阅读说明：本技术 用于改善的肝癌和肾癌诊断的计算超声 (Computational ultrasound for improved diagnosis of liver and kidney cancer ) 是由 J·诺谢尔 I·哈奇哈里洛格鲁 于 2018-02-27 设计创作，主要内容包括：本公开提供了用于生成超声图像的系统和方法。该方法包括接收从超声设备输出的原始超声信号,并执行操作以将原始超声信号变换为增强的超声图像。可以通过径向对称滤波来进一步处理增强的超声图像,以生成径向对称图像。增强的图像和径向对称图像都可以由医疗从业者基于超声数据进行肝癌或肾癌诊断。(The method includes receiving a raw ultrasound signal output from an ultrasound device and performing an operation to transform the raw ultrasound signal into an enhanced ultrasound image.)

A system for generating an ultrasound image, comprising:

a non-transitory computer readable memory;

or more processors, and

a computer readable medium containing programming instructions that, when executed by the or more processors, cause a system to:

acquiring an original ultrasonic signal output from an ultrasonic device;

generating filtered ultrasound image data by filtering the original ultrasound signal to transform the information from the signal domain to the frequency domain;

extracting image phase and energy features from the filtered ultrasound image data;

generating a filtered ultrasound signal by transforming image phase and energy characteristics from a frequency domain to a signal domain;

generating a transmission map estimate based on the backscattered ultrasound signals from the tissue interface; and

image phase and energy characteristics of the enhanced filtered ultrasound signal are estimated based on the transmission map such that portions of the soft tissue associated with the image phase and energy characteristics are darker or lighter in the enhanced ultrasound image.

2. The system of claim 1, further comprising programming instructions that, when executed by the or more processors, cause the system to

Performing radially symmetric filtering on the enhanced ultrasound image by analyzing a dark spherical shape in the enhanced ultrasound image; and

a radially symmetric image is generated to identify a region of interest in the enhanced ultrasound image.

3. The system of claim 2, further comprising programming instructions that, when executed by the or more processors, cause the system to output a surface topography map based on the radially symmetric image.

4. The system of claim 1, wherein the step of extracting image phase and energy features is performed by α scale filtering.

5. The system of claim 4, wherein the α scale filtering is performed in the frequency domain.

6. The system of claim 5, wherein the α scale filtering is defined by the expression

Where α is a constant derivative parameter, σ is a filter α scale parameter, n_cIs a unity return constant calculated from filter α.

7. The system of claim 6, wherein n is calculated according to the following mathematical equation_c

Where s is the scale parameter and a is the derivative parameter.

8. The system of claim 1, wherein the transmission map estimation comprises combining scattering and attenuation effects in soft tissue based on the following equation

US(x，y)＝U_SA(x，y)US_E(x，y)+(1-US_A(x，y))α

Wherein US (x, y) is a local energy image, US_A(x, y) is the signal transmission diagram, US_E(x, y) is the enhanced ultrasound image and α is a constant value representing echogenicity in soft tissue in the local region.

9. The system according to claim 8, wherein the signal transmission diagram US is estimated by using Beer-Lambert's law_A(x, y) to extract US_E(x, y) wherein the Beer-Lambert law models the attenuation function as a function of imaging depth.

10. The system of claim 9, wherein Beer-Lambert's law is defined by the following equation

US_T(x，y)＝US_o(x，y)exp(-ηd(x，y))

Wherein, US_o(x, y) is the initial intensity image, US_T(x, y) is the attenuation intensity image, η is the attenuation coefficient, and d (x, y) is the distance from the ultrasound transducer surface.

11. The system of claim 10, wherein the US is obtained by minimizing an objective function defined by the equation_A(x，y)

Where ω is the index set, o represents the element-by-element multiplication, is the convolution operation, and D is obtained using a higher order differential filter bank consisting of eight Kirsch filters and Laplacian filters_j，W_jIs a weighting matrix calculated according to the following mathematical equation:

W_j＝exp(-|D_j(x，y)*US(x，y)|²)。

12. the system of claim 11, wherein US is calculated according to the following equation_E(x，y)

13. The system of claim 2, wherein the radially symmetric filtering is specified by the equation

S_n＝F_n*A_n

Wherein "+" represents a convolution operation; a. the_nIs an isotropic Gaussian function, F_nIs defined as:

where α is the radial stringency parameter, k_nIs a scaling factor between the different radii.

