Super-resolution contrast imaging method, device, electronic equipment and storage medium

Abstract

Embodiments of the present application disclose a kind of super-resolution contrast imaging method, device, electronic equipment, storage medium.The method comprises: obtaining a plurality of first super-resolution contrast images, a plurality of first super-resolution contrast images are obtained for the same target region acquisition, there are at least two different resolutions in the resolution of a plurality of first super-resolution contrast images;For each first super-resolution contrast image, at least according to the resolution of the first super-resolution contrast image, determine the segmentation threshold value corresponding to the first super-resolution contrast image, and the pixel value of the first super-resolution contrast image is segmented, to obtain the mask image corresponding to the first super-resolution contrast image;A plurality of mask images are fused to obtain a first target image.The image obtained by this scheme can show the distribution of small blood vessels and large blood vessels in the target region, which is beneficial to the observation and analysis of the doctor to the lesion.

Description

Technical Field

[0001] This invention relates to the field of ultrasound imaging technology, and more specifically, to a super-resolution contrast imaging method, apparatus, electronic device, storage medium, and computer program product. Background Technology

[0002] Super-resolution contrast-enhanced ultrasound (SME) is a technique that precisely locates and tracks the actual positions of microbubbles within blood vessels during contrast imaging to obtain images of the vascular perfusion pathways of all contrast microbubbles. Since the diameter of contrast microbubbles is typically 1-10 micrometers, SME can obtain micrometer-level blood flow perfusion images. These images provide rich quantitative information about microvessels and have wide clinical applications in the diagnosis of tumors or various lesions, as well as in displaying the subtle blood flow distribution in biological tissues.

[0003] In related technologies, super-resolution imaging can obtain multiple super-resolution images with different resolutions when imaging the same target area. For example, a high-resolution super-resolution image may include fine blood vessels at a lower depth and some large blood vessels in the target area, while a low-resolution super-resolution image may include complete large blood vessels and other tissues at a higher depth in the target area. However, the relative positions of the blood vessels contained in each super-resolution image are not clearly displayed in the individual super-resolution images, and in clinical diagnosis, it is often necessary for doctors to compare and judge the position of the blood vessels in the target area. Summary of the Invention

[0004] The present invention addresses the aforementioned problems. It provides a super-resolution angiography method, apparatus, electronic device, storage medium, and computer program product. This method fuses multiple super-resolution angiography images of different resolutions to obtain an image that displays the distribution of small and large blood vessels in a target area. The clinical diagnostic information provided by this image is clearer and more intuitive, facilitating doctors' observation and analysis of lesions.

[0005] According to one aspect of the present invention, a super-resolution contrast imaging method is provided. The method includes: acquiring a plurality of first super-resolution contrast images, wherein the plurality of first super-resolution contrast images are acquired for the same target region, and the plurality of first super-resolution contrast images have at least two different resolutions; for each first super-resolution contrast image, determining a segmentation threshold corresponding to the first super-resolution contrast image based at least on the resolution of the first super-resolution contrast image, and segmenting the pixel values ​​of the first super-resolution contrast image according to the segmentation threshold to obtain a mask image corresponding to the first super-resolution contrast image, wherein the mask image uses different pixel values ​​to represent vascular regions and non-vascular regions; and fusing the plurality of mask images corresponding to the plurality of first super-resolution contrast images to obtain a first target image.

[0006] Optionally, determining the segmentation threshold corresponding to the first super-resolution imaging image based at least on the resolution of the first super-resolution imaging image includes: determining the segmentation threshold corresponding to the first super-resolution imaging image based at least on a preset suppression parameter corresponding to the first super-resolution imaging image; wherein, the higher the resolution of the first super-resolution imaging image, the smaller the corresponding preset suppression parameter.

[0007] Optionally, determining the segmentation threshold corresponding to the first super-resolution imaging image based at least on the preset suppression parameter corresponding to the first super-resolution imaging image includes: determining the product of the preset suppression parameter corresponding to the first super-resolution imaging image and the normalization result of the pixel value of the first super-resolution imaging image as the segmentation threshold corresponding to the first super-resolution imaging image.

[0008] Optionally, for any pixel in each first super-resolution imaging image, the normalized result of the pixel value is equal to the ratio between the difference between the pixel value of the pixel and the minimum pixel value of the first super-resolution imaging image and the difference between the maximum pixel value and the minimum pixel value of the first super-resolution imaging image.

[0009] Optionally, the pixel values ​​of the first super-resolution contrast image are segmented according to the segmentation threshold corresponding to the first super-resolution contrast image to obtain a mask image corresponding to the first super-resolution contrast image. This includes: for any pixel in the first super-resolution contrast image, if the pixel value of the pixel is greater than the segmentation threshold, the pixel value of the pixel is retained; if the pixel value of the pixel is less than or equal to the segmentation threshold, the pixel value of the pixel is set to 0, thereby obtaining a mask image corresponding to the first super-resolution contrast image; wherein, the region of the mask image corresponding to the first super-resolution contrast image with a pixel value greater than zero represents a blood vessel region.

[0010] Optionally, fusing multiple mask images corresponding to multiple first super-resolution imaging images to obtain a first target image includes: weighting the pixel values ​​of corresponding positions of the multiple mask images; and mapping the pixel values ​​of each position after weighting the average to the corresponding display color values ​​according to a preset rule to obtain the first target image.

