A method and device for selecting microflow gain in an ultrasound CDFI mode
Patent Information
- Application Number
- CN202611168211.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-08-03
- Publication Date
- 2026-10-30
AI Technical Summary
但超声微血流成像场景具备极强特殊性，人体微血管管径细小、血流速度极低、超声回波信号极其微弱，同时周围软组织、脂肪、筋膜等组织会产生大量杂波与背景噪声，成像清晰度比常规大血管血流成像低，采用深度学习等分割模型，很难将此类微血管分割出来
[0035]本发明提供一种超声CDFI模式下微血流增益选择方法，在超声CDFI模式下以固定增益步进值递增采集得到每个增益下的彩色血流图，并计算每个增益下的彩色血流图中彩色像素面积占采样面积的比值，构建增益与血流面积比值一一对应的离散数据集，基于所述离散数据集，分别对预设指数饱和函数和预设广义logistic函数进行拟合，得到第一拟合曲线的第一最佳增益和第一拟合误差以及第二拟合曲线的第二最佳增益和第二拟合误差，比较这两个拟合误差，将较小的拟合误差对应的最佳增益作为最终的最佳增益。本发明方法在超声图像中微血管难以分割计算血流盈亏比以调整微血流增益的情况下，通过分别对预设指数饱和函数和预设广义logistic函数进行拟合，并比较两者的拟合误差，确定微血流最佳增益，选择最佳增益下的超声图像，能够清楚地显示微血流信号，避免增益过低导致漏诊、增益过高导致背景噪声和伪像干扰，提高诊断效率与准确性。
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Figure CN122997749A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to a method and apparatus for microflow gain selection in ultrasound CDFI mode. Background Technology
[0002] When detecting blood flow in anatomical regions such as the neck, abdomen, thyroid, and breast in ultrasound CDFI mode, the initial blood flow gain may not meet the imaging quality requirements, necessitating adjustment of the blood flow gain in ultrasound CDFI mode. A common method for adjusting blood flow gain in ultrasound CDFI mode involves segmenting the vessel contours in the ultrasound image and calculating the flow gain-loss ratio at the current gain, then dynamically adjusting the gain based on this ratio. However, ultrasound microvascular imaging presents unique challenges. Human microvessels are extremely small in diameter, have very low blood flow velocities, and produce extremely weak ultrasound echo signals. Furthermore, surrounding soft tissues, fat, fascia, and other tissues generate significant clutter and background noise, resulting in lower imaging clarity compared to conventional large vessel blood flow imaging. Using segmentation models such as deep learning, it is difficult to segment these microvessels effectively. Summary of the Invention
[0003] In order to select the optimal microflow gain in ultrasound CDFI mode to improve the display effect of microflow signals in ultrasound images, thereby improving diagnostic efficiency and accuracy, this invention provides a method and device for selecting microflow gain in ultrasound CDFI mode.
[0004] In a first aspect, the present invention provides a method for micro-flow gain selection in ultrasound CDFI mode, the method comprising:
[0005] In ultrasound CDFI mode, starting from the initial gain, the scan is performed by increasing the gain step value to the maximum gain, and color blood flow images are acquired at each gain.
[0006] Calculate the ratio of the area of colored pixels to the sampling area in the color blood flow map under each gain, obtain the blood flow area ratio under each gain, and construct a discrete dataset with a one-to-one correspondence between the gain and the blood flow area ratio.
[0007] Based on the discrete dataset, a preset exponential saturation function is fitted to obtain the first optimal gain and the first fitting error of the first fitting curve.
[0008] Based on the discrete dataset, a preset generalized logistic function is fitted to obtain the second optimal gain and the second fitting error of the second fitting curve.
[0009] Compare the first fitting error and the second fitting error, and take the best gain corresponding to the smaller fitting error as the final best gain.
[0010] In one or more optional embodiments of this application, based on the discrete dataset, the following preset exponential saturation function is fitted to obtain the first optimal gain of the first fitted curve:
[0011] ,
[0012] In the formula, R represents the blood flow area ratio, G represents the gain, G0 represents the first optimal gain, and k represents the first exponential growth coefficient.
