Method and system for performing stiffness measurements using ultrasound shear wave elastography

Abstract

Systems and methods are provided for performing stiffness measurements of an anatomical structure in a patient using ultrasound shear wave elastography. The method includes acquiring a plurality of elastography frames from an ultrasound image of the anatomical structure, the plurality of elastography frames being provided by a cine loop performed by an ultrasound imaging system, automatically identifying a preferred elastography frame of the plurality of elastography frames for performing stiffness measurements of the anatomical structure, automatically identifying a preferred region of the preferred elastography frame based on a confidence level, automatically selecting at least one region of interest (ROI) based on stiffness measurements in the preferred region of the preferred elastography frame, and measuring stiffness of the anatomical structure in the at least one ROI.

Description

[Technical Field]

[0001] Although accepted as a quantitative biomarker for staging liver fibrosis, ultrasound shear wave elastography has not been fully adopted as a new clinical standard. [Background technology]

[0002] Some barriers to widespread adoption of shear wave elastography relate to subjective and potentially unreliable user interactions, including identifying a good field of view or imaging frame for disease assessment and selecting an area within the field of view for measurements. While expert users can overcome these challenges to obtain reliable data, such barriers prevent users with different levels of experience from obtaining reliable and reproducible tissue stiffness measurements in a rapid and standardized manner.

[0003] Some conventional ultrasound shear wave elastography products, such as ElastQ Imaging available from Koninklijke Philips NV, offer two-dimensional shear wave elastography for imaging and quantification. Real-time continuous imaging mode provides a color-coded stiffness map in units of Young's modulus (or shear wave velocity) superimposed on a B-mode ultrasound image, along with a confidence map indicating the quality of the stiffness measurement, viewed side-by-side and recorded with the shear wave elastography image. The confidence map can help guide the user through optimal real-time acquisition and frame and region-of-interest (ROI) selection in review mode. Low confidence levels in shear wave elastography are often associated with artifacts caused by, for example, hepatic vessels, acoustic shadows, reverberation near the liver capsule, and focal liver lesions. A confidence threshold can be set to mask unreliable regions containing such artifacts for quantification. Summary of the Invention [Problem to be solved by the invention]

[0004] However, depending on the patient's stage and underlying morphology, users still must make subjective judgments regarding imaging frame and ROI selection to obtain stiffness values, a process that is inconsistent and potentially error-prone, especially for inexperienced users. [Means for solving the problem]

[0005] The illustrative embodiments are best understood from the following detailed description when read in conjunction with the accompanying drawings. It is emphasized that the various features are not necessarily drawn to scale. In fact, dimensions may be arbitrarily increased or decreased for clarity of discussion. Where applicable and practical, like reference numerals refer to like elements. [Brief explanation of the drawings]

[0006] [Figure 1] FIG. 1 is a simplified block diagram of an ultrasound imaging system for performing stiffness measurements of a subject's anatomical structure using ultrasound shear wave elastography, according to a representative embodiment. [Figure 2] FIG. 1 is a flow diagram of a method for performing stiffness measurements of anatomical structures in a subject using ultrasound shear wave elastography, according to a representative embodiment. [Figure 3] 1 illustrates multiple frames acquired in an exemplary cine loop acquisition for performing stiffness measurements, according to a representative embodiment. [Figure 4] 1 illustrates a preferred elastography frame with multiple ROIs selected for performing stiffness measurements, according to a representative embodiment. [Figure 5] 10 illustrates a ROI placement heat map of a preferred elastography frame with multiple ROIs selected to perform stiffness measurements, according to a representative embodiment. [Figure 6] 10 shows a chart of exemplary histogram results for a global assessment of stiffness from a preferred elastography frame, according to a representative embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0007] According to a representative embodiment, a method is provided for performing stiffness measurements on a patient's anatomical structure using ultrasound shear wave elastography, the method including acquiring a plurality of elastography frames from an ultrasound image of the anatomical structure, the plurality of elastography frames being provided by a cine loop performed by an ultrasound imaging system, automatically identifying a preferred elastography frame of the plurality of elastography frames for performing stiffness measurements on the anatomical structure, automatically identifying a preferred region of the preferred elastography frame based on a confidence level, automatically selecting at least one region of interest based on stiffness measurements within the preferred region of the preferred elastography frame, and measuring stiffness of the anatomical structure in the at least one region of interest.

[0008] According to a representative embodiment, a system for performing stiffness measurements of a patient's anatomical structure using ultrasound shear wave elastography is provided. The system includes an ultrasound image source configured to provide ultrasound images of the anatomical structure, the plurality of elastography frames being provided by a cine loop performed by an ultrasound imaging system. When executed by a processing unit, the instructions cause the processing unit to automatically identify a preferred elastography frame of the plurality of elastography frames for performing stiffness measurements of the anatomical structure, automatically identify a preferred region of the preferred elastography frame based on a confidence level, automatically select at least one region of interest (ROI) based on stiffness measurements in the preferred region of the preferred elastography frame, and measure stiffness of the anatomical structure within the at least one ROI.

[0009] According to an exemplary embodiment, a non-transitory computer-readable medium stores instructions for performing stiffness measurements of a patient's anatomical structure using ultrasound shear wave elastography. When executed by one or more processors, the instructions cause the one or more processors to receive ultrasound images of the anatomical structure including a plurality of elastography frames, the plurality of elastography frames being provided by a cine loop performed by an ultrasound imaging system, automatically identify preferred elastography frames of the plurality of elastography frames for performing stiffness measurements of the anatomical structure, automatically identify preferred regions of the preferred elastography frames based on a confidence level, automatically select at least one ROI based on stiffness measurements in the preferred regions of the preferred elastography frames, and measure stiffness of the anatomical structure within the at least one ROI.