[0011] Optionally, the method further includes: acquiring multiple second super-resolution contrast images of different image categories from the multiple first super-resolution contrast images, wherein the multiple second super-resolution contrast images and the multiple first super-resolution contrast images are acquired for the same target region, the multiple second super-resolution contrast images correspond one-to-one with the multiple first super-resolution contrast images, and the resolutions of the corresponding first super-resolution contrast images and second super-resolution contrast images are consistent with each other; determining the vascular region indicated by the mask image of each first super-resolution contrast image as the vascular region in the corresponding second super-resolution contrast image to obtain the mask image corresponding to the second super-resolution contrast image; fusing the multiple mask images corresponding to the multiple second super-resolution contrast images to obtain a second target image; the image categories include blood flow density map, velocity density map, velocity direction map, velocity magnitude map, and energy map.

[0012] Optionally, after fusing multiple mask images corresponding to multiple first super-resolution contrast images to obtain a first target image, the method further includes: in response to a user's display command, displaying on a display interface the first target image, and / or at least one of the multiple first super-resolution contrast images, and / or an image acquired for the target area and obtained by an imaging method other than super-resolution contrast imaging.

[0013] According to another aspect of the present invention, a super-resolution contrast imaging apparatus is also provided, comprising: an acquisition module, configured to acquire a plurality of first super-resolution contrast images, wherein the plurality of first super-resolution contrast images are acquired for the same target region, and the plurality of first super-resolution contrast images have at least two different resolutions; a determination module, configured to, for each first super-resolution contrast image, determine a segmentation threshold corresponding to the first super-resolution contrast image at least based on the resolution of the first super-resolution contrast image, and segment the pixel values ​​of the first super-resolution contrast image according to the segmentation threshold corresponding to the first super-resolution contrast image to obtain a mask image corresponding to the first super-resolution contrast image, wherein the mask image uses different pixel values ​​to represent vascular regions and non-vascular regions; and a fusion module, configured to fuse the plurality of mask images corresponding to the plurality of first super-resolution contrast images to obtain a first target image.

[0014] According to another aspect of the present invention, an electronic device is also provided, comprising: a processor and a memory, wherein the memory stores computer program instructions, which are executed by the processor to perform the super-resolution contrast imaging method described above.

[0015] According to another aspect of the present invention, a storage medium is also provided, on which program instructions are stored, which are used to execute the above-described super-resolution contrast imaging method when running.

[0016] According to another aspect of the present invention, a computer program product is also provided, including computer program instructions, which, when executed, are used to perform the super-resolution contrast imaging method as described above.

[0017] The above technical solution determines the segmentation threshold corresponding to the first super-resolution angiography image based at least on the resolution of the first super-resolution angiography image. This allows for the adaptive setting of a more suitable segmentation threshold based on the different vascular information (e.g., size, color) contained in images of different resolutions. This makes the vascular region represented in the obtained mask image more accurate and effectively suppresses noise interference from non-vascular regions in the first super-resolution angiography image. On the other hand, by performing image fusion on each mask image, the obtained first target image can contain richer vascular information. Large and small blood vessels located at different depths within the target region can be clearly displayed in the first target image. Furthermore, the first target image can intuitively display the relative positions between the blood vessels, facilitating clinical diagnosis by doctors.

[0018] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0019] The above and other objects, features, and advantages of the present invention will become more apparent from the more detailed description of the embodiments of the invention in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same parts or steps.

[0020] Figure 1 A schematic diagram of the signal processing procedure of an ultrasound system according to an embodiment of the present invention is shown;

[0021] Figure 2 A schematic diagram illustrating the signal preprocessing of contrast imaging data according to an embodiment of the present invention is shown.

[0022] Figure 3 A schematic diagram illustrating the signal preprocessing of contrast imaging data according to another embodiment of the present invention is shown;

[0023] Figure 4 A schematic diagram of the signal processing procedure of an ultrasound system according to another embodiment of the present invention is shown;

[0024] Figure 5 A schematic flowchart of a super-resolution contrast imaging method according to an embodiment of the present invention is shown;

[0025] Figure 6 A schematic diagram of image arrangement according to an embodiment of the present invention is shown;

[0026] Figure 7 A schematic diagram of image arrangement according to another embodiment of the present invention is shown;

[0027] Figure 8 A schematic block diagram of a super-resolution contrast imaging apparatus according to an embodiment of the present invention is shown;

[0028] Figure 9 A schematic block diagram of an electronic device according to an embodiment of the present invention is shown. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of the present invention more apparent, exemplary embodiments according to the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are merely a part of the embodiments of the present invention, and not all of the embodiments of the present invention. It should be understood that the present invention is not limited to the exemplary embodiments described herein. Based on the embodiments of the present invention described herein, all other embodiments obtained by those skilled in the art without inventive effort should fall within the protection scope of the present invention.

[0030] To at least partially address the aforementioned technical problems, embodiments of the present invention provide a super-resolution angiography method, apparatus, electronic device, storage medium, and computer program product. This solution can fuse multiple super-resolution angiography images with different resolutions to obtain an image that displays the distribution of small and large blood vessels in the target area. The clinical diagnostic information provided by this image is clearer and more intuitive, which is beneficial for doctors to observe and analyze lesions.

