Ultrasound diagnostic apparatus and recording medium

Abstract

Problem: To provide an ultrasound diagnostic apparatus that allows a user to easily perform correction work on a data group. According to the solution, the invention discloses ultrasonic diagnosis equipment. The ultrasound diagnostic apparatus performs: selecting a first contrast image 2114 at a first staining time period T1, a second contrast image 211a at an early stage T2 after the staining time period T1, from among a plurality of contrast images acquired from a cross section containing a target of a subject to which a contrast agent has been administered, and a third contrast image 2212 at a later stage T3 after the early stage T2; calculating a difference [Delta] 1 between the average brightness of the target and the average brightness of the liver parenchyma in the contrast image 2114, a difference [Delta] 2 between the average brightness of the target and the average brightness of the liver parenchyma in the contrast image 211a, and a difference [Delta] 3 between the average brightness of the target and the average brightness of the liver parenchyma in the contrast image 2212; and if the differences [Delta] 1, [Delta] 2, and [Delta] 3 are all positive (or all negative), generating a WO image based on variations in the average brightness of the target over time to determine whether the contrast agent has been expelled from the target.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to Japanese Patent Application No. 2023-101286 filed on June 20, 2023, the disclosure of which is incorporated herein by reference in its entirety. Technical Field

[0003] The present invention relates to an ultrasonic diagnostic apparatus for selecting a contrast image of a certain time period from a plurality of contrast images, and a storage medium storing an instruction for selecting a contrast image of a certain time period from a plurality of contrast images. Background Art

[0004] One known examination method using ultrasound is contrast-enhanced ultrasound, in which a contrast agent is injected into a subject to create a contrast image of the subject.

[0005] Citation List

[0006] Patent Literature

[0007] Patent Document 1: Japanese Patent Publication No. 7053910 Summary of the invention

[0008] Technical issues

[0009] During contrast-enhanced ultrasonography, a technique is known for creating an image (Wash-in Parametric Image) in which the time difference (arrival time difference of the contrast agent) from the reference time of contrast-enhanced ultrasonography until the brightness of each point in the ultrasound image exceeds a threshold value during the early stage is expressed by color. This technique is very important for target diagnosis because it allows the difference between the staining speed of a target (e.g., a tumor) and the staining speed of surrounding tissues in the early stage to be expressed in a single image. For example, typical liver tumors are stained earlier than liver parenchyma, so generating an early stage image is very useful for the diagnosis of typical liver tumors.

[0010] On the other hand, according to the identification guidelines for hepatocellular carcinoma, the diagnostic criteria for hepatocellular carcinoma diagnosis using contrast-enhanced ultrasound (CT / MRI LI-RADS) also define the timing of contrast agent outflow targets and target brightness value reduction (washout). For example, metastatic liver cancer is known to show a brightness value reduction compared to the liver parenchyma about 1 minute after contrast agent administration, and hepatocellular carcinoma (HCC) is known to have a brightness reduction compared to the liver parenchyma 5 minutes to 10 minutes after the administration of a shrinking agent. This suggests that not only the arrival time of the contrast agent, but also an image representing the timing brightness reduction of the target (wash-out parameter image) will also be useful.

[0011] However, in reality, if the blood flow state or liver function of the target (e.g., tumor) is poor, or if the ultrasound attenuation is large at a deep depth, it may be difficult to observe the difference between the brightness value of the tumor and the brightness value of the liver parenchyma. In such cases, it may be impossible to determine the timing when the brightness of the target decreases (washout).

[0012] Therefore, it is desirable to develop an ultrasonic diagnostic device that can determine the timing (washout) when the brightness value of a target (e.g., a tumor) decreases, even when it is difficult to observe the difference between the brightness value of the target and the brightness value of the substance, such as when the blood flow state or liver function of the tumor is poor, or when ultrasound attenuation is large at deep depth.

[0013] Means to solve the above problems

[0014] A first aspect of the present invention is an ultrasonic diagnostic device, comprising:

[0015] one or more processors,

[0016] The one or more processors perform:

[0017] selecting a first contrast image, a second contrast image, and a third contrast image from among a plurality of contrast images acquired from a cross section of an object including a subject to which a contrast agent has been administered, the first contrast image indicating a state in which the object is stained in a first time period, the second contrast image for determining whether the contrast agent has been discharged from the object in a second time period after the first time period, and the third contrast image for determining whether the contrast agent has been discharged from the object in a third time period after the second time period;

[0018] Identifying a first large / small relationship between a characteristic value of the target in the first contrast image and a characteristic value of a tissue compared with the target, a second large / small relationship between the characteristic value of the target in the second contrast image and the characteristic value of the tissue, and a third large / small relationship between the characteristic value of the target in the third contrast image and the characteristic value of the tissue; and

[0019] When the first large / small relationship, the second large / small relationship, and the third large / small relationship all indicate that the characteristic value of the target is greater than the characteristic value of the tissue, or all indicate that the characteristic value of the target is less than the characteristic value of the tissue, based on the change of the characteristic value of the target over time, an image is generated to determine whether the contrast agent has been discharged from the target in the second time period and the third time period.

[0020] A second aspect of the present invention is a storage medium, which is one or more non-transitory computer-readable recording media storing one or more instructions executable by one or more processors, wherein the one or more instructions cause the one or more processors to execute:

[0021] selecting a first contrast image, a second contrast image, and a third contrast image from among a plurality of contrast images acquired from a cross section of an object including a subject to which a contrast agent has been administered, the first contrast image indicating a state in which the object is stained in a first time period, the second contrast image for determining whether the contrast agent has been discharged from the object in a second time period after the first time period, and the third contrast image for determining whether the contrast agent has been discharged from the object in a third time period after the second time period;

[0022] Identifying a first large / small relationship between a characteristic value of the target in the first contrast image and a characteristic value of a tissue compared with the target, a second large / small relationship between the characteristic value of the target in the second contrast image and the characteristic value of the tissue, and a third large / small relationship between the characteristic value of the target in the third contrast image and the characteristic value of the tissue; and

[0023] When the first large / small relationship, the second large / small relationship, and the third large / small relationship all indicate that the characteristic value of the target is greater than the characteristic value of the tissue, or all indicate that the characteristic value of the target is less than the characteristic value of the tissue, based on the change of the characteristic value of the target over time, an image is generated to determine whether the contrast agent has been discharged from the target in the second time period and the third time period.

[0024] Effects of the Invention

[0025] For each time stage, the present invention specifies the larger / smaller relationship between the characteristic value of the target and the characteristic value of the tissue to be compared with the target, and determines whether the specified larger / smaller relationship indicates that the characteristic value of the target is larger (or smaller) than the characteristic value of the tissue. Therefore, even if it is difficult to observe the difference between the brightness of the target and the brightness of the tissue to be compared, the time when the contrast agent is discharged from the target can be determined. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 is a diagram showing that a subject is being scanned using the ultrasonic diagnostic apparatus 1 according to an embodiment of the present invention.

[0027] Figure 2 is a block diagram of the ultrasonic diagnostic apparatus 1 .

[0028] Figure 3 It is a diagram illustrating the timeline of obtaining comparison images.

[0029] Figure 4 is based on Figure 3 The timeline shown is an explanatory diagram of the acquired data.

[0030] Figure 5 A diagram depicting a flow chart.

[0031] Figure 6 This is an explanatory diagram of the initial screen for selecting a comparison image.

[0032] Figure 7 is a diagram depicting the display unit after the image group 21 has been selected.

[0033] Figure 8 This is a diagram for explaining a method of allocating regions of interest.

[0034] Fig. 9 is a diagram depicting a state in which regions of interest 51 and 52 are assigned to tissues to be compared with a target.

[0035] Fig.10 This is a diagram for explaining a method of selecting a comparison image at a later stage.

[0036] Fig.11 This is a diagram for explaining a method of allocating regions of interest.

[0037] Fig.12 Described in chronological order Fig. 9 The depicted data sets 61 and 62 and Fig.11 A graph comparing data sets 63 and 64 is depicted.

[0038] Fig.13 This is a diagram for explaining the difference calculation method.

[0039] Fig.14 It is an explanatory diagram of the flowchart of creating a WO image in step ST5.

[0040] Fig.15 is a diagram depicting a comparison image 200 displayed on a display unit.

[0041] Fig.16 4 is an explanatory diagram of the region of interest 45 .

[0042] Fig.17 4 is an explanatory diagram of a procedure for determining a color to be assigned to a pixel P1 in the region of interest 45 .

[0043] Fig.18 This is an illustration of color image 81.

[0044] Fig.19 is a diagram depicting three regions 82 , 83 , and 84 of the color map 81 .

[0045] Fig. 20 This is an explanatory diagram depicting the case where the color is determined by the difference ΔE=B22-B221 and the difference ΔL=B32-B331.

[0046] Fig.21 4 is an explanatory diagram when determining the color to be assigned to the pixel P2 in the region of interest 45.

[0047] Fig. 22 This is a diagram depicting the case where color is determined by the difference ΔE=B22-B222 and the difference ΔL=B32-B332.

[0048] Fig.23 is a schematic diagram depicting a WO image.

[0049] Fig.24 This is an explanatory diagram of step ST1.

