Apparatus, system and method for contrast imaging

Abstract

Apparatuses, systems, and methods are provided for automatically selecting a first frame and a last frame of a sequence of frames from which a cumulative contrast image can be generated. In some examples, a statistical distribution of groups of pixels of the image frames can be analyzed to generate a parameter map. The parameter map can be analyzed to select a first image frame and a last image frame of the sequence. In some examples, an image frame corresponding to a parameter map having a value above a threshold can be selected as the first frame. In some examples, an image frame corresponding to a parameter map having a maximum value of all parameter maps can be selected as the last frame. In some examples, the parameter map can be used to segment features, such as tumors, from the image frames.

Description

Technical Field

[0001] This application relates to contrast-enhanced imaging. More specifically, this application relates to generating contrast-accumulated images.

[0002] Xiaolin Gu et al. (Proceedings of the IEEE-EMBS International Conference on Biomedical and Health Informatics 2012) disclosed "Parametric perfusion imaging using contrast enhanced ultrasound with bolus administration of contrastagents".

[0003] Matthew R. Lowerison et al. (IEEE International Ultrasonics Symposium Proceedings, 2014) disclosed "Use of accumulation images obtained by arterial-phasecontrast-enhanced harmonic grayscale ultrasonography to evaluate tumorvessels in focal nodular hyperplasia and other hepatic tumors". Background Technology

[0004] Contrast-enhanced ultrasound (CEUS) has been clinically used for imaging organs and tumor vessels, as well as for assessing tissue perfusion. A contrast agent is delivered to the region or volume to be imaged to provide a higher signal intensity from the region or volume of interest, or to selectively enhance the signal from regions or volumes with high contrast concentrations. For example, microbubbles can be delivered to a subject via a vein, and regions of interest (e.g., the liver) can be imaged using an ultrasound imaging system. The arrival, accumulation, and / or flushing of microbubbles within the region of interest can be captured in ultrasound images acquired by the ultrasound imaging system.

[0005] Contrast-enhanced imaging is a CEUS technique in which multiple contrast-enhanced images (e.g., multiple image frames) are acquired in a time-series manner. These multiple contrast-enhanced images are preprocessed using image processing techniques and then combined using statistical operators (typically the maximum value of pixels across all frames) to form a single image (e.g., a cumulative image). The resulting image can be used to map contrast agent progression and / or the enhancement of vascular topology and salience. Summary of the Invention

[0006] This paper discloses apparatus, systems, and methods for automatic frame selection in generating cumulative images. Because tissue signals are often incompletely suppressed in contrast-enhanced images, it is difficult to distinguish contrast echoes from tissue signals in grayscale contrast images. Therefore, image features within a small moving window on each contrast image can be used for automatic frame selection, rather than the grayscale intensity of individual pixels. In some paradigms, image features can be extracted from a statistical distribution of grayscale envelopes within multiple pixel windows spanning each contrast image frame. Examples of statistical distributions include, but are not limited to: signal-to-noise ratio (SNR) as a paradigmatic first-order feature, the Nakagami exponent (NI) as a paradigmatic second-order feature, and the product of SNR and NI (SNR×NI) as a paradigmatic third-order feature. The moving window, and the grayscale envelope statistical distribution therein, can be used to predict the arrival time of the contrast (e.g., the first image frame), and the intensity of the contrast enhancement will increase over time. In some paradigms, the frame with the peak intensity can be used as the last image frame. The apparatus, systems, and methods disclosed herein can provide faster and more consistent frame selection for generating contrast-accumulated images.

[0007] In some applications, the apparatus, systems, and methods disclosed herein for analyzing the statistical distribution of pixels in an image can be used for image segmentation. For example, segmenting tumors from an image.

[0008] According to at least one example disclosed herein, an apparatus for processing ultrasound images may include at least one processor configured to, for an individual image frame among a plurality of temporally spaced image frames, calculate a plurality of parameter values ​​based at least in part on the statistical distribution of corresponding pixel groups of a plurality of pixel groups of corresponding image frames of the plurality of temporally spaced image frames, wherein the plurality of pixel groups are at least in part defined on a multi-pixel window translated across image frames, wherein the plurality of temporally spaced image frames include contrast-enhanced ultrasound images, and generate a plurality of parameter maps including the plurality of parameter values, wherein an individual parameter map among the plurality of parameter maps corresponds to an individual image frame among the plurality of temporally spaced image frames.

[0009] According to at least one example disclosed herein, the method may include translating a multi-pixel window across individual image frames of multiple time-spaced image frames, wherein the multiple time-spaced image frames include contrast-enhanced ultrasound images, and for each translation of the multi-pixel window: determining a statistical distribution of pixels of the individual image frame included in the multi-pixel window to generate multiple statistical distributions, calculating parameter values ​​based at least in part on the corresponding statistical distributions of the multiple statistical distributions to generate multiple parameter values, and generating multiple parameter maps corresponding to the individual image frames of the multiple time-spaced image frames from the multiple parameter values. Attached Figure Description

[0010] Figure 1A These are example microvascular imaging (MVI) cumulative images;

[0011] Figure 1B This is an example of a cumulative time-of-arrival (ToA) image;

[0012] Figure 2 This is a block diagram of an ultrasound imaging system arranged according to some examples of this disclosure;

[0013] Figure 3 This is a block diagram illustrating an example processor according to some of the examples of this disclosure;

[0014] Figure 4 An analysis of an image frame based on the principles of this disclosure is shown.

[0015] Figure 5 The method illustrates the selection of image frames from a sequence of image frames in accordance with the principles of this disclosure;

[0016] Figure 6A-9B Examples of contrast-enhanced ultrasound image frames and parametric maps generated therefrom, based on the principles of this disclosure;

[0017] Figure 10 This is a flowchart of a method based on the principles of this disclosure. Detailed Implementation

[0018] The following description of certain examples is merely exemplary and is not intended to limit the invention or its application or use. In the following detailed description of examples of the apparatuses, systems, and methods of the invention, reference is made to the accompanying drawings, which form part of the invention, and specific examples of the described apparatuses, systems, and methods are shown by way of illustration. These examples are described in sufficient detail to enable those skilled in the art to practice the currently disclosed apparatuses, systems, and methods, and it should be understood that other embodiments can be utilized, and changes to the structure and logic can be made without departing from the spirit and scope of this disclosure. Furthermore, for clarity, certain features will not be discussed where detailed descriptions are obvious to those skilled in the art, so as not to obscure the description of the system. Therefore, the following detailed description is not intended to be limiting, but rather the scope of the system is defined only by the appended claims.