14. The system of claim 2, wherein the radially symmetric image is defined by the equation

Where S is the sum of all symmetric contributions within all considered ranges.

15, a method for generating an ultrasound image, comprising:

acquiring, by a computing device, an original ultrasound signal output from an ultrasound device;

generating filtered ultrasound image data by filtering the original ultrasound signal to transform the information from the signal domain to the frequency domain;

extracting image phase and energy features from the filtered ultrasound image data;

generating a filtered ultrasound signal by transforming image phase and energy characteristics from a frequency domain to a signal domain;

generating a transmission map estimate based on the backscattered ultrasound signals from the tissue interface; and

image phase and energy characteristics of the enhanced filtered ultrasound signal are estimated based on the transmission map such that portions of the soft tissue associated with the image phase and energy characteristics are darker or lighter in the enhanced ultrasound image.

16. The method of claim 15, further comprising:

performing, by the computing device, a radially symmetric filtering on the enhanced ultrasound image by analyzing a dark spherical shape in the enhanced ultrasound image;

by a computing device, a radially symmetric image is generated to identify a region of interest in an enhanced ultrasound image.

17. The method of claim 16, further comprising outputting a surface topography map based on the radially symmetric image.

18. The method of claim 15, wherein the backscattered ultrasound signal is modulated by interaction with tissue, including scattering and attenuation.

19. The method of claim 15, wherein the step of generating filtered ultrasound image data is performed by fourier transformation.

20. The method of claim 15, wherein the step of generating the filtered ultrasound signal is performed by an inverse fourier transform.

21. The method of claim 15, wherein the step of extracting image phase and energy features is performed by α scale filtering.

22. The method of claim 21, wherein the α scale filtering is performed in the frequency domain.

23. The method of claim 22, wherein the α scale filtering is defined by the expression

Where α is a constant derivative parameter, σ is a filter α scale parameter, n_cIs a unity return constant calculated from filter α.

24. The method of claim 23, wherein n is calculated according to the following equation_c

Where s is the scale parameter and a is the derivative parameter.

25. The method of claim 15, wherein the transmission map estimation comprises combining scattering and attenuation effects in soft tissue based on the following equation

US(x，y)＝USA(x，y)US_E(x，y)+(1-US_A(x，y))α

Wherein US (x, y) is a local energy image, US_A(x, y) is the signal transmission diagram, US_E(x, y) is the enhanced ultrasound image and α is a constant value representing echogenicity in soft tissue in the local region.

26. The method of claim 25, wherein the US signal transmission diagram is estimated by using Beer-Lambert's law_A(x, y) to extract US_E(x, y) wherein the Beer-Lambert law models the attenuation function as a function of imaging depth.

27. The method of claim 26, wherein Beer-Lambert's law is defined by the following equation

US_T(x，y)＝US_o(x，y)exp(-ηd(x，y))

Wherein, US_o(x, y) is the initial intensity image, US_T(x, y) is the attenuation intensity image, η is the attenuation coefficient, and d (x, y) is the distance from the ultrasound transducer surface.

28. The method of claim 27, wherein US is obtained by minimizing an objective function defined by the following equation_A(x，y)

Where ω is the index set, o represents the element-by-element multiplication, is the convolution operation, and D is obtained using a higher order differential filter bank consisting of eight Kirsch filters and Laplacian filters_j，W_jIs a weighting matrix calculated according to the following equation:

W_j＝exp(-|D_j(x，y)*US(x，y)|²)。

29. the method of claim 28, wherein US is calculated according to the following equation_E(x，y)

30. The method of claim 16, wherein the radially symmetric filtering is specified by the equation

S_n＝F_n*A_n

Wherein ". X" denotes a convolution operation, A_nIs an isotropic Gaussian function, F_nIs defined as:

where α is the radial stringency parameter, k_nIs a scaling factor between the different radii.

31. The method of claim 16, wherein the radially symmetric image is defined by the equation

Where S is the sum of all symmetric contributions within all considered ranges.

