Image processing methods and apparatus

Abstract

This invention provides an image processing method and apparatus, comprising: acquiring a first radio frequency (RF) data image of an ultrasound fundamental frequency to be processed; determining a first point spread function corresponding to the first RF data image; performing blind deconvolution on the first RF data image based on the first point spread function to obtain a second RF data image; generating a second ultrasound image based on the second RF data image; determining a second point spread function corresponding to the second RF data image; performing joint blind deconvolution on the first RF data image and the second RF data image based on the first point spread function and the second point spread function to obtain a third RF data image; generating a third ultrasound image based on the third RF data image; and generating a final ultrasound image based on the second and third ultrasound images. In this method, spatial resolution is simultaneously improved and speckle noise is reduced using only ultrasound fundamental frequency image information, without adding additional hardware requirements or image acquisition operations.

Description

Technical Field

[0001] This invention relates to the field of ultrasound imaging technology, and in particular to an image processing method and apparatus. Background Technology

[0002] Ultrasound imaging is one of the most commonly used medical imaging modalities, and it has been widely applied in disease diagnosis. However, due to the inherently limited bandwidth of ultrasound transducers, its imaging results are also plagued by low spatial resolution and speckle noise, which reduces image quality and hinders further image processing, making it difficult to identify body tissues, resulting in poor labeling and measurement results, and affecting the ultrasound diagnostic outcome.

[0003] Several methods exist to improve the spatial resolution of ultrasound images. However, existing image processing methods have certain limitations: the hardware requirements of ultrasound equipment and the operational complexity of image acquisition are too high; they cannot improve the spatial resolution when only one ultrasound fundamental frequency (or fundamental frequency) image information exists; the effect of improving spatial resolution is limited; and they cannot improve spatial resolution while reducing speckle noise.

[0004] To improve tissue identification and enhance ultrasound diagnostic results, it is necessary to improve the spatial resolution of ultrasound images and reduce speckle noise, thereby improving ultrasound image quality. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to provide an image processing method and apparatus that can simultaneously improve spatial resolution and reduce speckle noise using only ultrasound fundamental frequency image information, without adding additional hardware requirements or image acquisition operations, and can be easily integrated into existing ultrasound image processing workflows.

[0006] In a first aspect, embodiments of the present invention provide an image processing method, the method comprising: acquiring a first radio frequency data image of an ultrasound base frequency to be processed; determining a first point spread function corresponding to the first radio frequency data image, and performing blind deconvolution on the first radio frequency data image based on the first point spread function to obtain a second radio frequency data image; generating a second ultrasound image based on the second radio frequency data image; determining a second point spread function corresponding to the second radio frequency data image, and performing joint blind deconvolution on the first radio frequency data image and the second radio frequency data image based on the first point spread function and the second point spread function to obtain a third radio frequency data image; generating a third ultrasound image based on the third radio frequency data image; and generating a final ultrasound image based on the second ultrasound image and the third ultrasound image.

[0007] In an optional embodiment of this application, a second radio frequency data image is obtained by blindly deconvolving the first radio frequency data image based on a first point spread function using the following formula: Among them, yA For the first radio frequency data image, y B For the second radio frequency data image, p(y) B |y A ) is based on the standard maximum a posteriori estimate in y A Seeking y in B The maximum posterior distribution, H A It is a block cyclic matrix based on the first point diffusion function.

[0008] In an optional embodiment of this application, the second radio frequency data image is determined by the following formula: Where, μ B This is an adjustable parameter.

[0009] In an optional embodiment of this application, a third radio frequency data image is obtained by performing joint blind deconvolution on the first radio frequency data image and the second radio frequency data image based on the first point spread function and the second point spread function using the following formula: Among them, y A For the first radio frequency data image, y B For the second radio frequency data image, y C For the third radio frequency data image, (y C |y A ,y B ) is based on the standard maximum a posteriori estimate in y A and y B Seeking y in C The maximum posterior distribution, H A H is a block cyclic matrix constructed based on the first point diffusion function. B It is a block cyclic matrix based on the second point diffusion function.

[0010] In an optional embodiment of this application, the third radio frequency data image is determined by the following formula: Where, μ C This is an adjustable parameter.

[0011] In optional embodiments of this application, the step of generating a second ultrasound image based on a second radio frequency data image includes: performing signal processing on the second radio frequency data image to obtain a second ultrasound image; the step of generating a third ultrasound image based on a third radio frequency data image includes: performing signal processing on the third radio frequency data image to obtain a third ultrasound image; wherein, the signal processing includes envelope detection.