[0019] For cumulative imaging, a user can begin by acquiring images of a region of interest (ROI) within the object using an ultrasound imaging system, followed by the administration of a contrast agent, such as microbubbles. The contrast agent can be administered over a period of time via pills or continuous infusion. The user can continue acquiring images at the ROI for a period after drug administration. The imaging system can store the acquired images as a time series. After the user has completed image acquisition, they can select image frames from the stored time series for combination to generate one or more cumulative images.

[0020] Various techniques exist for cumulative imaging, each providing different diagnostic information. For example, angiographic cumulative imaging such as microvascular imaging (MVI) or maximum intensity projection (MIP) can combine multiple image frames to enhance the visualization of smaller vascular systems within a region of interest (ROI). Figure 1A An example of an MVI image is shown. Image 100 is a portion of the liver including a vascularized liver lesion (tumor) 102. Multiple ultrasound images are combined using MVI to produce image 100, such that larger and smaller vessels 104 within the tumor 102, as well as the vascular system 106 surrounding the tumor 102, are visualized. Examples of suitable techniques for performing MVI can be found in U.S. Provisional Application 62 / 938,554, filed November 21, 2019, which is incorporated herein by reference for any purpose.

[0021] Another example is called Time-of-Arrival (ToA) cumulative imaging. In ToA, different colors, grayscale values, and / or other codes are used to indicate when the contrast agent arrives in a specific region of the image, based on the time when the contrast agent appears in a region of the sequence of image frames used to generate the cumulative image. Figure 1BAn example of a ToA image is shown. Image 110 shows the same portion of the liver and tumor 102. However, different vascular systems in image 110 are encoded as different shadows. As shown, the larger vessels 104 and the central region of tumor 102 are encoded as a first shadow, indicating that contrast agent first reaches those areas. The periphery of tumor 102 and the surrounding smaller vascular systems 106 are encoded as a second shadow, indicating that contrast agent reaches those areas later. Examples of suitable techniques for performing ToA imaging can be found in U.S. Provisional Application 62 / 929,519, filed November 1, 2019, which is incorporated herein by reference for any purpose.

[0022] In some applications, the selection of the initial and / or final frames used to define the image frames combined for generating a cumulative image is important to ensuring the diagnostic value of the resulting cumulative image. For example, the initial frame of the sequence used to generate a cumulative image for ToA imaging is the frame in which the contrast agent first appears. If image frames in the sequence that appear before or after the first presentation of the contrast agent are selected, the apparent relative arrival of the contrast agent to different regions of the tissue can be altered. Such variations in the calculated ToA can make it difficult to compare ToA cumulative images between subjects and / or between examinations of the same subject.

[0023] In another example, in both ToA and MVI, incorrect selection of the final frame can lead to poor visualization of microvessels. For instance, if the final frame is too early in the sequence, the contrast agent may not have had time to accumulate in smaller vessels, resulting in poor visualization of microvessels. On the other hand, if the final frame is too late in the sequence, the contrast agent may have already accumulated in the ROI, causing image saturation and again leading to poor visualization of the vascular system.

[0024] Currently, users must manually review the captured time-series image frames and manually select the first and last frames from the time series to combine them into a cumulative image. Manually reviewing and selecting frames is time-consuming, especially when acquiring image frames at high frame rates and / or over long periods. Furthermore, manually determining the first and last image frames is prone to human error, leading to inconsistent selections. This, in turn, can degrade the diagnostic value of the resulting cumulative image. Therefore, an automated technique that provides more consistent frame selection is needed.

[0025] Automatic selection methods based on the grayscale intensity distribution of acquired image frames are insufficient. CEUS images are susceptible to artifacts caused by incomplete tissue suppression. Residual tissue signals can originate from multiple reflections within the interventional tissue layer, strong reflections from tissue interfaces (e.g., large vessel walls, peritoneum), and / or differences between the transmitted pulses used to stimulate the contrast agent. Furthermore, since tissue motion and acoustic signals are incoherent, simple frame differencing cannot generally be used to remove residual tissue signals.

[0026] This disclosure relates to apparatus, systems, and methods for extracting image features from a grayscale envelope statistical distribution within a multi-pixel window spanning each contrast image frame. Specifically, feature extraction is performed by analyzing the statistical characteristics of groups of pixels, rather than analyzing the grayscale values ​​(e.g., intensity) of individual pixels. Extracting features from the statistical distribution allows for the analysis of contrast image frames to select the first and / or last frame of a sequence for generating a cumulative image. The grayscale envelope statistical distribution can be used to select the first frame in a sequence of contrast agent presentation (e.g., arrival time). As contrast enhancement increases over time, the contrast agent will reach a peak intensity in one of the image frames, which can also be detected by the grayscale envelope statistical distribution therein. In some examples, the frame with peak intensity (e.g., the first frame reaching peak intensity) can be used as the last image frame of the sequence. In some examples, the frame with peak intensity can be used to determine the frame at the midpoint (time-by-time) between the frame with peak intensity and the first frame, and the frame at the midpoint can be selected as the last frame. Other techniques for selecting the last frame can also be used (e.g., finding a frame with 1 / 2 or 1 / 4 peak intensity within a predetermined number of frames after the first frame). The choice of technique can be at least in part based on the anatomical structure being imaged (e.g., liver, breast, or heart) and / or the type of cumulative imaging (e.g., MVI or ToA). The apparatuses, systems, and methods disclosed herein can provide faster and / or more consistent frame selection for generating contrast-enhanced cumulative images.

[0027] Figure 2A block diagram of an ultrasound imaging system 200 constructed according to the principles of this disclosure is shown. The ultrasound imaging system 200 according to this disclosure may include a transducer array 214, which may be included in an ultrasound probe 212 (e.g., an external or internal probe). The transducer array 214 is configured to transmit ultrasound signals (e.g., beams, waves) and receive echoes (e.g., received ultrasound signals) in response to the transmitted ultrasound signals. Various transducer arrays can be used, such as linear arrays, curved arrays, or phased arrays. For example, the transducer array 214 can include a two-dimensional array of transducer elements (as shown), capable of scanning in both elevation and azimuth dimensions for 2D and / or 3D imaging. It is well known that the axial direction is perpendicular to the array surface (in the case of a curved array, the axial direction is fanned out), the azimuth direction is generally defined by the longitudinal dimension of the array, and the elevation direction is transverse to the azimuth direction.

[0028] In some examples, transducer array 214 may be coupled to microwave beamformer 216, which may be located within ultrasonic probe 212 and may control the transmission and reception of signals by the transducer elements in array 214. In some examples, microwave beamformer 216 may control the transmission and reception of signals by the active elements in array 214 (e.g., an active subset of the elements of an array that defines an active aperture at any given time).

