Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent No. 3553968 specification Summary of the Invention [Problem to be solved by the invention]

[0004] In the method described in Patent Document 1, the heart and blood vessels are assumed to be a single compartment. Other organs are also assumed to be a single compartment with intravascular and extracellular spaces. Predictions are then made under the assumption that the contrast agent diffuses throughout each compartment upon arrival. However, because the volume of each tissue varies in reality, the predicted results can differ significantly from the actual changes in pixel values. In particular, when the injection volume of contrast agent is small, the injection time of contrast agent is short, or the concentration of contrast agent is low, the diffusion rate within the actual tissue is slow. Therefore, the predicted results are likely to differ significantly from the actual changes in pixel values. [Means for solving the problem]

[0005] In order to solve the above problem, one example of a simulator of the present invention is a simulator that predicts changes in pixel values ​​of tissues over time based on information about a subject, a contrast agent injection protocol, and information about subject tissues, and displays each tissue on a display unit in a color with a density corresponding to its pixel value.The simulator includes a prediction unit that predicts changes in pixel values ​​over time, and a display control unit that displays a predicted image that resembles a horizontal cross section of a human body in the craniocaudal direction on the display unit, and the display control unit controls the display unit so that multiple tissues are included in the predicted image and so that the shading of each tissue is changed according to the changes in pixel values ​​over time.

[0006] Another example of an injection device according to the present invention includes an injection head that injects a contrast medium according to an injection protocol, and the simulator.

[0007] Moreover, an imaging system as another example of the present invention includes a medical imaging device that images a subject, and the simulator.

[0008] Another example of a simulation program of the present invention is a simulation program that causes a computer to predict changes in pixel values ​​over time in the tissue of a subject, and 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 an injection protocol for a contrast agent, a tissue information acquisition unit that acquires information about the tissue, and a prediction unit that predicts changes over time in pixel values ​​of each of a plurality of compartments obtained by dividing the tissue along the blood flow direction based on the information about the subject, the injection protocol, and the information about the tissue.

[0009] This allows for highly 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 injection amount of contrast agent is small, the injection time of contrast agent is short, or the concentration of contrast agent is low. Furthermore, the position of the contrast agent within each tissue can be predicted. Therefore, optimal injection conditions or imaging conditions can be predicted before actual injection, preventing failure in imaging of tissue.

[0010] Further features of the invention will become apparent from the following description of an embodiment thereof, given by way of example only and with reference to the accompanying drawings, in which: [Brief explanation of the drawings]

[0011] [Figure 1] FIG. 2 is a schematic block diagram of a simulator. [Figure 2] 1 is a diagram illustrating a plurality of compartments according to the first embodiment. [Figure 3] 1 is a diagram illustrating a blood flow model. [Figure 4] 1 is a table showing stomach, spleen, pancreas and intestinal parameters. [Figure 5] 10 is a predicted image displayed on the display unit of the simulator. [Figure 6] 10 is an example of an operation screen displayed on a display unit. [Figure 7]This is a predicted image displayed on the display unit of the simulator when helical scan is selected. [Figure 8] 1 is a schematic diagram of an injection device and an imaging system. [Figure 9] 10 is a diagram illustrating a plurality of compartments according to a second embodiment. [Figure 10] 10 is a flowchart illustrating an addition process. [Figure 11] 10 is a diagram illustrating a blood flow model relating to a modified form. [Figure 12] 10 is a graph showing time density curves for a modified form. DETAILED DESCRIPTION OF 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 depending on 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 Fig. 1, a simulator (perfusion simulator) 20 that predicts changes in pixel values ​​over time in the tissue of a subject includes a prediction unit 16. The prediction unit 16 predicts changes over time attributable to at least the contrast agent in pixel values ​​of each of a plurality of compartments obtained by dividing the tissue of the subject along the blood flow direction, based on information about the subject, an injection protocol, and information about the tissue. Note that pixel values ​​change due to the influence of saline, blood, and the like in addition to the contrast agent.

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

[0015] The subject information acquiring unit 11 may also acquire subject information from the storage unit 24 of the simulator 20 or an external storage device (server). Examples of such servers include a Radiology Information System (RIS), a Picture Archiving and Communication System (PACS), a Hospital Information System (HIS), an image inspection system, and an image creation workstation. The subject information acquiring unit 11 may also acquire subject information from the imaging device 3 (FIG. 8) or the injection device 2 (FIG. 8). The subject information may include lean body mass, circulating blood volume, subject number (subject ID), subject name, sex, date of birth, age, height, blood volume, blood flow velocity, body surface area, the subject's illness, side effect history, creatinine level, heart rate, cardiac output, etc.

[0016] Simulator 20 also includes protocol acquisition unit 12, which acquires a contrast agent injection protocol. Prediction unit 16 then acquires the injection protocol, such as the contrast agent injection rate (mL / sec) and contrast agent injection time (sec), from protocol acquisition unit 12. Here, protocol acquisition unit 12 acquires the injection protocol input by the operator via input unit 27. Note that the injection protocol may include information on injection conditions, such as the injection method, contrast agent injection site, injection amount, injection timing, contrast agent concentration, and injection pressure. Protocol acquisition unit 12 may also acquire the injection protocol from memory unit 24, an external storage device, or injection device 2.

