A method, device and terminal equipment for mapping cardiac function parameters

Abstract

This invention belongs to the field of cardiac mapping technology, and particularly relates to a method, apparatus, and terminal device for mapping cardiac function parameters. The method includes acquiring cardiac image data; performing image registration on each image to be registered in the image data to obtain a registered image; extracting cardiac region images from the registered images; and outputting a result image for analyzing the cardiac function parameters based on the cardiac region images. This significantly improves the efficiency of data acquisition, reduces the difficulty of data acquisition, expands the mapping range, and improves resolution.

Description

Technical Field

[0001] This invention belongs to the field of cardiac mapping technology, and particularly relates to a method, device and terminal equipment for mapping cardiac function parameters. Background Technology

[0002] Cardiac arrhythmia (fibrillation) is an abnormal physiological phenomenon characterized by irregular heartbeats, which can lead to related cardiac diseases such as thrombosis, stroke, and heart failure. Currently, there are three main treatment methods: controlling heart rate and blood flow with medication, diluting the blood with medication to prevent embolism, and ablation surgery. During ablation surgery, mapping technology is used to identify, interpret, and locate cardiac electrical signals, thereby determining the ablation site and assessing the ablation effect. Mapping technology has become an important research tool in many cardiac disease-related fields, including arrhythmia research and treatment, drug cardiac safety evaluation, and antiarrhythmic drug screening. Mapping systems are mainly divided into two-dimensional and three-dimensional mapping systems. Two-dimensional mapping systems rely on two-dimensional images obtained after X-ray scanning, resulting in blurry results and difficulty in localization. Three-dimensional mapping systems can generate three-dimensional images, and through three-dimensional modeling, the mapping results are more intuitive, almost completely replacing two-dimensional mapping systems.

[0003] Existing three-dimensional mapping systems use implanted mapping catheters to acquire magnetic and electrical signals point by point in the heart, and then analyze these signals to map the heart. However, because these magnetic and electrical signals need to be acquired point by point, the mapping range is small and the mapping efficiency is low. Furthermore, the resulting image converted from the magnetic and electrical signals still suffers from low resolution. Summary of the Invention

[0004] In view of this, embodiments of the present invention provide a method, apparatus and terminal device for mapping cardiac function parameters, in order to solve the problems of small mapping range, low mapping efficiency and low resolution in the prior art.

[0005] A first aspect of this invention provides a method for mapping cardiac function parameters, comprising:

[0006] Acquire image data of the heart;

[0007] Image registration is performed on each image to be registered in the image data to obtain a registered image;

[0008] Extract the heart region image from the registered image;

[0009] The resulting image is output based on the heart region image to analyze the functional parameters of the heart.

[0010] A second aspect of the present invention provides a cardiac function parameter mapping device, comprising:

[0011] The image acquisition module is used to acquire image data of the heart;

[0012] The image registration module is used to perform image registration on each image to be registered in the image data to obtain a registered image;

[0013] An image extraction module is used to extract a heart region image from the registered image;

[0014] The result image generation module is used to output a result image for analyzing the functional parameters of the heart based on the heart region image.

[0015] A third aspect of the present invention provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method described above.

[0016] A fourth aspect of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method described above.

[0017] In a first aspect, this application provides a computational basis for angular changes in phase images by acquiring cardiac images, registering the images, extracting cardiac region images, and outputting a result image for lesion analysis based on the cardiac region images, thereby achieving lesion localization. This improves upon traditional electromagnetic signal-based mapping by using more readily available image signals for mapping, significantly increasing data acquisition efficiency, reducing data acquisition difficulty, expanding the mapping range, and improving resolution.

[0018] It is understood that the beneficial effects of the second and third aspects mentioned above can be found in the relevant descriptions in the first aspect above, and will not be repeated here. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a schematic diagram of the first process of a cardiac function parameter mapping method provided in an embodiment of the present invention;

[0021] Figure 2 This is a schematic diagram of the second process of a cardiac function parameter mapping method provided in an embodiment of the present invention;

[0022] Figure 3 This is a schematic diagram of the third process of a cardiac function parameter mapping method provided in an embodiment of the present invention;

[0023] Figure 4 This is a schematic diagram of the fourth process of a cardiac function parameter mapping method provided in an embodiment of the present invention;

[0024] Figure 5 This is a fifth flowchart illustrating a cardiac function parameter mapping method provided in an embodiment of the present invention;