[0050] Fig.25 This is a diagram for explaining the difference calculation method.

[0051] Fig.26 is a diagram depicting the region of interest 45 allocated in the comparison image 200 .

[0052] Fig. 27 This is a diagram for explaining the method of determining the assigned color.

[0053] Fig.28 1 is an explanatory diagram when the color to be assigned to the pixel Q1 is determined based on the two difference values ​​ΔE (= B13 - B23) and ΔL (= B13 - B33).

[0054] Fig.29 2 is an explanatory diagram of colors assigned to pixels Q2 and Q3.

[0055] Fig.30 is a schematic diagram depicting a WO image. DETAILED DESCRIPTION

[0056] Embodiments for carrying out the present invention will be described below; however, the present invention is not limited to the following embodiments.

[0057] Figure 1 is a diagram showing a subject being scanned using the ultrasonic diagnostic apparatus 1 according to an embodiment of the present invention, and Figure 2 is a block diagram of the ultrasonic diagnostic apparatus 1 .

[0058] The ultrasonic diagnostic apparatus 1 has an ultrasonic probe 2 , a transmit beamformer 3 , a transmitting device 4 , a receiving device 5 , a receive beamformer 6 , a processor 7 , a display unit 8 , a memory 9 , and a user interface 10 .

[0059] The ultrasonic probe 2 has a plurality of vibrating elements 2a arranged in an array. The transmission beamformer 3 and the transmitter 4 drive the plurality of vibrating elements 2a arranged in the ultrasonic probe 2, and transmit ultrasonic waves from the vibrating elements 2a. The ultrasonic waves transmitted from the vibrating elements 2a are reflected in the body of the subject 57 (see Figure 1 ), and the reflected echo is received by the vibration element 2a. The vibration element 2a converts the received echo into an electrical signal, and outputs the electrical signal as an echo signal to the receiver 5. The receiver 5 performs prescribed processing on the echo signal, and outputs the echo signal to the reception beamformer 6. The reception beamformer 6 performs reception beamforming on the signal received by the receiver 5, and outputs echo data.

[0060] The receive beamformer 6 may be a hardware beamformer or a software beamformer. If the receive beamformer 6 is a software beamformer, the receive beamformer 6 may include one or more processors, including one or more of the following: i) a graphics processing unit (GPU); ii) a microprocessor; iii) a central processing unit (CPU); iv) a digital signal processor (DSP); v) another type of processor capable of performing logical operations. The processor configuring the receive beamformer 6 may be configured by a processor different from the processor 7, or may be configured by the processor 7.

[0061] The ultrasound probe 2 may include circuits for performing all or part of transmit beamforming and / or receive beamforming. For example, all or part of the transmit beamformer 3, the transmitter 4, the receiver 5, and the receive beamformer 6 may be provided in the ultrasound probe 2.

[0062] The processor 7 controls the transmit beamformer 3, the transmitter 4, the receiver 5, and the receive beamformer 6. In addition, the processor 7 is in electronic communication with the ultrasound probe 2. The processor 7 controls which element of the vibration element 2a is active and the shape of the ultrasound beam emitted from the ultrasound probe 2. The processor 7 is in electronic communication with the display unit 8. The processor 7 can process the echo data to generate an ultrasound image. The term "electronic communication" can be defined as including both wired communication and wireless communication. According to one embodiment, the processor 7 may include a central processing unit (CPU). According to another embodiment, the processor 7 may include another electronic component that can perform processing functions, such as a digital signal processor, a field programmable gate array (FPGA), a graphics processing unit (GPU), another type of processor, etc. According to another embodiment, the processor 7 may include multiple electronic components capable of performing processing functions. For example, the processor 7 may include two or more electronic components selected from a list of electronic components, which include: a central processing unit, a digital signal processor, a field programmable gate array, and a graphics processing unit.

[0063] The processor 7 may also include a complex demodulator (not shown in the drawings) to demodulate the RF data. In another embodiment, the demodulation may be performed at an earlier step in the processing chain.

[0064] In addition, the processor 7 can generate various ultrasound images (e.g., B-mode images, color Doppler images, M-mode images, color M-mode images, spectral Doppler images, elastic imaging images, TVI images, strain images, and strain rate images) based on the data obtained by processing through the receive beamformer 6. In addition, one or more modules can generate these ultrasound images.

[0065] Image beams and / or image frames may be saved, and timing information indicating when data is retrieved into memory may be recorded. These modules may include, for example, a scan conversion module for performing a scan conversion operation to convert image frames from beam space coordinates to display space coordinates. A video processor module may also be provided for reading image frames from memory while the procedure is being performed on the subject, and displaying the image frames in real time. The video processor module may store image frames in an image memory, and may read ultrasound images from the image memory and display them on the display 8.

[0066] In this specification, the term "image" may broadly indicate both a visible image and data representing the visible image. In addition, the term "data" may include raw data, which is ultrasound data before a scan conversion operation, and image data, which is data after the scan conversion operation.

[0067] Note that the above-mentioned processing tasks handled by processor 7 may be performed by a plurality of processors.

[0068] Furthermore, when the reception beamformer 6 is a software beamformer, the processing performed by the beamformer may be performed by a single processor, or may be performed by a plurality of processors.

[0069] The display unit 8 is an LED (light emitting diode) display, an LCD (liquid crystal display), an organic EL (electroluminescence) display, etc. The display unit 8 may be configured by a single display, or may include two or more displays. In addition, the display unit 8 may also include a touch panel.

[0070] The memory 9 is any known data storage medium. In one example, the ultrasonic diagnostic device includes a non-transitory storage medium and a temporary storage medium. In addition, the ultrasonic diagnostic device may also include multiple memories. The non-transitory storage medium may be, for example, a non-volatile storage medium, such as a hard disk drive (HDD) drive, a read-only memory (ROM), etc. The non-transitory storage medium may include a portable storage medium, such as a CD (Compact Disc) and a DVD (Digital Versatile Disc). The program executed by the processor 7 is stored in the non-transitory storage medium. The temporary storage medium is a volatile storage medium, such as a random access memory (RAM).

[0071] The memory 9 stores one or more commands that can be executed by the processor 7. The one or more commands cause the processor 7 to perform predetermined operations.

[0072] Note that the processor 7 may also be configured to be connected to an external storage device via a wired connection or a wireless connection. In this case, the commands executed by the processor 7 may be distributed to both the memory 9 and the external storage device for storage.

[0073] The user interface 10 can accept input from the operator 56. For example, the user interface 10 accepts command and information input from the operator 56. The user interface 10 includes a touch panel and an operation panel. The operation panel can be configured to include a keyboard, hard keys, a trackball, a rotary controller, etc. The touch panel can also display soft keys, buttons, etc.

[0074] The ultrasonic diagnostic apparatus 1 is configured as described above.

[0075] Next, the operation of the ultrasonic diagnostic apparatus 1 in this example will be described. In this embodiment, an example will be described in which the ultrasonic diagnostic apparatus 1 operates to acquire data of a contrast image of a subject and creates an image indicating the timing when a contrast agent is discharged from a target based on the acquired contrast image data.

[0076] First, a method for acquiring data of a comparison image will be described. Specifically, the comparison image data is acquired as follows.

[0077] Figure 3 It is a diagram illustrating the timeline of obtaining comparison images.

[0078] The operator 56 injects a contrast agent into the body of the subject 57 and acquires a contrast image. A contrast agent containing microbubbles as an active ingredient can be used as the contrast agent. The operator 56 operates the probe 2 to acquire a contrast image at the examination site starting from time point t11 ​​(a time point when the contrast agent is injected or a time point close to the time point when the contrast agent is injected).

[0079] The processor 7 controls the ultrasonic probe 2 to transmit ultrasonic waves to the subject 57. The ultrasonic probe 2 receives ultrasonic waves (echoes) reflected by the contrast agent. The processor 7 performs predetermined processing on the echo signal and creates data indicating the signal strength of the ultrasonic waves reflected by the contrast agent. The data indicating the signal strength of the ultrasonic waves reflected by the contrast agent can be obtained as contrast image data.

[0080] The operator 56 intermittently acquires the contrast image data in a predetermined time period from the time point t11. For example, the operator 56 intermittently acquires the contrast image in the period from the time point t11 ​​to the time point tn2. The time length from the time point t11 ​​to the time point tn2 can be set to, for example, approximately 15 minutes.

[0081] During the period from time t11 to time tn2, the operator 56 does not continuously perform ultrasonic scanning and image recording, but performs data collection at a predetermined time. In the present embodiment, data acquisition of the subject 57 is continuously performed during the period SC1 (time point t11 ​​to t12), followed by data acquisition SC2 to SCn performed for several seconds every time a certain period of time passes. In the present embodiment, according to Figure 3 The timeline shown is used to scan the subject 57.

[0082] Figure 4 FIG. 3 is an explanatory diagram of data acquired according to the time line shown in FIG.

