Method and system for automatic strain measurement and strain ratio calculation for ultrasonic elastography

Abstract

The present invention is entitled “Method and system for automatic strain measurement and strain ratio calculation for ultrasonic elastography” and provides a system and method for automatic strain measurement and strain ratio calculation for ultrasonic elastography.

Description

Technical Field

[0001] Aspects of the present disclosure relate to medical imaging. More specifically, certain embodiments relate to methods and systems for automatic strain measurement and strain ratio calculation for ultrasound elastography, particularly using artificial intelligence (AI) algorithms. Background Art

[0002] Various medical imaging techniques can be used, for example, to image organs and soft tissues within the human body. Examples of medical imaging techniques include ultrasound imaging, computed tomography (CT) scanning, magnetic resonance imaging (MRI), etc. The manner in which images are generated during medical imaging depends on the particular technique.

[0003] For example, ultrasound imaging uses real-time, non-invasive, high-frequency sound waves to produce ultrasound images, typically of organs, tissues, objects (e.g., fetuses) within the human body. Images produced or generated during medical imaging can be two-dimensional (2D), three-dimensional (3D), and / or four-dimensional (4D) images (essentially real-time / continuous 3D images). During medical imaging, imaging data sets (including, for example, volumetric imaging data sets during 3D / 4D imaging) are acquired and corresponding images are generated and rendered in real time (e.g., via a display) using the imaging data sets.

[0004] In some cases, medical imaging systems can be used to perform specific types of examinations. One such type is an examination based on elastic imaging. Using medical imaging systems in conjunction with such examinations presents certain challenges, particularly with respect to evaluating the results of the examinations. Further limitations and disadvantages of conventional and traditional methods will become apparent to those skilled in the art by comparing such systems with some aspects of the present disclosure set forth in the remainder of this application with reference to the accompanying drawings. Summary of the invention

[0005] There is provided a system and method for automatic strain measurement and strain ratio calculation for ultrasound elastography, substantially as shown and / or described in conjunction with at least one of the accompanying drawings, and as more fully set forth in the claims.

[0006] These and other advantages, aspects and novel features of the present disclosure will be more fully understood from the following description and drawings, as well as details of one or more illustrated exemplary embodiments of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure 1 is a block diagram illustrating an exemplary medical imaging arrangement that may be configured to support automatic strain measurement and strain ratio calculation for ultrasound elastography.

[0008] Figure 2is a block diagram illustrating an exemplary ultrasound system that may be configured to support automatic strain measurement and strain ratio calculation for ultrasound elastography.

[0009] Figure 3 Exemplary images generated in an exemplary medical imaging system configured to support automatic strain measurement and strain ratio calculation for ultrasound elastography are shown.

[0010] Figure 4 Exemplary images generated in an exemplary medical imaging system configured to support automatic strain measurement and strain ratio calculation for ultrasound elastography are shown.

[0011] Figure 5 A flow chart of exemplary steps that may be performed for ultrasound imaging utilizing automatic strain measurement and strain ratio calculation for ultrasound elastography is shown. DETAILED DESCRIPTION

[0012] Certain specific implementations according to the present disclosure may involve automatic measurement of strain and calculation of strain ratios for ultrasound elastography. In particular, various embodiments have the technical effect of enhancing medical examinations based on elastography by using automatic measurement of strain values ​​and strain-related determinations (such as based on strain ratio calculations). Various aspects of the present disclosure have the technical effect of allowing the reliability and accuracy of the results of medical examinations based on elastography to be improved by allowing more accurate evaluation of strain-related data related to determining such results.

[0013] When reading in conjunction with the accompanying drawings, the following specific embodiments of the aforementioned invention content and certain embodiments will be better understood. In terms of the scope of the figures of the functional blocks of various embodiments shown in the accompanying drawings, these functional blocks do not necessarily represent the division between hardware circuits. Therefore, for example, one or more functional blocks (e.g., processors or memories) can be implemented in a single piece of hardware (e.g., a general signal processor or random access memory block, a hard disk, etc.) or multiple pieces of hardware. Similarly, a program can be an independent program, can be included in an operating system as a subroutine, can be a function in an installed software package, etc. It should be understood that various embodiments are not limited to the arrangements and tools shown in the accompanying drawings. It should also be understood that embodiments can be combined, or other embodiments can be utilized, and structural, logical and electrical changes can be made without departing from the scope of various embodiments. Therefore, the following detailed description should not be considered as a restrictive meaning, and the scope of the present disclosure is limited by the attached claims and their equivalents.

[0014] As used herein, elements or steps listed in the singular and beginning with the word "one" or "an" should be understood not to exclude a plurality of said elements or steps, unless such exclusion is explicitly stated. In addition, references to "exemplary embodiments," "various embodiments," "certain embodiments," "representative embodiments," etc. are not intended to be interpreted as excluding the presence of additional embodiments that also incorporate the features of the narration. In addition, unless explicitly stated to the contrary, embodiments that "comprise," "include," or "have" an element or multiple elements having a particular property may include additional elements that do not have that property.

[0015] In addition, as used herein, the term "image" refers broadly to both a visible image and the data representing the visible image. However, many embodiments generate (or are configured to generate) at least one visible image. In addition, as used herein, the phrase "image" is used to refer to ultrasound modes, such as B-mode (2D mode), M-mode, three-dimensional (3D) mode, CF mode, PW Doppler, CW Doppler, MGD, and / or a sub-mode of B-mode and / or CF, such as shear wave elastography (SWEI), TVI, Angio, B-flow, BMI, BMI_Angio, and in some cases also includes MM, CM, TVD, wherein "image" and / or "plane" include a single beam or multiple beams.

[0016] Furthermore, as used herein, the phrase “pixel” also includes embodiments in which data is represented by “voxels.” Thus, both the terms “pixel” and “voxel” may be used interchangeably throughout this document.

[0017] Furthermore, as used herein, the term processor or processing unit refers to any type of processing unit, whether single-core or multi-core: CPU, accelerated processing unit (APU), graphics board, DSP, FPGA, ASIC, or a combination thereof, that can perform the required computations required by various implementations.

[0018] It should be noted that various embodiments of generating or forming images described herein may include processing for forming an image that includes beamforming in some embodiments and does not include beamforming in other embodiments. For example, an image may be formed without beamforming, such as by multiplying a matrix of demodulated data by a matrix of coefficients such that the product is an image, and wherein the process does not form any "beams." Additionally, the formation of an image may be performed using a combination of channels that may originate from more than one transmit event (e.g., synthetic aperture techniques).

[0019] In various embodiments, ultrasound processing to form images, including ultrasound beamforming, such as receive beamforming, is performed, for example, in software, firmware, hardware, or a combination thereof. Figure 1 and Figure 2 One specific implementation of an ultrasound system having a software beamformer architecture formed according to various embodiments is shown in FIG.

[0020] Figure 1 is a block diagram illustrating an exemplary medical imaging arrangement that may be configured to support automatic strain measurement and strain ratio calculation for ultrasound elastography. Figure 1 An exemplary setup 100 including one or more medical imaging systems 110 and one or more computing systems 120 is shown in FIG.

