System optimization based ultrasonic transducer sensitivity attenuation compensation method and system

Abstract

The present application relates to the technical field of ultrasonic examination equipment, and particularly relates to an ultrasonic transducer sensitivity attenuation compensation method and system based on system optimization, which comprises the following steps: acquiring a first imaging sequence and identifying a sensitivity reference region; performing feature extraction on the sensitivity reference region and constructing an image quality score, and determining whether the sensitivity needs to be compensated according to the image quality score; finding a calibration site from a second imaging sequence and generating a prompt information; moving the ultrasonic probe to the calibration site according to the prompt information, and then performing closed-loop gain adjustment to calibrate the sensitivity. In view of the problem that the existing ultrasonic calibration scheme depends on a specific target for use, the process of judging whether the image quality of the current ultrasonic image needs to be calibrated in real time is introduced, and the echo information in the image is combined to screen out a uniform tissue site that can be used for calibration in the real-time scanning process, and the calibration is performed based on the site, so that the specific process of target calibration is omitted.

Description

Technical Field

[0001] This invention relates to the field of ultrasonic examination equipment technology, and specifically to a method and system for compensating for the sensitivity attenuation of ultrasonic transducers based on system optimization. Background Technology

[0002] A transducer is a device that converts electrical energy into acoustic energy, playing a crucial role in ultrasonic testing equipment. It utilizes the piezoelectric effect of materials to convert electrical energy into acoustic energy, thereby transmitting and receiving ultrasonic waves during ultrasonic testing. Medical ultrasound examination (ultrasound examination, ultrasound diagnostics) is a medical imaging diagnostic technique based on ultrasound (sound waves) to visualize muscles and internal organs, including their size, structure, and pathological lesions. Because the transducer uses piezoelectric materials, typically piezoelectric ceramics, to achieve sound-to-electric conversion, the elastic modulus of the material itself decreases with increasing vibration frequency, leading to a decrease in signal transmission and reception gain, thus affecting the imaging process.

[0003] To address this issue, existing technologies typically require sensitivity calibration and recalibration of the ultrasound equipment.

[0004] For example, Chinese patent CN201610680708.5 discloses an ultrasonic calibration method and apparatus. The ultrasonic calibration method includes: when a mobile device meets calibration conditions, emitting a first ultrasonic wave with a predetermined frequency using an ultrasonic transmitter at a predetermined emission intensity; receiving a second ultrasonic wave reflected from the first ultrasonic wave using an ultrasonic receiver to obtain the frequency response value of the second ultrasonic wave; calculating the absolute value of the difference between the frequency response value of the second ultrasonic wave and a standard frequency response value corresponding to a predetermined frequency; and increasing the emission intensity of the ultrasonic wave emitted by the ultrasonic transmitter when the absolute value is greater than a predetermined difference threshold. This solves the technical problem that mobile devices cannot automatically calibrate to the optimal ultrasonic emission intensity adapted to the current environment, achieving the technical effect of receiving stable intensity ultrasonic signals in various environments.

[0005] For example, Chinese patent CN201811472207.3 discloses an ultrasonic probe calibration method, which includes obtaining the blind zone of the ultrasonic probe at a preset detection depth; placing the ultrasonic probe under a three-dimensional magnetic field and obtaining a first image obtained by the ultrasonic probe scanning air and a second image obtained by the acoustic lens of the ultrasonic probe touching the tip of a free sensor coated with coupling agent; selecting a first region and a second region with the longest continuous color difference on the corresponding arc according to the color difference ratio, and calculating the intermediate coordinates of the first region and the second region; calculating the two-dimensional coordinates of the tip of the free sensor in the second image based on the intermediate coordinates of the first region and the second region and the blind zone, and converting them into the three-dimensional coordinates to be measured; establishing a ternary equation system based on the three-dimensional coordinates to be measured and the actual three-dimensional coordinates of the magnetic field received by the free sensor, and calculating the correction value of the ultrasonic probe under the three-dimensional magnetic field. This method can effectively reduce measurement errors and improve three-dimensional positioning accuracy.

[0006] However, in actual implementation, the inventors found that this type of calibration process usually requires a specific target, such as emitting ultrasonic signals and collecting echoes through a specific marker, and calibrating according to relevant calibration parameters. This leads to the problem of relatively inconvenient calibration in application. Summary of the Invention

[0007] To address the aforementioned problems in the existing technology, a method for compensating for the sensitivity attenuation of ultrasonic transducers based on system optimization is provided.

[0008] On the other hand, an ultrasonic transducer sensitivity attenuation compensation system for implementing the method is also provided.

[0009] The specific technical solution is as follows:

[0010] A method for compensating for sensitivity attenuation of ultrasonic transducers based on system optimization, comprising:

[0011] Step S1: Acquire the first imaging sequence and identify the sensitivity reference area during the scanning process;

[0012] Step S2: Extract features from the sensitivity reference region and construct an image quality score. When it is determined from the image quality score that sensitivity compensation is needed, proceed to step S3.