[0013] In one or more optional embodiments of this application, the first fitting error of the first fitting curve is calculated according to the following formula:
[0014] ,
[0015] In the formula, e1 represents the first fitting error of the first fitted curve, N+1 represents the total number of discrete data points, and R... n G represents the blood flow area ratio at the nth discrete data point. n This represents the gain of the nth discrete data point. This represents the first optimal gain obtained from the fitting. This represents the first exponential growth coefficient obtained from the fitting.
[0016] In one or more optional embodiments of this application, based on the discrete dataset, the following preset generalized logistic function is fitted to obtain the second optimal gain of the second fitting curve:
[0017] ,
[0018] In the formula, R represents the blood flow area ratio, and G represents the gain. This indicates the second-best gain. This represents the second exponential growth coefficient. This represents an asymmetric parameter.
[0019] In one or more optional embodiments of this application, the second fitting error of the second fitting curve is calculated according to the following formula:
[0020] ,
[0021] In the formula, e2 represents the second fitting error of the second fitted curve, and N+1 represents the total number of discrete data points. G represents the blood flow area ratio at the nth discrete data point. n This represents the gain of the nth discrete data point. This represents the second-best gain obtained from the fitting. This represents the second exponential growth coefficient obtained from the fitting. This represents the asymmetric parameters obtained from the fitting.
[0022] In one or more optional embodiments of this application, the ratio of the area of color pixels to the sampling area in the color flow map under each gain is calculated to obtain the flow area ratio under each gain, including:
[0023] Based on the preset RGB value difference threshold, the color blood flow map under each gain is divided into a blood flow mask map including color pixels and background pixels;
[0024] Calculate the ratio of the area of colored pixels to the sampling area in the blood flow mask image to obtain the blood flow area ratio for each gain.
[0025] Secondly, the present invention provides a micro-blood flow gain selection device in ultrasound CDFI mode, comprising:
[0026] The color flow imaging module is used to scan in ultrasound CDFI mode, starting from the initial gain and increasing to the maximum gain with a fixed gain step value, to acquire color flow images at each gain.
[0027] The discrete dataset construction module is used to calculate the ratio of the area of colored pixels to the sampling area in the color blood flow map under each gain, obtain the blood flow area ratio under each gain, and construct a discrete dataset with a one-to-one correspondence between the gain and the blood flow area ratio.
[0028] The first fitting error calculation module is used to fit a preset exponential saturation function based on the discrete dataset to obtain the first optimal gain and the first fitting error of the first fitting curve.
[0029] The second fitting error calculation module is used to fit a preset generalized logistic function based on the discrete dataset to obtain the second optimal gain and the second fitting error of the second fitting curve.
[0030] The optimal gain selection module is used to compare the first fitting error and the second fitting error, and select the optimal gain corresponding to the smaller fitting error as the final optimal gain.
[0031] Thirdly, embodiments of the present invention provide a computer-readable storage medium having a computer program / instruction stored thereon, which, when executed by a processor, implements the micro-flow gain selection method in ultrasound CDFI mode as described in the first aspect above.
[0032] Fourthly, embodiments of the present invention provide a computer program product, including a computer program / instruction, which, when executed by a processor, implements the micro-flow gain selection method in ultrasound CDFI mode as described in the first aspect above.
[0033] Fifthly, embodiments of the present invention provide a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the micro-flow gain selection method in ultrasound CDFI mode as described in the first aspect above.