[0031] For ease of understanding and description, the signal processing procedure of an ultrasound system according to an embodiment of the present invention will be introduced first. Please refer to... Figure 1The diagram illustrates the signal processing of an ultrasound system according to an embodiment of the present invention. Exemplarily, the ultrasound system may include an ultrasound probe, a host computer, and a front-end board connected to both the host computer and the ultrasound probe. The ultrasound probe may include multiple transducer elements, which may be arranged linearly to form a linear array, arranged in a two-dimensional matrix to form a planar array, or formed a convex array. The transducer elements are used to emit ultrasonic waves according to an excitation electrical signal and to convert the received ultrasonic waves into electrical signals. Each transducer element can perform the mutual conversion between electrical signals and ultrasonic waves, thereby emitting ultrasonic waves towards the target area of ​​the target object and receiving the ultrasonic wave echoes reflected back from the tissue of the target area.

[0032] For example, the host computer can store preset software (e.g., PC software) for generating transmission sequence parameters. When the scanning operation begins, the host computer can send the transmission sequence parameters to the front-end board via a transmission interface. The transmission sequence parameters may include the number and position of transducer elements for transmission and ultrasonic beam transmission parameters, such as amplitude, frequency, number of transmissions, transmission interval, transmission angle, waveform, and focusing position. The front-end board can control the ultrasonic probe to transmit ultrasonic waves / receive ultrasonic echoes according to the transmission sequence parameters. Specifically, the front-end board can generate a transmission waveform signal and direction and focusing information for transmission beamforming based on the transmission sequence parameters. Based on the transmission waveform signal, direction, and focusing information, the transmission circuit in the front-end board can be controlled to generate a transmission sequence. This transmission sequence is used to control some or all of the multiple transducer elements to transmit ultrasonic waves towards the target area. When the transmission circuit generates the transmission sequence, the transmit / receive switch in the front-end board is in a first connected state, and the transmission sequence generated by the transmission circuit can be sent to the transducer elements to control them to emit ultrasonic waves. In some embodiments, the transmission sequence parameters may further include a scanning timing sequence. The front-end board's transmission circuit can control the phase delay of the transmitted beam based on the scanning timing sequence, enabling different transducer elements to transmit ultrasonic waves at different times, so that each transmitted ultrasonic beam can be focused in a predetermined region of interest. After the transducer elements transmit ultrasonic waves, they can receive the ultrasonic echoes reflected back from the tissue in the target region. The transmit / receive switch in the front-end board can be in a second connected state. The analog front-end (AFE) of the front-end board can receive the ultrasonic echoes transmitted by the transducer elements and perform preliminary processing operations on the ultrasonic echoes, such as amplification and filtering. The ultrasonic echoes may include signals from multiple channels. The front-end board can also synthesize the pre-processed ultrasonic echoes to obtain synthesized echo data. The design line buffer in the front-end board can store the synthesized echo data. After obtaining the synthesized echo data, the front-end board can perform weighted processing on the echo data to obtain processed data and demodulate the processed data using multiple different demodulation center frequencies. Figure 1 The illustrated embodiment uses four demodulation center frequencies to demodulate the processed data, such as 4MHz, 6MHz, 8MHz, and 10MHz. After demodulation, four sets of contrast imaging data are obtained. The front-end board can perform signal preprocessing on these four sets of contrast imaging data, package the data, and upload it to the host computer via the transmission interface. Each set of contrast imaging data after signal preprocessing may include in-phase (I) and quadrature (Q) components, also known as "IQ data," and may also include amplitude data, which can be equal to the square root of the sum of the squares of the in-phase component and the quadrature vector.

[0033] After receiving four sets of contrast imaging data, the host computer can parse the data and process the four sets of contrast imaging data using a super-resolution imaging algorithm to obtain four sets of super-resolution contrast imaging images at different resolutions. The number of images in each set of super-resolution contrast imaging images can be greater than or equal to one. The host computer can also perform image fusion on the four sets of super-resolution contrast imaging images to obtain a target image, and display at least one super-resolution contrast imaging image and / or the target image through a display connected to the host computer.

[0034] Please see Figure 2 The diagram shown illustrates the signal preprocessing of contrast imaging data according to an embodiment of the present invention. Figure 2 In the illustrated embodiment, the echo data, processed data, and contrast data can all be digitized radio frequency (RF) signals. The specific process of preprocessing the contrast data may include: dividing the contrast data by sin(2π*f... 0* t) and cos(2π*f 0* The two sets of data (f0, t) are multiplied, where f0 represents the carrier frequency of the RF signal and t represents the time variable. After inputting these two sets of data into a low-pass filter, the I and Q components are obtained. The amplitude data can then be obtained using the amplitude calculation module. Please refer to [link to relevant documentation]. Figure 3 The diagram shown illustrates the signal preprocessing of contrast imaging data according to another embodiment of the present invention. Figure 3 In the illustrated embodiment, similarly, the contrast data can be a digitized radio frequency (RF) signal. The specific process of signal preprocessing for the contrast data may include: obtaining I and Q components from the contrast data through a Hilbert transform; and obtaining amplitude data from the I and Q components through an amplitude calculation module. Exemplarily, the specific process of signal preprocessing may also include operations such as logarithmic dynamic range transformation of the amplitude data, which will not be elaborated further.