[0013] Step S3: Locate the calibration area from the second imaging sequence and generate a prompt message;

[0014] Step S4: Move the ultrasound probe to the calibration location according to the prompt information, and then perform closed-loop gain adjustment to calibrate the sensitivity.

[0015] On the other hand, step S1 includes:

[0016] Step S11: During the scanning process, acquire the first imaging sequence and the corresponding ultrasound echo sequence and register them;

[0017] Step S12: Filter the bone tissue appearance time according to the echo gain of the ultrasound echo sequence;

[0018] Step S13: Extract the bone tissue region image from the first imaging requirement according to the bone tissue appearance time;

[0019] Step S14: Segment the bone tissue region image and determine the image uniformity. When the image uniformity meets the uniformity condition, output the bone tissue region image as the sensitivity reference region.

[0020] On the other hand, step S2 includes:

[0021] Step S21: Extract noise information from the sensitivity reference region and evaluate the image signal-to-noise ratio to form a first score;

[0022] Step S22: Dilate the sensitivity reference region to form an expanded region, and evaluate the edge pixel width of the sensitivity reference region in the expanded region to obtain a second score;

[0023] Step S23: Evaluate the gain value corresponding to the sensitivity reference region to obtain a third score;

[0024] Step S24: Generate the image quality score based on the first score, the second score, and the third score, and compare it with the image quality threshold.

[0025] On the other hand, step S3 includes:

[0026] Step S31: During the acquisition of the second imaging sequence, the second imaging sequence is stitched together to construct a complete view image;

[0027] Step S32: Perform edge extraction on the complete view image to obtain multiple closed regions;

[0028] Step S33: Filter the image gain value of the closed region to obtain the neutral tissue region;

[0029] Step S33: Calculate the image uniformity and the rate of change of image edge curvature for each of the neutral tissue regions to obtain a tissue score, and determine the calibration site according to the tissue score.

[0030] On the other hand, step S4 includes:

[0031] Step S41: Display the orientation of the calibration part in the complete view image on the display device;

[0032] Step S42: After starting the acquisition, pre-adjust the image gain value according to the calibration area;

[0033] Step S43: Adjust the sensitivity a second time according to the image edge of the calibration area to make the edge clear.

[0034] An ultrasonic transducer sensitivity attenuation compensation system based on system optimization is used to implement the above-mentioned ultrasonic transducer sensitivity attenuation compensation method.

[0035] The ultrasonic transducer sensitivity attenuation compensation system includes:

[0036] The first identification module acquires a first imaging sequence and identifies a sensitivity reference area during the scanning process;

[0037] An evaluation module is connected to the first identification module;

[0038] The evaluation module extracts features from the sensitivity reference region and constructs an image quality score;

[0039] A second identification module is connected to the evaluation module;

[0040] The second identification module locates the calibration site from the second imaging sequence and generates a prompt message;

[0041] An adjustment module, wherein the adjustment module is connected to the second identification module;

[0042] The adjustment module moves the ultrasound probe to the calibration position according to the prompt information, and then performs closed-loop gain adjustment to calibrate the sensitivity.

[0043] On the other hand, the first identification module includes:

[0044] A registration module, which acquires and registers the first imaging sequence and the corresponding ultrasound echo sequence during the scanning process;

[0045] A time determination module, which is connected to the registration module;

[0046] The time determination module obtains the bone tissue appearance time by filtering according to the echo gain of the ultrasound echo sequence;

[0047] An image cropping module, wherein the image cropping module is connected to the time determination module;

[0048] The image cropping module extracts images of bone tissue regions from the first imaging requirement according to the time of bone tissue appearance.

[0049] A uniformity determination module, which is connected to the image cropping module;

[0050] The uniformity judgment module segments the bone tissue region image and judges the image uniformity. When the image uniformity meets the uniformity condition, the bone tissue region image is output as the sensitivity reference region.

[0051] On the other hand, the evaluation module includes:

[0052] The first scoring module extracts noise information from the sensitivity reference region and evaluates the image signal-to-noise ratio to form a first score.

[0053] A second scoring module is connected to the first scoring module.

[0054] The second scoring module dilates the sensitivity reference region to form an expanded region, and evaluates the edge pixel width of the sensitivity reference region in the expanded region to obtain a second score;

[0055] A third scoring module, which is connected to the second scoring module;

[0056] The third scoring module evaluates the gain value corresponding to the sensitivity reference region to obtain a third score;

[0057] A scoring comparison module, which is connected to the third scoring module;

[0058] The scoring comparison module generates the image quality score based on the first score, the second score, and the third score, and compares it with the image quality threshold.

[0059] On the other hand, the second identification module includes:

[0060] An image stitching module, which stitches together the second imaging sequence during the acquisition of the second imaging sequence to construct a complete view image;

[0061] An edge extraction module, which is connected to the image stitching module;

[0062] The edge extraction module performs edge extraction on the complete view image to obtain multiple closed regions;

[0063] A brightness filtering module, wherein the brightness filtering module is connected to the edge extraction module;

[0064] The brightness filtering module filters the image gain value of the closed region to obtain a neutral tissue region;

[0065] A location determination module, wherein the location determination module is connected to the brightness filtering module;

[0066] The site determination module calculates a tissue score based on the image uniformity and the rate of change of image edge curvature for each neutral tissue region, and determines the calibration site based on the tissue score.