Technical Field

This document relates generally to image processing. More particularly, this document relates to systems and methods for ultrasound image processing that facilitate improved cancer diagnosis and therapy monitoring.

Background

The hepatitis C virus ("HCV") and non-alcoholic fatty liver disease ("NAFLD") are the two most common causes of chronic liver disease in North American areas, which is probably the most common type of liver disease in the United states in clinical practice, transabdominal ultrasound is most commonly used as the initial imaging modality because of its availability, low cost and no radiation exposure.

Disclosure of Invention

The present disclosure provides systems and methods for generating enhanced ultrasound images. Ultrasound images processed by the present system and method show soft tissue patterns with a level of visual clarity that enables medical practitioners to make liver or kidney disease diagnoses based solely on ultrasound data without the need for biopsy samples.

In , the system may include non-transitory computer readable memory, or more processors, and a computer readable medium containing programming instructions that, when executed by the or more processors, cause the system to perform the process of acquiring raw ultrasound signals output from an ultrasound device and generating filtered ultrasound image data by filtering the raw ultrasound signals to transform information from a signal domain to a frequency domain.

In embodiments , the system may perform a radially symmetric filtering on the enhanced ultrasound image by analyzing a dark spherical shape in the enhanced ultrasound image and generate a radially symmetric image to identify a region of interest in the enhanced ultrasound image in embodiments , the system may additionally output a surface topography map based on the radially symmetric image in embodiments , the step of generating filtered ultrasound image data is performed by a Fourier transform in embodiments , the step of generating a filtered ultrasound signal is performed by an inverse Fourier transform.

In some embodiments, the step of extracting image phase and energy features is performed by α scale filtering in some embodiments, α scale filtering is performed in the frequency domain, α scale filtering may be defined by the following expression

Where α is a constant derivative parameter, σ is a filter α scale parameter, n_cIs a unity return constant, n, calculated from filter α_cIs calculated according to the following mathematical equation

Where s is the scale parameter and a is the derivative parameter.

In embodiments, the transmission map estimation includes combining scattering and attenuation effects in soft tissue based on the following equation

US(x,y)＝US_A(x,y)US_E(x,y)+(1-US_A(x,y))α

Wherein US (x, y is the local energy image, US)_A(x, y) is the signal transmission diagram, US_E(x, y) are enhanced ultrasound images, α is a constant value representing echogenicity in soft tissue in the local region the signal transmission diagram US can be estimated by using Beer-Lambert's law_A(x, y) to extract US_E(x, y), Beer-Lambert law models the attenuation function as a function of imaging depth. The Beer-Lambert law is defined by the following equation

US_T(x,y)＝US_o(x,y)exp(-ηd(x,y))

Wherein, US_o(x, y) is the initial intensity image, US_T(x, y) is the attenuation intensity image, η is the attenuation coefficient, d (x, y) is the distance from the ultrasound transducer surface US is obtained by minimizing the objective function defined by the equation below_A(x,y)

Where ω is the index set, o represents the element-by-element multiplication, is the convolution operation, and D is obtained using a higher order differential filter bank consisting of eight Kirsch filters and Laplacian filters_j，W_jIs a weighting matrix calculated according to the following equation:

W_j＝exp(-|D_j(x,y)*US(x,y)|²)

US_E(x, y) is calculated according to the following equation

In embodiments, radial symmetric filtering is specified by the following equation

S_n＝F_n*A_n

Wherein "+" represents a convolution operation; a. the_nIs an isotropic Gaussian function, F_nIs defined as:

where α is the radial stringency parameter, k_nIs a scaling factor between the different radii. The radially symmetric image is defined by the following equation

Where S is the sum of all symmetric contributions within all considered ranges.

In another aspect, there is also provided a method for generating an ultrasound image, the method including acquiring, by a computing device, an original ultrasound signal output from an ultrasound device and generating filtered ultrasound image data by filtering the original ultrasound signal to transform information from a signal domain to a frequency domain.

In embodiments, the method further comprises performing a radial symmetry filter on the enhanced ultrasound image by analyzing the dark spherical shape in the enhanced ultrasound image and generating a radial symmetry image to identify a region of interest in the enhanced ultrasound image in embodiments, the method additionally comprises outputting a surface topography map based on the radial symmetry image.