[0035] Please see Figure 4As shown, it is a schematic diagram of the signal processing process of an ultrasound system according to another embodiment of the present invention. Figure 4 The illustrated embodiment describes the process of controlling the transmission and reception of ultrasonic waves. Figure 1 Similarly, this will not be elaborated upon. (And) Figure 1 The illustrated embodiment differs in that, after synthesizing the ultrasonic echoes to obtain synthesized echo data, the synthesized echo data can be directly packaged and uploaded to the host computer via a transmission interface. The host computer can parse the received echo data to obtain the actual echo data; a line buffer in the host computer can store the echo data. After obtaining the echo data through parsing, the host computer can further perform weighted processing on the echo data to obtain processed data, and demodulate the echo data using multiple different demodulation center frequencies. Similarly, Figure 4 The illustrated embodiment uses four demodulation center frequencies to demodulate the echo data, such as 4MHz, 6MHz, 8MHz, and 10MHz. After demodulation, four sets of contrast imaging data are obtained. The host computer can process these four sets of contrast imaging data using a super-resolution imaging algorithm to obtain four super-resolution contrast imaging images of different resolutions. The host computer can also perform image fusion on the four obtained super-resolution contrast imaging images to obtain a target image, and display at least one super-resolution contrast imaging image and / or the target image through a display connected to the host computer.

[0036] Please see Figure 5 The diagram shown is a schematic flowchart of a super-resolution contrast imaging method according to an embodiment of the present invention. According to one aspect of the present invention, a super-resolution contrast imaging method is provided, the method comprising: steps S510, S520, and S530.

[0037] In step S510, multiple first super-resolution imaging images are acquired. These multiple first super-resolution imaging images are acquired for the same target area, and at least two different resolutions exist among the multiple first super-resolution imaging images.

[0038] For example, the target region can be the area where the blood vessels to be analyzed are located on the target object (e.g., a human / animal body). The acquisition process can refer to the above embodiments, and will not be repeated here. Among the multiple acquired first super-resolution contrast images, there can be at least two or more resolutions, and the number of first super-resolution contrast images of each resolution can be one or more. In other words, the resolution of any two images of the multiple first super-resolution contrast images may be the same or different.

[0039] In step S520, for each first super-resolution contrast image, at least based on the resolution of the first super-resolution contrast image, a segmentation threshold corresponding to the first super-resolution contrast image is determined, and the pixel values ​​of the first super-resolution contrast image are segmented according to the segmentation threshold corresponding to the first super-resolution contrast image to obtain a mask image corresponding to the first super-resolution contrast image. The mask image uses different pixel values ​​to represent vascular regions and non-vascular regions.

[0040] For example, each first super-resolution contrast image at different resolutions can correspond to its own segmentation threshold. In some embodiments, for each first super-resolution contrast image, the segmentation threshold can be determined solely based on the resolution of the first super-resolution contrast image. Specifically, a higher-resolution super-resolution contrast image corresponds to higher-frequency contrast data, which may include fine blood vessels at lower depths in the target region and some large blood vessels. To minimize the filtering out of fine blood vessels in the image, a smaller segmentation threshold can be selected. Conversely, a lower-resolution super-resolution contrast image corresponds to lower-frequency contrast data, which may include complete large blood vessels at higher depths in the target region. To suppress noise in the image as much as possible, a larger segmentation threshold can be selected. In other embodiments, the segmentation threshold can be determined based on the resolution and pixel values ​​of the first super-resolution contrast image, a scheme described below. After determining the segmentation threshold of the first super-resolution contrast image, the pixel values ​​of the first super-resolution contrast image can be segmented based on the segmentation threshold. For example, the pixel values ​​of pixels less than or equal to the segmentation threshold can be set to 0 or other preset values. After segmentation, a mask image corresponding to the first super-resolution imaging image can be obtained. In the mask image, regions with pixel values ​​of 0 or preset values ​​can represent non-vascular regions, while other regions can represent vascular regions.

[0041] In step S530, multiple mask images corresponding to multiple first super-resolution imaging images are fused to obtain a first target image.

[0042] In some embodiments, the image sizes of the multiple mask images can be the same. The mask images are aligned to the same coordinate system. By averaging, selecting the maximum / minimum value, or calculating a weighted average of the pixel values ​​at the same positions in the mask images, the pixel values ​​of the pixels at each position in the first target image can be determined, thereby obtaining the first target image. In other embodiments, features (such as edges and textures) of each mask image can be extracted first, and then these features can be merged according to preset rules to obtain the first target image.

[0043] The above technical solution determines the segmentation threshold corresponding to the first super-resolution angiography image based at least on the resolution of the first super-resolution angiography image. This allows for the adaptive setting of a more suitable segmentation threshold based on the different vascular information (e.g., size, color) contained in images of different resolutions. This makes the vascular region represented in the obtained mask image more accurate and effectively suppresses noise interference from non-vascular regions in the first super-resolution angiography image. On the other hand, by performing image fusion on each mask image, the obtained first target image can contain richer vascular information. Large and small blood vessels located at different depths within the target region can be clearly displayed in the first target image. Furthermore, the first target image can intuitively display the relative positions between the blood vessels, facilitating clinical diagnosis by doctors.