[0012] In an optional embodiment of this application, the step of generating a final ultrasound image based on the second ultrasound image and the third ultrasound image includes: normalizing the pixel values ​​of the second ultrasound image and the third ultrasound image and performing a weighted summation of the pixel values ​​at the same pixel position to obtain the final ultrasound image.

[0013] In optional embodiments of this application, the step of determining the first point spread function corresponding to the first radio frequency data image includes: determining the first point spread function corresponding to the first radio frequency data image based on an estimation method using a parametric prior model or an estimation method using a non-parametric homomorphic filter; the step of determining the second point spread function corresponding to the second radio frequency data image includes: determining the second point spread function corresponding to the second radio frequency data image based on an estimation method using a parametric prior model or an estimation method using a non-parametric homomorphic filter.

[0014] In an optional embodiment of this application, the step of determining the first point spread function corresponding to the first radio frequency data image includes: using a pre-set first known point spread function as the first point spread function corresponding to the first radio frequency data image; the step of determining the second point spread function corresponding to the second radio frequency data image includes: using a pre-set second known point spread function as the second point spread function corresponding to the second radio frequency data image.

[0015] Secondly, embodiments of the present invention also provide an image processing apparatus, comprising: a first radio frequency data image acquisition module, configured to acquire a first radio frequency data image of an ultrasound base frequency to be processed; a second ultrasound image generation module, configured to determine a first point spread function corresponding to the first radio frequency data image, perform blind deconvolution on the first radio frequency data image based on the first point spread function to obtain a second radio frequency data image, and generate a second ultrasound image based on the second radio frequency data image; a third ultrasound image generation module, configured to determine a second point spread function corresponding to the second radio frequency data image, perform joint blind deconvolution on the first radio frequency data image and the second radio frequency data image based on the first point spread function and the second point spread function to obtain a third radio frequency data image, and generate a third ultrasound image based on the third radio frequency data image; and a final ultrasound image generation module, configured to generate a final ultrasound image based on the second ultrasound image and the third ultrasound image.

[0016] The embodiments of the present invention bring the following beneficial effects:

[0017] This invention provides an image processing method and apparatus, which acquires a first radio frequency (RF) data image of the ultrasound fundamental frequency to be processed; determines a first point spread function corresponding to the first RF data image; performs blind deconvolution on the first RF data image based on the first point spread function to obtain a second RF data image; generates a second ultrasound image based on the second RF data image; determines a second point spread function corresponding to the second RF data image; performs joint blind deconvolution on the first RF data image and the second RF data image based on the first point spread function and the second point spread function to obtain a third RF data image; generates a third ultrasound image based on the third RF data image; and generates a final ultrasound image based on the second and third ultrasound images. This method, using only ultrasound fundamental frequency image information, simultaneously improves spatial resolution and reduces speckle noise without adding extra hardware requirements or image acquisition operations, and can be easily integrated into existing ultrasound image processing workflows.

[0018] Other features and advantages of this disclosure will be set forth in the following description, or some features and advantages may be inferred from the description or determined without doubt, or may be learned by practicing the techniques described above.

[0019] To make the above-mentioned objects, features and advantages of this disclosure more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0020] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0021] Figure 1 A flowchart of an image processing method provided in an embodiment of the present invention;

[0022] Figure 2 A schematic diagram illustrating an image processing method provided in an embodiment of the present invention;

[0023] Figure 3 A flowchart of another intravascular image processing method provided in an embodiment of the present invention;

[0024] Figure 4 A schematic diagram illustrating another image processing method provided in an embodiment of the present invention;

[0025] Figure 5 This is a schematic diagram of the structure of an intravascular image processing device provided in an embodiment of the present invention;

[0026] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] Currently, ultrasound imaging is one of the most commonly used medical imaging modalities, and it has been widely applied in disease diagnosis. However, due to the inherently limited bandwidth of ultrasound transducers, its imaging results are also plagued by low spatial resolution and speckle noise, which reduces image quality and hinders further image processing, making it difficult to identify body tissues, resulting in poor labeling and measurement results, and affecting the ultrasound diagnostic outcome.

[0029] Several methods exist to improve the spatial resolution of ultrasound images, such as second harmonic imaging, dynamically focused phased array systems, and spatial compounding. However, these methods inevitably increase the hardware requirements of ultrasound equipment and the operational complexity of image acquisition, and cannot improve the spatial resolution when only one ultrasound fundamental frequency (or fundamental frequency) image is available.

