Imaging method and ultrasonic imaging device

Abstract

An imaging method and an ultrasonic imaging device using the imaging method, after obtaining an area of ​​interest (2) through an initial image, further determining the category and / or characteristics of an tissue structure of interest (3) in the area of ​​interest; obtaining a first imaging parameter (4) or a display parameter (4') according to the category and / or characteristics, and then obtaining a first imaging image (5, 5') through the first imaging parameter or the display parameter; imaging the entire area of ​​the object to be imaged based on a second imaging parameter that is at least partially different from the first imaging parameter to obtain a second imaging image (6); and fusing the first imaging image and the second imaging image to obtain an imaging image (7) of the object to be imaged. Since the first imaging parameter or the display parameter is associated with the category and / or characteristics of the tissue structure of interest, the imaging image obtained after fusion can better display the tissue structure of interest compared to the initial image, and the image effect is good.

Description

Technical Field

[0001] The present invention relates to the medical field, and in particular to an imaging method and an ultrasonic imaging device. Background Art

[0002] Medical ultrasound images have been increasingly widely used in clinical practice due to their non-invasive, low-cost, and real-time image display characteristics. Specifically, medical ultrasound imaging uses ultrasonic echo signals to detect the structural information of tissues and displays the structural information of tissues in real time through two-dimensional images. In this way, doctors can identify the structural information in the two-dimensional images to provide a basis for clinical diagnosis.

[0003] Currently, the mainstream medical ultrasound imaging technology is full-area imaging technology. This technology uses the same imaging parameters to image the entire area within the current imaging range, and compromises the imaging parameters to make the full-area image uniform and consistent, and to achieve the best display effect for the full-area image. However, this technology may not have the best display effect for the image within the area of ​​interest, and cannot highlight the features within the area of ​​interest.

[0004] For example, medical ultrasound 2D images have been widely used in the examination of the abdomen, heart, small organs, blood vessels, and obstetrics and gynecology, providing important diagnostic basis for the structural lesions of organs. However, in clinical examinations, the structural differences of many lesions are very subtle, especially small lesions and small calcification lesions of blood vessels, which are often difficult to identify on traditional ultrasound 2D images. Therefore, related diagnosis still faces many difficulties and challenges in clinical practice. Summary of the invention

[0005] In one embodiment, an imaging method is provided, comprising the following steps:

[0006] Acquire an initial image of the object to be imaged;

[0007] Acquire at least one region of interest of the object to be imaged based on the initial image;

[0008] determining a category and / or a characteristic of a tissue structure of interest in the region of interest based on the region of interest;

[0009] Obtaining a first imaging parameter or a display parameter based on the category and / or characteristic of the tissue structure of interest;

[0010] Imaging the region of interest based on the first imaging parameter, or processing an image obtained by scanning the region of interest based on the display parameter to obtain a first imaging image;

[0011] Imaging the entire area of ​​the object to be imaged based on second imaging parameters to obtain a second imaging image, wherein the first imaging parameters and the second imaging parameters are at least partially different;

[0012] The first imaging image and the second imaging image are fused to obtain an imaging image of the object to be imaged.

[0013] In one embodiment, an imaging method is provided, comprising the following steps:

[0014] Acquire an initial image of the object to be imaged;

[0015] Acquire at least one region of interest of the object to be imaged based on the initial image;

[0016] determining a category and / or a characteristic of a tissue structure of interest in the region of interest based on the region of interest;

[0017] Obtaining a first imaging parameter based on the category and / or characteristic of the tissue structure of interest; and

[0018] The region of interest is imaged based on the first imaging parameter to obtain a first imaging image.

[0019] In one embodiment, an ultrasonic imaging device is provided, comprising:

[0020] An ultrasonic probe, the ultrasonic probe being used to transmit ultrasonic waves to an object to be imaged so as to scan the object to be imaged, receive ultrasonic echoes returned from the object to be imaged, and convert the received ultrasonic echoes into electrical signals;

[0021] An echo processing module, the echo processing module is used to obtain an ultrasonic echo signal according to the electrical signal;

[0022] a processor, the processor being configured to obtain an imaging image of the object to be imaged according to the ultrasonic echo signal; and

[0023] A display, the display being used to display an image of the object to be imaged;

[0024] Wherein, the processor is further used for:

[0025] Acquire an initial image of the object to be imaged;

[0026] Acquire at least one region of interest of the object to be imaged based on the initial image;

[0027] determining a category and / or a characteristic of a tissue structure of interest in the region of interest based on the region of interest;

[0028] Obtaining a first imaging parameter based on the category and / or characteristic of the tissue structure of interest;

[0029] Imaging the region of interest based on a first imaging parameter to obtain a first imaging image;

[0030] Imaging the entire area of ​​the object to be imaged based on second imaging parameters to obtain a second imaging image, wherein the first imaging parameters and the second imaging parameters are at least partially different;

[0031] The first imaging image and the second imaging image are fused to obtain an imaging image of the object to be imaged.

[0032] In one embodiment, a computer-readable storage medium is provided, including a program, wherein the program can be executed by a processor to implement the method described above.

[0033] According to the imaging method and ultrasonic imaging device of the above-mentioned embodiment, after the region of interest is acquired through the initial image, the category and / or characteristics of the tissue structure of interest are further determined in the region of interest; a first imaging parameter or display parameter is obtained according to the category and / or characteristics, and then a first imaging image is obtained through the first imaging parameter or display parameter; the entire region of the object to be imaged is imaged based on the second imaging parameter to obtain a second imaging image, wherein the first imaging parameter and the second imaging parameter are at least partially different; the first imaging image and the second imaging image are fused to obtain an imaging image of the object to be imaged. Since the first imaging parameter or display parameter is associated with the category and / or characteristics of the tissue structure of interest, the imaging image obtained after fusion can better display the tissue structure of interest compared to the initial image, and the image effect is good. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 A structural block diagram of a medical imaging device provided by an embodiment of the present invention;

[0035] Figure 2 A structural block diagram of an ultrasonic imaging device provided by an embodiment of the present invention;

[0036] Figure 3 A flow chart of an imaging method provided by an embodiment of the present invention;

[0037] Figure 4 A schematic diagram of a scanning method for obtaining a first imaging image provided by an embodiment of the present invention;

[0038] Figure 5 A schematic diagram of another scanning method for obtaining a first imaging image provided by an embodiment of the present invention;

[0039] Figure 6A schematic diagram of first imaging parameter optimization provided by an embodiment of the present invention;

[0040] Figure 7 Another schematic diagram of optimizing the first imaging parameter provided by an embodiment of the present invention;

[0041] Figure 8 Another schematic diagram of optimizing the first imaging parameter provided by an embodiment of the present invention;

[0042] Fig. 9 Another flow chart of an imaging method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0043] The present invention is further described in detail below by specific embodiments in conjunction with the accompanying drawings. Wherein similar elements in different embodiments adopt associated similar element numbers. In the following embodiments, many detailed descriptions are for making the present application better understood. However, those skilled in the art can easily recognize that some features can be omitted in different situations, or can be replaced by other elements, materials, methods. In some cases, some operations related to the present application are not shown or described in the specification, this is to avoid the core part of the present application being overwhelmed by too much description, and for those skilled in the art, it is not necessary to describe these related operations in detail, and they can fully understand the related operations according to the description in the specification and the general technical knowledge in the art.

[0044] In addition, the features, operations or characteristics described in the specification can be combined in any appropriate manner to form various implementations. At the same time, the steps or actions in the method description can also be interchanged or adjusted in a manner that is obvious to those skilled in the art. Therefore, the various sequences in the specification and the drawings are only for the purpose of clearly describing a certain embodiment and are not meant to be a required sequence, unless otherwise specified that a certain sequence must be followed.

[0045] The serial numbers of the components in this document, such as "first", "second", etc., are only used to distinguish the objects described and do not have any order or technical meaning. The "connection" and "coupling" mentioned in this application, unless otherwise specified, include direct and indirect connections (couplings).

[0046] like Figure 1 As shown, the medical imaging device provided by the present invention includes: a scanning device 10, a processor 20 and a human-computer interaction device 30.