[0067] On the other hand, the adjustment module includes:

[0068] An indicator module displays the orientation of the calibration location in the complete view image on a display device;

[0069] A first adjustment module, which is connected to the indicator module;

[0070] After the first adjustment module starts acquiring data, it performs pre-adjustment according to the image gain value of the calibration area.

[0071] A second adjustment module is connected to the first adjustment module;

[0072] The second adjustment module performs a secondary adjustment on the sensitivity according to the image edge of the calibration area to make the edge clear.

[0073] The above technical solution has the following advantages or beneficial effects:

[0074] To address the inconvenience of existing ultrasound calibration schemes that rely on specific targets, a process is introduced to determine in real time whether the image quality of the current ultrasound image requires calibration. By combining the echo information in the image, uniform tissue areas that can be used for calibration during real-time scanning are selected, and calibration is performed based on these areas, eliminating the need for the specific target calibration process. Attached Figure Description

[0075] Embodiments of the invention will be described more fully with reference to the accompanying drawings. However, the drawings are for illustration and explanation only and do not constitute a limitation on the scope of the invention.

[0076] Figure 1 This is an overall schematic diagram of an embodiment of the present invention;

[0077] Figure 2 This is a schematic diagram of step S1 in an embodiment of the present invention;

[0078] Figure 3 This is a schematic diagram of step S2 in an embodiment of the present invention;

[0079] Figure 4 This is a schematic diagram of step S3 in an embodiment of the present invention;

[0080] Figure 5This is a schematic diagram of step S4 in an embodiment of the present invention;

[0081] Figure 6 This is a schematic diagram of the system in an embodiment of the present invention;

[0082] Figure 7 This is a schematic diagram of the first identification module in an embodiment of the present invention;

[0083] Figure 8 This is a schematic diagram of the evaluation module in an embodiment of the present invention;

[0084] Figure 9 This is a schematic diagram of the second identification module in an embodiment of the present invention;

[0085] Figure 10 This is a schematic diagram of the adjustment module in an embodiment of the present invention; Detailed Implementation

[0086] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0087] Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0088] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other.

[0089] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, but this is not intended to limit the scope of the invention.

[0090] This invention includes:

[0091] A method for compensating for the sensitivity attenuation of ultrasonic transducers based on system optimization, such as... Figure 1 As shown, it includes:

[0092] Step S1: Acquire the first imaging sequence and identify the sensitivity reference area during the scanning process;

[0093] Step S2: Extract features from the sensitivity reference region and construct an image quality score. When it is determined from the image quality score that sensitivity compensation is needed, proceed to step S3.

[0094] Step S3: Locate the calibration area from the second imaging sequence and generate a prompt message;

[0095] Step S4: Move the ultrasound probe to the calibration position according to the prompts, and then perform closed-loop gain adjustment to calibrate the sensitivity.

[0096] Specifically, in view of the problem that existing ultrasound calibration schemes rely on specific targets and are inconvenient to use, in this embodiment, firstly, during the normal scanning process, a first imaging sequence is acquired and the image is identified frame by frame to determine whether the reconstructed ultrasound image in the first imaging sequence includes a specific tissue region that can be used to judge image sensitivity and is used as a sensitivity reference region.

[0097] In ultrasound scanning, ultrasound waves are typically emitted into the patient's tissues, and echo sequences are acquired. Real-time image reconstruction is then performed based on these echo sequences. Each emission-acquisition-reconstruction cycle generates a corresponding image frame. These images are stored according to their acquisition time, forming a first imaging sequence and a second imaging sequence. Generally, the first imaging sequence refers to the sequence generated during the normal image reconstruction process, while the second imaging sequence refers to the sequence that needs to be acquired and buffered after compensation; they have different storage areas in their data.

[0098] A sensitivity reference region refers to a tissue area in an ultrasound image that possesses specific image features and exhibits good consistency across different images, such as bone or muscle. Feature extraction is performed on the sensitivity reference region to construct an image quality score, thereby evaluating the effectiveness of the current transducer array in acquiring images. An image quality score threshold is pre-calibrated; failure to meet this threshold indicates significant image quality degradation, necessitating sensitivity calibration.

[0099] During the calibration process, the ultrasound reconstructed image sequence is acquired simultaneously and stored as the second imaging sequence. Then, the images in the second imaging sequence are identified to determine the most suitable areas for calibration.

[0100] Finally, move the ultrasound probe to the calibration position according to the prompts, and then perform closed-loop gain adjustment. During the gain adjustment process, judge the changes in image quality until the requirements are met to calibrate the sensitivity.

[0101] In one embodiment, such as Figure 2 As shown, step S1 includes:

[0102] Step S11: During the scanning process, acquire the first imaging sequence and the corresponding ultrasound echo sequence and register them;

[0103] Step S12: Filter the bone tissue appearance time according to the echo gain of the ultrasound echo sequence;

[0104] Step S13: Extract bone tissue region images from the first imaging sequence according to the bone tissue appearance time;

[0105] Step S14: Segment the bone tissue region image and determine the image uniformity. When the image uniformity meets the uniformity condition, output the bone tissue region image as the sensitivity reference region.