[0034] The beneficial effects of the above-described technical solutions provided in the embodiments of the present invention include at least the following:
[0035] This invention provides a method for selecting micro-blood flow gain in ultrasound CDFI mode. In ultrasound CDFI mode, color blood flow images are acquired at each gain level with a fixed gain increment. The ratio of the area of colored pixels to the sampling area in the color blood flow image at each gain is calculated, constructing a discrete dataset with a one-to-one correspondence between gain and blood flow area ratio. Based on this discrete dataset, a preset exponential saturation function and a preset generalized logistic function are fitted to obtain a first optimal gain and a first fitting error for a first fitted curve, and a second optimal gain and a second fitting error for a second fitted curve. These two fitting errors are compared, and the optimal gain corresponding to the smaller fitting error is taken as the final optimal gain. This method addresses situations where it is difficult to segment microvessels in ultrasound images and calculate the blood flow surplus / deficit ratio to adjust micro-blood flow gain. By fitting the preset exponential saturation function and the preset generalized logistic function separately and comparing their fitting errors, the optimal gain for micro-blood flow is determined. Selecting the ultrasound image at the optimal gain clearly displays the micro-blood flow signal, avoiding missed diagnoses due to excessively low gain and background noise and artifact interference due to excessively high gain, thus improving diagnostic efficiency and accuracy. Attached Figure Description
[0036] The accompanying drawings are provided to further illustrate the technical solutions of this application and constitute a part of the specification. They are used together with the embodiments of this application to explain the technical solutions of this application, and do not constitute a limitation on the technical solutions of this application. In the accompanying drawings:
[0037] Figure 1 This is a schematic diagram of the microflow gain selection method in ultrasound CDFI mode provided in the embodiments of this application;
[0038] Figure 2 Color Doppler flow map and flow mask map in ultrasound CDFI mode provided in the embodiments of this application;
[0039] Figure 3 A schematic diagram of the exponential saturation function and the generalized logistic function provided in the embodiments of this application;
[0040] Figure 4 This is a schematic diagram of the micro-blood flow gain selection device in ultrasound CDFI mode provided in the embodiments of this application. Detailed Implementation
[0041] Embodiments of this application will now be described in more detail with reference to the accompanying drawings. However, this description is exemplary and not restrictive, and those skilled in the art can implement this application in various forms without being limited to the embodiments set forth herein. These embodiments are provided to enable a more thorough understanding of this disclosure and to fully convey the scope of this disclosure to those skilled in the art.
[0042] The embodiments, features, and elements disclosed in this application can also be combined with any conventional features or elements to form a unique inventive scheme as defined by the claims. Any feature or element of any embodiment can also be combined with features or elements from other inventive schemes to form another unique inventive scheme as defined by the claims. Therefore, it should be understood that any feature shown and / or discussed in this application can be implemented individually or in any suitable combination.
[0043] To facilitate understanding of the technical solutions of the embodiments of this application, some terms or concepts involved in the embodiments of this application will be briefly described first.
[0044] 1. CDFI (Color Doppler Flow Imaging) mode: Color Doppler blood flow imaging mode is an ultrasound imaging technology based on the Doppler effect. By detecting the movement speed, direction and blood flow state (such as laminar / turbulent flow) of red blood cells, the blood flow information is superimposed on a two-dimensional grayscale anatomical image in the form of color coding, thereby presenting the blood flow distribution and dynamic characteristics of the target area in real time and intuitively.
[0045] 2. Microflow: Specifically refers to blood flow in tiny blood vessels (such as capillaries, arterioles, and venules) with extremely small diameters (usually in the sub-millimeter range, such as <1mm or even <100μm) and extremely low blood flow velocities (such as a few millimeters to a few centimeters / second).
[0046] 3. Gain: This refers to the receiving gain of an ultrasound imaging device, which is a parameter used to amplify the echo signal received by the probe. Its value determines the brightness of the pixel grayscale value or the color of the blood flow signal in the image.
[0047] The inventors referenced existing methods for adjusting blood flow gain in ultrasound CDFI mode. However, these methods are only suitable for adjusting blood flow gain in conventional large blood vessels. In micro-blood flow scenarios, microvessels in ultrasound images may be unclear or indistinct, making accurate vessel segmentation impossible. Therefore, it's impossible to adjust micro-blood flow gain by calculating the blood flow surplus / deficient ratio, thus failing to achieve gain selection or adjustment in micro-blood flow scenarios. To address this issue, the inventors observed the variation characteristics of the blood flow area ratio with gain in ultrasound images and performed data fitting analysis on the gain and blood flow area ratio. They found that the mapping relationship between the blood flow area ratio and gain conforms to the characteristics of an exponential saturation function. After acquiring discrete datasets with one-to-one correspondence between gain and blood flow area ratio, they fitted the exponential saturation function and the generalized logistic function respectively, obtaining the optimal gain and fitting error of the first fitting curve and the optimal gain and fitting error of the second fitting curve. Comparing the fitting errors of the two, the optimal gain corresponding to the smaller fitting error was selected as the final optimal gain. The ultrasound image corresponding to this final optimal gain clearly displays the micro-blood flow signal. Based on this, the present invention provides a method and device for selecting micro-blood flow gain in ultrasound CDFI mode.