[0047] The human-computer interaction device 30 is used to receive user input and output visual information. For example, a touch screen can be used to receive user input commands and display visual information; a mouse, keyboard, trackball, joystick, etc. can also be used as the input device of the human-computer interaction device 30 to receive user input commands, and a display can be used as the display device of the human-computer interaction device 30 to display visual information.

[0048] The scanning device 10 is used to scan an object to be imaged to obtain image data of the object to be imaged.

[0049] The processor 20 is used to obtain an initial image of the object to be imaged; obtain at least one region of interest of the object to be imaged based on the initial image; determine the category and / or characteristics of the tissue structure of interest in the region of interest based on the region of interest; obtain a first imaging parameter or display parameter based on the category and / or characteristics of the tissue structure of interest; image the region of interest based on the first imaging parameter, or process the image obtained by scanning the region of interest based on the display parameter to obtain a first imaging image; image the entire region of the object to be imaged based on the second imaging parameter to obtain a second imaging image, wherein the first imaging parameter and the second imaging parameter are at least partially different; fuse the first imaging image and the second imaging image to obtain an imaging image of the object to be imaged. Since the first imaging parameter or the display parameter is associated with the category and / or characteristics of the tissue structure of interest, the imaging image obtained after fusion can better display the tissue structure of interest compared to the initial image, and the image effect is good. And the imaging image finally obtained is obtained by fusing the first imaging image and the second imaging image. Since the second imaging image is obtained by imaging the entire region, the transition effect between the part within the region of interest and the part outside the region of interest in the imaging image is improved.

[0050] Among them, in this embodiment, "and / or" includes three situations. Taking the category and / or characteristics of the tissue structure of interest as an example, one situation is the category and characteristics of the tissue structure of interest, another situation is the category of the tissue structure of interest, and another situation is the characteristics of the tissue structure of interest. The category of the tissue structure of interest refers to the category of the main tissue structure contained in the region of interest of the current image, which can be the same as the category of the object to be imaged, such as the heart, kidney, obstetric cerebellum, etc.; it can also be a tissue structure in the object to be imaged, such as a tumor, fluid cyst, calcification point, polyp, muscle fiber, fat and other finer tissue structures. The category of the tissue structure of interest can be one or more, such as only a tumor, or a tumor and a calcification point coexist. The characteristics of the tissue structure of interest refer to the corresponding physical, mathematical and statistical characteristics of the tissue structure, etc. Among them, the physical characteristics can be the hardness and softness of the tissue structure, such as the hardness and softness of the tissue structure measured by elastic imaging, and the mathematical and statistical characteristics can be one or more of the shape, length, width, area, number, and average brightness corresponding to the tissue structure.

[0051] The present invention can be applied to a variety of medical imaging systems, such as ultrasound imaging systems, X-ray imaging systems, magnetic resonance imaging (MRI) systems, positron emission tomography (PET) systems or single photon emission computed tomography (SPECT) systems, etc.; that is, the medical imaging device of the present invention can be an ultrasound imaging device, an X-ray imaging device, a magnetic resonance imaging device, a positron emission computed tomography (PET) imaging device, a single photon emission computed tomography (SPECT) imaging device, etc. The scanning device 10 can scan the object to be imaged to obtain image data of the object to be imaged. For example, for an ultrasound imaging system, the scanning device 10 includes a probe, a transmitting / receiving control circuit and an echo processing module. For other imaging systems, the scanning device 10 is a device for scanning the object to be imaged. The processor 20 can control the scanning device 10 or the imaging system to implement the imaging method of the embodiment of the present invention described in detail below. Here, although the term "image data" is used to describe the data obtained by the scanning device, in this article, the "image data" here can also include data received or obtained by the scanning device 10 after scanning, which is not processed or has been processed to a certain extent, but has not yet formed an image. For example, for an ultrasonic imaging system, the image data here also includes ultrasonic echo data obtained after the probe receives the ultrasonic echo, radio frequency data after a certain processing, or image data after an ultrasonic image is formed.

[0052] The present invention takes an ultrasonic imaging system as an example to illustrate the embodiments of the present invention, that is, the present invention takes an ultrasonic imaging device as an example to illustrate. Figure 2The ultrasonic imaging device includes an ultrasonic probe 110 , a transmitting / receiving control circuit 120 , an echo processing module 130 , a processor 20 and a human-computer interaction device 30 , and the human-computer interaction device 30 includes a display 310 .

[0053] The transmitting / receiving control circuit 120 sends an ultrasonic pulse with a certain amplitude and polarity after delayed focusing to the ultrasonic probe 110. The ultrasonic probe 110 is stimulated by the ultrasonic pulse to transmit ultrasonic waves to the object to be imaged, and after a certain delay, receives the ultrasonic echo with tissue information reflected from the object to be imaged, and reconverts the ultrasonic echo into an electrical signal. The echo processing module 130 receives the electrical signal converted and generated by the ultrasonic probe 110, obtains the ultrasonic echo signal, and performs filtering, amplification, beam synthesis and other processing on the ultrasonic echo signal, and then sends it to the processor 20 for related processing to obtain an imaging image of the object to be imaged. The echo processing module 130 includes, for example, a beam synthesis module. The ultrasonic image obtained by the processor 20 is sent to the display 310 for display. The imaging image obtained based on the ultrasonic imaging device mainly refers to the ultrasonic image.

[0054] In the embodiment of the present invention, the processor 20 may also implement the imaging method provided in the embodiment of the present invention, which will be described in detail below with reference to the accompanying drawings still taking the ultrasound imaging system as an example.

[0055] like Figure 3 As shown, it shows a flow chart of the imaging method provided by an embodiment of the present invention, comprising the following steps:

[0056] Step 1. The processor 20 obtains an initial image of the object to be imaged. It should be understood that the "initial" here refers only to the subsequent action or step of obtaining the region of interest, and does not refer to the initial or other specific meanings of the overall imaging process. For example, an imaging system (e.g., an ultrasonic imaging system) can be used to image the object to be imaged (e.g., using the full-area image imaging method described above) to obtain a full-area ultrasonic image of the object to be imaged (i.e., the so-called initial image). The "full-area ultrasonic image" mentioned here may mean that the ultrasonic image contains the entire area of ​​the object to be imaged. The "object to be imaged" mentioned here may be one or more organs or regions of the human body or animal that is currently being or will be subjected to ultrasonic scanning. Of course, the initial image can also be input externally.

[0057] Step 2, the processor 20 acquires at least one region of interest of the object to be imaged based on the initial image. The region of interest may be any region of the object to be imaged that a user (e.g., a doctor or operator of other ultrasonic imaging equipment, etc.) is interested in, such as a region suspected of having a microstructural lesion, etc. The structural information in this region can be used as a basis for clinical diagnosis. In a specific embodiment, the method of acquiring the region of interest includes but is not limited to three methods: a manual designation method by an operator, a semi-automatic method, and an automatic method. Among them, the automatic or semi-automatic method intelligently determines the region of interest in the initial image by identifying the content of the initial image. These three methods are introduced one by one below.

[0058] Manual designation by the operator: In this method, the processor 20 obtains the region of interest designated by the operator in the human-computer interaction interface, that is, the region of interest of the object to be imaged is manually designated by the operator. For example, the initial image of the object to be imaged as described above is displayed in the human-computer interaction interface of the ultrasound imaging device, and the human-computer interaction device 30 includes an input device, such as a trackball, and the sampling frame displayed on the initial image of the object to be imaged is operated by operating the trackball to change the position of the center point of the sampling frame and / or the size of the sampling frame, and the area within the sampling frame is the region of interest.

[0059] Semi-automatic method: This method is a combination of manual operation by the operator and image recognition technology. The process may be: the processor 20 obtains the image type of the initial image of the object to be imaged specified by the operator, and matches the initial image of the object to be imaged with the corresponding first sample template image based on the image type to obtain the region of interest.

[0060] The image type indicates which type of image the initial image of the current object to be imaged belongs to, such as liver image, kidney image, heart image, obstetric cerebellum image, etc. After obtaining the image type, the operator can determine what target of interest in the initial image of the object to be imaged is based on the image type, and the target of interest is the above-mentioned region of interest.

[0061] During the imaging process of the object to be imaged, the operator will select the inspection mode, that is, which organ to scan. For example, when the object to be imaged is the liver, the inspection mode will be selected as the liver mode. Therefore, in some embodiments, the inspection mode can be used to indicate the image type of the initial image of the object to be imaged.