[0106] Specifically, in order to achieve a better sensitivity verification process, in this embodiment, the first imaging sequence and the corresponding ultrasound echo sequence are first acquired and registered during the scanning process. That is, for multiple frames of images in the first imaging sequence, the corresponding ultrasound echo sequence is reconstructed and aligned in chronological order.

[0107] Specifically, for ultrasound echo sequences, the received signal gain intensity of the corresponding ultrasound echo signal can be directly read. Because bone tissue exhibits a significant change in hardness compared to other tissue types, it will show a significantly higher signal gain in the echo. The occurrence time of bone tissue is determined by filtering based on the echo gain of the ultrasound echo sequence.

[0108] By referring to the time of bone tissue appearance, the first imaging sequence can be searched to obtain the bone tissue image at the corresponding time point.

[0109] Then, edge extraction is performed by combining the grayscale values ​​in the image to segment the bone tissue region image, thereby removing irrelevant background parts. For the segmented bone tissue region image, image uniformity is judged, including obtaining edge points according to the image edges, determining the center point of the bone tissue region image based on the set of edge points, and constructing a diameter line passing through the center point between the two longest edge points. Subsequently, the grayscale values ​​of multiple pixels along this diameter line are read, and the rate of change between grayscale values ​​is calculated as the image uniformity.

[0110] In another embodiment, an exponentially weighted moving average (EWMA) model can be used to perform sliding image evaluation on bone tissue regions to determine image uniformity.

[0111] For example, S_avg(t) = α·S_avg(t-1) + (1-α)·S_i(t);

[0112] In the formula, S_avg(t) is the uniformity parameter obtained by the sliding window at time t, S_avg(t-1) is the uniformity parameter obtained by the sliding window at time t-1, S_i(t) is the pixel mean calculated by the sliding window at time t, and α is an adjustment factor. The uniformity of the image is judged by whether the uniformity parameter changes significantly.

[0113] The image uniformity is judged to determine whether the uniformity condition is met. If the image uniformity meets the uniformity condition, the bone tissue region image is output as the sensitivity reference region.

[0114] In one embodiment, such as Figure 3 As shown, step S2 includes:

[0115] Step S21: Extract noise information from the sensitivity reference region and evaluate the image signal-to-noise ratio to form a first score;

[0116] Step S22: Dilate the sensitivity reference region to form an expanded region, and evaluate the edge pixel width of the sensitivity reference region in the expanded region to obtain a second score;

[0117] Step S23: Evaluate the gain value corresponding to the sensitivity reference region to obtain the third score;

[0118] Step S24: Generate an image quality score based on the first score, the second score, and the third score, and compare it with the image quality threshold.

[0119] Specifically, in order to achieve a better evaluation of image quality, in this embodiment, noise information is first extracted from the sensitivity reference area, and the image signal-to-noise ratio is evaluated. The image signal-to-noise ratio is then combined with the corresponding scale to form a first score.

[0120] Subsequently, the sensitivity reference region is dilated to form an expanded region encompassing multiple pixel lengths. For each pixel length within the expanded region, the contrast between adjacent pixels is calculated sequentially. When the contrast value exceeds a contrast threshold, the actual edge of the sensitivity reference region is considered reached. At this point, the edge pixel width is determined based on the number of pixels traversed within the expanded region. The edge pixel width is then evaluated using a relevant scale; a wider edge pixel width indicates a more blurred image, resulting in a second score.

[0121] Finally, the gain values ​​of multiple pixels corresponding to the sensitivity reference area are calculated to obtain the mean gain and standard deviation, and the third score is obtained by evaluating according to the relevant scale.

[0122] An image quality score is generated by adding the first, second, and third scores together and comparing it with an image quality threshold to determine whether the image has deteriorated.

[0123] In one embodiment, such as Figure 4 As shown, step S3 includes:

[0124] Step S31: During the acquisition of the second imaging sequence, the second imaging sequence is stitched together to construct a complete view image;

[0125] Step S32: Extract edges from the complete view image to obtain multiple closed regions;

[0126] Step S33: Filter the image gain values ​​of the closed region to obtain the neutral tissue region;

[0127] Step S34: Calculate the image uniformity and the rate of change of image edge curvature for each neutral tissue region to obtain a tissue score, and determine the calibration site based on the tissue score.

[0128] Specifically, in order to achieve a better calibration location determination process, in this embodiment, during the acquisition of the second imaging sequence, the second imaging sequence is first stitched together, including superimposing according to the overlapping parts in the images, thereby constructing a complete view image.

[0129] Subsequently, edge extraction is performed on the full-view image to obtain multiple closed regions, each of which corresponds to a portion of the tissue region.

[0130] Since different tissue regions have different tissue densities, their ultrasound reflection intensity also varies. Therefore, tissues are screened according to a pre-defined range of image gain values ​​to obtain neutral tissue regions.

