Elasticity measurement method and ultrasound imaging device

Abstract

An elasticity measurement method and an ultrasonic imaging device, the elasticity measurement method comprising: acquiring a contrast image and an elasticity image of a target section of a target tissue; determining a first region of interest in the contrast image according to the contrast image; determining a second region of interest corresponding to the first region of interest in the elasticity image according to a correspondence between the contrast image and the elasticity image; obtaining elasticity data of the second region of interest, obtaining a first elasticity measurement result according to the elasticity data of the second region of interest; and displaying the contrast image and the elasticity image of the target section and displaying the first elasticity measurement result. The application obtains the contrast image and the elasticity image of the same target section, and determines the measurement region of the elasticity image based on the contrast image, thereby solving the problem that the measurement region of the elasticity image is difficult to determine.

Description

Technical Field

[0001] This application relates to the field of ultrasound imaging technology, and more specifically to an elasticity measurement method and an ultrasound imaging device. Background Technology

[0002] In modern medical imaging, ultrasound technology has become the most widely used, most frequently used, and fastest-adopted new technology due to its advantages such as high reliability, speed, convenience, real-time imaging, and repeatability. Ultrasound is often used for non-invasive examination of lesions such as tumors. Malignant tumors are often accompanied by increased stiffness compared to ordinary tissues; therefore, doctors typically use elastography to quantitatively measure the tissue stiffness at the lesion site to analyze its location.

[0003] In routine elasticity measurements, for measuring the hardness of lesion sites, doctors typically use a tracing method to delineate the boundaries of the lesion area according to the grayscale image corresponding to the elasticity image, thereby determining the measurement range and obtaining the corresponding elasticity result. However, for lesion sites that are not clearly visible in the grayscale image, it is difficult to identify the image boundaries for tracing measurements, and delineating the lesion area is time-consuming and labor-intensive, severely affecting measurement efficiency. Summary of the Invention

[0004] The summary section introduces a series of simplified concepts, which will be further explained in detail in the detailed description section. This summary section is not intended to limit the key and essential technical features of the claimed technical solution, nor is it intended to determine the scope of protection of the claimed technical solution.

[0005] One embodiment of this application provides an elasticity measurement method, the method comprising: acquiring a contrast image and an elasticity image of a target cross section of a target tissue; determining a first region of interest in the contrast image based on the contrast image; determining a second region of interest in the elasticity image corresponding to the first region of interest based on the correspondence between the contrast image and the elasticity image; obtaining elasticity data of the second region of interest; obtaining a first elasticity measurement result based on the elasticity data of the second region of interest; displaying the contrast image and the elasticity image of the target cross section, and displaying the first elasticity measurement result.

[0006] In one embodiment, determining the first region of interest in the contrast image based on the contrast image includes: obtaining contrast intensity data corresponding to each pixel in the contrast image; comparing the contrast intensity data with a preset threshold range; and determining the set of pixels corresponding to the contrast intensity data within the preset threshold range as the first region of interest.

[0007] In one embodiment, determining the first region of interest in the contrast image based on the contrast image includes: inputting the contrast image into a pre-trained machine learning model to obtain the first region of interest output by the machine learning model.

[0008] In one embodiment, determining a first region of interest in the contrast image based on the contrast image includes: performing edge detection on the contrast image to obtain the edge position of the first region of interest.

[0009] In one embodiment, acquiring the contrast image and elasticity image of a target section of a target tissue includes: emitting a first ultrasonic wave to the target tissue, receiving the echo signal of the first ultrasonic wave, and generating multiple frames of contrast images of the target tissue based on the echo signal of the first ultrasonic wave; determining the contrast image of the target section in the multiple frames of contrast images, and obtaining a reference grayscale image of the target section; emitting a second ultrasonic wave to the target tissue, receiving the echo signal of the second ultrasonic wave, and generating multiple frames of real-time grayscale images of the target tissue based on the echo signal of the second ultrasonic wave; determining the real-time grayscale image of the target section from the multiple frames of real-time grayscale images based on the matching degree between the real-time grayscale image and the reference grayscale image; and emitting a third ultrasonic wave to the target section based on the real-time grayscale image of the target section, receiving the echo signal of the third ultrasonic wave, and obtaining the elasticity image of the target section based on the echo signal of the third ultrasonic wave.

[0010] In one embodiment, acquiring the contrast image and elasticity image of a target section of a target tissue includes: emitting a fourth ultrasound wave toward the target tissue, receiving the echo signal of the fourth ultrasound wave, and generating multiple frames of elasticity images of the target tissue based on the echo signal of the fourth ultrasound wave; determining the elasticity image of the target section from the multiple frames of elasticity images, and obtaining a reference grayscale image of the target section; emitting a fifth ultrasound wave toward the target tissue, receiving the echo signal of the fifth ultrasound wave, and generating multiple frames of real-time grayscale images of the target tissue based on the echo signal of the fifth ultrasound wave; determining the real-time grayscale image of the target section from the multiple frames of real-time grayscale images based on the matching degree between the real-time grayscale image and the reference grayscale image; and emitting a sixth ultrasound wave toward the target section based on the real-time grayscale image of the target section, receiving the echo signal of the sixth ultrasound wave, and obtaining the contrast image of the target section based on the echo signal of the sixth ultrasound wave.

[0011] In one embodiment, the method further includes: simultaneously displaying the reference grayscale image, the contrast image of the target section, the real-time grayscale image of the target section, and the elastic image of the target section.