[0062] After acquiring the image type, the initial image of the object to be imaged can be matched with the corresponding first sample template image based on the image type to obtain one or more regions of interest. The corresponding first sample template image can be a sample image of the same image type as the initial image of the object to be imaged, and the sample image can be a template image of multiple samples obtained offline or established after multiple samples of the same image type are collected by an ultrasonic imaging device, and the template image is used as a matching reference to match with the initial image of the object to be imaged to obtain one or more regions of interest.

[0063] In some embodiments of the present invention, the process of matching an initial image of an object to be imaged with a corresponding first sample template image to obtain one or more regions of interest may be: traversing the initial image of the object to be imaged, selecting a region block centered on a currently traversed position and having the same size as the sample template image during the traversal process, and performing similarity calculation between the selected region block and the first sample template image, and after the traversal is completed, selecting the center point of the region block with the best similarity as the best matching position, and then defining the region of interest with the best matching position as the center, wherein the similarity calculation method may adopt a SAD method (Sum of Absolute Differences) or a correlation coefficient method or other suitable methods.

[0064] Automatic method: This method can determine the region of interest through image recognition technology. In some embodiments of the present invention, the method of determining the region of interest through image recognition method can include but is not limited to the following two methods:

[0065] One method is: extracting features from the initial image of the object to be imaged to obtain features of the initial image of the object to be imaged, matching the features of the initial image of the object to be imaged with features of the second sample image to obtain the image type of the initial image of the object to be imaged, and matching the initial image of the object to be imaged with the corresponding first sample template image based on the obtained image type to obtain the region of interest. In this method, the process of matching the initial image of the object to be imaged with the corresponding first sample template image based on the image type to obtain the region of interest can refer to the specific implementation in the above-mentioned semi-automatic method, which will not be elaborated in this embodiment of the present invention.

[0066] Furthermore, the above-mentioned process of obtaining the image type based on feature matching can be regarded as a process of automatically determining the image type. Compared with the operator-specified method, the process of automatically determining the image type can further refine the image type to which the initial image of the object to be imaged belongs, so as to determine which type of image of which department the initial image of the object to be imaged belongs to, such as which type of image in obstetrics or heart. In order to be able to refine the image type to which the initial image of the object to be imaged belongs, at least one second sample image can be obtained offline or collected through an ultrasonic imaging device for each refined image type, and the image type of each second sample image is known, so the refined image type of the initial image of the object to be imaged can be determined by matching the features of the second sample image, and the matching process can be as follows:

[0067] Step 21: Feature extraction; wherein the above-mentioned feature may refer to a general term for various attributes that can characterize the initial image of the object to be imaged and distinguish it from other images. In some embodiments of the present invention, feature extraction will be performed on any second sample image collected, so that the features of the second sample image are used as reference features to facilitate the subsequent matching of the initial image of the object to be imaged. Similarly, after acquiring the initial image of the object to be imaged, the same feature extraction method as the second sample image can be used to extract features from the initial image of the object to be imaged, and the features of the initial image of the object to be imaged can be obtained.

[0068] The feature extraction method can use image processing feature extraction methods, such as Sobel operator, Canny operator, Roberts operator and SIFT operator, etc.; machine learning methods can also be used to automatically extract image features, such as PCA (Principal Component Analysis), LDA (Linear Discriminant Analysis) and deep learning methods to automatically extract image features. Among them, the deep learning method can be: CNN (convolutional neural network), ResNet (residual network), VGG (Visual Geometry Group Network), etc.

[0069] Step 22: Feature matching; after obtaining the features of the initial image of the object to be imaged, the similarity can be calculated one by one with the features of the second sample image in the training sample library, and the image type of the second sample image with the most similar features is selected as the image type of the initial image of the object to be imaged, wherein the feature similarity measurement method can be the SAD algorithm, that is, calculating the sum of the absolute values ​​of the corresponding point differences of the two sets of features, the smaller the SAD value, the more similar; or the correlation coefficient of the two sets of features can be calculated to measure the similarity of the two sets of features, the larger the correlation coefficient, the more similar; or other suitable methods can be used. For the deep learning method, the process of feature matching is: input the image into the network trained by deep learning methods such as CNN in step 21 to directly determine the category of the image.

[0070] Step 23: Automatically delineate one or more regions of interest. After the image type is automatically determined, the method for obtaining one or more regions of interest based on the image type is the same as the semi-automatic method described above, which will not be described in detail here.

[0071] The method of determining the region of interest using image recognition technology described above is applicable to various image types.

[0072] In addition, for some image types that present periodic motion in the time dimension, such as fetal heart, adult heart, carotid artery and other image types, the motion area in the initial image of the object to be imaged of this type can be the region of interest. Therefore, when the image type of the initial image of the object to be imaged indicates that the object to be imaged is an object that presents periodic motion in the time dimension, the process of determining the region of interest by image recognition technology can be as follows (another way):

[0073] Step 21': obtain the motion features of the initial image of the object to be imaged; the motion features can be obtained by a variety of methods, such as using the frame difference method, specifically, the image information of the current frame can be directly subtracted from the image information of the previous frame or the previous frames to extract the motion features of the current frame. Of course, other methods such as OF (Optical Flow) and GMM (Gaussian Mixture Model) can also be used to extract motion features. When the frame difference method is used to obtain motion features, the difference between the points in the motion area in the current frame image and the previous frame (previous frames) is large, and the absolute value of the value obtained by the frame difference method is large. The difference between the points in other static areas in the current frame image and the previous frame (previous frames) is small, and the absolute value of the corresponding value obtained under the frame difference method is small, for example, close to 0.

[0074] Step 22 ′: segment the initial image of the object to be imaged based on the motion features to obtain the motion region in the initial image of the object to be imaged; after obtaining the motion features, threshold segmentation combined with morphological processing can be used to segment the motion region.

[0075] Step 23': Determine one or more regions of interest based on the motion region; after the motion region is segmented, the motion region can be used to locate the region of interest. Generally, the region of interest in the embodiment of the present invention can be a rectangle (for example, in the case of an ultrasound imaging system using a linear array probe for imaging coefficients) or a sector (for example, in the case of an ultrasound imaging system using a convex array or phased array probe for imaging coefficients), so one or more regions of interest positioning methods can be to fit one or more regular regions of interest based on the obtained motion region so that each motion region can be included respectively, and the fitting method can be to calculate the circumscribed rectangle or sector of the motion region, or to use least squares estimation rectangle fitting, or to use other suitable fitting methods for fitting.

[0076] The above method for locating the region of interest is also suitable for a semi-automatic method. For example, one semi-automatic method is: based on the operator's input, the positioning range is narrowed down, and then the automatic positioning method is used to locate the final region of interest within the narrowed range. The purpose of narrowing the positioning range is to improve positioning efficiency and accuracy, and the method for narrowing the positioning range can be: the operator draws at least one set of points on the motion area to indicate the range of the region of interest, or the positioning range is automatically narrowed down according to the operator's input information. Another semi-automatic method is that the operator draws one or more sets of points on the motion area to locate one or more initial regions of interest, and in the real-time scanning process, the above-mentioned automatic positioning or semi-automatic positioning method is used to change the position and size of the region of interest box in real time according to the image content.

[0077] One point that needs to be explained is that the above-mentioned method for locating the region of interest can perform real-time positioning of the initial image of each object to be imaged so as to change the region of interest in real time, or can perform positioning at intervals, or even after the operator triggers the positioning by pressing a button, etc. Even for a system that needs to monitor the region of interest in real time, the positioning of the region of interest can be real-time, and the image type acquisition method can be determined at intervals or after the image type acquisition is triggered, and the above-mentioned image type acquisition process can be obtained by the operator specifying or based on feature matching.

[0078] Step 3: The processor obtains the category and / or characteristics of the tissue structure of interest in the region of interest based on the region of interest. The image type in step 2 can be directly used as the category of the tissue structure of interest, and other methods can also be used. For example, in a specific embodiment, the method of obtaining the category and / or characteristics of the tissue structure of interest includes but is not limited to three methods: a manual designation method by an operator, a semi-automatic method, and an automatic method.