[0010] In the following detailed description, for purposes of explanation and not limitation, exemplary embodiments disclosing specific details are set forth to provide a thorough understanding of embodiments in accordance with the present teachings. Descriptions of known systems, devices, materials, methods of operation, and methods of manufacture may be omitted so as to avoid obscuring the description of the exemplary embodiments. Nevertheless, systems, devices, materials, and methods within the purview of those skilled in the art are within the scope of the present teachings and may be used in accordance with the exemplary embodiments. It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting. Defined terms are in addition to the technical and scientific meaning of the defined terms as commonly understood and accepted in the art of the present teachings.

[0011] Terms such as "first," "second," and "third" may be used herein to describe various components or constituent elements, but it should be understood that these components or constituent elements should not be limited by these terms. These terms are used only to distinguish one component or constituent element from another. Thus, a first element or component described below could be referred to as a second element or component without departing from the teachings of the inventive concept.

[0012] The terms used herein are for the purpose of describing particular embodiments only and are not intended to be limiting. As used in this specification and the appended claims, the singular forms of "a," "an," and "the" are intended to include both the singular and the plural unless the context clearly dictates otherwise. Additionally, the terms "comprises," "comprises," and / or similar terms specify the presence of stated features, elements, and / or components, but do not exclude the presence or addition of one or more other features, elements, components, 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.

[0013] Unless otherwise specified, when a component or component is said to be "connected," "coupled," or "adjacent" to another component or component, it is understood that the component or component may be directly connected or coupled to the other component or component, or there may be intervening components or components. That is, these and similar terms encompass the case where one or more intermediate components or components may be used to connect the two components or components. However, when a component or component is said to be "directly connected" to another component or component, this only encompasses the case where the two components or components are connected to each other without any intermediate or intervening components or components.

[0014] Thus, the present disclosure is intended to derive one or more of the advantages, as specifically set forth below, through one or more of its various aspects, embodiments, and / or specific features or subcomponents. For purposes of explanation and not limitation, exemplary embodiments disclosing specific details are described to provide a thorough understanding of embodiments in accordance with the present teachings. However, other embodiments consistent with the present disclosure that depart from the specific details disclosed herein remain within the scope of the appended claims. Furthermore, descriptions of well-known devices and methods may be omitted so as not to obscure the description of the exemplary embodiments. Such methods and devices are within the scope of the present disclosure.

[0015] Generally, various embodiments described herein provide systems and methods for efficiently and reliably performing ultrasound shear wave elastography by providing users with automated, standardized guidance in selecting optimal elastography imaging frames, regions within the selected imaging frames, and ROIs within the regions for quantification. Thus, users do not need to perform an integrated evaluation of confidence maps, B-mode, and elastography images to select regions for quantification, which relies on personal experience, as is the case with conventional techniques for performing ultrasound shear wave elastography. Various embodiments also provide a global assessment of liver stiffness instead of a single value output. This is beneficial because the development of liver fibrosis can exhibit spatially heterogeneous patterns, and a single value (average) stiffness output can lead to inaccurate determinations. Various embodiments facilitate a more global and accurate quantitative assessment of liver fibrosis in a rapid and standardized manner, driving shear wave elastography and other liver quantification tools to become the clinical standard for diffuse liver disease assessment.

[0016] Changes in the mechanical properties (e.g., stiffness) of soft tissues often signal underlying pathologies. Based on this principle, ultrasound shear wave elastography, a relatively new quantitative imaging modality, measures tissue stiffness and quantifies such mechanical properties to aid in diagnosis. Conventional clinical applications generally include staging of liver fibrosis and cancer detection in the breast, liver, prostate, and thyroid. Organs such as the liver, breast, prostate, and thyroid are often modeled as isotropic materials in ultrasound shear wave elastography, meaning that the corresponding mechanical properties or response to stress are assumed to be independent of the direction of loading. Under the further assumption that such tissues are isotropic, linear, and incompressible, only one physical parameter, namely, Young's modulus (E) or shear modulus (μ), is acquired to characterize stiffness. The physical parameter ultrasound elastography can be calculated using the equation E = 3μ = 3ρV sh 2 (1) where ρ denotes the tissue density.

[0017] For example, in the use case of liver fibrosis assessment, Young's modulus and shear wave velocity (V sh ) increases with the severity of liver fibrosis. This is because liver tissue becomes stiffer as liver disease progresses from normal to fibrosis to cirrhosis. With the prevalence of liver disease increasing worldwide, ultrasound shear wave elastography is being adopted clinically as a rapid, safe, cost-effective, and definitive diagnostic tool for staging liver fibrosis.

[0018] FIG. 1 is a simplified block diagram of an ultrasound imaging system for performing stiffness measurements of a subject's anatomical structure using ultrasound shear wave elastography, according to a representative embodiment.

[0019] 1, 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 in the subject 101. The shear wave elastography characteristics enable determination of stiffness in the region of interest.

[0020] The imaging device 110 can include a known transducer probe (not shown) of an ultrasound imaging system. The transducer probe can include a transducer array including a two-dimensional array of transducers, which can be scanned in two or three dimensions, for example, to emit ultrasound waves into the body of the subject 101 and receive echo signals in response. The transducer array can include, for example, capacitive micromachined ultrasound transducers (CMUTs) or piezoelectric transducers formed from materials such as PZT or PVDF. The transducer array is coupled to a microbeamformer within the transducer probe, which controls reception of signals by the transducers.