[0048] Example 1
[0049] Embodiment 1 of this invention provides a method for selecting microflow gain in ultrasound CDFI mode, referring to... Figure 1 As shown, the method includes the following steps:
[0050] S101: In ultrasound CDFI mode, starting from the initial gain, scan with a fixed gain step value increasing to the maximum gain to acquire color blood flow images at each gain.
[0051] S102: Calculate the ratio of the area of colored pixels to the sampling area in the color blood flow map under each gain, obtain the blood flow area ratio under each gain, and construct a discrete dataset with a one-to-one correspondence between the gain and the blood flow area ratio.
[0052] S103: Based on the discrete dataset, fit the preset exponential saturation function to obtain the first optimal gain and the first fitting error of the first fitting curve.
[0053] S104: Based on the discrete dataset, fit the preset generalized logistic function to obtain the second optimal gain and the second fitting error of the second fitting curve.
[0054] S105: Compare the first fitting error and the second fitting error, and take the best gain corresponding to the smaller fitting error as the final best gain.
[0055] The micro-blood flow gain selection method provided by this invention in ultrasound CDFI mode, when it is difficult to segment microvessels in ultrasound images and calculate the blood flow surplus / deficit ratio to adjust the micro-blood flow gain, determines the optimal micro-blood flow gain by fitting a preset exponential saturation function and a preset generalized logistic function respectively, and comparing the fitting errors of the two. The ultrasound image under the optimal gain can clearly display the micro-blood flow signal, avoid missed diagnosis due to too low gain, and background noise and artifact interference due to too high gain, thereby improving diagnostic efficiency and accuracy.
[0056] In this embodiment, the initial gain in step S101 is represented as G. s It can be set to a small value (such as 10), and the maximum gain is expressed as G. e It can be set to a large value (such as 90), and the fixed gain step value is expressed as: The value can be set according to the subsequent fitting accuracy requirements, for example, it can be set to 5. Each gain that increases in a fixed gain step value can be represented by the following formula 1:
[0057] ,
[0058] In Equation 1, G n This represents the nth gain. When n=0, it's the initial gain; when n=N, it's the maximum gain, resulting in N+1 gains. For example, the initial gain G... s Set to 10, maximum gain G e Set to 90, fixed gain step value When set to 5, N=16 can be calculated, resulting in a total of 17 different gains.
[0059] When acquiring ultrasound images at each gain, the ultrasound probe position and relevant parameters of the CDFI mode sampling frame remain unchanged. Color flow images at each gain in ultrasound CDFI mode are acquired and saved, as shown below. The nth gain G in ultrasound CDFI mode n Color flow imaging at each gain. During the acquisition of color flow imaging at each gain, since the relevant parameters of the sampling frame in CDFI mode remain unchanged, the position of the sampling frame in the color flow imaging at each gain also remains unchanged, so that the sampling frame area (i.e., the sampling area) remains unchanged.
[0060] In this embodiment, step S102 requires converting the color blood flow image into a blood flow mask image and then calculating the ratio of blood flow areas. The blood flow area ratio can be calculated through the following steps:
[0061] S1021: According to the preset difference threshold of RGB values, the color blood flow map under each gain is divided into a blood flow mask map including color pixels and background pixels.
[0062] S1022: Calculate the ratio of the area of colored pixels to the sampling area in the blood flow mask image to obtain the blood flow area ratio under each gain.