[0025] Figure 6 This is a sixth flowchart illustrating a cardiac function parameter mapping method provided in an embodiment of the present invention;

[0026] Figure 7 This is a seventh flowchart illustrating a cardiac function parameter mapping method provided in an embodiment of the present invention;

[0027] Figure 8 This is the eighth flowchart of a cardiac function parameter mapping method provided in an embodiment of the present invention;

[0028] Figure 9 This is a ninth flowchart illustrating a cardiac function parameter mapping method provided in an embodiment of the present invention;

[0029] Figure 10 This is a schematic diagram of the tenth process of a cardiac function parameter mapping method provided in an embodiment of the present invention;

[0030] Figure 11 This is a schematic flowchart of the image preprocessing method provided in an embodiment of the present invention;

[0031] Figure 12 This is a schematic flowchart of the image registration method provided in an embodiment of the present invention;

[0032] Figure 13 This is a schematic diagram of a cardiac function parameter mapping device provided in an embodiment of the present invention;

[0033] Figure 14 This is another structural schematic diagram of a cardiac function parameter mapping device provided in an embodiment of the present invention;

[0034] Figure 15 This is a schematic diagram of a cardiac function parameter mapping terminal device provided in an embodiment of the present invention. Detailed Implementation

[0035] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0036] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0037] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0038] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0039] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0040] The cardiac function parameter mapping method provided in this application embodiment can be applied to terminal devices such as mobile phones, tablets, wearable devices, in-vehicle devices, augmented reality (AR) / virtual reality (VR) devices, laptops, ultra-mobile personal computers (UMPCs), netbooks, and personal digital assistants (PDAs). This application embodiment does not impose any restrictions on the specific type of terminal device.

[0041] Example 1

[0042] like Figure 1 As shown, this embodiment of the invention provides a method for mapping cardiac function parameters, including:

[0043] Step 101: Obtain image data of the heart.

[0044] In this application, cardiac image data can be acquired using a dedicated cardiac image acquisition device, which can be a high-speed camera. The acquisition method involves removing the heart from a live animal or exposing it through thoracotomy, and then continuously acquiring data on the heartbeat at high speed for a specified period, without needing to acquire additional signals such as electrical or magnetic signals. Alternatively, historical cardiac image data can be directly acquired by loading data from the software interface. During image data loading, the software switches between GPU and CPU to process the imported image data based on the hardware environment.

[0045] Step 102: Perform image registration on each image to be registered in the image data to obtain the registered image.

[0046] In applications, the image registration stage can specifically use a two-dimensional vector field to represent the displacement of each pixel in the image, define the energy function of the optical flow field, and use the gradient descent algorithm to iteratively calculate the energy function. This allows for a more accurate determination of the pixel's displacement state, thereby achieving motion correction of the image. This renders the originally beating heart data static, removing the influence of motion for subsequent image analysis. It also ensures the accuracy of the grayscale value changes in the heart region image used for lesion analysis.

[0047] Step 103: Extract the heart region image from the registered image.

[0048] In applications, the heart region image, or Region of Interest (ROI), can be extracted from the registered image. This ROI can be extracted by setting a threshold and segmenting it. In this embodiment, OpenCV's `threshold` function is used for threshold segmentation, extracting the contour of the largest connected region as the final result. After segmentation, the result data is checked for format and the current process is judged to determine if it has failed. If it is normal, the temporal and spatial domain filtering process is entered; otherwise, the threshold parameters are adjusted or points are interactively selected to segment the heart and the process is repeated.

[0049] Step 104: Output the result image for analyzing the functional parameters of the heart based on the heart region image.

[0050] In applications, techniques such as grayscale value variation, conduction time, frequency domain analysis, and conduction direction analysis can be combined to obtain basic result diagrams such as isochronous diagrams, voltage diagrams, APD diagrams, frequency diagrams, phase diagrams, and velocity vector diagrams. When an isochronous diagram is required, the region at the same time is divided into areas similar to contour lines based on the grayscale change time, thus obtaining the isochronous diagram.

[0051] When a voltage map needs to be output, the image is stretched according to the maximum or minimum grayscale value to obtain the voltage map.

[0052] When a frequency map is needed, the image is transformed to the frequency domain using Fourier transform, and the dominant frequencies of the image are analyzed to obtain the frequency map.

[0053] When a phase map is needed, the image is transformed to the frequency domain using a Fourier transform, and then the phase is calculated using a Hilbert transform to obtain the phase map.

