Heart function parameter mapping method and device and terminal equipment

Abstract

The invention belongs to the technical field of cardiac mapping, and particularly relates to a cardiac function parameter mapping method and device and terminal equipment, and the method comprises the steps: obtaining the image data of a heart; performing image registration on each to-be-registered image in the image data to obtain a registered image; extracting a heart area image from the registration image; and outputting a result image for analyzing the functional parameters of the heart according to the heart region image. The data acquisition efficiency is greatly improved, the data acquisition difficulty is reduced, the mapping range is widened, and the resolution is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of cardiac mapping, and in particular relates to a cardiac function parameter mapping method, device and terminal equipment. Background Art

[0002] Arrhythmia (fibrillation) is an abnormal physiological phenomenon of irregular heart beats, which can lead to related heart diseases such as thrombosis, stroke, heart failure, etc. At present, there are three main treatment methods: controlling heart rate and heart rate through drugs, using drugs to dilute blood to avoid embolism, and ablation surgery. In the ablation surgery, the cardiac electrical signals are identified, judged and located through mapping technology, so as to determine the ablation position and check the ablation effect. Mapping technology has become an important research method in many heart disease-related fields such as arrhythmia research and treatment, drug cardiac safety evaluation, and antiarrhythmic drug screening. The mapping system mainly includes two-dimensional mapping system and three-dimensional mapping system. The two-dimensional mapping system relies on the two-dimensional image presented after X-ray scanning for mapping, and the results presented are fuzzy and difficult to locate. The three-dimensional mapping system can form a three-dimensional image map, and the mapping results are more intuitive through three-dimensional modeling, which has almost completely replaced the two-dimensional mapping system.

[0003] The existing three-dimensional mapping system uses an implanted mapping catheter to obtain magnetic and electrical signals point by point in the heart, and achieves cardiac mapping by analyzing the magnetic and electrical signals. However, since the magnetic and electrical signals need to be obtained point by point, the mapping range is small and the mapping efficiency is low. The result image obtained by converting the magnetic and electrical signals also has the defect of low resolution. Summary of the invention

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

[0005] A first aspect of an embodiment of the present invention provides a cardiac function parameter mapping method, comprising: acquiring image data of the heart; Performing image registration on each image to be registered in the image data to obtain a registered image; extracting a heart region image from the registered image; A result image for analyzing functional parameters of the heart is outputted according to the heart region image.

[0006] A second aspect of an embodiment of the present invention provides a cardiac function parameter mapping device, comprising: An image acquisition module, used for acquiring image data of the heart; An image registration module, used for performing image registration on each image to be registered in the image data to obtain a registered image; An image extraction module, used for extracting a heart region image from the registered image; A result image generating module is used to output a result image for analyzing the functional parameters of the heart according to the heart region image.

[0007] A third aspect of an embodiment 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 implements the steps of the above method when executing the computer program.

[0008] A fourth aspect of an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method described above are implemented.

[0009] In the first aspect of the embodiment of the present application, by acquiring a cardiac image, registering the image, extracting a cardiac region image, and outputting a result map for lesion analysis based on the cardiac region image, a calculation basis is provided for the angle change of the phase image, thereby realizing the location of the lesion point. The traditional electromagnetic signal-based mapping is improved to a mapping based on an easier-to-obtain image signal, which greatly improves the efficiency of data acquisition, reduces the difficulty of data acquisition, increases the scope of mapping, and improves the resolution.