Drawings

The present solution will be described with reference to the following drawings, wherein like reference numerals refer to like items throughout the drawings.

Fig. 1 shows an example of a process for generating an enhanced ultrasound image.

Fig. 2 shows an example of a process for generating a radially symmetric image.

Fig. 3 shows a biopsy (histopathology) image of an unhealthy liver indicative of fatty liver disease.

Fig. 4A-4D (collectively "fig. 4") show a B-mode (original) ultrasound image of a normal/healthy liver (fig. 4A) and an enhanced ultrasound image of a normal/healthy liver (fig. 4B), and a B-mode (original) ultrasound image of a diseased liver (fig. 4C) and an enhanced ultrasound image of a diseased liver (fig. 4D).

FIGS. 5A-5E (collectively "FIG. 5") illustrate a comparison of different image types; fig. 5A shows a B-mode (raw) ultrasound image of a normal/healthy liver; FIG. 5B shows a surface topography visualization of a radially symmetric image corresponding to normal/healthy liver tissue; FIG. 5C shows a surface topography visualization of a radially symmetric image corresponding to normal/healthy liver tissue, with different radially symmetric filter parameters applied compared to FIG. 5B; FIG. 5D shows a surface topography visualization of an enhanced ultrasound image corresponding to normal/healthy tissue; fig. 5E shows a surface topography visualization of an enhanced ultrasound image corresponding to normal/healthy tissue using different filter parameters than fig. 5D.

6A-6E (collectively "FIG. 6") illustrate a comparison of different image types; FIG. 6A shows a B-mode (raw) ultrasound image of a diseased liver; FIG. 6B shows a surface topography visualization corresponding to a radially symmetric image of diseased liver tissue; FIG. 6C shows a surface topography visualization of a radially symmetric image corresponding to diseased liver tissue, with different radially symmetric filter parameters applied compared to FIG. 6B; FIG. 6D shows a surface topography visualization of an enhanced ultrasound image corresponding to diseased tissue; fig. 6E shows a surface topography visualization of an enhanced ultrasound image corresponding to diseased tissue using different filter parameters than fig. 6D.

Fig. 7 is a schematic diagram of an exemplary ultrasound device in which the present solution may be implemented.

FIG. 8 is a schematic diagram of an exemplary architecture for a computing device.

Detailed Description

It will be readily understood that the components of the embodiments as generally described herein and illustrated in the figures can be arranged and designed in a widely differing configuration accordingly, the following more detailed description of the various embodiments as represented in the figures is not intended to limit the scope of the disclosure, but is merely representative of the various embodiments.

The present solution relates to a system and method for ultrasound image processing and design of the next generation ultrasound imaging platform in the context of cancer diagnosis and therapy monitoring with particular attention to the kidney and liver.

A biopsy (histopathological) image of an unhealthy liver indicative of fatty liver disease is shown in FIG. 3. As highlighted by the arrows, a circular pattern in the biopsy image indicates that the liver has fatty liver disease.

In cases, the solution is used for early detection of fatty liver, liver and kidney cancers biopsy is currently the standard technique for such early detection.

The invention combines different steps to create a framework that leads to enhancement of liver and/or kidney ultrasound data . optimization of each step is also a major contribution . the main part of the algorithm is based on extracting local phase image features from liver and kidney ultrasound data.

Referring now to fig. 1, an illustration of an exemplary method 100 for image enhancement is provided that is useful for understanding the present solution. The combination of the operations of step 102 and 108 creates a framework that results in enhancement of liver and/or kidney ultrasound data.

The main part of the process is based on extracting local phase image features from the ultrasound signal. Image phase information is a key component in scene interpretation because it contributes more to the visual appearance of the image than amplitude information. The phase feature is intensity invariant and more robust to noise, a characteristic that is particularly important for processing ultrasound images.

The extraction of phase information is performed in the frequency domain, as indicated by arrow 101, where the original ultrasound signal or B-mode ultrasound image is received from an ultrasound device, as indicated at 102, and the original ultrasound image signal is then transformed from the signal domain to the frequency domain.

In scenarios, the band pass quadrature filter includes a α scale filter, thus, α scale filtering may be employed in 104. α scale filtering is performed to extract phase and energy features from the filtered raw ultrasound signal.