[0017] In particular, the contrast agent injection site can be input on the injection setting screen of the simulator 20, and an upper limb vein is selected as the standard. Other injection sites that can be selected include the hepatic artery (CTHA: transcatheter arteriography), superior mesenteric artery (CTAP: transcatheter arteriography), right ventricle, ascending aorta, etc. The injection protocol may also include information such as a fixed contrast agent injection rate, whether or not a boost injection of contrast agent is performed, the injection rate of saline, the injection time of saline, whether or not to increase or decrease the injection rate, and the volume of the injection tube.

[0018] The simulator 20 also includes a tissue information acquisition unit 13 that acquires information about the tissue of the subject. The prediction unit 16 acquires tissue information from the tissue information acquisition unit 13, such as the number of compartments in the tissue (the number of divided compartments of blood vessels and organs), the volume of the tissue (volume of the vascular cavity), the volume of the capillaries, the volume of the extracellular fluid cavity, the blood flow rate per unit tissue (blood flow velocity), the seepage rate of the contrast agent in the tissue (capillary permeable surface area), the seepage back rate of the contrast agent in the tissue (capillary permeable surface area), and the inherent pixel value of the tissue.

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

[0020] Simulator 20 also includes a liquid medicine information acquisition unit 14 that acquires liquid medicine information related to the liquid medicine. Prediction unit 16 acquires liquid medicine information, such as contrast agent concentration (mgI / mL), contrast agent amount (mL), total iodine amount (mgI), and contrast agent half-life (contrast agent excretion rate), from liquid medicine information acquisition unit 14. Prediction unit 16 can also calculate the amount of iodine per kg of body weight (mgI / kg) from the total iodine amount and the subject's body weight. Here, liquid medicine information acquisition unit 14 acquires liquid medicine information input by an operator via input unit 27. The liquid medicine information may include product name, product ID, chemical classification, contained components, concentration, viscosity, expiration date, syringe capacity, syringe pressure resistance, cylinder inner diameter, piston stroke, lot number, etc.

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

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

[0023] Furthermore, the prediction unit 16 can acquire additional information, such as whether or not local perfusion (bolus delivery) 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 ( FIG. 6 ) showing the time-concentration curve (TDC curve) 43. Furthermore, if the operator selects to consider local perfusion, the prediction unit 16 considers the seepage of the contrast agent from capillaries into the extracellular fluid space within the tissue.

[0024] The prediction unit 16 then predicts the time-dependent change in pixel values ​​for each of a plurality of compartments obtained by dividing the tissue along the blood flow direction based on the subject information, the injection protocol, and the tissue information. The prediction unit 16 then stores the pixel values ​​of each compartment at each time in the memory unit 24 of the simulator 20 in association with each tissue.

[0025] The simulator 20 also includes a control unit 25 such as a CPU, and a memory unit 24 that stores the prediction results from the prediction unit 16 stores a control program and the like. The control unit 25 controls the simulator 20 in accordance with the control program stored in the memory unit 24. The control unit 25 also includes 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. The control unit 25 executes various processes in accordance with the control program implemented in the memory unit 24, thereby logically realizing each unit as a different function.

[0026] The storage unit 24 also stores a simulation program that causes a computer (control unit) to predict changes in pixel values ​​over time in the subject's tissue. 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 a contrast agent injection protocol, a tissue information acquisition unit 13 that acquires information about the tissue, and a prediction unit 16 that predicts changes over time in pixel values ​​for each of a plurality of compartments obtained by dividing the tissue along the blood flow direction based on the subject information, the injection protocol, and the tissue information. This simulation program can be stored in a computer-readable recording medium.

[0027] The storage unit 24 also includes a RAM (Random Access Memory) that is a system work memory for the operation of the control unit 25, a ROM (Read Only Memory) that stores programs or system software, a hard disk drive, etc. The control unit 25 can also control various processes according to programs stored in portable recording media such as a CD (Compact Disc), a DVD (Digital Versatile Disc), or a CF (Compact Flash) card, or in an external storage medium such as a server on the Internet.

[0028] Furthermore, the simulator 20 includes a display unit 26 that displays each tissue compartment in a color with a density corresponding to its pixel value. The display control unit 15 changes the shading of each tissue compartment displayed on the display unit 26 in accordance with changes in pixel value over time. To achieve this, the display control unit 15 reads the pixel value of the compartment at a predetermined time from the storage unit 24 and changes the shading of the compartment. The display unit 26 displays an operation screen such as an input screen. Various information such as the injection protocol, the input status of the device, the setting status, and the injection result may also be displayed.

[0029] 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. A keyboard or the like can be used as the input unit 27, but the input unit 27 can also serve as the display unit 26 by using a touch panel.