[0010] It can be understood that the beneficial effects of the second and third aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0012] Figure 1 This is a first flow chart of a cardiac function parameter mapping method provided by an embodiment of the present invention; Figure 2 is a second flow chart of a cardiac function parameter mapping method provided by an embodiment of the present invention; Figure 3 is a third flow chart of a cardiac function parameter mapping method provided by an embodiment of the present invention; Figure 4is a fourth flow chart of a cardiac function parameter mapping method provided by an embodiment of the present invention; Figure 5 is a fifth flow chart of a cardiac function parameter mapping method provided by an embodiment of the present invention; Figure 6 is a sixth flow chart of a cardiac function parameter mapping method provided by an embodiment of the present invention; Figure 7 is a seventh flow chart of a cardiac function parameter mapping method provided by an embodiment of the present invention; Figure 8 is an eighth flow chart of a cardiac function parameter mapping method provided by an embodiment of the present invention; Fig. 9 is a ninth flow chart of a cardiac function parameter mapping method provided by an embodiment of the present invention; Fig.10 is a tenth flow chart of a cardiac function parameter mapping method provided by an embodiment of the present invention; Fig.11 is a flow chart of an image preprocessing method provided by an embodiment of the present invention; Fig.12 is a flow chart of an image registration method provided by an embodiment of the present invention; Fig.13 It is a structural schematic diagram of a cardiac function parameter mapping device provided by an embodiment of the present invention; Fig.14 is another structural schematic diagram of a cardiac function parameter mapping device provided by an embodiment of the present invention; Fig.15 It is a schematic diagram of a cardiac function parameter mapping terminal device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0013] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0014] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0015] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0016] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0017] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0018] A cardiac function parameter mapping method provided in an embodiment of the present application can be applied to terminal devices such as mobile phones, tablet computers, wearable devices, vehicle-mounted devices, augmented reality (AR) / virtual reality (VR) devices, laptop computers, ultra-mobile personal computers (UMPC), netbooks, personal digital assistants (PDA), etc. The embodiment of the present application does not impose any restrictions on the specific type of the terminal device.

[0019] Embodiment 1 like Figure 1 As shown, an embodiment of the present invention provides a cardiac function parameter mapping method, comprising: Step 101, acquiring image data of the heart.

[0020] In the application, the image data of the heart can be acquired through a dedicated heart image acquisition device, which can be a high-speed camera. The acquisition method is: the heart is taken out from a living animal or the chest is opened to expose the heart, and then the heart beat data is continuously acquired at high speed for a certain period of time without the need to acquire additional electrical or magnetic signals. Historical heart image data can also be directly acquired by loading data from the software interface. When loading image data, the software switches the GPU or CPU according to the hardware environment to start processing the imported image data.

[0021] Step 102: perform image registration on each image to be registered in the image data to obtain a registered image.

[0022] In the application, 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, so as to obtain a more accurate displacement state of the pixel point, and then realize the motion correction of the image. The originally beating heart data is made still, and the influence of motion is removed for the subsequent image analysis. The gray value change of the heart area image is guaranteed to be accurate in the result map output for lesion analysis.

[0023] Step 103: extracting the heart region image from the registered image.

[0024] In the application, the heart region image can be extracted from the registered image, that is, the ROI region can be extracted, and the region can be extracted by setting a threshold and performing segmentation. This embodiment uses the threshold function of opencv for threshold segmentation, extracts the contour of the maximum connected area as the final result, and performs a format check on the result data after segmentation to determine whether the current process fails. If normal, enter the filtering process in the time domain and spatial domain, otherwise adjust the threshold parameters or interactively select points to segment the heart and re-execute.

[0025] Step 104: outputting a result image for analyzing functional parameters of the heart based on the heart region image.

[0026] In the application, the gray value change, conduction time, frequency domain analysis, and conduction direction analysis can be combined to obtain basic result diagrams such as isochronous diagram, voltage diagram, APD diagram, frequency diagram, phase diagram, and velocity vector diagram. When it is necessary to output an isochronous diagram, the area at the same time is divided into regions according to similar contour lines based on the gray value change time, so that an isochronous diagram can be obtained; When a voltage map needs to be output, the image is stretched according to the maximum or minimum grayscale, so that a voltage map can be obtained; When the frequency map needs to be output, the image is converted to the frequency domain through Fourier transform, and the dominant frequency of the image is analyzed to obtain the frequency map; When the phase map needs to be output, the image is converted to the frequency domain through Fourier transform, and then the phase is calculated through Hilbert transform, so that the phase map can be obtained; When it is necessary to output a velocity vector diagram, the velocity vector of the excitation conduction is obtained by fitting the isochronal diagram with a polynomial, thereby obtaining the velocity vector diagram; When an APD graph needs to be output, the peak value, trough, positive maximum slope, and negative maximum slope of the action potential signal of the image are calculated to calculate the duration of the action potential, thereby obtaining the APD graph.

