Contrast-enhanced ultrasound quantitative imaging system and method and computer-readable storage medium

Abstract

Systems and methods for contrast-enhanced ultrasound quantization imaging and computer-readable storage media are provided, including computer programs encoded on the computer storage medium. One of these methods includes: for each location in a region of interest, acquiring a time-varying ultrasound signal relative to the region of interest over a time period; for each location, setting a global threshold for the acquired time-varying ultrasound signal; for each location, determining a relative time at which the time-varying ultrasound signal reaches the global threshold; and generating a structural image of the region of interest based at least on the determined relative time for each location, wherein the generated structural image displays different times at which the ultrasound signal reaches the global threshold corresponding to different locations within the region of interest.

Description

Technical Field

[0001] This invention generally relates to contrast-enhanced ultrasound quantitative imaging, and more particularly to a method and system for visualizing color-coded time series of vascular structures in contrast-enhanced ultrasound quantitative images. Background Technology

[0002] Ultrasound imaging provides a wealth of medical information and can be applied in diagnosis, health monitoring, and assessment. Many ultrasound imaging systems utilize injectable ultrasound contrast agents to obtain the high-contrast images desired for visualizing the internal structures of organs. For example, vascular structures can help medical professionals assess disease conditions and prescribe treatment plans.

[0003] Traditional ultrasound imaging derives intensity information from B-mode images, which display the acoustic impedance of a two-dimensional cross-section of a tissue. However, B-mode image information is insufficient to resolve weak tissue boundaries or small blood vessels within the body. That is, in B-mode images, small blood vessels may appear blurry or be completely unimageable due to the limitations of resolution and grayscale imaging. Moreover, after injecting contrast agents through blood vessels, it takes time for the contrast agent concentration to reach a identifiable level in the region of interest prepared for ultrasound imaging. Therefore, although ultrasound images can enhance over time, this image enhancement cannot reflect the perfusion or flow rate of the contrast agent (or blood) in the blood vessels. Medical professionals are deprived of this crucial diagnostic information. Therefore, there is a need for non-invasive and accurate imaging of minute features such as vascular boundaries and to provide important parameters such as vascular perfusion. Summary of the Invention

[0004] Various embodiments of the present invention include, but are not limited to, systems, methods and non-transitory computer-readable media for contrast-enhanced ultrasound quantitative imaging.

[0005] In some embodiments, a non-transitory computer-readable storage medium for contrast-enhanced ultrasound quantitative imaging is configured with instructions executable by one or more processors to perform operations including: for each location in a region of interest, acquiring a time-varying ultrasound signal relative to the region of interest over a time period; setting a global threshold for the acquired time-varying ultrasound signal at each location; determining, for each location, a relative time at which the time-varying ultrasound signal reaches the global threshold; and generating a structural image of the region of interest based at least on the determined relative time for each location, wherein the generated structural image displays different times at which the ultrasound signal reaches the global threshold corresponding to different locations in the region of interest.

[0006] In some embodiments, prior to setting a global threshold, the operation further includes: for each location, determining a time-intensity curve based at least on the time-varying ultrasound signal obtained over the time period.

[0007] In some embodiments, setting a global threshold for the obtained time-varying ultrasound signal at each location includes: determining the peak value of the time-intensity curve at each location; obtaining a percentage threshold; and determining the global threshold at each location based at least on the peak value and the percentage threshold.

[0008] In some embodiments, the region of interest includes multiple blood vessels of different sizes; and the different times represent different blood perfusion rates in the multiple blood vessels.

[0009] In some embodiments, the different times displayed correspond to the different locations in the region of interest and are color-coded; and the color-coded times displayed on the generated structural image indicate the boundaries of the plurality of blood vessels.

[0010] In some embodiments, the operation further includes: obtaining an updated global threshold for each location; and updating the generated structural image based at least on the updated global threshold for each location.

[0011] Before generating the structural image, the operation further includes: determining one or more locations that show an anomaly based on a predetermined relative time and the relative time of each determined location; and the generated structural image includes one or more labels indicating the one or more locations that show an anomaly.

[0012] In some embodiments, prior to generating the structural image, the operation further includes: obtaining a window size for image smoothing; and generating the structural image of the region of interest based at least on the relative time at each determined position includes: generating the structural image of the region of interest based at least on the window size and the relative time at each determined position.

[0013] In some embodiments, before obtaining the time-varying ultrasound signal, the operation further includes obtaining user input to determine the end of the time period.

[0014] In some embodiments, a contrast-enhanced ultrasound quantitative imaging system includes one or more processors and one or more non-transitory computer-readable memories, the one or more non-transitory computer-readable memories being connected to the one or more processors and configured with instructions executable by the one or more processors to cause the one or more processors to perform operations including: for each location in the region of interest, acquiring a time-varying ultrasound signal relative to the region of interest over a time period; for each location, setting a global threshold for the acquired time-varying ultrasound signal; for each location, determining a relative time at which the time-varying ultrasound signal reaches the global threshold; and generating a structural image of the region of interest based at least on the determined relative time for each location, wherein the generated structural image displays different times at which the ultrasound signal reaches the global threshold corresponding to different locations in the region of interest.

[0015] In some embodiments, a contrast-enhanced ultrasound quantitative imaging method includes: for each location in a region of interest, acquiring an ultrasound signal that varies over time relative to the region of interest within a time period; for each location, setting a global threshold for the acquired ultrasound signal that varies over time; for each location, determining a relative time at which the ultrasound signal that varies over time reaches the global threshold; and generating a structural image of the region of interest based at least on the determined relative time for each location, wherein the generated structural image displays different times at which the ultrasound signal corresponding to different locations in the region of interest reaches the global threshold.