[0030] The simulator 20 described above can be mounted on an imaging system 100 including a medical imaging device 3, or an injection device 2 for injecting a contrast agent, as shown in Fig. 8 described below. The simulator 20 can also be mounted on an external computer connected by wire or wirelessly to the imaging device 3 or the injection device 2. Note that the imaging device 3 can be, for example, a magnetic resonance imaging (MRI) device, a computed tomography (CT) device, an angio imaging device, a positron emission tomography (PET) device, a single photon emission computed tomography (SPECT) device, a CT angiography device, an MR angiography device, an ultrasound diagnostic device, a vascular imaging device, or other various medical imaging devices, but this specification will be described with reference to a CT device.

[0031] Next, the prediction of the change in pixel value over time by the prediction unit 16 will be described with reference to Fig. 2. The upper part of Fig. 2 shows a schematic diagram in which a blood vessel A1, an organ A2, a blood vessel A3, and an 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 value of the entire tissue changes immediately after the contrast agent reaches each tissue. Therefore, regardless of the actual diffusion speed and position, the pixel value of the entire tissue is predicted to have changed.

[0032] That is, in organ A4 in FIG. 2, the contrast agent has just arrived, and in reality, there is no change in pixel values ​​in most parts of organ A4, but the pixel values ​​of the entire organ A4 are predicted to have increased (white). Also, in blood vessel A3, although almost no contrast agent has actually moved into organ A4, the pixel values ​​of the entire blood vessel A3 are predicted to have decreased (gray). Furthermore, in organ A2, although the contrast agent actually remains, the pixel values ​​of the entire organ A2 are predicted to have decreased (black). Therefore, it becomes impossible to accurately predict changes in pixel values ​​of tissues, especially when the diffusion rate within the actual tissue is slow.

[0033] 2 shows a schematic diagram according to the first embodiment. Blood vessel 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 in accordance with the number of divided compartments of the tissue acquired from the tissue information acquisition unit 13.

[0034] In this case, the prediction unit 16 makes a prediction for each compartment by dividing the volume of the tissue including the compartment to be predicted, the volume of the capillaries in the tissue, and the volume of the extracellular fluid space in the tissue by the number of divided compartments. For example, if the number of divided compartments is 15, the prediction unit 16 predicts the change in pixel value 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 pixel values ​​do not actually change in most parts, the pixel values ​​of only the compartment located on the blood vessel B3 side are predicted to have increased (white). Similarly, in blood vessel B3, since almost no contrast agent actually moved to organ B4, the pixel values ​​of only the compartment located on the organ B2 side are predicted to have decreased (gray). Furthermore, in organ B2, since contrast agent actually remains, the pixel values ​​of only the compartment located on the blood vessel B1 side are predicted to have decreased (black). This makes it possible to accurately predict changes in pixel values ​​of tissues even when the diffusion rate within the actual tissue is slow.

[0036] Next, a specific prediction of changes in pixel values ​​will be described with reference to Fig. 3. As shown in Fig. 3, the tissues of the subject in the first embodiment include the right ventricle, aorta, veins, arteries, brain (head), upper limbs, right coronary artery (myocardium dominated by the right coronary artery), anterior descending artery (myocardium dominated by the anterior descending artery), circumflex artery (myocardium dominated by the circumflex artery), lungs, liver, stomach, spleen, pancreas, intestinal tract, kidneys, lower limbs, left ventricle, ascending aorta, descending aorta, and abdominal aorta. A contrast medium injected through a vein in the upper limb travels to each organ via the right ventricle, lungs, left ventricle, and aorta (ascending aorta, descending aorta), and then reaches the right ventricle via a vein. The contrast medium injected into the body is then excreted from the body via the kidneys.

[0037] The prediction unit 16 predicts the temporal change in pixel values ​​of each tissue, starting from the right ventricle and proceeding upstream and downstream in the blood flow direction. That is, the prediction unit 16 first predicts 1-right ventricle, then predicts a second tissue group including 2-caval veins and 2-venes located upstream of the right ventricle in the blood flow direction, and 2-arteries located downstream of the right ventricle in the blood flow direction. The prediction unit 16 then predicts a third tissue group including veins, the brain, the upper limbs, the right coronary artery, the anterior descending artery, the circumflex artery, and the lungs; a fourth tissue group including arteries, veins, and the liver; a fifth tissue group including the left ventricle, the arteries, the ascending aorta, the descending aorta, the kidneys, and the lower limbs; a sixth tissue group including the abdominal aorta, the stomach, the spleen, the pancreas, the intestines, and the arteries; and a seventh tissue group including the arteries. In FIG. 3, the order of prediction is indicated by a hyphenated number before each tissue name.

[0038] The prediction unit 16 may predict the change in pixel value over time of each tissue, starting from the tissue closest to the injection site of the contrast agent toward the upstream and downstream 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. Thereafter, the prediction unit 16 predicts a group of tissues including an artery located upstream of the liver in the blood flow direction and a vein located downstream of the liver in the blood flow direction.