[0012] In one embodiment, the method further includes: identifying a third region of interest in a real-time grayscale image of the target section; determining a fourth region of interest in the elastic image corresponding to the third region of interest based on the correspondence between the elastic image and the real-time grayscale image; obtaining elastic data of the fourth region of interest; obtaining a second elasticity measurement result based on the elastic data of the fourth region of interest; and displaying the second elasticity measurement result.

[0013] In one embodiment, the method further includes: calculating the ratio of the first elasticity measurement result to the second elasticity measurement result, and displaying the ratio.

[0014] In one embodiment, the method further includes: determining the area of ​​the second region of interest and the area of ​​the fourth region of interest; calculating the ratio of the area of ​​the second region of interest to the area of ​​the fourth region of interest, and displaying the ratio.

[0015] In one embodiment, the method further includes: determining a fifth region of interest in the elastic image based on the elastic image; determining the area of ​​the fourth region of interest and the area of ​​the fifth region of interest; calculating the ratio of the area of ​​the fourth region of interest to the area of ​​the fifth region of interest, and displaying the ratio.

[0016] In one embodiment, the method further includes: determining a fifth region of interest in the elastic image based on the elastic image; determining the area of ​​the second region of interest and the area of ​​the fifth region of interest; calculating the ratio of the area of ​​the second region of interest to the area of ​​the fifth region of interest, and displaying the ratio.

[0017] In one embodiment, the method further includes: acquiring contrast intensity data of the first region of interest, obtaining a first contrast intensity measurement result based on the contrast intensity data of the first region of interest, and displaying the first contrast intensity measurement result.

[0018] In one embodiment, the method further includes: obtaining a reference grayscale image corresponding to the contrast image of the target section; identifying a sixth region of interest in the reference grayscale image; determining a seventh region of interest in the contrast image corresponding to the sixth region of interest based on the correspondence between the contrast image of the target section and the reference grayscale image; obtaining contrast intensity data of the seventh region of interest; obtaining a second contrast intensity measurement result based on the contrast intensity data of the seventh region of interest; and displaying the second contrast intensity measurement result.

[0019] In one embodiment, the method further includes: calculating the ratio of the first contrast intensity measurement result to the second contrast intensity measurement result, and displaying the ratio.

[0020] In one embodiment, the method further includes: displaying the outline of the first region of interest on the imaging image, and displaying the outline of the second region of interest on the elastic image.

[0021] In one embodiment, the method further includes: receiving an instruction to edit the contour of the first region of interest, redetermining the first region of interest based on the instruction to edit the contour of the first region of interest, and redetermining the second region of interest based on the redetermined first region of interest; and / or receiving an instruction to edit the contour of the second region of interest, and redetermining the second region of interest based on the instruction to edit the contour of the second region of interest.

[0022] In one embodiment, the first region of interest and the second region of interest correspond to the lesion location of the target tissue.

[0023] Another embodiment of this application provides an ultrasound imaging device, including: an ultrasound probe; a transmitting circuit for exciting the ultrasound probe to emit ultrasound waves toward a target tissue; a receiving circuit for controlling the ultrasound probe to receive the echo of the ultrasound waves to obtain the echo signal of the ultrasound waves; and a processor for executing the steps of the elasticity measurement method described above.

[0024] The elasticity measurement method and ultrasound imaging device of this application obtain contrast images and elasticity images of the same target section, and determine the measurement area of ​​the elasticity image based on the contrast image, thus solving the problem that the measurement area of ​​the elasticity image is difficult to determine. Attached Figure Description

[0025] The above and other objects, features, and advantages of this application will become more apparent from the more detailed description of the embodiments of this application in conjunction with the accompanying drawings. The accompanying drawings are used to provide a further understanding of the embodiments of this application and form part of the specification. They are used together with the embodiments of this application to explain this application and do not constitute a limitation thereof. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0026] Figure 1 This diagram shows a structural block diagram of an ultrasound imaging device according to an embodiment of the present application;

[0027] Figure 2 A schematic flowchart illustrating an elasticity measurement method according to an embodiment of this application is shown;

[0028] Figure 3A schematic diagram illustrating the simultaneous display of a reference grayscale image, a contrast image of the target section, a real-time grayscale image of the target section, and an elastic image of the target section according to an embodiment of the present application;

[0029] Figures 4A-4C This diagram illustrates editing a second region of interest according to one embodiment of the present application. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of this application more apparent, exemplary embodiments according to this application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments of this application. It should be understood that this application is not limited to the exemplary embodiments described herein. Based on the embodiments of this application described herein, all other embodiments obtained by those skilled in the art without inventive effort should fall within the protection scope of this application.

[0031] The following description provides numerous specific details to offer a more thorough understanding of this application. However, it will be apparent to those skilled in the art that this application can be practiced without one or more of these details. In other instances, certain technical features well-known in the art have not been described to avoid confusion with this application.

[0032] It should be understood that this application can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein. Rather, providing these embodiments will make the disclosure thorough and complete, and will fully convey the scope of this application to those skilled in the art.

[0033] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of this application. When used herein, the singular forms “a,” “an,” and “the” are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the terms “comprising” and / or “including,” when used in this specification, identify the presence of the stated features, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups. When used herein, the term “and / or” includes any and all combinations of the associated listed items.