[0083] First, the operator 56 applies a contrast agent, performs scanning continuously during a period SC1 (between time points t11 and t12), and acquires data of an image group 21 indicating a plurality of ultrasound images. The time length from time point t11 ​​to t12 is represented by "TS1". In the present embodiment, the time length TS1 is 60 seconds, but the time length TS1 may be shorter or longer than 60 seconds. Between time points t11 and t12, the processor 7 controls the ultrasound probe 2 to transmit ultrasound to the subject 57. The ultrasound probe 2 receives ultrasound waves (echoes) reflected by the contrast agent. The processor 7 performs a predetermined process on the echo signal and creates an ultrasound image indicating the signal intensity of the ultrasound waves reflected by the contrast agent. The ultrasound image may be, for example, a cross-sectional image of the liver of the subject 57. The processor 7 stores the acquired data of the image group 21 in the memory 9. In the present embodiment, two types of ultrasound images are created as ultrasound images. One is a contrast image in which a nonlinear signal included in an echo signal reflected by a contrast agent is enhanced, and the other is a B-mode image. Therefore, the image group 21 includes a contrast image set 211 including a plurality of contrast images arranged in time series and a B-mode image set 212 including a plurality of B-mode images arranged in time series.

[0084] After acquiring data from time point t11 ​​to t12, the operator 56 interrupts the data acquisition and waits until time point t21 when the next data acquisition starts after the contrast agent is injected. In addition, the operator 56 acquires data during the time period SC2 (between time points t21 and t22). The time length of the time period SC2 can be, for example, 5 seconds. The processor 7 performs predetermined processing on the echo signal to generate an image group 22. The image group 22 includes a contrast image set 221 and a B-mode image set 222, the contrast image set including a plurality of contrast images arranged in a time series, and the B-mode image set including a plurality of B-mode images arranged in a time series. The data of the image group 22 is stored in the memory 9.

[0085] After acquiring data from time point t21 to t22, the operator 56 interrupts the data acquisition and waits until time point t31 when the next data acquisition starts after the contrast agent is injected. In addition, the operator 56 acquires data between time points t31 and t32 (time length TS3). The time length TS3 can be, for example, 5 seconds. The processor 7 performs predetermined processing on the echo signal to generate an image group 23. The image group 23 includes a contrast image set 231 and a B-mode image set 232, the contrast image set including a plurality of contrast images arranged in a time series, and the B-mode image set including a plurality of B-mode images arranged in a time series. The data of the image group 23 is stored in the memory 9.

[0086] Subsequently, the step of acquiring data after waiting for a certain period of time is repeatedly performed.

[0087] Finally, the operator 56 acquires information during the period from time tn1 to time tn2 (time length TSn). The processor 7 performs predetermined processing on the echo signal to generate an image group 2n. The image group 2n includes a contrast image set 2n1 and a B-mode image set 2n2, the contrast image set including a plurality of contrast images arranged in time series, and the B-mode image set including a plurality of B-mode images arranged in time series. The data of the image group 2n is stored in the memory 9.

[0088] In this way, examination data on the subject 57 is acquired.

[0089] The data obtained by scanning the subject 57 is stored in the memory 9 .

[0090] Next, the operator 56 creates an image (hereinafter referred to as a "WO image") based on the data acquired from the subject 57 to determine whether the contrast agent has been discharged (washed out) from the target in the early stage and the late stage. Figure 5 The method for creating a WO image is described with the flowchart in FIG.

[0091] In step ST1, a contrast image of a time stage necessary for creating a WO image is selected from among a plurality of contrast images acquired during examination of a subject. In this embodiment, the following three time stages are considered necessary for creating a WO image.

[0092] (1) A time phase indicating a state in which a target (eg, a tumor) is stained with a contrast agent (hereinafter referred to as a "staining time phase")

[0093] (2) Early stage, and

[0094] (3) Late stage

[0095] The user selects a contrast image of a staining time stage, a contrast image of an early stage, and a contrast image of a late stage, and the mean brightness of the target and the mean brightness of the liver parenchyma at each time stage are calculated based on the selected contrast images.

[0096] The following describes how to select contrast images of the staining time phase, early phase contrast images, and late phase contrast images, and how to calculate the average brightness of the target and liver parenchyma.

[0097] First, an initial screen for selecting a comparison image is presented to the user.

[0098] Figure 6 This is an explanatory diagram of the initial screen for selecting a comparison image.

[0099] A thumbnail area 20 is depicted on the screen of the display unit 8. The thumbnail area 20 presents n thumbnails 121 to 12n. The n thumbnail images 121 to 12n corresponding to the n image groups 21 to 2n acquired by scanning the subject 57 are displayed. Figure 6 , thumbnails 121 , 122 , 123 , and 12n of four image groups 21 , 22 , 23 , and 2n from among the image groups 21 to 2n are depicted.

[0100] Herein, a method of selecting a contrast image of a staining time stage, a contrast image of an early stage, and a contrast image of a late stage is described.

[0101] The staining time phase is a time phase in which the subject (e.g., a tumor) is stained with a contrast agent, and is a time phase that occurs between the time when the contrast agent is applied and the time after about 1 minute has passed. On the other hand, in the case of the present embodiment, in the image groups 21 to 2n acquired by examining the subject, the first image group 21 represents images acquired after the contrast agent is applied until about 1 minute has passed. Therefore, the contrast images of the staining time phase are considered to be included in the image group 21. Next, the user then selects the image group 21 from the thumbnail area 20.

[0102] Figure 7 is a diagram depicting the display unit after the image group 21 has been selected.

[0103] The user operates the user interface 10 to input a command to select the image group 21. When the user interface 10 accepts the user input to select the image group 21, the processor selects the image group 21. Figure 7 , a schematic diagram of images included in the image group 21 is depicted for reference on the display unit 8. The image group 21 includes a contrast image set 211 and a B-mode image set 212. The contrast image set 211 contains contrast images 2111 to 211a, and the B-mode image set 212 contains B-mode images 2121 to 212a.

[0104] When the user selects the image group 21, images from the selected image group 21 are played back in the image display area 40. In the upper half of the image display area 40, the contrast images 2111 to 211a of the contrast image set 211 are reproduced, and in the lower half, the B-mode images 2121 to 212a of the B-mode image set 212 are reproduced. Thus, the user can view images acquired between time points t11 and t12 (see Figure 4 ) to confirm it as a video.

[0105] Referring to the video of the contrast image set 211 (and the B-mode image set 212), the user selects a contrast image from the contrast images 2111 to 211a in the contrast image set 211, starting from the time stage when the target (e.g., tumor) is sufficiently stained with the contrast agent. For example, if the user determines that the contrast image 2114 is suitable as the contrast image of the time stage when the target (e.g., tumor) is sufficiently stained with the contrast agent among the contrast images 2111 to 211a, the user inputs an instruction to select the contrast image 2114 from the contrast images 2111 to 211a by operating the user interface 10. When the user interface 10 accepts the user input of selecting the contrast image 2114, the processor selects the contrast image 2114 as the image of the staining time stage.

[0106] After selecting the contrast image 2114 of the staining time stage, the user selects the contrast image of the early stage. The early stage contrast image is a contrast image selected to determine whether the contrast agent has been discharged from the target in the early stage. The early stage is a time stage when, for example, about 1 minute has passed since the contrast agent was applied. Since the contrast images 2111 to 211a are images acquired between the time of contrast agent application and 1 minute has passed, the user can find the contrast images 2111 to 211a in the early stage. For example, if the user determines that among the contrast images 2111 to 211a, the contrast image 211a is suitable as an early stage contrast image, the user operates the user interface 10 and inputs an instruction to select the contrast image 211a as the early stage contrast image. When the user interface 10 accepts the user input of selecting the contrast image 211a as the early stage contrast image, the processor selects the contrast image 211a as the early stage contrast image.

[0107] In this way, the user can select the comparison image 2114 of the staining time stage and the comparison image 211 a of the early stage from the image group 21 .

[0108] Next, the user operates the user interface 10 to assign the region of interest on the comparison images 2111 to 211 a . Figure 8 is an explanatory diagram of a method for allocating regions of interest.

[0109] The user assigns a region of interest to a target (e.g., a tumor, a lesion) at the examination site while referring to the image set 211 (and 212) displayed on the image display area 40. Specifically, the user refers to the image sets 211 and 212 to identify the location of the target, and enters an instruction to assign the region of interest to the target location in the image sets 211 and 212. When the user interface 10 receives an input from the user to assign a region of interest, the processor 7 displays a graphic figure representing the regions of interest 41 and 42 on the image sets 211 and 212. Figure 8, the regions of interest 41 and 42 are indicated by circles. The position of the region of interest 42 in the image set 212 (B-mode image) corresponds to the position of the region of interest 41 in the image set 211 (contrast image). The user can move the regions of interest 41 and 42 simultaneously by operating the user interface 10.

[0110] Once the region of interest 41 has been assigned, the processor 7 calculates a feature value in the region of interest 41 relative to the contrast images 2111 to 211a of the image set 211. The feature value in the region of interest 41 may be calculated as, for example, a value representing brightness information in the region of interest 41. In one example, the feature value in the region of interest 41 may be calculated as an average value (average brightness) or a standard deviation of the brightness of a plurality of pixels included in the region of interest 41. In the following, for ease of description, the feature value of the region of interest is set to the average brightness. In this way, the feature value in the region of interest 41 may be calculated for each contrast image 2111 to 211a in the image set 211.

