Method and system for performing hardness measurements using ultrasonic shear wave elastography

Abstract

A system and method for performing a stiffness measurement on an anatomical structure in a patient using ultrasonic shear wave elastography is provided. The method includes acquiring a plurality of elastography frames from an ultrasound image of an anatomical structure, where the plurality of elastography frames are provided by an image loop performed by an ultrasound imaging system; automatically identifying a preferred elastography frame of the plurality of elastography frames for making a hardness measurement on the anatomical structure; automatically identifying a preferred area of the preferred elastography frame based on the confidence coefficient; automatically selecting at least one region of interest (ROI) based on hardness measurements within a preferred region of the preferred elastography frame; and measuring the hardness of the anatomical structure in the at least one ROI.

Description

Background Art

[0001] Despite acceptance as a quantitative biomarker for staging liver fibrosis, ultrasound shear wave elastography has not yet been fully adopted as a new clinical standard. Some barriers to widespread adoption of shear wave elastography are related to subjective and potentially unreliable user operations, including identifying a good field of view or imaging frame for disease assessment and selecting regions within the field of view for measurement. Although expert users can overcome these challenges to obtain reliable data, such barriers prevent users with different experience levels from obtaining reliable and reproducible tissue stiffness measurements in a rapid and standardized manner.

[0002] Some conventional ultrasound shear wave elastography products, such as ElastQ Imaging available from Koninklijke Philips NV, provide two-dimensional shear wave elastography for imaging and quantification. The real-time continuous imaging mode provides a color-coded hardness map in units of Young's modulus (or shear wave velocity) that is simultaneously superimposed on the B-mode ultrasound image with a confidence map indicating the quality of the hardness measurement, which is viewed side by side and co-registered with the shear wave elastography image. The confidence map can help guide the user to optimal acquisition in real time as well as frame and region of interest (ROI) selection in viewing mode. For example, low confidence levels in shear wave elastography are often associated with artifacts caused by hepatic vessels, acoustic shadowing, reverberation near the liver capsule, and localized liver lesions. Confidence thresholds can be set to mask unreliable areas containing such artifacts for quantification.

[0003] However, depending on the patient disease stage and underlying morphology, the user must still make subjective judgments on imaging frames and ROI selection to obtain hardness values. This is an inconsistent and potentially error-prone process, especially for inexperienced users. BRIEF DESCRIPTION OF THE DRAWINGS

[0004] When combined with Figure 1 The exemplary embodiments are best understood from the following detailed description when read together. It is emphasized that the various features are not necessarily drawn to scale. In fact, the dimensions may be increased or decreased arbitrarily for the sake of clarity of discussion. Whenever applicable and practical, similar reference numerals refer to similar elements.

[0005] Figure 1 is a simplified block diagram of an ultrasound imaging system for performing stiffness measurement of an anatomical structure in a subject using ultrasound shear wave elastography, according to a representative embodiment.

[0006] Figure 2 is a flow chart of a method for performing stiffness measurement of an anatomical structure in a subject using ultrasound shear wave elastography, according to a representative embodiment.

[0007] Figure 3 Shown are multiple frames acquired in an illustrative image acquisition cycle for performing hardness measurements, according to a representative embodiment.

[0008] Figure 4 A preferred elastography frame with multiple ROIs selected for performing stiffness measurements is shown in accordance with a representative embodiment.

[0009] Figure 5 A ROI placement heat map of a preferred elastography frame with multiple ROIs selected for performing stiffness measurements is shown in accordance with a representative embodiment.

[0010] Figure 6 A graph showing illustrative histogram results of an overall assessment of stiffness according to a preferred elastography frame, according to a representative embodiment. Summary of the invention

[0011] According to a representative embodiment, a method for performing stiffness measurement of an anatomical structure in a patient using ultrasonic shear wave elastography is provided. The method includes acquiring a plurality of elastic imaging frames from an ultrasonic image of the anatomical structure, wherein the plurality of elastic imaging frames are provided by an image cycle performed by an ultrasonic imaging system; automatically identifying a preferred elastic imaging frame among the plurality of elastic imaging frames for performing stiffness measurement of the anatomical structure; automatically identifying a preferred region of the preferred elastic imaging frame based on a confidence level; automatically selecting at least one region of interest (ROI) based on a stiffness measurement result within the preferred region of the preferred elastic imaging frame; and measuring the stiffness of the anatomical structure in the at least one ROI.

[0012] According to a representative embodiment, a system for performing stiffness measurement of an anatomical structure in a patient using ultrasonic shear wave elastography is provided. The system includes: an ultrasonic image source configured to provide an ultrasonic image of the anatomical structure including a plurality of elastic imaging frames, wherein the plurality of elastic imaging frames are provided by an image cycle performed by an ultrasonic imaging system; a processing unit; and a memory storing instructions. When executed by the processing unit, the instructions cause the processing unit to: automatically identify a preferred elastic imaging frame among the plurality of elastic imaging frames for performing stiffness measurement of the anatomical structure; automatically identify a preferred region of the preferred elastic imaging frame based on a confidence level; automatically select at least one ROI based on a stiffness measurement result within the preferred region of the preferred elastic imaging frame; and measure the stiffness of the anatomical structure in the at least one ROI.

[0013] According to a representative embodiment, a non-transitory computer-readable medium stores instructions for performing stiffness measurement of an anatomical structure in a patient using ultrasonic shear wave elastography. When executed by one or more processors, the instructions cause the one or more processors to: receive an ultrasonic image of the anatomical structure including a plurality of elastic imaging frames, wherein the plurality of elastic imaging frames are provided by an imaging cycle performed by an ultrasonic imaging system; automatically identify a preferred elastic imaging frame among the plurality of elastic imaging frames for performing stiffness measurement of the anatomical structure; automatically identify a preferred region of the preferred elastic imaging frame based on a confidence level; automatically select at least one ROI based on a stiffness measurement result within the preferred region of the preferred elastic imaging frame; and measure the stiffness of the anatomical structure in the at least one ROI. DETAILED DESCRIPTION

[0014] In the following specific embodiments, for the purpose of explanation rather than limitation, representative embodiments of the disclosure of specific details are described to provide a thorough understanding of the embodiments according to the present teachings. The description of known systems, equipment, materials, operating methods and manufacturing methods can be omitted to avoid blurring the description of representative embodiments. Nevertheless, systems, equipment, materials and methods within the knowledge of those of ordinary skill in the art are also within the scope of the present teachings and can be used according to representative embodiments. It should be understood that the terms used herein are only used for the purpose of describing specific embodiments and are not intended to be limiting. The defined terms are supplements to the technical and scientific meanings of the defined terms as generally understood and accepted in the technical field of the present teachings.