[0054] When a velocity vector diagram is required, the velocity vector of excitation conduction can be obtained by performing polynomial fitting on the isochronous diagram.

[0055] When an APD graph needs to be output, the peak, trough, slope (positive maximum value), and slope (negative maximum value) of the action potential signal in the image are calculated to determine the duration of the action potential, thus obtaining the APD graph.

[0056] like Figure 2 As shown, in one embodiment, step 202 is further included after step 101.

[0057] Step 202: Perform image preprocessing on the image data to make the surface texture of the heart clear and the brightness uniform.

[0058] In applications, processing of image data may include grayscale processing, geometric transformation, distortion correction, image enhancement, etc. One of these processes may be used, or multiple processes may be combined, with the aim of making the surface texture of the heart clear and the brightness uniform.

[0059] like Figure 3 As shown, in one embodiment, between steps 101 and 102, steps 201 to 204 are further included, for preprocessing the image data of the heart after acquisition, to make the surface texture of the heart clear and the brightness uniform. The execution flow of steps 201 to 204 is as follows:

[0060] Step 201: The data import process will check the data format and environment to determine whether the current system memory size supports the processing of the data. If everything is normal, proceed to step 202. If not, adjustments need to be made to the image, such as cropping, modifying the image size or format.

[0061] Step 202: Perform image preprocessing on the image data to make the surface texture of the heart clear and the brightness uniform.

[0062] Step 203: Perform format checks and judgments on the processed result data. Determine if the current processing result has failed. If it is normal, proceed to the image registration process (step 102); otherwise, proceed to step 204.

[0063] Step 204: Adjust the Gaussian template parameters and run the test again.

[0064] like Figure 4 As shown, in one embodiment, steps 301-302 are further included between steps 102 and 103, and their execution flow is as follows:

[0065] Step 301: After the registration process is completed, the format of the result data is checked again and it is determined whether the current process has failed. If it is normal, proceed to step 103; otherwise, proceed to step 302.

[0066] Step 302: Adjust the registration iteration count, smoothness, and other parameters, and run the process again.

[0067] like Figure 5 As shown, in one embodiment, a subsequent step 501 is further included between step 103 and step 104.

[0068] Step 501: In the spatial domain, an adjustable sliding window size is used for sliding window averaging, and in the temporal domain, an adjustable sliding window averaging is also used for the sequence value of each pixel.

[0069] like Figure 6As shown, in one embodiment, steps 401-402 are further included between step 103 and step 501, and their execution flow is as follows:

[0070] Step 401: After extracting the heart region image from the registered image in step 103, the format of the result data is checked and judged to determine whether step 103 has failed. If it is normal, step 501 is executed; if it fails, step 402 is executed.

[0071] Step 402: Adjust the threshold parameter or perform interactive segmentation of the heart to reacquire a new image of the heart region.

[0072] like Figure 7 As shown, in one embodiment, step 504 is included after step 501.

[0073] Step 504: Filter and enhance the signal.

[0074] In application, filtering and enhancement can be applied to each pixel sequence along the time dimension, stretching or suppressing the grayscale value of each pixel sequence. By observing the changes in the grayscale values ​​of each pixel in the grayscale image over time, an excitation transmission animation can be generated. The filter used is an IIR filter, and the filtered values ​​are reassigned back to each pixel sequence of the original image before proceeding to the next stage.

[0075] like Figure 8 As shown, in one embodiment, between step 103 and step 104, steps 501-504 are further included, for performing temporal and spatial domain filtering processing on the heart region image after extraction. The execution flow is as follows:

[0076] Step 501: In the spatial domain, an adjustable sliding window size is used for sliding window averaging, and in the temporal domain, an adjustable sliding window averaging is also used for the sequence value of each pixel.

[0077] Step 502: After processing, check the format of the result data again and determine whether the current step has failed. If it is normal, proceed to step 504; otherwise, proceed to step 503.

[0078] Step 503: Adjust the processing area size parameters for time or space and run the test again.

[0079] Step 504: Filter and enhance the signal.

[0080] like Figure 9As shown, in one embodiment, this method can select one or more result graphs for output, or output all of them. It can also analyze the functional parameters of the heart. Specifically, it includes steps 105-106, the execution flow of which is as follows:

[0081] Step 105: Analyze the functional parameters of the heart.