[0034] To fully understand this application, a detailed structure will be presented in the following description to illustrate the technical solution proposed in this application. Optional embodiments of this application are described in detail below; however, in addition to these detailed descriptions, this application may have other implementation methods.

[0035] Below, first refer to Figure 1Describes an ultrasound imaging apparatus according to an embodiment of this application. Figure 1 A schematic structural block diagram of an ultrasound imaging device 100 according to an embodiment of this application is shown.

[0036] like Figure 1 As shown, the ultrasound imaging device 100 includes an ultrasound probe 110, a transmitting circuit 112, a receiving circuit 114, a processor 116, and a display 118. Further, the ultrasound imaging device may also include a transmit / receive selection switch 120 and a beamforming module 122. The transmitting circuit 112 and the receiving circuit 114 can be connected to the ultrasound probe 110 via the transmit / receive selection switch 120.

[0037] The ultrasonic probe 110 includes multiple transducer elements. These elements can be arranged in a row to form a linear array, or in a two-dimensional matrix to form a planar array. They can also form a convex array. Each transducer element is used to emit ultrasonic waves based on an excitation electrical signal, or to convert received ultrasonic waves into electrical signals. Therefore, each transducer element can be used to achieve the mutual conversion between electrical pulse signals and ultrasonic waves, thereby enabling the emission of ultrasonic waves to the target area of ​​the object being tested, and also to receive ultrasonic wave echoes reflected back from the tissue. During ultrasonic testing, the transmission and reception sequences can be used to control which transducer elements are used to emit ultrasonic waves and which are used to receive ultrasonic waves, or to control the transducer elements to be used in time-slotted manner for emitting ultrasonic waves or receiving ultrasonic wave echoes. Transducer elements participating in ultrasonic wave emission can be simultaneously excited by electrical signals, thus emitting ultrasonic waves simultaneously; alternatively, transducer elements participating in ultrasonic beam emission can be excited by several electrical signals with a certain time interval, thus continuously emitting ultrasonic waves with a certain time interval.

[0038] During ultrasound imaging, processor 116 controls transmitting circuit 112 to send a delayed-focused transmission pulse to ultrasound probe 110 via transmit / receive selection switch 120. Excited by the transmission pulse, ultrasound probe 110 emits an ultrasonic beam towards the target tissue of the object being measured. After a certain delay, it receives the ultrasonic echo reflecting back from the target tissue, carrying tissue information, and converts this ultrasonic echo back into an electrical signal. Receiving circuit 114 receives the electrical signal generated by ultrasound probe 110, obtains the ultrasonic echo signal, and sends these ultrasonic echo signals to beamforming module 122. Beamforming module 122 performs focusing delay, weighting, and channel summation on the ultrasonic echo data before sending it to processor 116. Processor 116 performs signal detection, signal enhancement, data conversion, and logarithmic compression on the ultrasonic echo signal to form an ultrasound image. The ultrasound image obtained by processor 116 can be displayed on display 118 or stored in memory 124.

[0039] Optionally, the processor 116 can be implemented as software, hardware, firmware, or any combination thereof, and can use one or more application-specific integrated circuits (ASICs), one or more general-purpose integrated circuits, one or more microprocessors, one or more programmable logic devices, or any combination of the foregoing circuits and / or devices, or other suitable circuits or devices. Furthermore, the processor 116 can control other components in the ultrasound imaging device 100 to perform the corresponding steps of the methods in the various embodiments of this specification.

[0040] The display 118 is connected to the processor 116. The display 118 can be a touch screen, an LCD screen, etc.; or, the display 118 can be an independent display such as an LCD screen or a television, separate from the ultrasound imaging device 100; or, the display 118 can be the screen of an electronic device such as a smartphone or tablet, etc. The number of displays 118 can be one or more.

[0041] The display 118 can display the ultrasound images obtained by the processor 116. Furthermore, while displaying the ultrasound images, the display 118 can also provide a graphical user interface for human-machine interaction. One or more controlled objects can be set on the graphical interface, allowing the user to input operation commands using a human-machine interaction device to control these controlled objects and perform corresponding control operations. For example, icons can be displayed on the graphical interface, and the human-machine interaction device can be used to operate these icons to perform specific functions, such as drawing a region of interest bounding box on the ultrasound image.

[0042] Optionally, the ultrasound imaging device 100 may also include other human-machine interface devices besides the display 118, which are connected to the processor 116. For example, the processor 116 may be connected to the human-machine interface device via an external input / output port, which may be a wireless communication module, a wired communication module, or a combination of both. The external input / output port may also be implemented based on USB, bus protocols such as CAN, and / or wired network protocols.

[0043] The human-computer interaction device may include an input device for detecting user input information. This input information may be, for example, control commands for the timing of ultrasound transmission / reception, operational input commands for drawing points, lines, or boxes on an ultrasound image, or other types of commands. The input device may include one or a combination of several of the following: a keyboard, mouse, scroll wheel, trackball, mobile input device (e.g., a mobile device with a touchscreen, a mobile phone, etc.), a multi-function knob, etc. The human-computer interaction device may also include an output device such as a printer.

[0044] The ultrasound imaging device 100 may also include a memory 124 for storing instructions executed by the processor, storing received ultrasound echoes, storing ultrasound images, etc. The memory may be a flash memory card, solid-state memory, hard disk, etc. It may be volatile and / or non-volatile memory, removable memory and / or non-removable memory, etc.