[0111] Once the characteristic value (mean brightness) of the region of interest is calculated, the processor displays a data set 61 in the TIC area 60, which represents the time variation of the mean brightness of the region of interest 41 calculated for the contrast images 2111 to 211a. The horizontal axis of the data set 61 represents time, while the vertical axis represents the mean brightness in the region of interest. Figure 8 In , each data point included in the data set 61 is indicated by a black square. Figure 8 , in order to make it easier to visually grasp the data group 61, the total number of data included in the data group 61 is shown as being smaller than the actual total number of data. Each data in the data group 61 represents the average brightness of the region of interest 41 in the contrast images 2111 to 211a of the image set 211. For example, the data D11 represents the average brightness B11 of the target in the region of interest 41 in the contrast image 2114 of the staining time stage T1, and the data D21 represents the average brightness B21 of the target in the region of interest 41 in the contrast image 211a of the early stage T2.

[0112] Additionally, the user assigns regions of interest to the target tissues to be compared. Fig. 9 : is a diagram depicting a state where regions of interest 51 and 52 are assigned to tissue to be compared with a target. In this embodiment, the tissue to be compared with the target is the liver parenchyma, and an example is considered in which the regions of interest 51 (and 52) are assigned to the liver parenchyma, but regions of interest may be assigned to tissue other than the liver parenchyma. After the regions of interest 51 and 52 have been assigned, the average brightness in the region of interest 51 is calculated, and a data group 62 representing the temporal variation of the average brightness in the region of interest 51 is displayed. Fig. 9, the data group 62 for the target comparison tissue (liver parenchyma) is indicated by a white circle. Each data in the data group 62 represents the average brightness of the region of interest 51 in the contrast images 2111 to 211a of the image set 211. For example, the data D12 represents the average brightness B12 of the liver parenchyma in the region of interest 51 in the contrast image 2114 of the staining time stage T1, and the data D22 represents the average brightness B22 of the liver parenchyma in the region of interest 51 in the contrast image 211a of the early stage T2.

[0113] The data D11 and D12 of the staining time stage T1 and the data D21 and D22 of the early stage T2 can be obtained as described above.

[0114] Finally, the user selects a later stage to compare the images.

[0115] Fig.10 This is a diagram for explaining a method of selecting a comparison image at a later stage.

[0116] The late phase contrast images are contrast images selected to determine whether the contrast agent has been discharged from the target in the late phase. Contrast images in the late phase generally appear between 5 minutes and 10 minutes after the contrast agent is administered. Therefore, the user selects image groups 21 to 2n collected between 5 minutes and 10 minutes after the contrast agent is administered. It can be assumed that the user selects image group 22 collected 5 minutes after the contrast agent is administered. Thus, the user operates the user interface 10 to input a command to select image group 22 including contrast images of the late phase from among the image groups 21 to 2n. When the user interface 10 accepts the user input of selecting image group 22 from among the plurality of image groups 21 to 2n, the processor selects image group 22. Note that in Fig.10 , a schematic diagram of images included in the image group 22 is depicted for reference on the display unit 8. The image group 22 includes a contrast image set 221 and a B-mode image set 222. The contrast image set 221 contains contrast images 2211 to 221b, and the B-mode image set 222 contains B-mode images 2221 to 222b.

[0117] When the user selects an image group 22, images from the selected image group 22 are played back in the image display area 40. In the upper half of the image display area 40, contrast images 2211 to 221b of the contrast image set 221 are reproduced, and in the lower half, B-mode images 2221 to 222b of the B-mode image set 222 are reproduced. Thus, the user can view images acquired between time points t21 and t22 (see Figure 4 ) to confirm it as a video.

[0118] The user selects a later stage contrast image from contrast images 2211 to 221b in contrast image set 221 while referring to the video of contrast image set 221 (and B-mode image set 222). For example, if the user determines that contrast image 2212 is suitable as a later stage contrast image among contrast images 2211 to 221b, the user operates user interface 10 and inputs an instruction to select contrast image 2212 from among contrast images 2211 to 221b. When user interface 10 accepts the user input of selecting contrast image 2212, the processor selects contrast image 2212 as a later stage contrast image.

[0119] Next, the user operates the user interface 10 to assign regions of interest on the comparison images 221 1 to 221 b . Fig.11 is an explanatory diagram of a method for allocating regions of interest.

[0120] The user assigns a region of interest to a target (e.g., a tumor, a lesion) at the examination site while referring to the image set 221 (and 222) displayed on the image display area 40. Specifically, the user refers to the image sets 221 and 222 to identify the location of the target, and enters an instruction to assign the region of interest to the target location in the image sets 221 and 222. When the user interface 10 receives the input of assigning the region of interest from the user, the processor 7 displays a drawing graphic representing the regions of interest 43 and 44 on the image sets 221 and 222. Fig.11 , the regions of interest 43 and 44 are indicated by circles. The position of the region of interest 44 in the image set 222 (B-mode image) corresponds to the position of the region of interest 43 in the image set 221 (contrast image). The user can move the regions of interest 43 and 44 simultaneously by operating the user interface 10.

[0121] Once the region of interest 43 has been assigned, the processor 7 calculates a feature value in the region of interest 43 with respect to the contrast images 2211 to 221b of the image set 221. The feature value in the region of interest 43 may be calculated as, for example, a value representing brightness information in the region of interest 43. It may be assumed that the feature value in the region of interest 43 is an average value (average brightness) of brightness of a plurality of pixels in the region of interest 43. Therefore, the average brightness in the region of interest 43 may be calculated for each contrast image 2211 to 221b in the image set 221.

[0122] Once the average brightness in the region of interest 43 is calculated, the processor displays the average brightness in the region of interest 43 calculated for each of the contrast images 2211 to 221b in the image set 221, as shown in FIG. Fig.11The TIC region 60 depicts a data set 63 representing the temporal variation of the average brightness of the region of interest 43 calculated for the contrast images 2211 to 221b. Fig.11 In , each data point included in the data set 63 is indicated by a black square. Fig.11 In order to make it easier to visually grasp the data group 63, the total number of data included in the data group 63 is shown to be smaller than the actual total number of data. Each data in the data group 63 represents a feature value of the region of interest 43 in the contrast images 2211 to 221b of the image set 221. For example, data D31 represents the average brightness B31 of the target in the region of interest 43 in the contrast image 2212 of the later stage T3.

[0123] Additionally, the user assigns regions of interest to the target tissues to be compared. Fig.11 5 is a diagram depicting a state in which regions of interest 53 and 54 are assigned to tissue to be compared with a target. In this embodiment, the tissue to be compared with the target is the liver parenchyma, and an example is considered in which the regions of interest 53 (and 54) are assigned to the liver parenchyma. After the regions of interest 53 and 54 have been assigned, the average brightness in the region of interest 53 is calculated, and a data group 64 representing the temporal variation of the feature value in the region of interest 53 is displayed. Fig.11 , the data group 64 for the target comparison tissue (liver parenchyma) is indicated by a white circle. Each data in the data group 64 represents a feature value of the region of interest 53 in the contrast images 2211 to 221d of the image set 221. For example, data D32 represents the average brightness B32 of the liver parenchyma in the region of interest 53 in the contrast image 2212 of the late stage T3.

[0124] The data D31 and D32 of the later stage T3 can be obtained as described above.

[0125] Thus, brightness information of three time stages (staining time stage, early stage, and late stage) necessary for creating a WO image can be obtained.

[0126] Fig.12 Described in chronological order Fig. 9 The depicted data sets 61 and 62 and Fig.11 A graph comparing data sets 63 and 64 is depicted.

[0127] Data groups 61 and 62 represent the period of data acquisition SC1 (see Figure 4 ), and data groups 63 and 64 represent data groups acquired during the data acquisition period SC2 (see Figure 4 During the data acquisition periods SC1 and SC2, no data is acquired, so there is no data group.

[0128] In addition to data sets 61 to 64, Fig.12 Also depicted are time-intensity curves 71 and 72. The time-intensity curve 71 is a curve obtained by connecting the target data set 61 and the data set 63 in the direction of the time axis, and the time-intensity curve 72 is a curve obtained by connecting the liver parenchyma data set 62 and the data set 64 in the direction of the time axis.

[0129] from Fig.12 , the difference between the average brightness of the target and the average brightness of the liver parenchyma for the staining time stage T1, early stage T2 and late stage T3 can be identified.

[0130] Once the average brightness of the target and liver parenchyma in the staining time phase T1, the early phase T2 and the late phase T3 has been determined, the process proceeds to step ST2 (see Figure 5 ).

[0131] In step ST2, the processor identifies the large / small relationship between the average brightness of the target and the average brightness of the liver parenchyma for each of the staining time stage T1, the early stage T2, and the late stage T3. In the present embodiment, a case where the difference between the average brightness of the target and the average brightness of the liver parenchyma is calculated to identify the large / small relationship between the average brightness of the target and the average brightness of the liver parenchyma is described, but if the large / small relationship can be identified, a value other than the difference may be calculated.

[0132] Fig.13 This is a diagram for explaining the difference calculation method.