[0015] It will be understood that although the terms first, second, third, etc. may be used herein to describe various elements or components, these elements or components should not be limited by these terms. These terms are only used to distinguish one element or component from another element or component. Therefore, without departing from the teachings of the present invention, the first element or component discussed below may be referred to as the second element or component.

[0016] The terms used herein are only used for the purpose of describing specific embodiments and are not intended to be limiting. As used in the specification and claims, the singular terms "one", "an", and "said" are intended to include both the singular and plural forms, unless the context clearly dictates otherwise. In addition, the terms "include", "comprises", and / or similar terms specify the presence of stated features, elements, and / or parts, but do not exclude the presence or addition of one or more other features, elements, parts, and / or groups thereof. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0017] Unless otherwise indicated, when an element or component is referred to as being "connected to," "coupled to," or "adjacent to" another element or component, it will be understood that the element or component may be directly connected or coupled to the other element or component, or that there may be intervening elements or components. That is, these and similar terms cover situations where one or more intervening elements or components may be employed to connect two elements or components. However, when an element or component is referred to as being "directly connected" to another element or component, this only covers situations where two elements or components are connected to each other without any intervening or intermediary elements or components.

[0018] Therefore, the present disclosure is intended to present one or more of the advantages specifically noted below through one or more of its various aspects, embodiments, and / or specific features or subcomponents. For the purpose of explanation and not limitation, example embodiments disclosing specific details are set forth to provide a thorough understanding of the embodiments according to the present teachings. However, other embodiments consistent with the present disclosure that depart from the specific details disclosed herein are still within the scope of the claims. In addition, descriptions of well-known devices and methods may be omitted so as not to obscure the description of the example embodiments. Such methods and devices are within the scope of the present disclosure.

[0019] In general, the various embodiments described herein provide a system and method for efficiently and reliably performing ultrasonic shear wave elastography by providing automatic and standardized guidance to the user in selecting the best elastography imaging frame, the region within the selected imaging frame, and the ROI within the region for quantification. Therefore, the user does not have to perform an integrated assessment of the confidence map, B-mode, and elastography image to rely on personal experience to select the region for quantification as in conventional techniques for performing ultrasonic shear wave elastography. Various embodiments also provide a comprehensive assessment of liver stiffness rather than a single value output. This is beneficial because liver fibrosis development can present spatially heterogeneous patterns, and a single value (average) stiffness output may lead to inaccurate decisions. Various embodiments facilitate a more comprehensive and accurate quantitative assessment of liver fibrosis in a rapid and standardized manner, and can drive shear wave elastography and other liver quantification tools to become clinical standards for diffuse liver disease assessment.

[0020] Changes in the mechanical properties (e.g., stiffness) of soft tissues often indicate underlying pathology. Based on this principle, ultrasonic shear wave elastography, a relatively new quantitative imaging modality, measures tissue stiffness and quantifies such mechanical properties to support diagnosis. Routine clinical applications typically include staging of liver fibrosis and cancer detection for the breast, liver, prostate, and thyroid. Organs such as the liver, breast, prostate, and thyroid are often modeled as isotropic materials in ultrasonic shear wave elastography, which means that the corresponding mechanical properties or responses to stress are assumed to be independent of the loading direction. Under the further assumption that such tissues are isotropic, linear, and incompressible, only one physical parameter is obtained to characterize the stiffness: Young's modulus (E) or shear modulus (μ). The physical parameter ultrasonic elastography is provided by equation (1), where ρ refers to tissue density:

[0021] E=3μ=3ρV sh 2 (1)

[0022] In the use case of liver fibrosis assessment, for example, Young’s modulus and shear wave velocity (V sh ) increases with the severity of liver fibrosis. This is because, as liver disease progresses from normal to fibrosis to cirrhosis, liver tissue becomes stiffer. With the global growth of liver disease, ultrasound shear wave elastography has been gaining clinical adoption as a rapid, safe, cost-effective and definitive diagnostic tool for staging liver fibrosis.

[0023] Figure 1 is a simplified block diagram of an ultrasound imaging system for performing stiffness measurement of an anatomical structure in a subject using ultrasound shear wave elastography, according to a representative embodiment.

[0024] refer to Figure 1 , an ultrasound imaging system 100 includes an imaging device 110 and a computer system 105 for controlling imaging of a region of interest in a subject (patient) 101, such as an organ, tumor, or other anatomical structure within the subject 101. The imaging device 110 is illustratively an ultrasound imaging device capable of providing ultrasound images for shear wave elastography in the region of interest of the subject 101. The shear wave elastography feature enables determination of stiffness in the region of interest.

[0025] The imaging device 110 may include a known transducer probe (not shown) of an ultrasound imaging system. The transducer probe may include a transducer array including a two-dimensional array of transducers that can be scanned in two or three dimensions, for example, to transmit ultrasound waves into the body of the subject 101 and receive echo signals in response. For example, the transducer array may include a capacitive micromachined ultrasound transducer (CMUT) or a piezoelectric transducer formed of a material such as PZT or PVDF. The transducer array is coupled to a microbeamformer in the transducer probe that controls the reception of signals by the transducers.

[0026] The computer system 105 receives image data from the imaging device 110 and stores and processes the imaging data according to the representative embodiments described herein. The computer system 105 includes a processing unit 120, a memory 130, a display 140 including a graphical user interface (GUI) 145, and a user interface 150. The computer system 105 and / or the imaging device 110 also include an imaging interface (IF) 115, which is used to interface the imaging device 110 and the processing unit 120 to receive an ultrasound image (e.g., a B-mode ultrasound image). The imaging IF 115 receives imaging data (e.g., ultrasound image data) acquired by the imaging device 110 and converts the imaging data into a format readable by the processing unit 120. In an alternative configuration, the processing unit 120 can receive an ultrasound image from a database 135 that stores previously acquired ultrasound images of the object 101.