[0029] In some examples, the microwave beamformer 216 may be coupled, for example, via a probe cable or wirelessly, to a transmit / receive (T / R) switch 218, which toggles between transmitting and receiving and protects the main beamformer 222 from high-energy transmitted signals. In some examples, such as in portable ultrasound systems, the T / R switch 218 and other components of the system can be included in the ultrasound probe 212, rather than in an ultrasound system base that may house image processing electronics. An ultrasound system base typically includes software and hardware components, including circuitry for signal processing and image data generation, and executable instructions for providing a user interface.

[0030] Under the control of microwave beamformer 216, the transmission of ultrasonic signals from transducer array 214 is guided by transmit controller 220, which can be coupled to T / R switch 218 and main beamformer 222. Transmit controller 220 can control the direction in which the beam is manipulated. The beam can be manipulated directly forward (orthogonal to transducer array 214) from transducer array 214, or manipulated at different angles for a wider field of view. Transmit controller 220 can also be coupled to user interface 224 and receive input from user-to-user control operations. User interface 224 may include one or more input devices, such as control panel 252, which may include one or more mechanical controls (e.g., buttons, encoders, etc.), touch-sensitive controls (e.g., touchpads, touchscreens, etc.), and / or other known input devices.

[0031] In some examples, a portion of the beamforming signal generated by microwave beamformer 216 can be coupled to main beamformer 222, where the portion beamforming signals from individual patches of the transducer elements can be combined into a complete beamforming signal. In some examples, microwave beamformer 216 is omitted, and transducer array 214 is under the control of beamformer 222, which performs beamforming of all signals. In examples with and without microwave beamformer 216, the beamforming signal from beamformer 222 is coupled to processing circuitry 250, which may include one or more processors (e.g., signal processor 226, B-mode processor 228, Doppler processor 260, and one or more image generation and processing components 268) configured to generate an ultrasound image from the beamforming signal (i.e., beamformed RF data).

[0032] Signal processor 226 can be configured to process the received beamformed RF data in various ways, such as bandpass filtering, decimation, I and Q component separation, and harmonic signal separation. Signal processor 226 can also perform additional signal enhancement, such as speckle reduction, signal recombination, and electronic noise cancellation. The processed signal (also referred to as the I and Q components or IQ signal) can be coupled to additional downstream signal processing circuitry for image generation. The IQ signal can be coupled to multiple signal paths within the system, each signal path being associated with a specific arrangement of signal processing components suitable for generating different types of image data (e.g., B-mode image data, contrast image data, Doppler image data).

[0033] For example, system 200 may include a B-mode signal path 258 that couples a signal from signal processor 226 to B-mode processor 228 to generate B-mode image data for contrast imaging and / or conventional grayscale imaging. B-mode processor 228 is capable of employing amplitude detection to image organ structures of the body. In another example, system 200 may include a contrast signal path 272 that couples a signal from signal processor 226 to contrast processor 270 to generate contrast image data. Contrast processor 270 may employ amplitude detection, harmonic imaging techniques, and / or other processing techniques to detect contrast agents (e.g., microbubbles) within the body. In some examples, B-mode processor 228 and contrast processor 270 may be implemented by a single processor.

[0034] Signals generated by the B-mode processor 228 and / or the imaging processor 270 can be coupled to the scan converter 230 and / or the multiplane reformer 232. The scan converter 230 can be configured to arrange echo signals from their spatial relationship of reception into a desired image format. For example, the scan converter 230 can arrange echo signals into a two-dimensional (2D) fan-shaped format, or a pyramidal or other shaped three-dimensional (3D) format. The multiplane reformer 232 is capable of converting echoes received from points in a common plane of a volumetric region of the body into an ultrasound image (e.g., a B-mode image) of that plane, such as as described in U.S. Patent No. 6,443,896 (Detmer). In some examples, the scan converter 230 and the multiplane reformer 232 can be implemented as one or more processors.

[0035] Volumetric renderer 234 can generate an image (also known as a projection, drawing, or rendering) of a 3D dataset viewed from a given reference point, for example, as described in U.S. Patent No. 6,530,885 (Entrekin et al.). In some paradigms, volumetric renderer 234 can be implemented as one or more processors. Volumetric renderer 234 can generate drawings using any known or future known techniques, such as surface drawing and maximum intensity drawing, or positive or negative drawing. Although in Figure 2 The diagram shows data being received from the multiplane reformer 232, but in some examples, the volume plotter 234 may receive data from the scan converter 230.

[0036] In some examples, the system may include a Doppler signal path 262 that couples the output from signal processor 226 to Doppler processor 260. Doppler processor 260 may be configured to estimate the Doppler frequency shift and generate Doppler image data. The Doppler image data may include color data, which is then overlaid with B-mode (i.e., grayscale) image data for display. Doppler processor 260 may be configured, for example, to use a wall filter to filter out unwanted signals (i.e., noise or clutter associated with non-moving tissue). Doppler processor 260 may also be configured to estimate velocity and power according to known techniques. For example, the Doppler processor may include a Doppler estimator such as an autocorrelation function, wherein the velocity (Doppler frequency) estimate is based on the independent variable of a lag-one autocorrelation function, while the Doppler power estimate is based on the magnitude of a lag-zero autocorrelation function. Motion can also be estimated using known phase-domain (e.g., parametric frequency estimators such as MUSIC, ESPRIT, etc.) or time-domain (e.g., cross-correlation) signal processing techniques. Instead of a velocity estimator, or in addition to a velocity estimator, other estimators related to the temporal or spatial distribution of velocity (e.g., estimators of acceleration or temporal / / or spatial velocity derivatives) can be used. In some paradigms, velocity and power estimates may undergo additional threshold detection for further noise reduction, as well as segmentation and post-processing such as padding and smoothing. The velocity and power estimates can then be mapped to a desired display color gamut based on the color map. The color data, also known as Doppler image data, can then be coupled to a scan converter 230, where the Doppler image data can be converted to the desired image format and overlaid on a B-mode image of the tissue structure to form a color Doppler or power Doppler image. For example, Doppler image data can be overlaid on a B-mode image of the tissue structure.

[0037] The output from the scan converter 230, the multiplane reformer 232 and / or the volume plotter 234 (e.g., B-mode images, contrast images, Doppler images) can be coupled to the image processor 236 for further enhancement, buffering and temporary storage before being displayed on the image display 238.