[0063] Observation and actual testing revealed that the RGB values of gray pixels in the color Doppler flow map are nearly equal, while the RGB values of color pixels are not equal. Based on this characteristic, a preset RGB value difference threshold (e.g., 20) can be used to determine whether a pixel belongs to the color pixel category; pixels with values greater than this threshold are considered color pixels. The specific process of segmenting the color Doppler flow map into a flow mask image including color pixels and background pixels in step S1021 is as follows: traverse each pixel in the color Doppler flow map, compare its RGB value with the aforementioned difference threshold, mark pixels with values greater than the threshold as 1, and otherwise mark them as 0, thus segmenting the image into a flow mask image including color pixels and background pixels. (Refer to...) Figure 2 As shown, Figure 2 (a) Divided in the manner described above Figure 2 (b) The blood flow mask image is used to distinguish sampling frames. Figure 2 (a) In the rectangular box, the colored pixels and background pixels, the pixels marked as 0 are the background pixels in the sampling box, and the pixels marked as 1 are the background pixels. Figure 2 (b) The white pixels are the colored pixels in the sampling frame.
[0064] The area of colored pixels is obtained by statistically analyzing the area of white pixels in the blood flow mask image, which represents the blood flow area. The area of the sampling frame in the colored blood flow image is the sampling area. In step S1022 above, the blood flow area ratio under each gain is calculated using the following formula 2:
[0065] ,
[0066] In Equation 2, A represents the area of the colored pixel at the nth gain. s Indicates the sampling area. Color pixel area. Smaller than the sampling frame area A s Therefore .
[0067] Based on the blood flow area ratio for each gain, a discrete dataset with a one-to-one correspondence between gain and blood flow area ratio is constructed, i.e., N+1 discrete data points. , .
[0068] With gain G n The independent variable is the ratio of blood flow area R. n As the dependent variable, based on N+1 measured discrete data points The functional relationship between the blood flow area ratio and the gain can be obtained by fitting.
[0069] When there is no vascular tissue in the sampling frame of the ultrasound CDFI mode, there are no color pixels formed by blood flow, only noise pseudo-pixels. As the gain increases, the relational expression between the gain and the blood flow area ratio can be expressed by the following exponential saturation function Formula 3:
[0070] Formula 3,
[0071] In Formula 3, R represents the blood flow area ratio, G represents the gain, G0 represents the threshold gain, that is, the first optimal gain, and k represents the first exponential growth coefficient. The threshold gain represents the critical gain value at which the micro blood flow signal starts to be detected. When the gain G < G0, even if a micro blood flow signal exists, it will be suppressed (or lost) due to insufficient gain, and effective color pixels cannot be formed; when the gain G ≥ G0, noise pseudo-pixels start to be amplified, and the color pixel area (or the equivalent area of noise pseudo-pixels) increases exponentially and saturates as the gain increases (as Figure 3 shown in the exponential saturation function curve in (a)).
[0072] When microvessels exist in the sampling frame of the ultrasound CDFI mode, at a low gain, the weak echo signal of blood flow is suppressed or lost, the displayed blood flow area is too small, and the blood flow of the microvessels is invisible; as the gain gradually increases, the real blood flow signal is amplified, the real blood flow area gradually increases, and noise pseudo-pixels increase slightly; when the gain is too high, the real blood flow area no longer increases, but the noise pseudo-pixels still continue to increase with the gain, the blood flow boundary expands outward, and the number of noise pseudo-pixels increases sharply. The relationship curve between gain and blood flow area basically presents an S-shaped curve. Since the area of the sampling frame remains unchanged during the sampling process, the blood flow area and the blood flow area ratio are in a direct proportional relationship. Therefore, the following generalized logistic function Formula 4 can be used to approximately fit the S-shaped curve relationship between the gain and the blood flow area ratio in the micro blood flow scenario:
[0073] Formula 4,
[0074] In Formula 4, R represents the blood flow area ratio, G represents the gain, represents the inflection point gain, that is, the second optimal gain, represents the second exponential growth coefficient, represents the asymmetry parameter. The inflection point gain represents the position where the concavity and convexity of the curve changes, that is, the position with the maximum slope (the fastest rising). When the gain G < G0, the real blood flow signal is weak, and the increase in real blood flow area brought by the gain increase is not significant, even masked by noise; when the gain G ≥ G0, the real blood flow signal is strong enough, the continuous increase in gain leads to a sharp increase in noise pseudo-pixels, while the growth of the real blood flow area slows down (as Figure 3 shown in the generalized logistic function curve in (b)).