[0133] The processor calculates the difference Δ1 between the average brightness B11 of the target region of interest 41 (in other words, the average brightness B11 represented by the data D11) and the average brightness B12 of the region of interest 51 of the liver parenchyma (that is, the average brightness B12 represented by the data D12) in the contrast image 2114 of the staining time stage T1. When calculating the difference Δ1, the difference Δ1 (= B12-B11) can be calculated by subtracting the average brightness B11 of the target region of interest 41 from the average brightness B12 of the region of interest 51 of the liver parenchyma, or the difference Δ1 (= B11-B12) can be calculated by subtracting the average brightness B12 of the region of interest 51 of the liver parenchyma from the average brightness B11 of the target region of interest 41. In the present embodiment, the difference Δ1 is calculated by subtracting the average brightness of the target region of interest 41 from the average brightness of the region of interest 51 of the liver parenchyma. Thus, the processor calculates a difference value Δ1 (= B12 − B11 ) by subtracting the average brightness B11 of the target region of interest 41 from the average brightness B12 of the liver parenchyma region of interest 51 .

[0134] In addition, the processor calculates the difference Δ2 between the average luminance B21 of the region of interest 41 of the target in the comparison image 211a in the early stage T2 of the processor calculation (in other words, the average luminance B21 represented by the data D21) and the average luminance B22 of the region of interest 51 of the liver parenchyma (that is, the average luminance B22 represented by the data D22). When calculating the difference Δ2, the difference Δ2 (= B22 - B21) can be calculated by subtracting the average luminance B21 of the region of interest 41 of the target from the average luminance B22 of the region of interest 51 of the liver parenchyma, or the difference Δ2 (= B21 - B22) can be calculated by subtracting the average luminance B22 of the region of interest 51 of the liver parenchyma from the average luminance B21 of the region of interest 41 of the target. In the present embodiment, similar to the difference Δ1, the difference Δ2 is calculated by subtracting the average luminance of the region of interest 41 of the target from the average luminance of the region of interest 51 of the liver parenchyma. Therefore, the processor calculates the difference Δ2 (= B22 - B21) by subtracting the average luminance B21 of the region of interest 41 of the target from the average luminance B22 of the region of interest 51 of the liver parenchyma.

[0135] In addition, the processor calculates the difference Δ3 between the average luminance B31 of the region of interest 41 of the target in the comparison image 2212 in the later stage T3 (in other words, the average luminance B31 represented by the data D31) and the average luminance B32 of the region of interest 51 of the liver parenchyma (that is, the average luminance B32 represented by the data D32). When calculating the difference Δ3, the difference Δ3 (= B32 - B31) can be calculated by subtracting the average luminance B31 of the region of interest 41 of the target from the average luminance B32 of the region of interest 51 of the liver parenchyma, or the difference Δ3 (= B31 - B32) can be calculated by subtracting the average luminance B32 of the region of interest 51 of the liver parenchyma from the average luminance B31 of the region of interest 41 of the target. In the present embodiment, similar to the difference Δ1, the difference Δ3 is calculated by subtracting the average luminance of the region of interest 41 of the target from the average luminance of the region of interest 51 of the liver parenchyma. Therefore, the processor calculates the difference Δ3 (= B32 - B31) by subtracting the average luminance B31 of the region of interest 41 of the target from the average luminance B32 of the region of interest 51 of the liver parenchyma.

[0136] After calculating the differences Δ1, Δ2, and Δ3, the process proceeds to step ST3.

[0137] In step ST3, the processor determines whether the differences Δ1, Δ2, and Δ3 are all positive (or negative).

[0138] In the staining time stage T1, the average luminance B12 of the liver parenchyma is less than the average luminance B11 of the target (B12 < B11). Therefore, the difference Δ1 < 0.

[0139] In the early stage T2, the average brightness B22 of the liver parenchyma is less than the average brightness B21 of the target (B22 < B21). Therefore, the difference Δ2 < 0.

[0140] On the other hand, in the late stage T3, the average brightness B32 of the liver parenchyma is greater than the average brightness B31 of the target (B32 > B31). Therefore, the difference Δ3 > 0.

[0141] Thus, the differences Δ1 and Δ2 are negative, but the difference Δ3 is positive. Therefore, it is determined that the differences Δ1, Δ2, and Δ3 include both positive and negative differences. When the differences Δ1, Δ2, and Δ3 include both positive and negative differences, it means that in the early stage, the brightness of the target is greater than that of the liver parenchyma, but in the late stage, the brightness of the target is less than that of the liver parenchyma. Therefore, it is considered that the brightness of the target is highly variable relative to the brightness of the liver parenchyma, which indicates a case of good target staining. In the case of good target staining, using data from both the liver parenchyma and the target can provide useful information for determining whether the contrast agent has been excreted from the target. Therefore, if the differences Δ1, Δ2, and Δ3 include both positive and negative differences, the process proceeds to step ST4, and the processor determines to use the data of the liver parenchyma and the target to create a WO image. Then the process proceeds to step ST5.

[0142] Fig.14 It is an explanatory diagram of the flowchart for creating a WO image in step ST5.

[0143] In step ST51, the user identifies the region in the subject's body where it is necessary to determine whether the contrast agent has been excreted from the target, and assigns a region of interest to the identified region.

[0144] Fig.15 and Fig.16 depicts how to assign a region of interest to the region where it is necessary to determine whether the contrast agent has been excreted from the target.

[0145] The user operates the user interface 10 and types a command to display the contrast image 200. When the user interface 10 accepts the user input to display the contrast image 200, the contrast image 200 is displayed on the display unit 8, as Fig.15 shown. The contrast image 200 can be selected, for example, from the contrast images 2111 to 211a included in the image group 21 (or from the contrast images 2211 to 221b included in the image group 22). In addition, the contrast image can be selected from the three contrast images selected in step ST1 (the contrast image 2114 at the staining time stage T1, the contrast image 211a in the early stage, and the contrast image 2212 in the late stage). After selecting the contrast image 200, the user assigns a region of interest to the point on the contrast image 200 where the user wants to view the timing of the excretion of the contrast agent.

[0146] Fig.16 4 is an explanatory diagram of the region of interest 45 .

[0147] The user operates the user interface 10 and enters an instruction to assign the region of interest 45 to a position (e.g., a target) where the user wants to view the timing of the discharge of the contrast agent. When the user interface 10 accepts the user input to assign the region of interest 45, the processor assigns the region of interest 45 to the contrast image 200. The region of interest 45 is, for example, a region containing the target. Once the region of interest 45 has been assigned to the contrast image 200, the process proceeds to step ST52.

[0148] In step ST52, the processor selects a pixel from the region of interest 45, and determines a color to be assigned to the selected pixel. Here, an example of assigning the color of the pixel P1 in the region of interest 45 is described.

[0149] Fig.17 4 is an explanatory diagram of a procedure for determining a color to be assigned to a pixel P1 in the region of interest 45 .

[0150] The processor reads the average brightness B22 of the liver parenchyma (region of interest 51) of the contrast image 211a in the early stage T2 from the memory. In addition, the processor also reads the brightness value B221 of the pixel P21 from the contrast image 211a in the early stage T2. The pixel P21 corresponds to the pixel P1 in the region of interest 45 of the contrast image 200. The processor then calculates the difference ΔE between the average brightness B22 of the liver parenchyma and the brightness value B221 of the pixel P21. When calculating the difference ΔE, the difference ΔE (= B22-B221) can be calculated by subtracting the brightness value B221 of the pixel P21 from the average brightness B22 of the liver parenchyma, or the difference ΔE (= B221-B22) can be calculated by subtracting the average brightness B22 of the liver parenchyma from the brightness value B221 of the pixel P21. In the present embodiment, the difference ΔE is calculated by subtracting the brightness value of the pixel from the average brightness of the liver parenchyma. Thus, the processor calculates a difference ΔE (= B22 − B221 ) by subtracting the brightness value B221 of the pixel P21 from the average brightness B22 of the liver parenchyma.

[0151] In addition, the processor also reads the average brightness B32 of the liver parenchyma (region of interest 51) of the contrast image 2212 in the later stage T3 from the memory, and the brightness value B331 of the pixel P31 of the contrast image 2212 in the later stage T3. The pixel P31 corresponds to the pixel P1 in the region of interest 45 of the contrast image 200. In addition, the processor then calculates the difference ΔL between the average brightness B32 of the liver parenchyma and the brightness value B331 of the pixel P31. When calculating the difference ΔL, the difference ΔL (= B32-B331) can be calculated by subtracting the brightness value B331 of the pixel P31 from the average brightness B32 of the liver parenchyma, or the difference ΔL (= B331-B32) can be calculated by subtracting the average brightness B32 of the liver parenchyma from the brightness value B331 of the pixel P31. In the present embodiment, similar to the difference ΔE, the difference ΔL is calculated by subtracting the pixel brightness value from the average brightness of the liver parenchyma. Thus, the processor calculates a difference value ΔL (= B32 − B331 ) by subtracting the brightness value B331 of the pixel P31 from the average brightness B32 of the liver parenchyma.

[0152] The processor then determines the pixel P1 to be assigned to the region of interest 45 based on the combination of these two differences ΔE (= B22 - B221) and ΔL (= B32 - B331) (see Fig.16 )’s color.