[0027] The memory 130 stores instructions executable by the processing unit 120. When executed, and as described more fully below, the instructions cause the processing unit 120 to perform various processes discussed below regarding shear wave elastography. The instructions also allow a user (e.g., an ultrasound physician or clinician) to use the GUI 145 and / or the user interface 150 to perform different steps of the examination and initialize the imaging device 110. In addition, the processing unit 120 can implement additional operations based on the execution of the instructions (such as instructing another element of the computer system 105 (including the memory 130 and the display 140) or otherwise communicating with it) to perform one or more of the above-mentioned processes.

[0028] The memory 130 may include a main memory and / or a static memory, wherein such memories may communicate with each other and with the processing unit 120 via one or more buses. The memory 130 stores instructions for implementing some or all aspects of the methods and processes described herein. When executed, the instructions cause the processing unit 120 to implement one or more processes for performing stiffness measurement of anatomical structures in an object using ultrasonic shear wave elastography (e.g., as described below with reference to Figure 2The memory 130 may be implemented, for example, by any number, type, and combination of random access memory (RAM) and read-only memory (ROM), and may store various types of information, such as software algorithms, which serve as instructions that, when executed by the processing unit 120, cause the processing unit 120 to perform various steps and methods according to the present teachings. In addition, updates to the methods and processes described herein may also be provided to the computer system 105 and stored in the memory 130.

[0029] The various types of ROM and RAM may include any number, type, and combination of computer-readable storage media, such as disk drives, flash memory, electrically programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, removable disks, magnetic tapes, compact disk read-only memory (CD-ROM), digital versatile disks (DVDs), floppy disks, Blu-ray disks, universal serial bus (USB) drives, or any other form of storage media known in the art.

[0030] The memory 130 may also include instructions for interfacing the transducer probe of the imaging device 110 with the processing unit 120 to control the acquisition of an ultrasound image of the object 101. The interface for the transducer probe may include a transmit / receive (T / R) switch that is coupled to a microbeamformer of the transducer probe via a probe cable. The T / R switch switches between transmit and receive modes, for example, under the control of the processing unit 120 and / or the user interface 150. The processing unit 120 also controls the direction to which the beam is turned and focused via the transducer probe interface. The beam can be turned straight forward from the transducer array (orthogonal to the transducer array), or turned to different angles for a wider field of view. The processing unit 120 may also include a main beamformer that provides final beamforming after digitization. In general, the transmission of ultrasound signals and the reception of echo signals in response are well known, so additional details in this regard are not included in this article.

[0031] Each of the memory 130 and the database 135 is a tangible storage medium for storing data and executable software instructions, and is non-transient while the software instructions are stored therein. As used herein, the term "non-transient" should be interpreted as a characteristic that is not a permanent characteristic of a state, but a characteristic of a state that will last for a period of time. The term "non-transient" specifically negates transient characteristics, such as characteristics of a carrier or signal or other forms that only exist temporarily anywhere at any time. As described above, the memory 130 can store software instructions and / or computer readable codes that enable the execution of various functions, and the database 135 can store previously acquired ultrasound images of the object 105. The memory 130 and / or the database 135 can be secure and / or encrypted, or unsecure and / or unencrypted.

[0032] "Memory" is an example of a computer-readable storage medium, and should be interpreted as possibly being multiple memories or databases. The memory or database may be, for example, multiple memories or databases local to a computer, and / or distributed between multiple computer systems or computing devices, or set up in the "cloud" according to known components and methods. Computer-readable storage media are defined as any medium that constitutes patentable subject matter under 35 U.S.C. §101, and do not include any medium that does not constitute patentable subject matter under 35 U.S.C. §101. Examples of such media include non-transient media, such as computer memory devices that store information in a format readable by a computer or data processing system. More specific examples of non-transient media include computer disks and non-volatile memory.

[0033] Processing unit 120 represents one or more processing devices and is configured to run software instructions stored in memory 130 to perform functions as described in various embodiments herein. Processing unit 120 can be implemented by any combination of hardware, software, firmware, hard-wired logic circuits, or a combination thereof using the following: a general-purpose computer, a central processing unit (CPU), a graphics processing unit (GPU), a computer processor, a microprocessor, a microcontroller, a state machine, a programmable logic device, a field programmable gate array (FPGA), an application-specific integrated circuit (ASIC), a system on a chip (SOC), or a combination thereof. In addition, any processing unit or processor herein may include multiple processors, parallel processors, or both. Multiple processors may be included in a single device or multiple devices or coupled to a single device or multiple devices.

[0034] Processing unit 120 may include or have access to an AI engine or module, which may be implemented as software that provides artificial intelligence and applies machine learning (such as neural network modeling). The AI ​​engine may reside in any of a variety of components (such as memory 130, external servers, and / or the cloud) in addition to or in addition to processing unit 120. For example, when the AI ​​engine is implemented in the cloud (such as at a data center), the AI ​​engine may be connected to processing unit 120 via the Internet using one or more wired and / or wireless connections.

[0035] As used herein, the term "processor" encompasses an electronic component capable of running a program or machine-executable instructions. References to a computing device that includes a "processor" should be interpreted as including more than one processor or processing core, such as in a multi-core processor. A processor may also refer to a collection of processors within a single computer system or distributed among multiple computer systems, such as in a cloud-based or other multi-site application. The term computing device should also be interpreted to include a collection or network of computing devices, each of which includes one or more processors. A module has software instructions to perform various functions using one or more processors, which may be within the same computing device or may be distributed across multiple computing devices.

[0036] For example, the display 140 can be any compatible monitor for displaying at least an ultrasound image, such as a computer monitor, a television, a liquid crystal display (LCD), a light emitting diode (LED) display, a flat panel display, a solid state display, or a cathode ray tube (CRT) display. The display 140 can also provide the GUI 145 discussed above, which is used to display information to a user and receive information from a user.