[0131] Finally, a tissue score is calculated for the image uniformity and the rate of change of image edge curvature for each neutral tissue region. The calibration site is determined based on the tissue score. The calibration site has good image uniformity and relatively smooth edges, which makes it easy to observe the calibration effect.

[0132] In one embodiment, such as Figure 5 As shown, step S4 includes:

[0133] Step S41: Display the orientation of the calibration area in the complete view image on the display device;

[0134] Step S42: After starting the acquisition, pre-adjust the image gain value of the calibration site in conjunction with the human body reflection model;

[0135] Step S43: Adjust the sensitivity a second time according to the image edge of the calibration area to make the edge clear.

[0136] Specifically, in order to achieve better calibration results, in this embodiment, the position of the calibration site in the full view image is first displayed on the display device, so that the doctor can point the probe to a specific position according to the displayed image.

[0137] The probe is then controlled to begin acquisition, generating echoes and reconstructing images. To accurately measure image gain, the human body structure was pre-calibrated before acquisition began, and a set of tissue type-impedance compensation coefficient mapping tables was constructed.

[0138] Specifically, ultrasound imaging often requires traversing multiple interfaces, including coupling gel, skin layer, fat layer, muscle layer, and even bone. The skin layer can be simply represented by adipose tissue.

[0139] For the aforementioned tissue components, a tissue type-impedance compensation coefficient mapping table is pre-constructed.

[0140]

[0141] Based on the aforementioned compensation coefficients, the image gain coefficients can be compensated during the acquisition process to determine the actual image gain value at the corresponding calibration location.

[0142] Then, pre-adjustment is performed according to the image gain value so that the image gain value at the calibration location meets the set range.

[0143] Subsequently, the sensitivity is adjusted a second time according to the image edge of the calibration area to make the edge clear, so as to realize the closed-loop adjustment process.

[0144] An ultrasonic transducer sensitivity attenuation compensation system based on system optimization is used to implement the above-mentioned ultrasonic transducer sensitivity attenuation compensation method.

[0145] like Figure 6 As shown, the ultrasonic transducer sensitivity attenuation compensation system includes:

[0146] The first identification module 1 acquires the first imaging sequence and identifies the sensitivity reference area during the scanning process;

[0147] Evaluation module 2 is connected to the first identification module 1;

[0148] Evaluation module 2 extracts features from the sensitivity reference area and constructs an image quality score;

[0149] The second identification module 3 is connected to the evaluation module 2.

[0150] The second identification module 3 locates the calibration area from the second imaging sequence and generates a prompt message;

[0151] Adjustment module 4 is connected to the second identification module 3;

[0152] The adjustment module 4 moves the ultrasound probe to the calibration position according to the prompts, and then performs closed-loop gain adjustment to calibrate the sensitivity.

[0153] Specifically, in view of the problem that the existing ultrasound calibration schemes rely on specific targets and are inconvenient to use, in this embodiment, firstly, during the normal scanning process, the first identification module 1 acquires the first imaging sequence and performs frame-by-frame identification of the image to determine whether the reconstructed ultrasound image in the first imaging sequence includes a specific tissue region that can be used for image sensitivity judgment and serves as a sensitivity reference region.

[0154] In ultrasound scanning, ultrasound waves are typically emitted into the patient's tissues, and echo sequences are acquired. Real-time image reconstruction is then performed based on these echo sequences. Each emission-acquisition-reconstruction cycle generates a corresponding image frame. These images are stored according to their acquisition time, forming a first imaging sequence and a second imaging sequence. Generally, the first imaging sequence refers to the sequence generated during the normal image reconstruction process, while the second imaging sequence refers to the sequence that needs to be acquired and buffered after compensation; they have different storage areas in their data.

[0155] The sensitivity reference region refers to a tissue area in an ultrasound image that possesses specific image features and exhibits good consistency across different images, such as bone and muscle. Evaluation module 2 extracts features from the sensitivity reference region and constructs an image quality score to measure the effectiveness of the current transducer array in acquiring images. A pre-calibrated image quality score threshold is established; failure to meet this threshold indicates significant image quality degradation, necessitating sensitivity calibration.

[0156] During the calibration process, the second identification module 3 simultaneously acquires the image sequence of ultrasound reconstruction and stores it as the second imaging sequence. Then, it identifies the images in the second imaging sequence and determines the parts in the second imaging sequence that are more suitable for calibration as the calibration parts.

[0157] Finally, the adjustment module 4 moves the ultrasound probe to the calibration position according to the prompt information, and then performs closed-loop gain adjustment. During the gain adjustment process, it judges the changes in image quality until the requirements are met to calibrate the sensitivity.

[0158] In one embodiment, such as Figure 7 As shown, the first identification module 1 includes:

[0159] Registration module 11 acquires and registers the first imaging sequence and the corresponding ultrasound echo sequence during the scanning process;

[0160] Time determination module 12, which is connected to registration module 11;

[0161] The time determination module 12 obtains the bone tissue appearance time by filtering according to the echo gain of the ultrasound echo sequence;

[0162] Image capture module 13, image capture module 13 is connected to time determination module 12;

[0163] Image cropping module 13 extracts bone tissue region images from the first imaging sequence according to the bone tissue appearance time;

[0164] Uniformity judgment module 14, which is connected to image cropping module 13;

[0165] The uniformity judgment module 14 segments the bone tissue region image and judges the image uniformity. When the image uniformity meets the uniformity condition, the bone tissue region image is output as the sensitivity reference region.