[0153] Figures 18 to 21 It is depicted how the color to be assigned to the pixel P1 in the region of interest 45 is determined based on the two difference values ​​ΔE and ΔL.

[0154] The processor determines from the color map 81 the color to be assigned to the pixel P1 based on the two difference values ​​ΔE and ΔL.

[0155] The color map 81 is stored in the memory. The horizontal axis of the color map 81 represents the difference ΔE in the previous stage T2, and the vertical axis represents the difference ΔL in the later stage T3. The difference ΔE and ΔL may have negative values, but if the difference ΔE and ΔL are negative, on the color map 81, these values ​​are considered to be "zero".

[0156] The color map 81 represents a color determined by a combination of two difference values ​​ΔE and ΔL. For example, if the difference ΔE is ΔE=ΔEa and the difference ΔL is ΔL=ΔLa, the color determined by the difference combination is Ca. Thus, a color can be determined by a combination of two difference values ​​ΔE and ΔL. Although the color map 81 can determine thousands to tens of thousands of colors according to the combination of two difference values ​​ΔE and ΔL, here, for ease of description, the area of ​​the color map 81 is divided into three areas 82, 83, and 84, as shown in FIG. Fig.19 As depicted, regions 82, 83, and 84 are assigned red, yellow, and blue, respectively.

[0157] Thus, in the present embodiment, each pixel in the region of interest 45 is assigned a color selected from red, yellow, and blue.

[0158] Return to Fig.17 , the difference ΔE in the early stage T2 is ΔE = B22 - B221, and the difference ΔL in the later stage T3 is ΔL = B32 - B331. Thus, the processor determines the color to be assigned to the pixel P1 through the combination of the difference ΔE = B22 - B221 and the difference ΔL = B32 - B331.

[0159] Fig. 20 is an explanatory diagram when determining the color through the difference ΔE = B22 - B221 and the difference ΔL = B32 - B331.

[0160] The difference ΔE in the early stage T2 is ΔE = B22 - B221. Here, B22 < B221 (see Fig.17 ). Therefore, since ΔE = B22 - B221 is negative, the processor determines that ΔE = B22 - B221 is "zero".

[0161] On the other hand, the difference ΔL in the later stage T3 is ΔL = B32 - B331. Here, since B32 > B331 (see Fig.17 ), ΔL = B32 - B331 is positive. Thus, the processor identifies the point (B32 - B331) from the axis ΔL.

[0162] Therefore, the processor can identify the color C1 corresponding to the combination of the difference ΔE = B22 - B221 and the difference ΔL = B32 - B331 from the color map 81. The color C1 is included in the region 82, so the color C1 represents red. Thus, the processor assigns red as the color at the pixel P1 in the region of interest 45. After determining the color to be assigned, the process proceeds to step ST53.

[0163] In step ST53, the processor determines whether all pixels from the region of interest 45 have been selected. Here, similarly, only the pixel P1 is selected, so the process returns to step ST52.

[0164] In addition, the processor then determines the color to be assigned to other pixels in the same way.

[0165] Fig.21 is an explanatory diagram when determining the color to be assigned to the pixel P2 in the region of interest 45.

[0166] The processor reads from the memory the average brightness B22 of the liver parenchyma (region of interest 51) of the contrast image 211a in the early stage T2. In addition, the processor also reads the brightness value B222 of the pixel P22 from the contrast image 211a in the early stage T2. This pixel P22 corresponds to the pixel P2 in the region of interest 45 of the contrast image 200 (see Fig.16 ). In addition, the processor calculates the difference ΔE (= B22 - B222) by subtracting the brightness value B222 of the pixel P22 from the average brightness B22 of the liver parenchyma.

[0167] In addition, the processor also reads from the memory the average brightness B32 of the liver parenchyma (region of interest 51) of the contrast image 2212 in the late stage T3, and the brightness value B332 of the pixel P32 from the contrast image 2212 in the late stage T3. This pixel P32 corresponds to the pixel P2 in the region of interest 45 of the contrast image 200. In addition, the processor calculates the difference ΔL (= B32 - B332) by subtracting the brightness value B332 of the pixel P32 from the average brightness B32 of the liver parenchyma.

[0168] Thus, two differences ΔE (= B22 - B222) and ΔL (= B32 - B332) can be calculated.

[0169] After calculating the two differences ΔE and ΔL, the processor determines the color to be assigned to the pixel P2 in the region of interest 45 based on these two differences ΔE (= B22 - B222) and ΔL (= B32 - B332) (see Fig.16 ).

[0170] Fig. 22 is an explanatory diagram depicting the determination of color by the differences ΔE = B22 - B222 and ΔL = B32 - B332.

[0171] The difference ΔE in the early stage T2 is ΔE = B22 - B222. Here, B22 < B222 (see Fig.21 ). Therefore, since ΔE = B22 - B222 is negative, the processor determines that ΔE = B22 - B222 is "zero".

[0172] In addition, the difference ΔL in the late stage T3 is ΔL = B32 - B332. Here, since B32 < B3322 (see Fig.21 ), ΔL = B32 - B332 is positive. Therefore, the processor determines that ΔL = B32 - B332 is "zero".

[0173] Therefore, the processor can identify the color C2 corresponding to the combination of the difference value ΔE=B22-B222 and the difference value ΔL=B32-B332 from the color map 81. The color C2 is included in the area 84, so the color C2 represents blue. Therefore, the processor assigns blue as the color at the pixel P2 in the area of ​​interest 45. After determining the color to be assigned, the process proceeds to step ST53.

[0174] In step ST53, the processor determines whether all pixels from the region of interest 45 have been selected. Here, only pixels P1 and P2 have been selected, so the process returns to step ST52.

[0175] In the same manner, the processor determines the colors to be assigned to other pixels based on the combination of the two difference values ​​ΔE and ΔL.

[0176] Thus, a color can be assigned to each pixel within the region of interest 45. If it is determined in step ST53 that all pixels have been selected, the process proceeds to step ST54 to create and display a WO image. Fig.23 is a schematic diagram depicting a WO image.

[0177] Fig.23 An example is depicted in which the target brightness is decreasing (contrast agent is being discharged) in the late stage. Once the WO image is displayed, Figure 5 The process in is completed.

[0178] The WO image may be color-coded to indicate the time when the contrast agent is discharged. Thus, by viewing the WO image, the user can easily visually recognize the time when the brightness of the target decreases.

[0179] The above examples describe situations with good target staining. Next, refer to Figure 5 The process in Figure 2 is used to describe the situation of poor target staining.

[0180] In step ST1 , similar to the method described previously, contrast images in the staining time stage T1 , the early stage T2 , and the late stage T3 are selected, and regions of interest are assigned to them.

[0181] Fig.24 This is an explanatory diagram of step ST1.

[0182] The processor selects the contrast image 2114 of the staining time stage T1, the contrast image 211a of the early stage T2, and the contrast image 2212 of the late stage T3. In addition, the regions of interest 41 and 51 are assigned, and data groups 91 and 93 representing the time change of the average brightness at the target and data groups 92 and 94 representing the time change of the average brightness at the liver parenchyma are obtained.

[0183] Data group 91 represents a data group of a target acquired during data acquisition period SC1, and data group 93 represents a data group of a target acquired during data acquisition period SC2. In addition, data group 92 represents a data group of liver parenchyma acquired during data acquisition period SC1, and data group 94 represents a data group of liver parenchyma acquired during data acquisition period SC2. Fig.24 A case of poor target staining is indicated, and an example is depicted in which the average brightness of the target in the staining time stage T1, the early stage T2, and the late stage T3 is lower than the average brightness of the liver parenchyma.

[0184] Once the average brightness of the target and the liver parenchyma in the staining time phase T1 , the early phase T2 , and the late phase T3 has been determined, the process proceeds to step ST2 .

[0185] In step ST2, the processor determines the large / small relationship between the average brightness of the target and the average brightness of the liver parenchyma for each of the staining time stage T1, the early stage T2, and the late stage T3. In the present embodiment, a case where the difference between the average brightness of the target and the average brightness of the liver parenchyma is calculated as the large / small relationship between the average brightness of the target and the average brightness of the liver parenchyma is described.

[0186] Fig.25 This is a diagram for explaining the difference calculation method.

[0187] The processor calculates the difference Δ1 between the average brightness B13 of the target region of interest 41 (in other words, the average brightness B13 represented by data D13) and the average brightness B13 of the region of interest 51 of the liver parenchyma (i.e., the average brightness B14 represented by data D14) in the contrast image 2114 of the staining time stage T1. When calculating the difference Δ1, the difference Δ1 (= B14-B13) can be calculated by subtracting the average brightness B13 of the target region of interest 41 from the average brightness B14 of the region of interest 51 of the liver parenchyma, or the difference Δ1 (= B13-B14) can be calculated by subtracting the average brightness B14 of the region of interest 51 of the liver parenchyma from the average brightness B13 of the target region of interest 41. In the present embodiment, the difference Δ1 is calculated by subtracting the average brightness of the target region of interest 41 from the average brightness of the region of interest 51 of the liver parenchyma. Thus, the processor calculates the difference Δ1 (= B14 − B13 ) by subtracting the average brightness B13 of the target region of interest 41 from the average brightness B14 of the liver parenchyma region of interest 51 .