[0027] like Figure 2 As shown, in one embodiment, step 202 is further included after step 101 .

[0028] Step 202: perform image preprocessing on the image data to make the heart surface texture clear and the brightness uniform.

[0029] In the application, the image data may be processed by grayscale, geometric transformation, distortion correction, image enhancement, etc., and one of them or a combination of them may be used, with the goal of making the heart surface texture clear and the brightness uniform.

[0030] like Figure 3 As shown, in one embodiment, between step 101 and step 102, steps 201 to 204 are also included, which are used to perform image preprocessing on the image data after acquiring the image data of the heart, so that the texture of the heart surface is clear and the brightness is uniform. The execution process of steps 201 to 204 is as follows: Step 201, the process of importing data will check the format and environment of the data to determine whether the current system memory size supports processing the data. If everything is normal, proceed to step 202. If not, it is necessary to adjust the image to crop, modify the image size or format.

[0031] Step 202: perform image preprocessing on the image data to make the heart surface texture clear and the brightness uniform.

[0032] Step 203, check and judge the format of the processed result data. Judge whether the current processing result fails. If it is normal, enter the image registration process, that is, step 102, otherwise enter step 204. Step 204, adjust the Gaussian template parameters and re-run.

[0033] like Figure 4As shown, in one embodiment, between steps 102 and 103, steps 301 and 302 are also included, and the execution process is as follows: Step 301, after the registration process is completed, the format of the result data is checked again to determine whether the current process fails. If it is normal, go to step 103, otherwise go to step 302; Step 302, adjust the parameters such as the number of iterations and smoothness of the registration and re-run.

[0034] like Figure 5 As shown, in one embodiment, a post-step 501 is further included between step 103 and step 104 .

[0035] Step 501, a sliding window mean processing is performed in the spatial domain using an adjustable sliding window size, and a sliding window mean processing with an adjustable size is also performed on the sequence value of each pixel point in the time domain.

[0036] like Figure 6 As shown, in one embodiment, between step 103 and step 501, steps 401 to 402 are also included, and the execution process is as follows: Step 401, after extracting the heart area image from the registered image in step 103, the format of the result data is checked and judged to determine whether step 103 fails. If normal, step 501 is executed; if failed, step 402 is executed.

[0037] Step 402: adjust the threshold parameters or interactively segment the heart to reacquire a new heart region image.

[0038] like Figure 7 As shown, in one embodiment, after step 501, step 504 is also included.

[0039] Step 504: filter and enhance the signal.

[0040] In the application, each pixel sequence can be filtered and enhanced in the dimension of time, so that the gray value of each pixel sequence can be stretched or suppressed. Through the change of the gray value of each pixel in the grayscale image over time, an excitation conduction animation can be generated. The filter uses an IIR filter, which reassigns the filtered value back to each pixel sequence of the original image and directly enters the next link.

[0041] like Figure 8 As shown, in one embodiment, between step 103 and step 104, steps 501 to 504 are also included for performing temporal and spatial filtering on the cardiac region image after extracting the cardiac region image. The execution process is as follows: Step 501, a sliding window mean processing is performed in the spatial domain using an adjustable sliding window size, and a sliding window mean processing with an adjustable size is also performed on the sequence value of each pixel point in the time domain.

[0042] Step 502, after processing, the format of the result data is checked again and it is determined whether the current step fails. If it is normal, step 504 is executed, otherwise step 503 is executed.

[0043] Step 503, adjust the time or space processing area size parameter and re-run.

[0044] Step 504: filter and enhance the signal.