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

[0040]

number

[0041] Furthermore, in order to determine changes in pixel values ​​in tissues other than the right ventricle, left ventricle, and blood vessels, the prediction unit 16 takes into account the seepage speed of the contrast agent when it permeates from the capillaries to the extracellular fluid space and the seepage back speed when it permeates from the extracellular fluid space to the capillaries. For this purpose, the prediction unit 16 uses differential equations such as the following Equations 2 and 3. Note that the volume of the extracellular fluid space is Vec, the concentration of the contrast agent in the extracellular fluid space is Cec, the volume of the capillaries is Viv, the concentration of the contrast agent in the capillaries is Civ, the seepage speed is PS1, and the seepage back speed is PS2.

[0042]

number

[0043]

number

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

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

[0046] For the spleen, the following values ​​are used: tissue volume of 120 mL to 160 mL, capillary volume of 10 mL to 15 mL, extracellular fluid space volume of 45 mL to 65 mL, blood flow rate per unit tissue of 150 mL / min to 250 mL / min, seepage rate 15-25 of 15 to 25, and seepage return rate of 15 to 25.

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

[0048] 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 back rate of 150 to 250.

[0049] Here, the seepage rate and the return rate can be calculated by multiplying the capillary area by the permeability. For example, if the total area of ​​the capillaries in the human body is 800 m 2 Assuming that the permeability of all organs is 1 ml / min / g, the permeation rate and return rate can be calculated.

[0050] The prediction unit 16 sequentially stores the prediction results in the storage unit 24 (FIG. 1). The prediction results include information on pixel values ​​associated with tissues at each time point. The display unit 26 (FIG. 1) then displays a schematic predicted image of each tissue including 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 shading of each compartment according to the change in pixel values ​​over time.

[0051] FIG. 5 shows an example of changes in shading in a predicted image, corresponding to a horizontal cross section in the craniocaudal direction of the body. However, unlike an actual cross section, all tissues are shown so that they can be seen at a glance. A window width of 350 and a window level of 40 are set. The window width corresponds to the range of contrast of 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 it in black. If the pixel value is greater than the value obtained by adding half the window width value to the window level value, the display unit 26 displays it in white.

[0052] The upper part of Figure 5 shows an image N1 of each tissue immediately after the injection of contrast agent into the veins of the upper limb, with the veins of the upper limb shown in their natural pixel values ​​(dark gray). 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 natural pixel values ​​(dark gray). Next, the center of Figure 5 shows an image N2 of each tissue approximately 25 seconds after the start of injection, with the abdominal aorta, celiac artery, and internal jugular vein appearing particularly white. Meanwhile, the veins of the upper limb are shown in light gray, with pixel values ​​decreasing due to the contrast agent having flowed out early.

[0053] The bottom of Figure 5 shows image N3 of each tissue taken approximately 120 seconds after the start of injection. Because the contrast agent has diffused and distributed uniformly throughout the blood vessels and organs of the body, the pixel values ​​have decreased compared to image N2, and the image is shown in light gray overall.

[0054] Next, referring to FIG. 6, an example of an operation screen displayed on the display unit will be described. As shown on the right side of FIG. 6, when prediction by the prediction unit 16 is completed, the display control unit 15 reads out 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 a predicted image 41 and displays it on the display unit 26. For example, in FIG. 6, a time point of 9.90 seconds is selected, and the predicted image 41 at that time point is displayed. Note that, by default, the predicted image 41 at the time of injection, i.e., at 0 seconds, is displayed.

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

[0056] Operation buttons 42 are displayed below the predicted image 41. These operation buttons 42 include, from left to right in FIG. 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 the play button, the predicted image 41 is played back continuously as a moving image over time. This allows the operator to visually recognize the position of the contrast agent in each tissue at a desired time.

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

[0058] The operator can select tissues to display on the time-density curve 43 using display options 44 below the time-density curve 43. For example, in FIG. 6, the liver, portal vein, and hepatic artery are selected from among the brain, liver, portal vein, hepatic artery, and right ventricle. It is also possible to display the amount of contrast agent excreted from the body. Other selectable tissues include the upper limbs, lungs, left ventricle, myocardium, right coronary artery, anterior descending artery, circumflex artery, bronchus, spleen, intestine, kidney, 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 artery, upper limb vein, lower limb artery, lower limb vein, superior vena cava, and inferior vena cava.

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

[0060] Also, 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 (9.90 seconds in FIG. 6) corresponding to the predicted image 41. The current time bar 47 moves along the X-axis corresponding to the elapsed time as the predicted images 41 are continuously played back.

[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 FIG. 6, a bed movement speed of 8.0 cm / sec is input.

[0062] When the helical scan box 48 is selected, the display control unit 15 acquires the delay time for helical scanning. Here, the delay time corresponds to the elapsed time (the time it takes for the bed to move) from imaging the head until imaging each tissue, and is calculated based on the length from the top of the predicted image 41 to each tissue. Then, the display control unit 15 reads out the pixel values ​​at a time obtained by adding the delay time to a predetermined time from the storage unit 24. That is, the display control unit 15 reads out 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 FIG. 6, the head (brain) shows the pixel value at the time 9.90 seconds after the current time has elapsed, and the right ventricle shows the pixel value at the time 14.90 seconds after the current time has elapsed.