[0021] The medical imaging system 110 includes suitable hardware, software, or a combination thereof for supporting medical imaging (i.e., enabling data to be obtained for generating and / or rendering images during a medical imaging examination). This may require capturing specific types of data in specific ways, which in turn may be used to generate the data for the images. For example, the medical imaging system 110 may be an ultrasound system configured for generating and / or rendering ultrasound images. Reference Figure 2 An exemplary implementation of an ultrasound system that may correspond to the medical imaging system 110 is described in more detail. Figure 1 As shown, the medical imaging system 110 may include a scanner device 112 , which may be portable and movable, and a display / control unit 114 .

[0022] The scanner device 112 may be configured to generate and / or capture a particular type of imaging signal (and / or data corresponding thereto), such as by moving over a patient's body (or a portion thereof), and may include suitable circuitry for performing and / or supporting such functionality. The scanner device 112 may be an ultrasound probe, an MRI scanner, a CT scanner, or any suitable imaging device. For example, where the medical imaging system 110 is an ultrasound system, the scanner device 112 may transmit ultrasound signals and capture echo ultrasound images.

[0023] The display / control unit 114 may be configured to display images (e.g., via screen 116). In some cases, the display / control unit 114 may also be configured to generate, at least in part, the displayed images. In addition, the display / control unit 114 may also support user input / output. For example, in addition to the images, the display / control unit 114 may also provide (e.g., via screen 116) user feedback (e.g., information related to the system, its functions, its settings, etc.). The display / control unit 114 may also support user input (e.g., via user controls 118), such as to allow control of medical imaging. The user input may involve controlling the display of images, selecting settings, specifying user preferences, requesting feedback, etc.

[0024] In some implementations, the medical imaging system 110 may also incorporate additional and specialized computing resources, such as one or more computing systems 120. In this regard, each computing system 120 may include appropriate circuitry, interfaces, logic, and / or code for processing, storing, and / or communicating data. The computing system 120 may be a dedicated device configured for use specifically in conjunction with medical imaging, or it may be a general-purpose computing system (e.g., a personal computer, a server, etc.) that is configured and / or configured to perform the operations described below with respect to the computing system 120. The computing system 120 may be configured to support the operation of the medical imaging system 110, as described below. In this regard, various functions and / or operations may be offloaded from the imaging system. Doing so may simplify and / or centralize certain aspects of processing to reduce costs (by eliminating the need to increase processing resources in the imaging system).

[0025] The computing system 120 can be configured and / or arranged for use in different ways. For example, in some implementations, a single computing system 120 can be used; in other implementations, multiple computing systems 120 are configured to work together (e.g., based on a distributed processing configuration), or work separately, where each computing system 120 is configured to handle specific aspects and / or functions, and / or process data only for a specific medical imaging system 110.

[0026] In some implementations, the computing system 120 can be local (e.g., co-located with one or more medical imaging systems 110, such as within the same facility and / or the same local network); in other implementations, the computing system 120 can be remote and thus accessible only via a remote connection (e.g., via the Internet or other available remote access technology). In certain implementations, the computing system 120 can be configured in a cloud-based manner and can be accessed and / or used in a manner substantially similar to accessing and using other cloud-based systems.

[0027] Once the data is generated and / or configured in the computing system 120, the data may be copied and / or loaded into the medical imaging system 110. This may be done in different ways. For example, the data may be loaded via a directional connection or link between the medical imaging system 110 and the computing system 120. In this regard, the communication between the different elements in the arrangement 100 may be performed using available wired and / or wireless connections and / or according to any suitable communication (and / or networking) standards or protocols. Alternatively or additionally, the data may be loaded into the medical imaging system 110 indirectly. For example, the data may be stored in a suitable machine-readable medium (e.g., a flash memory card, etc.), which may then be used to load the data into the medical imaging system 110 (on-site, such as by a user of the system (e.g., an imaging clinician) or authorized personnel); or the data may be downloaded to a local electronic device capable of communication (e.g., a laptop, etc.), which may then be used on-site (e.g., by a user of the system or authorized personnel) to upload the data into the medical imaging system 110 via a direct connection (e.g., a USB connector, etc.).

[0028] In operation, the medical imaging system 110 may be used to generate and present (e.g., render or display) images during a medical examination, and / or to support user input / output in conjunction therewith. The images may be 2D, 3D, and / or 4D images. The specific operations or functions performed in the medical imaging system 110 to facilitate the generation and / or presentation of the images depend on the type of system (i.e., the manner in which data corresponding to the images are obtained and / or generated). For example, in ultrasound imaging, the data is based on transmitted ultrasound signals and echoed ultrasound signals, such as relative to Figure 2 Described in more detail.

[0029] According to the present disclosure, a medical imaging system (e.g., medical imaging system 110) may be configured to support automatic strain measurement and strain ratio calculation for ultrasound elastography. In this regard, medical imaging (e.g., ultrasound imaging) may be used for elastic imaging-related applications (referred to as ultrasound elastography in the context of ultrasound imaging), in which the elastic properties and stiffness of soft tissues (e.g., of a particular organ or body part) may be evaluated and / or determined (e.g., mapped). In this regard, elastic imaging may be used in situations where the softness of soft tissues and changes therein (e.g., softening or hardening of tissues) may have medical significance.

[0030] For example, elastic imaging can be used to check whether a pregnant woman's cervix is ​​softening. In this regard, softening of the cervix during pregnancy poses a serious risk of premature birth. Early softening of the cervix usually starts in the center of the cervical canal and spreads from there until the entire cervix is ​​affected. Therefore, elastic imaging (e.g., ultrasound elastic imaging) of the cervix can show whether it is rigid and open, or whether softening has begun or has occurred.

[0031] For example, ultrasound elastography can be performed in the form of an intravaginal examination, where the application of strain elastography can confirm the condition of the cervix. In strain elastography, the clinician can determine the pliability of the tissue (and thereby determine whether softening of the cervix has occurred or is occurring) based on an assessment of strain and / or strain ratio. In this regard, strain is a measure of the deformation exhibited by a particular tissue (or region thereof) in response to pressure or stress applied thereto, and can therefore be used as an indicator of the pliability of the tissue; the strain ratio calculation measures the ratio of strain exhibited or measured for two different regions (corresponding to, for example, different tissues and / or different portions of the same tissue), ideally when exposed to the same amount of stress. For example, during strain elastography, external compression is applied to the tissue, and ultrasound images before and after the compression are taken and compared, where the image areas with the least deformation are the most rigid (i.e., stiffest) image areas, and the areas with the greatest deformation are the least rigid (softest) image areas. What can typically be displayed to the clinician during strain elastography is an image of the relative distortion (i.e., strain) in the examined tissue. Therefore, in conventional solutions, the clinician's determination of tissue pliability is typically based on assessing strain and / or strain ratio based on visual inspection of displayed images during an examination (medical imaging).