[0045] like Fig. 9 As shown, in one embodiment, the method can select one or more result graphs to output, or can output all of them. It can also analyze the functional parameters of the heart. Specifically, it includes steps 105-106, and its execution process is as follows: Step 105: analyzing the functional parameters of the heart.

[0046] When performing specific analysis in the 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, thereby providing a guiding scheme for subsequent ablation. Among them, the technical means of calculating the angle change of the phase image and locating the lesion point based on the angle change belong to the existing technology, such as the technology mentioned in the document "A New Efficient Method for Detecting Phase Singularity in Cardiac Fibrillation", which can be applied to this link. In addition to the above schemes, other schemes can also be used instead, which are not limited here. In addition, the excitement map, phase map, etc. in the result map can also be directly analyzed, and the lesion point can be located through the abnormal and irregular parts of the image that have spiral rotation phenomenon.

[0047] Step 106, the image data and result graphs of each link obtained in the method are displayed and output for analysis by medical personnel.

[0048] like Fig.10 As shown, this embodiment provides a method for measuring cardiac function parameters with better effect, which includes the steps in all the above embodiments, and its specific process is as follows: Step 101, acquiring image data of the heart.

[0049] Step 201, the process of importing data will check the format and environment of the data to determine whether the current system memory size supports processing the data. If everything is normal, proceed to step 202. If not, it is necessary to adjust the image to crop, modify the image size or format.

[0050] Step 202: perform image preprocessing on the image data to make the heart surface texture clear and the brightness uniform.

[0051] Step 203, check and judge the format of the processed result data. Judge whether the current processing result fails. If it is normal, enter the image registration process, that is, step 102, otherwise enter step 204. Step 204, adjust the Gaussian template parameters and re-run.

[0052] Step 102: perform image registration on each image to be registered in the image data to obtain a registered image.

[0053] Step 301, after the registration process is completed, the format of the result data is checked again to determine whether the current process fails. If it is normal, go to step 103, otherwise go to step 302; Step 302, adjust the parameters such as the number of iterations and smoothness of the registration and re-run.

[0054] Step 103: extracting the heart region image from the registered image.

[0055] Step 401, after extracting the heart area image from the registered image in step 103, the format of the result data is checked and judged to determine whether step 103 fails. If normal, step 501 is executed; if failed, step 402 is executed.

[0056] Step 402: adjust the threshold parameters or interactively segment the heart to reacquire a new heart region image.

[0057] Step 501, a sliding window mean processing is performed in the spatial domain using an adjustable sliding window size, and a sliding window mean processing with an adjustable size is also performed on the sequence value of each pixel point in the time domain.

[0058] Step 502, after processing, the format of the result data is checked again and it is determined whether the current step fails. If it is normal, step 504 is executed, otherwise step 503 is executed.

[0059] Step 503, adjust the time or space processing area size parameter and re-run.

[0060] Step 504: filter and enhance the signal.

[0061] Step 104: outputting a result image for analyzing functional parameters of the heart based on the heart region image.

[0062] Step 105: analyzing the functional parameters of the heart.

[0063] Step 106, the image data and result graphs of each link obtained in the method are displayed and output for analysis by medical personnel.

[0064] The specific execution process of the above steps can be found in the previous description and will not be repeated here.

[0065] like Fig.11 As shown, in one embodiment, step 202 specifically includes steps 2021 to 2025. The preprocessing of steps 2021 to 2025 uses the Retinex algorithm. The basic assumption of the Retinex theory is that the original image S is the product of the illumination image L and the reflectance image R. The purpose of image enhancement based on Retinex is to estimate the illumination L from the original image S, thereby decomposing R and eliminating the influence of uneven illumination. The image preprocessing process of this embodiment specifically includes: Step 2021, using the logarithmic method to separate the illumination light component and the reflected light component.

[0066] Step 2022, convolve the original image with a Gaussian template to obtain an image after low-pass filtering.

[0067] Step 2023, in the logarithmic domain, subtract the low-pass filtered image from the original image to obtain a high-frequency enhanced image.

[0068] Step 2024, taking the antilogarithm of the high-frequency enhanced image to obtain an enhanced image.