[0079] Manual designation by the operator: In this method, the processor 20 obtains the preset category and / or characteristic selected by the operator for the tissue structure of interest using the human-machine interaction interface through the human-machine interaction device 30. That is, the human-machine interaction interface displays the pre-set tissue structure type and corresponding characteristics that can correspond to each region of interest, and the operator can select them. For features, in addition to selection, they can also be determined based on information input by the operator. In this method, the preset category and / or characteristic selected by the operator for the tissue structure of interest using the human-machine interaction interface is determined as the category and / or characteristic of the tissue structure of interest.

[0080] Semi-automatic method: This method is a combination of manual operation by the operator and image recognition technology. The process may be: the processor 20 obtains the points set by the operator using the human-computer interaction interface (manual), determines the scope of image recognition according to the points, and obtains the category and / or characteristics of the tissue structure of interest through the following automatic method, such as determining the category and / or characteristics of the tissue structure of interest based on the region of interest through an image recognition method (automatic). For example, in step 2, while obtaining the region of interest based on a set or multiple sets of points selected by the operator, the type and corresponding characteristics of the tissue structure of interest in the region of interest are automatically identified; it is also possible not to combine the semi-automatic method in step 2, re-receive a set or multiple sets of points input by the operator in each region of interest, and automatically identify the category and / or characteristics of the tissue structure in the region of interest according to the points input by the operator. When the above semi-automatic method is used, the scope of identification and detection of the type and / or characteristics is narrowed by the operator's input information, thereby improving the processing speed. Of course, the above-mentioned semi-automatic method can also be a group or multiple groups of points input by the operator to determine one or more initial tissue structure categories and / or characteristics for each region of interest, and then the following automatic method is used to update the categories and / or characteristics of the tissue structures in the identified region of interest in real time during the real-time ultrasound scanning process.

[0081] Automatic method: This method can determine the category and / or characteristics of the tissue structure of interest based on the region of interest through image recognition methods. Among them, there are two methods of determining the category and / or characteristics of the tissue structure of interest based on the region of interest through image recognition methods in the semi-automatic method and the automatic method. The following two methods are introduced one by one.

[0082] The first method is to extract features from the region of interest of the initial image to obtain features of the region of interest of the initial image, match the features of the region of interest of the initial image with the features of the corresponding first sample image, and obtain the category and / or characteristics of the tissue structure of interest in the region of interest. The corresponding first sample image may be a sample image of a tissue structure having the same category and / or characteristics as the region of interest of the initial image, and the sample image may be an image of multiple samples obtained offline or established by collecting multiple samples of tissue structures of the same category and / or characteristics through an ultrasonic imaging device, and its features are used as a matching reference to match the features of the region of interest of the initial image to obtain the category and / or characteristics of the tissue structure of interest. Multiple regions of interest may involve tissue structures of different types and / or characteristics, and each first sample image that performs feature matching with each region of interest corresponds to a tissue structure of different categories and / or characteristics.

[0083] The above process of obtaining the category and / or characteristics of the tissue structure based on feature matching can be regarded as an automatic acquisition process. Compared with the operator-specified method, the automatic acquisition process can further refine the category and / or characteristics of the tissue structure to determine what kind of tissue structure and characteristics the tissue structure of interest belongs to. In order to be able to refine the category and / or characteristics of the tissue structure of the region of interest, at least one first sample image can be obtained offline or collected through an ultrasonic imaging device for each tissue structure category and / or characteristic, and the tissue structure category and / or characteristics of each first sample image are known. Therefore, the refined category and / or characteristics of the tissue structure of the region of interest can be determined by matching the features of the first sample image. The matching process can be as follows:

[0084] Step 31: Feature extraction; In some embodiments of the present invention, a training sample library is pre-established, and feature extraction is performed on any first sample image, so that the features of the first sample image are used as reference features to facilitate the subsequent matching of the region of interest of the initial image; the first sample image and its corresponding features, categories and / or characteristics are stored in the training sample library. The feature in step 3 may refer to a general term for various attributes that can characterize the region of interest of the initial image and distinguish it from other images or from other regions of the initial image. Similarly, after obtaining the region of interest of the initial image, the same feature extraction method as the first sample image may be used to extract features of the region of interest of the initial image to obtain the features of the region of interest of the initial image.

[0085] The feature extraction method can use image processing feature extraction methods, such as Sobel operator, Canny operator, Roberts operator and SIFT operator, etc.; machine learning methods can also be used to automatically extract features of the region of interest, such as PCA (Principal Component Analysis), LDA (Linear Discriminant Analysis) and deep learning methods to automatically extract image features. Among them, the deep learning method can be: CNN (convolutional neural network), ResNet (residual network), VGG (Visual Geometry Group Network), etc.

[0086] Step 32: Feature matching; after obtaining the features of the region of interest of the initial image, similarity calculations can be performed one by one with the features of the first sample image in the training sample library, and the category and / or characteristics of the first sample image with the most similar features are selected as the category and / or characteristics of the tissue structure of interest in the region of interest, wherein the feature similarity measurement method can be the SAD algorithm, that is, calculating the sum of the absolute values ​​of the corresponding point differences of the two sets of features, the smaller the SAD value, the more similar; or the correlation coefficient of the two sets of features can be calculated to measure the similarity of the two sets of features, the larger the correlation coefficient, the more similar; or other suitable methods can be used. For the deep learning method, the feature matching process is: the image is input into the network trained by the deep learning method such as CNN in step 31 to directly obtain the category and / or characteristics of the region of interest.

[0087] Of course, a lot of first sample images are stored in the training sample library. In order to reduce the amount of calculation, the association between the image type of the initial image and the first sample image can be established in advance, that is, one type of initial image corresponds to a part of the first sample image. In step 32, after obtaining the features of the initial image, the image type of the initial image is obtained, and the first sample image that needs to be further feature matched with the region of interest of the initial image is determined according to the image type, and then the similarity is calculated one by one with the features of the first sample image determined in the training sample library, and the category and / or characteristics of the first sample image with the most similar features are selected as the category and / or characteristics of the tissue structure of interest in the region of interest. Among them, the image type of the initial image can be obtained by directly using the image type obtained in step 2, or the image type of the initial image can be obtained again in the manner of step 2.

[0088] The method of determining the category and / or characteristics of the tissue structure of interest using the image recognition technology described above is applicable to the categories and / or characteristics of various tissue structures.

[0089] In addition, for some tissue structures that exhibit periodic motion in the time dimension, such as fetal heart, adult heart, carotid artery, etc., when the category and / or characteristics of the tissue structure of the region of interest indicate that the region of interest exhibits periodic motion in the time dimension, the process of determining the category and / or characteristics of the tissue structure of interest by image recognition technology can be as follows (another way):

[0090] Step 31': obtain the motion features of the region of interest of the initial image; the motion features can be obtained by a variety of methods, such as using the frame difference method, specifically, the image information of the current frame can be directly subtracted from the image information of the previous frame or the previous frames to extract the motion features of the current frame. Of course, other methods such as OF (Optical Flow) and GMM (Gaussian Mixture Model) can also be used to extract the motion features. When the frame difference method is used to obtain the motion features, the difference between the points in the motion area in the current frame image and the previous frame (previous frames) is large, and the absolute value of the value obtained by the frame difference method is large. The difference between the points in other static areas in the current frame image and the previous frame (previous frames) is small, and the absolute value of the corresponding value obtained under the frame difference method is small, for example, close to 0.

[0091] Step 32 ′: Determine the category and / or characteristic of the tissue structure of interest in the region of interest based on the motion feature.

[0092] It should be noted that the acquisition of the category and / or characteristic of the organizational structure of interest may be performed at intervals, or in real time, or after a category and / or characteristic acquisition instruction is triggered.