[0037] The user interface 150 may include a user and / or network interface for providing information and data output by the processing unit 120 and / or the memory 130 to the user and / or for receiving information and data input by the user. That is, the user interface 150 enables the user to operate the imaging device as described herein, and can schedule, control or manipulate various aspects of the ultrasound imaging system 100 of the present teaching. It is worth noting that the user interface 150 enables the processing unit 120 to indicate the effect of the user's control or manipulation. The user interface 150 may include one or more of the following: a port, a disk drive, a wireless antenna or other type of receiver circuit. For example, the user interface 150 may also connect one or more interface devices, such as a mouse, a keyboard, a mouse, a trackball, a joystick, a microphone, a camera, a touch pad, a touch screen, a voice or gesture recognition captured by a microphone or a camera.

[0038] For example, all or part of the user interface 150 may be implemented by a GUI 145 on a touch screen of the display 140. The user interface 150 includes graphics, such as buttons, fields, slides, and other visual markers, which are operable by a user to initiate various commands for manipulating displayed images, performing measurements and calculations, etc. during an ultrasound examination. The buttons may be displayed by the GUI 145 on the touch screen.

[0039] It is noteworthy that the processing unit 120, memory 130, display 140, GUI 145 and user interface 150 can be located away from the imaging device 110 operated by the sonographer (e.g., in another location in the building or in another building). The processing unit 120, memory 130, display 140, GUI 145 and user interface 150 can be located, for example, at the location where the radiologist / clinician is located. It is noteworthy that additional processing units, memories, displays, GUIs and user interfaces can be located near the sonographer for controlling various functions of the imaging device 110 required to perform shear wave elastography operations. In a more general sense, the ultrasound imaging system 100 can include a source of ultrasound signal data from an examination, which can be the imaging device 110 or a database previously populated with ultrasound images from previous examinations.

[0040] Figure 2 is a flow chart illustrating a method of performing stiffness measurement of an anatomical structure in a subject using ultrasonic shear wave elastography according to a representative embodiment. For example, the method may be implemented by the ultrasonic imaging system 100 discussed above under the control of the processing unit 120 executing instructions stored in the memory 130.

[0041] refer to Figure 2 , in box S211, multiple elastic imaging frames from an ultrasound image of the anatomical structure are acquired. The ultrasound image can be, for example, a B-mode ultrasound image, but other types of ultrasound images can be incorporated without departing from the scope of the present teachings. The elastic imaging frames are provided by an image cycle performed by an ultrasound imaging device (e.g., imaging device 110) that images the object. The image cycle can be acquired within a minimum time length (e.g., approximately six seconds) during which the patient performs shallow breathing or pauses breathing. The elastic imaging frames can be acquired directly from the ultrasound imaging device during or after the imaging process, or can be acquired from a previously populated ultrasound image database (e.g., database 135). The ultrasound imaging device and the database can be collectively referred to as an ultrasound image source.

[0042] In box S212, a preferred elastic imaging frame among a plurality of elastic imaging frames is automatically identified by a processing unit (e.g., processing unit 120). The preferred elastic imaging frame is used to perform hardness measurement on the anatomical structure. The preferred elastic imaging frame provides a hardness map that is consistent across elastic imaging frames that are temporally adjacent to the preferred elastic imaging frame, which elastic imaging frames mean elastic imaging frames acquired immediately before and after the preferred elastic imaging frame. Compared to the subjective manual selection of elastic imaging frames in conventional techniques, automatic identification always provides high-quality elastic imaging frames as preferred elastic imaging frames, regardless of user experience and expertise.

[0043] In an embodiment, a preferred elastic imaging frame may be automatically identified based on temporal stability. Temporal stability means that the preferred elastic imaging frame provides a consistent hardness map across elastic imaging frames adjacent to the preferred elastic imaging frame (occurring immediately before and after the preferred elastic imaging frame). Identifying the preferred elastic imaging frame based on temporal stability may include assigning a temporal stability score to each elastic imaging frame in the plurality of frames acquired in box S211 based on a correlation of hardness between the elastic imaging frame and the temporally preceding and succeeding elastic imaging frames, and identifying the elastic imaging frame with the highest temporal stability score as the preferred elastic imaging frame. The correlation may be based on corresponding pixels across the elastic imaging frames, for example, which are respectively associated with numbers indicating hardness.

[0044] For example, for each elastic imaging frame in a plurality of elastic imaging frames, a correlation coefficient between the elastic imaging frame and the elastic imaging frame immediately preceding is determined, and another correlation coefficient between the elastic imaging frame and the elastic imaging frame immediately following is determined. The correlation coefficient may be determined by the error between the pixel-by-pixel hardness measurements in each pair of concurrent frames. Then, based on the average value of the correlation coefficient between (i) the elastic imaging frame and the elastic imaging frame immediately preceding and (ii) the elastic imaging frame and the elastic imaging frame immediately following, a temporal stability score is assigned to each elastic imaging frame. For example, the mean square error (MSE) may be used to determine the average value. A higher score is assigned to a higher average value of the correlation coefficient. Therefore, the elastic imaging frame with the highest score is identified as a preferred elastic imaging frame. In an embodiment, the supplement or inverse of the MSE may be used as a final temporal stability score. In various embodiments, without departing from the scope of the present teachings, the MSE may be normalized or not normalized.

[0045] In an embodiment, the MSE may be based on a combination of the hardness MSE and the confidence MSE. More specifically, the hardness MSE (MSE) may be calculated based on the hardness map of each elastic imaging frame. S ) and the confidence MSE (MSE) can be calculated based on the confidence map of each elastic imaging frame CThen, the combined MSE (MSE) for each elastic imaging frame is determined according to equation (2): 组合 , where p is the pixel value, f1 is the elastic imaging frame, and f2 is the adjacent frame of the elastic imaging frame:

[0046] MSE 组合 (p,f1,f2)=MSE S (p,f1,f2)x MSE C (p,f1,f2)Equation (2)

[0047] The elastography frame with the lowest combined MSE is selected as the preferred elastography frame.

