Method and device for estimating motion of a chamber wall segment based on multi-resolution optical flow calculation

Abstract

This application relates to a method and apparatus for estimating segmental wall motion based on multi-resolution optical flow calculation, belonging to the field of ultrasound image processing technology. Addressing the issues of myocardial dysfunction relying on physician subjective judgment and echocardiogram image quality, this application proposes an optical flow method based on multi-resolution analysis. Combined with image correction techniques, an image "grayscale matrix" is introduced, and weighting coefficients are used to effectively solve calculation errors caused by image quality issues such as "shadows," improving the accuracy of optical flow calculation. Furthermore, dynamic tracking of the ROI region is achieved through motion vector decomposition, more accurately reflecting the motion characteristics of myocardial segments. This method can effectively track the motion state of each myocardial segment, significantly reducing calculation errors caused by missing information at high resolution, thereby improving the accuracy of myocardial ventricular wall motion calculation.

Description

Technical Field

[0001] This application relates to the field of ultrasonic image processing technology, and in particular to a method and apparatus for estimating segmental motion of the ventricular wall based on multi-resolution optical flow calculation. Background Technology

[0002] Cardiovascular disease is a major threat to human health, ranking first among causes of death worldwide. According to the World Health Organization, 17.9 million people died from cardiovascular disease in 2019, accounting for 32% of all deaths globally. Ischemic heart disease accounts for the largest proportion of these deaths, at 16%. Ischemic heart disease is a clinical condition primarily caused by insufficient blood and oxygen supply to the myocardium, leading to myocardial ischemia. Abnormal myocardial function can cause heart failure, myocardial infarction, and even sudden death. Therefore, effectively assessing and analyzing myocardial function in real time is crucial.

[0003] In recent years, echocardiography has been widely used in the diagnosis of myocardial dysfunction and the evaluation of viable myocardium due to its advantages of being non-invasive, safe, inexpensive, and simple. Echocardiography is simple to operate, the examination procedure is not complicated, it involves no radiation, and the examination cost is low. It can accurately obtain data on cardiac structure and myocardial function, and can accurately assess myocardial contractile function and myocardial wall motion. Therefore, echocardiography has high value in the diagnosis of cardiovascular diseases and has become one of the important auxiliary diagnostic tools for physicians.

[0004] Whether myocardial function is normal can be evaluated through various indicators such as left ventricular ejection fraction, myocardial function index, and myocardial motion coordination. To better assess regional left ventricular myocardial function, a 17-segment model recommended by the American College of Echocardiography can be used to reflect the area of ​​coronary blood supply. However, since the 17th segment (the apex cap) is difficult to detect under normal circumstances, a 16-segment model is used in echocardiography to assess left ventricular wall motion, and myocardial function is assessed through strain analysis between different segments.

[0005] Tissue Doppler imaging (DTI) is an echocardiographic technique that improves the accuracy of myocardial function diagnosis by analyzing ventricular wall motion. Two-dimensional speckle tracking imaging (2D-STI) is a quantitative assessment method that tracks the relative position and motion velocity of regions of interest in the myocardium. The coordination of myocardial function can also be obtained by measuring the time difference of peak systolic parameters in different segments of the left ventricle, which can be acquired using M-mode ultrasound, spectral Doppler, tissue Doppler, speckle tracking, and three-dimensional ultrasound. However, DTI is often affected by cardiac rotation, oscillation, and respiratory motion, which can influence the image quality. 2D-STI also has limitations; it requires high image quality and high temporal and spatial resolution. If it cannot accurately reflect the motion information of all myocardial segments within the same cardiac cycle, tracking may fail. Summary of the Invention

[0006] This application provides a method and apparatus for estimating segmental wall motion based on multi-resolution optical flow calculation, which can improve image quality and obtain more accurate wall motion mechanical parameters to more effectively assess myocardial function.

[0007] The technical solution of this application is as follows:

[0008] According to a first aspect of the embodiments of this application, a method for estimating segmental wall motion based on multi-resolution optical flow calculation is provided, comprising:

[0009] Obtain a sorted combination of ultrasound images within a cardiac cycle based on ultrasound echocardiography.

[0010] The myocardial contour was obtained by sorting and combining the ultrasound images, and myocardial segments of different sections were marked using a 16-segment model.