[0166] Specifically, in order to achieve a better sensitivity verification process, in this embodiment, the registration module 11 first acquires and registers the first imaging sequence and the corresponding ultrasound echo sequence during the scanning process. That is, for multiple frames of images in the first imaging sequence, the corresponding ultrasound echo sequence is reconstructed and aligned in chronological order.

[0167] Specifically, for the ultrasound echo sequence, the received signal gain intensity of the corresponding ultrasound echo signal can be directly read. Because bone tissue exhibits a significant change in hardness compared to other tissue types, it will show a significantly higher signal gain in the echo. The time determination module 12 filters for the bone tissue appearance time based on the echo gain of the ultrasound echo sequence.

[0168] The image cropping module 13 then searches the first imaging sequence with reference to the time when the bone tissue appears, and can obtain the bone tissue image at the corresponding time point.

[0169] Then, the uniformity judgment module 14 performs edge extraction based on the grayscale values ​​in the image, segments the bone tissue region image, thereby removing irrelevant background parts. For the segmented bone tissue region image, it performs image uniformity judgment, including obtaining edge points according to the image edges, determining the center point of the bone tissue region image according to the set of edge points, and constructing a diameter line passing through the center point between the two longest edge points. Subsequently, it reads the grayscale values ​​of multiple pixels on the diameter line and calculates the rate of change between the grayscale values ​​as the image uniformity.

[0170] The image uniformity is judged to determine whether the uniformity condition is met. If the image uniformity meets the uniformity condition, the bone tissue region image is output as the sensitivity reference region.

[0171] In one embodiment, such as Figure 8 As shown, evaluation module 2 includes:

[0172] The first scoring module 21 extracts noise information from the sensitivity reference area and evaluates the image signal-to-noise ratio to form a first score.

[0173] The second scoring module 22 is connected to the first scoring module 21.

[0174] The second scoring module 22 expands the sensitivity reference region to form an expanded region, and evaluates the edge pixel width of the sensitivity reference region in the expanded region to obtain a second score.

[0175] The third scoring module 23 is connected to the second scoring module 22.

[0176] The third scoring module 23 evaluates the gain value corresponding to the sensitivity reference area to obtain the third score;

[0177] The scoring comparison module 24 is connected to the third scoring module 23.

[0178] The scoring comparison module 24 generates an image quality score based on the first score, the second score, and the third score, and compares it with the image quality threshold.

[0179] Specifically, in order to achieve a better evaluation of image quality, in this embodiment, the first scoring module 21 first extracts noise information from the sensitivity reference area and evaluates the image signal-to-noise ratio. Based on the image signal-to-noise ratio and the corresponding scale, it evaluates and forms a first score.

[0180] Subsequently, the second scoring module 22 dilates the sensitivity reference region to form an expanded region, which includes multiple pixel lengths. For each pixel length of the expanded region, the contrast between any two adjacent pixels is calculated sequentially. When the contrast value exceeds a contrast threshold, the actual edge of the sensitivity reference region is considered to have been reached. At this point, the edge pixel width is determined based on the number of pixels traversed within the expanded region. The edge pixel width is then evaluated according to a relevant scale; a larger edge pixel width indicates a more blurred image, resulting in a second score.

[0181] Finally, the third scoring module 23 calculates the gain value of each pixel corresponding to the sensitivity reference area, obtains the mean gain and standard deviation, and evaluates them according to the relevant scale to obtain the third score.

[0182] The scoring comparison module 24 adds up the first score, the second score and the third score to generate an image quality score, and compares it with the image quality threshold to determine whether the image has deterioration problems.

[0183] In one embodiment, such as Figure 9 As shown, the second identification module 3 includes:

[0184] Image stitching module 31 stitches the second imaging sequence during the acquisition of the second imaging sequence to construct a complete view image;

[0185] Edge extraction module 32, which is connected to image stitching module 31;

[0186] The edge extraction module 32 performs edge extraction on the complete view image to obtain multiple closed regions;

[0187] Brightness filtering module 33, which is connected to edge extraction module 32;

[0188] The brightness filtering module 33 filters the image gain values ​​of the closed region to obtain the neutral tissue region;

[0189] Location determination module 34, which is connected to brightness filtering module 33;

[0190] The site determination module 34 calculates the tissue score based on the image uniformity and the rate of change of image edge curvature for each neutral tissue region, and determines the calibration site based on the tissue score.

[0191] Specifically, in order to achieve a better calibration location determination process, in this embodiment, the image stitching module 31 first stitches the second imaging sequence during the acquisition of the second imaging sequence, including superimposing according to the overlapping parts in the image, thereby constructing a complete view image.

[0192] Subsequently, the edge extraction module 32 performs edge extraction in the complete view image to obtain multiple closed regions, each of which corresponds to a portion of the tissue region.