[0038] According to the principles of this disclosure, in some examples, image processor 236 can receive imaging data corresponding to image frames of a sequence of contrast-enhanced images (e.g., multi-frame loop, cinematic loop). Each image frame in the sequence may have been acquired at different times (e.g., image frames may be time-spaced). In some examples, image processor 236 can combine two or more image frames in the sequence to generate a cumulative image. In some examples, image processor 236 can analyze each image frame in the sequence to select image frames from the sequence and subsequent image frames from the sequence. In examples, the image frame and the subsequent image frame may be the first and last frame, respectively, of a subset (e.g., a subsequence) of image frames from the sequence. The first image frame, the last image frame, and images acquired between the first and last image frames (e.g., frames of a subsequence) can be combined to generate a cumulative image. In some examples, image processor 236 can analyze each frame in the sequence to segment one or more features from the image. For example, tumors or other features of interest can be segmented. Reference Figure 4-10 The analysis of image frames is described in more detail. The analysis results of accumulated images and / or image frames can be provided to display 238 and / or local memory 242.

[0039] Graphics processor 240 can generate graphic overlays for display alongside images, such as cumulative images generated by image processor 236. These graphic overlays may contain, for example, standard identification information such as patient name, date and time of image, imaging parameters, etc. For this purpose, the graphics processor can be configured to receive input from user interface 224, such as typed patient name or other annotations. User interface 224 can also be coupled to multiplane reformer 232 to select and control the display of multiple multiplane reformatted (MPR) images.

[0040] System 200 may include local memory 242. Local memory 242 may be implemented as any suitable non-transitory computer-readable medium (e.g., flash drive, disk drive). Local memory 242 may store data generated by system 200, including B-mode images, parameter diagrams, executable instructions, input provided by the user via user interface 224, or any other information required for operation of system 200.

[0041] As previously described, system 200 includes a user interface 224. User interface 224 may include a display 238 and a control panel 252. Display 238 may include a display device implemented using various known display technologies, such as LCD, LED, OLED, or plasma display technologies. In some examples, display 238 may include multiple displays. Control panel 252 may be configured to receive user input (e.g., examination type, contrast agent injection time, selection of ROI in an image). Control panel 252 may include one or more hardware controls (e.g., buttons, knobs, dials, encoders, mice, trackballs, or others). In some examples, control panel 252 may additionally or alternatively include software controls (e.g., GUI control elements or simply GUI controls) provided on a touch-sensitive display. In some examples, display 238 may be a touch-sensitive display that includes one or more software controls of control panel 252.

[0042] In some examples, combinations are possible. Figure 2 The various components are shown. For example, image processor 236 and graphics processor 240 can be implemented as a single processor. In another example, scan converter 230 and multi-plane reformer 232 can be implemented as a single processor. In some examples, Figure 2 The various components shown can be implemented as separate components. For example, signal processor 226 can be implemented as a separate signal processor for each imaging mode (e.g., B-mode, angiography, Doppler). In some examples, Figure 2 One or more of the various processors shown can be implemented by a general-purpose processor and / or microprocessor configured to perform a specified task. In some examples, one or more of the various processors can be implemented as dedicated circuitry. In some examples, one or more of the various processors can be implemented using one or more graphics processing units (GPUs) (e.g., image processor 236).

[0043] Figure 3 This is a block diagram illustrating an exemplary processor 300 according to the principles of this disclosure. Processor 300 can be used to implement one or more processors described herein, such as the image processor 236 shown in FIG. 1. Processor 300 can be any suitable processor type, including but not limited to microprocessors, microcontrollers, digital signal processors (DSPs), field-programmable arrays (FPGAs) in which an FPGA has been programmed to form a processor, graphics processing units (GPUs), application-specific circuits (ASICs) in which an ASIC has been designed to form a processor, or combinations thereof.

[0044] Processor 300 may include one or more cores 302. Core 302 may include one or more arithmetic logic units (ALUs) 304. In some examples, in addition to or in place of ALU 304, core 302 may include a floating-point logic unit (FPLU) 306 and / or a digital signal processing unit (DSPU) 308.

[0045] Processor 300 may include one or more registers 312 communicatively coupled to core 302. Registers 312 may be implemented using dedicated logic gates (e.g., flip-flops) and / or any memory technology. In some examples, registers 312 may be implemented using static memory. Registers may provide data, instructions, and addresses to core 302.

[0046] In some examples, processor 300 may include one or more levels of cache memory 310 communicatively coupled to core 302. Cache memory 310 may provide computer-readable instructions to core 302 for execution. Cache memory 310 may provide data for processing by core 302. In some examples, the computer-readable instructions may have already been provided to cache memory 310 by local memory (e.g., local memory attached to external bus 316). Cache memory 310 may be implemented using any suitable cache memory type, such as metal-oxide-semiconductor (MOS) memory, static random access memory (SRAM), dynamic random access memory, and / or any other suitable memory technology.

[0047] Processor 300 may include controller 314, which controls inputs to processor 300 from other processors and / or components included in the system (e.g., control panel 252 and scan converter 230 shown in FIG. 1) and / or outputs from processor 300 to other processors and / or components included in the system (e.g., display 238 and volume plotter 234 shown in FIG. 1). Controller 314 may control data paths in ALU 304, FPLU 306, and / or DSPU 308. Controller 314 may be implemented as one or more state machines, data paths, and / or dedicated control logic. The gates of controller 314 may be implemented as stand-alone gates, FPGAs, ASICs, or any other suitable technology.

[0048] Register 312 and cache 310 can communicate with controller 314 and core 302 via internal connections 320A, 320B, 320C and 320D. The internal connections can be implemented as a bus, multiplexer, crossbar switch and / or any other suitable connection technology.

[0049] Inputs and outputs for processor 300 may be provided via bus 316, which may include one or more wires. Bus 316 may be communicatively coupled to one or more components of processor 300, such as controller 314, cache 310, and / or register 312. Bus 316 may be coupled to one or more components of the system, such as the aforementioned display 238 and control panel 252.

[0050] Bus 316 can be coupled to one or more external memories. The external memory may include read-only memory (ROM) 332. ROM 332 may be a mask ROM, electrically programmable read-only memory (EPROM), or any other suitable technology. The external memory may include random access memory (RAM) 333. RAM 333 may be static RAM, battery-backed static RAM, dynamic RAM (DRAM), or any other suitable technology. The external memory may include electrically erasable programmable read-only memory (EEPROM) 335. The external memory may include flash memory 334. The external memory may include a magnetic storage device such as a disk 336. In some examples, the external memory may be included in the system (e.g., in…) Figure 2 The ultrasound imaging system 200 shown includes, for example, local memory 242.

[0051] In some examples, processor 300 may be included in a computing system separate from the ultrasound imaging system, such as system 200. For example, the computing system may be a workstation for post-ultrasound examination processing of images acquired during the examination. In these examples, processor 300 may perform some or all of the processes described as being performed by image processor 236 in system 200. Examples include image frame analysis and selection, generating cumulative images, and / or segmenting features from image frames.