[0011] Myocardial ventricular wall motion displacement data obtained by segmenting the myocardial contour using multi-resolution optical flow calculation;

[0012] Motion decomposition is performed at the myocardial boundary of at least one myocardial segment, decomposing it into a circular vector parallel to it and a normal vector perpendicular to it, and dynamic tracking of the ROI region of the myocardial segment is realized based on the circular vector and the normal vector.

[0013] Calculate the displacement data of each myocardial segment, and determine the displacement curve of the myocardial ventricular wall segment motion based on the displacement data.

[0014] Optionally, the myocardial ventricular wall motion displacement data obtained by multi-resolution optical flow calculation and segmentation includes:

[0015] The relationship between image pixel motion and grayscale is established by using the optical flow constraint equation and the assumption of constant brightness, and is expressed as I(x+dx,y+dy,t+dt)=I(x,y,t), where (x,y) is the image pixel position, (dx,dy) is the displacement vector per unit time, t represents time, and dt represents the time interval between two consecutive images.

[0016] Based on the relationship between image pixel motion and grayscale, multi-resolution optical flow calculation is used to obtain myocardial ventricular wall motion displacement data of the segmented myocardial contour.

[0017] Optionally, the myocardial ventricular wall motion displacement data obtained by multi-resolution optical flow calculation and segmentation includes:

[0018] according to Determine the optical flow velocity vectors u and v, where u1 and v1 are the optical flow velocity vectors calculated at low resolution, u2 and v2 are the optical flow velocity vectors calculated at high resolution, and s is the weighting coefficient.

[0019] Optionally, the weighting coefficient s is determined using the grayscale matrix of the image at the current time.

[0020] Optionally, the step of performing motion decomposition at the myocardial boundary of at least one myocardial segment, decomposing it into a periodic vector parallel to it and a normal vector perpendicular to it, includes:

[0021] At the myocardial boundary of at least one myocardial segment, motion is decomposed into a periodic vector parallel to it and a normal vector perpendicular to it, wherein the normal vector points in the direction of myocardial contraction. The normal vector is obtained by rotating the tangent vector by 90°, and the tangent vector is obtained using the formula... Calculate t i Let v be the i-th tangent vector. i is the i-th tangent point on the myocardial segment.

[0022] Optionally, the step of dynamically tracking the ROI region of the myocardial segment based on the periodic vector and the normal vector includes:

[0023] The labeled myocardial segments are then subjected to secondary segmentation and vector labeling;

[0024] Normalization is performed on the myocardial segments after vector labeling to obtain multiple circumferential and radial normalized vectors;

[0025] The normalized vector is combined with the region of interest to obtain the ROI vector field within the region, and the ROI region of the myocardial segment is dynamically tracked based on the ROI vector field.

[0026] Optionally, the step of dynamically tracking the ROI region of the myocardial segment based on the ROI vector field includes:

[0027] The ROI region of the myocardial segment is dynamically tracked according to the formula I' = warp(I, w), where I' is the new ROI, I is the old ROI, and w is the velocity vector calculated by comparing two consecutive frames of images.

[0028] According to a second aspect of the embodiments of this application, a device for estimating segmental wall motion based on multi-resolution optical flow calculation is provided, comprising:

[0029] The acquisition module is used to obtain a sorted combination of ultrasound images within a cardiac cycle based on the ultrasound echocardiogram.

[0030] The segmentation module is used to obtain the myocardial contour by sorting and combining the ultrasound images and to mark the myocardial segments of different sections using a 16-segment model;

[0031] The calculation module is used to calculate the myocardial ventricular wall motion displacement data of the myocardial contour obtained by multi-resolution optical flow calculation segmentation;

[0032] The decomposition module is used to perform motion decomposition at the myocardial boundary of at least one myocardial segment, decomposing it into a circumferential vector parallel to it and a normal vector perpendicular to it, and to realize dynamic tracking of the ROI region of the myocardial segment based on the circumferential vector and the normal vector.

[0033] The processing module is used to calculate the displacement data of each myocardial segment and determine the displacement curve of the myocardial ventricular wall segment motion based on the displacement data.

[0034] According to a third aspect of the embodiments of this application, a non-volatile storage device is provided, comprising: a processor, and a memory communicatively connected to the processor;

[0035] The memory stores computer-executed instructions;

[0036] The processor executes computer execution instructions stored in the memory to implement the method provided in the first aspect.

[0037] According to a fourth aspect of the embodiments of this application, a computer-readable storage medium is provided, wherein computer-executable instructions are stored in the computer-readable storage medium, and the computer-executable instructions are executed by a processor to implement the method provided in the first aspect.