[0032] When a clinician determines (or suspects) that softening of the cervix has occurred or is occurring, the patient is typically referred for further investigation. However, as described above, due to the risks posed by softening of the cervix, clinicians tend to be overly cautious and thus identify most detected deformations (e.g., strain and / or strain ratio) as indicating softening of the cervix, which may lead to overdiagnosis. Therefore, it is desirable to improve the reliability and accuracy of elastic imaging of the cervix.

[0033] Therefore, in various embodiments of the present disclosure, the medical imaging system may be configured to support automatic measurement of strain and calculation of strain ratios for ultrasound elastography to allow for improved reliability of strain elastography and thereby reduce overdiagnosis (e.g., overdiagnosis of such possible problems as cervical softening). In addition, in many embodiments, this may be accomplished in particular by using artificial intelligence (AI) algorithms to perform and / or optimize such automatic measurements and / or calculations, and / or further enhance related functions (e.g., setting up and conducting examinations, assessments, etc.). The use of automatic measurement of strain and calculation of strain ratios for ultrasound elastography (particularly in conjunction with cervical examinations) is described in more detail below.

[0034] In addition to enhancing the reliability of cervical examination, automatic strain measurement and strain ratio calculation can also be used for other pregnancy-related applications. For example, in the case where labor has not started and is induced by drugs, automatic strain measurement and strain ratio calculation in ultrasound elastography can be used to check whether the cervix responds (or does not respond) to the drug. In addition, although various exemplary implementations are described with respect to the use of strain elastography in conjunction with cervical examination, the present disclosure is not limited to such applications, and the solutions described herein (particularly with respect to the use of automatic strain measurement and strain ratio calculation in ultrasound elastography, and specifically through AI algorithms) can be similarly used for other applications in which strain elastography is used, such as breast examinations (e.g., diagnostic imaging of lesions), small part examinations (e.g., imaging of thyroid and testicular lesions), prostate examinations (e.g., ultrasound imaging of lesions), etc.

[0035] Figure 2 is a block diagram illustrating an exemplary ultrasound system that may be configured to support automatic strain measurement and strain ratio calculation for ultrasound elastography. Figure 2 An ultrasound system 200 is shown in FIG.

[0036] The ultrasound system 200 may be configured to provide ultrasound imaging and, therefore, may include suitable circuitry, interfaces, logic and / or code for performing and / or supporting ultrasound imaging related functions. The ultrasound system 200 may correspond to Figure 1 A medical imaging system 110 is provided.

[0037] The ultrasound system 200 includes, for example, a transmitter 202, an ultrasound probe 204, a transmit beamformer 210, a receiver 218, a receive beamformer 220, an RF processor 224, an RF / IQ buffer 226, a user input module 230, a signal processor 240, an image buffer 250, a display system 260, an archive 270, and a training engine 280.

[0038] The transmitter 202 may include suitable circuitry, interfaces, logic and / or code operable to drive the ultrasound probe 204. The ultrasound probe 204 may include a two-dimensional (2D) array of piezoelectric elements. The ultrasound probe 204 may include a set of transmit transducer elements 206 and a set of receive transducer elements 208 that are generally constructed as identical elements. In certain embodiments, the ultrasound probe 204 may be operable to acquire ultrasound image data covering at least a substantial portion of an anatomical structure, such as a heart, a blood vessel, or any suitable anatomical structure.

[0039] The transmit beamformer 210 may include suitable circuitry, interfaces, logic and / or code operable to control the transmitter 202, which drives the set of transmit transducer elements 206 via the transmit sub-aperture beamformer 214 to transmit ultrasound transmit signals into an area of ​​interest (e.g., a person, an animal, an underground cavity, a physical structure, etc.). The transmitted ultrasound signals may be backscattered from structures (e.g., blood cells or tissue) in the object of interest to produce echoes. The echoes are received by the receive transducer elements 208.

[0040] The set of receive transducer elements 208 in the ultrasound probe 204 may be operable to convert received echoes into analog signals, which are sub-aperture beamformed by the receive sub-aperture beamformer 216 and then transmitted to the receiver 218. The receiver 218 may include suitable circuitry, interfaces, logic, and / or code that may be operable to receive the signals from the receive sub-aperture beamformer 216. The analog signals may be transmitted to one or more of the plurality of A / D converters 222.

[0041] The plurality of A / D converters 222 may include suitable circuits, interfaces, logic, and / or code that are operable to convert analog signals from the receiver 218 into corresponding digital signals. The plurality of A / D converters 222 are disposed between the receiver 218 and the RF processor 224. Nevertheless, the present disclosure is not limited in this regard. Therefore, in some embodiments, the plurality of A / D converters 222 may be integrated within the receiver 218.

[0042] The RF processor 224 may include suitable circuits, interfaces, logic, and / or code that may be operable to demodulate the digital signals output by the plurality of A / D converters 222. According to one embodiment, the RF processor 224 may include a complex demodulator (not shown) that may be operable to demodulate the digital signals to form I / Q data pairs representing corresponding echo signals. The RF or I / Q signal data may then be transferred to the RF / IQ buffer 226. The RF / IQ buffer 226 may include suitable circuits, interfaces, logic, and / or code that may be operable to provide temporary storage of the RF or I / Q signal data generated by the RF processor 224.

[0043] The receive beamformer 220 may include suitable circuitry, interfaces, logic, and / or code operable to perform digital beamforming processing to, for example, sum delayed channel signals received from the RF processor 224 via the RF / IQ buffer 226 and output a beam summed signal. The resulting processed information may be a beam summed signal output from the receive beamformer 220 and communicated to the signal processor 240. According to some embodiments, the receiver 218, the plurality of A / D converters 222, the RF processor 224, and the beamformer 220 may be integrated into a single beamformer, which may be digital. In various embodiments, the ultrasound system 200 includes a plurality of receive beamformers 220.

[0044] The user input device 230 may be used to input patient data, scan parameters, settings, select protocols and / or templates, interact with the artificial intelligence segmentation processor to select tracking targets, etc. In an exemplary embodiment, the user input device 230 is operable to configure, manage, and / or control the operation of one or more components and / or modules in the ultrasound system 200. In this regard, the user input device 230 is operable to configure, manage, and / or control the operation of the transmitter 202, the ultrasound probe 204, the transmit beamformer 210, the receiver 218, the receive beamformer 220, the RF processor 224, the RF / IQ buffer 226, the user input device 230, the signal processor 240, the image buffer 250, the display system 260, and / or the archive 270.

[0045] For example, the user input device 230 may include a button, a rotary encoder, a touch screen, motion tracking, voice recognition, a mouse device, a keyboard, a camera, and / or any other device capable of receiving user instructions. In certain embodiments, for example, one or more user input devices in the user input device 230 may be integrated into other components (such as a display system 260 or an ultrasound probe 204). As an example, the user input device 230 may include a touch screen display. As another example, the user input device 230 may include an accelerometer, a gyroscope, and / or a magnetometer attached to and / or integrated with the probe 204 to provide gesture motion recognition of the probe 204, such as identifying one or more probe compressions against the patient's body, predefined probe movement or tilt operations, etc. Additionally and / or alternatively, the user input device 230 may include image analysis processing to identify the probe posture by analyzing the collected image data.