[0069] Step 2025, performing contrast enhancement on the enhanced image, that is, grayscale linear stretching, to obtain a final result image.

[0070] In one embodiment, the registration process in step 102 is to remove the influence of motion. The optical flow motion compensation algorithm is used to perform point-to-point non-rigid registration on each image to be registered, so that the originally beating heart data can be stationary, and the influence of motion is removed for the subsequent image analysis. The processing flow is as follows: Fig.12 As shown, steps 1021 to 1025 are included: Step 1021, define a parameterized optical flow field, and record the parameter vector of the optical flow field as U； Step 1022, using a two-dimensional vector field to represent the displacement of each pixel in the image. In this embodiment, u and vrepresents the displacement component of the pixel represented by the two-dimensional vector field; Step 1023, define the energy function of the optical flow field as E ( U ), and use the gradient descent algorithm to E ( U ) to perform iterative calculations until the convergence condition is reached or the preset number of iterations is reached, and the parameter vector calculated by the current iteration is U As a result of iterative operations; Among them, the energy function E ( U ) consists of two parts: the data term and the smoothing term. The data term measures the difference between the predicted optical flow field and the actually observed image flow, while the smoothing term makes the optical flow field change more smoothly in space. Among them, the data term is used to measure the degree of adaptation of the current optical flow field to the change of image brightness, and is represented by the dot product of the image brightness gradient and the optical flow field. The smoothing term penalizes the spatial gradient of the optical flow field to make the change of the optical flow field smoother.

[0071] In one embodiment, the energy function defining the optical flow field is: E ( U )include: The energy function of the optical flow field is expressed by the following formula: ; in, is the gradient term of the energy function, is the smoothing term of the energy function, , are the brightness gradients of the image in the x and y directions, 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 the two-dimensional vector field.

[0072] In one embodiment, in order to E ( U ) performs gradient descent iterations, and uses the variational method to transform the energy function minimization problem into a partial differential equation problem. By varying the energy function with respect to the optical flow field parameters through the Euler-Lagrange equation, the partial differential equation for updating the optical flow field parameters is obtained. After obtaining the partial differential equation, the gradient descent algorithm can be used to iteratively solve it. By updating the parameters along the negative gradient direction, the value of the energy function is gradually reduced. Suppose there is an energy function E ( U ),in U is the parameter vector that needs to be optimized for this iterative operation. The iterative update rule of gradient descent is as follows:

[0073] in, U n+1 is the parameter vector for the next iteration, U n is the parameter vector of the current iteration, λ is the learning rate (step size), which is used to control the update amplitude of each step, is the energy function in U n The gradient vector at , we need to calculate the energy function with respect to the parameter U The gradient of can be obtained by varying the energy function. .

[0074] When the iteration result changes little (i.e., tends to be stable), it is considered that the convergence condition has been met. For example, when the difference between the results of several consecutive iterations is less than a certain set value, it can be considered that the iteration result tends to be stable.

[0075] Step 1024, calculating the displacement component of each pixel point according to the result of the iterative operation, indicating the movement direction and size of the object in the image; Step 1025: motion correction is performed on the image to be registered using the calculated displacement component, so as to obtain a registered image.

[0076] This method can measure the adaptability of the current optical flow field to the image brightness change by introducing data terms in the energy function, and can impose a smoothing term penalty on the spatial gradient of the optical flow field by introducing a smoothing term to make the change of the optical flow field smoother. This energy function can more accurately reflect the energy change of the optical flow field, thereby further improving the accuracy of the image registration stage.

[0077] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean 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 the present application.

[0078] The embodiment of the present invention also provides a cardiac function parameter mapping device, the structure of which is as follows: Fig.13 As shown, it includes an image acquisition module 111, an image registration module 113, an image extraction module 114 and a result map generation module 117.

[0079] The image acquisition module 111 is used to acquire image data of the heart, and a high-speed camera may be used.

[0080] 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, which can make the originally beating heart data static, thereby removing the influence of motion factors for subsequent image analysis.