[0038] According to a fifth aspect of the embodiments of this application, a computer program product is provided, including a computer program that, when executed by a processor, implements the method provided in the first aspect.

[0039] Beneficial effects:

[0040] This application discloses a method and device for estimating segmental wall motion based on multi-resolution optical flow calculation. Addressing the issues of relying on physician subjective judgment and echocardiogram image quality for myocardial dysfunction, this application proposes an optical flow method based on multi-resolution analysis. By combining image correction techniques and introducing an image "grayscale matrix," weighting coefficients are used to effectively resolve calculation errors caused by image quality issues such as "shadows," thus improving the accuracy of optical flow calculation. Furthermore, dynamic tracking of the ROI region is achieved through motion vector decomposition, more accurately reflecting the motion characteristics of myocardial segments. This method can effectively track the motion state of each myocardial segment, significantly reducing calculation errors caused by missing information at high resolution, thereby improving the accuracy of myocardial ventricular wall motion calculation.

[0041] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0042] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application, and do not constitute an undue limitation of this application.

[0043] Figure 1 This is a flowchart illustrating an exemplary embodiment of a method for estimating segmental motion of the ventricular wall based on multi-resolution optical flow calculation provided in this application.

[0044] Figure 2 This is a schematic diagram illustrating an image correction technique according to an exemplary embodiment;

[0045] Figure 3 This is a schematic diagram of an ultrasound image “shadow” according to an exemplary embodiment;

[0046] Figure 4 This is a grayscale matrix image illustrated according to an exemplary embodiment;

[0047] Figure 5 This is a schematic diagram of the vector decomposition of myocardial ventricular wall motion according to an exemplary embodiment;

[0048] Figure 6 This is a schematic diagram of an ROI vector field according to an exemplary embodiment;

[0049] Figure 7 This is a schematic diagram illustrating the error in the calculation result of segmental displacement according to an exemplary embodiment;

[0050] Figure 8 This is a hypersonic simulation diagram of the corresponding "shade" shown according to an exemplary embodiment;

[0051] Figure 9This is a schematic diagram illustrating the calculation results before and after using a grayscale matrix, according to an exemplary embodiment.

[0052] Figure 10 This is a schematic diagram illustrating dynamic ROI tracking according to an exemplary embodiment;

[0053] Figure 11 This is a simulation diagram of a myocardial wall with "shading" shown according to an exemplary embodiment;

[0054] Figure 12 This is a schematic diagram illustrating the comparison of experimental results of the dual-flow method according to an exemplary embodiment;

[0055] Figure 13 This is a comparative schematic diagram showing the normalized experimental results according to an exemplary embodiment;

[0056] Figure 14 This is a schematic diagram of the region of interest after segmentation of the myocardial ventricular wall in a normal patient, according to an exemplary embodiment.

[0057] Figure 15 This is a schematic diagram illustrating the comparison of experimental results according to an exemplary embodiment;

[0058] Figure 16 This is a schematic diagram illustrating a normalized comparison of another experimental result according to an exemplary embodiment;

[0059] Figure 17 This is a schematic diagram of a myocardial motion displacement curve according to an exemplary embodiment.

[0060] Figure 18 This is a schematic diagram of a wall segment motion estimation device based on multi-resolution optical flow calculation, according to an exemplary embodiment. Detailed Implementation

[0061] To enable those skilled in the art to better understand the technical solutions of this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0062] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0063] To improve image quality and obtain more accurate wall motion biomechanical parameters for more effective assessment of myocardial function, this application proposes an optical flow method based on multi-resolution analysis, using echocardiographic images from 15 patients with myocardial motion abnormalities and 15 normal patients. Simultaneously, dynamic tracking of the Region of Interest (ROI) is achieved through motion vector decomposition, ultimately calculating the displacement of each segment to accurately reflect the motion information of myocardial segments. Experimental results show that this method is highly accurate and robust, image quality can be corrected, myocardial function coordination analysis is easily implemented, and it has suitable quantification capabilities for myocardial wall motion, which can assist in rapid clinical diagnosis.

[0064] Figure 1 A flowchart illustrating a method for estimating segmental wall motion based on multi-resolution optical flow calculation, provided as an exemplary embodiment of this application. Figure 1 As shown, the specific steps of this method for calculating segmental motion of the myocardial ventricular wall are as follows:

[0065] Step 110: Obtain a sorted combination of ultrasound images within a cardiac cycle based on the ultrasound echocardiogram.