[0046] The signal processor 240 may include suitable circuits, interfaces, logic and / or codes that are operable to process ultrasound scan data (i.e., summed IQ signals) to generate ultrasound images for presentation on the display system 260. The signal processor 240 is operable to perform one or more processing operations based on a plurality of selectable ultrasound modalities on the acquired ultrasound scan data. In an exemplary embodiment, the signal processor 240 is operable to perform display processing and / or control processing, etc. The acquired ultrasound scan data may be processed in real time during a scanning session as echo signals are received. Additionally or alternatively, the ultrasound scan data may be temporarily stored in the RF / IQ buffer 226 during the scanning session and processed in a less real-time manner in an online or offline operation. In various embodiments, the processed image data may be presented at the display system 260 and / or may be stored at the archive 270. The archive 270 may be a local archive, a picture archiving and communication system (PACS), or any suitable device for storing images and related information.

[0047] The signal processor 240 may be one or more central processing units, microprocessors, microcontrollers, etc. For example, the signal processor 240 may be an integrated component, or may be distributed in various locations. The signal processor 240 may be configured to receive input information from the user input device 230 and / or the archive 270, generate output that can be displayed by the display system 260, and manipulate the output in response to the input information from the user input device 230, etc. The signal processor 240 may be capable of executing, for example, any of the one or more methods and / or one or more instruction sets discussed herein according to various embodiments.

[0048] The ultrasound system 200 is operable to continuously acquire ultrasound scan data at a frame rate suitable for the imaging situation under consideration. Typical frame rates are in the range of 20 to 220, but may be lower or higher. The acquired ultrasound scan data may be displayed on the display system 260 at a display rate that is the same as the frame rate, or slower or faster. An image buffer 250 is included for storing processed frames of acquired ultrasound scan data that are not scheduled for immediate display. Preferably, the image buffer 250 has sufficient capacity to store frames of ultrasound scan data for at least several minutes. The frames of ultrasound scan data are stored in a manner that is easy to retrieve from them according to their acquisition order or time. The image buffer 250 may be embodied as any known data storage medium.

[0049] In an exemplary embodiment, the signal processor 240 may include a strain assessment module 242, which includes suitable circuits, interfaces, logic and / or code that can be configured to perform and / or support various functions or operations related to or supporting automatic strain measurement and strain ratio calculation for ultrasound elastography, as described in more detail below.

[0050] In some implementations, the signal processor 240 (and / or its components, such as the strain assessment module 242) may be configured to implement and / or use artificial intelligence and / or machine learning techniques to enhance and / or optimize imaging-related functions or operations. For example, the signal processor 240 (and / or its components, such as the strain assessment module 242) may be configured to implement and / or use deep learning techniques and / or algorithms (such as using a deep neural network (e.g., a convolutional neural network)), and / or may utilize any suitable form of artificial intelligence image analysis technology or machine learning processing functionality that may be configured to analyze the acquired ultrasound images, such as identifying, segmenting, marking, and tracking structures (or tissues thereof) that meet specific criteria and / or have specific features.

[0051] In an exemplary implementation, the signal processor 240 (and / or its components, such as the strain assessment module 242) may be provided as a deep neural network, which may be composed of, for example, an input layer, an output layer, and one or more hidden layers between the input layer and the output layer. Each layer may be composed of a plurality of processing nodes, which may be referred to as neurons. For example, the input layer may have a neuron for each pixel or a group of pixels from a scan plane of an anatomical structure. The output layer may have neurons corresponding to a plurality of predefined structures or predefined types of structures (or tissues therein). Each neuron of each layer may perform a processing function and pass the processed ultrasound image information to one of a plurality of neurons in a downstream layer for further processing. For example, neurons of a first layer may learn to identify structural edges in ultrasound image data. Neurons of a second layer may learn to recognize shapes based on detected edges from the first layer. Neurons of a third layer may learn the position of the recognized shapes relative to landmarks in the ultrasound image data. Neurons of a fourth layer may learn the characteristics of a particular tissue type present in a particular structure, etc. Therefore, the processing performed by a deep neural network (e.g., a convolutional neural network) may allow biological structures and / or artificial structures in ultrasound image data to be recognized with high probability.

[0052] In some implementations, the signal processor 240 (and / or components thereof, such as the strain assessment module 242) can be configured to perform or otherwise control at least some of the functions performed thereby based on user instructions via the user input device 230. As an example, a user can provide voice commands, probe gestures, button presses, etc. to issue specific instructions, such as controlling various aspects of automatic strain measurement and strain ratio calculation, and / or providing or otherwise specifying various parameters or settings related thereto, as described in more detail below.

[0053] The training engine 280 may comprise suitable circuitry, interfaces, logic and / or code operable to train neurons of a deep neural network of the signal processor 240 (and / or components thereof, such as the strain assessment module 242). For example, the signal processor 240 may be trained to recognize specific structures and / or tissues (or types thereof) provided in an ultrasound scan plane, wherein the training engine 280 trains its deep neural network to perform some of the desired functions, such as using a database of classified ultrasound images of various structures.

[0054] As an example, the training engine 280 may be configured to train the signal processor 240 (and / or components thereof, such as the strain assessment module 242) using ultrasound images of specific structures relative to characteristics of the specific structures (such as the appearance of edges of the structures, the appearance of the shapes of the structures based on the edges, the location of the shapes relative to landmarks in the ultrasound image data, etc.) and / or relative to characteristics of specific tissues (e.g., their softness). In various embodiments, the database of training images may be stored in the archive 270 or any suitable data storage medium. In certain embodiments, the training engine 280 and / or the database of training images may be an external system communicatively coupled to the ultrasound system 200 via a wired or wireless connection.

[0055] In operation, the ultrasound system 200 can be used to generate ultrasound images, including two-dimensional (2D), three-dimensional (3D) and / or four-dimensional (4D) images. In this regard, the ultrasound system 200 is operable to continuously acquire ultrasound scan data at a specific frame rate, which can be applicable to the imaging situation in question. For example, the frame rate can be in the range of 20-70, and can also be lower or higher. The acquired ultrasound scan data can be displayed on the display system 260 at a display rate that is the same as the frame rate, or slower or faster. The image buffer 250 is included to store processed frames of the acquired ultrasound scan data that are not scheduled for immediate display. Preferably, the image buffer 250 has sufficient capacity to store frames of ultrasound scan data for at least a few seconds. The frames of ultrasound scan data are stored in a manner that is easy to retrieve from them according to their acquisition order or time. The image buffer 250 can be embodied as any known data storage medium.