[0045] It should be understood that Figure 1 The components included in the ultrasound imaging device 100 shown are merely illustrative and may include more or fewer components. This application is not limiting in this regard.

[0046] Below, we will refer to Figure 2 A method for measuring elasticity according to an embodiment of this application is described. Figure 2 This is a schematic flowchart of an elasticity measurement method 200 according to an embodiment of this application.

[0047] like Figure 2 As shown, an embodiment of the elasticity measurement method 200 of this application includes the following steps:

[0048] In step S210, a contrast image and an elasticity image of the target cross section of the target tissue are acquired;

[0049] In step S220, a first region of interest in the contrast image is determined based on the contrast image;

[0050] In step S230, based on the correspondence between the imaging image and the elastic image, a second region of interest corresponding to the first region of interest is determined in the elastic image;

[0051] In step S240, elastic data of the second region of interest is obtained, and a first elasticity measurement result is obtained based on the elastic data of the second region of interest;

[0052] In step S250, the contrast image and elasticity image of the target cross-section are displayed, and the first elasticity measurement result is displayed.

[0053] The elasticity measurement method 200 of this application uses contrast elastography to obtain contrast images and elasticity images of the same target section. Based on the blood perfusion information provided by the contrast images, the location of regions of interest such as tumor regions can be determined, thereby determining the measurement area of ​​the elasticity image. This avoids the problem that the measurement area of ​​the elasticity image cannot be determined based solely on grayscale images, thus improving the accuracy and efficiency of elasticity measurement.

[0054] Specifically, in step S210, an angiographic image and an elastic image of the target cross-section of the target tissue are first acquired. In one embodiment, angiographic imaging of the target tissue is first performed: a first ultrasound wave is emitted towards the target tissue, the echo signal of the first ultrasound wave is received, and multiple frames of angiographic images of the target tissue are generated based on the echo signal of the first ultrasound wave. The angiographic image of the target cross-section is determined from the multiple frames of angiographic images, and a reference grayscale image of the target cross-section is obtained. Then, a second ultrasound wave is emitted towards the target tissue, the echo signal of the second ultrasound wave is received, and multiple frames of real-time grayscale images of the target tissue are generated based on the echo signal of the second ultrasound wave. Based on the matching degree between the real-time grayscale image and the reference grayscale image, the real-time grayscale image of the target cross-section can be determined from the multiple frames of real-time grayscale images. Finally, based on the real-time grayscale image of the target cross-section, elastic imaging of the target tissue is performed, a third ultrasound wave is emitted towards the target cross-section, the echo signal of the third ultrasound wave is received, and the elastic image of the target cross-section is obtained based on the echo signal of the third ultrasound wave. By performing the above steps, an angiographic image and an elastic image corresponding to the same target cross-section can be obtained.

[0055] Before performing contrast imaging on the target tissue, a contrast agent needs to be injected into the target tissue, and the ultrasound imaging mode needs to be activated to obtain multiple contrast images and corresponding grayscale images. The contrast images reflect the contrast intensity, which in turn reflects the strength of blood perfusion. Contrast agent microbubbles can enhance the intensity of reflected echoes and can diffuse to various organs of the body with the bloodstream. Due to the active blood vessels inside lesions such as tumors, the contrast agent microbubble perfusion is rich, making the lesion area clearly visible in the contrast images. The image frame whose image features meet the requirements of clinical observation is selected from the multiple contrast images, which is the contrast image of the target section. In some embodiments, the contrast image corresponding to the moment of maximum contrast intensity can be selected as the contrast image of the target section. The moment of maximum contrast intensity indicates that the blood perfusion at the lesion site has reached its peak, which can better reflect the microcirculation status of the lesion site. When obtaining a reference grayscale image of the target section, the grayscale image corresponding to the target section can be extracted from the grayscale image obtained when the contrast image was previously obtained, or the target section can be re-enhanced with grayscale imaging after obtaining the contrast image corresponding to the target section to obtain the grayscale image of the target section.

[0056] Subsequently, grayscale imaging of the target tissue is performed in real time, continuously acquiring multiple frames of real-time grayscale images of the target tissue. While acquiring real-time grayscale images, the matching degree between each frame of the real-time grayscale image and the reference grayscale image is calculated in real time. Based on the calculated matching degree, the real-time grayscale image corresponding to the target section is obtained. The matching degree can be calculated using an image matching algorithm, or the reference grayscale image and the real-time grayscale image can be displayed simultaneously, allowing the user to subjectively estimate the matching degree. When the matching degree is higher than a preset threshold, it can be approximately assumed that the current real-time grayscale image and the reference grayscale image correspond to the same section. Elastic imaging is then performed under the position and orientation of the ultrasound probe corresponding to the current real-time grayscale image to obtain the elastic image corresponding to the target section.

[0057] The elastic imaging in this application embodiment can be either strain elastic imaging or shear wave elastic imaging. Strain elastic imaging primarily involves applying pressure to the target tissue using a handheld ultrasonic probe, acquiring ultrasonic echo signals before and after compression, and calculating the displacement at corresponding positions before and after compression—that is, the spatial positional change of the target tissue at two different times. By calculating the axial gradient of the displacement, the strain value at each point in the target tissue can be obtained, and the strain value is represented in image form, which is the strain elastic image. The strain elastic image can intuitively reflect the differences in hardness or elasticity between different tissues. Under the same external force compression, a larger strain indicates a softer tissue, and a smaller strain indicates a harder tissue. Shear wave elastic imaging, on the other hand, first uses an ultrasonic probe to excite a focused ultrasonic beam, forming acoustic radiation force, creating a shear wave source within the target tissue, and generating transversely propagating shear waves. By identifying and detecting the shear waves generated within the tissue and their propagation parameters, and imaging these propagation parameters, the hardness differences of the target tissue can be quantitatively and visually obtained.