[0052] Figure 4 An analysis of an image frame according to the principles of this disclosure is illustrated. In some examples, the analysis can be performed by a processor, such as... Figure 2 The image processor 236 shown does not analyze the value of each individual pixel (e.g., grayscale value, intensity value) for image frame 402, but rather analyzes the statistical distribution of the values ​​of groups of pixels. Figure 4As shown, the moving window 406 translates across image 402, as indicated by arrows 408 and 410. The moving window 406 can include any number of pixels; for example, it can have dimensions of 20x20 pixels (400 pixels total) or 16x16 pixels (256 pixels total). In some examples, the window 406 may not be square (e.g., circular, rectangular). The size and / or shape of the window 406 can be at least partially based on the size of image 402. For example, a 16x16 pixel moving window could be selected for a 600x800 pixel image 402.

[0053] Each time the window 406 is translated across image 402, the statistical distribution of pixel values ​​is analyzed, and parameter values ​​are generated. These parameter values ​​are then assigned to the center of window 406 (e.g., to a position equal to the pixel at the center of window 406). Various parameters and / or combinations thereof can be calculated for the parameter values. For example, the signal-to-noise ratio (SNR) can be calculated for each window 406. In another example, the Nakagami exponent (NI) can be calculated for each window 406. In yet another example, SNR and NI can be calculated, and the final parameter value can be the product of SNR and NI. These parameters are merely exemplary, and other distributions or combinations thereof (e.g., gamma, Hoyt) can be used in other examples. The parameter values ​​can be used to generate a parameter map 412. In some examples, the number of parameter values ​​calculated can be equal to the number of pixels in image 402. That is, each pixel in image 402 can have a corresponding parameter value. Therefore, in some examples, the parameter map 412 can have the same dimensions as image 402. Figure 4 In the example shown, parameter graph 412 illustrates the SNR for image 402. Parameter graphs, such as parameter graph 412, can be generated for each frame in a sequence of frames.

[0054] Alternatively, in some examples, instead of translating window 406 across the entire image 402, a large ROI (not shown) can be selected automatically and / or by user input via a user interface (e.g., user interface 224) within image 402. In these examples, window 406 can be translated through the ROI instead of the entire image 402, and parametric graph 412 can be generated only for the ROI. This can be advantageous in applications where significant artifacts exist in image 402. ROIs can be selected to exclude areas containing artifacts (e.g., distortion from nearby implanted devices, skin surface reflections).

[0055] Optionally, in some examples, it is known that image frames acquired before the arrival of the contrast agent (e.g., at least two seconds before the expected arrival of the contrast agent) can be used as a mask applied to image 402, which can exclude some or all areas of image 402, including residual tissue signals, from parameter figure 412. In some examples, a user can provide input via a user interface to indicate when the contrast agent is injected, which can be used to determine which image frame to use as a mask.

[0056] By generating parameter graph 412, the dynamic range of the values ​​associated with each image frame 402 can be increased. In the example shown, for the limited dynamic range of 0-1 for the original grayscale image 402 (as shown in scale bar 404), the grayscale value for the residual tissue signal from the peritoneum (indicated by arrow 420) is comparable to the grayscale value for the strong contrast echo inside the tumor (indicated by dashed circle 422). For the amplified dynamic range of 0-10 (as shown in scale bar 414), the order of magnitude is larger, and for parameter graph 412, the parameter value for the residual tissue signal from the peritoneum (indicated by arrow 424) is almost invisible and much lower than the parameter value for the strong contrast echo inside the tumor (indicated by dashed circle 426). By increasing the dynamic range, it becomes easier to distinguish signals from the contrast agent from the tissue. For example, there is a greater difference between the pixel values ​​of the tumor 416 in image 402 than in ROI 418 in image 402. By increasing the difference between the values ​​used for tissue and contrast agent, it becomes easier to automatically select the first and last frames from the sequence to form subsequences to generate cumulative images.

[0057] Figure 5 The selection of image frames from an image frame sequence is illustrated according to the principles of this disclosure. In some examples, it can be performed by, for example... Figure 2 The selection is performed by a processor such as image processor 236 shown. The sequence of parameter graphs 502 may correspond to the sequence of image frames 504. The parameter graphs of sequence 502 can be analyzed to determine whether any of the parameter graphs has a value equal to or higher than a threshold. The threshold may be selected based at least in part on the maximum value of the parameter for the type of tissue being imaged. For example, in liver imaging, the threshold may be equal to 4 when SNR or NI is used as a parameter, and the threshold may be equal to 16 when SNR x NI is used as the parameter. Other values ​​may be selected as thresholds when imaging different tissues (e.g., breast tissue, heart tissue) and / or using different parameters. In some examples, the user can set the threshold. In some examples, the threshold may be based on the type of examination and / or the user's indication of what type of tissue is included in the image frame.

[0058] The earliest acquired parameter map with a value equal to or greater than a threshold, parameter map 506 in the example, can be used to select the first image frame 516 of subsequence 510 from image sequence 504. In some examples, image frame 516 can be the image frame corresponding to parameter map 506. That is, as referenced Figure 4 The parameter graph 506 is generated from image frame 516. In some examples, image frame 516 may be an image frame or a predetermined number of frames acquired at a predetermined time before (or after) the image frame used to generate parameter graph 506. For example, image frame 516 may be an image frame acquired one second before (or after) the image frame used to generate parameter graph 506. In another example, image frame 516 may be an image frame acquired 100 frames before (or after) the image frame used to generate parameter graph 506. In other examples, other selection criteria may be used for image frame 516.

[0059] The sequence of parameter maps 502 can also be analyzed to determine which frames include the largest (e.g., peak) parameter value among the parameter values ​​included in the sequence of parameter maps 502. In some examples, the earliest acquired parameter map including the maximum parameter value, i.e., parameter map 508 in the example, can be used to select the last frame 518 of subsequence 510. In some examples, image frame 518 can be the image frame corresponding to parameter map 508. In some examples, the maximum parameter value found in parameter map 508 can be used to calculate a parameter value equal to a fraction of the maximum value (e.g., 1 / 2, 1 / 4). The parameter maps of sequence 502 can then be analyzed to find the earliest acquired parameter map including the calculated parameter value, and the corresponding image frame can be selected as image frame 518.

[0060] Alternatively, instead of analyzing the sequence of parameter graphs 502 to find the maximum parameter value, image frame 518 can be selected at least in part based on image frame 516. For example, an image frame acquired after a predetermined amount of time following image frame 516 can be selected as image frame 518. In another example, an image frame acquired at a predetermined number of frames following image frame 516 can be selected as image frame 518.