[0093] In some embodiments, the above steps 2 and 3 may also be performed in the following manner:

[0094] The category and / or characteristics of at least one region of interest and its tissue structure of interest are obtained by learning through a deep learning method. For example, a convolutional neural network is used to perform deep learning on the second sample image in the training sample library to obtain the characteristics of the second sample image, which corresponds to the image type of the initial image; a convolutional neural network is used to perform deep learning on the first sample template image in the training sample library to obtain the characteristics of the first sample template image, which corresponds to the region of interest; a convolutional neural network is used to perform deep learning on the first sample image in the training sample library to obtain the characteristics of the first sample image, which corresponds to the category and / or characteristics of the tissue structure of interest. Then, based on the learned characteristics, the candidate regions of interest are classified and regressed to obtain the region of interest of the initial image and the category and / or characteristics of its tissue structure. There are many ways of deep learning, and the present invention gives examples of three ways. The first method is based on sliding window, specifically: first, extract features from the area within the sliding window of the input image. The method of extracting features is the same as that described in step 21 and step S31, so it is not repeated. Then, the extracted features are classified by discriminators such as SVM (Support Vector Machine) and / or random forest to determine whether the current sliding window is a region of interest. If so, the corresponding category and characteristics of the tissue structure in the region of interest are determined. The second method is based on deep learning Bounding-Box (border regression) method detection and recognition. The common form is: for the input image, the feature learning and parameter regression are performed by stacking the basic convolution layer and the fully connected layer. For an input image, such as the initial image, the corresponding Bounding-Box of the region of interest can be directly regressed through the network, and the category and characteristics of the tissue structure in the region of interest can be determined at the same time. Common networks include R-CNN (Region-CNN), Fast R-CNN, Faster-RCNN, SSD, YoLo, etc. The third method is an end-to-end semantic segmentation network method based on deep learning. This method is similar to the second method based on deep learning Bounding-Box. The difference is that the fully connected layer is removed, and an upsampling or deconvolution layer is added to make the input and output sizes the same, so as to directly obtain the region of interest of the initial image and the corresponding category and characteristics of the organizational structure in the region of interest. For example, by stacking the basic convolutional layers to learn features and regress parameters, and by upsampling or deconvolution layers to make the input and output sizes the same, for an input image, such as the initial image, the Bounding-Box of the corresponding region of interest can be directly regressed through the network, and the category and characteristics of the organizational structure in the region of interest can be determined at the same time. Common networks include FCN, U-Net, Mask R-CNN, etc.

[0095] Step 4, the processor obtains the first imaging parameter based on the category of the tissue structure of interest, the characteristics of the tissue structure of interest, or the information based on the combination of the category and characteristics of the tissue structure of interest. For example, the category and / or characteristics of the tissue structure of interest are matched with a preset category-parameter correspondence table and / or characteristic-parameter correspondence table to obtain the corresponding first imaging parameter. Alternatively, according to the category and / or characteristics of the tissue structure of interest in each region of interest, the optimal imaging parameter is automatically iterated as the first imaging parameter. The automatic iteration method can be to first construct an objective function, and then automatically iterate the optimal imaging parameter using an optimization method such as a gradient descent method and a Newton method.

[0096] Step 5: The processor images the region of interest based on the first imaging parameter to obtain a first imaging image. Since there may be multiple regions of interest, in step 5, the corresponding region of interest on the object to be imaged may be imaged by scanning once based on each first imaging parameter to obtain a first imaging image of each region of interest. For example, Figure 4 As shown, in one scan, different regions of interest on the imaging object are scanned using different first imaging parameters (e.g., different emission voltages, different line densities, etc.) to obtain first imaging images of each region of interest. For example, in one scan, the first region of interest and the second region of interest on the imaging object are scanned using the same scanning parameters, but the first region of interest and the second region of interest are processed using different signal / image processing parameters (e.g., different contrasts), so that the first imaging images of each region of interest are obtained through one scan. Figure 5 As shown, in step 5, the corresponding region of interest on the object to be imaged can also be imaged by multiple scanning based on each first imaging parameter to obtain the first imaging image of each region of interest. Here, "one scan" and "multiple scans" refer not only to the front-end scanning step of transmitting ultrasound to scan the object to be imaged, but also to the back-end signal / image processing step of imaging based on ultrasound echo signals.

[0097] Step 6: The processor images the entire area of ​​the object to be imaged based on the second imaging parameters to obtain a second imaging image. The "entire area" of the scanning target for imaging using the second imaging parameters is the entire area of ​​the current object to be imaged including the aforementioned region of interest, that is, when imaging using the second imaging parameters, the scanned area (or the imaging area at this time) includes the region of interest itself in addition to the area outside the aforementioned region of interest. Therefore, accordingly, the second imaging image obtained is an image of the entire area of ​​the current object to be imaged including the region of interest, rather than an image of only the area outside the region of interest.

[0098] Wherein the first imaging parameter and the second imaging parameter are at least partially different, it can be: the first imaging parameter and the second imaging parameter are parameters of the same type, and the values ​​of the first imaging parameter and the second imaging parameter are different; or the first imaging parameter and the second imaging parameter are parameters of different types, such as the first imaging parameter includes parameter A and parameter B, and the second imaging parameter includes parameter C and parameter D; or the first imaging parameter includes the second imaging parameter, such as the first imaging parameter includes parameter A and parameter B, and the second imaging parameter includes parameter A, then it is determined that the first imaging parameter includes the second imaging parameter; and so on. In the ultrasonic imaging device, the first imaging parameter and the second imaging parameter may include scanning parameters and signal / image processing parameters. In the ultrasonic imaging device, the first imaging parameter and the second imaging parameter may be at least one of: transmission frequency, transmission voltage, line density, number of focal points, focal position, speckle noise suppression parameter and image enhancement parameter. In the embodiment of the present invention, the secondary imaging method of the whole area and the area of ​​interest is adopted, so in the imaging process of the area of ​​interest, the first imaging parameter can be optimized according to the category and / or characteristics of the tissue structure of interest to optimize the area of ​​interest.

[0099] For example, by optimizing the emission frequency within the region of interest, the region of interest may be free from restrictions on the emission frequency during the imaging process of the entire region. When the region of interest is located in the near field of the emission source, the emission frequency of the region of interest during the scanning imaging process may be increased, thereby improving the resolution of the first imaging image. When the region of interest is located in the far field of the emission source, the emission frequency of the region of interest during the scanning imaging process may be reduced, thereby improving the penetration of the first imaging image.

[0100] Alternatively, when other parameters of the ultrasound system are fixed, the higher the transmit voltage, the greater the transmit power of the ultrasound system, and the better the quality of the image. Therefore, in the embodiment of the present invention, the transmit voltage can be optimized, such as using a lower transmit voltage when scanning the entire area, and using a higher transmit voltage when scanning the area of ​​interest. Figure 6 As shown, the image quality in the region of interest is improved when the transmission power meets the acoustic field limitation of the ultrasound system. For example, one of the acoustic field limitation indicators of the ultrasound system, Ispta (spatial peak time average sound intensity), is less than or equal to 480 mW / cm 2 .

[0101] The line density, number of focal spots and scanning frame rate of the ultrasound system are mutually restricted. The greater the line density or the greater the number of focal spots, the lower the scanning frame rate. Therefore, the line density or the number of focal spots can be optimized. When scanning and imaging the entire area, a lower line density or a smaller number of focal spots is used, while when scanning and imaging the area of ​​interest, a higher line density or a larger number of focal spots is used. In this way, the quality of the first imaging image is improved when the scanning frame rate meets the requirements. Figure 7 or Figure 8 As shown, Figures 6 to 8 The horizontal axis in is the probe position in the ultrasound system.

[0102] It can be seen that the present invention can realize adaptive local enhanced imaging, and can optimize the speckle noise suppression parameters and image enhancement parameters in the local region of interest according to the type and characteristics of the tissue structure in the local region of interest. Since the local region of interest is usually smaller than the entire region, more complex algorithms and more effective parameters can be applied when the computing power is limited, thereby improving the image quality of the image in the region of interest.

[0103] The adaptive local enhanced imaging method is not limited to the parameter optimization described above, but also includes the optimization of various other transmission, reception, and post-processing image parameters, such as transmission aperture, transmission waveform, spatial compounding, frequency compounding, line compounding, and frame correlation, etc., by optimizing the first imaging parameters corresponding to the local region of interest, thereby obtaining a better first imaging image.

[0104] In embodiments where the imaging system is another type of imaging system, the aforementioned first parameter and second parameter may be set accordingly.

[0105] Step 7: The processor fuses the first imaging image and the second imaging image to obtain an imaging image of the object to be imaged. In the embodiment of the present invention, the fusion process may be: obtaining a first fusion parameter of the first imaging image and a second fusion parameter of the second imaging image, and then fusing the first imaging image and the second imaging image based on the first fusion parameter and the second fusion parameter to obtain an imaging image of the object to be imaged, and displaying it on a display. In the final imaging image seen by the operator, the region of interest has a very good image effect because the imaging parameters and display parameters are optimized.