[0058] In another embodiment, to obtain contrast images and elastic images of a target section of the target tissue, elastic imaging of the target tissue can be performed first to determine the elastic image of the target section. Then, the contrast image of the target section is determined based on the matching result between the grayscale image of the target section and the real-time grayscale image. Specifically, firstly, a fourth ultrasound wave is emitted towards the target tissue, and the echo signal of the fourth ultrasound wave is received. Multiple frames of elastic images of the target tissue are generated based on the echo signal of the fourth ultrasound wave. The elastic image of the target section is determined from the multiple frames of elastic images, and a reference grayscale image of the target section is obtained. Next, a fifth ultrasound wave is emitted towards the target tissue, and the echo signal of the fifth ultrasound wave is received. Multiple frames of real-time grayscale images of the target tissue are generated based on the echo signal of the fifth ultrasound wave. The real-time grayscale image of the target section is determined from the multiple frames of real-time grayscale images based on the matching degree between the real-time grayscale image and the reference grayscale image. Finally, based on the real-time grayscale image of the target section, a sixth ultrasound wave is emitted towards the target section, and the echo signal of the sixth ultrasound wave is received. The contrast image of the target section is obtained based on the echo signal of the sixth ultrasound wave.

[0059] See Figure 3 After acquiring the contrast image and elasticity image of the target section of the target tissue, the reference grayscale image 301, the contrast image 302 of the target section, the real-time grayscale image 303 of the target section, and the elasticity image 304 of the target section can be displayed simultaneously, thereby presenting the tissue structure information, blood flow microcirculation perfusion information, and elasticity distribution information of the target section at the same time, providing users with more comprehensive clinical information.

[0060] In step S220, a first region of interest (ROI) is determined in the contrast-enhanced image. The first ROI can correspond to the location of a lesion in the target tissue, such as the location of a tumor. For some lesions that are not clearly visible on grayscale images, it is difficult to locate the lesion area using grayscale images. However, on contrast-enhanced images, the blood supply to the lesion area is usually relatively rich, and it can be clearly seen from the contrast-enhanced image that the contrast signal intensity of the lesion area is significantly higher than that of the surrounding area. Therefore, regardless of whether the lesion area can be located using grayscale images, its location can be determined using the blood supply information provided by the contrast-enhanced image.

[0061] In one embodiment, a first region of interest (ROI) can be determined based on the contrast intensity information contained in the contrast image. Specifically, contrast intensity data corresponding to each pixel in the contrast image is obtained, the contrast intensity data corresponding to each pixel is compared with a preset threshold range, and the set of pixels corresponding to contrast intensity data within the preset threshold range is determined as the first ROI. Since contrast intensity can reflect blood flow supply information, the first ROI determined based on contrast intensity data corresponds to the region of tissue with unique blood flow supply characteristics, especially the region in the contrast image corresponding to the location of tissue lesions, which can be located relatively accurately based on contrast intensity data.

[0062] In other embodiments, the first region of interest can also be determined based on image information contained in the contrast image. For example, the contrast image can be input into a pre-trained machine learning model to obtain the first region of interest output by the machine learning model. When training the machine learning model, features are learned from a pre-built database by stacking convolutional layers and fully connected layers, thereby directly obtaining the first region of interest of the input image.

[0063] Alternatively, edge detection can be performed on the contrast image to obtain the edge positions of the first region of interest, i.e., the contour of the first region of interest. Edge detection detects all points in the contrast image with large variations in grayscale values, and these points, when connected, form lines, i.e., image edges. Edge detection algorithms include the Sobel edge detection algorithm, the Laplacian edge detection algorithm, and the Canny edge detection algorithm, among others.

[0064] After identifying the first region of interest in the contrast image, the outline of the first region of interest can be displayed on the contrast image to indicate to the user the shape, size, and location of the first region of interest.

[0065] In step S230, based on the correspondence between the contrast image and the elastic image, a second region of interest (ROI) corresponding to the first ROI is determined in the elastic image. Since the elastic image and the contrast image correspond to the same target cross-section, the position of the target tissue in the contrast image is the same as its position in the elastic image. Therefore, the second ROI is the region in the elastic image that has the same shape, size, and position as the first ROI. After determining the second ROI in the elastic image, its outline can be displayed in the elastic image to indicate its shape, size, and position to the user.

[0066] Based on the outlines of a first region of interest (ROI) displayed on the contrast image and a second ROI displayed on the elasticity image, the user can assess whether the automatically determined ROIs align with their understanding of the target tissue. If the user deems the automatically determined first and second ROIs unsatisfactory, they can activate the editing function to edit the outline of the first ROI. Specifically, the user can edit the first ROI; the system can receive instructions to edit the outline of the first ROI, redetermine the first ROI based on these instructions, and then redetermine the second ROI based on the redefined first ROI. Alternatively, the user can also edit the second ROI; the system can receive instructions to edit the outline of the second ROI and redetermine the second ROI based on these instructions.