[0044] Optionally, determining the segmentation threshold corresponding to the first super-resolution imaging image based at least on the resolution of the first super-resolution imaging image includes: determining the segmentation threshold corresponding to the first super-resolution imaging image based at least on a preset suppression parameter corresponding to the first super-resolution imaging image; wherein, the higher the resolution of the first super-resolution imaging image, the smaller the corresponding preset suppression parameter.

[0045] For example, first super-resolution imaging images of different resolutions can correspond to different preset suppression parameters. The preset suppression parameter can be negatively correlated with the resolution; the higher the resolution, the smaller the preset suppression parameter can be. In some embodiments, the pixel value range of the first super-resolution imaging image is 0-255, in which case the preset suppression parameter can be an integer between 0 and 255. In other embodiments, the first super-resolution imaging image is stored as a floating-point number, with each pixel value between 0.0 and 1.0, in which case the preset suppression parameter can be between 0 and 1. When determining the segmentation threshold of the first super-resolution imaging image solely based on its resolution, the preset suppression parameter of each first super-resolution imaging image can be determined as the corresponding segmentation threshold.

[0046] The above technical solution can determine the segmentation threshold of the corresponding first super-resolution angiography image based on the preset suppression parameters. This can quickly and accurately determine the vascular region in the image based on the vascular information in each first super-resolution angiography image. This method of setting the segmentation threshold is highly flexible, and the obtained segmentation result can closely approximate the actual vascular distribution.

[0047] Optionally, determining the segmentation threshold corresponding to the first super-resolution imaging image based at least on the preset suppression parameter corresponding to the first super-resolution imaging image includes: determining the product of the preset suppression parameter corresponding to the first super-resolution imaging image and the normalization result of the pixel value of the first super-resolution imaging image as the segmentation threshold corresponding to the first super-resolution imaging image.

[0048] For example, for each pixel in each first super-resolution imaging image, the segmentation threshold corresponding to that pixel can be equal to the product of the corresponding preset suppression parameter and the normalized result of the pixel value. When the pixel value of that pixel is greater than the corresponding segmentation threshold, the pixel value can be retained. When the pixel value of that pixel is less than or equal to the corresponding segmentation threshold, the pixel value can be set to 0 or other preset values. The parameters used for normalizing the pixel value can include, for example, one or more of the mean, standard deviation, variance, range, median absolute deviation, and interquartile range of the pixel values ​​of the corresponding first super-resolution imaging image. For example, taking the mean and variance as examples, if the normalized result of the pixel value is denoted as I', the normalization result can be optionally determined by the formula I' = (I - μ) / s, where I is the pixel value of that pixel, and μ and s are the mean and variance of the pixel values ​​of the corresponding first super-resolution imaging image, respectively. For example, taking the interquartile range as an example, if the normalized result of the pixel value is denoted as I', the normalization feature can be optionally determined by the formula I'=(I-Q1) / (Q3-Q1), where I is the pixel value of the pixel point, and Q1 and Q3 are the first quartile and the third quartile of the pixel value corresponding to the first super-resolution imaging image, respectively.

[0049] The above technical solution can dynamically determine whether each pixel belongs to the vascular region based on the pixel value of each pixel in the first super-resolution contrast image. This segmentation method can focus on the information of the local region of the first super-resolution contrast image to a certain extent, so as to divide the vascular region and non-vascular region in the image more flexibly and accurately.

[0050] Optionally, for any pixel in each first super-resolution imaging image, the normalized result of the pixel value is equal to the ratio between the difference between the pixel value of the pixel and the minimum pixel value of the first super-resolution imaging image and the difference between the maximum pixel value and the minimum pixel value of the first super-resolution imaging image.

[0051] For example, the parameter used to normalize the pixel value of a pixel can be, for example, the range of pixel values ​​corresponding to the first super-resolution imaging image. Specifically, if the normalized result of the pixel value is denoted as I', the normalization feature can be optionally determined by the formula I' = (I - Imin) / (Imax - Imin), where I is the pixel value of the pixel, and Imax and Imin are the maximum and minimum pixel values ​​corresponding to the first super-resolution imaging image, respectively.

[0052] The above technical solution can quickly normalize the pixel values ​​of each pixel, and the normalization result is in the range of 0 to 1. This is equivalent to adaptively setting a proportional coefficient for the preset suppression parameter for each pixel, which helps to improve the robustness of setting the segmentation threshold, and thus allows for setting a more appropriate segmentation threshold for each pixel.

[0053] Optionally, the pixel values ​​of the first super-resolution contrast image are segmented according to the segmentation threshold corresponding to the first super-resolution contrast image to obtain a mask image corresponding to the first super-resolution contrast image. This includes: for any pixel in the first super-resolution contrast image, if the pixel value of the pixel is greater than the segmentation threshold, the pixel value of the pixel is retained; if the pixel value of the pixel is less than or equal to the segmentation threshold, the pixel value of the pixel is set to 0, thereby obtaining a mask image corresponding to the first super-resolution contrast image; wherein, the region of the mask image corresponding to the first super-resolution contrast image with a pixel value greater than zero represents a blood vessel region.

[0054] For example, for each pixel in each first super-resolution imaging image, if the pixel value is greater than the segmentation threshold, the pixel value can be retained; if the pixel value is less than or equal to the segmentation threshold, the pixel value can be set to 0. In the obtained mask image, regions with a pixel value of 0 can represent non-vascular regions, and regions with a pixel value greater than 0 can represent vascular regions.