[0016] In some embodiments, a contrast-enhanced ultrasound quantitative imaging system includes: an ultrasound transducer configured to emit ultrasound waves and receive ultrasound waves reflected from a region of interest, and to generate an ultrasound signal based on the received ultrasound waves; and a computing system including one or more processors and one or more non-transitory computer-readable storage media connected to the one or more processors and storing instructions executable by the one or more processors to cause the one or more processors to perform operations. The operations include: for each location within the region of interest, acquiring a time-varying ultrasound signal relative to the region of interest from the generated ultrasound signal over a time period; for each location, setting a global threshold for the acquired time-varying ultrasound signal; for each location, determining a relative time at which the time-varying ultrasound signal reaches the global threshold; and generating a structural image of the region of interest based at least on the determined relative time for each location, wherein the generated structural image displays different times at which the ultrasound signal reaches the global threshold corresponding to different locations within the region of interest.

[0017] The embodiments disclosed herein have one or more technical effects. In some embodiments, the methods and systems can provide highly precise visualization of tissue, blood vessels, or other structures in a region of interest. For example, visualization can be achieved by color-coding different portions of the region of interest (e.g., down to the pixel level) based on different times. This time can be determined based on the ultrasound signal corresponding to the different portions reaching a global threshold. In one embodiment, using the contrast-enhanced ultrasound quantization imaging method disclosed herein, minute features such as vascular boundaries can be distinguished. In other embodiments, the generated images provide visualization of the boundaries of vascular structures (e.g., blood vessels of different sizes) based on encoded colors. This allows users to directly observe minute-sized features (e.g., capillaries) on ultrasound images. In other embodiments, the generated images provide information on perfusion rates in vascular structures. In some embodiments, vascular structure and perfusion rate provide key information that allows users to identify abnormal areas based on encoded colors. In one embodiment, a computer can compare the generated image to a baseline (e.g., flow velocity data or tissue images of a healthy body) and, based on this comparison, precisely locate the location causing the health problem on the generated image (e.g., achieving problem-level localization).

[0018] These and other features of the systems, methods, and non-transitory computer-readable media disclosed herein, as well as the methods of operation and function of the related elements of the structure, and the economical combination of components and manufacture, will become more apparent when considered in conjunction with the accompanying drawings (all of which form a part of this specification), in which the same reference numerals denote corresponding parts. However, it should be clearly understood that the drawings are for illustrative and descriptive purposes only and are not intended to be construed as limiting the invention. It should be understood that the foregoing general description and the following detailed description are exemplary and illustrative only and do not limit the claimed invention. Attached Figure Description

[0019] Figure 1 An exemplary environment for the contrast-enhanced ultrasound quantitative imaging system of various embodiments is shown.

[0020] Figure 2 Exemplary computing systems for contrast-enhanced ultrasound quantitative imaging are shown in various embodiments.

[0021] Figures 3A-3B An exemplary workflow of the setup module for each embodiment is shown.

[0022] Figures 4A-4B Exemplary workflows of the determination and generation modules of various embodiments are shown.

[0023] Figures 5A-5B Examples of the generated structural images from each embodiment are shown.

[0024] Figure 6 Flowcharts depicting exemplary methods for contrast-enhanced ultrasound quantitative imaging according to various embodiments are provided.

[0025] Figure 7 This is a block diagram illustrating a computer system on which any of the embodiments described herein may be implemented. Detailed Implementation

[0026] Figure 1 Exemplary environments for a contrast-enhanced ultrasound quantization (CEUS QI) system 100 for contrast-enhanced ultrasound quantization imaging according to various embodiments are shown. Depending on the implementation, the CEUS QI system 100 may include more, fewer, or alternative components.

[0027] In some embodiments, the CEUS QI system 100 may include an ultrasonic transducer 112, a data storage 114, an image forming module 116, a computing system 120, a user interface 122, and a display 130, one or more of these components may be optional. The ultrasonic transducer 112 may be connected to the data storage 114 and the computing system 120. The data storage 114 may be connected to the image forming module 116. The image forming module 116 may be connected to the computing system 120. The computing system 120 may be connected to the display 130 and the user interface 122. Any connected modules can transmit signals between each other. The ultrasonic transducer 112, data storage 114, image forming module 116, computing system 120, user interface 122, and display 130 may be integrated into a single system or device, or may be distributed across multiple interconnected systems or devices.

[0028] In some embodiments, CEUS QI can be performed on a region of interest (ROI) (e.g., abdomen, heart, etc.) of a target 102 (e.g., a person, pet, tissue cross-section, living sample). For example, a contrast agent can be injected into the human body via intravenous injection, after which the ROI can be exposed to ultrasound. In one embodiment, computing system 120 can trigger ultrasound transducer 112 to emit ultrasound towards the ROI. For example, the ultrasound transducer can be placed on a patient's body, allowing ultrasound to be emitted towards the ROI of the body. The ROI may include the skin, nails, hair, etc., of the body surface, beneath which various organ structures (e.g., tissues, blood vessels) can reflect ultrasound back to varying degrees. In one embodiment, multiple blood vessels of different sizes, such as arteries, veins, and capillaries, may be present in the ROI. In some embodiments, ultrasound transducer 112 may include a detector. The detector can be configured to receive ultrasound reflected from the ROI and generate an ultrasound signal based on the received ultrasound. For example, the detector can convert the reflected ultrasound into an electrical signal to obtain an ultrasound signal. In some embodiments, ultrasound transducer 112 can send the ultrasound signal to data storage 114. Data storage 114 can be configured to store the ultrasonic signal generated by ultrasonic transducer 112 and send it to image forming module 116. Image forming module 116 is optional and can be configured to adjust the amplitude and phase of the ultrasonic signal, for example, by performing time-delay focusing, weighting, channel summation, etc. Image forming module 116 can then send the adjusted ultrasonic signal to computing system 120 for related signal processing. Computing system 120 can be a system for CEUS QI. (Refer to below...) Figure 2 Describe the signal processing of CEUS QI.