[0063] This makes it possible to obtain a predicted image when a helical scan is performed. For example, if the current time point is set to the time point immediately after the start of injection (0 seconds), the pixel value immediately after the start of injection can be displayed for the brain, the pixel value 5 seconds after the start of injection can be displayed for the right ventricle, and the pixel value 7.5 seconds after the start of injection can be displayed for the liver. The display control unit 15 may obtain the delay time by calculation. Alternatively, the delay time associated with the bed movement speed may be stored in the storage unit 24 in advance, and the display control unit 15 may obtain the delay time from the storage unit 24.

[0064] The predicted image obtained in this manner will be described with reference to Fig. 7. Fig. 7 shows the change in density of the predicted image as an example during helical scanning. Note that the image in Fig. 7 corresponds to a horizontal cross section in the craniocaudal direction of the body, but unlike an actual cross section, it shows all tissues. In addition, 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 the contrast agent was injected into the veins of the upper limb. However, unlike image N1 in Figure 5, the upper limb veins show pixel values ​​at the time when the delay time was added. Therefore, the veins of the upper limb are stained white with the contrast agent. The contrast agent has not yet reached the other blood vessels, and the other blood vessels are shown with their original pixel values ​​(dark gray).

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

[0067] The lower part of Figure 7 shows image H3 of each tissue taken approximately 120 seconds after the start of injection. As the contrast agent has diffused and distributed uniformly throughout the blood vessels and organs of the body, the pixel values ​​have decreased compared to image H2, and the entire image is shown in light gray.

[0068] Returning to the explanation of FIG. 6, an analysis button 49 is displayed below the helical scan box 48. When the operator selects the analysis button 49, the prediction unit 16 starts predicting pixel values. Note that the prediction unit 16 may start predicting pixel values ​​when it acquires the subject information, the injection protocol, and the tissue information.

[0069] Next, with reference to Fig. 8, an imaging system 100 including a simulator 20 will be described. The imaging system 100 includes an injection device 2 for injecting a contrast agent, and a medical imaging device 3 that is connected to the injection device 2 by wire or wirelessly and captures an image of a subject. The injection device 2 or the imaging device 3 includes the above-mentioned simulator 20.

[0070] Imaging device 3 has an imaging section 31 that images the subject according to an imaging plan, a control device 32 that controls the entire imaging device 3, and a display 33 as display section 26. Note that control device 32 and display 33 can also be configured integrally. Imaging device 3 is connected to injector 2 by wire or wirelessly, for example, via a gateway device (not shown).

[0071] The imaging plan of the imaging device 3 can include information such as the imaging region, 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 in accordance with the imaging plan. The control device 32 is also connected to a display 33, which displays the input status, setting status, imaging results, and various information of the device.

[0072] Imaging unit 31 includes a bed, an X-ray source that irradiates the subject (i.e., the subject) with X-rays, and an X-ray detector that detects the X-rays that have passed through the subject. Imaging unit 31 exposes the subject to X-rays and captures a fluoroscopic image of the subject by back-projecting the inside of the subject's body based on the X-rays that have passed through the subject. Imaging unit 31 may capture images using radio waves or ultrasound instead of X-rays. Control device 32 can communicate with imaging unit 31, injection device 2, etc., via wired or wireless communication.

[0073] Injector 2, which injects contrast media, injects medicinal liquids, such as various contrast media and physiological saline, filled in a syringe into the body of a subject. Injector 2 also includes injection head 21, which injects the contrast media according to an injection protocol. Injector 2 also includes stand 22, which holds injection head 21, and console 23, which is connected to injection head 21 by wire or wirelessly.

[0074] Console 23 functions as a control device that controls injection head 21 and also functions as simulator 20. Console 23 also includes a touch panel that functions as input unit 27 and display unit 26, and can communicate with injection head 21, imaging device 3, and the like via wired or wireless communication. Note that injection device 2 may also include a display as display unit 26 and a user interface such as a keyboard as input unit 27, instead of a touch panel.

[0075] Simulator 20, input unit 27, and display unit 26 can also be configured as separate units. For example, instead of console 23, injection device 2 may have a control device connected to injection head 21 and display unit 26 (e.g., a touch panel display) connected to the control device and displaying the injection status of the medicinal liquid. Such a control device also functions as simulator 20. Injection head 21 and the control device can also be configured integrally with stand 22. Alternatively, a hanging member can be provided instead of stand 22, and injection head 21 can be suspended from the ceiling via the hanging member.

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

[0077] Injection head 21 has first holder 214 on which a syringe filled with a contrast agent is mounted, and second holder 215 on which a syringe filled with physiological saline as a medicinal liquid for boosting the contrast agent is mounted. Injection head 21 also has a drive mechanism (not shown) that pushes out the medicinal liquid in the syringe mounted in first holder 214 in accordance with an injection protocol, and a drive mechanism (not shown) that pushes out the medicinal liquid in the syringe mounted in second holder 215 in accordance with the injection protocol.