[0055] The above technical solution can quickly obtain a mask image, and the mask image can accurately indicate pixels belonging to blood vessel areas and pixels belonging to non-blood vessel areas.

[0056] Optionally, fusing multiple mask images corresponding to multiple first super-resolution imaging images to obtain a first target image includes: weighting the pixel values ​​of corresponding positions of the multiple mask images; and mapping the pixel values ​​of each position after weighting the average to the corresponding display color values ​​according to a preset rule to obtain the first target image.

[0057] For example, for each pixel in any mask image, if the pixel value is 0, the corresponding weight value of the pixel can be 0; if the pixel value is greater than 0, the corresponding weight value of the pixel is 1. For any pixel in the first target image, the pixel value of the pixel is equal to the ratio between the sum of the pixel values ​​of the corresponding pixels in each mask image and a specific value. The specific value is equal to the sum of the weight values ​​of the corresponding pixels in each mask image and a preset constant. The preset constant is greater than 0 and can be used to ensure that the denominator is non-zero. For example, the pixel values ​​of each pixel at each position after weighted averaging are mapped to the corresponding display color values ​​according to a preset rule. The preset rule can be, for example, a colormap defined / selected by the user. Through the colormap, the pixel values ​​of each pixel at each position after weighted averaging can be mapped to the corresponding display color values, and the first target image can be displayed on the display interface of the monitor according to the display color values.

[0058] The above technical solution can effectively fuse the image information of each mask image by weighted averaging of the pixels at each position of multiple mask images, so that the first target image can express the image information contained in each mask image as much as possible. On the other hand, by mapping the pixel values ​​of each weighted average position to the corresponding display color values ​​according to preset rules, the visual expressiveness of the image can be effectively enhanced, which is conducive to making the first target image clearer and more intuitive.

[0059] Optionally, the method further includes: acquiring multiple second super-resolution contrast images of different image categories from the multiple first super-resolution contrast images, wherein the multiple second super-resolution contrast images and the multiple first super-resolution contrast images are acquired for the same target region, the multiple second super-resolution contrast images correspond one-to-one with the multiple first super-resolution contrast images, and the resolutions of the corresponding first super-resolution contrast images and second super-resolution contrast images are consistent with each other; determining the vascular region indicated by the mask image of each first super-resolution contrast image as the vascular region in the corresponding second super-resolution contrast image to obtain the mask image corresponding to the second super-resolution contrast image; fusing the multiple mask images corresponding to the multiple second super-resolution contrast images to obtain a second target image; the image categories include blood flow density map, velocity density map, velocity direction map, velocity magnitude map, and energy map.

[0060] For example, the image category of the first super-resolution contrast image may be, for example, a blood flow density map, a velocity density map, a velocity direction map, a velocity magnitude map, or an energy map. Similarly, the image category of the second super-resolution contrast image may be, for example, a blood flow density map, a velocity density map, a velocity direction map, a velocity magnitude map, or an energy map. The image category of the second super-resolution contrast image is different from that of the first super-resolution contrast image. Multiple second super-resolution contrast images correspond one-to-one with multiple first super-resolution contrast images, and all are images acquired for the same target region. For each second super-resolution contrast image, the resolution is the same as the corresponding first super-resolution contrast image. The vascular region indicated by the mask image of each first super-resolution contrast image can be used as the vascular region in the corresponding second super-resolution contrast image. By setting the pixel values ​​of non-vascular regions (excluding vascular regions) in the second super-resolution contrast image to 0 or other preset values, the mask image corresponding to the second super-resolution contrast image can be obtained. By fusing multiple mask images corresponding to multiple second super-resolution imaging images, a second target image can be obtained. The fusion method can refer to the aforementioned process of fusing multiple mask images corresponding to multiple first super-resolution imaging images, and will not be repeated here.

[0061] The above technical solution can directly use the vascular region in the mask image corresponding to the first super-resolution contrast image as the vascular region of the corresponding second super-resolution contrast image with different image categories after determining the vascular region in the mask image, and obtain the mask region corresponding to the second super-resolution contrast image without having to perform pixel value segmentation on the second super-resolution contrast image again. This method can save time and improve the efficiency of image fusion.

[0062] Optionally, after fusing multiple mask images corresponding to multiple first super-resolution contrast images to obtain a first target image, the method further includes: in response to a user's display command, displaying on a display interface the first target image, and / or at least one of the multiple first super-resolution contrast images, and / or an image acquired for the target area and obtained by an imaging method other than super-resolution contrast imaging.

[0063] For example, a user can input display commands via an input device (such as a mouse, keyboard, touchscreen, etc.) connected to a host computer. Responding to the user's command, the host computer can display one or more of the following on the display interface of a monitor connected to the host computer, arranged according to the display command: a first target image, multiple first super-resolution contrast images, and images obtained by imaging methods other than super-resolution contrast imaging. Other imaging methods may include, for example, Doppler imaging. See also... Figure 6 As shown, it is a schematic diagram of image arrangement according to an embodiment of the present invention. Figure 6 The tissue image shown can be an image obtained using the Doppler imaging method. Figure 6 The super-resolution contrast-enhanced fused image shown can be either the first target image or the second target image in this embodiment of the invention. In response to the user's horizontal display command, the tissue image and the super-resolution contrast-enhanced fused image can be displayed on the display interface in a horizontal arrangement. Please refer to... Figure 7 As shown, it is a schematic diagram of image arrangement according to another embodiment of the present invention. Figure 7 In the illustrated embodiment, the tissue image, multiple first super-resolution contrast images, and super-resolution contrast fusion image (i.e., first target image / second target image) can be displayed on the display interface in both horizontal and vertical directions.