[0029] In some embodiments, depending on the different imaging modes set via the user interface 122, the computing system 120 can perform different processing on the ultrasound signal to generate corresponding images. For example, the intensity of the ultrasound signal and the intensity changes at each location can be recorded over a period of time to generate an image. The quantized image can be displayed to the user on the display 130. Through the user interface 122, the user can update settings or otherwise input instructions to modify the image, such as changing the display mode.

[0030] Figure 2 An exemplary computing system 120 for CEUS QI is shown in various embodiments. Depending on the implementation, the computing system 120 may include more, fewer, or alternative components.

[0031] In some embodiments, the computing system 120 for CEUS QI may include one or more processors (e.g., digital processors, analog processors, digital circuitry designed to process information, central processing units, graphics processing units, microcontrollers or microprocessors, analog circuitry designed to process information, state machines and / or other mechanisms for electronically processing information) and one or more non-transitory computer-readable memories (e.g., permanent memory, temporary memory, non-transitory computer-readable storage media) connected to each other. The one or more memories may be configured with instructions executable by the one or more processors. The processors may be configured to perform various operations by interpreting machine-readable instructions stored in the memory. The computing system 120 may include other computing resources. The computing system 120 may be equipped with appropriate software (e.g., ultrasound imaging control programs) and / or hardware (e.g., wired connections, wireless connections) to access these other computing resources.

[0032] In some embodiments, the computing system 120 for CEUS QI may include an acquisition module 202, a setting module 204, a determination module 206, and a generation module 208. That is, the acquisition module 202, setting module 204, determination module 206, and generation module 208 may be implemented as software (e.g., as part of software instructions), hardware, or a combination of software and hardware. As software instructions, the various modules may be executed by one or more processors of the computing system 120 to perform various operations.

[0033] Although Figure 2 The computing system 120 used for CEUS QI is shown as a single entity, but this is for ease of reference only and not as limiting. One or more modules or functions of the computing system 120 described herein may be implemented in a single computing device or distributed across multiple computing devices. In some embodiments, one or more modules or functions of the computing system 120 described herein may be implemented in one or more networks (e.g., an enterprise network accessible to the ultrasound machine), one or more endpoints (e.g., the ultrasound machine), one or more servers (e.g., servers connected to the ultrasound machine), or one or more clouds (e.g., a cloud accessible to the ultrasound machine).

[0034] In some embodiments, the acquisition module 202 may be configured to acquire, for each location of the ROI, time-varying ultrasound signals associated with that ROI from a CEUS image over a time period. The CEUS image over this time period may be generated based on intensity changes at each location of the ROI and may be stored in the computing system 120 for CEUS QI. For the entire ROI, the information obtained from the CEUS image may, in general, include the ultrasound signals received during that time period. Acquiring information may include one or more of the following: accessing, acquiring, analyzing, determining, examining, identifying, loading, locating, opening, receiving, retrieving, viewing, storing, or otherwise acquiring information. The ultrasound signals may be transmitted from the ultrasound transducer 112 or the image forming module 116 as described above, or otherwise retrieved by the computing system 120. For the time-varying ultrasound signals, the ultrasound signals may be acquired over that time period to capture time-related changes in intensity or one or more other parameters. A Region of Interest (ROI) can refer to any two-dimensional surface (e.g., the body surface, a cross-section below the body surface) and the ultrasound signal obtained can be obtained from ultrasound waves reflected by structures located at any depth below the surface (e.g., blood vessels of different sizes, tissue between blood vessels). Alternatively, an ROI can refer to any three-dimensional space (e.g., the volume below the body surface).

[0035] CEUS QI can be performed simultaneously with contrast agent injection and passage through the ROI. CEUS images can be generated in real time based on the contrast-enhanced ultrasound signals at each location within the ROI. For example, the intensity and intensity changes of the ultrasound signal at each location within the ROI can be recorded in real time to generate CEUS images. In other words, in response to the injection of contrast agent into the patient, time-varying ultrasound signals are generated and received, and the corresponding CEUS images are generated and stored as input to the CEUS QI system.

[0036] In some embodiments, before or after acquiring ultrasound signals that vary over time, the computing system 120 (e.g., another acquisition module) can be configured to acquire user input to determine the end of the time period. For example, the computing system 120 can obtain an end frame via a user interface 122. This end frame may indicate the end of a time period for the CEUS QI. As another example, the computing system 120 can be configured to set this end frame. Using this end frame, the computing system 120 can acquire ultrasound signals from various locations within the ROI from the time of contrast agent injection or thereafter until the end frame, improving efficiency and optimizing storage. Similarly, the computing system 120 (e.g., another acquisition module) can be configured to acquire user input to determine the start of the time period (e.g., a start frame). The acquired ultrasound signals can be sent to the setting module 204 for further processing.

[0037] In some embodiments, before or after acquiring the time-varying ultrasound signal, the calculation system 120 (e.g., another acquisition module) can be configured to acquire a percentage threshold. This percentage threshold can be a parameter used for CEUS QI as described below (e.g., 50%). For example, the calculation system 120 can acquire this percentage threshold via a user interface 122. As another example, the calculation system 120 can be configured to set this percentage threshold. The acquired percentage threshold can be sent to the setting module 204 for further processing. This percentage threshold can be adjustable in real time, allowing the generated image to be updated in real time.