[0066] In this embodiment of the invention, echocardiogram images of 30 patients were collected as raw image data. Ultrasound images within one cardiac cycle were extracted, sorted, and combined for analysis to calculate the displacement data of myocardial ventricular wall motion. This has very important reference value for the diagnosis of myocardial ischemia in cardiovascular diseases.

[0067] Step 120: Based on the sorting and combination of the ultrasound images, the myocardial contour is obtained and the myocardial segments of different sections are marked using a 16-segment model.

[0068] For example, you can first manually segment the myocardial outline, and then label the myocardial segments in different sections according to the 16-segment model recommended by the American Society of Echocardiography.

[0069] Step 130: Calculate the myocardial ventricular wall motion displacement data of the segmented myocardial contour using multi-resolution optical flow calculation.

[0070] Specifically, the relationship between image pixel motion and grayscale is established through optical flow constraint equations and the assumption of constant brightness, expressed as I(x+dx,y+dy,t+dt)=I(x,y,t), where (x,y) is the image pixel position, (dx,dy) is the displacement vector per unit time, t represents time, and dt represents the time interval between two consecutive images. Based on the relationship between image pixel motion and grayscale, multi-resolution optical flow is used to calculate the myocardial ventricular wall motion displacement data of the segmented myocardial contour.

[0071] Optical flow was first proposed by Horn and Schunk in 1981 to calculate the motion representation of brightness patterns on the surface of an image. It establishes the relationship between pixel motion and grayscale through optical flow constraint equations and the assumption of constant brightness. This relationship can be expressed by the formula I(x+dx,y+dy,t+dt)=I(x,y,t), where (x,y) are the image pixel positions, (dx,dy) are the displacement vectors per unit time, t represents time, and dt represents the time interval between two consecutive images.

[0072] Brox et al. proposed the Brox optical flow method in 2004, which further optimized and improved the optical flow calculation method. Based on the Brox optical flow method, this invention introduces a multi-resolution analysis method to further improve the calculation accuracy of image pixel motion.

[0073] By introducing the Gaussian pyramid algorithm, the optical flow calculation results of the top layer are fed back to the next layer as the optical flow estimate of the next layer. In this way, the motion is corrected from top to bottom along the Gaussian pyramid until the bottom layer of the pyramid is reached.

[0074] To implement the multi-layer pyramid strategy, this invention innovatively employs image correction technology. Specifically, after optical flow calculation is performed on the original echocardiogram image data to obtain the velocity vector w of each pixel, a new image is generated through inverse image deformation and compared with the original image to evaluate the algorithm's accuracy. The principle of the image correction technology used in this invention is as follows: Figure 2 As shown, the introduction of image correction technology can greatly improve the efficiency of optical flow calculation, and can also track fast-moving image pixels.

[0075] During echocardiography, clinicians typically acquire ultrasound images with the patient in the left lateral decubitus position. However, rib obstruction often results in "shadows" in the echocardiogram data. Figure 3 As shown. Typically, the amount of information contained in this "shadow" region is limited. Therefore, this embodiment of the invention proposes a weighting coefficient based on image grayscale to minimize the impact of the "shadow" on optical flow calculation.

[0076] Like color images, grayscale images describe the overall and local distribution and characteristics of chromaticity and brightness levels. As defined by optical flow, the instantaneous rate of change of grayscale at a specific coordinate point on a two-dimensional image plane is typically defined as the optical flow vector. The grayscale value directly reflects the image quality. Due to "shadow" occlusion, the grayscale change between two frames is relatively small, thus affecting the accuracy of optical flow calculation. Correspondingly, the grayscale value also reflects image information to some extent; the image information at "shadow" locations is relatively low. This invention innovatively uses a "confidence weight matrix" obtained from a "grayscale matrix" to solve this problem. This is the matrix obtained after normalizing the grayscale image, such as... Figure 4 As shown.

[0077] The value of each pixel in the grayscale matrix represents its weight in the next pyramid iteration. During the iteration process, the grayscale matrix at the corresponding resolution is updated synchronously, resulting in a new optical flow pyramid iteration formula, as shown in equation [equation missing]. As shown, according to Determine the optical flow velocity vectors u and v, where u1 and v1 are the optical flow velocity vectors calculated under low resolution, u2 and v2 are the optical flow velocity vectors calculated under high resolution, and s is the weighting coefficient, which is determined by the grayscale matrix of the image at the current time.