[0021] The computer system 105 receives image data from the imaging device 110 and stores and processes the imaging data according to the exemplary embodiments described herein. The computer system 105 includes a processing unit 120, a memory 130, a display 140 with a graphical user interface (GUI) 145, and a user interface 150. The computer system 105 and / or the imaging device 110 further include an imaging interface (IF) 115 for interfacing with the imaging device 110 and the processing unit 120 to receive ultrasound images (e.g., B-mode ultrasound images). The imaging IF 115 receives imaging data (e.g., ultrasound image data) acquired by the imaging device 110 and converts it into a format readable by the processing unit 120. In an alternative configuration, the processing unit 120 can receive ultrasound images from a database 135 that stores previously acquired ultrasound images of the subject 101.

[0022] Memory 130 stores instructions executable by processing unit 120. When executed, the instructions cause processing unit 120 to perform various processes related to shear wave elastography, as described more fully below. The instructions further enable a user (e.g., a sonographer or clinician) to use GUI 145 and / or user interface 150 to perform different steps of an examination and initialize imaging device 110. Additionally, processing unit 120 may implement additional operations based on executing the instructions, such as instructing or otherwise communicating with other elements of computer system 105, including memory 130 and display 140, to perform one or more of the processes described above.

[0023] Memory 130 can include main memory and / or static memory, and such memories can communicate with each other and with processing unit 120 via one or more buses. Memory 130 stores instructions used to implement some or all aspects of the methods and processes described herein. When executed, the instructions cause processing unit 120 to perform one or more processes for performing stiffness measurements of anatomical structures in a subject, for example, using ultrasound shear wave elastography, as described below with reference to FIG. 2, as well as control the performance of ultrasound images. Memory 130 can be implemented, for example, by any number, type, and combination of random access memory (RAM) and read-only memory (ROM), and can store various types of information, such as software algorithms, that, when executed by processing unit 120, act as instructions to cause processing unit 120 to perform various steps and methods according to the present teachings. Additionally, updates to the methods and processes described herein may be provided to computer system 105 and stored in memory 130.

[0024] 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 field-programmable gate array read-only memory (EPROM), electrically erasable field-programmable gate array read-only memory (EEPROM), registers, hard disks, removable disks, tapes, compact disk read-only memories (CD-ROMs), 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.

[0025] The memory 130 may further include instructions for interfacing the transducer probe of the imaging device 110 with the processing unit 120 to control the acquisition of ultrasound images of the subject 101. The interface for the transducer probe may include a transmit / receive (T / R) switch coupled to the transducer probe's microbeamformer by 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 in which the beam is steered and focused via the transducer probe interface. The beam may be steered straight ahead (orthogonal) from the transducer array or at a different angle for a wider field of view. The processing unit 120 may also include a main beamformer that provides the final beamforming following digitization. In general, the transmission of ultrasound signals and the reception of echo signals in response are well known, and therefore further details in this regard are not included herein.

[0026] Each of memory 130 and database 135 is a tangible storage medium for storing data and executable software instructions and is non-transitory while the software instructions are stored therein. As used herein, the term "non-transitory" should be interpreted as a characteristic of a state that persists over a period of time, rather than as a permanent characteristic of a state. The term "non-transitory" specifically negates fleeting characteristics, such as characteristics of a carrier wave or signal, or other formation that exists only temporarily at any time and in any place. As described above, memory 130 can store software instructions and / or computer-readable code that enable the performance of various functions, and database 135 can store previously acquired ultrasound images of subject 105. Memory 130 and / or database 135 may be secured and / or encrypted, or may be unsecured and / or unencrypted.

[0027] "Memory" is an example of a computer-readable storage medium, and should be interpreted as multiple memories or databases, as the case may be. The memory or database may, for example, be multiple memories or databases local to a computer and / or distributed among multiple computer systems or computing devices, or located in a "cloud" in accordance with known components and methods. Computer-readable storage media is defined as any medium that constitutes patentable subject matter under 35 U.S.C. §101, and excludes any medium that does not constitute patentable subject matter under 35 U.S.C. §101. Examples of such media include non-transitory 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-transitory media include computer disks and non-volatile memory.

[0028] Processing unit 120 represents one or more processing devices configured to execute software instructions stored in memory 130 to perform functions as described in various embodiments herein. Processing unit 120 may be implemented using any combination of hardware, software, firmware, hardwired logic circuitry, or a combination thereof, such as 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. Furthermore, any processing unit or processor herein may include multiple processors, parallel processors, or both. Multiple processors may be included in or combined with a single device or multiple devices.

[0029] 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 in addition to or other than processing unit 120, such as, for example, memory 130, an external server, and / or the cloud. If the AI ​​engine is implemented in the cloud, such as, for example, 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.

[0030] As used herein, the term "processor" encompasses an electronic component capable of executing a program or machine-executable instructions. References to a computing device with a "processor" should be interpreted to include two or more processors or processing cores, 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 containing one or more processors. Modules contain software instructions for performing various functions using one or more processors, which may be within the same computing device or distributed across multiple computing devices.

[0031] Display 140 may be any compatible monitor for displaying at least ultrasound images, such as, for example, 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 (liquid crystal) display. Display 140 may also provide GUI 145, as described above, for displaying and receiving information from a user.

[0032] The user interface 150 may include a user and / or network interface for providing a user with information and data output by the processing unit 120 and / or memory 130 and / or receiving information and data input by a user. That is, the user interface 150 allows a user to operate an imaging device as described herein and to schedule, control, or operate aspects of the ultrasound imaging system 100 of the present teachings. In particular, the user interface 150 allows the processing unit 120 to indicate the effects of the user's controls or operations. The user interface 150 may include one or more of a port, a disk drive, a wireless antenna, or other type of receiver circuitry. The user interface 150 may further connect to one or more interface devices, such as, for example, a mouse, keyboard, trackball, joystick, microphone, video camera, touchpad, touchscreen, voice or gesture recognition captured by a microphone or video camera, etc.