[0048] In another embodiment, using MSE S and MSE C The combined MSE for each elastic imaging frame is determined by the weighted sum of 组合 , as shown in equation (3), where p is the pixel value, f1 is the elastic imaging frame, and f2 is the adjacent frame of the elastic imaging frame:

[0049] MSE 组合 (p, f1, f2) = A*MSE S (p, f1, f2) + B*MSE C (p, f1, f2) Equation (3)

[0050] A and B are the MSEs that are assigned to the hardness S and confidence MSE C For example, the weighting coefficients A and B may be determined empirically or based on typical or expected ranges of the underlying hardness and confidence maps. For example, when the confidence is in the range of 0-100% but the hardness is typically in the range of 0-30 kPa, a weight of 3 (A=3, B=1) may be assigned to the hardness MSE S The elastic imaging frame with the lowest combined MSE is selected as the preferred elastic imaging frame.

[0051] Figure 3 FIG. 2 shows a plurality of frames acquired in an illustrative image acquisition cycle for performing hardness measurements according to a representative embodiment. Figure 3 , the image cycle acquisition 300 includes five elastography frames 301, 302, 303, 304, and 305 depicting a stiffness map. For example, using the temporal stability analysis, the elastography frame 304 is identified as having the highest temporal stability score with respect to temporal stability between the elastography frame 304 and the adjacent elastography frames 303 and 305.

[0052] In another embodiment, the preferred elastic imaging frame can be automatically identified based on thresholding according to confidence. Identifying the preferred elastic imaging frame based on thresholding according to confidence may include: assigning a confidence score to pixels in each elastic imaging frame of a plurality of elastic imaging frames; excluding each elastic imaging frame in which more than a threshold number of pixels in the elastic imaging frame have a low confidence score; and identifying the remaining elastic imaging frame with the highest confidence score as the preferred elastic imaging frame. By first excluding elastic imaging frames in which more than a threshold number of pixels have a low confidence score, a situation in which all elastic imaging frames are too unreliable to be used can be identified. For example, the threshold number of pixels can be set to 60% of the total pixels in the elastic imaging frame, but other percentages (e.g., 50%) can be included without departing from the scope of the present teachings. For example, as will be apparent to those skilled in the art, a hardness map and a corresponding confidence map can be used to determine a confidence score for a pixel. When the confidence score is less than a confidence threshold set by a user (e.g., the default threshold can be 60%), the confidence score is considered low.

[0053] Reference again Figure 2 In block S213, a preferred region of the preferred elastography frame is automatically identified by a processing unit (e.g., processing unit 120) based on confidence levels respectively associated with different regions of the preferred elastography frame. The confidence level indicates the likelihood of obtaining an accurate representative stiffness measurement result from a particular region of the preferred elastography frame. Typically, the B-mode anatomy is used to identify and exclude elastography signals from small blood vessels and abnormally bright peripheral areas in the preferred elastography frame.

[0054] Preferred areas can be identified by creating a confidence map of the selected elastography frames and excluding low confidence areas, as will be apparent to one skilled in the art. For example, a low confidence area can be an area where the confidence level is less than a previously defined confidence threshold. Thus, areas of low confidence areas can be excluded by thresholding the confidence map to exclude pixels below the previously defined confidence threshold. After excluding the low confidence areas, the remaining areas include high confidence areas and are considered to be preferred areas. In practice, removing low confidence areas from consideration typically removes most of the vascular signals from the hardness map.

[0055] However, some small blood vessels in the B-mode image may still remain within the preferred area (high confidence area of ​​the hardness map), thereby reducing the confidence level. Therefore, in an embodiment, Doppler imaging can be optionally used to further refine the preferred area. More specifically, Doppler imaging can be interleaved with acquiring elastic imaging frames in box S211 to capture blood flow signals in anatomical structures. Based on the blood flow signal, areas with strong Doppler signals are identified and excluded, which indicate high blood flow (e.g., exceeding a previously defined blood flow threshold). The remaining low blood flow areas are identified as preferred areas.

[0056] In another embodiment, the local spatial standard deviation of hardness is used to automatically identify preferred regions of a preferred elastic imaging frame. This includes calculating the local spatial standard deviation of hardness associated with different regions in the entire preferred elastic imaging frame, and assigning spatial stability scores based on the calculated local spatial standard deviation of hardness, respectively, wherein a lower spatial standard deviation of hardness is assigned a higher spatial stability score. These spatial stability scores can be calculated as the complement or inverse of the local standard deviation. The region of the preferred elastic imaging frame with the highest spatial stability score (indicating the lowest local standard deviation) is identified as the preferred region.

[0057] In block S214, at least one region of interest (ROI) is automatically selected by a processing unit (e.g., processing unit 120) based on the hardness measurement results within the preferred region of the preferred elastic imaging frame. For the purpose of the following discussion, it is assumed that one ROI is selected for performing hardness measurement on the anatomical structure. However, it should be understood that multiple ROIs can be selected according to the same process without departing from the scope of the present teachings. For example, Figure 4 Shown are the selections of three ROIs in which hardness measurements are to be performed, as discussed below.

[0058] Typically, regions with stable and consistent hardness values ​​are desired as ROIs. A variety of techniques can be used to identify such regions, examples of which include: (i) determining spatial stability based on local spatial standard deviation, (ii) determining temporal stability based on the squared error of temporally adjacent elastic imaging frames, (iii) determining hardness value probabilities based on pixel-by-pixel hardness values, and (iv) determining ROI placement heat maps based on a combination of other techniques, as described below. The ROI has a predefined size and shape, for example, provided by a user. For example, the ROI can be a circle with a diameter in the range of, for example, about 0.5 cm to about 2.0 cm. For example, the size and shape of the ROI can correspond to the size and shape of the sampling caliper of an ultrasonic imaging device. Of course, ROIs of other sizes and shapes can be incorporated without departing from the scope of the present teachings.

[0059] Automatically selecting an ROI by determining spatial stability includes: determining a local standard deviation of the hardness of a region in a preferred zone of a preferred elastic imaging frame, determining a spatial stability score for each region in the region, and selecting at least one region in the region with the highest spatial stability score to be the ROI as the ROI. In an embodiment, the highest spatial stability score indicates the lowest spatial variability, in which case the spatial stability score can be the complement or inverse of the local standard deviation of the hardness. In various embodiments, the local standard deviation of the hardness can be normalized or not normalized without departing from the scope of the present teachings.