[0038] In some embodiments, the setting module 204 can be configured to set a global threshold for the acquired time-varying ultrasound signal at each location. This global threshold may be a threshold signal for which the same threshold signal is applied to each location of the ROI. The global threshold may refer to a predetermined threshold for the acquired signal. The global threshold can be determined through one or more steps. In one embodiment, the setting module 204 can be configured to determine a time-intensity curve (TIC) for each location, at least based on the acquired time-varying ultrasound signal over that time period. For example, the setting module 204 can organize the acquired time-varying ultrasound signals at each location into a TIC. The TIC can depict the intensity change of the ultrasound signal within that time period. The setting module 204 can then set a global threshold for the location in the ROI based on the corresponding TIC and the acquired percentage threshold. The TIC is referenced... Figure 3A Provide a detailed description. The same process can be repeated for all locations within the ROI.

[0039] refer to Figure 3A This illustrates an exemplary workflow 300 of the setting module 204 in various embodiments. In some embodiments, the setting module 204 may determine the peak value of the time-intensity curve for each location; obtain a percentage threshold; and determine a global threshold for each location based at least on the peak value and the percentage threshold. Figure 3A As shown, based on the time-varying ultrasound signals obtained at locations A and B, the setting module 204 can determine the intensity change of the corresponding ultrasound signal within that time period. Locations A and B can correspond to different points on different structures within the ROI. As shown in graph 312, the intensity change at location A is represented by TIC 321A, and the intensity change at location B is represented by TIC 321B. As shown, the TIC is plotted relative to the x-axis representing frame time and the y-axis representing signal intensity. Data points in the TIC can be fitted with a smoothing function. In one example, location A and TIC 321A can correspond to wider blood vessels (e.g., arteries), and location B and TIC 321B can correspond to thinner blood vessels (e.g., capillaries).

[0040] In some embodiments, to set a global threshold for each location, the setting module 204 may first determine the peak value (i.e., Imax). The peak value at each location may be determined based on the obtained ultrasound signal that varies over time. For ease of illustration, the peak values ​​at locations A and B, as shown in the figure, are determined based on TIC 321A and 321B. For example, as shown in curve 314, the peak value at location A may be determined as the maximum intensity I1max of curve 321A, and the peak value at location B may be determined as the maximum intensity I2max of curve 321B. Because the contrast agent arrives at different points at different times, the contrast enhancement at different locations may vary, and the Imax at each location may be different.

[0041] In some embodiments, using Imax, the setting module 204 can be configured to set a global threshold based on Imax and the obtained percentage threshold. For example, when the obtained percentage threshold is 50%, the setting module 204 can be configured to determine the global threshold for each location in the ROI as Imax * 50%. For example, the global thresholds at locations A and B can be determined as I1 and I2, respectively, based on the determined peak values ​​I1max and I2max and the percentage threshold 50%. For example, as shown in graph 314, the global threshold I1 for location A can be determined as I1max multiplied by 50%, and the global threshold I2 for location B can be determined as I2max multiplied by 50%.

[0042] refer to Figure 2 In some embodiments, the determining module 206 may be configured to determine, for each location, the relative moment when the time-varying ultrasound signal reaches the global threshold. A relative moment means that, as long as the relativity between different moments is indicated, the moment need not necessarily be an absolute measure of time. Relativity may be the basis for color coding as described below. In one embodiment, the relative moment of a location within the ROI may be determined and recorded when the ultrasound signal at that location first reaches its corresponding global threshold. This process is referenced... Figure 3B To provide a more detailed description.

[0043] refer to Figure 3B This illustrates an exemplary workflow 302 of the determination module 206 in various embodiments. For example... Figure 3BAs shown, curve 314 is the same as described above. Then, as shown in curve 316, the determining module 206 can be configured to determine the time corresponding to the determined global thresholds I1 and I2. In some embodiments, the determined time may be the first time when the ultrasound signal at each location reaches the corresponding global threshold. For example, the determining module 206 can determine the time t1 when the ultrasound signal at location A first reaches the global threshold I1 as a relative time based on curve 321A, and the time t2 when the ultrasound signal at location B first reaches the global threshold I2 as a relative time. The same process can be performed at each location in the ROI to determine the corresponding relative time.

[0044] refer to Figure 2 In some embodiments, the generation module 208 can be configured to generate a structural image of the ROI based at least on the relative time of each determined location. This structural image may refer to an image of the organ structure (e.g., tissue structure, vascular structure) of the ROI. The generated structural image can display different times at which the ultrasound signal reaches a global threshold corresponding to different locations within the region of interest, for example, through color encoding. In one embodiment, the generation module 208 can be configured to encode different relative times with different colors to generate the structural image. The encoded colors may include grayscale colors, RGB colors, etc. Different times may correspond to different colors in the corresponding color spectrum. For example, when the relative time of a determined first location is the same as the relative time of a determined second location in the ROI, the first and second locations can be encoded with the same color. In another example, when the relative time of a determined first location is different from the relative time of a determined third location in the ROI, the color encoded at the first location may be different from the color encoded at the third location. The generated image in Figure 4A and 4B As shown in the image.