[0033] All or part of user interface 150 may be implemented, for example, by GUI 145 on the touchscreen of display 140. User interface 150 includes graphics such as buttons, fields, slides, and other visual markers that can be actuated by a user to initiate various commands for manipulating images displayed during an ultrasound examination, performing measurements, calculations, etc. Push buttons may be displayed by GUI 145 on the touchscreen.

[0034] In particular, the processing unit 120, memory 130, display 140, GUI 145, and user interface 150 may be located remotely (e.g., in another location in the building or another premises) from the imaging device 110 operated by the sonographer. The processing unit 120, memory 130, display 140, GUI 145, and user interface 150 may be located, for example, at the location where the radiologist / clinician is located. In particular, additional processing units, memory, displays, GUIs, and user interfaces may be located near the sonographer to control various functions of the imaging device 110 required to perform shear wave elastography operations. In a more general sense, the ultrasound imaging system 100 may include a source of ultrasound signal data from an examination, which may be the imaging device 110 or a database previously populated with ultrasound images from previous examinations.

[0035] 2 is a flow diagram illustrating a method for performing stiffness measurements of anatomical structures in a subject using ultrasound shear wave elastography, according to a representative embodiment. The method may be performed, for example, by the ultrasound imaging system 100 described above under control of the processing unit 120 executing instructions stored in memory 130.

[0036] Referring to FIG. 2 , in block S211, multiple elastography frames are acquired from an ultrasound image of an anatomical structure. The ultrasound image may be, for example, a B-mode ultrasound image, although other types of ultrasound images may be incorporated without departing from the scope of the present teachings. The elastography frames are provided by a cine loop performed by an ultrasound imaging device imaging the subject. The cine loop may be acquired over a minimal length of time (e.g., approximately 6 seconds) while the patient takes shallow breaths or pauses breathing during that length of time. The elastography frames may be acquired directly from the ultrasound imaging device during or after the imaging process, or may be acquired from a previously populated database of ultrasound images (e.g., database 135). The ultrasound imaging device and database may collectively be referred to as an ultrasound image source.

[0037] In block S212, a preferred elastography frame of the plurality of elastography frames is automatically identified by a processing unit (e.g., processing unit 120). The preferred elastography frame is used to perform stiffness measurements of anatomical structures. The preferred elastography frame provides a consistent stiffness map across elastography frames that are temporally adjacent to the preferred elastography frame, meaning elastography frames acquired immediately before and after the preferred elastography frame. Compared to subjective manual selection of elastography frames in conventional techniques, automatic identification consistently provides a high-quality elastography frame as the preferred elastography frame regardless of user experience and expertise.

[0038] In one embodiment, preferred elastography frames can be automatically identified based on temporal stability, meaning that a preferred elastography frame provides a consistent stiffness map across adjacent elastography frames (those occurring immediately before and after the preferred elastography frame). Identifying preferred elastography frames based on temporal stability may include assigning a temporal stability score to each elastography frame of the multiple frames acquired in block S211 based on stiffness correlation between the elastography frame and previous and subsequent temporal elastography frames, and identifying the elastography frame with the highest temporal stability score as the preferred elastography frame. The correlation may be based, for example, on corresponding pixels across the elastography frames, each associated with a stiffness number.

[0039] For example, for each elastography frame of a plurality of elastography frames, a correlation coefficient is determined between the elastography frame and the immediately preceding elastography frame, and another correlation coefficient is determined between the elastography frame and the immediately succeeding elastography frame. The correlation coefficient may be determined by the error between pixel-by-pixel stiffness measurements in pairs of simultaneous frames. A temporal stability score is then assigned to each elastography frame based on the average of (i) the correlation coefficient between the elastography frame and the immediately preceding elastography frame and (ii) the correlation coefficient between the elastography frame and the immediately succeeding elastography frame. The average may be determined, for example, using mean square error (MSE). A higher score is assigned to a higher average of the correlation coefficients. Thus, the elastography frame with the highest score is identified as the preferred elastography frame. In one embodiment, the complement or inverse of the MSE may be used as the final temporal stability score. In various embodiments, the MSE may or may not be normalized without departing from the scope of the present teachings.

[0040] In one embodiment, the MSE may be based on a combination of stiffness MSE and confidence MSE. More specifically, stiffness MSE (MSE S ) can be calculated from the stiffness map of each elastography frame, and the reliability MSE (MSE C ) can be calculated from the confidence map of each elastography frame. Then, the combined MSE (MSE combined ) is expressed as Eq. (2) MSE combine (p, f1, f2) = MSE S (p, f1, f2) × MSE C (p, f1, f2) Equation (2) where p is the pixel value, f1 is the elastography frame, and f2 is a frame adjacent to the elastography frame.

[0041] The elastography frame with the lowest combined MSE is selected as the preferred elastography frame.

[0042] In another embodiment, the combined MSE of each elastography frame combined is expressed as equation (3). MSE combine (p, f1, f2)=A×MSE S (p, f1, f2)+B×MSE C (p, f1, f2) Equation (3) As shown in the MSE S and MSE C where pixel values ​​are 1 for the elastography frame and 2 for the frames adjacent to the elastography frame.