[0106] For example, fusion can be performed according to the following formula:

[0107]

[0108]

[0109] Where (x, y) represents the position of each pixel in the i-th region of interest (ROI), and N is an integer greater than or equal to 1. is the i-th region of interest corresponding to the first image, is the i-th region of interest corresponding to the second image, and is numbered 1-N to distinguish; (i.e., α1, α2, ... α N) is a first fusion parameter of the region of interest corresponding to each first imaging image, and the first fusion parameters of each first imaging image may be the same or different; (i.e., β1, β2, ... β N ) is the second fusion parameter corresponding to the i-th region of interest of the second imaging image; I o is the imaged image of the object to be imaged, that is, the fusion result. In the embodiment of the present invention, the image corresponding to the fusion result depends on the values ​​of the first fusion parameter and the second fusion parameter.

[0110] The first fusion parameter and the second fusion parameter can be set according to actual conditions. A is generally 1, but may also be other values ​​close to 1, for example, when A>1, the overall brightness level of the image output after fusion can be improved. In other embodiments, it may also be set in other ways.

[0111] It should be noted here that the values ​​of the first fusion parameter α and the second fusion parameter β are not fixed, and may vary according to the different pixels in the image, the different positions in the image, and the generation time of the image. N ) and β(β1, β2...β N ) can also be equal to 1 or 0.

[0112] For example, during the fusion process, when the grayscale value of each pixel in the first image and the second image is between [0, 255], the value of the first fusion parameter and the second fusion parameter may not be less than 0; if the grayscale value of each pixel in the first image or the second image is less than 0, the value of the corresponding fusion parameter may be less than 0, and the values ​​of the first fusion parameter and the second fusion parameter may not be 0 at the same time; or during the fusion process, the first fusion parameter α corresponding to each position in the first image is different, and the second fusion parameter β corresponding to each position in the second image may also be different. For example, if the edge position of the region of interest needs to fuse more image information of the second image, the value of the second fusion parameter β at the edge position of the region of interest may be greater than the value of the second fusion parameter β at other positions. If more image information of the first image is fused at other positions except the edge position, the value of the first fusion parameter α at other positions is greater than the value of the first fusion parameter α at the edge position; if the first image and the second image are real-time images, which may change with time, the values ​​of the first fusion parameter α and the second fusion parameter β may also be different with time; and so on.

[0113] It can be seen from the above technical solution that the imaging method provided by the embodiment of the present invention can respectively use the first imaging parameter to image the area of ​​interest of the imaging object and the second imaging parameter to image the entire area of ​​the imaging object, so as to obtain a first imaging image of the area of ​​interest and a second imaging image of the entire area. In this way, the first imaging parameter can be set specifically according to the category and / or characteristics of the tissue structure of interest, so as to perform targeted enhancement and optimization on the desired aspects of the image of the tissue structure of interest; in the process of fusing the first imaging image and the second imaging image, more image information of the second imaging image can be fused at the edge position of the first imaging image, so as to achieve a smooth transition between the area of ​​interest and outside the area of ​​interest, improve the transition effect, and thereby maintain visual consistency in the overall effect of the fused imaging image.

[0114] Furthermore, the first imaging parameter and the second imaging parameter are different, so that during the fusion process, the first imaging image can use the image information of the area corresponding to the area of ​​interest in the second imaging image to enhance the image quality of the area of ​​interest. Furthermore, the second imaging image is an image corresponding to the entire area of ​​the object to be imaged, and its image shape is a regular shape. Compared with an unconventional shape, the parameter control of the second imaging parameter is relatively simple. Furthermore, because the second imaging image is an image corresponding to the entire area, in addition to the area of ​​interest, the image outside the area of ​​interest is also displayed in real time, realizing real-time display of the entire area.

[0115] The first imaging image can be obtained by imaging the region of interest based only on the first imaging parameter, or further image processing can be performed on this basis, for example, display parameters can be obtained based on the category of the tissue structure of interest, based on the characteristics of the tissue structure of interest, or based on information combining the category and characteristics of the tissue structure of interest; the region of interest is imaged based on the first imaging parameter, and the image of the region of interest obtained based on the first imaging parameter is processed based on the display parameter to obtain the first imaging image. The first imaging image can be obtained by processing the image obtained by scanning the region of interest based only on the display parameter.

[0116] The region of interest includes a tissue structure background of interest and a tissue structure of interest; the display parameters are at least one of clarity, contrast of the tissue structure of interest, color of the tissue structure of interest, contrast of the boundary of the tissue structure of interest, color of the boundary of the tissue structure of interest, contrast of the background of the tissue structure of interest, and color of the background of the tissue structure of interest.

[0117] Among them, the display parameters are obtained based on the category of the tissue structure of interest, based on the characteristics of the tissue structure of interest, or based on the combination of the category and characteristics of the tissue structure of interest, including: matching the category and / or characteristics of the tissue structure of interest with a preset category-parameter correspondence table and / or characteristic-parameter correspondence table to obtain corresponding display parameters; or automatically iterating the optimal display parameters according to the category and / or characteristics of the tissue structure of interest in each region of interest.

[0118] In some embodiments, the display parameters can be used to form a first imaging image (as described above), and in some embodiments, the display parameters can also be used to form an imaging image. For example, the first imaging image and the second imaging image are fused, and the region of interest on the fused image is processed based on the display parameters to obtain an imaging image of the object to be imaged.

[0119] In addition to re-imaging the region of interest by calling different first imaging parameters according to the type and / or characteristics of the tissue structure, the enhanced imaging of the region of interest can also be performed by changing the display mode of the tissue structure in the region of interest during the second or multiple imaging of the local region of interest. Fig. 9 In the embodiment shown, except for the method of obtaining the first image (step 4', step 5') and Figure 3 Except for the different embodiments shown, other steps are the same.

[0120] In step 4', the processor derives display parameters based on the category and / or characteristics of the tissue structure of interest.

[0121] In step 5', the processor processes the image obtained by imaging the region of interest based on the display parameters to obtain a first imaging image.

[0122] It can be seen that this embodiment can highlight or display some or all important tissue structures in the region of interest in different colors according to the categories and / or characteristics of the tissue structures in the region of interest identified, for example, highlight the identified calcification points and display the areas that are not calcification points in blue. It is also possible to draw corresponding contour edges for important tissue structures, such as drawing the boundaries of masses in the region of interest. It is also possible to enhance the boundary contrast of important tissues, such as using different enhanced contrasts according to the sizes and characteristics of different tumors identified. It is also possible to use different image enhancement algorithms for different regions of interest according to the identified categories and / or characteristics, such as for region of interest 1, the image contrast is not high, and the histogram equalization method is used to improve the clarity of the image, while for region of interest 2, more noise is detected, and bilateral filtering is used for noise reduction.

[0123] Since most of the steps and features in this embodiment are Figure 3The above has been described in detail in the illustrated embodiment and will not be described in detail here.

[0124] This document is described with reference to various exemplary embodiments. However, those skilled in the art will recognize that changes and modifications may be made to the exemplary embodiments without departing from the scope of this document. For example, various operating steps and components for performing the operating steps may be implemented in different ways (e.g., one or more steps may be deleted, modified, or incorporated into other steps) depending on the specific application or considering any number of cost functions associated with the operation of the system.

[0125] In addition, as will be appreciated by those skilled in the art, the principles of the present invention may be reflected in a computer program product on a computer-readable storage medium, which is pre-installed with a computer-readable program code. Any tangible, non-temporary computer-readable storage medium may be used, including magnetic storage devices (hard disks, floppy disks, etc.), optical storage devices (CD-ROMs, DVDs, Blu Ray disks, etc.), flash memory, and / or the like. These computer program instructions may be loaded onto a general-purpose computer, a special-purpose computer, or other programmable data processing device to form a machine, so that these instructions executed on a computer or other programmable data processing device may generate a device that implements a specified function. These computer program instructions may also be stored in a computer-readable memory, which may instruct a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory may form a manufactured product, including an implementation device that implements a specified function. The computer program instructions may also be loaded onto a computer or other programmable data processing device, so that a series of operating steps are performed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device may provide steps for implementing a specified function.