[0067] See Figures 4A-4C , Figures 4A-4C The diagram illustrates editing the second region of interest (ROI). Users can draw traces on the second ROI, which replace the outline to its left, redefining part of the second ROI's outline and thus correcting its contour. Editing the second ROI also changes the first ROI accordingly. Figure 4A The outline of the second region of interest is shown. Figure 4B The trace line drawn by the user is shown. Figure 4C The corrected second region of interest is shown.

[0068] In some embodiments, the editing function can be enabled after automatic measurement is completed. The user can decide whether to re-edit the first region of interest or the second region of interest based on the shape, position, or internal image information of the first region of interest and the second region of interest, or decide whether to re-edit the first region of interest or the second region of interest based on whether the measurement result is within the expected range.

[0069] Next, in step S240, elastic data of the second region of interest is obtained, and a first elasticity measurement result is obtained based on the elastic data of the second region of interest. Since the elastic image is generated based on the distribution of elastic data, statistical calculations can be performed on the elastic data such as shear wave velocity, shear modulus, or Young's modulus corresponding to each pixel in the second region of interest to obtain the first elasticity measurement result. The first elasticity measurement result includes, but is not limited to, statistical results such as the average, minimum, maximum, quartile, or standard deviation of the elastic data in the second region of interest.

[0070] In some embodiments, the first elasticity measurement result may also be obtained by statistically analyzing the elasticity data corresponding to a portion of pixels within the second region of interest. For example, a region of predetermined size and shape may be drawn within the second region of interest, and the elasticity data within the region of interest may be statistically analyzed to obtain the first elasticity measurement result. After obtaining the first elasticity measurement result, in step S250, the imaging image and elasticity image of the target cross-section are displayed, and the first elasticity measurement result is also displayed.

[0071] The second region of interest (ROI) mentioned above is determined based on the contrast image. In some embodiments, the ROI in the elastic image can also be determined and measured based on the grayscale image, and then compared and analyzed with the second ROI determined based on the contrast image and the first elasticity measurement result. Specifically, firstly, the third ROI in the real-time grayscale image of the target section is identified. Based on the correspondence between the elastic image and the real-time grayscale image, a fourth ROI corresponding to the third ROI is determined in the elastic image. This fourth ROI is the ROI determined based on the grayscale image. Then, the elasticity data of the fourth ROI is obtained, and the second elasticity measurement result is obtained and displayed based on the elasticity data of the fourth ROI. Generally, the second elasticity measurement result should be close to the first elasticity measurement result, and the user can evaluate the accuracy of the measurement result based on the deviation between the two. To quantitatively reflect the deviation between the first and second elasticity measurement results, the ratio of the first and second elasticity measurement results can also be calculated and displayed.

[0072] In addition to the elasticity measurement results, since the fourth region of interest and the second region of interest correspond to the same target tissue, ideally the fourth region of interest should also be close to the second region of interest. Therefore, the areas of the second region of interest and the fourth region of interest can also be determined, the ratio of the area of ​​the second region of interest to the area of ​​the fourth region of interest can be calculated and displayed, so that users can evaluate the accuracy of the region of interest automatically determined by the system based on the ratio.

[0073] Since lesions such as tumors are also clearly reflected in elastography images—for example, malignant tumors are often accompanied by increased stiffness—in some embodiments, regions of interest (ROIs) can be determined within the elastography image itself for measurement. The ROI determined based on the elastography image itself can be defined as the fifth ROI. Then, the areas of the fourth and fifth ROIs can be determined, their ratio calculated, and displayed; alternatively, the areas of the second and fifth ROIs can be determined, their ratio calculated, and displayed. The first and second ROIs mentioned above are determined based on blood perfusion information provided by the contrast imaging image; the third and fourth ROIs are determined based on tissue structure information provided by the grayscale image; and the fifth ROI is determined based on elasticity information provided by the elastography image. Displaying the elasticity measurement results corresponding to each of the above ROIs and the ratios between these results, as well as the ratios of the areas of different ROIs, helps users to perform a comprehensive analysis of the target tissue.

[0074] Since a contrast image is also generated during the contrast elastography process, in one embodiment, contrast intensity data of a first region of interest in the contrast image can also be acquired. A first contrast intensity measurement result is obtained and displayed based on the contrast intensity data of the first region of interest. The method for obtaining the first contrast intensity measurement result based on the contrast intensity data is similar to the method for obtaining the first elasticity measurement result based on the elasticity data; that is, statistical analysis is performed based on the contrast intensity data corresponding to some or all pixels in the first region of interest to obtain the first contrast intensity measurement result.

[0075] The first region of interest (ROI) corresponding to the aforementioned first contrast intensity measurement result is determined based on the contrast intensity itself. In some embodiments, an additional ROI for contrast intensity measurement can be determined based on the grayscale image. The ROI identified in the reference grayscale image corresponding to the contrast image can be defined as the sixth ROI. After determining the sixth ROI, a seventh ROI corresponding to the sixth ROI can be determined in the contrast image based on the correspondence between the contrast image and the reference grayscale image. The contrast intensity data of the seventh ROI is obtained, and the second contrast intensity measurement result is obtained and displayed based on the contrast intensity data of the seventh ROI. Furthermore, the ratio of the first contrast intensity measurement result to the second contrast intensity measurement result can be calculated and displayed, providing users with a basis for judging the accuracy of the contrast intensity measurement result.