[0064] The above technical solution can respond to user commands and display multiple images simultaneously on the display interface, providing greater flexibility in image display. This facilitates comprehensive observation and analysis of the images by doctors, which is helpful for clinical diagnosis.

[0065] Please see Figure 8 The diagram shown is a schematic block diagram of a super-resolution contrast imaging apparatus according to an embodiment of the present invention. According to another aspect of the present invention, a super-resolution contrast imaging apparatus 800 is also provided, the apparatus 800 comprising:

[0066] The acquisition module 810 is used to acquire multiple first super-resolution imaging images. The multiple first super-resolution imaging images are acquired for the same target area, and there are at least two different resolutions among the multiple first super-resolution imaging images.

[0067] The determination module 820 is used to determine, for each first super-resolution contrast image, at least based on the resolution of the first super-resolution contrast image, the segmentation threshold corresponding to the first super-resolution contrast image, and to segment the pixel values ​​of the first super-resolution contrast image according to the segmentation threshold corresponding to the first super-resolution contrast image, so as to obtain a mask image corresponding to the first super-resolution contrast image, wherein the mask image uses different pixel values ​​to represent vascular regions and non-vascular regions.

[0068] The fusion module 830 is used to fuse multiple mask images corresponding to multiple first super-resolution imaging images to obtain a first target image.

[0069] Please see Figure 9The diagram shown is a schematic block diagram of an electronic device 900 according to an embodiment of the present invention. According to another aspect of the present invention, an electronic device is also provided, comprising: a processor 910 and a memory 920, wherein the memory 920 stores computer program instructions, which are executed by the processor 910 to perform the aforementioned super-resolution contrast imaging method. Specifically, the electronic device may be an ultrasound diagnostic device.

[0070] According to another aspect of the present invention, a storage medium is also provided, on which program instructions are stored. When the program instructions are executed by a computer or processor, the computer or processor performs corresponding steps of the super-resolution contrast imaging method described in the embodiments of the present invention, and is used to implement corresponding modules in the super-resolution contrast imaging apparatus described in the embodiments of the present invention, or corresponding modules in the super-resolution contrast imaging apparatus described above. The storage medium may, for example, include a memory card of a smartphone, a storage component of a tablet computer, a hard disk of a personal computer, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a portable compact disc read-only memory (CD-ROM), a USB memory, or any combination of the above storage media. A computer-readable storage medium may be any combination of one or more computer-readable storage media.

[0071] According to another aspect of the present invention, a computer program product is also provided, including computer program instructions, which, when executed, are used to perform the super-resolution contrast imaging method as described above.

[0072] Those skilled in the art can understand the specific implementation and beneficial effects of the super-resolution contrast imaging device by reading the above detailed description of the super-resolution contrast imaging method, and will not be elaborated further here for the sake of brevity.

[0073] Although exemplary embodiments have been described herein with reference to the accompanying drawings, it should be understood that the above exemplary embodiments are merely illustrative and are not intended to limit the scope of the invention. Various changes and modifications can be made therein by those skilled in the art without departing from the scope and spirit of the invention. All such changes and modifications are intended to be included within the scope of the invention as claimed in the appended claims.

[0074] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0075] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed.

[0076] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0077] Similarly, it should be understood that, in order to streamline the invention and aid in understanding one or more of the various aspects of the invention, features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof in the description of exemplary embodiments of the invention. However, this approach should not be construed as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as reflected in the corresponding claims, its inventive point lies in solving the corresponding technical problem with fewer features than all of those in a single disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of the invention.

[0078] Those skilled in the art will understand that, apart from the mutual exclusion of features, all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or apparatus so disclosed can be combined in any combination. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0079] Furthermore, those skilled in the art will understand that although some embodiments herein include certain features included in other embodiments but not others, combinations of features from different embodiments are intended to be within the scope of the invention and form different embodiments. For example, in the claims, any of the claimed embodiments can be used in any combination.

[0080] The various component embodiments of the present invention can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some modules in the super-resolution contrast imaging apparatus according to embodiments of the present invention. The present invention can also be implemented as an apparatus program (e.g., a computer program and computer program product) for performing some or all of the methods described herein. Such programs implementing the present invention can be stored on a computer-readable medium or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0081] It should be noted that the above embodiments are illustrative of the invention and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.

[0082] The above are merely specific embodiments or descriptions of the present invention, and the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention. The scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A super-resolution contrast imaging method, characterized in that, The method includes: Multiple first super-resolution contrast images are acquired, wherein the multiple first super-resolution contrast images are acquired for the same target region, and the resolution of the multiple first super-resolution contrast images contains at least two different resolutions; For each first super-resolution contrast image, at least the segmentation threshold corresponding to the first super-resolution contrast image is determined based on the resolution of the first super-resolution contrast image, and the pixel values ​​of the first super-resolution contrast image are segmented according to the segmentation threshold corresponding to the first super-resolution contrast image to obtain a mask image corresponding to the first super-resolution contrast image, wherein the mask image uses different pixel values ​​to represent vascular regions and non-vascular regions. The multiple mask images corresponding to the multiple first super-resolution imaging images are fused to obtain the first target image.