[0078] Selecting an echocardiogram with localized "shading," and cropping a frame from it, along with its corresponding simulated image, reveals that at low resolution, the size of the "shading" area is correspondingly reduced, while in the high-resolution image, the motion in the corresponding "shading" area better reflects the overall myocardial motion within that region. At high resolution, the calculation of optical flow is more accurate in areas with noticeable brightness, while the "shading" area suffers from information loss, leading to errors in optical flow calculation. The simulated image is shown below. Figure 8 As shown, Figure 8 (a), (b), and (c) represent 30%, 60%, and 100% of the original image resolution, respectively.

[0079] Select an echocardiogram with localized "shading," crop a frame from it, and apply the "grayscale matrix" of this invention to compare it with the multi-resolution optical flow calculation results based on image grayscale, as shown in the example. Figure 9 As shown, Figure 9 (a) shows the calculation results without using the grayscale matrix. Figure 9 (b) shows the calculation results after using the grayscale matrix. (Reference) Figure 9 As shown, by using the method of adding the gray matrix, the weight of the "shaded" area is relatively small during the iteration process, which can reduce the calculation errors caused by missing information at high resolution and effectively solve the "shaded" problem.

[0080] Step 140: Perform motion decomposition at the myocardial boundary of at least one myocardial segment, decomposing it into a periodic vector parallel to it and a normal vector perpendicular to it, and realize dynamic tracking of the ROI region of the myocardial segment based on the periodic vector and the normal vector.

[0081] Furthermore, at the myocardial boundary of at least one myocardial segment, the motion is decomposed into a periodic vector parallel to it and a normal vector perpendicular to it, wherein the normal vector points in the direction of myocardial contraction. The normal vector is obtained by rotating the tangent vector by 90°, and the tangent vector is obtained using the formula... Calculate t i Let v be the i-th tangent vector. i is the i-th tangent point on the myocardial segment.

[0082] Furthermore, the labeled myocardial segments are further segmented and vector-labeled; the vector-labeled myocardial segments are normalized to obtain multiple circumferential and radial normalized vectors;

[0083] The normalized vector is combined with the region of interest to obtain the ROI vector field within the region, and the ROI region of the myocardial segment is dynamically tracked according to the ROI vector field. The ROI region of the myocardial segment is dynamically tracked according to the formula I' = warp(I, w), where I' is the new region of interest, I is the old region of interest, and w is the velocity vector calculated by comparing the two frames of images.

[0084] The ventricular wall of the myocardium is arched in shape. Under the influence of pumping and other processes, the wall moves, and its displacement can be decomposed into two mutually perpendicular components: radial and circumferential. To better analyze the myocardial motion of different segments of the ventricular wall, this invention proposes a vector separation method, with the radial direction as the primary analysis target, such as... Figure 5 As shown.

[0085] After segmenting the myocardium, assuming the circumferential motion of each segment is along the ventricular wall, the corresponding normal vector points in the direction of myocardial contraction. The normal vector can be obtained by rotating the tangent vector by 90°, using the formula... Calculate, where t i Let v be the i-th tangent vector. i is the i-th tangent point on the myocardial segment.

[0086] The six myocardial segments obtained after initial segmentation were further segmented to better observe the motion information within each segment. The twelve myocardial segments obtained from the secondary segmentation were then vector-labeled and normalized to obtain twelve circumferential and radial normalized vectors. These vectors were then combined with the region of interest (ROI) to obtain the ROI vector field. The displacement data calculated by optical flow was then directionally separated to reduce the influence of anomalous motions caused by uneven stress on the ventricular wall. The final ROI vector field is shown in Figure 6.

[0087] During contraction, the relative position and shape of the myocardial ventricular wall change significantly. If the displacement of each segment is calculated based on the initial state of the myocardial region, the actual myocardial position may differ from the region of interest at a certain contraction stage, leading to substantial errors in the calculated segmental displacement. Figure 7 As shown.

[0088] To better track the motion of different segments of the myocardium, this embodiment of the invention employs a region of interest (ROI) dynamic tracking algorithm for the myocardial ventricular wall, ensuring that the ROI region remains consistent with the actual ventricular wall region. A velocity vector w is calculated by comparing two frames of images. Then, image correction techniques are used to map each pixel in the initial ROI onto another plane under the influence of the velocity vector, thereby generating a new ROI I'. The ROI dynamic tracking algorithm can be represented by the formula I' = warp(I, w), where I' is the new ROI, I is the old ROI, and w is the velocity vector calculated by comparing the two frames of images.