[0075] During actual ultrasound scanning, the distribution of blood vessels within the sampling frame is not clear during sampling in CDFI mode. Therefore, it is necessary to rely on the measured discrete data points. The preset exponential saturation function and the preset generalized logistic function are fitted separately, and the fitting errors of the two are compared. The more suitable fitting function is selected based on the magnitude of the error, and finally, an appropriate gain is selected.
[0076] In this embodiment, the first optimal gain (threshold gain) of the first fitted curve can be obtained in step S103 in the following manner:
[0077] Based on the discrete dataset, the following preset exponential saturation function formula 3 is fitted to obtain the first optimal gain of the first fitted curve:
[0078] Formula 3,
[0079] In the formula, R represents the blood flow area ratio, G represents the gain, G0 represents the first optimal gain, and k represents the first exponential growth coefficient.
[0080] The first optimal gain G0 and the first exponential growth coefficient k are determined by linear transformation and least squares method, as follows:
[0081] Transforming formula 3, we obtain formula 5:
[0082] Formula 5,
[0083] Known discrete dataset Fitting a linear model The slope a and intercept b. The least squares objective function is given by equation 6:
[0084] Formula 6,
[0085] Taking the partial derivative with respect to a and setting it to 0, we obtain the following equation 7:
[0086] Formula 7,
[0087] Taking the partial derivative with respect to b and setting it to 0, we obtain the following equation 8:
[0088] Formula 8,
[0089] Simplify equations (7) and (8), and we get:
[0090] Formula 9,
[0091] In Equation 9, , .
[0092] Write equation (5) as Form, that is , , , Substituting into equation (9) yields the first optimal gain after linear fitting. and the first exponential growth rate for:
[0093] Formula 10,
[0094] In Equation 10, .
[0095] This yields the first fitted curve and the first optimal gain of the first fitted curve. .
[0096] In this embodiment, after obtaining the first fitting curve by fitting a preset exponential saturation function in step S103, the first fitting error of the first fitting curve is calculated according to the following formula 11:
[0097] Formula 11,
[0098] In Equation 11, e1 represents the first fitting error of the first fitted curve, and N+1 represents the total number of discrete data points. G represents the blood flow area ratio at the nth discrete data point. n This represents the gain of the nth discrete data point. This represents the first optimal gain obtained from the fitting. This represents the first exponential growth coefficient obtained from the fitting.
[0099] In this embodiment, the second optimal gain (inflection point gain) of the second fitted curve can be obtained in step S104 in the following manner:
[0100] Based on the discrete dataset, the following preset generalized logistic function formula 4 is fitted to obtain the second optimal gain of the second fitting curve:
[0101] Formula 4,
[0102] In Equation 4, R represents the blood flow area ratio, and G represents the gain. This indicates the second-best gain. This represents the second exponential growth coefficient. This represents an asymmetric parameter.
[0103] Due to the asymmetric parameter in equation (4) The existence of ... , and Nonlinear least squares methods are highly sensitive to initial values; good initial values can significantly improve the fitting efficiency of nonlinear least squares methods. Let the asymmetric parameter... First estimate through linear transformation and The initial value.
[0104] In equation (4), let Then, by transformation, we obtain the following equation 12:
[0105] Formula 12,
[0106] Equation (12) can be written as Form, , , , Substituting into equation (9) yields the initial second optimal gain after linear fitting. and the initial second exponential growth coefficient for:
[0107] Formula 13,
[0108] In Equation 13, .
[0109] This yields the initial values of the three parameters in the generalized logistic function. , as well as .
[0110] The nonlinear least squares objective function of the generalized logistic function can be expressed as Equation 14:
[0111] Formula 14,
[0112] The Levenberg-Marquardt algorithm is used to solve the above nonlinear least squares objective function. The initial value of the nonlinear fitting is... , as well as The final second exponential growth coefficient was obtained by fitting the data. Second best gain and asymmetric parameters .
[0113] This yields the second fitted curve and the second optimal gain of the second fitted curve. .