[0081] The image extraction module 114 is used to extract the heart region image from the registered image.

[0082] The result graph generation module 117 is used to combine the gray value change, conduction time, frequency domain analysis, and conduction direction analysis techniques to obtain basic result graphs such as isochronal graph, voltage graph, APD graph, frequency graph, phase graph, and velocity vector graph. The details are as follows: When it is necessary to output an isochronous map, the area at the same time is divided into regions according to similar contour lines based on the grayscale change time, so that an isochronous map can be obtained; When a voltage map needs to be output, the image is stretched according to the maximum or minimum grayscale, so that a voltage map can be obtained; When the frequency map needs to be output, the image is converted to the frequency domain through Fourier transform, and the dominant frequency of the image is analyzed to obtain the frequency map; When the phase map needs to be output, the image is converted to the frequency domain through Fourier transform, and then the phase is calculated through Hilbert transform, so that the phase map can be obtained; When it is necessary to output a velocity vector diagram, the velocity vector of the excitation conduction is obtained by fitting the isochronal diagram with a polynomial, thereby obtaining the velocity vector diagram; When an APD graph needs to be output, the peak value, trough, positive maximum slope, and negative maximum slope of the action potential signal of the image are calculated to calculate the duration of the action potential, thereby obtaining the APD graph.

[0083] like Fig.14 As shown, in one embodiment, it may also include an image preprocessing module 112 , a filtering module 115 , a signal processing module 116 , a result graph output module 118 and a lesion analysis module 119 .

[0084] The image preprocessing module 112 is used to perform image preprocessing on the image data to make the heart surface texture clear and the brightness uniform.

[0085] The filtering module 115 is used to perform temporal and spatial filtering processing on the heart region image.

[0086] The signal processing module 116 is used to filter and enhance the signal so that the grayscale value of each pixel sequence is stretched or suppressed, and to obtain an excitation conduction dynamic image through the change of the grayscale value of each pixel in the grayscale image over time.

[0087] In this embodiment, one or more result graphs may be selected for output, or all of them may be output.

[0088] The result graph output module 118 is used to output the result graph.

[0089] The lesion analysis module 119 is used to calculate the angle change of the phase image based on the result image, and locate the lesion point based on the angle change, thereby providing a guiding plan for subsequent ablation, or directly analyze the excitement map, phase map, etc. in the result image, and locate the lesion point through the abnormal, irregular and spiral rotation parts of the image.

[0090] An embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0091] An embodiment of the present 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, wherein the processor implements the steps of the above method when executing the computer program.

[0092] like Fig.15 As shown, the embodiment of the present application further provides a terminal device 200, including: at least one processor 201 ( Fig.15 Only one processor is shown in the figure), a memory 202, and a computer program 203 stored in the memory 202 and executable on at least one processor 201, wherein the processor 201 implements the steps in the above-mentioned various method embodiments when executing the computer program 203.

[0093] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The terminal device may include, but is not limited to, a processor and a memory. Those skilled in the art will understand that Fig.15 It is only an example of the terminal device and does not constitute a limitation of the terminal device. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, it may also include input and output devices, network access devices, etc.

[0094] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0095] In some embodiments, the memory may be an internal storage unit of the above-mentioned terminal device, such as a hard disk or memory of the terminal device. In other embodiments, the memory may also be an external storage device of the terminal device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the terminal device. Further, the memory may also include both an internal storage unit of the terminal device and an external storage device. The memory is used to store an operating system, an application program, a boot loader (BootLoader), data and other programs, such as the program code of the computer program, etc. The memory may also be used to temporarily store data that has been output or is to be output.

[0096] For example, the terminal device can 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 function, a computing device or other processing device connected to a wireless modem, a vehicle-mounted 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 TV set top box (STB), a customer premise equipment (CPE) and / or other devices for communicating on a wireless system and a next-generation communication system, such as a mobile terminal in a 5G network or a mobile terminal in a future evolved Public Land Mobile Network (PLMN) network, etc.