[0043] A and B are the stiffness MSEs, respectively. S and reliability MSE C are weighting factors indicating the weighting to assign to the stiffness MSE. For example, weighting factors A and B may be determined empirically or based on typical or expected ranges of the underlying stiffness and confidence maps. For example, if confidence ranges from 0 to 100% but stiffness typically ranges from 0 to 30 kPa, a weighting of 3 (A=3, B=1) may be used to assign approximately equal contributions to the stiffness MSE. S The elastography frame with the lowest combined MSE is selected as the preferred elastography frame.

[0044] 3 illustrates multiple frames acquired in an exemplary cine loop acquisition for performing stiffness measurements, according to a representative embodiment. Referring to FIG. 3, cine loop acquisition 300 includes five elastography frames 301, 302, 303, 304, and 305 that show stiffness maps. Using temporal stability analysis, for example, elastography frame 304 is identified as having the highest temporal stability score for the temporal stability between elastography frame 304 and adjacent elastography frames 303 and 305.

[0045] In another embodiment, preferred elastography frames can be automatically identified based on a reliability-based threshold. Identifying preferred elastography frames based on a reliability-based threshold can include assigning a reliability score to pixels in each elastography frame of a plurality of elastography frames, eliminating each elastography frame in which more than a threshold number of pixels in the elastography frame have a low reliability score, and identifying the remaining elastography frame with the highest reliability score as the preferred elastography frame. By first eliminating elastography frames in which more than a threshold number of pixels have a low reliability score, situations in which all of the elastography frames are too reliable to use can be identified. The threshold number of pixels can be set, for example, at 60 percent of all pixels in the elastography frame, although other percentages (e.g., 50 percent) can be incorporated without departing from the scope of the present teachings. The reliability scores of pixels can be determined, for example, using a stiffness map and a corresponding reliability map, as would be apparent to one skilled in the art. A confidence score is considered low when it is below a confidence threshold set by the user (eg, the default threshold may be 60 percent).

[0046] 2, in block S213, preferred regions of the preferred elastography frame are automatically identified by a processing unit (e.g., processing unit 120) based on confidence levels associated with different regions of the preferred elastography frame. The confidence levels indicate the likelihood of obtaining accurately representative stiffness measurements from particular regions of the preferred elastography frame. Typically, B-mode anatomy is used to distinguish and exclude elastography signals in the preferred elastography frame from small vessels and abnormally bright outlying regions.

[0047] Preferred regions may be identified by creating a confidence map of the selected elastography frame and removing regions of low confidence, as will be apparent to those skilled in the art. Regions of low confidence may, for example, be regions having a confidence level below a previously defined confidence threshold. Thus, regions of low confidence may be removed by thresholding the confidence map to remove pixels below the previously defined confidence threshold. Following removal of the low confidence regions, the remaining regions, including high confidence regions, are considered to be preferred regions. Removing low confidence regions from consideration typically removes most of the vascular signal from the stiffness map in practice.

[0048] However, some small vessels in the B-mode image may still remain within the preferred region (high confidence region of the stiffness map), reducing the confidence level. Therefore, in one embodiment, Doppler images may be selectively used to further refine the preferred region. More specifically, Doppler images may be interleaved with the acquisition of elastography frames in block S211 to capture blood flow signals within the anatomical structure. Regions with strong Doppler signals indicating high blood flow (e.g., exceeding a previously defined blood flow threshold) are identified and removed based on the blood flow signals. The remaining low blood flow regions are identified as preferred regions.

[0049] In another embodiment, the local spatial standard deviation of stiffness is used to automatically identify preferred regions of the preferred elastography frame. This involves calculating the local spatial standard deviation of stiffness associated with different regions throughout the preferred elastography frame and assigning spatial stability scores based on the calculated local spatial standard deviation of stiffness, with lower spatial standard deviations of stiffness being assigned higher spatial stability scores. These spatial stability scores can be calculated as the complement or reciprocal of the local standard deviation. The region of the preferred elastography frame with the highest spatial stability score (indicating the lowest local standard deviation) is identified as the preferred region.

[0050] In block S214, at least one region of interest (ROI) is automatically selected by a processing unit (e.g., processing unit 120) based on stiffness measurements within a preferred region of the preferred elastography frame. For purposes of the following discussion, it is assumed that one ROI is selected to perform stiffness measurements on an anatomical structure. However, it is understood that multiple ROIs may be selected following the same process without departing from the scope of the present teachings. For example, FIG. 4 illustrates the selection of three ROIs to perform stiffness measurements on, as described below.

[0051] Generally, regions with stable and consistent stiffness values ​​are desirable as ROIs. Such regions can be identified using several techniques, including (i) determining spatial stability based on local spatial standard deviation, (ii) determining temporal stability based on the squared error of temporally adjacent elastography frames, (iii) determining stiffness value probability based on pixel-wise stiffness values, and (iv) determining an ROI placement heat map based on a combination of other techniques described below. The ROI has a predetermined size and shape, for example, provided by the user. For example, the ROI may be circular, e.g., with a diameter ranging from about 0.5 cm to about 2.0 cm. The size and shape of the ROI may correspond, for example, to the size and shape of the sampling caliper of an ultrasound imaging device. Of course, other sizes and shapes of ROIs may be incorporated without departing from the scope of the present teachings.

[0052] Automatically selecting an ROI by determining spatial stability includes determining a local standard deviation of stiffness of regions in a preferred region of a preferred elastography frame, determining a spatial stability score for each of the regions, and selecting at least one of the regions as the ROI with the highest spatial stability score. In one embodiment, the highest spatial stability score indicates the lowest spatial variability, in which case the spatial stability score may be the complement or reciprocal of the local standard deviation of stiffness. In various embodiments, the local standard deviation of stiffness may or may not be normalized without departing from the scope of the present teachings.