[0082] In practical application, the angle change of the phase image can be calculated based on the result image, and the lesion point can be located based on the angle change, thus providing guidance for subsequent ablation. The technique of calculating the angle change of the phase image and locating the lesion point based on the angle change is an existing technology; for example, the technique mentioned in the literature "A New Efficient Method for Detecting Phase Singularity in Cardiac Fibrillation" can be applied to this step. Besides the above method, other methods can be used instead, which are not limited here. Furthermore, the activation map and phase map in the result image can be directly analyzed, and the lesion point can be located by identifying abnormal or irregular spiral rotation phenomena in the image.

[0083] Step 106: Display and output the image data and result diagrams obtained from each step of this method for analysis by medical personnel.

[0084] like Figure 10 As shown, this embodiment provides a more effective method for mapping cardiac function parameters, which includes the steps in all the embodiments described above. The specific process is as follows:

[0085] Step 101: Obtain image data of the heart.

[0086] Step 201: The data import process will check the data format and environment to determine whether the current system memory size supports the processing of the data. If everything is normal, proceed to step 202. If not, adjustments need to be made to the image, such as cropping, modifying the image size or format.

[0087] Step 202: Perform image preprocessing on the image data to make the surface texture of the heart clear and the brightness uniform.

[0088] Step 203: Perform format checks and judgments on the processed result data. Determine if the current processing result has failed. If it is normal, proceed to the image registration process (step 102); otherwise, proceed to step 204.

[0089] Step 204: Adjust the Gaussian template parameters and run the test again.

[0090] Step 102: Perform image registration on each image to be registered in the image data to obtain the registered image.

[0091] Step 301: After the registration process is completed, the format of the result data is checked again and it is determined whether the current process has failed. If it is normal, proceed to step 103; otherwise, proceed to step 302.

[0092] Step 302: Adjust the registration iteration count, smoothness, and other parameters, and run the process again.

[0093] Step 103: Extract the heart region image from the registered image.

[0094] Step 401: After extracting the heart region image from the registered image in step 103, the format of the result data is checked and judged to determine whether step 103 has failed. If it is normal, step 501 is executed; if it fails, step 402 is executed.

[0095] Step 402: Adjust the threshold parameter or perform interactive segmentation of the heart to reacquire a new image of the heart region.

[0096] Step 501: In the spatial domain, an adjustable sliding window size is used for sliding window averaging, and in the temporal domain, an adjustable sliding window averaging is also used for the sequence value of each pixel.

[0097] Step 502: After processing, check the format of the result data again and determine whether the current step has failed. If it is normal, proceed to step 504; otherwise, proceed to step 503.

[0098] Step 503: Adjust the processing area size parameters for time or space and run the test again.

[0099] Step 504: Filter and enhance the signal.

[0100] Step 104: Output the result image for analyzing the functional parameters of the heart based on the heart region image.

[0101] Step 105: Analyze the functional parameters of the heart.

[0102] Step 106: Display and output the image data and result diagrams obtained from each step of this method for analysis by medical personnel.

[0103] The specific execution process for the above steps can be found in the previous description, and will not be repeated here.

[0104] like Figure 11As shown, in one embodiment, step 202 specifically includes steps 2021-2025. The preprocessing in steps 2021-2025 employs the Retinex algorithm. The basic assumption of Retinex theory is that the original image S is the product of the illumination image L and the reflectance image R. The purpose of Retinex-based image enhancement is to estimate the illumination L from the original image S, thereby decomposing R and eliminating the influence of uneven illumination. The image preprocessing flow in this embodiment specifically includes:

[0105] Step 2021: Separate the irradiated light component and the reflected light component using the logarithm method.

[0106] Step 2022: Convolve the original image using a Gaussian template to obtain the low-pass filtered image.

[0107] Step 2023: In the logarithmic domain, subtract the low-pass filtered image from the original image to obtain the high-frequency enhanced image.

[0108] Step 2024: Take the inverse number of the high-frequency enhanced image to obtain the enhanced image.

[0109] Step 2025: Perform contrast enhancement on the enhanced image, i.e., grayscale linear stretching, to obtain the final result image.