[0089] In this embodiment of the invention, the root-mean-square error (RMSE) is used as an evaluation index for the accuracy of the velocity vector field. RMSE reflects the degree of dispersion between the estimated velocity vector field and the actual vector field, and its range is taken within the region of interest. The calculation formula for error measurement is as follows:

[0090] To assess ventricular wall motion, each segment should be evaluated over multiple cardiac cycles, and a scoring system for wall motion status should be used for quantitative assessment to compare segmental wall motion abnormalities among patients. A four-level scoring system is generally used: (1) normal or hyperactive, (2) hypoactive (reduced thickening), (3) absent (negligible thickening), and (4) dyskinesia (contractional thinning or stretching). Normal or hyperactive wall motion: 1 point. Hypoactive wall motion, i.e., endocardial motion amplitude less than 5 mm: 2 points. Absent wall motion, i.e., endocardial motion amplitude less than 2 mm: 3 points. Paradoxical wall motion, i.e., antagonistic motion: 4 points. The wall motion score index (WMSI) is calculated after scoring. The formula for calculating the WMSI is:

[0091]

[0092] Select a normal echocardiogram image, capture the process of the myocardium transitioning from diastole to systole within one cardiac cycle, and record the experimental results as follows: Figure 10 As shown. The ROI region dynamic tracking algorithm applied in this embodiment of the invention can effectively track the motion state of each segment of the myocardium, thereby improving the calculation accuracy of myocardial ventricular wall motion.

[0093] Step 150: Calculate the displacement data of each myocardial segment, and determine the displacement curve of the myocardial ventricular wall segment motion based on the displacement data.

[0094] By calculating the displacement data of each myocardial segment and determining the displacement curve of the myocardial ventricular wall segment motion based on the displacement data, the motion information of the myocardial segment is accurately reflected.

[0095] This invention embodiment uses a simulated image of the right ventricular wall with a "shadow" added to it for experimental purposes. Figure 11 As shown. This experiment used warp technology to simulate myocardial contraction. Specifically, the left wall shifted 2 pixels to the right, the right wall shifted 2 pixels to the left, and the entire ventricular wall shifted 1 pixel downward.

[0096] The optical flow method based on multi-resolution analysis in this invention is compared with the HS optical flow method, LK optical flow method, and Brox optical flow method. The experimental results are as follows: Figure 12 As shown, the experimental results after normalization are as follows: Figure 13 As shown. Furthermore, this embodiment of the invention selects an echocardiogram of a normal patient for experimentation. The warp technique is also used to simulate myocardial ventricular wall motion: the left wall moves 1 pixel to the right, the right wall moves 1 pixel to the left, and the entire ventricular wall moves downwards by 0.5 pixels. The region of interest segmented within the error calculation range is shown below. Figure 14As shown, the optical flow method based on multi-resolution analysis in this embodiment of the invention is compared with the HS optical flow method, LK optical flow method, and Brox optical flow method. The experimental results are as follows: Figure 15 As shown, the experimental results after normalization are as follows: Figure 16 As shown.

[0097] Depend on Figure 12 and Figure 13 , Figure 15 and Figure 16 The experimental comparison results show that the HS optical flow method and LK optical flow method have high calculation error rates. Moreover, the HS optical flow method, LK optical flow method and Brox optical flow method all have directional errors in the calculation of the "shadow" area and the segmented region of interest. However, the improved optical flow method based on multi-resolution analysis in this embodiment of the invention has significantly better results than the other three algorithms. It can reduce the calculation errors caused by missing information at high resolution, effectively solve the problem of motion calculation errors caused by the darkness of the "shadow" area, and thus improve the accuracy of calculation.

[0098] In this embodiment of the invention, the final displacement curve can be obtained by calculating the average displacement of pixels within the ROI region of each segment of the myocardium, such as... Figure 17 As shown. Figure 17 The 12 curves represent the displacement of each segment after directional separation. Besides reflecting the overall displacement of each myocardial segment, the curves also reveal the motion details within the segment. The results show that the displacement curves, plotted over two cardiac cycles, exhibit a regular arched shape, demonstrating the accuracy of the ventricular wall segment motion estimation algorithm based on multi-resolution optical flow calculation in this embodiment of the invention.