[0114] In this embodiment, after obtaining the second fitting curve by fitting the generalized logistic function in step S104, the fitting error of the second fitting curve is calculated according to the following formula 15:
[0115] Formula 15,
[0116] In Equation 15, e2 represents the fitting error of the second fitted curve, and N+1 represents the total number of discrete data points. G represents the blood flow area ratio at the nth discrete data point. n This represents the gain of the nth discrete data point. This represents the second-best gain obtained from the fitting. This represents the second exponential growth coefficient obtained from the fitting. This represents the asymmetric parameters obtained from the fitting.
[0117] In this embodiment, in step S105, the magnitudes of two fitting errors, e1 and e2, are compared, and the optimal gain corresponding to the smaller fitting error is selected as the final optimal gain. In ultrasound CDFI mode, it is necessary to acquire and save the most suitable color Doppler flow map for the ultrasound physician to review after the scan. Therefore, based on the final optimal gain, the most suitable flow map can be selected from the multiple color Doppler flow maps acquired and saved in step S101 for the physician to review. By iterating through the gains corresponding to each color Doppler flow map, the gain closest to the final optimal gain is selected. The color Doppler flow map corresponding to this gain is the one that needs to be saved during the ultrasound scan for the ultrasound physician to review.
[0118] Example 2
[0119] Based on the same inventive concept, this invention also provides a micro-blood flow gain selection device in ultrasound CDFI mode, referring to... Figure 4 As shown, it includes:
[0120] The color flow imaging module 101 is used to scan from the initial gain to the maximum gain in ultrasound CDFI mode, acquiring color flow images at each gain.
[0121] The discrete dataset construction module 102 is used to calculate the ratio of the area of the color pixels to the sampling area in the color blood flow map under each gain, obtain the blood flow area ratio under each gain, and construct a discrete dataset in which the gain and the blood flow area ratio correspond one-to-one.
[0122] The first fitting error calculation module 103 is used to fit a preset exponential saturation function based on the discrete dataset to obtain the first optimal gain and the first fitting error of the first fitting curve.
[0123] The second fitting error calculation module 104 is used to fit a preset generalized logistic function based on the discrete dataset to obtain the second optimal gain and the second fitting error of the second fitting curve.
[0124] The optimal gain selection module 105 is used to compare the first fitting error and the second fitting error, and select the optimal gain corresponding to the smaller fitting error as the final optimal gain.
[0125] Example 3
[0126] Based on the same inventive concept, embodiments of the present invention provide a computer-readable storage medium storing a computer program / instruction thereon, which, when executed by a processor, implements the micro-flow gain selection method in ultrasound CDFI mode as described in Embodiment 1 above.
[0127] Example 4
[0128] Based on the same inventive concept, this invention provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the micro-flow gain selection method in ultrasound CDFI mode as described in Embodiment 1 above.
[0129] Example 5
[0130] Based on the same inventive concept, this embodiment of the invention provides a computer device, including a memory, a processor, and a computer program stored in the memory. The processor executes the computer program to implement the micro-flow gain selection method in ultrasound CDFI mode as described in Embodiment 1 above.
[0131] In this embodiment, when describing representative embodiments, the specification may have presented the method and / or process as a specific sequence of steps. However, the method or process should not be limited to the specific order of steps described herein, to the extent that the method or process does not depend on the specific order of steps described herein. Other sequences of steps are also possible, as understood by those skilled in the art. Therefore, the specific order of steps set forth in the specification should not be construed as a limitation on the claims. Furthermore, the claims for the method and / or process should not be limited to the steps performed in the written order, and those skilled in the art will readily understand that these orders can be varied and still remain within the spirit and scope of the embodiments of the invention.
[0132] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.