[0030] Several methods exist to enhance spatial resolution when only fundamental frequency ultrasound image information is available. One such method is 2D blind deconvolution of the fundamental frequency ultrasound image information. This works by estimating the point spread function of the imaging system for an ultrasound radio frequency image based on radio frequency signal information. Subsequently, deconvolution is used to reconstruct the ultrasound radio frequency image, generating a reconstructed ultrasound image that enhances axial and lateral spatial resolution. However, since this operation only reconstructs the fundamental frequency ultrasound radio frequency image, its effect on improving spatial resolution is limited. Another existing method is to add second harmonic imaging radio frequency image information to the aforementioned fundamental frequency-based 2D blind deconvolution operation to improve spatial resolution and the reconstruction effect of the ultrasound image. However, using second harmonic imaging inevitably increases the hardware requirements of the ultrasound equipment and the algorithmic requirements for extracting the second harmonic image.

[0031] Ultrasound imaging results are affected not only by low spatial resolution but also by speckle noise. Several methods exist to reduce speckle noise in ultrasound images, such as frequency diversity techniques, spatial composite methods, and filtering approaches. However, none of these methods can improve spatial resolution while simultaneously reducing speckle noise.

[0032] Given the limitations of the above methods, in order to improve tissue identification and enhance ultrasound diagnostic results, it is necessary to improve the spatial resolution of ultrasound images and reduce speckle noise to improve ultrasound image quality.

[0033] Based on this, the image processing method and apparatus provided in this embodiment of the invention specifically provide a method that can simultaneously improve spatial resolution and reduce speckle noise using only ultrasound fundamental frequency image information, without adding any additional hardware requirements or image acquisition operations, and can be easily integrated into existing ultrasound image processing workflows.

[0034] To facilitate understanding of this embodiment, a detailed description of an image processing method disclosed in this embodiment of the invention will be provided first.

[0035] Example 1:

[0036] This invention provides an image processing method, see [link to relevant documentation]. Figure 1 The flowchart shown illustrates an image processing method, which includes the following steps:

[0037] Step S102: Obtain the first radio frequency data image of the ultrasound base frequency to be processed.

[0038] See Figure 2 The diagram illustrates an image processing method. In this embodiment, a first radio frequency data image y of the ultrasound fundamental frequency to be processed can be acquired first. A .

[0039] In this embodiment, the input only uses a single first radio frequency data image at the ultrasonic base frequency. A It does not require additional hardware or image acquisition operations and can be easily integrated into existing ultrasound image processing workflows.

[0040] Step S104: Determine the first point spread function corresponding to the first radio frequency data image; perform blind deconvolution on the first radio frequency data image based on the first point spread function to obtain the second radio frequency data image; generate the second ultrasound image based on the second radio frequency data image.

[0041] like Figure 2 As shown, in acquiring the first radio frequency data image y A Subsequently, this embodiment can estimate the first radio frequency data image y. A The two-dimensional point spread function is called the first point spread function (PSF). A Based on the first-point diffusion function PSF A For the first radio frequency data image y A Blind deconvolution is performed to generate a new second radio frequency data image y. B Processing the second radio frequency data image y B New second ultrasound images (US) can be generated. B .

[0042] Step S106: Determine the second point spread function corresponding to the second radio frequency data image; perform joint blind deconvolution on the first radio frequency data image and the second radio frequency data image based on the first point spread function and the second point spread function to obtain the third radio frequency data image; generate the third ultrasound image based on the third radio frequency data image.

[0043] like Figure 2 As shown, in generating the second radio frequency data image y B Subsequently, this embodiment can estimate the second radio frequency data image y. B The two-dimensional point spread function is called the second point spread function (PSF). B Based on the first-point diffusion function PSF A Second point diffusion function PSF B For the first radio frequency data image y A Second radio frequency data image y B Performing two-dimensional joint blind deconvolution generates a new third radio frequency data image y with improved spatial resolution. C Processing third-radio frequency data image y CNew third ultrasound images (US) can be generated. C .

[0044] Step S108: Generate the final ultrasound image based on the second and third ultrasound images.

[0045] like Figure 2 As shown, this embodiment can display the second ultrasound image US B and third ultrasound image US C The images are then processed to obtain the final ultrasound image.

[0046] Second ultrasound image US B and third ultrasound image US C The average value is higher than that of the first radio frequency data image from the original base frequency. A The first ultrasound image generated (US) A It has higher spatial resolution. Therefore, using a second ultrasound image (US) B and third ultrasound image US C The final ultrasound image generated US D It will be better than the original first ultrasound image US A It has a higher spatial resolution.