[0110] In one embodiment, the registration process in step 102 is to remove the influence of motion. It employs an optical flow motion compensation algorithm, performing point-to-point non-rigid registration on each image to be registered. This renders the originally beating heart data static, thus removing the influence of motion for subsequent image analysis. The processing flow is as follows: Figure 12 As shown, steps 1021-1025 are included:

[0111] Step 1021: Define a parameterized optical flow field, and denote the parameter vector of the optical flow field as... U；

[0112] Step 1022: Use a two-dimensional vector field to represent the displacement of each pixel in the image. This embodiment uses... u and v Represents the displacement component of a pixel as represented by a two-dimensional vector field;

[0113] Step 1023, define the energy function of the optical flow field as: E ( U And use the gradient descent algorithm to apply the energy function. E ( U Perform iterative calculations until the convergence condition is met or the preset number of iterations is reached, then convert the parameter vector obtained from the current iteration. U As the result of iterative operations;

[0114] Among them, the energy function E ( U The algorithm comprises two parts: a data term and a smoothing term. The data term measures the difference between the predicted optical flow field and the actual observed image flow, while the smoothing term makes the spatial variation of the optical flow field smoother. Specifically, the data term measures how well the current optical flow field adapts to changes in image brightness, and is represented by the dot product of the image brightness gradient and the optical flow field. The smoothing term applies a smoothing penalty to the spatial gradient of the optical flow field to make its variations smoother.

[0115] In one embodiment, the energy function of the optical flow field is defined as: E ( U )include:

[0116] The energy function of the optical flow field can be expressed by the following formula:

[0117] ;

[0118] in, The gradient term of the energy function. This is the smoothing term of the energy function. , These represent the brightness gradients of the image in the x and y directions, respectively. It is the rate of change of the image over time. is the weight of the smoothing term, and u and v are the displacement components of the pixel represented by a two-dimensional vector field.

[0119] In one embodiment, in order to adjust the energy function E ( U Gradient descent iterative computation is performed, and the problem of minimizing the energy function is transformed into a partial differential equation problem using a variational method. By varying the energy function with respect to the optical flow parameters through the Euler-Lagrange equations, a partial differential equation for updating the optical flow parameters is obtained. After obtaining the partial differential equation, the gradient descent algorithm can be used for iterative solution. The parameters are updated along the negative gradient direction to gradually reduce the value of the energy function. Assume there is an energy function... E ( U ),in U This is the parameter vector that needs to be optimized in this iterative operation. The iterative update rule for gradient descent is as follows:

[0120]

[0121] in, U n+1 It is the parameter vector for the next iteration. U n This is the parameter vector for the current iteration, and λ is the learning rate (step size), used to control the update magnitude at each step. Is the energy function in U n The gradient vector at a given point requires calculating the energy function with respect to the parameters. U The gradient can be obtained by variational applying the energy function. .

[0122] When the iterative results change little (i.e., tend to stabilize), the convergence condition is considered to have been met. For example, when the difference between the results of several consecutive iterations is less than a certain set value, the iterative results can be considered to have tended to stabilize.

[0123] Step 1024: Calculate the displacement component of each pixel based on the results of the iterative calculation, representing the direction and magnitude of the object's motion in the image;

[0124] Step 1025: Motion correction is performed on the image to be registered using the calculated displacement components, thus obtaining the registered image.

[0125] This method, by introducing a data term into the energy function, measures the degree to which the current optical flow field adapts to changes in image brightness. By introducing a smoothing term, the spatial gradient of the optical flow field is penalized, resulting in smoother changes in the optical flow field. This energy function more accurately reflects the energy changes of the optical flow field, thereby further improving the accuracy of the image registration stage.

[0126] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0127] This invention also provides a cardiac function parameter mapping device, the structure of which is as follows: Figure 13 As shown, it includes an image acquisition module 111, an image registration module 113, an image extraction module 114, and a result image generation module 117.

[0128] The image acquisition module 111 is used to acquire image data of the heart and can be a high-speed camera.

[0129] The image registration module 113 is used to register images. It uses an optical flow motion compensation algorithm to perform point-to-point non-rigid registration on each image to be registered. This can make the originally beating heart data stand still, thus removing the influence of motion for subsequent image analysis.

[0130] Image extraction module 114 is used to extract the heart region image from the registered image.

[0131] The result graph generation module 117 combines techniques such as grayscale value variation, conduction time, frequency domain analysis, and conduction direction analysis to obtain basic result graphs such as isochronous plots, voltage plots, APD plots, frequency plots, phase plots, and velocity vector plots. Specifically:

[0132] When an isochronous plot needs to be output, the regions at the same time are divided into regions according to the grayscale change time using contour lines, thus obtaining the isochronous plot;

[0133] When a voltage map needs to be output, the image is stretched according to the maximum or minimum grayscale value to obtain the voltage map.