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

[0079] Operation unit 212 is equipped with a drive mechanism forward button, a drive mechanism reverse button, a final confirmation button, and the like. When injecting a medicinal liquid, a mixing tube or the like is connected to the tip of a syringe mounted on injection head 21. When the operator completes preparations for injection, such as connecting the mixing tube, he or she presses the final confirmation button. This puts injection head 21 into a standby state ready to start injection.

[0080] The liquid medicine pushed out from the syringe is then injected into the body of the subject via a mixing tube or the like. This mixing tube functions as a mixer for mixing the contrast medium and the diluted liquid medicine. Examples of such mixers include "SPIRAL FLOW (registered trademark)" manufactured by Nemoto Kyorindo Co., Ltd.

[0081] Injection head 21 can be equipped with various syringes, such as pre-filled syringes with data carriers such as RFID chips, IC tags, and barcodes. Injection head 21 also incorporates a reading unit (not shown) that reads the data carriers attached to the syringes. This data carrier stores information about the medicinal liquid.

[0082] 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. Furthermore, imaging device 3 can receive information from the server and transmit information to the server. Test orders are stored in advance in this server. The test order includes subject information about the subject and test information about the test content. Furthermore, the server can store information about the imaging results, such as image data, transmitted from imaging device 3, and information about the injection results transmitted from injection device 2.

[0083] According to the 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. Furthermore, the imaging device 3 may change the imaging plan in accordance with the prediction result by the prediction unit 16. For example, when 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, tube current, or the like 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 depending on the prediction result by the prediction unit 16. For example, if the operator inputs a desired pixel value into 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 rate, injection time, etc. so that the predicted result matches the desired pixel value.

[0085] According to the invention of the first embodiment described above, highly accurate predictions can be made that approximate the time-dependent changes in pixel values ​​in actual tissues. In particular, highly accurate predictions can be made even when the injection amount of contrast agent is small, the injection time of contrast agent is short, or the concentration of contrast agent is low. Furthermore, the position of the contrast agent in each tissue can be predicted. Furthermore, the operator can visually recognize the position of the contrast agent in each tissue at a desired time.

[0086] Furthermore, according to the first embodiment of the present invention, it is possible to predict pixel values ​​of each tissue even if the injection protocol includes boost injection of contrast agent or an increase or decrease in injection rate. For example, it is possible to predict actual captured images even in the case of a so-called cross injection method, in which the injection rate of contrast agent is gradually decreased while the injection rate of saline is increased. Furthermore, when a new injection protocol or imaging plan is created, it is possible to predict actual captured images.

[0087] [Second embodiment] The second embodiment will be described with reference to FIG. 9. In the first embodiment, each tissue is divided into the same number of compartments. On the other hand, in the second embodiment, a tissue having a large volume is divided into a greater number of compartments. That is, the prediction unit 16 according to the second embodiment predicts the change over time in pixel values ​​in each of a plurality of compartments obtained by dividing a tissue having a small volume (first tissue), and also predicts the change over time in pixel values ​​in each of a plurality of compartments obtained by dividing a tissue having a large volume (second tissue) into a greater number of compartments than the tissue having a small volume.

[0088] In the description of the second embodiment, differences from the first embodiment will be described, and the components described in the first embodiment will be given the same reference numerals and their description will be omitted. Unless otherwise specified, the components given the same reference numerals perform substantially the same operations and functions, and their effects are also substantially the same.

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

[0090] Therefore, in the second embodiment, tissues with large volumes are divided into a greater number of compartments than tissues with small volumes. Specifically, in Fig. 9, organ B6 is divided into three compartments, and organ B8 is divided into 15 compartments. This makes the volumes of the compartments of organ B6 and organ B8 similar, allowing the operator to visually recognize accurately the position of the contrast agent within each tissue.

[0091] In the second embodiment, the optimal number of divided compartments according to the volume of each tissue is determined in advance and stored in the storage unit 24. Then, the tissue information acquisition unit 13 acquires the number of divided compartments from the storage unit 24, and the prediction unit 16 predicts a change in pixel value based on the acquired number of divided compartments.

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

[0093] Furthermore, according to the invention of the second embodiment, even if the predicted image 41 contains tissues with large volumes and tissues with small volumes, the operator can accurately visually recognize the position of the contrast agent in each tissue.

[0094] 9, blood vessels A5, A7, B5, and B7 are each divided into the same number of compartments, i.e., 15. However, blood vessels with large volumes may be divided into a greater number of compartments than blood vessels with small volumes. Furthermore, the number of compartments may be set so that the volumes of the compartments of tissue B8 with a large volume and tissue B6 with a small volume are approximately the same.

[0095] Although the present invention has been described above with reference to various embodiments, the present invention is not limited to the above-described embodiments. The present invention also includes inventions that have been modified without departing from the scope of the present invention, and inventions equivalent to the present invention. Furthermore, the above-described embodiments and modifications can be combined as appropriate without departing from the scope of 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 coronal cross sections. The number of compartments into which tissue is divided is not limited to 15, and any number equal to or greater than 2 can be selected.