[0045] Figure 4A An exemplary workflow 400 of the generation module 208 of various embodiments is shown. For example... Figure 4AAs shown, graph 316 is the same as described above. In some embodiments, generation module 208 can generate a structured image 404 with color coding (e.g., from palette 420) for each location in the ROI based on their relative times. Palette 420 can refer to color coding corresponding to relative times. Image 404 can display the ROI in two dimensions (e.g., xy coordinates) to indicate relative positions. For example, one or more pixels of image 404 can correspond to a location in the ROI. This location can be color-coded based on its corresponding relative time. For example, as in graph 316, determination module 206 can determine that the relative time t1 of location A is 20 and the relative time t2 of location B is 40. Location A can be on an artery, and location B can be on a capillary. Therefore, based on palette 420, generation module 208 can encode location A with color 1 and location B with color 2 to reflect different times. After all locations in the ROI are color-coded, image 404 can be generated with colors that provide a visualization of the ROI.

[0046] The generated images can provide rich medical information. In some embodiments, the different times displayed can correspond to different locations within the ROI and are color-coded; the color-coded times displayed on the generated structural image can indicate the boundaries of multiple blood vessels. In one embodiment, the generated image displays different regions in different colors, and the boundaries of these color regions can indicate the boundaries of different organ structures (e.g., blood vessels receiving injected contrast agent at different rates). For example, location A encoded with color 1 and location B encoded with color 2 can represent that these two locations correspond to different biological parts, such as two locally distinct blood vessels. Figure 4A As shown, the colored area around location A represents the region that receives contrast agent fastest to reach its global threshold, and is represented by the cross-sectional shape of an artery in the ROI. The colored area around location B represents the region that receives contrast agent later to reach its global threshold, and is represented by the cross-sectional shape of a capillary in the ROI. The broad background of the image (e.g., location C) can represent the tissue region that receives contrast agent latest. Therefore, different organ structures within the body can be imaged and visualized in color. Based on the generated images, the computational system 120 can measure the size of blood vessels.

[0047] Furthermore, time-based color coding can distinguish finer features at a high resolution. For example, locations on the same cross-section of an artery around location A can be coded with shading in various colors based on their relative time, which can be influenced by distance from the center of the cross-section and location. Since the arterial wall may have an uneven structure, resulting in non-uniform blood flow carrying contrast agent, this feature of the wall can be identified through color coding. As shown in image 404, the fine structure of the wall is discerned, at least because this non-uniformity causes blood flow to slow down near the wall, leading to an increase in relative time.

[0048] In some embodiments, the generated structural image can provide blood perfusion rate information. For example, the ROI may include multiple blood vessels of different sizes, and different times indicate different blood perfusion rates in these vessels. The perfusion rate of contrast agent carried by the blood in different vessels may be affected by vessel size, distance from the injection site, etc. Since the generated structural image displays different relative times at these locations in different colors, different colors can represent different blood perfusion rates in different blood vessels. If the relative time of a first location is greater than the relative time of a second location, the perfusion rate at the first location may be slower than the perfusion rate at the second location. For example, since the relative time at location B is greater than the relative time at location A, the perfusion rate at location B may be slower than the perfusion rate at location A. That is, according to the generated image, the artery near location A appears to receive the contrast agent faster than the capillaries around location B.

[0049] In some embodiments, the computing system 120 for CEUS QI can be configured to determine one or more locations exhibiting anomalies. In one embodiment, before generating a structural image, the computing system 120 can determine one or more locations exhibiting anomalies based on predetermined relative times (e.g., a baseline) and the relative times of each determined location, and the generated structural image can include one or more labels indicating the one or more locations exhibiting anomalies. For example, the computing system 120 can compare these relative times to a baseline (e.g., blood flow rate data from a healthy body). Alternatively, the computing system 120 can compare the generated image to a baseline image of a healthy body. For example, a predetermined relative time of a first location in a ROI from a healthy person can be encoded with color 1. If the relative time of the same first location in the same ROI of a determined patient is encoded with color 2 in the structural image, the computing system 120 can determine an anomaly at the first location. Based on any comparison, the computing system 120 can precisely pinpoint one or more locations on the generated image that may cause a health problem (e.g., problem-level). The generation module 208 can then be configured to indicate the one or more locations exhibiting anomalies on the generated image, for example, by marking, highlighting, etc. Precise location can indicate the origin of cancer cells, malignant tissue, blood vessel blockage, and so on.

[0050] Figure 4B Another exemplary workflow 402 of the generation module 208 in various embodiments is illustrated. In some embodiments, the global threshold of a location in the ROI can be changed based on an update of the percentage threshold, and the computation system 120 can adjust the generated structure image accordingly in real time. In one embodiment, the computation system 120 can be configured to acquire the updated global threshold for each location. The generation module 208 can be configured to update the generated structure image at least based on the updated global threshold for each location. For example, in Figure 4A In graph 316, the percentage threshold before the update was 50%. After the update, the percentage threshold became 80%. Figure 4B As shown in curve 416, for location A, at time t3 (e.g., relative to time 30), the global threshold I3 first reaches 80% of the peak intensity of curve 321A. Therefore, location A is re-encoded with color 3 in image 406. As another example, for location B, at time t4 (e.g., relative to time 50), the global threshold I4 first reaches 80% of the peak intensity of curve 321B. Therefore, location B is re-encoded with color 4 in image 406. The same update can be performed on all locations in the ROI and all pixels in the generated image. Accordingly, as... Figure 4B As shown in structure image 406, the structure image is re-encoded based on updates to relative times.