[0099] This application discloses a method and device for estimating segmental wall motion based on multi-resolution optical flow calculation. Addressing the issues of relying on physician subjective judgment and echocardiogram image quality for myocardial dysfunction, this application proposes an optical flow method based on multi-resolution analysis. By combining image correction techniques and introducing an image "grayscale matrix," weighting coefficients are used to effectively resolve calculation errors caused by image quality issues such as "shadows," thus improving the accuracy of optical flow calculation. Furthermore, dynamic tracking of the ROI region is achieved through motion vector decomposition, more accurately reflecting the motion characteristics of myocardial segments. This method can effectively track the motion state of each myocardial segment, significantly reducing calculation errors caused by missing information at high resolution, thereby improving the accuracy of myocardial ventricular wall motion calculation.

[0100] Figure 18 This is a schematic diagram of a ventricular wall segment motion estimation device based on multi-resolution optical flow calculation, provided as an exemplary embodiment of this application. The ventricular wall segment motion estimation device based on multi-resolution optical flow calculation provided in this embodiment can execute the processing flow provided in an embodiment of a ventricular wall segment motion estimation method based on multi-resolution optical flow calculation. For example... Figure 18As shown, the wall segment motion estimation device 20 based on multi-resolution optical flow calculation provided in this application includes:

[0101] The acquisition module 201 is used to acquire a sorted combination of ultrasound images within a cardiac cycle based on the ultrasound echocardiogram.

[0102] The segmentation module 202 is used to obtain the myocardial contour by sorting and combining the ultrasound images and to mark the myocardial segments of different sections using a 16-segment model;

[0103] Calculation module 203 is used to calculate the myocardial ventricular wall motion displacement data of the myocardial contour obtained by multi-resolution optical flow calculation segmentation;

[0104] The decomposition module 204 is used to perform motion decomposition at the myocardial boundary of at least one myocardial segment, decomposing it into a circumferential vector parallel to it and a normal vector perpendicular to it, and to realize dynamic tracking of the ROI region of the myocardial segment based on the circumferential vector and the normal vector.

[0105] The processing module 205 is used to calculate the displacement data of each myocardial segment and determine the displacement curve of the myocardial ventricular wall segment motion based on the displacement data.

[0106] The apparatus provided in this application embodiment can be specifically used to perform the above-described... Figure 1 The specific functions and technical effects of the solutions provided in the corresponding method embodiments will not be elaborated here.

[0107] This invention also provides a non-volatile storage device comprising: a processor, and a memory communicatively connected to the processor;

[0108] The memory stores the instructions that the computer executes;

[0109] The processor executes computer execution instructions stored in the memory to implement the solution provided in any of the above method embodiments; the specific functions and technical effects achieved are not elaborated here. The electronic device can be the server mentioned above.

[0110] This application also provides a computer-readable storage medium storing computer-executable instructions. When executed by a processor, the computer-executable instructions are used to implement the solution provided in any of the above method embodiments. The specific functions and technical effects to be achieved are not described here.

[0111] This application also provides a computer program product, which includes a computer program stored in a readable storage medium. At least one processor of the electronic device can read the computer program from the readable storage medium. The at least one processor executes the computer program to cause the electronic device to perform the solution provided in any of the above method embodiments. The specific functions and technical effects that can be achieved are not described here.

[0112] The application scenarios described in this application are for the purpose of more clearly illustrating the technical solutions of this application, and do not constitute a limitation on the technical solutions provided in this application. As those skilled in the art will know, with the emergence of new application scenarios, the technical solutions provided in this application are also applicable to similar technical problems.

[0113] Those skilled in the art will understand that various aspects of this application can be implemented as a system, method, or program product. Therefore, various aspects of this application can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, collectively referred to herein as a "circuit," "module," or "system."

[0114] In some possible implementations, the electronic device according to this application may include at least one processor and at least one memory. The memory stores program code that, when executed by the processor, causes the processor to perform the operational data management methods according to the various exemplary embodiments of this application described above. For example, the processor may perform steps such as those in the operational data management method.

[0115] It should be noted that although several units or sub-units of the device have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of this application, the features and functions of two or more units described above can be embodied in one unit. Conversely, the features and functions of one unit described above can be further divided and embodied by multiple units.

[0116] Furthermore, although the operations of the method of this application are described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0117] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0118] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and 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 image scaling device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable image scaling device, 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.

[0119] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable image scaling 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.