[0097] Alternatively, noise information of pixel values ​​may be stored in advance in the storage unit 24, and the display control unit 15 may read the noise information from the storage unit 24 and add the noise information to the predicted image 41 of each compartment. An example of this noise information is an image showing radial noise that occurs between tissues stained white by a contrast agent. By adding an image showing the noise to the predicted image 41 in a superimposed manner, a predicted image 41 that more closely resembles the actual captured image can be obtained.

[0098] In each of the above embodiments, the display unit 26 arranges the compartments so that multiple tissues form a schematic diagram in which they are connected in the blood flow direction, and displays each compartment in a color with a density corresponding to the pixel value. However, the display unit 26 may arrange the compartments so that each tissue is displayed individually, and display each compartment in a color with a density corresponding to the pixel value. The display control unit 15 may also control the display unit 26 so that the number of compartments in each tissue is different. In this case, the display control unit 15 displays each tissue so that it includes the number of compartments set by the operator or the number of compartments previously stored in the memory unit 24. The display unit 26 may display each compartment in a color other than black and white.

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

[0100] 6, the display control unit 15 may display each tissue (compartment) in a color other than black and white shading (grayscale), for example, in a shade of blue, green, red, or yellow. Furthermore, the display control unit 15 may display a predetermined tissue in a shade of a color other than black and white. For example, when the liver, portal vein, and hepatic artery are selected in the display options 44, the display control unit 15 can display the liver, portal vein, and hepatic artery in the predicted image 41 in a shade of red, blue, and green, respectively.

[0101] [Transformation] The prediction unit 16 may take into account changes in the blood flow rate (blood flow velocity) per unit tissue due to the injection of the drug solution. That is, when the 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 of the injected drug solution. In particular, when the drug solution is injected at a rate faster than the normal blood flow velocity, the blood flow velocity increases in downstream tissues. Therefore, when the injection rate 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 subtracting the blood flow velocity from the injection rate and adding the difference to the blood flow velocity.

[0102] Therefore, the prediction unit 16 predicts the change over time in pixel values ​​based on the blood flow velocity obtained by the addition. That is, when predicting the change over time in pixel values ​​using the above-mentioned formula, the prediction unit 16 adds the obtained difference to the blood flow velocity Q per unit tissue in the compartment. Specifically, as shown in the flowchart of the addition process in FIG. 10, the prediction unit 16 acquires the normal blood flow velocity of the tissue corresponding to the injection site, i.e., the tissue into which the medicinal liquid is injected, as subject information from the subject information acquisition unit 11 (S101). The prediction unit 16 also acquires the infusion rate of the medicinal liquid included in the injection protocol received from the protocol acquisition unit 12 (S102). Note that the prediction unit 16 may acquire the blood flow velocity after the infusion rate. Alternatively, the prediction unit 16 may acquire the blood flow velocity by calculating it from the subject's weight instead of acquiring it from the subject information acquisition unit 11.

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

[0104] Here, the injection rate is the amount of medicinal liquid injected per unit time. Therefore, when contrast medium and saline are injected simultaneously, the prediction unit 16 performs addition based on the total injection rate of both. Also, when a boost injection of saline is performed at a rate faster than the normal blood flow rate, the prediction unit 16 performs addition based on the injection rate of the saline. After completing the addition process, the prediction unit 16 predicts the change in pixel value over time for each of multiple compartments obtained by dividing the tissue along the blood flow direction based on the subject information including the blood flow rate obtained by the addition, the injection protocol, and tissue information. This makes it possible to bring the blood flow rate closer to the actual rate and improve the prediction accuracy of pixel values.

[0105] Furthermore, in the addition process, the prediction unit 16 can add the difference to the blood flow velocity for all of the multiple tissues. However, the prediction unit 16 may also add the difference to only some of the multiple tissues. Specifically, the difference may be added to the blood flow velocity for only tissues from the tissue immediately downstream of the tissue corresponding to the injection site in the blood flow direction to the tissue corresponding to the injection site in the blood flow direction. For example, with reference to FIG. 11, an example of adding the difference to the blood flow velocity for only some of the multiple tissues when injecting a drug solution through an upper limb vein will be described. In FIG. 11, the right ventricle, artery, lung, vein, left ventricle, ascending aorta, artery, myocardium (myocardium dominated by the right coronary artery, myocardium dominated by the anterior descending artery, and myocardium dominated by the circumflex artery), and vein form a closed circuit originating from the right ventricle. In other words, these tissues form a closed circuit originating from the right ventricle, which is immediately downstream of the tissue corresponding to the injection site in the blood flow direction. Therefore, the prediction unit 16 targets only the tissues included in the closed circuit, from the right ventricle to the veins, and does not add the difference to the blood flow velocity of the tissues in the lower limbs, for example. This allows the effect of the addition to be limited to the closed circuit, simplifying the simulation and reducing the calculation time.