[0060] Automatically selecting a ROI by determining temporal stability includes determining a correlation coefficient between each pair of temporally adjacent elastic imaging frames, averaging the correlation coefficients between a preferred elastic imaging frame and temporally adjacent elastic imaging frames, determining a temporal stability score for each region in the region based on the average correlation coefficient, and selecting at least one region in the region with the highest temporal stability score as the ROI, thereby indicating the most temporally stable region. For example, the correlation coefficients can be averaged using MSE (using equations (2) and (3) discussed above) or squared error (SE) (using these same equations with SE replacing MSE). In an embodiment, the highest temporal stability score indicates the lowest temporal variability, in which case the temporal stability score can be the complement or inverse of the MSE or SE.

[0061] Automatically selecting a ROI by determining a hardness value probability based on a pixel-by-pixel hardness value includes: evaluating the distribution of pixel-by-pixel hardness values ​​within the identified preferred area, and selecting a local hardness region with a predefined size and shape of the ROI containing hardness values ​​representing most hardness values ​​as the ROI based on the distribution of the pixel-by-pixel hardness values. The majority hardness value is the most common hardness value in the elastic imaging frame. The distribution of the pixel-by-pixel hardness values ​​can be evaluated using a hardness map of the preferred elastic imaging frame, for example, by a histogram analysis of the pixel-by-pixel hardness values ​​in the frame. Typically, the highest bar in the histogram indicates the majority hardness value. In this case, identifying the local hardness region representing the majority hardness value may include identifying the highest bar in the histogram.

[0062] Automatically selecting a ROI by determining a ROI placement heat map includes: providing a smoothness heat map from a determination of spatial stability, a temporal stability heat map from a determination of temporal stability, and a hardness frequency heat map from a determination of hardness value probabilities for a preferred region of a preferred elastic imaging frame, and combining (e.g., multiplying) the smoothness heat map, the temporal stability heat map, and the hardness frequency heat map to derive a pixel-by-pixel ROI placement heat map. Calculating a local average for the pixel-by-pixel ROI placement heat map to derive a final ROI placement heat map. The final ROI placement heat map is then used to select a ROI. For example, the ROI center position can be determined as the position of the maximum pixel-by-pixel value of the final ROI placement heat map. The following discussion Figure 5 An example of a ROI placement heat map is shown in .

[0063] In an embodiment, a user may specify the number (N) of desired ROIs to perform hardness measurements using a GUI (e.g., GUI 145). The N ROIs will then be automatically placed in the N regions determined to be optimal for hardness measurements. The N regions may be identified as ROIs using any of the above techniques.

[0064] Figure 4 FIG. 4 shows a preferred elastography frame with multiple ROIs selected for performing stiffness measurements according to a representative embodiment. Figure 4 , ROIs 401, 402, and 403 are selected according to one or more of the embodiments of step S214 described above. In this example, the associated hardness values ​​are ultimately determined to be 7.4 kilopascals (kPa) for ROI 401, 8.2 kPa for ROI 402, and 6.1 kPa for ROI 403, such that ROI 403 would be the most preferred ROI.

[0065] Figure 5 ROI placement heatmap of a preferred elastography frame with multiple ROIs selected for performing stiffness measurements according to a representative embodiment is shown. Figure 5 , the geometric means of the smoothness heatmap 510, the temporal stability heatmap 520, and the hardness frequency heatmap 530 are multiplied together to provide a pixel-by-pixel ROI placement heatmap 540. A local spatial average filter (e.g., equal to the diameter of the caliper) is applied to the pixel-by-pixel ROI placement heatmap 540 to generate a final ROI placement heatmap 545 for identifying preferred locations of one or more ROIs. In the depicted example, the ROI placement provides a first ROI 501 centered at the brightest pixel in the final ROI placement heatmap 545, a second ROI 502 centered at the second brightest pixel in the final ROI placement heatmap 545, and a third ROI 503 centered at the third brightest pixel in the heatmap.

[0066] Reference again Figure 2 In block S215, the hardness of the anatomical structure is measured in the ROI. The hardness is measured using any compatible technique apparent to one skilled in the art. For example, the average hardness value within the circular ROI may be used as the output hardness measurement result.

[0067] In block S216 (optional), a comprehensive assessment of the overall stiffness of the anatomical structure may be performed using the stiffness measurements across the entire preferred elastography frame (including the measurements of the ROI performed in block S215). Alternatively, the comprehensive assessment of the overall stiffness may be limited to stiffness pixels above a confidence threshold, as described above. The comprehensive assessment provides a more accurate picture of the overall stiffness and corresponding health or status of the anatomical structure than a single (e.g., average) stiffness value representing the anatomical structure provided by conventional stiffness measurement techniques.

[0068] The evaluation can include classifying the stiffness measurements in the preferred elastography frame according to severity (e.g., greater stiffness corresponds to greater severity) and indicating the percentage of the preferred elastography frame that falls into the corresponding category. For example, a histogram of all stiffness measurements within the preferred elastography frame can be created, where the histogram "bins" are defined by empirically determined cutoff values ​​for different categories. The bins can be displayed as corresponding bars in the histogram. For example, the bins can include cutoff values ​​for different disease stages present in the anatomical structure. The percentage of pixels within each category can then be easily displayed as discussed below. Figure 6 In the simple diagram shown in .

[0069] For example, when the anatomical structure is the liver of a subject, a comprehensive assessment can be performed based on a meta-analysis of histological data in the viral hepatitis (METAVIR) scoring system, in which stiffness is classified into five groups, indicated as F0, F1, F2, F3, and F4. In this case, the categories are liver fibrosis stages arranged in increasing severity from F0 to F4.