[0047] An ultrasound radio frequency data image A can generally be considered as being obtained by convolving an image B acquired by an ideal ultrasound system with the point spread function C of an actual ultrasound system and adding noise. Image A is usually blurry compared to image B because the point spread function C includes physical diffraction. The purpose of deconvolution is to reconstruct a clear ultrasound image B that is unaffected by diffraction, given A and C.

[0048] Based on the above principles, the second radio frequency data image y B First radio frequency data image y A Based on this, the third radio frequency data image y C First radio frequency data image y A Second radio frequency data image y B Based on the second radio frequency data image y, it is obtained. B and third radio frequency data image y C The second ultrasound image obtained (US) B and third ultrasound image US C It will be better than the original first ultrasound image US A Clear. Using a second ultrasound image (US) B and third ultrasound image US C The final ultrasound image generated US D It will be better than the original first ultrasound image US A It has a higher spatial resolution.

[0049] Due to the second ultrasound image US B and third ultrasound image US C The second radio frequency data image y is obtained by two-dimensional blind deconvolution. B The third radio frequency data image y obtained by joint blind deconvolution of two dimensions C Generate, therefore, the second ultrasound image US B and third ultrasound image US C They have different speckle noise. Based on this, based on the second ultrasound image US B and third ultrasound image US C Generate the final ultrasound image US D This can reduce speckle noise.

[0050] This invention provides an image processing method that involves acquiring a first radio frequency (RF) data image of the ultrasound fundamental frequency to be processed; determining a first point spread function corresponding to the first RF data image; performing blind deconvolution on the first RF data image based on the first point spread function to obtain a second RF data image; generating a second ultrasound image based on the second RF data image; determining a second point spread function corresponding to the second RF data image; performing joint blind deconvolution on the first RF data image and the second RF data image based on the first point spread function and the second point spread function to obtain a third RF data image; generating a third ultrasound image based on the third RF data image; and generating a final ultrasound image based on the second and third ultrasound images. This method, using only ultrasound fundamental frequency image information, simultaneously improves spatial resolution and reduces speckle noise without adding extra hardware requirements or image acquisition operations, and can be easily integrated into existing ultrasound image processing workflows.

[0051] Example 2:

[0052] This embodiment provides another intravascular image processing method, which is implemented based on the above embodiment. See [link to previous embodiment]. Figure 3 The flowchart illustrates another intravascular image processing method. The intravascular image processing method in this embodiment includes the following steps:

[0053] Step S302: Obtain the first radio frequency data image of the ultrasound base frequency to be processed.

[0054] Step S304: Determine the first point spread function corresponding to the first radio frequency data image; perform blind deconvolution on the first radio frequency data image based on the first point spread function to obtain the second radio frequency data image; generate the second ultrasound image based on the second radio frequency data image.

[0055] Specifically, in this embodiment, signal processing can be performed on the second radio frequency data image to obtain a second ultrasound image. Correspondingly, this embodiment can also perform the above signal processing on the third radio frequency data image to obtain a third ultrasound image; wherein, the signal processing includes envelope detection. The radio frequency data image in this embodiment needs to undergo signal processing to convert it into a commonly seen ultrasound image. The above signal processing can use envelope detection.

[0056] In this embodiment, two-dimensional blind deconvolution can be used to process the first radio frequency data image of the original base frequency. (N is the number of image pixels) Perform image reconstruction calculations to generate a new second radio frequency data image. Second radio frequency data y B The second radio frequency (RF) data image is obtained by blindly deconvolving the first RF data image based on the first point spread function using the following formula:

[0057] Where log[…] represents the logarithmic operation, and ||…| represents the modulus of the vector. A For the first radio frequency data image, y B For the second radio frequency data image, p(y) B |y A ) is based on the standard maximum a posteriori (MAP) estimate in y A Seeking y in B The maximum posterior distribution, H A Let [p(y) be a block cyclic matrix constructed based on the first point diffusion function. B )] represents the prior probability, derived from Bayesian statistics.

[0058] Solving the above formula can be transformed into solving a minimum value problem, that is, determining the second radio frequency data image through the following formula: Where, μ B It is an adjustable parameter.

[0059] Specifically, this embodiment can determine the first point spread function corresponding to the first radio frequency data image based on an estimation method using a parametric prior model or an estimation method using non-parametric homomorphic filtering. Correspondingly, this embodiment can also determine the second point spread function corresponding to the second radio frequency data image based on an estimation method using a parametric prior model or an estimation method using non-parametric homomorphic filtering.