[0053] Automatically selecting an ROI by determining temporal stability includes determining a correlation coefficient between a pair of temporally adjacent elastography frames, averaging the correlation coefficients between the preferred elastography frame and the temporally adjacent elastography frames, determining a temporal stability score for each of the regions based on the averaged correlation coefficients, and selecting at least one of the regions as the ROI with the highest temporal stability score, indicating the most temporally stable region. The correlation coefficients may be averaged using MSE, using equations (2) and (3) above, or using squared error (SE), e.g., using these same equations with SE instead of MSE. In one embodiment, the highest temporal stability score indicates the lowest temporal variability, in which case the temporal stability score may be the complement or reciprocal of MSE or SE.

[0054] Automatically selecting an ROI by determining stiffness value probabilities based on pixel-wise stiffness values ​​includes evaluating the distribution of pixel-wise stiffness values ​​within the identified preferred region, and selecting a local stiffness region having a predetermined size and shape of the ROI that includes stiffness values ​​representing a majority stiffness value based on the distribution of pixel-wise stiffness values. The majority stiffness value is a common stiffness value in the elastography frame. The distribution of pixel-wise stiffness values ​​can be evaluated using a stiffness map of the preferred elastography frame, for example, by histogram analysis of pixel-wise stiffness values ​​within the frame. Generally, the highest bar in the histogram indicates the majority stiffness value. In this case, identifying the local stiffness region representing the majority stiffness value can include identifying the highest bar in the histogram.

[0055] Automatically selecting one ROI by determining an ROI placement heat map includes providing a smoothness heat map from the spatial stability determination, providing a temporal stability heat map from the temporal stability determination, and providing a stiffness frequency heat map from the stiffness value probability determination of the preferred region of the preferred elastography frame, and combining (e.g., multiplying) the smoothness heat map, the temporal stability heat map, and the stiffness frequency heat map to derive a pixel-by-pixel ROI placement heat map. To derive the final ROI placement heat map, a local average is calculated for the pixel-by-pixel ROI placement heat map. An ROI is then selected using the final ROI placement heat map. For example, the ROI center position may be determined as the position of the maximum pixel-wise value in the final ROI placement heat map. An example of an ROI placement heat map is shown in FIG. 5, described below.

[0056] In one embodiment, a user can specify the number of ROIs (N) desired for performing stiffness measurements using a GUI (e.g., GUI 145). The N ROIs are then automatically placed in the N regions determined to be most optimal for stiffness measurements. The N regions may be identified as ROIs using any of the techniques described above.

[0057] 4 illustrates a preferred elastography frame with multiple ROIs selected for performing stiffness measurements, according to a representative embodiment. Referring to FIG. 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 stiffness 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, resulting in ROI 403 being the most preferred ROI.

[0058] FIG. 5 illustrates an ROI placement heat map of a preferred elastography frame with multiple ROIs selected for performing stiffness measurements, according to a representative embodiment. Referring to FIG. 5 , the geometric means of the smoothness heat map 510, the temporal stability heat map 520, and the stiffness frequency heat map 530 are multiplied together to provide a pixel-wise ROI placement heat map 540. A local spatial averaging filter (e.g., equal to the caliper diameter) is applied to the pixel-wise ROI placement heat map 540 to generate a final ROI placement heat map 545, which is used to identify preferred locations for one or more ROIs. In the illustrated example, the ROI placement provides a first ROI 501 centered on the brightest pixel in the final ROI placement heat map 545, a second ROI 502 centered on the second brightest pixel in the final ROI placement heat map 545, and a third ROI 503 centered on the third brightest pixel in the heat map.

[0059] 2, in block S215, the stiffness of the anatomical structure is measured in the ROI. The stiffness may be measured using any suitable technique apparent to one skilled in the art. For example, the average stiffness value within the circular ROI may be used as the output stiffness measurement.

[0060] In block S216 (optional), a global assessment of the overall stiffness of the anatomical structure may be performed using stiffness measurements across the preferred elastography frame, including the ROI measurements performed in block S215. Alternatively, the global assessment of overall stiffness may be limited to stiff pixels above the confidence threshold described above. The global assessment provides a more accurate image of the overall stiffness of the anatomical structure and corresponding health or condition than a single (e.g., averaged) stiffness value representing the anatomical structure as provided by conventional stiffness measurement techniques.

[0061] The evaluation can include categorizing the stiffness measurements in the preferred elastography frames according to severity (e.g., greater stiffness corresponds to greater severity) and displaying the percentage of the preferred elastography frames that fall into each category. For example, a histogram of all stiffness measurements in the preferred elastography frames can be created, with "bins" in the histogram defined by empirically determined cutoffs for different categories. The bins can be displayed as corresponding bars in the histogram. For example, the bins can include cutoffs for different grades of disease present in the anatomical structure. The percentage of pixels in each category can then be easily displayed in a simple chart, such as that shown in FIG. 6, described below.

[0062] For example, if the anatomical structure is the subject's liver, the overall assessment can be performed according to the Meta-Analysis of Histological Data in Viral Hepatitis (METAVIR) scoring system, in which stiffness is classified into five groups designated F0, F1, F2, F3, and F4, where the categories are grades of liver fibrosis arranged in increasing severity from F0 to F4.