[0120] These computer program instructions can also be loaded onto a computer or other programmable image scaling device, causing a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable device 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.

[0121] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0122] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for estimating segmental wall motion based on multi-resolution optical flow calculation, characterized in that, The method includes: Based on the acquisition of ultrasound images within a preset cardiac cycle, a sorted combination of ultrasound images is obtained; The myocardial contour was obtained by sorting and combining the ultrasound images, and myocardial segments of different sections were marked using a 16-segment model. Myocardial ventricular wall motion displacement data obtained by segmenting the myocardial contour using multi-resolution optical flow calculation; Motion decomposition is performed at the myocardial boundary of at least one myocardial segment, decomposing it into a circular vector parallel to it and a normal vector perpendicular to it, and dynamic tracking of the ROI region of the myocardial segment is realized based on the circular vector and the normal vector. Calculate the displacement data of each myocardial segment, and determine the displacement curve of the myocardial ventricular wall segment motion based on the displacement data; The myocardial ventricular wall motion displacement data obtained by multi-resolution optical flow calculation and segmentation includes: according to Determine the optical flow velocity vector , ,in , This is the optical flow velocity vector calculated at low resolution. , is the optical flow velocity vector calculated at high resolution, and s is the weighting coefficient. The weighting coefficient s is determined by the grayscale matrix of the image at the current time. The value of each pixel in the grayscale matrix represents its weight in the next pyramid iteration.

2. The method according to claim 1, characterized in that, The myocardial ventricular wall motion displacement data obtained by multi-resolution optical flow calculation and segmentation includes: The relationship between image pixel motion and grayscale is established using optical flow constraint equations and the assumption of constant brightness. It means that in the formula Image pixel position, dt is the displacement vector per unit time, where t represents time and dt represents the time interval between two consecutive images. Based on the relationship between image pixel motion and grayscale, multi-resolution optical flow calculation is used to obtain myocardial ventricular wall motion displacement data of the segmented myocardial contour.

3. The method according to claim 1, characterized in that, The step of performing motion decomposition at the myocardial boundary of at least one myocardial segment, decomposing it into a periodic vector parallel to it and a normal vector perpendicular to it, includes: At the myocardial boundary of at least one myocardial segment, motion is decomposed into a periodic vector parallel to it and a normal vector perpendicular to it, wherein the normal vector points in the direction of myocardial contraction. The normal vector is obtained by rotating the tangent vector by 90°, and the tangent vector is obtained using the formula... Calculate t i Let v be the i-th tangent vector. i is the i-th tangent point on the myocardial segment.

4. The method according to claim 3, characterized in that, The dynamic tracking of the ROI region of the myocardial segment based on the periodic vector and the normal vector includes: The labeled myocardial segments are then subjected to secondary segmentation and vector labeling; Normalization is performed on the myocardial segments after vector labeling to obtain multiple circumferential and radial normalized vectors; The normalized vector is combined with the region of interest to obtain the ROI vector field within the region, and the ROI region of the myocardial segment is dynamically tracked based on the ROI vector field.

5. The method according to claim 4, characterized in that, The dynamic tracking of the ROI region of the myocardial segment based on the ROI vector field includes: According to the formula The ROI region of the myocardial segment is dynamically tracked, where I' is the new ROI, I is the old ROI, and w is the velocity vector calculated by comparing two consecutive frames of images.

6. A device for estimating segmental motion of the ventricular wall based on multi-resolution optical flow calculation, used to implement the method described in any one of claims 1-5, characterized in that, The ventricular wall segment motion estimation device includes: The acquisition module is used to obtain a sorted combination of ultrasound images within a cardiac cycle based on the ultrasound echocardiogram. The segmentation module is used to obtain the myocardial contour by sorting and combining the ultrasound images and to mark the myocardial segments of different sections using a 16-segment model; The calculation module is used to calculate the myocardial ventricular wall motion displacement data of the myocardial contour obtained by multi-resolution optical flow calculation segmentation; The decomposition module is used to perform motion decomposition at the myocardial boundary of at least one myocardial segment, decomposing it into a circumferential vector parallel to it and a normal vector perpendicular to it, and to realize dynamic tracking of the ROI region of the myocardial segment based on the circumferential vector and the normal vector. The processing module is used to calculate the displacement data of each myocardial segment and determine the displacement curve of the myocardial ventricular wall segment motion based on the displacement data.

7. A non-volatile storage device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-5.

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