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

[0107] FIG. 12 is a graph showing time density curves that are prediction results obtained by performing addition processing according to a modified embodiment. FIG. 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 is set to 170 cm, the subject's weight is set to 70 kg, and the contrast agent concentration is set to 300 mgI / mL. FIG. 12 also shows the time density curves obtained 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 saline at an injection rate of 2.0 mL / sec for 15 seconds. The time density curve obtained when addition processing is performed is shown by a solid line, and the time density curve obtained when addition processing is not performed is shown by a dashed line.

[0108] When addition processing is not performed, the time it takes for the contrast agent to reach the tissue is delayed. Therefore, compared to when addition processing is performed, the contrast agent is simulated to arrive at a later timing. Therefore, in the time density curve shown by the dashed line, a second peak in pixel values ​​occurs, as indicated by arrow A in FIG. 12. On the other hand, when addition processing is performed, circulation of the contrast agent is simulated. Therefore, the second peak does not occur, as indicated by the time density curve shown by the solid line.

[0109] In this way, by performing the addition process, it is possible to predict pixel values ​​that are closer to the actual values. Furthermore, it is also possible to simulate a boost injection using saline. Specifically, the boost injection pushes out the contrast agent that has been stagnating after the end of the injection of the contrast agent at an earlier timing. Furthermore, in the time density curve shown by the solid line in FIG. 12, as a result of simulating the boost injection, the prediction accuracy of the pixel value at the peak is improved, and the pixel value becomes higher.

[0110] Furthermore, the prediction unit 16 may take into account the diffusion of the contrast agent between adjacent compartments. That is, when there is a difference in the concentration of the contrast agent between adjacent compartments, the contrast agent diffuses from the compartment with a higher concentration to the compartment with a lower concentration. Therefore, the prediction unit 16 may take into account the diffusion of the contrast agent by predicting pixel values ​​so as to reduce the contrast agent concentration in the compartment with a higher concentration and increase the contrast agent concentration in the compartment with a lower concentration.

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

[0112] The predictor 16 may calculate the contact area, and if the calculated contact area is large, may increase the amount of decrease or increase in the contrast agent concentration. Furthermore, each of the above-described modified embodiments may be appropriately combined with other embodiments or modified embodiments within the scope of the present invention.

[0113] This application claims priority from Japanese Patent Application No. 2014-240006, filed November 27, 2014, the entire contents of which are incorporated herein by reference. [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 display unit; a display control unit that controls the display unit to display a predicted image based on prediction of pixel values ​​that change over time in tissue of a subject, the predicted image of the tissue of the subject corresponding to the pixel values; the display control unit causes a time-density curve of a selected tissue selected by an operator from tissues of the subject for which the change in pixel value over time is to be predicted to be displayed on the display unit simultaneously with the predicted image.

2. a prediction unit that predicts a change in the pixel value over time in the tissue of the subject; a subject information acquisition unit that acquires information about the subject; a protocol acquisition unit that acquires an injection protocol of a contrast agent; further comprising an organization information acquisition unit that acquires information about the organization; the prediction unit predicts a change in the pixel value over time based on information about the subject, the injection protocol, and information about the tissue; The display device according to claim 1 , wherein the display control unit controls the display unit so as to display the predicted image in a color having a density according to the pixel value.

3. A display unit; a display control unit that controls the display unit to display a predicted image based on prediction of pixel values ​​that change over time in tissue of a subject, the predicted image of the tissue of the subject corresponding to the pixel values; The display device, wherein the display control unit controls the display unit so that the predicted image is played back as a continuous moving image along the elapsed time from the start of injection of a contrast agent.

4. The display device according to claim 1 , wherein the display control unit controls the display unit so that the predicted image at a time when a predetermined time has elapsed since the start of injection of a contrast agent is displayed.

5. The display device according to claim 1 , wherein the display control unit controls the display unit so as to change the shade of the predicted image in accordance with a change over time in the pixel values.

6. an injection head for injecting contrast media according to an injection protocol; An injection device comprising a display device according to any one of claims 1 to 5.

7. a medical imaging device for imaging a subject; An imaging system comprising: the display device according to claim 1 .

8. A control program for a display device including a computer and a display unit, The computer a display control unit that controls the display unit to display a predicted image based on prediction of pixel values ​​that change over time in tissue of a subject, the predicted image being a predicted image of the tissue of the subject according to the pixel values; the display control unit causes a time-density curve of a selected tissue selected by an operator from tissues of the subject for which the change in pixel value over time is to be predicted to be displayed on the display unit simultaneously with the predicted image.

9. A method for controlling a display device including a computer and a display unit, The computer, controlling the display unit to display a time-density curve of a selected tissue selected by an operator simultaneously with a predicted image of the tissue of the subject based on prediction of pixel values ​​that change over time in the tissue of the subject, the predicted image corresponding to the pixel values; A control method, wherein the selected tissue is selected from tissues of the subject for which the change in pixel value over time is to be predicted.

Images