[0133] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus, and computer program products according to embodiments of the invention. Those skilled in the art will understand that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0134] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0135] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
Claims
1. A method for selecting microflow gain in ultrasound CDFI mode, characterized in that, The method includes: In ultrasound CDFI mode, starting from the initial gain, the scan is performed by increasing the gain step value to the maximum gain, and color blood flow images are acquired at each gain. Calculate the ratio of the area of colored pixels to the sampling area in the color blood flow map under each gain, obtain the blood flow area ratio under each gain, and construct a discrete dataset with a one-to-one correspondence between the gain and the blood flow area ratio. Based on the discrete dataset, a preset exponential saturation function is fitted to obtain the first optimal gain and the first fitting error of the first fitting curve. Based on the discrete dataset, a preset generalized logistic function is fitted to obtain the second optimal gain and the second fitting error of the second fitting curve. Compare the first fitting error and the second fitting error, and take the best gain corresponding to the smaller fitting error as the final best gain.
2. The method for selecting microflow gain in ultrasound CDFI mode as described in claim 1, characterized in that, Based on the discrete dataset, the following preset exponential saturation function is fitted to obtain the first optimal gain of the first fitted curve: ， In the formula, R represents the blood flow area ratio, G represents the gain, G0 represents the first optimal gain, and k represents the first exponential growth coefficient.
3. The method for selecting microflow gain in ultrasound CDFI mode as described in claim 2, characterized in that, The first fitting error of the first fitted curve is calculated using the following formula: ， In the formula, e1 represents the first fitting error of the first fitted curve, N+1 represents the total number of discrete data points, and R... n G represents the blood flow area ratio at the nth discrete data point. n This represents the gain of the nth discrete data point. This represents the first optimal gain obtained from the fitting. This represents the first exponential growth coefficient obtained from the fitting.
4. The method for selecting micro-blood flow gain in ultrasound CDFI mode as described in claim 1 or 3, characterized in that, Based on the discrete dataset, the following preset generalized logistic function is fitted to obtain the second optimal gain of the second fitted curve: ， In the formula, R represents the blood flow area ratio, and G represents the gain. This indicates the second-best gain. This represents the second exponential growth coefficient. This represents an asymmetric parameter.
5. The method for selecting micro-blood flow gain in ultrasound CDFI mode as described in claim 4, characterized in that, The second fitting error of the second fitted curve is calculated using the following formula: ， In the formula, e2 represents the second fitting error of the second fitted curve, N+1 represents the total number of discrete data points, and R... n G represents the blood flow area ratio at the nth discrete data point. n This represents the gain of the nth discrete data point. This represents the second-best gain obtained from the fitting. This represents the second exponential growth coefficient obtained from the fitting. This represents the asymmetric parameters obtained from the fitting.
6. The method for selecting micro-blood flow gain in ultrasound CDFI mode as described in claim 1, characterized in that, Calculate the ratio of the area of colored pixels to the area of the sampled region in the color flow map at each gain to obtain the flow area ratio at each gain, including: Based on the preset RGB value difference threshold, the color blood flow map under each gain is divided into a blood flow mask map including color pixels and background pixels; Calculate the ratio of the area of colored pixels to the sampling area in the blood flow mask image to obtain the blood flow area ratio for each gain.
7. A micro-blood flow gain selection device in ultrasound CDFI mode, characterized in that, include: The color flow imaging module is used to scan in ultrasound CDFI mode, starting from the initial gain and increasing to the maximum gain with a fixed gain step value, to acquire color flow images at each gain. The discrete dataset construction module is used to calculate the ratio of the area of colored pixels to the sampling area in the color blood flow map under each gain, obtain the blood flow area ratio under each gain, and construct a discrete dataset with a one-to-one correspondence between the gain and the blood flow area ratio. The first fitting error calculation module is used to fit a preset exponential saturation function based on the discrete dataset to obtain the first optimal gain and the first fitting error of the first fitting curve. The second fitting error calculation module is used to fit a preset generalized logistic function based on the discrete dataset to obtain the second optimal gain and the second fitting error of the second fitting curve. The optimal gain selection module is used to compare the first fitting error and the second fitting error, and select the optimal gain corresponding to the smaller fitting error as the final optimal gain.
8. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that, When the computer program / instruction is executed by the processor, it implements the microflow gain selection method in ultrasound CDFI mode as described in any one of claims 1-6.
9. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instruction is executed by the processor, it implements the microflow gain selection method in ultrasound CDFI mode as described in any one of claims 1-6.
10. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the microflow gain selection method in ultrasound CDFI mode as described in any one of claims 1-6.