[0063] FIG. 6 shows a chart of exemplary histogram results for a global assessment of stiffness from a preferred elastography frame, according to a representative embodiment. Referring to FIG. 6 , chart 600 shows categories F0, F1, F2, F3, and F4 corresponding to histogram bins, with the size and shading of each category visually corresponding to the percentage of stiffness measurements that fall within that category. Thus, in the illustrated example, 55 percent of the stiffness measurements fall in category F0 (the largest and lightest shaded blocks), 15 percent of the stiffness measurements fall in category F1, 12 percent of the stiffness measurements fall in category F2, 10 percent of the stiffness measurements fall in category F3, and 8 percent of the stiffness measurements fall in category F4 (the smallest and darkest shaded blocks). Chart 600 can be displayed on a display (e.g., display 140) to allow a user to quickly and accurately assess the overall stiffness characteristics and overall health of the liver.

[0064] According to various embodiments of the present disclosure, the methods described herein may be implemented using a hardware computer system executing a software program stored on a non-transitory storage medium. Furthermore, in exemplary, non-limiting embodiments, implementations may include distributed processing, component / object distributed processing, and parallel processing. A virtual computer system process may implement one or more of the methods or functions described herein, and the processors described herein may be used to support a virtual processing environment.

[0065] While performing stiffness measurements 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 purview of the appended claims, as presently stated and as amended, without departing from the scope and spirit of the teachings. Also, while performing stiffness measurements has been described with reference to particular means, materials, and embodiments, the intention is not to be limited to the details disclosed, but rather, the embodiments extend to all functionally equivalent structures, methods, and uses, as may be within the scope of the appended claims.

[0066] The descriptions of the embodiments described herein are intended to provide a general understanding of the structure of various embodiments. The illustrations are not intended to serve as a complete description of all of the elements and features of the present disclosure described herein. Many other embodiments may be apparent to those skilled in the art upon reviewing the present disclosure. Other embodiments may be utilized and derived from the present disclosure, such that structural and logical substitutions and changes may be made without departing from the scope of the present disclosure. Additionally, the illustrations are merely representational and may not be drawn to scale. Certain proportions in the figures are exaggerated, while other proportions are minimized. Therefore, the present disclosure and the drawings should be considered illustrative and not limiting.

[0067] One or more embodiments of the present disclosure may be individually and / or collectively referred to herein by the term "invention" merely for convenience and without any intention to intentionally limit the scope of the present application to any particular invention or inventive concept. Furthermore, although specific likenesses have been illustrated and described herein, it should be understood that any subsequent configuration designed to achieve the same or similar purpose may be substituted for the specific likeness shown. The present disclosure is intended to cover any and all subsequent adaptations or modifications of the various embodiments. Combinations of the above embodiments, and other embodiments not specifically described herein, will become apparent to those skilled in the art upon reviewing the description.

[0068] This Abstract of the Disclosure is provided for purposes of compliance 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. Moreover, in the foregoing Detailed Description, various features may be grouped together or described in a single embodiment for the purpose of streamlining the disclosure. This disclosure should not be interpreted as reflecting an intention that the claimed embodiments require more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive subject matter may be directed to less than all features of any of the disclosed embodiments. Accordingly, the following claims are hereby incorporated into the Detailed Description, with each claim standing on its own as defining separately claimed subject matter.

[0069] The foregoing description of the disclosed embodiments is provided to enable those skilled in the art to practice the concepts described in this disclosure. Accordingly, the subject matter disclosed above should be considered illustrative and not limiting, and the appended claims are intended to encompass all such modifications, enhancements, and other embodiments that fall within the true spirit and scope of the present disclosure. Accordingly, to the maximum extent permitted by law, the scope of the present disclosure should be determined by the broadest permissible interpretation of the following claims and their equivalents, and should not be limited or constrained by the foregoing detailed description.

Claims

1. 1. A method of performing stiffness measurements of an anatomical structure in a patient using ultrasound shear wave elastography, comprising the steps of acquiring a plurality of elastography frames from an ultrasound image of the anatomical structure, the plurality of elastography frames being provided by a cine loop performed by an ultrasound imaging system; automatically identifying a preferred elastography frame of a plurality of elastography frames for performing stiffness measurements on the anatomical structure; automatically identifying preferred regions of the preferred elastography frames based on a confidence level; automatically selecting at least one region of interest based on stiffness measurements within a preferred region of the preferred elastography frame; measuring the stiffness of an anatomical structure in said at least one region of interest; A method comprising:

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

3. wherein the step of automatically identifying preferred elastography frames is based on confidence-based thresholding; removing each elastography frame of the plurality of elastography frames in which more than 60% of the pixels in the elastography frame have a low confidence score; identifying the remaining elastography frame having the highest confidence score as the preferred elastography frame; 2. The method of claim 1, comprising:

4. The step of automatically identifying preferred regions of the preferred elastography frames comprises: generating a confidence map of the selected elastography frames; removing regions of low confidence from the confidence map based on a previously defined confidence threshold; and the preferred region of the preferred elastography frame comprises the remaining region of the confidence map after removing the low confidence regions. The method of claim 1.

5. The step of automatically identifying preferred regions of the preferred elastography frames comprises: performing interleaved Doppler imaging by acquiring the plurality of elastography frames to capture blood flow signals within the anatomical structure; removing regions of the preferred elastography frame having Doppler signals from the Doppler imaging that indicate high blood flow; and the preferred region of the preferred elastography frame further comprises a remaining region after removing the region having high blood flow. The method of claim 4.