[0056] In some cases, the ultrasound system 200 can be configured to support grayscale and color-based operations. For example, the signal processor 240 can be operated to perform grayscale B-model processing and / or color processing. Grayscale B-model processing can include processing B-model RF signal data or IQ data pairs. For example, grayscale B-model processing can cause the grayscale B-model processing to be performed by calculating the amount (I 2 +Q 2 ) 1 / 2 The envelope of the beam-summed receive signal can be formed. The envelope can be subjected to additional B-model processing, such as logarithmic compression to form display data. The display data can be converted to an XY format for video display. The scan converted frame can be mapped to grayscale for display. The B-model frame is provided to the image buffer 250 and / or the display system 260. The color processing may include processing color-based RF signal data or IQ data pairs to form a frame to cover the B-model frame provided to the image buffer 250 and / or the display system 260. The grayscale and / or color processing can be adaptively adjusted based on user input (e.g., selection from the user input device 230), for example, to enhance the grayscale and / or color of a specific area.

[0057] In some cases, ultrasound imaging may include the generation and / or display of volumetric ultrasound images (i.e., the location of an object (e.g., an organ, tissue, etc.) displayed in three dimensions, 3D). In this regard, with 3D (and similarly with 4D) imaging, a volumetric ultrasound dataset may be acquired that includes voxels corresponding to the imaged object. This may be accomplished, for example, by transmitting sound waves at different angles rather than transmitting them in only one direction (e.g., straight down), and then capturing their reflections back. The returning echoes (of the transmission at different angles) are then captured and processed (e.g., via signal processor 240) to generate a corresponding volumetric dataset, which in turn may be used to create and / or display a volumetric (e.g., 3D) image, such as via display 250. This may require the use of specific processing techniques to provide the desired 3D perception.

[0058] For example, volume rendering techniques may be used to display projections (e.g., 2D projections) of volumetric (e.g., 3D) data sets. In this regard, rendering a 2D projection of a 3D data set may include setting or defining a perceived angle in space relative to the object being displayed, and then defining or calculating the necessary information (e.g., opacity and color) for each voxel in the data set. This may be done, for example, using a suitable transfer function to define RGBA (red, green, blue, and alpha) values ​​for each voxel.

[0059] In various specific implementations, the ultrasound system 200 may be configured to support automatic strain measurement and strain ratio calculation for ultrasound elastography. In this regard, as described above, elastography (e.g., ultrasound elastography) can be used to check whether the cervix of a pregnant woman is softened, which is a condition that poses a serious health risk (e.g., premature birth). For example, strain elastography of the cervix (e.g., applied in the form of an intravaginal examination) can show whether the cervix is ​​rigid and open, or whether softening has begun or has occurred. However, due to the serious risks posed by the softening of the cervix, clinicians who perform such examinations may tend to over-diagnose. This is further exacerbated by the fact that the examination is usually performed by a clinician based on a visual inspection of the displayed image, which may lead to a large number of cases in which the cervix is ​​incorrectly suspected of softening and the patient is unnecessarily referred for further examination. As described above, strain elastography can also be used for other applications (e.g., verifying the response to drugs used to induce labor, breast examination (diagnosing lesions), examination of certain small organs or body structures (imaging of thyroid and testicular lesions), prostate examination (ultrasound imaging of lesions), etc.). Problems similar to those noted with respect to strain elastography of the cervix may also arise in these other applications, in a substantially similar manner - namely, overdiagnosis by the clinician based on visual inspection of the displayed images.

[0060] Some solutions have been proposed to try to address some of these diagnosis-related problems. For example, clinical studies have been conducted on many of the above-mentioned elastography applications, and in many of these studies, cutoff values ​​for strain ratios were proposed—i.e., to define which strain ratios may indicate a problem. Nevertheless, such solutions do not address the primary contributing factor—i.e., the user dependence of conventional solutions—where misdiagnosis or overdiagnosis may occur.

[0061] Solutions according to the present disclosure address these issues by simplifying the workflow of the examination, reducing user dependency, and speeding up the screening of images during the examination. This can be accomplished by incorporating automated or semi-automated methods for measuring strain and / or calculating strain ratios, particularly using artificial intelligence (AI) techniques to enhance the reliability and accuracy of strain elastography. These solutions can be applied to any of the strain elastography applications described above.

[0062] In various implementations, a medical imaging system (e.g., the system ultrasound system 200) may be configured to automatically measure strain and / or strain ratio calculations during an imaging operation. In this regard, the medical imaging system may be configured to measure strain (e.g., strain in a particular portion) and provide strain-related feedback to a user (e.g., a clinician) to reduce ambiguity or uncertainty about the examination results, rather than simply displaying an image that reflects changes in strain characteristics in the imaging area. For example, in some implementations, a medical imaging system (e.g., the system ultrasound system 200) may be configured to allow a user to specify a particular area (e.g., by placing a single region of interest (ROI) box over the area), and provide automatic strain-related measurements (and / or assessments based thereon) of the particular area. Such methods provide a quick and simple way to make clinical decisions about the state of the tissue (or related structure / organ) being examined, and thereby make better decisions about the next action to take.

[0063] In a use case related to cervical softening, for example, a user may only need to place a single ROI box over a region of the cervical canal, and the system may then perform an automated softness assessment of the region of the cervical canal—e.g., automatically performing strain measurements, automatically determining the softest and stiffest regions based on the measured strains, and / or automatically performing ratio measurements. In some cases, predefined data may be used during such automated functions. For example, strain measurements and / or strain ratio calculations may be performed based on preprogrammed cutoff values ​​(e.g., cutoff values ​​obtained from clinical studies, etc.). In some cases, these values ​​may be configurable to allow the operator to adjust any of these values, if necessary.

[0064] In some implementations, the system can be configured to enable a user to control various aspects or features of the scan. For example, in addition to controlling the placement of the ROI box (i.e., where it is placed) relative to the ROI box, the user can also control or adjust the size of the ROI box (e.g., on the screen / display, by moving its edges to expand or contract the box in a particular direction, by setting minimum and / or maximum limits on the size of the ROI box, etc.). In such implementations, in the event that the user does not set or adjust the size of the ROI box, the system can be configured to use a predefined default size.

[0065] Measurements / calculations and evaluations based thereon may be performed as part of imaging-related processing via appropriate processing resources in the system. For example, in the ultrasound system 200, the signal processor 240 (and / or components thereof, such as the strain evaluation module 242) may be configured to perform automatic strain measurements and strain ratio calculations in the process of processing received echoes and signals and / or data based thereon. In this regard, in various specific implementations, artificial intelligence (AI) techniques and / or algorithms (e.g., using deep learning networks, as described above) may be used to perform and continuously enhance automatic strain measurements and strain ratio calculations and evaluations based thereon. With respect to Figure 3 and Figure 4 Exemplary usage scenarios are described.