[0061] Once image frames 516 and 518 have been selected, image frames 516, 518, and the image frames acquired between image frames 516 and 518 can be included in subsequence 510. The image frames of subsequence 510 can be combined to generate cumulative image 512. In some examples, such as in... Figure 5In the example shown, cumulative image 512 can be a ToA cumulative image, where the contrast agent is encoded (e.g., different intensities or colors) to indicate those regions where the contrast agent arrived earlier or later relative to other regions. However, in other examples, other types of cumulative images can be generated from subsequence 510.

[0062] Optionally, in some examples, the user can view automatically selected frames 516 and 518 and override one or both selections. The user can then select different image frames from a sequence 504 for image frame 516 and / or image frame 518. In some cases, such as when the user provides an incorrect threshold or tissue type, allowing the user to override the selection can be advantageous.

[0063] although Figure 5 The analysis of a single sequence in parameter graph 502 is described, but in some examples, multiple sequences can be analyzed. For example, parameter graphs can be generated based on multiple parameters (e.g., SNR, NI).

[0064] This paper discloses a technique for automatically selecting the first and last image frames of an image frame sequence for generating a cumulative image. This technique reduces or eliminates the need for manual review of the image frames in the sequence to select the images used to generate the cumulative image. The technique disclosed herein can provide a more consistent selection of the first and last images in the sequence used to generate the cumulative image. In some applications, this can improve the consistency and / or diagnostic value of the cumulative image.

[0065] Although references have been described regarding the use of selecting the first and last frames for generating the cumulative image. Figure 4 and Figure 5 The parametric plot is publicly available, but it can be used for other applications in contrast imaging. For example, the range scale and rate of change of parameter values ​​in the parametric plot can be used to control the range scale and temporal gradient of time-varying codes used for ToA imaging. For example, the temporal gradient can be proportional to the temporal gradient of image features in the parametric plot. For rapid changes in contrast images, a higher temporal gradient in image features can provide a higher temporal gradient in codes with a larger coding range over short time frames (e.g., color range, grayscale intensity). For slow changes, a lower temporal gradient can result in a lower temporal gradient in codes with a smaller coding range over a longer time span. This adaptation of the coding used for ToA can provide better visualization of fast and slow processes (e.g., rapid arrival and slow uptake in tissue), respectively.

[0066] In some paradigms, parametric maps can be used for image segmentation (e.g., segmenting features from an image). For example, as referenced... Figure 4The parameter value of tumor 416 has a higher intensity value compared to the surrounding tissue than the pixel intensity value of tumor 416 compared to the surrounding tissue. That is, the difference in values ​​between normal tissue and tumor can be greater in the parameter map than in the original image frame. Therefore, certain segmentation techniques, such as those that differentiate tissue types based at least in part on thresholding, can be more reliable when applied to the parameter map rather than directly to the original image frame.

[0067] Figure 6A-9B Examples of contrast-enhanced ultrasound image frames and parametric maps generated therefrom, based on the principles of this disclosure. Figure 6A-9B The ultrasound images and parameter diagrams shown include those with... Figure 1A and 1B The same liver lesion (tumor) is shown in the image. Figure 6A-9B The ultrasound images A6A-A6B, A7A-A7B, A8A-A8B, and A9A-A9B shown are exemplary contrast-enhanced images from a sequence of contrast-enhanced images. In some examples, the ultrasound images may have already been acquired by an ultrasound imaging system such as system 200. The parametric graphs may be generated by a processor, such as image processor 236 in some examples.

[0068] Figures 6A-6B Image frames A6A-A6B from the sequence are shown. Image frames A6A-A6B were acquired approximately 1 second before the contrast agent reached the area included in the image frame. Figure 6A In this process, parameter values ​​are determined by moving a 16x16 pixel window and analyzing the statistical distribution, generating parameter maps B6A, C6A, and D6A from image frame A6A. Parameter map B6A ​​shows the values ​​for SNR, parameter map C6A shows the values ​​for NI, and parameter map D6A shows the values ​​for SNR x NI. As can be seen from the scale bar on the right side of image frame A1 and parameter maps B6A-D6A, all three parameter maps have a larger dynamic range than the image frame.

[0069] Since the contrast agent has not yet arrived. Figure 6A The parameter maps B6A-D6A in the diagram include parameter values ​​based on signals from tissue and artifacts. The maximum signal in parameter maps B6A and C6A is 3.8, and the maximum value in parameter map D6A is 14.5. Therefore, since there is no contrast agent present, these maximum values ​​from tissue and other artifacts can be used to set the threshold. For example, the threshold for parameter maps B6A and C6A could be 4, while the threshold for parameter map D6A could be 16.

[0070] Figure 6B It shows the relationship with Figure 6AThe same image frames and parametric maps are shown. However, parametric maps B6B-D6B have been thresholded to remove any values ​​below the thresholds shown above. Therefore, Figure 6B All three parametric graphs in the graph have zero for all parameter values.

[0071] Figures 7A-7B The 76th image frames A7A-A7B from the sequence are shown. Image frames A7A-A7B were acquired approximately one second after the contrast agent reached the area included in the image frame. Figure 7A The parameter diagrams B7A-D7A in the reference are shown. Figure 6A The generation follows a similar pattern to that described. As can be seen in all three parameter plots, the maximum parameter value has increased. However, the parameter values ​​at the tissue sites are comparable to those at the tumor sites indicated by the dashed circles. Figure 7B The parameter diagrams B7B-D7B are shown, which have been compared with... Figure 6B The parameters shown in the B6B-D6B graphs are thresholded in a similar manner. Using data from... Figure 6A The threshold is determined by parameters B6A-D6A in the diagram. After thresholding, while all tissue signals are removed, only the signal from the contrast agent in the tumor is visible.

[0072] Figures 8A-8B Image frames A8A-A8B from the sequence are shown. Image frames A8A-A8B were acquired approximately 4 seconds after the contrast agent reached the area included in the image frame. Figure 8A The parameter diagrams B8A-D8A in the reference diagram are shown. Figure 6A The generation follows a similar pattern to what was described. As can be seen in all three parameter plots, the maximum parameter value has now increased significantly, especially at the tumor site within the dashed circle. The parameter value at the tumor site is now significantly greater than the parameter value at the tissue site. Figure 8B As shown, when the parametric maps B8A-D8A are thresholded to generate parametric maps B8B-D8B, most tumor sites remain visible in the parametric maps, and there is almost no tissue noise around the tumor sites.