[0126] Although the principles of this invention have been shown in various embodiments, many modifications of structures, arrangements, proportions, elements, materials and components particularly suitable for specific environments and operational requirements can be used without departing from the principles and scope of this invention. The above modifications and other changes or amendments will be included in the scope of this invention.

[0127] The foregoing specific description has been described with reference to various embodiments. However, those skilled in the art will recognize that various modifications and changes can be made without departing from the scope of the present disclosure. Therefore, the consideration of the present disclosure will be illustrative rather than restrictive, and all these modifications will be included in its scope. Similarly, the advantages, other advantages and solutions to the problems of various embodiments have been described above. However, the benefits, advantages, solutions to the problems and any elements that can produce these, or make them more clear, should not be interpreted as critical, necessary or necessary. The term "include" and any other variants used in this article are all non-exclusive inclusions, so that the process, method, article or device including the list of elements not only includes these elements, but also includes other elements that are not explicitly listed or do not belong to the process, method, system, article or device. In addition, the term "coupled" and any other variants used in this article refer to physical connections, electrical connections, magnetic connections, optical connections, communication connections, functional connections and / or any other connections.

[0128] Those skilled in the art will appreciate that many changes may be made to the details of the above-described embodiments without departing from the basic principles of the invention. Therefore, the scope of the invention should be determined from the following claims.

Claims

1. An imaging method, It is characterized in that The steps include: Acquire an initial image of the object to be imaged; Acquire at least one region of interest of the object to be imaged based on the initial image; the region of interest includes a tissue structure background of interest and a tissue structure of interest; determining a category and / or a characteristic of a tissue structure of interest in the region of interest based on the region of interest; Obtaining first imaging parameters and display parameters based on the category and / or characteristics of the tissue structure of interest; The display parameter is at least one of: clarity, contrast of the tissue structure of interest, color of the tissue structure of interest, contrast of the boundary of the tissue structure of interest, color of the boundary of the tissue structure of interest, contrast of the background of the tissue structure of interest, and color of the background of the tissue structure of interest; Imaging the region of interest based on the first imaging parameter, and processing the image obtained by imaging the region of interest based on the display parameter to obtain a first imaging image; Imaging the entire area of ​​the object to be imaged based on second imaging parameters to obtain a second imaging image, wherein the first imaging parameters and the second imaging parameters are at least partially different; The first imaging image and the second imaging image are fused to obtain an imaging image of the object to be imaged.

2. The method according to claim 1, It is characterized in that The step of obtaining a first imaging parameter or a display parameter based on the category and / or characteristic of the tissue structure of interest comprises: Matching the category and / or characteristic of the tissue structure of interest with a preset category-parameter correspondence table or characteristic-parameter correspondence table to obtain a corresponding first imaging parameter or display parameter; or, According to the category and / or characteristics of the tissue structure of interest in each region of interest, the optimal imaging parameters are automatically iterated to be used as the first imaging parameters or the optimal display parameters are automatically iterated to be obtained.

3. The method according to claim 1, It is characterized in that The first imaging parameter and the second imaging parameter are at least one of: an emission frequency, an emission voltage, a line density, a number of focal points, a focal point position, a speckle noise suppression parameter and an image enhancement parameter.

4. The method according to claim 1, It is characterized in that The step of acquiring at least one region of interest of the object to be imaged based on the initial image, and determining the category or characteristic of the tissue structure of interest in the region of interest based on the region of interest comprises: The category and / or characteristics of at least one region of interest and its tissue structure of interest are learned by a deep learning method.

5. The method according to claim 1, It is characterized in that The step of determining the category and / or characteristic of the tissue structure of interest in the region of interest based on the region of interest comprises: Determining the preset category and / or characteristic selected by the operator using the human-computer interaction interface for the tissue structure of interest as the category and / or characteristic of the tissue structure of interest; or, Acquire the points set by the operator using the human-computer interaction interface, determine the range of image recognition according to the points, and determine the category and / or characteristics of the tissue structure of interest based on the region of interest by an image recognition method; or, The category and / or characteristics of the tissue structure of interest are determined based on the region of interest by an image recognition method.

6. The method according to claim 5, It is characterized in that The step of determining the category and / or characteristics of the tissue structure of interest based on the region of interest by an image recognition method comprises: Extracting features of the region of interest of the initial image to obtain features of the region of interest, matching the features of the region of interest with features of the first sample image, and using the category and / or characteristic corresponding to the matched first sample image as the category and / or characteristic of the tissue structure of interest in the region of interest; or, Acquire motion features in the region of interest of the initial image, and determine the category and / or characteristic of the tissue structure of interest in the region of interest based on the motion features; or, The category and / or characteristics of the tissue structure of interest in at least one region of interest are learned by a deep learning method.

7. The method according to claim 1, It is characterized in that The acquiring at least one region of interest of the object to be imaged based on the initial image includes: acquiring the image type of the initial image, and matching the region of interest of the initial image with a corresponding first sample template image based on the image type of the initial image to obtain the at least one region of interest.

8. The method according to claim 7, It is characterized in that The step of obtaining the image type of the initial image comprises: Acquire an image type of the initial image specified by an operator; or, Extracting features from the initial image to obtain features of the initial image, matching the features of the initial image with features of the first sample feature image to obtain an image type of the initial image; or, The image type of the initial image is obtained by learning through a deep learning method.

9. The method according to claim 1, It is characterized in that The step of fusing the first imaging image and the second imaging image to obtain the imaging image of the object to be imaged comprises: Acquire a first fusion parameter of the first imaging image and a second fusion parameter of the second imaging image; The first imaging image and the second imaging image are fused based on the first fusion parameter and the second fusion parameter to obtain an imaging image of the object to be imaged.

10. The method according to claim 1, It is characterized in that The acquired regions of interest are multiple; the step of imaging the regions of interest based on the first imaging parameters to obtain a first imaging image comprises: Based on each first imaging parameter, a corresponding region of interest on the object to be imaged is imaged through one scan to obtain a first imaging image of each region of interest.

11. The method according to claim 1, It is characterized in that The acquired regions of interest are multiple; the step of imaging the regions of interest based on the first imaging parameters to obtain a first imaging image comprises: Based on each first imaging parameter, the corresponding region of interest on the object to be imaged is imaged through multiple scans to obtain a first imaging image of each region of interest.

12. The method according to any one of claims 1 to 11, It is characterized in that The step of fusing the first imaging image and the second imaging image to obtain an imaging image of the object to be imaged comprises: The first imaging image and the second imaging image are fused, and a region of interest on the fused image is processed based on the display parameters to obtain an imaging image of the object to be imaged.

13. An imaging method, It is characterized in that The steps include: Acquire an initial image of the object to be imaged; Acquire at least one region of interest of the object to be imaged based on the initial image; determining a category and / or a characteristic of a tissue structure of interest in the region of interest based on the region of interest; Obtaining first imaging parameters and display parameters based on the category and / or characteristics of the tissue structure of interest; The display parameter is at least one of: clarity, contrast of the tissue structure of interest, color of the tissue structure of interest, contrast of the boundary of the tissue structure of interest, color of the boundary of the tissue structure of interest, contrast of the background of the tissue structure of interest, and color of the background of the tissue structure of interest; Imaging the region of interest based on the first imaging parameter to obtain a first imaging image; Imaging the entire area of ​​the object to be imaged based on second imaging parameters to obtain a second imaging image, wherein the first imaging parameters and the second imaging parameters are at least partially different; The first imaging image and the second imaging image are fused, and a region of interest on the fused image is processed based on the display parameters to obtain an imaging image of the object to be imaged.

14. The method according to claim 13, It is characterized in that The step of obtaining the first imaging parameter based on the category and / or characteristic of the tissue structure of interest comprises: Matching the category and / or characteristic of the tissue structure of interest with a preset category-parameter correspondence table and / or characteristic-parameter correspondence table to obtain corresponding first imaging parameters; or, According to the category and / or characteristics of the tissue structure of interest in each region of interest, the optimal imaging parameters are automatically iterated to be used as the first imaging parameters.