However, the inventors have recognized that the combination of the α scale filter with the Fourier transform process of 102 and the transmission map filtering process of 106 and 108 provides certain non-obvious advantages when there is interest in visualizing certain soft tissue features.

In scenarios, the α scale filter in the frequency domain is constructed as defined by equation (1) below:

where α is a constant derivative parameter that is selected to be α ═ 0.2 so that the filter satisfies the DC condition σ is the filter α scale parameter (e.g., in embodiments, the filter α scale parameter is 25), and n is_cIs a unity return constant calculated from the filter α value using equation (2) below:

where s is the scale parameter and a is the derivative parameter, in embodiments, the scale parameter is 2 and the derivative parameter is 1.83.

The Fourier transformed ultrasound image is filtered using a constructed α scale filter, and image phase and energy features are extracted from the ultrasound image using an inverse Fourier transform operation.

After α scale filtering is completed, the method 100 continues with transforming ultrasound image data from the frequency domain to the signal domain via an inverse Fourier transform process 105 then uses the results of 105 in 106 to obtain a transmission map estimate the interaction of ultrasound signals within the tissue can be characterized into two main categories (i.e., scatter and attenuation). since the information of backscattered ultrasound signals (from the tissue interface to the ultrasound transducer) is modulated by both interactions, they can be considered as a mechanism for structural information encoding.

US(x,y)＝US_A(x,y)US_E(x,y)+(1-US_A(x,y))α (3)

Where US (x, y) is the local energy image computed in 104, US_A(x, y) is the signal transmission diagram, US_E(x, y) is an enhanced liver/kidney ultrasound image, α is a representative officeIn the present solution three different values of α are used to obtain three different enhancement results.

The local energy image generated in 104 is used in subsequent filtering processes as shown at 106 and 108. 106, and 108 are typically performed to enhance the extracted phase and energy features such that the portion of soft tissue associated therewith becomes darker or lighter in the enhanced ultrasound image (e.g., as shown in fig. 3).

In 106, the signal transmission diagram US is estimated by using the well-known Beer-Lambert law_A(x, y) to extract US_E(x, y), Beer-Lambert law models the attenuation function as a function of imaging depth. The Beer-Lambert law is defined by the following equation (4):

US_T(x,y)＝US_o(x,y)exp(-ηd(x,y)) (4)

wherein, US_o(x, y) is the initial intensity image (filtered image obtained from the α scale filtering step), US_T(x, y) is the attenuation intensity image, η is the attenuation coefficient (e.g., in embodiments , the attenuation coefficient used is 2), and d (x, y) is the distance from the ultrasound transducer surface.

denier to obtain US_T(x, y), US is obtained by minimizing the objective function defined by equation (5)_A(x,y)。

Where ω is the index set, o represents the element wise multiplication (element wise multiplication), and x is the convolution operation, D is obtained using a higher order differential filter bank consisting of eight Kirsch filters and Laplacian filters_j。W_jIs a weighting matrix calculated using equation (6) below:

W_j＝exp(-|D_j(x,y)*US(x,y)|²) (6)

denier estimates US_A(x, y), US is calculated using equation (7) below_E(x,y)：

FIG. 3 shows a biopsy (histopathological) image of an unhealthy liver indicative of fatty liver disease, as highlighted by arrows, a circular pattern in the biopsy image indicates that the liver has fatty liver disease, however, the circular pattern is invisible or undetectable by studying the ultrasound images (FIGS. 4A and 4C). The circular pattern is visible in the enhanced ultrasound image generated by the present solution, as shown in FIG. 4D, FIG. 4D is generated from the B-mode ultrasound image of FIG. 4C.