2. The method according to claim 1, characterized in that, The step of determining the segmentation threshold corresponding to the first super-resolution imaging image based at least on the resolution of the first super-resolution imaging image includes: The segmentation threshold corresponding to the first super-resolution imaging image is determined at least based on the preset suppression parameters corresponding to the first super-resolution imaging image; The higher the resolution of the first super-resolution imaging image, the smaller the corresponding preset suppression parameter.

3. The method according to claim 2, characterized in that, The step of determining the segmentation threshold corresponding to the first super-resolution imaging image based at least on the preset suppression parameters corresponding to the first super-resolution imaging image includes: The product of the preset suppression parameter corresponding to the first super-resolution imaging image and the normalization result of the pixel value of the first super-resolution imaging image is determined as the segmentation threshold corresponding to the first super-resolution imaging image.

4. The method according to claim 3, characterized in that, For any pixel in each first super-resolution imaging image, the normalized pixel value is equal to the ratio between the difference between the pixel value and the minimum pixel value of the first super-resolution imaging image and the difference between the maximum pixel value and the minimum pixel value of the first super-resolution imaging image.

5. The method according to any one of claims 1-3, characterized in that, The step of segmenting the pixel values ​​of the first super-resolution imaging image according to the segmentation threshold corresponding to the first super-resolution imaging image to obtain the mask image corresponding to the first super-resolution imaging image includes: For any pixel in the first super-resolution imaging image, if the pixel value of the pixel is greater than the segmentation threshold, the pixel value of the pixel is retained; if the pixel value of the pixel is less than or equal to the segmentation threshold, the pixel value of the pixel is set to 0, so as to obtain the mask image corresponding to the first super-resolution imaging image. The regions in the mask image corresponding to the first super-resolution angiography image where the pixel value is greater than zero represent the blood vessel region.

6. The method according to any one of claims 1-3, characterized in that, The step of fusing the multiple mask images corresponding to the multiple first super-resolution imaging images to obtain the first target image includes: The pixel values ​​of corresponding positions in the multiple mask images are weighted and averaged. The pixel values ​​of each pixel at each position, after weighted averaging, are mapped to the corresponding display color values ​​according to a preset rule to obtain the first target image.

7. The method according to any one of claims 1-3, characterized in that, The method further includes: Acquire multiple second super-resolution imaging images that are different in image category from the multiple first super-resolution imaging images. The multiple second super-resolution imaging images and the multiple first super-resolution imaging images are acquired for the same target area. The multiple second super-resolution imaging images correspond one-to-one with the multiple first super-resolution imaging images, and the resolutions of the corresponding first super-resolution imaging images and second super-resolution imaging images are consistent with each other. The vascular region indicated by the mask image of each first super-resolution contrast image is determined as the vascular region in the corresponding second super-resolution contrast image, so as to obtain the mask image corresponding to the second super-resolution contrast image. The multiple mask images corresponding to the multiple second super-resolution imaging images are fused to obtain the second target image; The image categories include blood flow density maps, velocity density maps, velocity direction maps, velocity magnitude maps, and energy maps.

8. The method according to any one of claims 1-3, characterized in that, After fusing the multiple mask images corresponding to the multiple first super-resolution imaging images to obtain the first target image, the method further includes: In response to a user's display command, the following are displayed on the display interface according to the arrangement indicated by the display command: the first target image, and / or at least one of the plurality of first super-resolution contrast images, and / or an image acquired for the target region and obtained by an imaging method other than super-resolution contrast imaging.

9. A super-resolution contrast imaging device, characterized in that, The device includes: The acquisition module is used to acquire multiple first super-resolution contrast images, wherein the multiple first super-resolution contrast images are acquired for the same target area, and the resolution of the multiple first super-resolution contrast images includes at least two different resolutions; The determination module is used to determine, for each first super-resolution contrast image, at least based on the resolution of the first super-resolution contrast image, the segmentation threshold corresponding to the first super-resolution contrast image, and to segment the pixel values ​​of the first super-resolution contrast image according to the segmentation threshold corresponding to the first super-resolution contrast image, so as to obtain a mask image corresponding to the first super-resolution contrast image, wherein the mask image uses different pixel values ​​to represent vascular regions and non-vascular regions. The fusion module is used to fuse multiple mask images corresponding to the multiple first super-resolution imaging images to obtain a first target image.

10. An electronic device comprising a processor and a memory, characterized in that, The memory stores computer program instructions, which, when executed by the processor, are used to perform super-resolution contrast imaging as described in any one of claims 1-8.

11. A storage medium on which program instructions are stored, characterized in that, The program instructions are used to execute the super-resolution contrast imaging method as described in any one of claims 1-8 when the program is run.

12. A computer program product comprising computer program instructions, characterized in that, The computer program instructions, when executed, are used to perform the super-resolution contrast imaging method as described in any one of claims 1-8.

Images