[0060] It should also be noted that in this embodiment, any two-dimensional point spread function estimation method can be used to estimate the two-dimensional point spread function, such as estimation methods based on parametric prior models or estimation methods based on non-parametric homomorphic filtering.

[0061] Step S306: Determine the second point spread function corresponding to the second radio frequency data image; perform joint blind deconvolution on the first radio frequency data image and the second radio frequency data image based on the first point spread function and the second point spread function to obtain the third radio frequency data image; generate the third ultrasound image based on the third radio frequency data image.

[0062] In this embodiment, two-dimensional joint blind deconvolution can be used to process the first radio frequency data image y. A Second radio frequency data image y B Image reconstruction calculations are performed to jointly generate a new third radio frequency data image y. C .

[0063] Newly generated third radio frequency data image y C Not only does it contain the first radio frequency data image y of the original baseband A The information also includes a second radio frequency data image with improved spatial resolution. B The information, therefore the newly generated y C Compared to y B It has better ultrasound image reconstruction results. Simultaneously, it utilizes third radiofrequency data images... C The generated third ultrasound image US C It also has and is composed of second radio frequency data image y B The generated second ultrasound image US B Different speckle noises exist. Therefore, image composite methods (e.g., weighted summation) can reduce the interference of speckle noise in the merged ultrasound images.

[0064] Specifically, the third radio frequency data y C The calculation is based on solving the following cost function. The third radio frequency data image is obtained by performing joint blind deconvolution on the first and second radio frequency data images based on the first and second point spread functions using the following formula:

[0065]

[0066] Among them, y A For the first radio frequency data image, y B For the second radio frequency data image, y C For the third radio frequency data image, p(y) C |y A ,y B ) is based on the standard maximum a posteriori estimate in y A and y B Seeking y in C The maximum posterior distribution, H A H is a block cyclic matrix constructed based on the first point diffusion function. BIt is a block cyclic matrix based on the second point diffusion function.

[0067] Solving the above formula can be transformed into solving a minimum value problem, that is, determining the third radio frequency data image through the following formula: Where, μ C This is an adjustable parameter.

[0068] In this embodiment, an iterative optimization method can be used to calculate the minimum value problem in the two-dimensional joint blind deconvolution computation. This embodiment can... C The minimum value problem is rewritten as Where, μ C This is an adjustable parameter.

[0069] The above formula is solved iteratively using steps one through seven as follows:

[0070] Step 1: Initialize μ C β is a user-input parameter greater than zero, used to initialize u. 0 =w 0 =z 0 =y A Initialize λ 0 = 0. Here, β is an adjustable parameter, and λ is a vector of Lagrangian multipliers. The superscript to the right of the variable is used to indicate that the current iteration number is k and to initialize k = 0.

[0071] Step 2: Calculate u k+1 , can be obtained

[0072] Where I is an N×N identity matrix, F and F * These are the two-dimensional Fourier transform and inverse Fourier transform matrices, Θ. A =diag(FPSF) A ) and Θ B =diag(FPSF) B ), where diag(...) is a diagonal matrix.

[0073] Step 3: Calculate w k+1 Using soft thresholding, we can obtain

[0074] Step 4: Calculate z k+1 , can be obtained

[0075] Step 5: Calculate λ k+1 , can be obtained

[0076] Step 6: Calculate the value r of the cost function at iteration number k+1. k+1 , can be written as

[0077] Step 7: If r k+1 With r k The absolute value of the difference is less than a set threshold (which can be 10 in this embodiment). -4 If the value is greater than the set threshold, the calculation ends. If it is greater than the set threshold, return to step two to calculate the variable value for the next iteration, until r... k+1 With r k The calculation ends when the absolute value of the difference is less than the set threshold.

[0078] Similarly, in the aforementioned two-dimensional blind deconvolution calculation, y can be... B * The minimum value problem is rewritten as Where, μ B This is an adjustable parameter. Solve using the same steps as described above (steps one through seven), where z... 0 =0,Θ B =0, H B =0.

[0079] Step S308: Normalize the pixel values ​​of the second and third ultrasound images and perform a weighted summation of the pixel values ​​at the same pixel position to obtain the final ultrasound image.

[0080] like Figure 2 As shown, in this embodiment, the second ultrasound image US can be processed. B and third ultrasound image US C The pixel values ​​are normalized and then weighted and summed at the same pixel locations to generate a new final ultrasound image (US). D =US B +US C In this embodiment, the following settings can be made: α = β = 0.5.