[0076] In summary, the elasticity measurement method 200 of this application obtains an imaging image and an elasticity image of the same target cross-section, and determines the measurement area of ​​the elasticity image based on the imaging image, thus solving the problem that the measurement area of ​​the elasticity image is difficult to determine.

[0077] This application also provides an ultrasonic imaging device for implementing the above-described elasticity measurement method 200. Now refer back to... Figure 1 This ultrasound imaging device can achieve the following: Figure 1 The ultrasound imaging device 100 shown may include an ultrasound probe 110, a transmitting circuit 112, a receiving circuit 114, a processor 116, and a display 118. Optionally, the ultrasound imaging device 100 may also include a transmit / receive selection switch 120 and a beamforming module 122. The transmitting circuit 112 and the receiving circuit 114 can be connected to the ultrasound probe 110 through the transmit / receive selection switch 120. The relevant descriptions of each component can be referred to the relevant descriptions above, and will not be repeated here.

[0078] The transmitting circuit 112 is used to excite the ultrasound probe 110 to emit ultrasound waves toward the target tissue; the receiving circuit 114 is used to control the ultrasound probe 110 to receive the echo of the ultrasound waves to obtain an ultrasound echo signal; the processor 116 is used to execute the steps of the elasticity measurement method 200 described above, specifically including: acquiring a contrast image and an elasticity image of a target section of the target tissue; determining a first region of interest in the contrast image based on the contrast image; determining a second region of interest in the elasticity image corresponding to the first region of interest based on the correspondence between the contrast image and the elasticity image; obtaining elasticity data of the second region of interest; obtaining a first elasticity measurement result based on the elasticity data of the second region of interest; displaying the contrast image and the elasticity image of the target section, and displaying the first elasticity measurement result.

[0079] The above only describes the main functions of each component of the ultrasound imaging device; for more details, please refer to the relevant description of the elasticity measurement method 200. The ultrasound imaging device of this application embodiment obtains contrast images and elasticity images of the same target cross-section, and determines the measurement area of ​​the elasticity image based on the contrast image, thus solving the problem of the difficulty in determining the measurement area of ​​the elasticity image.

[0080] Although exemplary embodiments have been described herein with reference to the accompanying drawings, it should be understood that the above exemplary embodiments are merely illustrative and are not intended to limit the scope of this application. Various changes and modifications can be made therein by those skilled in the art without departing from the scope and spirit of this application. All such changes and modifications are intended to be included within the scope of this application as claimed in the appended claims.

[0081] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0082] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed.

[0083] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of this application may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0084] Similarly, it should be understood that, in order to streamline this application and aid in understanding one or more of the various inventive aspects, features of this application may sometimes be grouped together in a single embodiment, figure, or description thereof in the description of exemplary embodiments of this application. However, this approach should not be construed as reflecting an intention that the claimed application requires more features than are expressly recited in each claim. Rather, as reflected in the corresponding claims, its inventive point lies in solving the corresponding technical problem with features fewer than all features of a single disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of this application.

[0085] Those skilled in the art will understand that, apart from the mutual exclusion of features, all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or apparatus so disclosed can be combined in any combination. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0086] Furthermore, those skilled in the art will understand that although some embodiments described herein include certain features but not others included in other embodiments, combinations of features from different embodiments are intended to be within the scope of this application and form different embodiments. For example, in the claims, any one of the claimed embodiments can be used in any combination.

[0087] The various component embodiments of this application can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some modules according to the embodiments of this application. This application can also be implemented as an apparatus program (e.g., a computer program and computer program product) for performing part or all of the methods described herein. Such an implementation of this application can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0088] It should be noted that the above embodiments are illustrative of this application and not limiting of it, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. This application can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.

[0089] The above description is merely a specific embodiment or illustration of the embodiments of this application. The scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. The scope of protection of this application shall be determined by the scope of the claims.

Claims

1. A method for measuring elasticity, characterized in that, The method includes: Acquire real-time grayscale images, contrast images, and elasticity images of the target cross-section of the target tissue; determine a first region of interest in the contrast image based on the contrast image; Based on the correspondence between the imaging image and the elastic image, a second region of interest corresponding to the first region of interest is determined in the elastic image; Obtain elasticity data of the second region of interest, and obtain a first elasticity measurement result based on the elasticity data of the second region of interest; Identify the third region of interest in the real-time grayscale image of the target section; Based on the correspondence between the elastic image and the real-time grayscale image, a fourth region of interest corresponding to the third region of interest is determined in the elastic image; Obtain elasticity data of the fourth region of interest, and obtain a second elasticity measurement result based on the elasticity data of the fourth region of interest; Display the contrast image and elasticity image of the target section, and display the first elasticity measurement result and the second elasticity measurement result; A fifth region of interest is determined in the elastic image based on the elastic image, wherein the second region of interest, the fourth region of interest, and the fifth region of interest correspond to the same target tissue; Determine the areas of the second region of interest, the fourth region of interest, and the fifth region of interest; Calculate the ratio of the area of ​​the fourth region of interest to the area of ​​the fifth region of interest, and calculate the ratio of the area of ​​the second region of interest to the area of ​​the fifth region of interest, and display the ratios.

2. The elasticity measurement method according to claim 1, characterized in that, Determining the first region of interest in the contrast image based on the contrast image includes: Obtain the contrast intensity data corresponding to each pixel on the contrast image; compare the contrast intensity data with a preset threshold range; The set of pixels corresponding to the imaging intensity data within the preset threshold range is determined as the first region of interest.