[0070] Figure 6 A graph showing illustrative histogram results of a comprehensive assessment of stiffness from a preferred elastography frame according to a representative embodiment. Figure 6, chart 600 shows categories F0, F1, F2, F3, and F4 corresponding to histogram bins, where the respective sizes and shading of the categories visually correspond to the percentages of hardness measurements that fall within the categories. Thus, in the depicted example, 55% of the hardness measurements are in category F0 (largest and lightest shaded box), 15% of the hardness measurements are in category F1, 12% of the hardness measurements are in category F2, 10% of the hardness measurements are in category F3, and 8% of the hardness measurements are in category F4 (smallest and darkest shaded box). Chart 600 can be displayed on a display (e.g., display 140) so that a user can quickly and accurately assess the overall hardness characteristics of the liver, as well as the overall health.

[0071] According to various embodiments of the present disclosure, a hardware computer system that executes a software program stored on a non-transient storage medium can be used to implement the methods described herein. In addition, in an exemplary non-limiting embodiment, implementations may include distributed processing, component / object distributed processing, and parallel processing. Virtual computer system processing can implement one or more of the methods or functions described herein, and the processors described herein can be used to support a virtual processing environment.

[0072] Although performing hardness measurement has been described with reference to exemplary embodiments, it should be understood that the words that have been used are words of description and illustration, rather than words of limitation. Changes may be made within the scope of the claims as presently stated and amended without departing from the scope and spirit of the teachings. Moreover, although performing hardness measurement has been described with reference to specific modules, materials, and embodiments, it is not intended to be limited to the details disclosed; rather, the embodiments extend to all functionally equivalent structures, methods, and uses such as are within the scope of the claims.

[0073] The illustrations of the embodiments described herein are intended to provide an overall understanding of the structures of the various embodiments. These illustrations are not intended to be used as a complete description of all elements and features of the disclosure described herein. After viewing the disclosure, many other embodiments may be apparent to those skilled in the art. Other embodiments may be utilized and derived from the disclosure so that structural and logical replacements and changes may be made without departing from the scope of the disclosure. In addition, the illustrations are merely representative and may not be drawn to scale. Certain proportions within the illustrations may be exaggerated, while other proportions may be minimized. Therefore, the disclosure and the accompanying drawings should be considered illustrative and not restrictive.

[0074] One or more embodiments of the present disclosure may be referred to herein individually and / or collectively by the term "invention", which is merely for convenience and is not intended to voluntarily limit the scope of the present application to any particular invention or inventive concept. In addition, although specific embodiments have been illustrated and described herein, it should be appreciated that any subsequent arrangements designed to achieve the same or similar purposes may replace the specific embodiments shown. The present disclosure is intended to cover any and all subsequent adjustments or variations of the various embodiments. After reviewing this specification, the combination of the above-described embodiments and other embodiments not specifically described herein will be apparent to those skilled in the art.

[0075] The Abstract of the present disclosure is provided to comply with 37 CFR § 1.72 (b) and is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. In addition, in the foregoing Detailed Description, various features may be grouped together or described in a single embodiment for the purpose of simplifying the disclosure. This disclosure should not be interpreted as reflecting an intention that the claimed embodiments require more features than those expressly recited in each claim. On the contrary, as reflected in the following claims, the inventive subject matter may involve less than all the features of any disclosed embodiment. Therefore, the following claims are incorporated into the Detailed Description, with each claim itself as defining a separately claimed subject matter.

[0076] The previous description of the disclosed embodiments is provided to enable any person skilled in the art to practice the concepts described in the present disclosure. As such, the subject matter disclosed above should be considered illustrative rather than restrictive, and the claims are intended to cover all such modifications, enhancements, and other embodiments that fall within the true spirit and scope of the present disclosure. Therefore, to the maximum extent permitted by law, the scope of the present disclosure is to be determined by the broadest permissible interpretation of the claims and their equivalents, and shall not be constrained or limited by the foregoing detailed description.

Claims

1. A method of performing stiffness measurement of an anatomical structure in a patient using ultrasonic shear wave elastography, the method comprising: Acquiring ( S211 ) a plurality of elastic imaging frames from the ultrasound image of the anatomical structure, wherein the plurality of elastic imaging frames are provided by an imaging cycle performed by an ultrasound imaging system; automatically identifying ( S212 ) a preferred elastic imaging frame among the plurality of elastic imaging frames for use in performing hardness measurement on the anatomical structure; automatically identifying ( S213 ) a preferred region of the preferred elastic imaging frame based on a confidence level; automatically selecting (S214) at least one region of interest (ROI) based on the stiffness measurement results within the preferred region of the preferred elastography frame; and The stiffness of the anatomical structure in the at least one ROI is measured (S215).

2. The method according to claim 1, wherein: Automatically identifying the preferred elastography frame is based on temporal stability and includes: calculating a correlation coefficient between each pair of adjacent elastic imaging frames in the plurality of elastic imaging frames; assigning a score to each of the plurality of elastography frames based on an average of the correlation coefficient with the immediately preceding elastography frame and the correlation coefficient with the immediately succeeding elastography frame, wherein a higher average of the correlation coefficients is assigned a higher score; and The elastography frame having the highest score is identified as the preferred elastography frame.

3. The method according to claim 1, wherein: Automatically identifying the preferred elastography frame is based on thresholding according to confidence and comprises: excluding each of the plurality of elastography frames in which more than 60% of the pixels in the elastography frame have a low confidence score; and The remaining elastography frames having the highest confidence scores are identified as the preferred elastography frames.

4. The method according to claim 1, wherein: Automatically identifying the preferred region of the preferred elastic imaging frame comprises: creating a confidence map of the selected elastography frame; and excluding low confidence regions from the confidence map based on a previously defined confidence threshold, The preferred area of ​​the preferred elastic imaging frame includes a remaining area of ​​the confidence map after excluding the low confidence area.

5. The method according to claim 4, wherein: Automatically identifying the preferred region of the preferred elastic imaging frame further comprises: performing Doppler imaging interleaved with acquiring the plurality of elastography frames to capture blood flow signals in the anatomical structure; and excluding regions of the preferred elastography frames having Doppler signals from the Doppler imaging indicating high blood flow, The preferred area of ​​the preferred elastic imaging frame further includes a remaining area after excluding the area with high blood flow.