[0081] It should be noted here that the final ultrasound image US generated through normalization... D Due to the second ultrasound image US B and third ultrasound image US C The images are obtained by merging, and therefore have the same characteristics as the second ultrasound image US. B and third ultrasound image US C Similar clarity. However, the second ultrasound image (US) B and third ultrasound image US C The clarity of the images is higher than that of the original first ultrasound image (US). AThe clarity of the ultrasound image, therefore, the final ultrasound image US D The clarity compared to the original first ultrasound image US A This will also improve performance. Furthermore, by using the weighted summation method described above at the same pixel location, the final output ultrasound image US can be reduced. D The speckle noise in the image.

[0082] It should also be noted that the image processing method provided in this embodiment is applicable not only to processing one ultrasound image, but also to processing multiple ultrasound images.

[0083] Given a radio frequency data image of the fundamental frequency of ultrasound, the image processing method provided in the preceding steps can be used directly. First point spread function (PSF) A Second point diffusion function PSF B It can be calculated using any two-dimensional point spread function estimation method, or it can be calculated using a known point spread function of the imaging system input by the user.

[0084] When multiple ultrasound radio frequency data images are given (e.g., multiple recorded ultrasound images or continuous ultrasound images acquired in real time), the image processing method provided in the preceding steps can also be used. However, the method provided in the preceding steps requires estimation of the two-dimensional point spread function in each ultrasound radio frequency data image, which results in high computational cost and extended computation time when processing multiple images.

[0085] Therefore, in this embodiment, a pre-set first known point spread function can be used as the first point spread function corresponding to the first radio frequency data image; and a pre-set second known point spread function can be used as the second point spread function corresponding to the second radio frequency data image.

[0086] See Figure 4 A schematic diagram of another image processing method is shown. Figure 4 The superscript to the right of the variable indicates that the image is the current i-th ultrasound image. This process is similar to... Figure 2 The difference lies in the fact that this process uses the initially estimated two-dimensional point spread function. and The first and second known point spread functions are used respectively, instead of being based on the i-th ultrasound radio frequency image. and The estimated and

[0087] Figure 4 The method shown is based on the assumption that, under the same imaging equipment conditions, the two-dimensional point spread function (PSF) is... A and PSF B It is considered constant across multiple ultrasound images, i.e. and Figure 4 The advantage of the method shown is that it only requires estimating the initial two-dimensional point spread function. and Instead of recalculating the point spread function for each ultrasound image, computation time is reduced, thus increasing the processing speed of multiple ultrasound images.

[0088] Example 3:

[0089] Corresponding to the above method embodiments, this invention provides an intravascular image processing device, see [link to relevant documentation]. Figure 5 The diagram shows a structural schematic of an intravascular image processing device, which includes:

[0090] The first radio frequency data image acquisition module 51 is used to acquire the first radio frequency data image of the ultrasound base frequency to be processed.

[0091] The second ultrasound image generation module 52 is used to determine the first point spread function corresponding to the first radio frequency data image, perform blind deconvolution on the first radio frequency data image based on the first point spread function to obtain the second radio frequency data image, and generate a second ultrasound image based on the second radio frequency data image.

[0092] The third ultrasound image generation module 53 is used to determine the second point spread function corresponding to the second radio frequency data image, perform joint blind deconvolution on the first radio frequency data image and the second radio frequency data image based on the first point spread function and the second point spread function to obtain the third radio frequency data image, and generate a third ultrasound image based on the third radio frequency data image.

[0093] The final ultrasound image generation module 54 is used to generate a final ultrasound image based on the second ultrasound image and the third ultrasound image.

[0094] This invention provides an image processing apparatus that acquires a first radio frequency (RF) data image of the ultrasound fundamental frequency to be processed; determines a first point spread function corresponding to the first RF data image; performs blind deconvolution on the first RF data image based on the first point spread function to obtain a second RF data image; generates a second ultrasound image based on the second RF data image; determines a second point spread function corresponding to the second RF data image; performs joint blind deconvolution on the first RF data image and the second RF data image based on the first point spread function and the second point spread function to obtain a third RF data image; generates a third ultrasound image based on the third RF data image; and generates a final ultrasound image based on the second and third ultrasound images. This method, using only ultrasound fundamental frequency image information, simultaneously improves spatial resolution and reduces speckle noise without adding extra hardware requirements or image acquisition operations, and can be easily integrated into existing ultrasound image processing workflows.