[0188] In addition, the processor calculates the difference Δ2 between the average brightness B23 of the target region of interest 41 in the contrast image 211a of the early stage T2 (in other words, the average brightness B23 represented by the data D23) and the average brightness B24 of the region of interest 51 of the liver parenchyma (that is, the average brightness B24 represented by the data D24). When calculating the difference Δ2, the difference Δ2 (= B24-B23) can be calculated by subtracting the average brightness B23 of the target region of interest 41 from the average brightness B24 of the region of interest 51 of the liver parenchyma, or the difference Δ2 (= B23-B24) can be calculated by subtracting the average brightness B24 of the region of interest 51 of the liver parenchyma from the average brightness B23 of the target region of interest 41. In the present embodiment, similar to the difference Δ1, the difference Δ2 is calculated by subtracting the average brightness of the target region of interest 41 from the average brightness of the region of interest 51 of the liver parenchyma. Thus, the processor calculates a difference value Δ2 (= B24 − B23 ) by subtracting the average brightness B23 of the target region of interest 41 from the average brightness B24 of the liver parenchyma region of interest 51 .

[0189] In addition, the processor calculates the difference Δ3 between the average brightness B33 of the target region of interest 41 in the contrast image 2212 of the later stage T3 (in other words, the average brightness B33 represented by the data D33) and the average brightness B34 of the region of interest 51 of the liver parenchyma (that is, the average brightness B34 represented by the data D34). When calculating the difference Δ3, the difference Δ3 (= B34-B33) can be calculated by subtracting the average brightness B33 of the target region of interest 41 from the average brightness B34 of the region of interest 51 of the liver parenchyma, or the difference Δ3 (= B33-B34) can be calculated by subtracting the average brightness B34 of the region of interest 51 of the liver parenchyma from the average brightness B33 of the target region of interest 41. In the present embodiment, similar to the difference Δ1, the difference Δ3 is calculated by subtracting the average brightness of the target region of interest 41 from the average brightness of the region of interest 51 of the liver parenchyma. Thus, the processor calculates a difference value Δ3 (= B34 − B33 ) by subtracting the average brightness B33 of the target region of interest 41 from the average brightness B34 of the liver parenchyma region of interest 51 .

[0190] After the difference values ​​Δ1, Δ2, and Δ3 are calculated, the process proceeds to step ST3.

[0191] In step ST3 , the processor determines whether the differences Δ1 , Δ2 , and Δ3 are all positive (or negative).

[0192] In the staining time stage T1, the average brightness B14 of the liver parenchyma is greater than the average brightness B13 of the target (B14>B13). Therefore, the difference Δ1>0.

[0193] In the early stage T2, the average brightness B24 of the liver parenchyma is greater than the average brightness B23 of the target (B24>B23). Therefore, the difference Δ2>0.

[0194] In the later stage T3, the average brightness B34 of the liver parenchyma is greater than the average brightness B33 of the target (B34>B33). Therefore, the difference Δ3>0.

[0195] Thus, the three differences Δ1, Δ2 and Δ3 are all positive. Therefore, the differences Δ1, Δ2 and Δ3 are all determined to be positive. If the differences Δ1, Δ2 and Δ3 are all positive, it is considered that the target is not adequately stained. In this case, if an attempt is made to obtain a WO image using the difference between the target and the liver parenchyma as in the case of good target staining, it will be difficult to obtain a suitable WO image because it is difficult to see a significant difference between the difference between the target and the liver parenchyma in the early stage and the difference between the target and the liver parenchyma in the later stage. Therefore, if all the differences Δ1, Δ2 and Δ3 are determined to be positive (or negative), the processor determines to proceed to step ST6 and create a WO image based on the temporal variation of the brightness (characteristic value) of the target instead of using the difference between the target and the liver parenchyma. The process then proceeds to step ST5. Reference will be made to Fig.14 Step ST5 is described by referring to the process.

[0196] In step ST51, the user displays the comparison image 200 on the display unit 8, and allocates the region of interest 45 in the comparison image 200, such as Fig.26 Once the region of interest 45 has been assigned to the comparison image 200, the process proceeds to step ST52.

[0197] In step ST52, the processor determines a color to be assigned to each pixel of the region of interest 45. Step ST52 is described below.

[0198] In step ST52, the processor selects a pixel from the region of interest 45, and determines a color to be assigned to the selected pixel. Here, an example of assigning the color of the pixel Q1 in the region of interest 45 is described.

[0199] Fig. 27 This is a diagram for explaining the method of determining the assigned color.

[0200] The processor reads the brightness value B13 of the pixel Q13 from the contrast image 2114 at the staining time stage T1. The pixel Q13 corresponds to the pixel Q1 in the region of interest 45 of the contrast image 200.

[0201] In addition, the processor also reads the brightness value B23 of the pixel Q23 of the contrast image 211 a in the early stage T2 . The pixel Q23 corresponds to the pixel Q1 in the region of interest 45 of the contrast image 200 .

[0202] The processor calculates the difference ΔE between the brightness value B13 of the dyeing time stage T1 and the brightness value B23 of the early stage T2. When calculating the difference ΔE, the difference ΔE (= B13-B23) can be calculated by subtracting the brightness value B23 of the early stage T2 from the brightness value B13 of the dyeing time stage T1, or the difference ΔE (= B23-B13) can be calculated by subtracting the brightness value B13 of the early stage T2 from the brightness value B23 of the dyeing time stage T1. In the present embodiment, the difference ΔE is calculated by subtracting the brightness value of the early stage T2 from the brightness value of the dyeing time stage T1. Therefore, the processor calculates the difference ΔE (= B13-B23) by subtracting the brightness value B23 of the early stage T2 from the brightness value B13 of the dyeing time stage T1.

[0203] In addition, the processor reads the brightness value B33 of the pixel Q33 of the contrast image 2212 from the later stage T3. The pixel Q33 corresponds to the pixel Q1 in the region of interest 45 of the contrast image 200. In addition, the processor calculates the difference ΔL between the brightness value B13 of the staining time stage T1 and the brightness value B33 of the later stage T3. When calculating the difference ΔL, the difference ΔL (= B13-B33) can be calculated by subtracting the brightness value B33 of the later stage T3 from the brightness value B13 of the staining time stage T1, or the difference ΔL (= B33-B13) can be calculated by subtracting the brightness value B13 of the later stage T3 from the brightness value B33 of the staining time stage T1. In the present embodiment, the difference ΔL is calculated by subtracting the brightness value of the later stage T3 from the brightness value of the staining time stage T1. Therefore, the processor calculates the difference ΔL (= B13 − B33 ) by subtracting the brightness value B33 of the later stage T3 from the brightness value B13 of the dyeing time stage T1 .

[0204] The processor then determines the pixel Q1 to be assigned to the region of interest 45 based on the combination of these two differences ΔE (= B13 - B23) and ΔL (= B13 - B33) (see Fig.26 )’s color.

[0205] Fig.28 1 is an explanatory diagram when the color to be assigned to the pixel Q1 is determined based on the two difference values ​​ΔE (= B13 - B23) and ΔL (= B13 - B33).

[0206] The processor determines from the color map 81 the color to be assigned to the pixel Q1 based on the two difference values ​​ΔE and ΔL.

[0207] The color map 81 is stored in the memory. The horizontal axis of the color map 81 represents the difference ΔE between the dyeing time stage T1 and the early stage T2, and the vertical axis represents the difference ΔL between the dyeing time stage T1 and the late stage T3. Although the color map 81 can determine thousands to tens of thousands of colors according to the combination of the two difference values ​​ΔE and ΔL, here, for the convenience of description, the area of ​​the color map 81 is divided into three areas 82, 83 and 84, as shown in FIG. Fig.28 As depicted, regions 82, 83, and 84 are assigned red, yellow, and blue, respectively.

[0208] Thus, in the present embodiment, each pixel in the region of interest 45 is assigned a color selected from red, yellow, and blue.

[0209] like Fig. 27 As depicted, the difference ΔE between the dyeing time stage T1 and the early stage T2 is ΔE=B13-B23, and the difference ΔL between the dyeing time stage T1 and the late stage T3 is ΔL=B13-B33. Therefore, the processor can identify the color C1 corresponding to the combination of the difference ΔE=B13-B23 and the difference ΔL=B13-B33 from the color map 81. The color C1 is included in the area 83, so the color C1 represents yellow. Therefore, the processor assigns yellow as the color at the pixel Q1 in the area of ​​interest 45. After determining the color to be assigned, the process proceeds to step ST53.

[0210] In step ST53, the processor determines whether all pixels from the region of interest 45 have been selected. Here, only the pixel Q1 is selected, so the process returns to step ST52 to assign the color of each pixel in the same way.

[0211] Fig.29 As other pixels, color C2 assigned to pixel Q2 and color C3 assigned to pixel Q3 are depicted. Color C2 is red, and color C3 is blue.

[0212] In the same manner, the processor determines the colors to be assigned to other pixels based on the combination of the two difference values ​​ΔE and ΔL.