3. The elasticity measurement method according to claim 1, characterized in that, Determining the first region of interest in the contrast image based on the contrast image includes: The contrast image is input into a pre-trained machine learning model to obtain the first region of interest output by the machine learning model.

4. The elasticity measurement method according to claim 1, characterized in that, Determining the first region of interest in the contrast image based on the contrast image includes: Edge detection is performed on the imaging image to obtain the edge position of the first region of interest.

5. The elasticity measurement method according to claim 1, characterized in that, The acquisition of contrast images and elasticity images of the target cross-section of the target tissue includes: A first ultrasonic wave is emitted toward the target tissue, the echo signal of the first ultrasonic wave is received, and a multi-frame contrast image of the target tissue is generated based on the echo signal of the first ultrasonic wave. The contrast image of the target section is determined from the multiple contrast images, and a reference grayscale image of the target section is obtained; A second ultrasonic wave is emitted toward the target tissue, the echo signal of the second ultrasonic wave is received, and a multi-frame real-time grayscale image of the target tissue is generated based on the echo signal of the second ultrasonic wave. Based on the matching degree between the real-time grayscale image and the reference grayscale image, the real-time grayscale image of the target section is determined from multiple frames of real-time grayscale images; Based on the real-time grayscale image of the target cross-section, a third ultrasonic wave is emitted toward the target cross-section, the echo signal of the third ultrasonic wave is received, and the elasticity image of the target cross-section is obtained based on the echo signal of the third ultrasonic wave.

6. The elasticity measurement method according to claim 1, characterized in that, The acquisition of real-time grayscale images, contrast images, and elasticity images of the target cross-section of the target tissue includes: A fourth ultrasonic wave is emitted toward the target tissue, the echo signal of the fourth ultrasonic wave is received, and a multi-frame elastic image of the target tissue is generated based on the echo signal of the fourth ultrasonic wave. The elastic image of the target section is determined from the multiple elastic images, and a reference grayscale image of the target section is obtained; A fifth ultrasonic wave is emitted toward the target tissue, the echo signal of the fifth ultrasonic wave is received, and a multi-frame real-time grayscale image of the target tissue is generated based on the echo signal of the fifth ultrasonic wave. Based on the matching degree between the real-time grayscale image and the reference grayscale image, the real-time grayscale image of the target section is determined from multiple frames of real-time grayscale images; Based on the real-time grayscale image of the target section, a sixth ultrasonic wave is emitted toward the target section, the echo signal of the sixth ultrasonic wave is received, and an imaging image of the target section is obtained based on the echo signal of the sixth ultrasonic wave.

7. The elasticity measurement method according to claim 5 or 6, characterized in that, Also includes: Simultaneously display the reference grayscale image, the imaging image of the target section, the real-time grayscale image of the target section, and the elastic image of the target section.

8. The elasticity measurement method according to claim 1, characterized in that, Also includes: Calculate the ratio of the first elasticity measurement result to the second elasticity measurement result, and display the ratio.

9. The elasticity measurement method according to claim 1, characterized in that, Also includes: Determine the area of ​​the second region of interest and the area of ​​the fourth region of interest; Calculate the ratio of the area of ​​the second region of interest to the area of ​​the fourth region of interest, and display the ratio.

10. The elasticity measurement method according to claim 1, characterized in that, Also includes: Obtain the contrast intensity data of the first region of interest, obtain the first contrast intensity measurement result based on the contrast intensity data of the first region of interest, and display the first contrast intensity measurement result.

11. The elasticity measurement method according to claim 10, characterized in that, Also includes: Obtain a reference grayscale image corresponding to the imaging image of the target section; Identify the sixth region of interest in the reference grayscale image; Based on the correspondence between the imaging image of the target section and the reference grayscale image, a seventh region of interest corresponding to the sixth region of interest is determined in the imaging image; Obtain the contrast intensity data of the seventh region of interest, obtain the second contrast intensity measurement result based on the contrast intensity data of the seventh region of interest, and display the second contrast intensity measurement result.

12. The elasticity measurement method according to claim 11, characterized in that, Also includes: Calculate the ratio of the first contrast intensity measurement result to the second contrast intensity measurement result, and display the ratio.

13. The elasticity measurement method according to claim 1, characterized in that, Also includes: The outline of the first region of interest is displayed on the imaging image, and the outline of the second region of interest is displayed on the elastic image.

14. The elasticity measurement method according to claim 13, characterized in that, Also includes: Receive an instruction to edit the outline of the first region of interest, redetermine the first region of interest according to the instruction to edit the outline of the first region of interest, and redetermine the second region of interest according to the redetermined first region of interest; And / or, receive an instruction to edit the outline of the second region of interest, and redetermine the second region of interest based on the instruction to edit the outline of the second region of interest.

15. The elasticity measurement method according to claim 1, characterized in that, The first region of interest and the second region of interest correspond to the lesion location of the target tissue.

16. An ultrasonic imaging device, characterized in that, include: Ultrasonic probe; A transmitting circuit is used to excite the ultrasound probe to emit ultrasound waves toward the target tissue. A receiving circuit is used to control the ultrasonic probe to receive the echo of the ultrasonic wave in order to obtain the echo signal of the ultrasonic wave. A processor for performing the steps of the elasticity measurement method according to any one of claims 1-15.

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