[0051] Figure 5A and 5B Examples of generated structural images 502 and 504 from various embodiments are shown. The two structural images 502 and 504 are displayed with smoothing windows of different sizes at the same percentage threshold (e.g., threshold grayscale = 80). The smoothing window can be implemented using smoothing functions in image processing. Applying a smoothing window can reduce signal noise in a single pixel that is higher than the signal of its neighboring pixels, creating a smoother image. In some embodiments, image 504 is generated with an input smoothing window size 15, and image 502 is generated with an input smoothing window size 5. Like the percentage threshold, the smoothing window size can be determined and adjusted via the user interface 122 or set by the computing system 120.

[0052] In some embodiments, before generating the structural image, the computing system 120 may obtain a window size for image smoothing, and the generation module 208 may generate a structural image of the region of interest based at least on the window size and a determined relative time for each location. By adjusting the size of the smoothing window, the computing system 120 can reduce the signal-to-noise ratio of the generated image, but this requires a trade-off in the resolution loss of smaller features. For example, as Figure 5A As shown, when the input smoothing window size is 5, the generated structural image 502 can distinguish various fine vascular structures. Figure 5B When the input of the smoothing window size is adjusted to 15, the generated image 504 produces a smoother image compared to the structured image 502, but many small features are blended into the background and are not distinguished.

[0053] Figure 6 A flowchart of an exemplary method 600 of various embodiments is shown. Method 600 can be performed by one or more components of the CEUS QI system 100, such as the CEUS QI computing system 120. The operation of method 600 presented below is illustrative. Depending on the implementation, method 600 may include more, fewer, or alternative steps performed in various orders or in parallel.

[0054] Block 610 includes acquiring a time-varying ultrasound signal relative to the region of interest for each location within a time period. Block 620 includes setting a global threshold for the acquired time-varying ultrasound signal at each location. Block 630 includes determining the relative time at which the time-varying ultrasound signal reaches the global threshold for each location. Block 640 includes generating a structural image of the region of interest based at least on the determined relative time for each location, wherein the generated structural image shows different times at which the ultrasound signal reaches the global threshold corresponding to different locations within the region of interest.

[0055] In some embodiments, before setting a global threshold, method 600 further includes: for each location, determining a time-intensity curve based at least on the time-varying ultrasound signal obtained within that time period.

[0056] In some embodiments, setting a global threshold for the obtained time-varying ultrasound signal at each location includes: determining the peak value of the time-intensity curve at each location; obtaining a percentage threshold; and determining a global threshold for each location based at least on the peak value and the percentage threshold.

[0057] In some embodiments, the region of interest includes multiple blood vessels of different sizes; and different times represent different blood perfusion rates in these multiple blood vessels.

[0058] In some embodiments, the different times displayed correspond to different locations in the region of interest and are color-coded; and the color-coded times displayed on the generated structural image indicate the boundaries of the plurality of blood vessels.

[0059] In some embodiments, method 600 further includes: for each location, obtaining an updated global threshold; and updating the generated structural image based at least on the updated global threshold for each location.

[0060] In some embodiments, before generating the structural image, method 600 further includes: determining one or more locations that exhibit anomalies based on a predetermined relative time and the relative time of each determined location; and the generated structural image includes one or more labels indicating the one or more locations that exhibit anomalies.

[0061] In some embodiments, before generating the structural image, method 600 further includes: obtaining a window size for image smoothing; and generating a structural image of the region of interest based at least on the relative time of each determined location includes: generating a structural image of the region of interest based at least on the window size and the relative time of each determined location.

[0062] In some embodiments, before obtaining the time-varying ultrasound signal, the operation further includes obtaining user input to determine the end of the time period.

[0063] Figure 7 This is a block diagram illustrating a computer system 700 in which any of the embodiments described herein may be implemented. The computer system 700 can... Figure 1-6 Implemented in any component of the device, apparatus, or system shown. For example, computing system 120 may implement computer system 700. Reference Figure 1-6One or more of the described methods (such as method 600) can be executed by one or more implementations of computer system 700. Computer system 700 includes bus 702 or other communication mechanisms for transmitting information, and one or more hardware processors 704 connected to bus 702 for processing information. Hardware processor 704 may be, for example, one or more general-purpose microprocessors.

[0064] Computer system 700 may include bus 702 or other communication mechanisms for transmitting information, and one or more hardware processors 704 connected to bus 702 for processing information. Hardware processor 704 may be, for example, one or more general-purpose microprocessors.

[0065] Computer system 700 may also include main memory 706, such as random access memory (RAM), cache, and / or other dynamic storage devices, connected to bus 702, for storing information and instructions executable by the processor. Main memory 706 may also be used to store temporary variables or other intermediate information during the execution of instructions executable by processor 704. When these instructions are stored in storage media accessible to processor 704, they transform computer system 700 into a dedicated machine specifically designed to perform the operations specified by those instructions. Computer system 700 may also include read-only memory (ROM) 708 or other static storage devices connected to bus 702 for storing static information and instructions for processor 704. Storage devices 710 (e.g., disks, optical discs, USB thumb drives (flash drives), etc.) may be provided and connected to bus 702 to store information and instructions.

[0066] Computer system 700 can implement the techniques described herein using custom hard-wired logic, one or more ASICs or FPGAs, firmware, and / or program logic, which, when combined with the computer system, enables or programs computer system 700 to become a special-purpose machine. According to one embodiment, the operations, methods, and processes described herein are executed by computer system 700 in response to processor 704 executing one or more sequences of one or more instructions contained in main memory 706. These instructions may be read into main memory 706 from another storage medium (such as storage device 710). Executing the sequence of instructions contained in main memory 706 causes processor 704 to perform the processing steps described herein. In alternative embodiments, hard-wired circuitry may be used instead of software instructions or in combination with software instructions.