[0213] Thus, a color can be assigned to each pixel within the region of interest 45. If it is determined in step ST53 that all pixels have been selected, the process proceeds to step ST54 to create and display a WO image. Fig.30 is a schematic diagram depicting a WO image. Fig.30 An example of the reduction in brightness of a target in the early stages is depicted.

[0214] WO images can be created in this way.

[0215] In the case of the present embodiment, in the case of good target staining, a WO image is created using data from both the target and the liver parenchyma (using the difference between the liver parenchyma and the target). On the other hand, in the case of poor target staining, if an attempt is made to obtain a high-quality WO image using both the data of the target and the data of the liver parenchyma similar to the case of good target staining, it will be difficult to obtain a suitable WO image because it is difficult to see a significant difference between the difference between the target and the liver parenchyma in the early stage and the difference between the target and the liver parenchyma in the later stage. Therefore, in the case of poor target staining, a WO image is created using only the brightness of the target without using the brightness of the liver parenchyma. Thus, even in the case of poor target staining, a WO image suitable for determining at which time stage the contrast agent is discharged from the target can be obtained.

[0216] In the case where the target is well colored, the color assigned to the pixel is determined based on the difference in the early stage and the difference in the late stage. Therefore, the color is assigned not only based on the difference in the early stage (or the late stage) but also based on the difference in both the early stage and the late stage, so that the color reflecting the information in both the early stage and the late stage can be determined, and thus a high-quality WO image can be obtained.

[0217] Although the WO image is created based on the contrast images of the staining time stage T1 , the early stage T2 , and the late stage T3 , the WO image may be created using contrast images of other time stages instead of the contrast images of these time stages.

[0218] Furthermore, in the case of the present embodiment, a contrast image of a time stage when the brightness of the target is at a peak is selected as a contrast image of the staining time stage T1. However, if a contrast image of a time stage in which the target is sufficiently stained with a contrast agent can be selected, the contrast image of the staining time stage T1 does not have to be limited to the contrast image of the peak time stage, and a contrast image of a time stage deviating from the peak can be selected as a contrast image of the staining time stage. For example, a contrast image of a time stage when the difference between the brightness of the target and the brightness of the liver parenchyma is the largest can be selected as a contrast image of the staining time stage T1.

[0219] Furthermore, in the case of this embodiment, the contrast image at about 5 minutes after the contrast agent is administered is selected as the contrast image at the later stage T3. However, the contrast image at the later stage T3 is not limited to the contrast image at about 5 minutes after the contrast agent is administered. For example, the contrast image at about 10 minutes after the contrast agent is administered may be selected as the contrast image at the later stage T3.

[0220] Reference numerals list

[0221] 1. Ultrasonic diagnostic equipment

[0222] 2. Ultrasound probe

[0223] 2a Vibration element

[0224] 3. Transmit beamformer

[0225] 4. Transmitter

[0226] 5. Receiver

[0227] 6. Receive beamformer

[0228] 7. Processor

[0229] 8. Display unit

[0230] 9. Memory

[0231] 10. User Interface

[0232] 20. Thumbnail area

[0233] 21, 22, 23, 2n. Image group

[0234] 40. Image display area

[0235] 41, 42, 43, 44, 45, 51, 53. Area of ​​interest

[0236] 56. Operator

[0237] 57. Subject

[0238] 60.TIC Area

[0239] 61, 62, 63, 64. Data Group

[0240] 71, 72. Time intensity curve

[0241] 81. Color map

[0242] 82, 83, 84. Area

[0243] 91, 92, 93, 94. Data Group

[0244] 200. Compare images

Claims

1. An ultrasonic diagnostic device, comprising: one or more processors, The one or more processors execute: selecting a first contrast image, a second contrast image, and a third contrast image from among a plurality of contrast images acquired from a cross section of an object including a subject to which a contrast agent has been administered, the first contrast image representing a state in which the object is stained in a first time period, the second contrast image used to determine whether the contrast agent has been discharged from the object in a second time period after the first time period, and the third contrast image used to determine whether the contrast agent has been discharged from the object in a third time period after the second time period; Identifying a first large / small relationship between a characteristic value of the target in the first contrast image and a characteristic value of a tissue to be compared with the target, a second large / small relationship between the characteristic value of the target in the second contrast image and the characteristic value of the tissue, and a third large / small relationship between the characteristic value of the target in the third contrast image and the characteristic value of the tissue; as well as When the first large / small relationship, the second large / small relationship, and the third large / small relationship all indicate that the characteristic value of the target is greater than the characteristic value of the tissue, or all indicate that the characteristic value of the target is less than the characteristic value of the tissue, based on the change of the characteristic value of the target over time, an image is generated to determine whether the contrast agent has been discharged from the target in the second time stage and the third time stage.

2. The ultrasonic diagnostic apparatus according to claim 1, wherein The first large / small relationship represents a first difference between the feature value of the region of interest of the target and the feature value of the region of interest of the tissue in the first contrast image; The second large / small relationship represents a second difference between the feature value of the region of interest of the target and the feature value of the region of interest of the tissue in the second contrast image; as well as The third large / small relationship represents a third difference between the feature value of the region of interest of the object and the feature value of the region of interest of the tissue in the third contrast image.

3. The ultrasonic diagnostic apparatus according to claim 1, wherein When the user interface accepts a user command to assign a region of interest to the comparison image, the one or more processors assign the region of interest to the comparison image; and The region of interest is located at a point where the user wants to confirm the timing when the contrast agent is discharged.

4. The ultrasonic diagnostic apparatus according to claim 1, wherein The one or more processors execute: selecting a first pixel from the region of interest assigned to the contrast image; reading the characteristic value of the tissue of the second contrast image at the second time stage; reading a brightness value of a second pixel corresponding to the first pixel in the comparison image from the second comparison image during the second time period; calculating a fourth difference between the characteristic value of the tissue and the brightness value of the second pixel in the second contrast image at the second time stage; reading the characteristic value of the tissue of the third contrast image at the third time stage; reading a brightness value of a third pixel corresponding to the first pixel in the comparison image from the third comparison image at the third time stage; calculating a fifth difference between the characteristic value of the tissue and the brightness value of the third pixel in the third contrast image at the third time stage; as well as A color is assigned in the first pixel of the compared image based on a combination of the fourth difference value and the fifth difference value.

5. The ultrasonic diagnostic apparatus according to claim 4, wherein The color map is stored in memory; and The one or more processors execute: A color corresponding to the combination of the fourth difference value and the fifth difference value is specified from the color map, and the specified color is assigned as the color in the first pixel.

6. The ultrasonic diagnostic apparatus according to claim 1, wherein The one or more processors execute: selecting the first pixel from the region of interest in the compared image; reading a brightness value of a second pixel corresponding to the first pixel in the comparison image from the first comparison image during the first time period; reading a brightness value of a third pixel corresponding to the first pixel in the comparison image from the second comparison image in the second time period; reading a brightness value of a fourth pixel corresponding to the first pixel in the comparison image from the third comparison image at the third time stage; calculating a fourth difference between the brightness value of the second pixel and the brightness value of the third pixel; calculating a fifth difference between the brightness value of the second pixel and the brightness value of the fourth pixel; as well as A color is assigned in the first pixel of the compared image based on a combination of the fourth difference value and the fifth difference value.

7. The ultrasonic diagnostic apparatus according to claim 6, wherein The color map is stored in memory; and The one or more processors execute: A color corresponding to the combination of the fourth difference value and the fifth difference value is specified from the color map, and the specified color is assigned as the color in the first pixel. 8 . The ultrasonic diagnostic apparatus according to claim 4 , wherein the second time stage is an early stage, and the third time stage is a late stage. 9 . The ultrasonic diagnostic apparatus according to claim 2 , wherein the characteristic value is an average brightness within the region of interest.

10. The ultrasonic diagnostic apparatus according to claim 1, wherein the target is a tumor, and the tissue is liver parenchyma.

11. A storage medium, the storage medium being one or more non-transitory computer-readable recording media storing one or more instructions executable by one or more processors, wherein the one or more instructions cause the one or more processors to execute: selecting a first contrast image, a second contrast image, and a third contrast image from among a plurality of contrast images acquired from a cross section of an object including a subject to which a contrast agent has been administered, the first contrast image representing a state in which the object is stained in a first time period, the second contrast image used to determine whether the contrast agent has been discharged from the object in a second time period after the first time period, and the third contrast image used to determine whether the contrast agent has been discharged from the object in a third time period after the second time period; Identifying a first large / small relationship between a characteristic value of the target in the first contrast image and a characteristic value of a tissue to be compared with the target, a second large / small relationship between the characteristic value of the target in the second contrast image and the characteristic value of the tissue, and a third large / small relationship between the characteristic value of the target in the third contrast image and the characteristic value of the tissue; as well as When the first large / small relationship, the second large / small relationship, and the third large / small relationship all indicate that the characteristic value of the target is greater than the characteristic value of the tissue, or all indicate that the characteristic value of the target is less than the characteristic value of the tissue, based on the change of the characteristic value of the target over time, an image is generated to determine whether the contrast agent has been discharged from the target in the second time stage and the third time stage.

Images