6. The step of automatically selecting at least one region of interest includes: determining a local spatial standard deviation of stiffness in a preferred region of the preferred elastography frame; assigning a spatial stability score based on the local spatial standard deviation of stiffness, respectively, wherein a lower spatial standard deviation of stiffness is assigned a higher spatial stability score; selecting a region with the preferred region having the highest spatial stability score as the preferred region; 2. The method of claim 1, comprising:

7. The step of automatically selecting at least one region of interest includes: assessing a distribution of stiffness values ​​per pixel within a preferred region of the preferred elastography frame; identifying localized stiffness regions in the preferred region; selecting a local stiffness region representing a majority stiffness value based on the distribution of stiffness values ​​per pixel as the at least one region of interest; 2. The method of claim 1, comprising:

8. classifying a stiffness probability of an anatomical structure at each pixel in the preferred elastography frame; displaying the sorted stiffness probabilities in a histogram having a plurality of bars associated with the sorted stiffness probabilities; and selecting the local stiffness region representing the majority stiffness value comprises identifying the tallest bar in the histogram. The method of claim 7.

9. The step of automatically selecting at least one region of interest includes: determining a correlation coefficient between pairs of temporally adjacent elastography frames; averaging correlation coefficients between the preferred elastography frame and the temporally adjacent elastography frames; determining a temporal stability score for each of the regions based on the averaged correlation coefficients; selecting the at least one region of interest as the region having the highest temporal stability score; 4. The method of claim 3, comprising:

10. The step of automatically selecting at least one region of interest includes: deriving a region of interest location heatmap from the smoothness heatmap, the stiffness frequency heatmap, and the temporal stability heatmap; selecting the at least one region of interest using the region of interest location heatmap; 2. The method of claim 1, comprising:

11. 1. A system for performing stiffness measurements of anatomical structures in a patient using ultrasound shear wave elastography, the system comprising: an ultrasound image source configured to provide an ultrasound image of an anatomical structure comprising a plurality of elastography frames, the plurality of elastography frames being provided by a cine loop performed by an ultrasound imaging system; a processing unit; When executed by the processing unit, the processing unit automatically identifying a preferred elastography frame of a plurality of elastography frames for performing stiffness measurements on the anatomical structure; automatically identifying preferred regions of the preferred elastography frames based on a confidence level; automatically selecting at least one region of interest based on stiffness measurements within a preferred region of the preferred elastography frame; measuring the stiffness of an anatomical structure in said at least one region of interest; a memory for storing instructions for executing the A system having:

12. The instructions are sent to the processing unit: calculating a correlation coefficient between each pair of adjacent elastography frames of the plurality of elastography frames; assigning a score to each elastography frame of the plurality of elastography frames based on an average of a correlation coefficient with a immediately preceding elastography frame and a correlation coefficient with a immediately succeeding elastography frame, wherein a higher average of the correlation coefficients is assigned a higher score; identifying the elastography frame having the highest score as the preferred elastography frame; 12. The system of claim 11, wherein the step of automatically identifying the preferred elastography frame based on temporal stability is performed by performing:

13. The instructions are sent to the processing unit: removing each elastography frame of the plurality of elastography frames in which more than 60% of the pixels in the elastography frame have a low confidence score; identifying the remaining elastography frame having the highest confidence score as the preferred elastography frame; 12. The system of claim 11, wherein the step of automatically identifying the preferred elastography frame is performed based on a confidence-based threshold by performing:

14. The instructions are sent to the processing unit: generating a confidence map of the selected elastography frames; removing regions of low confidence from the confidence map based on a previously defined confidence threshold; automatically identifying a preferred region of the preferred elastography frame by performing the preferred region of the preferred elastography frame comprises the remaining region of the confidence map after removing the low confidence regions. The system of claim 11.

15. The instructions are sent to the processing unit: determining a local spatial standard deviation of stiffness in a preferred region of the preferred elastography frame; assigning a spatial stability score based on the local spatial standard deviation of stiffness, respectively, wherein a lower spatial standard deviation of stiffness is assigned a higher spatial stability score; selecting a region with the preferred region having the highest spatial stability score as the preferred region; The system of claim 11 , further comprising: a step of automatically selecting the at least one region of interest by executing:

16. The instructions are sent to the processing unit: assessing a distribution of stiffness values ​​per pixel within a preferred region of the preferred elastography frame; identifying localized stiffness regions in the preferred region; selecting a local stiffness region representing a majority stiffness value based on the distribution of stiffness values ​​per pixel as the at least one region of interest; The system of claim 11 , further comprising: a step of automatically selecting the at least one region of interest by executing:

17. The instructions are sent to the processing unit: selecting the most temporally stable region as the at least one region of interest using a correlation coefficient between pairs of adjacent elastography frames calculated to automatically identify the preferred elastography frame; The system of claim 11 , further comprising: a step of automatically selecting the at least one region of interest by executing:

18. The instructions are sent to the processing unit: deriving a region of interest location heatmap from the smoothness heatmap, the stiffness frequency heatmap, and the temporal stability heatmap; selecting the at least one region of interest using the region of interest location heatmap; The system of claim 11 , further comprising: a step of automatically selecting the at least one region of interest by executing:

19. 13. The system of claim 12, wherein the at least one region of interest is circular with a diameter in the range of about 0.5 cm to about 2.0 cm set by a sampling caliper.

20. 1. A non-transitory computer-readable medium storing instructions that, when executed by one or more processors, perform stiffness measurements of anatomical structures in a patient using ultrasound shear wave elastography, the instructions causing the one or more processors to: receiving an ultrasound image of an anatomical structure having the plurality of elastography frames, the plurality of elastography frames being provided by a cine loop performed by an ultrasound imaging system; automatically identifying a preferred elastography frame of a plurality of elastography frames for performing stiffness measurements on the anatomical structure; automatically identifying preferred regions of the preferred elastography frames based on a confidence level; automatically selecting at least one region of interest based on stiffness measurements within a preferred region of the preferred elastography frame; measuring the stiffness of an anatomical structure in said at least one region of interest; A non-transitory computer-readable medium for executing the method.

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