[0066] In an exemplary use scenario, during strain elastography, tissue in an examined body part or region (e.g., the cervix and surrounding areas) may be manually compressed / decompressed using an ultrasound probe (e.g., in the case of strain elastography of the cervix, using a dedicated probe or probe attachment for intravaginal examination). The compression / decompression causes strain in the tissue beneath the probe. The system may automatically measure, such as based on calculations based on speckle correlation of signals of compressed tissue relative to uncompressed tissue. In this regard, the measured strain values ​​may be assigned based on a predefined range—e.g., between 0% and 2%, where 0% indicates no strain (i.e., no elastic deformation and no tissue deformation, thereby indicating that the tissue is very hard), and 2% indicates that the tissue is very soft (e.g., the tissue is very elastic and easily deformed).

[0067] The system may use the measured strain values ​​to make other strain-related determinations and / or assessments based thereon. For example, the system may automatically calculate an average strain value and / or a strain ratio based on the measured strain values. In some cases, the measured strain values ​​may be used to adjust or otherwise control the display of an image. For example, different colors (and their hues) may be used to represent soft and / or hard tissue areas—for example, in a non-limiting embodiment, tissue areas with higher strain values ​​(softer) may be colored red (with increasing color saturation), while tissue areas with lower strain values ​​(harder) are colored blue (with increasing color saturation). The system may also be configured to provide feedback related to the measured strain values ​​and / or determinations and / or assessments based thereon. For example, strain-related feedback may be visual, displayed with the displayed image (e.g., incorporated into the displayed image).

[0068] In some cases, the feedback can be configured to indicate or indicate the results of the examination. For example, the manner in which the visual feedback (e.g., its color) is provided can be adjusted based on the strain-related measurement or calculation to indicate whether any problems are detected or suspected. An exemplary use case of these features is described in the context of cervical strain elastography and with respect to Figure 3 and Figure 4 Show and describe.

[0069] As described above, in various specific implementations, various actions or functions performed during operations related to strain elastography according to the present disclosure may be performed using artificial intelligence (AI). Exemplary actions that may be processed and / or performed using artificial intelligence (AI) related resources in the system may include: analyzing all strain values ​​(e.g., strain values ​​within a selected measurement ROI-frame); identifying average strain values ​​and their locations; identifying upper percentile strain maxima and their locations; identifying lower percentile strain minima and their locations; excluding average regions; calculating hard to soft strain ratios; displaying hard values, soft values, and ratios; etc.

[0070] Figure 3 Exemplary images generated in an exemplary medical imaging system configured to support automatic strain measurement and strain ratio calculation for ultrasound elastography are shown. Figure 3 In a medical imaging system (e.g., Figure 2 A screenshot of an image 300 (e.g., an ultrasound image) generated in an ultrasound system 200.

[0071] The ultrasound image 300 may correspond to an image captured in a system (such as Figure 2 In particular, ultrasound image 300 illustrates exemplary real-time visual feedback that may be provided during an exemplary use scenario of ultrasound image 200 during strain ultrasound elastography of the cervix to examine its softening.

[0072] In this regard, as described above, during strain elastography, the area being examined may be manually compressed / decompressed using an ultrasound probe. In the case of strain elastography of the cervix, this may be performed as an intravaginal examination using an ultrasound probe configured for such an examination. The compression / decompression causes strain in the cervical tissue and surrounding area beneath the probe. The strain value may be automatically measured by the system (e.g., based on a speckle-based calculation, as described above with respect to Figure 2 described), and the average strain value can also be calculated by the system.

[0073] The system can also automatically evaluate the results of the examination based on the strain measurements. For example, the evaluation can be based on the determination of the strain ratio of hard tissue to soft tissue. In this regard, the evaluation of the calculated strain ratio can be based on predefined criteria and / or parameters - for example, a maximum (or minimum) threshold, such as based on the measured strain, for determining when the softness (or hardness) of the tissue meets certain limits for being considered soft (or hard) tissue. The evaluation can be limited to specific areas in the image that can be specified by the user (clinician). For example, if Figure 3 As shown, the user can select an area to be evaluated on the screen by using a user-selected ROI box 310 (eg, using appropriate user controls). In the context of strain elastography of the cervix, the clinician can select a region corresponding to and / or including the uterine tube and surrounding tissue.

[0074] The system can then provide feedback related to the strain measurement and / or the assessment based thereon. This can be done visually, such as by incorporating strain-related feedback into the displayed image. For example, after measuring strain values ​​based on an ultrasound image (or data corresponding thereto), the system can identify the location of an area (e.g., green) corresponding to or having an average strain value, identify the location of an area (e.g., red) corresponding to an upper percentile strain maximum value (e.g., soft tissue), and identify the location of an area (e.g., blue) corresponding to a lower percentile strain minimum value (e.g., hard tissue). The system can then perform a strain ratio calculation. In this regard, the system can exclude the average area (green) and calculate the strain ratio of hard (blue) to soft (red), and then provide feedback (e.g., display) related to the identified hard tissue, soft tissue, and the calculated ratio. In this regard, in some specific implementations, the manner in which feedback is provided (e.g., visually) can be configured to indicate the result of the assessment, such as based on a predetermined standard. For example, based on a predefined cutoff value, the manner in which feedback about the ratio is provided (the color used to display the ratio) can be adaptively adjusted for each specific application.

[0075] For example, Figure 3 As shown, tissue with higher strain values ​​(softer) is colored red (with increasing color saturation); while tissue with lower strain values ​​(harder) is colored blue (with increasing color saturation). The ratio of soft tissue to hard tissue within the user-selected ROI box 310 is calculated and displayed. In this regard, the manner in which the calculated ratio is displayed can be configured to indicate the result of the assessment based on a predefined cutoff value for cervical strain assessment. For example, the color used to display the ratio can be configured to indicate whether the ratio indicates that softening of the cervix has occurred or is occurring based on an applicable cutoff value - for example, green when the ratio is within a predefined acceptable limit, and red if the ratio is not within the predefined acceptable limit.

[0076] exist Figure 3 In the exemplary use case shown, for example, the ratio is 1 (because the soft tissue and hard tissue within the ROI box 310 are each the same, approximately 0.17%), which is within acceptable limits. Therefore, the word "ratio" and the calculated ratio value ("1") are displayed in green.

[0077] Figure 4Exemplary images generated in an exemplary medical imaging system configured to support automatic strain measurement and strain ratio calculation for ultrasound elastography are shown. Figure 4 In a medical imaging system (e.g., Figure 2 A screenshot of an image 400 (e.g., an ultrasound image) generated in an ultrasound system 200.

[0078] Image 400 may be generated in the same manner as image 300, such as with respect to Figure 3 That is, using a system implemented according to the present disclosure (e.g., Figure 2 Ultrasound system 200), wherein the system is configured to perform automatic strain-related measurements and calculations and assessments based thereon, particularly with respect to softening of the cervix. Figure 4 In the exemplary use case shown, the ratio (hard tissue to soft tissue) is calculated to be 0.46% because the soft tissue within the user-selected ROI box 410 (e.g., including the cervical canal and surrounding areas) constitutes 0.39% of the area enclosed in the ROI box 410, as determined in the system based on automatic strain measurements and applicable softness criteria, while the hard tissue within the ROI box 410 constitutes 0.18%, as determined in the system based on automatic strain measurements and applicable hardness criteria.