[0134] When a frequency map is needed, the image is transformed to the frequency domain using Fourier transform, and the dominant frequencies of the image are analyzed to obtain the frequency map.

[0135] When a phase map is needed, the image is transformed to the frequency domain using a Fourier transform, and then the phase is calculated using a Hilbert transform to obtain the phase map.

[0136] When a velocity vector diagram is required, the velocity vector of excitation conduction can be obtained by performing polynomial fitting on the isochronous diagram.

[0137] When an APD graph needs to be output, the peak, trough, slope (positive maximum value), and slope (negative maximum value) of the action potential signal in the image are calculated to determine the duration of the action potential, thus obtaining the APD graph.

[0138] like Figure 14 As shown, in one embodiment, it may further include an image preprocessing module 112, a filtering module 115, a signal processing module 116, a result image output module 118, and a lesion analysis module 119.

[0139] The image preprocessing module 112 is used to preprocess the image data to make the surface texture of the heart clear and the brightness uniform.

[0140] The filtering module 115 is used to perform temporal and spatial filtering on images of the heart region.

[0141] The signal processing module 116 is used to filter and enhance the signal, so that the gray value of each pixel sequence is stretched or suppressed, and to obtain an excitation transmission animation by observing the change of gray value of each pixel in the grayscale image over time.

[0142] This embodiment can select one or more result images for output, or all of them can be output.

[0143] The result graph output module 118 is used to output the result graph.

[0144] The lesion analysis module 119 is used to calculate the angle change of the phase image based on the result image, locate the lesion point based on the angle change, thereby providing a guiding plan for subsequent ablation, or directly analyze the activation image, phase image, etc. in the result image, and locate the lesion point through the abnormal, irregular spiral rotation phenomenon in the image.

[0145] This invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method described above.

[0146] This invention provides a cardiac function parameter mapping terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the method described above.

[0147] like Figure 15 As shown, this application embodiment also provides a terminal device 200, including: at least one processor 201 ( Figure 15 The diagram shows only one processor, memory 202, and computer program 203 stored in memory 202 and executable on at least one processor 201. When processor 201 executes computer program 203, it implements the steps in the various method embodiments described above.

[0148] The terminal device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that... Figure 15 This is merely an example of the terminal device and does not constitute a limitation on the terminal device. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components, such as input / output devices, network access devices, etc.

[0149] The processor referred to can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0150] In some embodiments, the memory may be an internal storage unit of the terminal device, such as a hard drive or memory. In other embodiments, the memory may be an external storage device of the terminal device, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, the memory may include both internal and external storage units of the terminal device. The memory is used to store the operating system, applications, bootloader, data, and other programs, such as the program code of the computer program. The memory can also be used to temporarily store data that has been output or will be output.

[0151] For example, the terminal device may be a station (STAION, ST) in a WLAN, a cellular phone, a cordless phone, a Session Initiation Protocol (SIP) phone, a Wireless Local Loop (WLL) station, a Personal Digital Assistant (PDA) device, a handheld device with wireless communication capabilities, a computing device or other processing device connected to a wireless modem, an in-vehicle device, a vehicle networking terminal, a computer, a laptop computer, a handheld communication device, a handheld computing device, a satellite wireless device, a wireless modem card, a set-top box (STB), customer premises equipment (CPE), and / or other devices used for communication over a wireless system, as well as next-generation communication systems, such as mobile terminals in 5G networks or mobile terminals in future evolved Public Land Mobile Network (PLMN) networks, etc.

[0152] It should be noted that the information interaction and execution process between the above-mentioned units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, which will not be repeated here.

[0153] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0154] This application also provides a network device, which includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement the steps in any of the above method embodiments.

[0155] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps described in the various method embodiments above.

[0156] This application provides a computer program product that, when run on a mobile terminal, enables the mobile terminal to implement the steps described in the above-described method embodiments.