Referring now to FIG. 2, methods are provided for generating a radially symmetric image based on an enhanced ultrasound image, the process begins at 201 by performing a radially symmetric filter on the enhanced ultrasound image, the enhanced ultrasound image is further processed steps by utilizing local radial symmetry to identify a region of interest in the enhanced image, the analysis relies on the fact that the diseased liver has a higher degree of radial symmetry than a healthy liver, the analysis is performed by searching for a dark spherical shape in the enhanced image using a fast radial feature detection algorithm, the process includes generating a radially symmetric image at 203, as shown in FIGS. 5 and 6, FIG. 5B shows a visualization of a surface topography corresponding to a radially symmetric image of normal/healthy liver tissue, FIG. 5C shows a visualization of a surface topography corresponding to a radially symmetric image of normal/healthy liver tissue, to which a different radially symmetric filter parameter is applied than FIG. 5B, FIG. 6B shows a visualization of a surface topography corresponding to a radially symmetric image of diseased liver tissue, FIG. 6C shows a visualization of a surface topography corresponding to a radially symmetric image of diseased liver tissue, to which a different radially symmetric filter parameter is applied than FIG. 6B, optionally including a fast symmetric filter parameter output based on the fast radial symmetry algorithm 205.

Fast radial symmetry is used to process multimedia images. For each radius n, the algorithm uses image gradients to resolve positively and negatively affected pixels. These pixels are calculated using equations (8) and (9);

in the above equation, "round" rounds each vector element to the nearest integer, "g" is the gradient of the image (e.g., the gradient of the enhanced image), "n" represents the radius value of the spherical structure searched in the image. p is a radical of_-veAnd p_+veCorresponding to pixels having gradients g (p) pointing towards and away from the center, respectively. Using these pixels, the calculations are represented as O, respectively_nAnd M_nThe orientation and magnitude of the projected image. For each affected pixel, O_nAnd M_nCorresponding point p in (1)_+ve1 and g (p) are respectively added. Similarly, for negatively affected pixels, the corresponding points are reduced by the same amount in each image, as defined by equations (10) - (13):

O_n(p_+ve(p))＝O_n(p_+ve(p))+1， (10)

O_n(p_-ve(p))＝O_n(p_-ve(p))-1， (11)

M_n(p_+ve(p))＝M_n(p_+ve(p))+||g(p)||， (12)

M_n(p_-ve(p))＝M_n(p_-ve(p))-||g(p)||. (13)

using these images, a radially symmetric response image is defined as: s_n＝F_n*A_nWhere "+" denotes a convolution operation. A. the_nIs an isotropic Gaussian function, F_nDefined in equations (14) and (15):

here, α is the radial stringency parameter, k_nIs a scaling factor between the different radii. The final full radial symmetry transform is defined by performing this operation on various radius values and summing the resulting feature maps, as shown in equation (16):

the final topography image is obtained by displaying the topography map based on the image intensity values over the radial symmetry and phase images. Diseased livers have a higher morphology than healthy livers. Fig. 5D-5E show examples of surface topography images of a healthy liver, and fig. 6D-6E show examples of surface topography images of a diseased liver. In particular, fig. 5D shows a surface topography visualization of an enhanced ultrasound image corresponding to normal/healthy tissue; fig. 5E shows a surface topography visualization of an enhanced ultrasound image corresponding to normal/healthy tissue using different filter parameters than fig. 5D. FIG. 6D shows a surface topography visualization of an enhanced ultrasound image corresponding to diseased tissue; fig. 6E shows a surface topography visualization of an enhanced ultrasound image corresponding to diseased tissue using different filter parameters than fig. 6D.

Referring now to fig. 7, a schematic diagram of an exemplary ultrasound device 700 is provided in which the present solution may be implemented. The ultrasound device 700 includes a device dock 702, with a computing device 704 disposed in the device cradle 702. The computing device 704 is generally configured to control the operation of the ultrasound device 700. Such control may be responsive to user-software interaction via input device 716 and/or according to predefined rules. The robotic arm 706 is movably attached to the device base 702. The robotic arm 706 includes, but is not limited to, an articulated arm having a plurality of joints 708. An ultrasonic transducer 710 is disposed at the distal end of the robotic arm 706. The ultrasonic transducer 710 moves over the surface 712 of the object to be inspected and generates the original ultrasonic signal. The object is arranged on a patient positioning table 714. The raw ultrasound signals are provided to a local computing device 704 or a remote computing device for processing in accordance with the present solution. The result of this processing is to transform the raw ultrasound data into an enhanced ultrasound image (e.g., such as that shown in fig. 4).