[0073] In some examples, threshold parameter maps B8B-D8B can be used to segment tumors from image frames. For instance, pixels A8A-A8B in image frames corresponding to parameter values ​​above the threshold can be classified as belonging to a tumor. In some examples, only pixels within a Region of Interest (ROI) corresponding to parameter values ​​above the threshold, such as dashed circles, can be assigned as tumors. Segmentation can be performed based on one or more of the parameter maps B8B-D8B.

[0074] Figures 9A-9BImage frames A9A-A9B from the sequence are shown. Image frames A9A-A9B were acquired approximately 10 seconds after the contrast agent reached the area included in the image frame. Figure 9A The parameter diagrams B9A-D9A in the reference are shown. Figure 6A A similar manner to that described is used. As can be seen from all three parameter plots, the maximum parameter value continuously increases, but is now concentrated in the tissue surrounding the tumor site indicated by the dashed circle. This is due to the accumulation of contrast agent in the tissue. However, as... Figure 9B As shown, when the parametric map B9A-D9A is thresholded to generate parametric map B9A-D9B, clutter around the tumor is still reduced. Therefore, tumor segmentation from image frames can still be improved using parametric map B9A-D9B.

[0075] exist Figures 9A-9B Within the tumor (within the dashed circle), the maximum parameter value (peak) can be found. Generally, the contrast dynamics (e.g., arrival time and peak time) differ at different locations. For an entire image with a complex contrast dynamic distribution, the initial appearance of the contrast signal (grayscale intensity and parameter value) at any location on the image can be considered the arrival time, and the maximum contrast echo (grayscale intensity and parameter value) across the entire image can be considered the peak time.

[0076] Therefore, in addition to selecting the first and last image frames of the sequence used to generate the cumulative images, the parametric map can also be used, or alternatively, to segment tumors or other objects of interest from the image frames.

[0077] Figure 10 This is a flowchart of the method based on the principles of this disclosure. Flowchart 1000 summarizes the analysis and selection techniques described herein, such as those referenced... Figure 4-9B The techniques described herein. In some examples, the methods shown in flowchart 1000 may be performed by one or more components of an ultrasound imaging system, such as ultrasound imaging system 200. For example, an image processor, such as image processor 236, may perform some or all of the methods shown in flowchart 1000. In some examples, the methods shown in flowchart 1000 may be performed by a processor included in a computing system separate from ultrasound imaging system 200. The computing system may include a processor such as processor 300, and / or an equivalent processor to processor 236 for post-processing of images acquired during examination.

[0078] In some examples, as shown in box 1002, at least one processor, such as image processor 236, can translate a multi-pixel window across individual image frames that are spaced out in multiple time periods. In some examples, the image frame may be an ultrasound image frame. In some examples, the image frame may be a CEUS image.

[0079] For each translation of the multipixel window, as shown in box 1004, at least one processor can determine the statistical distribution of pixels in the various image frames included within the multipixel window to generate multiple statistical distributions. In some examples, the multipixel window can be square. In some examples, at least one processor can translate the multipixel window one pixel at a time. In some examples, at least one processor can translate the multipixel window multiple times such that the number of statistical distributions computed for each image frame is equal to the number of pixels in the image frame. In some examples, at least one processor can translate the multipixel window only across ROIs in the image frame. In some examples, the ROI can be selected by the user via a user interface such as user interface 224.

[0080] As shown in box 1006, at least one processor can compute parameter values ​​at least in part based on one of a plurality of statistical distributions to generate a plurality of parameter values. In some examples, the parameter values ​​may include signal-to-noise ratio, Nakagami exponent, or a combination thereof.

[0081] As shown in box 1008, at least one processor can generate multiple parameter maps corresponding to individual image frames in a plurality of time-spaced image frames from multiple parameter values. In some examples, at least one processor can provide one or more parameter maps for display, for example, on display 238.

[0082] In some examples, at least one processor may use a parametric map to define a subsequence to generate a cumulative image (e.g., a ToA image or an MVI image). For example, at least one processor may select a first frame from a plurality of time-spaced image frames based at least in part on a first parametric map among a plurality of parametric maps that includes a first parameter value exceeding a threshold. In some examples, the threshold is at least in part based on the tissue type included in the plurality of time-spaced image frames. In other examples, the threshold may be at least in part based on user input provided via a user interface. In some examples, at least one processor may select a second frame from a plurality of time-spaced image frames based at least in part on a second parametric map among a plurality of parametric maps that includes the largest parameter value among a plurality of parameter values. In some examples, the first and second frames at least in part define a subsequence of a plurality of time-spaced image frames, and the method further includes combining the subsequence to generate a cumulative image.

[0083] In some examples, at least one processor may segment one or more features from an image frame using a parametric map. For example, at least one processor may determine the maximum parameter value among multiple parameter values ​​corresponding to a first parametric map of multiple parametric maps, and threshold multiple parameter values ​​corresponding to the remaining parametric maps of multiple parametric maps to remove parameter values ​​below the maximum parameter value. At least one processor may segment at least one image frame from a plurality of time-spaced image frames corresponding to at least one parametric map in the remaining parametric maps of multiple parametric maps. In some examples, segmentation may include assigning pixels of at least one image frame from the plurality of time-spaced image frames to features, wherein the pixels correspond to parameter values ​​equal to or greater than a threshold.

[0084] In various examples of implementing components, systems, and / or methods using programmable devices (such as computer-based systems or programmable logic), it should be understood that the aforementioned systems and methods can be implemented using any of a variety of known or later-developed programming languages, such as "C", "C++", "FORTRAN", "Pascal", "VHDL", etc. Therefore, various storage media, such as computer disks, optical disks, electronic storage devices, etc., can be prepared, which can contain information that can instruct devices (e.g., computers) to implement the aforementioned systems and / or methods. Once a suitable device accesses the information and programs contained on the storage medium, the storage medium can provide the device with the information and programs, thereby enabling the device to perform the functions of the systems and / or methods described herein. For example, if a computer disk containing appropriate materials (such as source files, object files, executable files, etc.) is provided to a computer, the computer can receive the information, configure itself appropriately, and perform the functions of the various systems and methods outlined in the above figures and flowcharts to implement various functions. That is, the computer can receive various portions of information from the disk relating to different elements of the aforementioned systems and / or methods, implement the respective systems and / or methods, and coordinate the functions of the aforementioned systems and / or methods.

[0085] In view of this disclosure, it should be noted that the various methods and apparatuses described herein can be implemented in hardware, software, and / or firmware. Furthermore, various methods and parameters are included by way of example only and not in any limiting sense. In view of this disclosure, those skilled in the art can implement the teachings while remaining within the scope of the invention, in determining their own techniques and the means required to influence these techniques. The functionality of one or more processors described herein can be incorporated into a smaller number or a single processing unit (e.g., a CPU) and can be implemented using application-specific integrated circuits (ASICs) or general-purpose processing circuitry programmed in response to executable instructions to perform the functions described herein.