[0157] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0158] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0159] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0160] In the embodiments provided in this application, it should be understood that the disclosed network devices and methods can be implemented in other ways. For example, the network device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0161] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0162] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for mapping cardiac function parameters, characterized in that, include: The image data of the heartbeat is continuously and rapidly acquired by a high-speed camera over a certain period of time. An optical flow motion compensation algorithm is used to perform point-to-point non-rigid registration on each image to be registered in the image data to obtain a registered image, in which the cardiac data described is at rest. Extract the heart region image from the registered image; Output the result image for analyzing the functional parameters of the heart based on the heart region image; The non-rigid registration includes: Define a parameterized optical flow field, and denote the parameter vector of the optical flow field as U; The displacement of each pixel in the image is represented by a two-dimensional vector field; The energy function of the optical flow field is defined as E(U), and the gradient descent algorithm is used to iteratively calculate the energy function E(U) until the convergence condition is met or the preset number of iterations is reached. The parameter vector U obtained by the current iteration is used as the result of the iteration. The energy function includes a data term and a smoothing term. The data term is used to measure the degree of adaptation of the current optical flow field to changes in image brightness. The smoothing term is used to apply a smoothing penalty to the spatial gradient of the optical flow field to make the changes in the optical flow field smoother. Calculate the displacement component of each pixel based on the result of the iterative calculation; The calculated displacement components are used to perform motion correction on the image to be registered.

2. The cardiac function parameter mapping method as described in claim 1, characterized in that, After acquiring the image data of the heartbeat, the method further includes image preprocessing of the image data to make the surface texture of the heart clear and the brightness uniform.

3. The method for mapping cardiac function parameters as described in claim 1, characterized in that, After extracting the heart region image, the process further includes performing temporal and spatial filtering on the heart region image.

4. The method for mapping cardiac function parameters as described in claim 3, characterized in that, After the time-domain and spatial-domain filtering processes, the signal is further subjected to filtering and enhancement processes, which are used to stretch or suppress the grayscale value of each pixel sequence.

5. The method for mapping cardiac function parameters as described in claim 1, characterized in that, The resulting images include any one or more of the following: isochronous plot, voltage plot, APD plot, frequency plot, phase plot, and velocity vector plot; When the resulting image includes an isochronous map, the regions at the same time are divided into regions according to contour lines based on the grayscale change time to obtain the isochronous map; When the resulting image includes a voltage map, the image is stretched according to the maximum or minimum grayscale value to obtain the voltage map; When the resulting image includes a frequency map, the image is transformed to the frequency domain using a Fourier transform, and the dominant frequencies of the image are analyzed to obtain the frequency map. When the resulting image includes a phase map, the image is transformed to the frequency domain by Fourier transform, and then the phase is calculated by Hilbert transform to obtain the phase map; When the resulting image includes a velocity vector map, the velocity vector of excitation conduction is obtained by performing polynomial fitting on the isochronous diagram; When the resulting image includes an APD map, the peak value, trough value, slope with a positive maximum value, and slope with a negative maximum value of the action potential signal in the image are calculated to determine the duration of the action potential, thus obtaining the APD map.

6. The method for mapping cardiac function parameters as described in claim 1, characterized in that, The energy function of the defined optical flow field is: E ( U )include: The energy function of the optical flow field can be expressed by the following formula: ； in, The gradient term of the energy function. This is the smoothing term of the energy function. , These represent the brightness gradients of the image in the x and y directions, respectively. It is the rate of change of the image over time. is the weight of the smoothing term, and u and v are the displacement components of the pixel represented by a two-dimensional vector field.

7. A cardiac function parameter mapping device, characterized in that, include: The image acquisition module is used to continuously and rapidly acquire image data of the heartbeat over a certain period of time using a high-speed camera. The image registration module is used to perform point-to-point non-rigid registration on each image to be registered in the image data using an optical flow motion compensation algorithm to obtain a registered image, wherein the heart data described in the registered image is at rest. An image extraction module is used to extract a heart region image from the registered image; The result image generation module is used to output a result image for analyzing the functional parameters of the heart based on the heart region image; The non-rigid registration includes: Define a parameterized optical flow field, and denote the parameter vector of the optical flow field as U; The displacement of each pixel in the image is represented by a two-dimensional vector field; The energy function of the optical flow field is defined as E(U), and the gradient descent algorithm is used to iteratively calculate the energy function E(U) until the convergence condition is met or the preset number of iterations is reached. The parameter vector U obtained by the current iteration is used as the result of the iteration. The energy function includes a data term and a smoothing term. The data term is used to measure the degree of adaptation of the current optical flow field to changes in image brightness. The smoothing term is used to apply a smoothing penalty to the spatial gradient of the optical flow field to make the changes in the optical flow field smoother. Calculate the displacement component of each pixel based on the result of the iterative calculation; The calculated displacement components are used to perform motion correction on the image to be registered.

8. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 6.

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