[0193] Since different tissue regions have different tissue densities, their ultrasound reflection intensity also varies. Therefore, the brightness screening module 33 screens the tissues according to the range of pre-calibrated image gain values ​​to obtain neutral tissue regions.

[0194] Finally, the site determination module 34 calculates the image uniformity and the rate of change of image edge curvature for each neutral tissue region to obtain a tissue score, and determines the calibration site according to the tissue score. The calibration site has good image uniformity and relatively smooth edges, making it easy to observe the calibration effect.

[0195] In one embodiment, such as Figure 10 As shown, the adjustment module 4 includes:

[0196] Indicator module 41 displays the orientation of the calibration part in the full view image on the display device;

[0197] First adjustment module 42, first adjustment module 42 is connected to indicator module 41;

[0198] After the first adjustment module 42 starts acquiring data, it performs pre-adjustment based on the image gain value of the calibration site and the human body reflection model.

[0199] The second adjustment module 43 is connected to the first adjustment module 42.

[0200] The second adjustment module 43 performs secondary sensitivity adjustment based on the image edge of the calibration area to make the edge clear.

[0201] Specifically, in order to achieve a better calibration effect, in this embodiment, the indicator module 41 first displays the position of the calibration site in the complete view image on the display device, so that the doctor can point the probe to a specific position according to the displayed image.

[0202] Subsequently, the first adjustment module 42 controls the probe to start acquiring data, generating echoes and reconstructing images. Based on the reconstructed images, the image gain value of the calibration area is first pre-adjusted to ensure that the image gain value of the calibration area meets expectations.

[0203] Subsequently, the second adjustment module 43 adjusts the sensitivity a second time according to the image edge of the calibration area to make the edge clear, so as to realize the closed-loop adjustment process.

[0204] Those skilled in the art will understand that various aspects, or possible implementations of various aspects, of the present invention can be embodied as systems, methods, or computer program products. Therefore, various aspects, or possible implementations of various aspects, of the present invention can take the form of entirely hardware embodiments, entirely software embodiments (including firmware, resident software, etc.), or embodiments combining software and hardware aspects, all collectively referred to herein as "circuit," "module," or "system." Furthermore, various aspects, or possible implementations of various aspects, of the present invention can take the form of computer program products, which are computer instructions stored in memory.

[0205] The memory can be a computer-readable signal medium or a computer-readable storage medium. Computer-readable storage media include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or apparatuses, or any suitable combination thereof, such as random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, and portable read-only memory (CD-ROM).

[0206] A processor in a computer reads computer instructions stored in memory, enabling the processor to execute the functional actions specified in each step or combination of steps in a flowchart; and to generate means for implementing the functional actions specified in each block or combination of blocks in a flowchart.

[0207] It should be understood that a processor in a computer can be understood as one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers (MCUs), microprocessors, or other electronic components used to execute the aforementioned computer instructions.

[0208] Computer instructions may be executed entirely on the user's local computer, partially on the user's local computer, as a separate software package, partially on the user's local computer and partially on a remote computer, or entirely on a remote computer or server. It should also be noted that in some alternative implementations, the functions indicated by the steps in the flowchart or the blocks in the block diagram may not occur in the order shown in the diagram. For example, depending on the functions involved, two consecutive steps or blocks may actually be executed approximately simultaneously, or these blocks may sometimes be executed in reverse order.

[0209] Of course, in practical applications, the various components of a computer system are coupled together through a bus system. The bus system is used to enable communication and connection between these components. In addition to the data bus, the bus system also includes a power bus, a control bus, and a status signal bus.

[0210] The above are merely preferred embodiments of the present invention and are not intended to limit the implementation methods and protection scope of the present invention. Those skilled in the art should recognize that any equivalent substitutions and obvious changes made based on the description and illustrations of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for compensating for sensitivity attenuation of an ultrasonic transducer based on system optimization, characterized in that, include: Step S1: Acquire the first imaging sequence and identify the sensitivity reference area during the scanning process; The sensitivity reference area is a bone tissue region image where the image uniformity meets the uniformity condition. Step S2: Extract features from the sensitivity reference region and construct an image quality score. When it is determined from the image quality score that sensitivity compensation is needed, proceed to step S3. Step S3: Locate the calibration area from the second imaging sequence and generate a prompt message; Step S4: Move the ultrasound probe to the calibration location according to the prompt information, and then perform closed-loop gain adjustment to calibrate the sensitivity.

2. The ultrasonic transducer sensitivity attenuation compensation method according to claim 1, characterized in that, Step S1 includes: Step S11: During the scanning process, acquire the first imaging sequence and the corresponding ultrasound echo sequence and register them; Step S12: Filter the bone tissue appearance time according to the echo gain of the ultrasound echo sequence; Step S13: Extract bone tissue region images from the first imaging sequence according to the bone tissue appearance time; Step S14: Segment the bone tissue region image and determine the image uniformity. When the image uniformity meets the uniformity condition, output the bone tissue region image as the sensitivity reference region.