[0097] It should be noted that the information interaction, execution process and other contents between the above-mentioned units are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0098] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by 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 embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0099] An embodiment of the present 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 implements the steps in any of the above-mentioned method embodiments when executing the computer program.

[0100] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0101] An embodiment of the present application provides a computer program product. When the computer program product runs on a mobile terminal, the mobile terminal can implement the steps in the above-mentioned method embodiments when executing the computer program product.

[0102] If the integrated unit is implemented in the form of 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, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the terminal device, a recording medium, a computer memory, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), an electric carrier signal, a telecommunication signal, and a software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0103] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0104] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example 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 performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0105] In the embodiments provided in the present 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 only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0106] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0107] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for measuring cardiac function parameters, characterized in that: include: acquiring image data of the heart; Performing image registration on each image to be registered in the image data to obtain a registered image; extracting a heart region image from the registered image; A result image for analyzing functional parameters of the heart is outputted according to the heart region image.

2. The cardiac function parameter mapping method according to claim 1, characterized in that: After acquiring the image data of the heart, the method further includes performing image preprocessing on the image data, wherein the image preprocessing is used to make the heart surface texture clear and the brightness uniform.

3. The cardiac function parameter mapping method according to claim 1, characterized in that: After the heart region image is extracted, the method further includes performing time domain and space domain filtering processing on the heart region image.

4. The cardiac function parameter mapping method according to claim 3, characterized in that: After the temporal and spatial filtering processes, the signal is further filtered and enhanced, and the filtering and enhancement processes are used to stretch or suppress the grayscale value of each pixel sequence.

5. The cardiac function parameter mapping method according to claim 1, characterized in that: The result image includes any one or more of an isochronous diagram, a voltage diagram, an APD diagram, a frequency diagram, a phase diagram, and a velocity vector diagram; When the result map includes an isochronous map, the regions at the same time are divided into regions according to similar contour lines according to the grayscale change time to obtain the isochronous map; When the result image includes a voltage image, the image is stretched according to the maximum or minimum grayscale to obtain the voltage image; When the result graph includes a frequency graph, the image is converted into the frequency domain by Fourier transform, and the dominant frequency of the image is analyzed to obtain the frequency graph; When the result image includes a phase image, the image is converted into the frequency domain by Fourier transform, and then the phase is calculated by Hilbert transform to obtain the phase image; When the result graph includes a velocity vector graph, the velocity vector of the excitation conduction is obtained by performing polynomial fitting on the isochronal graph to obtain the velocity vector graph; When the result graph includes an APD graph, the peak value, the trough value, the positive maximum slope, and the negative maximum slope of the action potential signal of the image are calculated to calculate the duration of the action potential and obtain the APD graph.

6. The cardiac function parameter mapping method according to claim 1, characterized in that: The image registration comprises: Define a parameterized optical flow field and record the parameter vector of the optical flow field as U； Use a two-dimensional vector field to represent the displacement of each pixel in the image; The energy function of the optical flow field is defined as E ( U ), and use the gradient descent algorithm to E ( U ) to perform iterative calculations until the convergence condition is reached or the preset number of iterations is reached, and the parameter vector calculated by the current iteration is U As a result of iterative operations; Calculate the displacement component of each pixel point according to the result of the iterative operation; The calculated displacement component is used to perform motion correction on the image to be registered.

7. The cardiac function parameter mapping method according to claim 6, characterized in that: The energy function defining the optical flow field is: E ( U )include: The energy function of the optical flow field is expressed by the following formula: ； in, is the gradient term of the energy function, is the smoothing term of the energy function, , are the brightness gradients of the image in the x and y directions, 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 the two-dimensional vector field.

8. A cardiac function parameter mapping device, characterized in that: include: An image acquisition module, used for acquiring image data of the heart; An image registration module, used for performing image registration on each image to be registered in the image data to obtain a registered image; An image extraction module, used for extracting a heart region image from the registered image; A result image generating module is used to output a result image for analyzing the functional parameters of the heart according to the heart region image.

9. 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, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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