6. The method according to claim 1, wherein: Automatically selecting the at least one ROI comprises: determining a local spatial standard deviation of stiffness in said preferred region of said preferred elastography frame; assigning spatial stability scores based on the local spatial standard deviations of hardness, respectively, wherein lower spatial standard deviations of hardness are assigned higher spatial stability scores; and The region with the highest steric stability score among the preferred regions is selected as the preferred region.

7. The method according to claim 1, wherein: Automatically selecting the at least one ROI comprises: evaluating a distribution of pixel-by-pixel hardness values ​​within the preferred region of the preferred elastography frame; identifying a localized hardness region in the preferred zone; and A local hardness region representing most hardness values ​​is selected as the at least one ROI based on the distribution of pixel-by-pixel hardness values.

8. The method according to claim 7, further comprising: classifying a hardness probability of the anatomical structure at each pixel in the preferred elastography frame; and displaying the classified hardness probabilities in a histogram including a plurality of bars associated with the classified hardness probabilities, Wherein selecting the local hardness region representing a majority of hardness values ​​comprises identifying a highest bar in the histogram.

9. The method according to claim 3, wherein: Automatically selecting the at least one ROI comprises: determining a correlation coefficient between each pair of temporally adjacent elastography frames; averaging correlation coefficients between the preferred elastic imaging frame and the temporally adjacent elastic imaging frames; determining a temporal stability score for each of the regions based on the average correlation coefficient; and The at least one ROI is selected as the region having the highest temporal stability score.

10. The method according to claim 1, wherein: Automatically selecting the at least one ROI comprises: deriving a ROI placement heatmap based on the smoothness heatmap, the hardness frequency heatmap, and the temporal stability heatmap; and The at least one ROI is selected using the ROI placement heat map.

11. A system for performing stiffness measurement of an anatomical structure in a patient using ultrasonic shear wave elastography, the system comprising: an ultrasound image source (110) configured to provide an ultrasound image of the anatomical structure including a plurality of elastic imaging frames, wherein the plurality of elastic imaging frames are provided by an imaging cycle performed by an ultrasound imaging system; a processing unit (120); and A memory (130) storing instructions which, when executed by the processing unit, cause the processing unit to: automatically identifying ( S212 ) a preferred elastic imaging frame among the plurality of elastic imaging frames for use in performing hardness measurement on the anatomical structure; automatically identifying ( S213 ) a preferred region of the preferred elastic imaging frame based on a confidence level; automatically selecting (S214) at least one region of interest (ROI) based on the stiffness measurement results within the preferred region of the preferred elastography frame; and The stiffness of the anatomical structure in the at least one ROI is measured (S215).

12. The system according to claim 11, wherein: The instructions cause the processing unit to automatically identify the preferred elastography frame based on temporal stability by: calculating a correlation coefficient between each pair of adjacent elastic imaging frames in the plurality of elastic imaging frames; assigning a score to each of the plurality of elastography frames based on an average of the correlation coefficient with the immediately preceding elastography frame and the correlation coefficient with the immediately succeeding elastography frame, wherein a higher average of the correlation coefficients is assigned a higher score; and The elastography frame having the highest score is identified as the preferred elastography frame.

13. The system according to claim 11, wherein: The instructions cause the processing unit to automatically identify the preferred elastic imaging frame based on thresholding according to a confidence level by: excluding each of the plurality of elastography frames in which more than 60% of the pixels in the elastography frame have a low confidence score; and The remaining elastography frames having the highest confidence scores are identified as the preferred elastography frames.

14. The system according to claim 11, wherein: The instructions cause the processing unit to automatically identify the preferred region of the preferred elastic imaging frame by: creating a confidence map of the selected elastography frame; and excluding low confidence regions from the confidence map based on a previously defined confidence threshold, The preferred area of ​​the preferred elastic imaging frame includes a remaining area of ​​the confidence map after excluding the low confidence area.

15. The system according to claim 11, wherein: The instructions cause the processing unit to automatically select the at least one ROI by: determining a local spatial standard deviation of stiffness in said preferred region of said preferred elastography frame; assigning spatial stability scores based on the local spatial standard deviations of hardness, respectively, wherein lower spatial standard deviations of hardness are assigned higher spatial stability scores; and The region with the highest steric stability score among the preferred regions is selected as the preferred region.

16. The system of claim 11, wherein: The instructions cause the processing unit to automatically select the at least one ROI by: evaluating a distribution of pixel-by-pixel hardness values ​​within the preferred region of the preferred elastography frame; identifying a localized hardness region in the preferred zone; and A local hardness region representing most hardness values ​​is selected as the at least one ROI based on the distribution of pixel-by-pixel hardness values.

17. The system of claim 11, wherein: The instructions cause the processing unit to automatically select the at least one ROI by: The temporally most stable region is selected as the at least one ROI using the correlation coefficient between each pair of adjacent elastography frames calculated for automatically identifying the preferred elastography frame.

18. The system of claim 11, wherein: The instructions cause the processing unit to automatically select the at least one ROI by: deriving a ROI placement heatmap based on the smoothness heatmap, the hardness frequency heatmap, and the temporal stability heatmap; and The at least one ROI is selected using the ROI placement heat map.

19. The system of claim 12, wherein: The at least one ROI is circular having a diameter set by a sampling caliper within a range of about 0.5 cm to about 2.0 cm.

20. A non-transitory computer readable medium storing instructions for performing stiffness measurement of an anatomical structure in a patient using ultrasonic shear wave elastography, the instructions, when executed by one or more processors (120), causing the one or more processors to: receiving (S211) an ultrasound image of the anatomical structure including a plurality of elastic imaging frames, wherein: The plurality of elastography frames are provided by an imaging cycle performed by an ultrasound imaging system; automatically identifying ( S212 ) a preferred elastic imaging frame among the plurality of elastic imaging frames for use in performing hardness measurement on the anatomical structure; automatically identifying ( S213 ) a preferred region of the preferred elastic imaging frame based on a confidence level; automatically selecting (S214) at least one region of interest (ROI) based on the stiffness measurement results within the preferred region of the preferred elastography frame; and The stiffness of the anatomical structure in the at least one ROI is measured (S215).

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