15. The method according to claim 13 or 14, It is characterized in that The step of imaging the region of interest based on the first imaging parameter to obtain a first imaging image comprises: Obtaining display parameters based on the category and / or characteristics of the tissue structure of interest; The region of interest is imaged based on the first imaging parameter, and the image obtained by imaging the region of interest is processed based on the display parameter to obtain a first imaging image.

16. The method according to claim 13, It is characterized in that The step of obtaining display parameters based on the category and / or characteristics of the tissue structure of interest comprises: Matching the category and / or characteristic of the tissue structure of interest with a preset category-parameter correspondence table and / or characteristic-parameter correspondence table to obtain corresponding display parameters; or, According to the category and / or characteristics of the tissue structure of interest in each region of interest, the optimal display parameters are automatically iterated.

17. The method according to claim 13, It is characterized in that The acquired regions of interest are multiple; the step of imaging the regions of interest based on the first imaging parameters to obtain a first imaging image comprises: Based on each first imaging parameter, a corresponding region of interest on the object to be imaged is imaged through one scan to obtain a first imaging image of each region of interest.

18. The method according to claim 13, It is characterized in that The acquired regions of interest are multiple; the step of imaging the regions of interest based on the first imaging parameters to obtain a first imaging image comprises: Based on each first imaging parameter, the corresponding region of interest on the object to be imaged is imaged through multiple scans to obtain a first imaging image of each region of interest.

19. An ultrasonic imaging device, It is characterized in that include: An ultrasonic probe, the ultrasonic probe being used to transmit ultrasonic waves to an object to be imaged so as to scan the object to be imaged, receive ultrasonic echoes returned from the object to be imaged, and convert the received ultrasonic echoes into electrical signals; An echo processing module, the echo processing module is used to obtain an ultrasonic echo signal according to the electrical signal; a processor, the processor being configured to obtain an imaging image of the object to be imaged according to the ultrasonic echo signal; and A display, the display being used to display an image of the object to be imaged: Wherein, the processor is further used for: Acquire an initial image of the object to be imaged; Acquire at least one region of interest of the object to be imaged based on the initial image; the region of interest includes a tissue structure background of interest and a tissue structure of interest; determining a category and / or a characteristic of a tissue structure of interest in the region of interest based on the region of interest; A first imaging parameter and a display parameter are obtained based on the category and / or characteristics of the tissue structure of interest; the display parameter is at least one of clarity, contrast of the tissue structure of interest, color of the tissue structure of interest, contrast of the boundary of the tissue structure of interest, color of the boundary of the tissue structure of interest, contrast of the background of the tissue structure of interest, and color of the background of the tissue structure of interest; Imaging the region of interest based on the first imaging parameter, processing the image obtained by imaging the region of interest based on the display parameter to obtain a first imaging image, imaging the entire region of the object to be imaged based on the second imaging parameter to obtain a second imaging image; or imaging the region of interest based on the first imaging parameter to obtain a first imaging image, fusing the first imaging image and the second imaging image, and processing the region of interest on the fused image based on the display parameter to obtain an imaging image of the object to be imaged; wherein the first imaging parameter and the second imaging parameter are at least partially different; fusing the first imaging image and the second imaging image to obtain a fused imaging image of the object to be imaged; The display is also used to display the fused imaging image.

20. The ultrasonic imaging device according to claim 19, It is characterized in that The processor obtains a first imaging parameter based on the category and / or characteristic of the tissue structure of interest, including: Matching the category and / or characteristic of the tissue structure of interest with a preset category-parameter correspondence table and / or characteristic-parameter correspondence table to obtain corresponding first imaging parameters; or, According to the category and / or characteristics of the tissue structure of interest in each region of interest, the optimal imaging parameters are automatically iterated to be used as the first imaging parameters.

21. The ultrasonic imaging device according to claim 19, It is characterized in that The acquired regions of interest are multiple; the processor images the regions of interest based on the first imaging parameter to obtain a first imaging image, including: Based on each first imaging parameter, a corresponding region of interest on the object to be imaged is imaged through one scan to obtain a first imaging image of each region of interest.

22. The ultrasonic imaging device according to claim 19, It is characterized in that The acquired regions of interest are multiple; the processor images the regions of interest based on the first imaging parameter to obtain a first imaging image, including: Based on each first imaging parameter, the corresponding region of interest on the object to be imaged is imaged through multiple scans to obtain a first imaging image of each region of interest.

23. The ultrasonic imaging device according to claim 19, It is characterized in that The first imaging parameter and the second imaging parameter are at least one of: an emission frequency, an emission voltage, a line density, a number of focal points, a focal point position, a speckle noise suppression parameter and an image enhancement parameter.

24. The ultrasonic imaging device according to claim 19, It is characterized in that The processor acquires at least one region of interest of the object to be imaged based on the initial image, and determines the category and / or characteristic of the tissue structure of interest in the region of interest based on the region of interest, including: The category and / or characteristics of at least one region of interest and its tissue structure of interest are learned by a deep learning method.

25. The ultrasonic imaging device according to claim 19, It is characterized in that The processor determines, based on the region of interest, a category and / or a characteristic of an interested tissue structure in the region of interest, including: Determining the preset category and / or characteristic selected by the operator using the human-computer interaction interface for the tissue structure of interest as the category and / or characteristic of the tissue structure of interest; or, Acquire the points set by the operator using the human-computer interaction interface, determine the range of image recognition according to the points, and determine the category and / or characteristics of the tissue structure of interest based on the region of interest by an image recognition method; or, The category and / or characteristics of the tissue structure of interest are determined based on the region of interest by an image recognition method.

26. The ultrasonic imaging device according to claim 25, It is characterized in that The processor determines the category and / or characteristics of the tissue structure of interest based on the region of interest by an image recognition method, including: Extracting features of the region of interest of the initial image to obtain features of the region of interest, matching the features of the region of interest with features of the first sample image, and using the category and / or characteristic corresponding to the matched first sample image as the category and / or characteristic of the tissue structure of interest in the region of interest; or, Acquire motion features in the region of interest of the initial image, and determine the category and / or characteristic of the tissue structure of interest in the region of interest based on the motion features; or, The category and / or characteristics of the tissue structure of interest in at least one region of interest are learned by a deep learning method.

27. The ultrasonic imaging device according to claim 19, It is characterized in that The processor acquires at least one region of interest of the object to be imaged based on the initial image, including: acquiring an image type of the initial image, and matching the region of interest of the initial image with a corresponding first sample template image based on the image type of the initial image to obtain the at least one region of interest.

28. The ultrasonic imaging device according to claim 27, It is characterized in that The processor obtains the image type of the initial image, including: Acquire an image type of the initial image specified by an operator; or, Extracting features from the initial image to obtain features of the initial image, matching the features of the initial image with features of the first sample feature image to obtain an image type of the initial image; or, The image type of the initial image is obtained by learning through a deep learning method.

29. The ultrasonic imaging device according to claim 19, It is characterized in that The fusing the first imaging image and the second imaging image to obtain the imaging image of the object to be imaged includes: Acquire a first fusion parameter of the first imaging image and a second fusion parameter of the second imaging image; The first imaging image and the second imaging image are fused based on the first fusion parameter and the second fusion parameter to obtain an imaging image of the object to be imaged.

30. A computer readable storage medium, It is characterized in that The method comprises a program which can be executed by a processor to implement the method according to any one of claims 1 to 18. 31.An imaging method, It is characterized in that The steps include: Acquire an initial image of the object to be imaged; Acquire at least one region of interest of the object to be imaged based on the initial image; the region of interest includes a tissue structure background of interest and a tissue structure of interest; Acquire the category and / or characteristic of the tissue structure of interest in the region of interest based on the region of interest; Obtaining display parameters based on the category and / or characteristics of the tissue structure of interest; The display parameter is at least one of: clarity, contrast of the tissue structure of interest, color of the tissue structure of interest, contrast of the boundary of the tissue structure of interest, color of the boundary of the tissue structure of interest, contrast of the background of the tissue structure of interest, and color of the background of the tissue structure of interest; Imaging the region of interest based on the first imaging parameter, and processing the image obtained by imaging the region of interest based on the display parameter to obtain a first imaging image; Imaging the entire area of ​​the object to be imaged based on second imaging parameters to obtain a second imaging image, wherein the first imaging parameters and the second imaging parameters are at least partially different; The first imaging image and the second imaging image are fused to obtain an imaging image of the object to be imaged.

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