[0095] The aforementioned second ultrasound image generation module is used to obtain a second radio frequency data image by blindly deconvolving the first radio frequency data image based on the first point spread function using the following formula: Among them, y A For the first radio frequency data image, y B For the second radio frequency data image, p(y) B |y A ) is based on the standard maximum a posteriori estimate in y A Seeking y in B The maximum posterior distribution, H A It is a block cyclic matrix based on the first point diffusion function.

[0096] The aforementioned second ultrasound image generation module is used to determine the second radiofrequency data image using the following formula: Where, μ B This is an adjustable parameter.

[0097] The aforementioned third ultrasound image generation module is used to obtain a third radio frequency data image by performing joint blind deconvolution on the first radio frequency data image and the second radio frequency data image based on the first point spread function and the second point spread function using the following formula: Among them, y A For the first radio frequency data image, y B For the second radio frequency data image, y C For the third radio frequency data image, p(y) C |y A ,y B ) is based on the standard maximum a posteriori estimate in y A and y B Seeking y in C The maximum posterior distribution, H A H is a block cyclic matrix constructed based on the first point diffusion function. B It is a block cyclic matrix based on the second point diffusion function.

[0098] The aforementioned third ultrasound image generation module is used to determine the third radiofrequency data image using the following formula: Where, μ C This is an adjustable parameter.

[0099] The aforementioned second ultrasound image generation module is used to perform signal processing on the second radio frequency data image to obtain a second ultrasound image; the aforementioned third ultrasound image generation module is used to perform signal processing on the third radio frequency data image to obtain a third ultrasound image; wherein, the signal processing includes envelope detection.

[0100] The aforementioned final ultrasound image generation module is used to normalize the pixel values ​​of the second and third ultrasound images and perform a weighted summation of the pixel values ​​at the same pixel position to obtain the final ultrasound image.

[0101] The aforementioned second ultrasound image generation module is used to determine the first point spread function corresponding to the first radio frequency data image based on an estimation method using a parametric prior model or an estimation method using a non-parametric homomorphic filter; the aforementioned third ultrasound image generation module is used to determine the second point spread function corresponding to the second radio frequency data image based on an estimation method using a parametric prior model or an estimation method using a non-parametric homomorphic filter.

[0102] The aforementioned second ultrasound image generation module is used to use a pre-set first known point spread function as the first point spread function corresponding to the first radio frequency data image; the aforementioned third ultrasound image generation module is used to use a pre-set second known point spread function as the second point spread function corresponding to the second radio frequency data image.

[0103] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the intravascular image processing device described above can be referred to the corresponding process in the embodiments of the aforementioned intravascular image processing method, and will not be repeated here.

[0104] Example 4:

[0105] This invention also provides an electronic device for running the above-described intravascular image processing method; see [link to previous document]. Figure 6 The diagram shows the structure of an electronic device, which includes a memory 100 and a processor 101. The memory 100 is used to store one or more computer instructions, which are executed by the processor 101 to implement the above-mentioned intravascular image processing method.

[0106] Furthermore, Figure 6 The electronic device shown also includes a bus 102 and a communication interface 103, with the processor 101, the communication interface 103 and the memory 100 connected via the bus 102.

[0107] The memory 100 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 103 (which can be wired or wireless), such as the Internet, wide area network, local area network, or metropolitan area network. The bus 102 may be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 6 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0108] Processor 101 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of processor 101 or by instructions in software form. Processor 101 can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software module can reside in a readily available storage medium in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory 100, and processor 101 reads information from memory 100 and, in conjunction with its hardware, completes the steps of the method described in the foregoing embodiments.

[0109] This invention also provides a computer-readable storage medium storing computer-executable instructions. When these computer-executable instructions are called and executed by a processor, they cause the processor to implement the above-described intravascular image processing method. For specific implementation details, please refer to the method embodiments, which will not be repeated here.

[0110] The computer program product of the intravascular image processing method and apparatus provided in the embodiments of the present invention includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods in the preceding method embodiments. For specific implementation, please refer to the method embodiments, which will not be repeated here.

[0111] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the system and / or device described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0112] Furthermore, in the description of the embodiments of the present invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in the present invention based on the specific circumstances.