[0086] Although this system has been described with specific reference to ultrasound imaging systems, it is also conceivable that this system can be extended to other medical imaging systems that acquire one or more images in a systematic manner. Therefore, this system can be used to acquire and / or record image information involving, but not limited to, the kidneys, testes, breasts, ovaries, uterus, thyroid gland, liver, lungs, musculoskeletal system, spleen, heart, arteries, and vascular systems, as well as other imaging applications related to ultrasound-guided interventions. Furthermore, this system may include one or more procedures that can be used with conventional imaging systems such that they can provide the features and advantages of this system. Certain additional advantages and features of this disclosure may be apparent to those skilled in the art upon studying this disclosure, or may be appreciated by those employing the novel systems and methods of this disclosure. Another advantage of this system and method is the ability to readily upgrade conventional medical imaging systems to incorporate the features and advantages of this system, device, and method.

[0087] Of course, it should be understood that any of the examples, instances, or processes described herein may be combined with one or more other examples, instances, and / or processes of the systems, devices, and methods according to the present invention, or may be separated and / or performed between individual devices or device parts.

[0088] Finally, the foregoing discussion is intended to be illustrative of the system and method only and should not be construed as limiting the appended claims to any specific example or set of examples. Therefore, while the system has been described in particular detail with reference to exemplary examples, it should be understood that many modifications and alternatives can be devised by those skilled in the art without departing from the broader and contemplated spirit and scope of the system and method as set forth in the following claims. Thus, the specification and drawings should be viewed in an illustrative manner and not as intended to limit the scope of the appended claims.

Claims

1. An apparatus for processing ultrasound images, the apparatus comprising: At least one processor (236, 300) is configured as follows: For an individual image frame in a plurality of temporally spaced image frames, a plurality of parameter values ​​are calculated at least in part based on the statistical distribution of corresponding pixel groups in a plurality of pixel groups of the corresponding image frames in the plurality of temporally spaced image frames, wherein the plurality of pixel groups are defined at least in part over a multi-pixel window that is translated across the image frames, wherein the plurality of temporally spaced image frames include contrast-enhanced ultrasound images; Generate multiple parameter maps including the multiple parameter values, wherein an individual parameter map in the multiple parameter maps corresponds to an individual image frame in the multiple time-spaced image frames; The first frame is selected from the plurality of time-spaced image frames, based at least in part on a parameter map that includes parameter values ​​equal to or higher than a threshold. The last frame is selected from the plurality of time-spaced image frames, based at least in part on a second parameter map that includes the maximum parameter value among the plurality of parameter maps; and The first frame, the last frame, and any of the plurality of time-spaced image frames spaced between the first frame and the last frame are combined to generate a cumulative image.

2. The apparatus according to claim 1, wherein, The last frame corresponds to the second parameter map.

3. The apparatus according to claim 1, wherein, The accumulated images include arrival time images.

4. The apparatus according to claim 3, wherein, At least one of the temporal gradients or encoding ranges of the arrival time image is based, at least in part, on the plurality of parameter maps.

5. The apparatus according to claim 1, wherein, The first frame corresponds to the parameter map that includes parameter values ​​equal to or higher than the threshold in the plurality of parameter maps.

6. The apparatus according to claim 1, wherein, At least one of the plurality of parameter maps corresponds to an image frame in the plurality of time-spaced image frames acquired before the contrast agent arrives, and the at least one processor is further configured to determine the maximum value among the plurality of parameter values ​​of the at least one parameter map in the plurality of parameter maps; and The remaining parameter maps in the plurality of parameter maps are thresholded, the remaining parameter maps corresponding to image frames in the plurality of time-spaced image frames acquired after the image frames in the plurality of time-spaced image frames acquired before the arrival of the contrast agent.

7. The apparatus according to claim 6, wherein, The at least one processor is further configured to segment features from at least one image frame of the plurality of time-spaced image frames acquired after an image frame of the plurality of time-spaced image frames acquired before the arrival of the contrast agent, based at least in part on a corresponding thresholded parameter map in the remaining parameter map of the plurality of parameter maps.

8. A method (1000) comprising: Translate (1002) a multi-pixel window across individual image frames in a plurality of time-spaced image frames, wherein the plurality of time-spaced image frames include contrast-enhanced ultrasound images; For each translation of the multi-pixel window: Determine (1004) the statistical distribution of pixels in the individual image frames included in the multi-pixel window to generate multiple statistical distributions; The parameter values ​​(1006) are calculated at least in part based on the corresponding statistical distributions among the plurality of statistical distributions to generate the plurality of parameter values; Generate (1008) multiple parameter maps corresponding to the individual image frames in the multiple time-spaced image frames from the multiple parameter values; The first frame of the plurality of time-spaced image frames is selected based at least in part on a first parameter map that includes a first parameter value above a threshold in the plurality of parameter maps; and The second frame among the plurality of time-spaced image frames is selected based at least in part on a second parameter map that includes the maximum parameter value among the plurality of parameter maps; The first frame and the second frame at least partially define subsequences of the plurality of time-spaced image frames, and the method further includes combining the subsequences to generate an accumulated image.

9. The method (1000) according to claim 8, wherein, The parameter values ​​include at least one of the signal-to-noise ratio, the Nakagami index, or a combination thereof.

10. The method (1000) according to claim 8, wherein, The multi-pixel window includes a square shape.

11. The method (1000) according to claim 8, wherein, The threshold is based at least in part on the tissue type included in the image frames spaced apart at the plurality of times.

12. The method (1000) according to claim 8, further comprising: Determine the maximum parameter value among the multiple parameter values ​​corresponding to the first parameter map in the multiple parameter maps; and The parameter values ​​corresponding to the remaining parameter graphs in the plurality of parameter graphs are thresholded to remove parameter values ​​that are lower than the maximum parameter value.

13. The method (1000) of claim 12, further comprising segmenting at least one of the plurality of time-spaced image frames corresponding to at least one of the remaining parameter maps in the plurality of parameter maps.

14. The method (1000) according to claim 13, wherein, Segmentation includes assigning pixels of at least one of the plurality of time-spaced image frames to features, wherein the pixels correspond to parameter values ​​equal to or greater than a threshold.

15. The method (1000) according to claim 8, wherein, The multi-pixel window translates only across the region of interest in the individual image frames among the plurality of time-spaced image frames, wherein the region of interest is smaller than the individual image frame.

16. An ultrasound imaging system (200) for acquiring and processing ultrasound images, comprising the means for processing ultrasound images according to claim 1.

Images