Referring now to fig. 8, an illustration of an exemplary architecture of a computing device 800 is provided. Computing device 800 may be a device local to an ultrasound device (e.g., ultrasound device 700 of fig. 7). In this case, computing device 704 is the same as or substantially similar to computing device 800. In other scenarios, the computing device 800 is located remotely from the ultrasound device. In this case, the computing device 704 of the ultrasound device includes a network interface that facilitates communication between the two computing devices over a network (e.g., an intranet or the internet).

The computing device 800 of FIG. 8 represents embodiments of a representative computing device configured to facilitate generating an enhanced ultrasound image based on raw ultrasound signals, thus, the computing device 800 of FIG. 8 implements at least portions of a method for providing such an enhanced ultrasound image in accordance with the present solution.

some or all of the components of computing device 800 may be implemented as hardware, software, and/or a combination of hardware and software hardware, including but not limited to or more electronic circuits, may include but not be limited to passive components (e.g., resistors and capacitors) and/or active components (e.g., amplifiers and/or microprocessors).

As shown in fig. 8, computing device 800 includes a user interface 802, a central processing unit ("CPU") 806, a system bus 810, a memory 812 connected to and accessible by other portions of the computing device 800 by the system bus 810, and a hardware entity 814 connected to the system bus 810. The user interface may include input devices (e.g., keyboard 850, cursor control device 858, and/or camera 860) and output devices (e.g., speaker 852, display 854, and/or light emitting diodes 856) that facilitate user-software interaction to control operation of computing device 800.

At least the hardware entities 814 perform actions directed to accessing and using the memory 812, the memory 812 may be a random access memory ("RAM"), a disk drive, and/or a compact disc read only memory ("CD-ROM"). the hardware entities 814 may include a disk drive unit 816, the disk drive unit 816 includes a computer-readable storage medium 818 having stored thereon or more sets of instructions 820 (e.g., software code) configured to implement or more of the methods, processes, or functions described herein. the instructions 820 may also reside, completely or at least partially, within the memory 812 and/or within the CPU 806 during execution by the computing device 800. the memory 812 and the CPU 806 may also constitute machine-readable media.

In some scenarios , hardware entity 814 includes electronic circuitry (e.g., a processor) programmed to facilitate providing the enhanced ultrasound images.

A software application 864 implementing the present solution described herein is stored as a software program in a computer-readable storage medium and configured to run on CPU 806. Furthermore, software implementations of the present solution may include, but are not limited to, distributed processing, component/object distributed processing, parallel processing, virtual machine processing. In various scenarios, a network interface device 862 connected to the network environment communicates over the network using instructions 820.

The present solution may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The described embodiments are to be considered in all respects only as illustrative and not restrictive. The scope of the invention is, therefore, indicated by the appended claims rather than by this detailed description. All changes which come within the meaning and range of equivalency of the claims are to be embraced within their scope.

Rather, language referring to the features and advantages is understood to mean that a specific feature, advantage, or characteristic described in connection with an embodiment is included in at least embodiments of the present invention.

Furthermore, the described features, advantages, and characteristics of the invention may be combined in any suitable manner in or more embodiments in accordance with the description herein, one skilled in the relevant art will recognize that the invention may be practiced without the specific features or advantages of or more of the specific embodiments.

Reference throughout this specification to " embodiments," "an embodiment," or similar language means that a particular feature, structure, or characteristic described in connection with the indicated embodiment is included in at least embodiments of the present invention.

As used in this document, the singular forms "," "," and "the" include plural referents unless the context clearly dictates otherwise.

All of the devices, methods and algorithms disclosed and claimed herein can be made and executed without undue experimentation in light of the present disclosure. While the invention has been described in terms of preferred embodiments, it will be apparent to those of ordinary skill in the art that variations may be applied to the apparatus, methods and sequence of steps of the method without departing from the concept, spirit and scope of the invention. More specifically, it will be apparent that certain components may be added to, combined with, or substituted for the components described herein while the same or similar results would be achieved. All such similar substitutes and modifications apparent to those skilled in the art are deemed to be within the spirit, scope and concept of the invention as defined.

Various presently unforeseen or unanticipated alternatives, modifications, variations or improvements therein may be subsequently made by those skilled in the art, and each of which is also intended to be encompassed by the disclosed embodiments.