[0067] Main memory 706, ROM 708, and / or storage device 710 may include non-transitory storage media. As used herein, the term "non-transitory media" and similar terms refer to a medium that stores data and / or instructions that cause a machine to operate in a particular manner, excluding transient signals. Such non-transitory media may include non-volatile media and / or volatile media. Non-volatile media include, for example, optical discs or magnetic disks, such as storage device 710. Volatile media include dynamic memory, such as main memory 706. Common forms of non-transitory media include, for example, magnetic disks, floppy disks, hard disks, solid-state drives, magnetic tape or any other magnetic data storage media, CD-ROMs, any other optical data storage media, any physical media with a perforated pattern, RAM, PROMs and EPROMs, FLASH-EPROMs, NVRAMs, any other memory chips or cassette tapes, and their network versions.

[0068] Computer system 700 may include a network interface 718 connected to bus 702. Network interface 718 can provide bidirectional data communication to one or more network links connected to one or more local networks. For example, network interface 718 may be an Integrated Services Digital Network (ISDN) card, cable modem, satellite modem, or modem to provide data communication connectivity to a corresponding type of telephone line. As another example, network interface 718 may be a Local Area Network (LAN) card to provide data communication connectivity to a LAN-compatible network (or a WAN component communicating with a WAN). Wireless links may also be implemented. In any such implementation, network interface 718 can transmit and receive electrical, electromagnetic, or optical signals carrying digital data streams representing various types of information.

[0069] Computer system 700 can send messages and receive data, including program code, via a network, network link, and network interface 718. In the example of the Internet, the server can send request code to the application via the Internet, ISP, local area network, and network interface 718.

[0070] The received code can be executed by processor 704 upon receipt and / or stored in storage device 710 or other non-volatile memory for later execution.

[0071] Each process, method, and algorithm described above can be implemented in a code module executed by one or more computer systems or a computer processor including computer hardware, and can be executed automatically, wholly or partially. The process and algorithm can be implemented, partially or entirely, in a dedicated circuit.

[0072] The various features and processes described above can be used independently of each other or combined in various ways. All possible combinations and sub-combinations are intended to fall within the scope of this invention. Furthermore, in some embodiments, certain method or process blocks may be omitted. The methods and processes described herein are not limited to any particular order, and the blocks or states associated with them may be performed in other suitable orders. For example, described blocks or states may be performed in an order different from that specifically disclosed, or multiple blocks or states may be combined in a single block or state. Examples of blocks or states may be performed serially, in parallel, or otherwise. Blocks or states may be added to or removed from the disclosed embodiments. Examples of systems and components described herein may be configured differently from those described. For example, elements may be added, removed, or rearranged compared to the disclosed embodiments.

[0073] The various operations of the methods described herein can be performed, at least in part, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors can constitute the engine of a processor implementation that operates to perform one or more of the operations or functions described herein.

[0074] Similarly, the methods described herein can be implemented at least in part by a processor, where one or more specific processors are examples of hardware. For example, at least some operations of a method can be performed by one or more processors or an engine implemented by a processor. Furthermore, the one or more processors can also function to support the implementation of related operations in a “cloud computing” environment or as “Software as a Service” (SaaS). For example, at least some operations can be performed by a set of computers (as an example of a machine including processors) that can be accessed via a network (e.g., the Internet) and one or more appropriate interfaces (e.g., application programming interfaces (APIs)).

[0075] The execution of certain operations can be distributed across processors, residing not only within a single computer but also deployed across multiple computers. In some embodiments, the processor or processor-implemented engine may reside in a single geographic location (e.g., in a home environment, office environment, or server farm). In other embodiments, the processor or processor-implemented engine may be distributed across multiple geographic locations.

[0076] Throughout this specification, multiple instances can implement components, operations, or structures described as single instances. Although a single operation of one or more methods is illustrated and described as a separate operation, one or more individual operations can be performed simultaneously, and they do not need to be performed in the order shown. Structures and functionalities presented as individual components can be implemented as combined structures or components. Similarly, structures and functionalities presented as single components can be implemented as separate components. These, and other variations, modifications, additions, and improvements fall within the scope of this document.

[0077] Although an overview of the subject matter of the invention has been described with reference to specific embodiments, various modifications and changes can be made to these embodiments without departing from the broad scope of the embodiments of the invention. The detailed description should not be construed in a limiting sense, and the scope of the embodiments should be defined by the appended claims and the full scope of their equivalents. Furthermore, related terms used herein (such as “first,” “second,” “third,” etc.) do not indicate any order, importance, or significance, but are used to distinguish one element from another. Additionally, the terms “a,” “an,” and “a plurality” do not indicate a quantity limitation, but rather the presence of at least one of the mentioned items.

Claims

1. A non-transitory computer-readable storage medium for contrast-enhanced ultrasound quantitative imaging, said non-transitory computer-readable storage medium being configured with instructions executable by one or more processors to cause said one or more processors to perform operations, said operations including: For each location within a region of interest (ROI) where contrast agent is injected, acquire the contrast agent-enhanced ultrasound signal over time relative to the ROI, the ROI comprising multiple blood vessels of different sizes; For each location, a global threshold is set for the obtained time-varying ultrasound signal; For each location, the relative time when the time-varying ultrasound signal reaches the global threshold is determined, and the relative time is determined and recorded when the ultrasound signal at that location first reaches its corresponding global threshold; and A structural image of the region of interest is generated based at least on the relative time at each determined location, wherein the generated structural image shows different times at which the ultrasound signal corresponding to different locations in the region of interest reaches the global threshold, the different times representing different blood perfusion rates in the plurality of blood vessels, the different times shown corresponding to the different locations in the region of interest and being color-coded, and the color-coded times shown on the generated structural image indicate the boundaries of the plurality of blood vessels.