[0079] Since the ratio of 0.46% may exceed (or fall below) the acceptable limit, it may be different from Figure 3 The exemplary usage scenario shown is used to provide feedback. Figure 4 As shown, the word "Ratio" and the calculated ratio value ("0.46%") are displayed in green.

[0080] Figure 5 A flow chart of exemplary steps that may be performed for ultrasound imaging utilizing automatic strain measurement and strain ratio calculation for ultrasound elastography is shown.

[0081] Figure 5 A flowchart 500 is shown, which includes a number of exemplary steps (represented as blocks 502 to 514) that may be performed in a suitable medical imaging system (e.g., Figure 2 The method is performed in an ultrasound system 200 .

[0082] In a start step 502, the system may be set up and operation may be initiated.

[0083] In step 504, imaging signals may be obtained during an elastography examination (eg, cervical elastography). For example, in an ultrasound imaging system, this may include transmitting an ultrasound signal and receiving a corresponding echo of the signal.

[0084] In step 506 , the imaging signals (eg, echoes of received ultrasound signals) may be processed to generate a corresponding data set for generating a corresponding medical image (eg, an ultrasound image).

[0085] In step 508, strain values ​​in the selected region of interest (ROI) box may be determined. In this regard, the ROI box may be user-selected, such as via interaction with a display unit of the system. The strain values ​​may be determined, for example, based on calculations of speckle correlations of signals based on compressed tissue relative to uncompressed tissue, as described above.

[0086] In step 510, the strain values ​​may be processed using an AI algorithm implemented in the system (such as based on applicable criteria). For example, processing the strain values ​​may include determining (e.g., based on a preset cutoff) a low value, an average value, and a high value, and the corresponding portion of the ROI box. This may allow, for example, identification of tissue that may be assessed as soft, hard, or somewhere in between.

[0087] In step 512, the results of the elastography examination (eg, softening of the cervix) may be evaluated based on the strain values ​​and related processing. For example, in the context of a cervical examination, softening of the cervix may be evaluated based on the ratio of hard tissue to soft tissue within the ROI box.

[0088] Real-time feedback regarding the strain values, related calculations, and / or examination results may be provided to the user in step 514. For example, this may include an indicator showing the strain values ​​(or related information) and the results of the elastography examination.

[0089] According to the present disclosure, an exemplary method for an elastic imaging-based examination utilizing automatic strain measurement and strain ratio calculation includes processing, by at least one processor, imaging data obtained during an elastic imaging examination of a patient, wherein the imaging data corresponds to a specific medical imaging technique; generating, by at least one processor, corresponding one or more medical images based on the processing of the imaging data; calculating strain values ​​corresponding to a region of interest (ROI) within the one or more medical images, and processing the calculated strain values, wherein the processing includes: determining, based on the elastic imaging examination, a plurality of ranges corresponding to the calculated strain values, identifying a portion of the region of interest (ROI) corresponding to each of the plurality of ranges, and evaluating the results of the elastic imaging examination based on one or more of: the calculated strain values, the plurality of ranges, and a portion of the region of interest (ROI); and providing strain-related feedback based on the processing of the calculated strain values.

[0090] In one exemplary implementation, the method further includes automatically processing the calculated strain values ​​via at least one processor using one or more artificial intelligence based algorithms.

[0091] In one exemplary implementation, the method further includes processing the calculated strain value based on a type of elastography examination. The type of elastography examination may include one of cervical elastography, prostate elastography, breast elastography, and thyroid elastography.

[0092] In one exemplary implementation, the method further includes determining a plurality of ranges based on one or more preset cutoff values.

[0093] In one exemplary implementation, the method further includes determining one or more preset cutoff values ​​based on a type of elastography examination.

[0094] In one exemplary implementation, the plurality of ranges include at least a low range, a high range, and an average range, and providing strain-related feedback includes displaying strain values ​​for at least one of the high range and the low range in at least one displayed medical image of the one or more medical images.

[0095] In one exemplary implementation, evaluating the results of the elastography examination includes calculating a ratio between a high range and a low range.

[0096] In one exemplary implementation, providing strain-related feedback includes providing feedback related to results of an elastography examination.

[0097] In one exemplary implementation, providing feedback related to the result of the elastography examination includes displaying an indicator of the result of the elastography examination in at least one displayed medical image of the one or more medical images.

[0098] In one exemplary implementation, wherein the elastography examination includes cervical elastography, the results of the elastography examination include softening of the patient's cervix.

[0099] In one exemplary implementation, providing strain-related feedback includes providing feedback indicative of risk of preterm birth based on determining that softening of the cervix has occurred or is occurring.

[0100] In one exemplary implementation, the medical imaging technique includes ultrasound imaging, and the medical image includes an ultrasound image generated based on received echo ultrasound signals.

[0101] An exemplary system for an elastic imaging-based examination using automatic strain measurement and strain ratio calculation according to the present disclosure includes: a scanning device configured to obtain imaging signals during an elastic imaging examination of a patient, wherein the imaging data corresponds to a specific medical imaging technology; a display device configured to display an image; and at least one processor configured to: process the imaging data obtained via the scanner device; generate corresponding one or more medical images based on the processing of the imaging data; display the one or more medical images via the display device; use one or more artificial intelligence-based algorithms to automatically: calculate strain values ​​corresponding to a region of interest (ROI) within at least one of the one or more medical images, and process the calculated strain values, wherein the processing includes: determining multiple ranges corresponding to the calculated strain values ​​based on the elastic imaging examination, identifying a portion of the region of interest (ROI) corresponding to each of the multiple ranges, and evaluating the results of the elastic imaging examination based on one or more of the following: the calculated strain values, the multiple ranges, and the portion of the region of interest (ROI); and providing strain-related feedback based on the processing of the calculated strain values.

[0102] In one exemplary implementation, the at least one processor is further configured to process the calculated strain values ​​based on a type of elastography examination.

[0103] In one exemplary implementation, the at least one processor is further configured to determine a plurality of ranges based on one or more preset cutoff values.

[0104] In one exemplary implementation, the at least one processor is further configured to determine one or more preset cutoff values ​​based on a type of elastography examination.

[0105] In one exemplary embodiment, the multiple ranges include at least a low range, a high range, and an average range, and the at least one processor is further configured to display strain values ​​of at least one of the high range and the low range in at least one of the one or more medical images displayed via a display device when providing strain-related feedback.

[0106] In one exemplary implementation, the at least one processor is further configured to calculate a ratio between the high range and the low range when evaluating the results of the elastography examination.

[0107] In one exemplary embodiment, providing strain-related feedback includes providing feedback related to the results of the elastic imaging examination, and the at least one processor is further configured to display an indicator of the results of the elastic imaging examination in at least one of the one or more medical images displayed via a display device when the strain-related feedback is provided.

[0108] In one exemplary embodiment, the elastography examination includes cervical elastography, the results of the elastography examination include softening of the patient's cervix, and the at least one processor is further configured to provide an indicator of risk of preterm birth based on determining that softening of the cervix has occurred or is occurring when providing strain-related feedback.