3. The ultrasonic transducer sensitivity attenuation compensation method according to claim 1, characterized in that, Step S2 includes: Step S21: Extract noise information from the sensitivity reference region and evaluate the image signal-to-noise ratio to form a first score; Step S22: Dilate the sensitivity reference region to form an expanded region, and evaluate the edge pixel width of the sensitivity reference region in the expanded region to obtain a second score; Step S23: Evaluate the gain value corresponding to the sensitivity reference region to obtain a third score; Step S24: Generate the image quality score based on the first score, the second score, and the third score, and compare it with the image quality threshold.

4. The ultrasonic transducer sensitivity attenuation compensation method according to claim 1, characterized in that, Step S3 includes: Step S31: During the acquisition of the second imaging sequence, the second imaging sequence is stitched together to construct a complete view image; Step S32: Perform edge extraction on the complete view image to obtain multiple closed regions; Step S33: Filter the image gain value of the closed region to obtain the neutral tissue region; Step S34: Calculate the image uniformity and the rate of change of image edge curvature for each of the neutral tissue regions to obtain a tissue score, and determine the calibration site according to the tissue score.

5. The ultrasonic transducer sensitivity attenuation compensation method according to claim 4, characterized in that, Step S4 includes: Step S41: Display the orientation of the calibration part in the complete view image on the display device; Step S42: After starting the acquisition, pre-adjust the image gain value of the calibration area in combination with the human body reflection model; Step S43: Adjust the sensitivity a second time according to the image edge of the calibration area to make the edge clear.

6. A system for compensating for the sensitivity attenuation of an ultrasonic transducer based on system optimization, characterized in that, Used to implement the ultrasonic transducer sensitivity attenuation compensation method as described in any one of claims 1-5; The ultrasonic transducer sensitivity attenuation compensation system includes: The first identification module acquires a first imaging sequence and identifies a sensitivity reference area during the scanning process; The sensitivity reference area is a bone tissue region image where the image uniformity meets the uniformity condition. An evaluation module is connected to the first identification module; The evaluation module extracts features from the sensitivity reference region and constructs an image quality score; A second identification module is connected to the evaluation module; The second identification module locates the calibration site from the second imaging sequence and generates a prompt message; An adjustment module, wherein the adjustment module is connected to the second identification module; The adjustment module moves the ultrasound probe to the calibration position according to the prompt information, and then performs closed-loop gain adjustment to calibrate the sensitivity.

7. The ultrasonic transducer sensitivity attenuation compensation system according to claim 6, characterized in that, The first identification module includes: A registration module, which acquires and registers the first imaging sequence and the corresponding ultrasound echo sequence during the scanning process; A time determination module, which is connected to the registration module; The time determination module obtains the bone tissue appearance time by filtering according to the echo gain of the ultrasound echo sequence; An image cropping module, wherein the image cropping module is connected to the time determination module; The image cropping module extracts images of bone tissue regions from the first imaging sequence according to the time of bone tissue appearance. A uniformity determination module, which is connected to the image cropping module; The uniformity judgment module segments the bone tissue region image and judges the image uniformity. When the image uniformity meets the uniformity condition, the bone tissue region image is output as the sensitivity reference region.

8. The ultrasonic transducer sensitivity attenuation compensation system according to claim 6, characterized in that, The evaluation module includes: The first scoring module extracts noise information from the sensitivity reference region and evaluates the image signal-to-noise ratio to form a first score. A second scoring module is connected to the first scoring module. The second scoring module dilates the sensitivity reference region to form an expanded region, and evaluates the edge pixel width of the sensitivity reference region in the expanded region to obtain a second score; A third scoring module, which is connected to the second scoring module; The third scoring module evaluates the gain value corresponding to the sensitivity reference region to obtain a third score; A scoring comparison module, which is connected to the third scoring module; The scoring comparison module generates the image quality score based on the first score, the second score, and the third score, and compares it with the image quality threshold.

9. The ultrasonic transducer sensitivity attenuation compensation system according to claim 6, characterized in that, The second identification module includes: An image stitching module, which stitches together the second imaging sequence during the acquisition of the second imaging sequence to construct a complete view image; An edge extraction module, which is connected to the image stitching module; The edge extraction module performs edge extraction on the complete view image to obtain multiple closed regions; A brightness filtering module, wherein the brightness filtering module is connected to the edge extraction module; The brightness filtering module filters the image gain value of the closed region to obtain a neutral tissue region; A location determination module, wherein the location determination module is connected to the brightness filtering module; The site determination module calculates a tissue score based on the image uniformity and the rate of change of image edge curvature for each neutral tissue region, and determines the calibration site based on the tissue score.

10. The ultrasonic transducer sensitivity attenuation compensation system according to claim 9, characterized in that, The adjustment module includes: An indicator module displays the orientation of the calibration location in the complete view image on a display device; A first adjustment module, which is connected to the indicator module; After the first adjustment module starts acquiring data, it performs pre-adjustment based on the image gain value of the calibration area and the human body reflection model. A second adjustment module is connected to the first adjustment module; The second adjustment module performs a secondary adjustment on the sensitivity according to the image edge of the calibration area to make the edge clear.

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