[0113] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0114] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0115] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, 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 the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. An image processing method, characterized in that, The method includes: Acquire the first radio frequency data image of the ultrasound fundamental frequency to be processed; Determine the first point spread function corresponding to the first radio frequency data image, perform blind deconvolution on the first radio frequency data image based on the first point spread function to obtain a second radio frequency data image, and generate a second ultrasound image based on the second radio frequency data image; Determine the second point spread function corresponding to the second radio frequency data image, and perform joint blind deconvolution on the first radio frequency data image and the second radio frequency data image based on the first point spread function and the second point spread function to obtain a third radio frequency data image; generate a third ultrasound image based on the third radio frequency data image; A final ultrasound image is generated based on the second ultrasound image and the third ultrasound image; The second radio frequency data image is obtained by blindly deconvolving the first radio frequency data image based on the first point spread function using the following formula: ;in, The first radio frequency data image, This is the second radio frequency data image. For standard maximum a posteriori estimation in Seeking in The maximum posterior distribution, This is a block cyclic matrix constructed based on the diffusion function of the first point; The third radio frequency data image is obtained by performing joint blind deconvolution on the first radio frequency data image and the second radio frequency data image based on the first point spread function and the second point spread function using the following formula: ;in, The first radio frequency data image, This is the second radio frequency data image. The third radio frequency data image, For standard maximum a posteriori estimation in and Seeking in The maximum posterior distribution, The block cyclic matrix is ​​constructed based on the diffusion function of the first point. It is a block cyclic matrix constructed based on the second point diffusion function.

2. The method according to claim 1, characterized in that, The second radio frequency data image is determined using the following formula: , , ;in, This is an adjustable parameter.

3. The method according to claim 1, characterized in that, The third radio frequency data image is determined by the following formula: , , ;in, This is an adjustable parameter.

4. The method according to claim 1, characterized in that, The step of generating a second ultrasound image based on the second radio frequency data image includes: performing signal processing on the second radio frequency data image to obtain a second ultrasound image; The step of generating a third ultrasound image based on the third radio frequency data image includes: performing the signal processing on the third radio frequency data image to obtain a third ultrasound image; wherein the signal processing includes envelope detection.

5. The method according to claim 1, characterized in that, The step of generating a final ultrasound image based on the second ultrasound image and the third ultrasound image includes: The pixel values ​​of the second and third ultrasound images are normalized, and the pixel values ​​at the same pixel position are weighted and summed to obtain the final ultrasound image.

6. The method according to claim 1, characterized in that, The step of determining the first point spread function corresponding to the first radio frequency data image includes: determining the first point spread function corresponding to the first radio frequency data image based on an estimation method of a parameterized prior model or an estimation method of a non-parametric homomorphic filter; The step of determining the second point spread function corresponding to the second radio frequency data image includes: determining the second point spread function corresponding to the second radio frequency data image based on an estimation method of a parameterized prior model or an estimation method of a non-parametric homomorphic filter.

7. The method according to claim 1, characterized in that, The step of determining the first point spread function corresponding to the first radio frequency data image includes: using a pre-set first known point spread function as the first point spread function corresponding to the first radio frequency data image; The step of determining the second point spread function corresponding to the second radio frequency data image includes: using a pre-set second known point spread function as the second point spread function corresponding to the second radio frequency data image.

8. An image processing apparatus, characterized in that, The device includes: The first radio frequency data image acquisition module is used to acquire the first radio frequency data image of the ultrasound base frequency to be processed. The second ultrasound image generation module is used to determine the first point spread function corresponding to the first radio frequency data image, perform blind deconvolution on the first radio frequency data image based on the first point spread function to obtain the second radio frequency data image, and generate a second ultrasound image based on the second radio frequency data image. The third ultrasound image generation module is used to determine the second point spread function corresponding to the second radio frequency data image, perform joint blind deconvolution on the first radio frequency data image and the second radio frequency data image based on the first point spread function and the second point spread function to obtain a third radio frequency data image, and generate a third ultrasound image based on the third radio frequency data image. The final ultrasound image generation module is used to generate a final ultrasound image based on the second ultrasound image and the third ultrasound image; The second ultrasound image generation module is used to obtain a second radio frequency data image by blindly deconvolving the first radio frequency data image based on the first point spread function using the following formula: ;in, The first radio frequency data image, This is the second radio frequency data image. For standard maximum a posteriori estimation in Seeking in The maximum posterior distribution, This is a block cyclic matrix constructed based on the diffusion function of the first point; The third ultrasound image generation module is used to perform joint blind deconvolution on the first radio frequency data image and the second radio frequency data image based on the first point spread function and the second point spread function to obtain the third radio frequency data image using the following formula: ;in, The first radio frequency data image, This is the second radio frequency data image. The third radio frequency data image, For standard maximum a posteriori estimation in and Seeking in The maximum posterior distribution, The block cyclic matrix is ​​constructed based on the diffusion function of the first point. It is a block cyclic matrix constructed based on the second point diffusion function.

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