[0109] As used herein, the term "circuit / circuitry" refers to physical electronic components (e.g., hardware) and any software and / or firmware ("code") that can configure hardware, is executed by hardware, and / or is otherwise associated with hardware. For example, as used herein, when executing one or more first codes, a specific processor and memory may include a first "circuit", and when executing one or more second codes, a specific processor and memory may include a second "circuit". As used herein, "and / or" represents any one or more of the items in the list connected by "and / or". As an example, "x and / or y" represents any element in the three-element set {(x), (y), (x, y)}. In other words, "x and / or y" means "one or both of x and y". As another example, "x, y and / or z" represents any element in the seven-element set {(x), (y), (z), (x, y), (x, z), (y, z), (x, y, z)}. In other words, "x, y, and / or z" means "one or more of x, y, and z." As used herein, the terms "box" and "module" refer to functions that can be performed by one or more circuits. As used herein, the term "exemplary" means serving as a non-limiting example, instance, or illustration. As used herein, the term "for example / eg" brings up a list of one or more non-limiting examples, instances, or illustrations. As used herein, a circuit is "operable to" perform a function whenever the circuit includes the necessary hardware (and code, if necessary) to perform the function, regardless of whether the performance of the function is disabled or not enabled (e.g., by some user-configurable setting, factory trim, etc.).

[0110] Other embodiments of the present invention may provide a non-transitory computer-readable medium and / or storage medium and / or a non-transitory machine-readable medium and / or storage medium having machine code and / or a computer program stored thereon, the machine code and / or computer program having at least one code segment, and the at least one code segment can be executed by a machine and / or a computer, thereby causing the machine and / or the computer to perform a process as described herein.

[0111] Therefore, the present disclosure can be implemented in hardware, software, or a combination of hardware and software. The present invention can be implemented in a centralized manner in at least one computing system, or in a distributed manner, where different elements are distributed on several interconnected computing systems. Any type of computing system or other device suitable for executing the methods described herein is suitable. A typical combination of hardware and software can be a general-purpose computing system with a program or other code that controls the computing system when loaded and executed so that it executes the methods described herein. Another typical implementation can include a dedicated integrated circuit or chip.

[0112] Various embodiments according to the present disclosure may also be embedded in a computer program product, which includes all the features capable of implementing the methods described herein and is capable of executing these methods when loaded into a computer system. A computer program herein refers to any expression of a set of instructions in any language, code or symbol, which is intended to enable a system with information processing capabilities to perform specific functions directly or after one or both of the following: a) conversion into another language, code or symbol; b) reproduction in a different material form.

[0113] Although the present invention has been described with reference to certain embodiments, it will be appreciated by those skilled in the art that various changes may be made and equivalents may be substituted without departing from the scope of the present invention. In addition, many modifications may be made to adapt specific circumstances or materials to the teachings of the present invention without departing from the scope of the present invention. Therefore, the present invention is not intended to be limited to the disclosed specific embodiments, but the present invention will include all embodiments falling within the scope of the appended claims.

Claims

1. A method comprising: processing, by at least one processor, imaging data acquired during an elastography examination of a patient, wherein the imaging data corresponds to a particular medical imaging technique; generating, by the at least one processor, corresponding one or more medical images based on processing the imaging data; calculating strain values ​​corresponding to a selected region of interest (ROI) within the one or more medical images; processing the calculated strain values, wherein the processing comprises: determining a plurality of ranges corresponding to the calculated strain values ​​based on the elastography examination, wherein the plurality of ranges include at least a low range, a high range, and an average range; identifying a portion of the selected region of interest (ROI) corresponding to each of the plurality of ranges; and evaluating a result of the elastography examination based on one or more of: the calculated strain value, the plurality of ranges, and the portion of the selected region of interest (ROI); and providing strain-related feedback based on said processing of the calculated strain values, Wherein providing the strain-related feedback comprises displaying strain values ​​of at least one of the high range and the low range in at least one displayed medical image of the one or more medical images. 2 . The method of claim 1 , further comprising automatically processing the calculated strain values ​​via the at least one processor using one or more artificial intelligence based algorithms. 3 . The method of claim 1 , further comprising processing the calculated strain values ​​based on a type of the elastography examination. 4 . The method of claim 3 , wherein the type of the elastography examination comprises one of cervical elastography, prostate elastography, breast elastography, and thyroid elastography. The method of claim 1 , comprising determining the plurality of ranges based on one or more preset cutoff values. 6 . The method of claim 5 , comprising determining the one or more preset cut-off values ​​based on a type of the elastography examination. 7 . The method of claim 1 , wherein evaluating the results of the elastography examination comprises calculating a ratio between the high range and the low range.

8. The method of claim 1, wherein the elastography examination comprises cervical elastography, and wherein the results of the elastography examination comprise softening of the patient's cervix.

9. The method of claim 8, wherein providing the strain-related feedback comprises providing feedback indicative of risk of preterm birth based on determining that softening of the cervix has occurred or is occurring.

10. A system comprising: a scanning device configured to obtain imaging signals during an elastography examination of a patient, wherein the imaging data corresponds to a specific medical imaging technique; A display device, the display device being configured to display an image; and at least one processor, the at least one processor being configured to: processing the imaging data obtained via the scanner device; generating corresponding one or more medical images based on processing the imaging data; displaying the one or more medical images via the display device; Use one or more artificial intelligence-based algorithms to automatically: calculating a strain value corresponding to a selected region of interest (ROI) within at least one of the one or more medical images; processing the calculated strain values, wherein the processing comprises: determining a plurality of ranges corresponding to the calculated strain values ​​based on the elastography examination, wherein the plurality of ranges include at least a low range, a high range, and an average range; identifying a portion of the selected region of interest (ROI) corresponding to each of the plurality of ranges; and evaluating a result of the elastography examination based on one or more of: the calculated strain value, the plurality of ranges, and the portion of the selected region of interest (ROI); and providing strain-related feedback based on said processing of the calculated strain values, Wherein, the at least one processor is configured to display strain values ​​of at least one range of the high range and the low range in a medical image displayed by at least one of the one or more medical images through the display device when providing the strain-related feedback.

11. The system of claim 10, wherein the at least one processor is configured to process the calculated strain values ​​based on a type of the elastography examination. 12 . The system of claim 10 , wherein the at least one processor is configured to determine the plurality of ranges based on one or more preset cutoff values.

13. A system according to claim 10, wherein providing the strain-related feedback includes providing feedback related to the result of the elastic imaging examination, and wherein the at least one processor is configured to display an indicator of the result of the elastic imaging examination in at least one of the one or more medical images displayed via the display device when providing the strain-related feedback.

14. A system according to claim 10, wherein the elastography examination includes cervical elastography, wherein the results of the elastography examination include softening of the patient's cervix, and wherein the at least one processor is configured to provide an indicator of risk of preterm birth based on determining that softening of the cervix has occurred or is occurring when providing the strain-related feedback.

Images