2. The non-transitory computer-readable storage medium according to claim 1, wherein, Before setting the global threshold, the operation further includes: For each location, a time-intensity curve is determined based at least on the time-varying ultrasound signal within the obtained time period.

3. The non-transitory computer-readable storage medium according to claim 2, wherein, Setting a global threshold for the obtained time-varying ultrasound signal at each location includes: For each location, determine the peak value of the time-intensity curve; Obtain the percentage threshold; and For each location, the global threshold is determined based at least on the peak value and the percentage threshold.

4. The non-transitory computer-readable storage medium according to claim 1, wherein, The operation also includes: For each of the aforementioned locations, obtain the updated global threshold; and The generated structural image is updated based at least on the updated global threshold at each location.

5. The non-transitory computer-readable storage medium according to claim 1, wherein: Before generating the structural image, the operation further includes: determining one or more locations exhibiting anomalies based on a predetermined relative time and the determined relative time for each location; and The generated structural image includes one or more labels indicating the one or more locations that show anomalies.

6. The non-transitory computer-readable storage medium according to claim 1, wherein: Before generating the structure diagram, the operation further includes: obtaining the window size for image smoothing; and Generating the structural image of the region of interest based at least on the relative time of each determined position includes: generating the structural image of the region of interest based at least on the window size and the relative time of each determined position.

7. The non-transitory computer-readable storage medium according to claim 1, wherein, Before obtaining the time-varying ultrasound signal, the operation further includes: Obtain user input to determine the end of the time period.

8. A contrast-enhanced ultrasound quantitative imaging system, comprising: An ultrasonic transducer is configured to emit ultrasonic waves and receive ultrasonic waves reflected from a region of interest, and to generate an ultrasonic signal based on the received ultrasonic waves. and A computing system comprising one or more processors and one or more non-transitory computer-readable storage media connected to the one or more processors and storing instructions executable by the one or more processors to cause the one or more processors to perform operations, the operations including: For each location in the region of interest where the contrast agent is injected, the contrast agent-enhanced ultrasound signal over a time period relative to the region of interest is obtained from the generated ultrasound signal, the region of interest comprising multiple blood vessels of different sizes; For each location, a global threshold is set for the obtained time-varying ultrasound signal, and the relative time when the ultrasound signal at that location first reaches its corresponding global threshold is determined and recorded. For each location, determine the relative time at which the time-varying ultrasound signal reaches the global threshold; and A structural image of the region of interest is generated based at least on the relative time at each determined location, wherein the generated structural image shows different times at which the ultrasound signal corresponding to different locations in the region of interest reaches the global threshold, the different times representing different blood perfusion rates in the plurality of blood vessels, the different times shown corresponding to the different locations in the region of interest and being color-coded, and the color-coded times shown on the generated structural image indicate the boundaries of the plurality of blood vessels.

9. The system according to claim 8, wherein, Before setting the global threshold, the operation further includes: For each location, a time-intensity curve is determined based at least on the time-varying ultrasound signal within the obtained time period.

10. The system according to claim 9, wherein, Setting a global threshold for the obtained time-varying ultrasound signal at each location includes: For each location, determine the peak value of the time-intensity curve; Obtain the percentage threshold; and For each location, the global threshold is determined based at least on the peak value and the percentage threshold.

11. The system according to claim 8, wherein: Before generating the structural image, the operation further includes: determining one or more locations exhibiting anomalies based on a predetermined relative time and the determined relative time for each location; and The generated structural image includes one or more labels indicating the one or more locations that show anomalies.

12. The system according to claim 8, wherein: Before generating the structure diagram, the operation further includes: obtaining the window size for image smoothing; and Generating the structural image of the region of interest based at least on the relative time of each determined position includes: generating the structural image of the region of interest based at least on the window size and the relative time of each determined position.

13. A method for quantitative imaging with contrast-enhanced ultrasound, comprising: For each location within a region of interest (ROI) where contrast agent is injected, acquire the contrast agent-enhanced ultrasound signal over time relative to the ROI, the ROI comprising multiple blood vessels of different sizes; For each location, a global threshold is set for the obtained time-varying ultrasound signal; For each location, the relative time when the time-varying ultrasound signal reaches the global threshold is determined, and the relative time is determined and recorded when the ultrasound signal at that location first reaches its corresponding global threshold; and A structural image of the region of interest is generated based at least on the relative time at each determined location, wherein the generated structural image shows different times at which the ultrasound signal corresponding to different locations in the region of interest reaches the global threshold, the different times representing different blood perfusion rates in the plurality of blood vessels, the different times shown corresponding to the different locations in the region of interest and being color-coded, and the color-coded times shown on the generated structural image indicate the boundaries of the plurality of blood vessels.

14. The method of claim 13, further comprising, before setting the global threshold: For each location, a time-intensity curve is determined based at least on the time-varying ultrasound signal within the obtained time period.

15. The method according to claim 14, wherein, Setting a global threshold for the obtained time-varying ultrasound signal at each location includes: For each location, determine the peak value of the time-intensity curve; Obtain the percentage threshold; and For each location, the global threshold is determined based at least on the peak value and the percentage threshold.

16. The method of claim 13, further comprising, before generating the structural image: Based on a predetermined relative time and the relative time of each determined location, one or more locations exhibiting anomalies are identified; and The generated structural image includes one or more labels indicating one or more locations that show an anomaly.

Images